Journal articles on the topic 'Electronic data processing China'
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1
Li, YaoGuang, and HeChi Gan. "Tourism Information Data Processing Method Based on Multi-Source Data Fusion." Journal of Sensors 2021 (July 31, 2021): 1–12. http://dx.doi.org/10.1155/2021/7047119.
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Urban social civilization and the quality of life of residents are gradually improved, and the development scale and trend of the leisure tourism industry have been growing. This paper constructs a multi-source data fusion model based on an ensemble learning algorithm, uses Ctrip 2020 open data set to train the model, and then obtains the tourism information data processing and prediction results. This paper takes the data of Ctrip as the training set and compares the trained model with the data of tunic and Feizhu. In this paper, sensor detection technology is used to analyze many famous scenic spots in China, including tourist type, gender, and location. The results show that tourism feature extraction results are consistent with data from trending flying bamboo, tunics, and other websites, according to the results of a multi-source fusion of tourism information. Among them, in the data of the first half of 2020, the prediction accuracy of the model after data processing is about 62%. Affected by the epidemic situation, the accuracy of the model is low. In the second half of the year, the prediction accuracy is 78%, which can be used to fuse tourism information in a short time. Therefore, the data show that the model has high learning ability and high trend prediction ability in tourism data processing, which can provide necessary information support for tourists.
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Wu, Zhong, and Chuan Zhou. "Construction of an Intelligent Processing Platform for Equestrian Event Information Based on Data Fusion and Data Mining." Journal of Sensors 2021 (July 23, 2021): 1–9. http://dx.doi.org/10.1155/2021/1869281.
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In the past two years, equestrian sports have become more and more popular with the public. Due to the comprehensive development of equestrian preparations for the 2020 Olympic Games in China, the equestrian sports industry presents an unprecedented favorable development environment in China. This article is aimed at studying the construction of an equestrian event information intelligent processing platform based on data fusion and data mining. This article introduces the relevant theoretical knowledge of data mining and data fusion, including the description of the concept of data mining, the common analysis methods and algorithms of data mining, the basic concepts of data fusion, and the functional structure of data fusion. It discusses various algorithms in cluster analysis and focuses on the analysis of distance measurement and similarity coefficient in cluster analysis. In the experimental part, in order to intelligently process and acquire information, an information intelligent processing platform is constructed based on data fusion and data mining technology. The experimental results of this paper show that the precision rate, recall rate, and F -score of the platform under closed test are much higher than those under open test, and the precision rate is increased by about 7.26%.
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Ye, Hongjun, Xiaojun Jing, Liang Liu, Maolei Wang, Shuo Hao, Xingkang Lang, and Baoguo Yu. "Analysis of Quasi-Zenith Satellite System Signal Acquisition and Multiplexing Characteristics in China Area." Sensors 20, no. 6 (March 11, 2020): 1547. http://dx.doi.org/10.3390/s20061547.
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On the basis of realizing regional navigation, the Quasi-Zenith Satellite System (QZSS) has advanced navigation function, which leads to the broadcasting of more signals in a single frequency of QZSS signals. Current signal transmission technology cannot solve this problem, so it is necessary to design a signal multiplexing method. The current QZSS satellite interface document does not disclose the multiplexing modulation method of the signal transmission, which has a certain impact on the acquisition of high-precision observation data and further data processing. The iGMAS (International GNSS Monitoring & Assessment System) Monitoring and Evaluation Center of the 54th Research Institute of China Electronics Technology Group Corporation has used the low-distortion data acquisition and processing platform and refined signal software receiving processing algorithm of the iGMAS to complete the signal acquisition and analysis of QZSS satellites. Analysis of the multiplexing and modulation method and signal characteristics for the QZSS has been carried out, which can provide a reference for the design and data processing of high-precision receivers.
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Feng, Yong Qin, Yan Yin, Xiao Rui Zhang, and Jin jian Xiao. "Testing and Evaluation of Baby Walkers Based on Product Injury Data." Advanced Materials Research 834-836 (October 2013): 1628–31. http://dx.doi.org/10.4028/www.scientific.net/amr.834-836.1628.
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The injury characteristics of baby walkers were analyzed based on injury data about baby walkers from the National Electronic Injury Surveillance System (NEISS) of U.S. and the National Injury Related Products Surveillance System (NISS) of China. The main causes for the baby walkers injuries were identified by testing and evaluation, including insufficient anti-collision space, low designed static and dynamic strength, substandard outsourced casters and the processing technique not in conformity with the requirements for accessible parts. Such suggestions were put forward as increasing the anti-collision space, enhancing the strength of structural materials, performing stricter acceptance check for the outsourced casters and confirming the process inspection of semi-finished products.
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Zu, Yun Xiao, Shan Shan Jia, Zhe Li, and Yue Jia. "Study on the Layout and Structure of the Waste Electronic Products Recycling Network." Advanced Materials Research 518-523 (May 2012): 3613–20. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.3613.
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The current recycling networks of the waste electronic products in China are analyzed from the view of the layout of the recycling center and the structure of recycling channel. The optimization model of an e-waste recycling network is established according to our national actual condition, and solved with simulated data. The results show that setting up detecting centers dispersedly can reduce the transportation amount of the waste electronic products and reduce transportation costs, and the relative concentration of recycling processing center can achieve economies of scale and lower the total cost.
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Wu, Meng. "The impact of Internet finance to commercial banks in China." Finance and Market 6, no. 1 (April 21, 2021): 29. http://dx.doi.org/10.18686/fm.v6i1.3165.
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In recent years, network technology, information technology and data processing technology have become the basis of the competition of Internet financial institutions. With the rapid development of the electronic platform, the accumulation of massive customer data information, Internet finance has a more and broader range of customer resources than that of a single bank. These advantages of Internet finance will have a significant impact on commercial banks. The traditional banks have a mature business model and rich industry experience in finance. Internet finance and traditional commercial banks interact with each other. In this case, this paper will focus on the influence of Internet finance on traditional commercial banks in China.
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Zhang, Wenkang, Dan Liu, Yu Du, Ruitao Liu, Daqian Wang, Longzhou Yu, and Shuming Wen. "Mill Feed Control System and Algorithm Based on Python." Minerals 12, no. 7 (June 24, 2022): 804. http://dx.doi.org/10.3390/min12070804.
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Grinding is an important link in the process of mineral processing. It plays a vital role in mineral processing by optimizing the grinding process, improving the quality of grinding products and ensuring the follow-up operation indicators. In this paper, the Python language, intelligent theoretical control technology and mineral processing were combined to solve the problem of ore feeding control in mineral processing. Using error factor analysis, an extended control algorithm was designed. The NumPy library and data collected from the Yuan YangMou concentrator in China were used to quantitatively analyze the factors affecting the error of electronic belt scales. This paper introduces the use of Kalman filtering for electronic belt scale weight data to reduce the effect of noise and hence reduce errors. The factors affecting the process of mill feeding are also analyzed. The core ideas and methods of fuzzy control theory are summarized, and a Python-based fuzzy controller suitable for the mill feeding process that improves the overall robustness and accuracy of feeding system is implemented.
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Xu, Guoxiang, and Hao Jin. "Using Artificial Intelligence Technology to Solve the Electronic Health Service by Processing the Online Case Information." Journal of Healthcare Engineering 2021 (November 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/9637018.
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With the continuous improvement of economic level and the continuous development of science and technology in China, information technology has begun to integrate into all walks of life. Medical units have begun to change from the traditional medical system to the intelligent system, and the processing of online case information has become an important component of medical informationization. To improve the efficiency of dealing with online case information, this study proposes to establish a fully connected neural network model to deal with online cases. Using jieba word segmentation tool and data preprocessing technology, the data of electronic medical records are sorted out, and the data are quantified using Word2Vec and other tools, and the data on electronic medical records are converted into one-hot binary variables. The quantified data are trained into a fully connected neural model, and the accuracy rate is about 88%. It is compared with naive Bayes and decision tree classification methods, and then a comparative experiment is carried out by solving e-health services in different ways. The results show that the fully connected neural network model has the best classification effect: the highest accuracy rate is about 93.7%, the highest precision rate is about 94.0%, the highest recall rate is about 95.3%, and the highest F1 score is about 94.6%. However, using artificial intelligence technology to solve electronic health services has great advantages, among which efficiency, assistance, and service satisfaction are all higher than 90%, which provides favorable technical support for electronic health services.
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Liu, Xing, Zhen Li, Jing Wang, and Jiankun Chu. "Research on the Efficiency of “Dual-Chain” Integration of Talent Chain and Industrial Chain of Vocational Education Based on Big Data Technology." Journal of Electrical and Computer Engineering 2022 (May 9, 2022): 1–12. http://dx.doi.org/10.1155/2022/8751815.
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Since China has implemented the policy of “dual-chain” integration of vocational education, the level of vocational education in China has been developed rapidly. In order to study the current efficiency of dual-chain integration of vocational education in China, taking 31 Chinese provinces and cities as examples, the DEA-BPNN calculation model is used to calculate the efficiency of Chinese vocational education from 2010 to 2019, and the results of the study show that although the development of vocational education in China has achieved certain results, the crude development mode, which is mainly based on the increase of investment, has not been fundamentally changed.
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Wang, Hang, Yunfeng Chen, Rui Min, and Yangkang Chen. "Urban DAS Data Processing and Its Preliminary Application to City Traffic Monitoring." Sensors 22, no. 24 (December 18, 2022): 9976. http://dx.doi.org/10.3390/s22249976.
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Distributed acoustic sensing (DAS) is an emerging technology for recording vibration signals via the optical fibers buried in subsurface conduits. Its relatively easy-to-deploy and high spatial and temporal sampling characteristics make DAS an appealing tool to record seismic wavefields at higher quantity and quality than traditional geophones. Considering that the usage of optical fibers in the urban environment has drawn relatively less attention aside from its functionality as a telecommunication cable, we examine its ability to record seismic signals and investigate its preliminary application in city traffic monitoring. To solve the problems that DAS signals are prone to a variety of environmental noise and are generally of weak amplitude compared to noise, we propose a fast workflow for real-time DAS data processing, which can enhance the detection of regular car signals and suppress the other components. We conduct a DAS experiment in Hangzhou, China, a typical metropolitan area that can provide us with a rich data library to validate our DAS data-processing workflow. The well-processed data enable us to extract their slope and coherency attributes that can provide an estimate of real traffic situations. The one-minute (with video validations) and 24 h statistics of these attributes show that the speed and volume of car flow are well correlated demonstrates the robustness of the proposed data processing workflow and great potential of DAS for city traffic monitoring with high precision and convenience. However, challenges also exist in view that all the attributes are statistically analyzed based on the behaviors of a large number of cars, which is meaningful but lacking in precision. Therefore, we suggest developing more quantitative processing and analyzing methods to provide precise information on individual cars in future works.
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Huu, Phat Nguyen, Vinh Tran Quang, Chau Nguyen Le Bao, and Quang Tran Minh. "Proposed Detection Face Model by MobileNetV2 Using Asian Data Set." Journal of Electrical and Computer Engineering 2022 (October 11, 2022): 1–19. http://dx.doi.org/10.1155/2022/9984275.
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In 2019, the infectious coronavirus disease 2019 (COVID-19) was first reported in Wuhan, China. It has then become a public health problem in the world. This pandemic is having a heavy impact on the lives of people in our country. All countries are trying to control the spread of this disease. To solve the problem, each person needs to wear masks in a public place. Therefore, we propose a model capable of distinguishing between masked and nonmasked faces using a convolutional neural network (CNN) based on deep learning (DL)—MobileNetV2 in this paper. The model can detect people who are not wearing masks. It has an accuracy of up to 99.37%. The model will be applied in places such as schools, offices, and so on to monitor the wearing masks.
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Wang, Jili, Weidong Yu, Yunkai Deng, Robert Wang, Yingjie Wang, Heng Zhang, and Mingjie Zheng. "Demonstration of Time-Series InSAR Processing in Beijing Using a Small Stack of Gaofen-3 Differential Interferograms." Journal of Sensors 2019 (April 8, 2019): 1–13. http://dx.doi.org/10.1155/2019/4204580.
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More and more synthetic aperture radar (SAR) satellites in orbit provide abundant data for remote sensing applications. In August 2016, China launched a new Earth observation SAR satellite, Gaofen-3 (GF-3). In this paper, we utilize a small stack of GF-3 differential interferograms to map land subsidence in Beijing (China) using the time-series SAR interferometry (InSAR) technique. The small stack of differential interferograms is generated with 5 GF-3 SAR images from March 2017 to January 2018. Orbit errors are carefully addressed and removed during differential InSAR (DInSAR) processing. Truncated singular-value decomposition (TSVD) is applied to strengthen the robustness of deformation rate estimation. To validate the results of GF-3 data, an additional deformation measurement using 26 Sentinel-1B images from March 2017 to February 2018 is carried out using the persistent scatterer interferometry (PSI) technique. By implementing a cross-comparison, we find that the retrieved results from GF-3 images and Sentinel-1 images are spatially consistent. The standard deviation of vertical deformation rate differences between two data stacks is 11.24 mm/y in the study area. The results shown in this paper demonstrate the reasonable potential of GF-3 SAR images to monitor land subsidence.
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Li, Zhucong, Zhen Gan, Baoli Zhang, Yubo Chen, Jing Wan, Kang Liu, Jun Zhao, and Shengping Liu. "Semi-Supervised Noisy Label Learning for Chinese Clinical Named Entity Recognition." Data Intelligence 3, no. 3 (2021): 389–401. http://dx.doi.org/10.1162/dint_a_00099.
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This paper describes our approach for the Chinese clinical named entity recognition (CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing (CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record (EMR). We constructed a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule post-processing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we used post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.
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Pan, Chen. "Quality Management System for Clinical Nutrition: On the processing of the Artificial Intelligence into Quality Assessment." Nutrition and Food Processing 04, no. 03 (May 26, 2021): 01–06. http://dx.doi.org/10.31579/2637-8914/038.
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Objective: To critically evaluate the Quality Management System (QMS) for Clinical Nutrition (CN) in Jiangsu. Monitor its performance in quality assessment as well as human resource management from nutrition aspect. Investigate the appliance and development of Artificial Intelligence (AI) in medical quality control. Subjects: The study source of this research was all the staffs of 70 Clinical Nutrition Department (CND) of the tertiary hospitals in Jiangsu Province, China. These departments are all members of the Quality Management System of Clinical Nutrition in Jiangsu (QMSNJ). Methods: An online survey was conducted on all 341 employees within all these CNDs based on the staff information from the surveyed medical institutions. The questionnaire contains 5 aspects, while data analysis and AI evaluation were focused on human resource information. Results: 330 questionnaires were collected with the respondent rate of 96.77%. The QMS for CN has been build up for CNDs in Jiangsu, which achieved its target in human resource improvements, especially among dietitians. The increasing number of participated departments (42.8%) and the significant growth of dietitians (p=0.02, t=-0.42) are all expressions of the advancements of QMSNJ. Conclusion: As the first innovation of an online platform for QM in Jiangsu, JPCNMP has been successfully implemented among QMS from this research. This multidimensional electronic system can help QMSNJ and CND achieve quality assessment from various aspects, so as to realize the continuous improvement of clinical nutrition. The instrument of online platform, as well as AI technology for quality assessment is worth to be recommended and promoted in the future. Strengths This is the first evaluation of the online QM platform after its implementation in daily disciplinary management among the QMS in china. This research has been designed to investigate the status of CND multidimensionally. This analysis is emphasizing on the human resource approvement after the designation and application of QMS. A clearer forecast of AI in medical quality assessment and disciplinary construction was achieved, while some modifications are recommended in human resource management to improve its efficiency and accuracy.
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Cheng, Xiao, Daojin Yao, Lin Yang, and Wentao Dong. "Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors." Sensors 22, no. 20 (October 14, 2022): 7795. http://dx.doi.org/10.3390/s22207795.
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With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways.
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Cheng, Yishuang, and Juan Pan. "A Study on Resource Security under Legal Protection of e-Commerce Data Sovereignty Based on Blockchain Network." Security and Communication Networks 2022 (May 13, 2022): 1–7. http://dx.doi.org/10.1155/2022/3713891.
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With the growing demand for cross-border electronic data forensics, national sovereignty and international network security have ushered in great challenges, and how to achieve dynamic balance in various fields under cross-border electronic data forensics, advance the process of global cyberspace governance, reach international consensus, and achieve win-win cooperation is a question that China needs to solve. Based on the above status quo, a blockchain-based secure e-commerce system is researched and implemented. Using the distributed architecture of the blockchain system and the automatic execution feature of smart contracts, a secure transaction application system is designed to realize decentralization and solve the problem of untrustworthy intermediate parties. Data storage is based on the mechanism of combining blockchain and offchain databases to ensure data security, and the characteristic of blockchain can only be appended and read to prevent the system from being tampered. The database index and the storage structure of smart contract commodity information and transaction information are established to improve the query efficiency. The functional and performance tests of the system show that the transaction processing efficiency is high in the multinode environment, and the response time of order and commodity status query can reach 80 ms. The design and development process of the system is introduced in detail, which is an important reference value for the application of blockchain technology in e-commerce.
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Chunta, Li, Bi Huiyu, and Chen Zhenha. "A Point of Interest Data Method of Pingshan Village’s Spatial Pattern Coupling with Business Form." International Journal of Circuits, Systems and Signal Processing 15 (August 25, 2021): 1041–50. http://dx.doi.org/10.46300/9106.2021.15.112.
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In the context of the rapid development of China's urbanization economy and the current situation of 'hollow villages' era., how to effectively inherit and protect the Chinese traditional villages as cultural heritage is an important issue for China’s beautiful rural construction. The economic development of villages is based on the reasonable configuration of business, as an important carrier of the layout of the format，the architectural space and street space of the village are linked together for analysis and research, so as to achieve the 'win-win' effect of high-quality business form and the rational use and protection of historical building space.This paper takes Pingshan Village, a famous historical and cultural village in Yi county, China, as the research object, collects data through historical record, on-site investigations and interviews, and uses geographic information technology, point of interest combing and spatial syntax analysis to study the spatial characteristics of the current status of the Pingshan Village’s commercial concentrated water street. Besides, this paper makes quantitative analysis of the number of business forms in JiyangXi water street, spatial analysis of the commercial layout, and fixed-point analysis of the flow of people. With the results above, this paper takes correlation analysis on the results of analyzing spatial characteristics and the layout of business forms, draws up the advantages and disadvantages of the main commercial space layout of Pingshan Village as well as the optimization strategy, thereby reaches the design suggestions on the adjustment of the business forms in different spaces in traditional village planning.
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Ye, Hongjun, Xiaojun Jing, Liang Liu, Shuo Hao, and Baoguo Yu. "Analysis of the Multiplexing Method of New System Navigation Signals of GPS III First Star L1 Frequency in China’s Regional." Sensors 19, no. 24 (December 5, 2019): 5360. http://dx.doi.org/10.3390/s19245360.
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Compared with the previous GPS satellites, the first GPS III satellite adds a new civil signal L1C to the signal components of the L1 frequency in addition to the improvement of positioning accuracy, anti-interference ability, and service life. The selection and combination of signal modulation and multiplexing methods will affect the power ratio and phase relationship in the process of signal transmission. In the distribution of constellation of different modulation modes, the signal amplitudes of different signal constellation points will be affected by the nonlinear amplifiers of satellites. The analysis can assess its impact on navigation performance. The iGMAS monitoring and evaluation center of the 54th Research Institute of China Electronics Technology Group Corporation uses the low-distortion data acquisition and processing platform and refined signal software receiving processing algorithm of the iGMAS monitoring and evaluation center to complete the signal acquisition of the first satellite of GPS III over China, and processes accordingly for its signal modulation mode. Compared with the previous generation GPS of old system signals, it is found that the GPS signal of the new system not only adds the L1C frequency, but also the constant envelope multiplexing mode of the L1 frequency signal, and the power ratio of the internal signal components are also adjusted.
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Chen, Chao, Jun Fu, Li Zou, Yi Liu, and Yakun Sun. "Analysis of the Development Trend and Scheme of Agricultural Electrification Intelligence Based on Big Data Mining and OLAP Tool Analysis Algorithm." International Transactions on Electrical Energy Systems 2022 (September 16, 2022): 1–13. http://dx.doi.org/10.1155/2022/1614939.
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To transform from a large agricultural country to a strong agricultural country, China must take the road of modernized, automated, and information-based agriculture. With the advantages of high efficiency, low cost, flexibility, stability, and reliability, electrified production is the material and technical basis for agricultural mechanization, automation, industrialization, and management modernization. Big data mining and OLAP tool analysis algorithm has powerful data processing ability and occupies a very important part in various fields. This article analyzes the current situation of agricultural electrification development, electrification development dynamics, and electrification application scope based on big data mining and OLAP tool analysis algorithm, and finally comes up with several suggestions for agricultural electrification development.
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Determann, Lothar. "Healthy Data Protection." Michigan Technology Law Review, no. 26.2 (2020): 229. http://dx.doi.org/10.36645/mtlr.26.2.healthy.
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Modern medicine is evolving at a tremendous speed. On a daily basis, we learn about new treatments, drugs, medical devices, and diagnoses. Both established technology companies and start-ups focus on health-related products and services in competition with traditional healthcare businesses. Telemedicine and electronic health records have the potential to improve the effectiveness of treatments significantly. Progress in the medical field depends above all on data, specifically health information. Physicians, researchers, and developers need health information to help patients by improving diagnoses, customizing treatments and finding new cures. Yet law and policymakers are currently more focused on the fact that health information can also be used to harm individuals. Even after the outbreak of the COVID-19 pandemic (which occurred after the manuscript for this article was largely finalized), the California Attorney General Becera made a point of announcing that he will not delay enforcement of the California Consumer Privacy Act (“CCPA”), which his office estimated imposes a $55 billion cost (approximately 1.8% of California Gross State Product) for initial compliance, not including costs of ongoing compliance, responses to data subject requests, and litigation. Risks resulting from health information processing are very real. Contact tracing and quarantines in response to SARS, MERS, and COVID-19 outbreaks curb civil liberties with similar effects to law enforcement investigations, arrests, and imprisonment. Even outside the unusual circumstances of a global pandemic, employers or insurance companies may disfavor individuals with pre-existing health conditions in connections with job offers and promotions as well as coverage and eligibility decisions. Some diseases carry a negative stigma in social circumstances. To reduce the risks of such harms and protect individual dignity, governments around the world regulate the collection, use, and sharing of health information with ever-stricter laws. European countries have generally prohibited the processing of personal data, subject to limited exceptions, for which companies have to identify and then document or apply. The General Data Protection Regulation (“GDPR”) that took effect in 2018 confirms and amplifies a rigid regulatory regime that was first introduced in the German State Hessen in 1970 and demands that organizations minimize the amount of data they collect, use, share, and retain. Healthcare and healthtech organizations have struggled to comply with this regime and have found EU data protection laws fundamentally hostile to data-driven progress in medicine. The United States, on the other hand, has traditionally relied on sector- and harm-specific laws to protect privacy, including data privacy and security rules under the federal Health Insurance Portability and Accountability Act (“HIPAA”) and numerous state laws including the Confidentiality of Medical Information Act (“CMIA”) in California, which specifically address the collection and use of health information. So long as organizations observe the specific restrictions and prohibitions in sector-specific privacy laws, they may collect, use, and share health information. As a default rule in the United States, businesses are generally permitted to process personal information, including health information. Yet, recently, extremely broad and complex privacy laws have been proposed or enacted in some states, including the California Consumer Privacy Act of 2018 (“CCPA”), which have a potential to render compliance with data privacy laws impractical for most businesses, including those in the healthcare and healthtech sectors. Meanwhile, the People’s Republic of China is encouraging and incentivizing data-driven research and development by Chinese companies, including in the healthcare sector. Data-related legislation is focused on cybersecurity and securing access to data for Chinese government agencies and much less on individual privacy interests. In Europe and the United States, the political pendulum has swung too far in the direction of ever more rigid data regulation and privacy laws, at the expense of potential benefits through medical progress. This is literally unhealthy. Governments, businesses, and other organizations need to collect, use and share more personal health information, not less. The potential benefits of health data processing far outweigh privacy risks, which can be better tackled by harm-specific laws. If discrimination by employers and insurance companies is a concern, then lawmakers and law enforcement agencies need to focus on anti-discrimination rules for employers and insurance companies - not prohibit or restrict the processing of personal data, which does not per se harm anyone. The notion of only allowing data processing under specific conditions leads to a significant hindrance of medical progress by slowing down treatments, referrals, research, and development. It also prevents the use of medical data as a tool for averting dangers for the public good. Data “anonymization” and requirements for specific consent based on overly detailed privacy notices do not protect patient privacy effectively and unnecessarily complicate the processing of health data for medical purposes. Property rights to personal data offer no solutions. Even if individuals - not companies creating databases - were granted property rights to their own data originally, this would not ultimately benefit individuals. Given that transfer and exclusion rights are at the core of property regimes, data property rights would threaten information freedom and privacy alike: after an individual sells her data, the buyer and new owner could exercise his data property rights to enjoin her and her friends and family from continued use of her personal data. Physicians, researchers, and developers would not benefit either; they would have to deal with property rights in addition to privacy and medical confidentiality requirements. Instead of overregulating data processing or creating new property rights in data, lawmakers should require and incentivize organizations to earn and maintain the trust of patients and other data subjects and penalize organizations that use data in specifically prohibited ways to harm individuals. Electronic health records, improved notice and consent mechanisms, and clear legal frameworks will promote medical progress, reduce risks of human error, lower costs, and make data processing and sharing more reliable. We need fewer laws like the GDPR or the CCPA that discourage organizations from collecting, using, retaining, and sharing personal information. Physicians, researchers, developers, drug companies, medical device manufacturers and governments urgently need better and increased access to personal health information. The future of medicine offers enormous opportunities. It depends on trust and healthy data protection. Some degree of data regulation is necessary, but the dose makes the poison. Laws that require or intend to promote the minimization of data collection, use, and sharing may end up killing more patients than hospital germs. In this article, I promote a view that is decidedly different from that supported by the vast majority of privacy scholars, politicians, the media, and the broader zeitgeist in Europe and the United States. I am arguing for a healthier balance between data access and data protection needs in the interest of patients’ health and privacy. I strive to identify ways to protect health data privacy without excessively hindering healthcare and medical progress. After an introduction (I), I examine current approaches to data protection regulation, privacy law, and the protection of patient confidentiality (II), risks associated with the processing of health data (III), needs to protect patient confidence (IV), risks for healthcare and medical progress (V), and possible solutions (VI). I conclude with an outlook and call for healthier approaches to data protection (VII).
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Wang, Li, Yaoyun Zhang, Min Jiang, Jingqi Wang, Jiancheng Dong, Yun Liu, Cui Tao, Guoqian Jiang, Yi Zhou, and Hua Xu. "Toward a normalized clinical drug knowledge base in China—applying the RxNorm model to Chinese clinical drugs." Journal of the American Medical Informatics Association 25, no. 7 (April 4, 2018): 809–18. http://dx.doi.org/10.1093/jamia/ocy020.
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Abstract Objective In recent years, electronic health record systems have been widely implemented in China, making clinical data available electronically. However, little effort has been devoted to making drug information exchangeable among these systems. This study aimed to build a Normalized Chinese Clinical Drug (NCCD) knowledge base, by applying and extending the information model of RxNorm to Chinese clinical drugs. Methods Chinese drugs were collected from 4 major resources—China Food and Drug Administration, China Health Insurance Systems, Hospital Pharmacy Systems, and China Pharmacopoeia—for integration and normalization in NCCD. Chemical drugs were normalized using the information model in RxNorm without much change. Chinese patent drugs (i.e., Chinese herbal extracts), however, were represented using an expanded RxNorm model to incorporate the unique characteristics of these drugs. A hybrid approach combining automated natural language processing technologies and manual review by domain experts was then applied to drug attribute extraction, normalization, and further generation of drug names at different specification levels. Lastly, we reported the statistics of NCCD, as well as the evaluation results using several sets of randomly selected Chinese drugs. Results The current version of NCCD contains 16 976 chemical drugs and 2663 Chinese patent medicines, resulting in 19 639 clinical drugs, 250 267 unique concepts, and 2 602 760 relations. By manual review of 1700 chemical drugs and 250 Chinese patent drugs randomly selected from NCCD (about 10%), we showed that the hybrid approach could achieve an accuracy of 98.60% for drug name extraction and normalization. Using a collection of 500 chemical drugs and 500 Chinese patent drugs from other resources, we showed that NCCD achieved coverages of 97.0% and 90.0% for chemical drugs and Chinese patent drugs, respectively. Conclusion Evaluation results demonstrated the potential to improve interoperability across various electronic drug systems in China.
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Xie, L., P. Cao, T. Yu, Z. Jiang, Q. An, J. Li, C. Li, et al. "Prototype of readout electronics for GAEA gamma spectrometer of Back-n facility at CSNS." Journal of Instrumentation 17, no. 03 (March 1, 2022): P03022. http://dx.doi.org/10.1088/1748-0221/17/03/p03022.
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Abstract The back-streaming neutrons (Back-n) facility at China Spallation Neutron Source (CSNS) provides an excellent white neutron source for accurate nuclear data measurements. At Back-n, the GAmma spectrometer with GErmanium Array (GAEA) in planning is designed to measure neutron-induced cross-sections, nuclear structure, and in-beam nuclear spectroscopy. As GAEA consists of various detectors, developing and maintaining the readout electronics for this spectrometer is challenging. A prototype of readout electronics based on the universal digital readout platform is proposed in this paper. The digitizer in the platform consists of a data processing carrier and a signal acquire mezzanine. Different mezzanines are designed to accommodate various detectors in GAEA. The carrier's firmware also has a universal part for data readout and a reconfigurable module for real-time data processing. In order to synchronize all digitizers, a time synchronization module is designed, based upon which a global synchronous network is constructed with PXIe chassis. Critical signals as the clock, trigger, and control can be synchronously distributed over this network with high precision, significantly simplifying the whole readout electronics. Several tests were carried out to evaluate the prototype. The measured FWHM of the 22Na characteristic peak at 1274.5 keV is better than 0.15% for the HPGe detector. The measured time resolution of the readout electronics is 34.7 ps. Test results show that the performance of the readout electronics can meet GAEA requirements.
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Nadeem, Muhammad Athar, Zhiying Liu, Abdul Hameed Pitafi, Amna Younis, and Yi Xu. "Investigating the Adoption Factors of Cryptocurrencies—A Case of Bitcoin: Empirical Evidence From China." SAGE Open 11, no. 1 (January 2021): 215824402199870. http://dx.doi.org/10.1177/2158244021998704.
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The share of electronic transactions in the global payments continues to increase all around the globe. In the recent years, cryptocurrencies (also known as a system of electronic transaction) have caught significant attention from governments, policymakers, and practitioners worldwide. Cryptocurrencies as an innovative method of exchanges without any physical form boast several potential benefits such as speedy transactions, cross-border usage, low transaction fee, transparency, high security, anonymity, and privacy, and thus are expected to bring huge revolution in the future economic system. This study aims to investigate the adoption factors of Bitcoin, a most known cryptocurrency in China. Based on Technology Acceptance Model, a research framework has been developed to test the proposed hypotheses. The data have been collected via a survey questionnaire from 385 Chinese respondents. The findings show that the perceived ease of use and the perceived usefulness have a positive relationship with the intention to use Bitcoin. The perceived usefulness mediates the relationship between the perceived ease of use and the intention to use Bitcoin. Furthermore, the results reveal that the transaction processing and the perceived ease of use have significant impacts on the perceived usefulness. However, the security and control shows an insignificant effect on the perceived usefulness. This study contributes to the growing literature of Bitcoin and offers valuable information to individual users (payees), fund managers (investors), and companies/businesses (receiving Bitcoin as a payment method). Research implications and limitations are also discussed.
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Li, Xiaoying. "Teacher Education and Management: Innovative Application of Ecological Management System of Big Data Management System." Journal of Sensors 2022 (September 13, 2022): 1–15. http://dx.doi.org/10.1155/2022/6048083.
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At present, with the rapid growth of China’s comprehensive national power, China wants to accelerate the construction of world-class universities and world-class disciplines to match it. Therefore, the education and management of university teachers is crucial, and the current research results at home and abroad in this area are remarkable. In this paper, we will start from the innovative application of university teachers’ management system and use the Oracle and SQL Server database processors commonly used in big data processing to process data. It is concluded that China’s teaching force is gradually balanced; the comprehensive quality of the teaching force is improving; the academic atmosphere is more open; the fields and perspectives of academic research are richer; the distribution of teachers at all levels in China is more balanced; the comprehensive quality of our teachers is improving; the system of education and scientific research in China’s universities is improving; and the ecological management system of universities is improving. In addition, in view of the current situation of education and management of teachers in China’s colleges and universities, according to the results of big data analysis, we also propose (1) to improve the academic level and establish a strict institutional threshold for the college teaching profession; (2) to effectively implement the policy of freedom and equality in academic academia and improve the appointment and evaluation system of teachers and principals in higher education institutions; (3) to insist on implementing the principle of academic fairness and optimizing the teacher evaluation system; (4) to innovate and implement the incentive mechanism for teachers in colleges and universities according to the talent training characteristics of teachers’ academic career ability; and (5) to establish the access system of college teachers’ career development and promote academic career development and other suggestions of big data management for college teachers.
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Zhang, Yiliu, and Jie Liu. "Prediction of Overall Energy Consumption of Data Centers in Different Locations." Sensors 22, no. 10 (May 12, 2022): 3704. http://dx.doi.org/10.3390/s22103704.
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The use of big data leads to higher demands for hyperscale data centers (HDCs) in terms of the scale and quantity required for data storage and processing. Before the construction of an HDC, it is necessary to comprehensively analyze the economic budget according to the energy requirements and potential energy cost. We propose a global energy consumption prediction framework based on the power usage effectiveness (PUE) calculation that considers all heat sources and power consumption. The framework integrates physical models and a statistical framework that combines IT equipment energy consumption and data center energy consuming predictions. Furthermore, the framework provides a method to calculate the carbon emissions and electricity cost of the data center. Using hourly meteorological data as climate parameters, combined with a limited range of energy parameters, the annual PUE values of 60 regions were estimated, and a further analysis of the Carbon Usage Effectiveness (CUE) and electricity costs in China was conducted as an example. Based on experimental validation and an evaluation of real-time data, our framework can predict the overall energy consumption of HDCs effectively, filling a gap in HDC research in the Asia-Pacific region and providing a basis for HDC feasibility analysis.
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Li, Na, Xinchen Huang, Huijie Zhao, Xianfei Qiu, Kewang Deng, Guorui Jia, Zhenhong Li, David Fairbairn, and Xuemei Gong. "A Combined Quantitative Evaluation Model for the Capability of Hyperspectral Imagery for Mineral Mapping." Sensors 19, no. 2 (January 15, 2019): 328. http://dx.doi.org/10.3390/s19020328.
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To analyze the influence factors of hyperspectral remote sensing data processing, and quantitatively evaluate the application capability of hyperspectral data, a combined evaluation model based on the physical process of imaging and statistical analysis was proposed. The normalized average distance between different classes of ground cover is selected as the evaluation index. The proposed model considers the influence factors of the full radiation transmission process and processing algorithms. First- and second-order statistical characteristics (mean and covariance) were applied to calculate the changes for the imaging process based on the radiation energy transfer. The statistical analysis was combined with the remote sensing process and the application performance, which consists of the imaging system parameters and imaging conditions, by building the imaging system and processing models. The season (solar zenith angle), sensor parameters (ground sampling distance, modulation transfer function, spectral resolution, spectral response function, and signal to noise ratio), and number of features were considered in order to analyze the influence factors of the application capability level. Simulated and real data collected by Hymap in the Dongtianshan area (Xinjiang Province, China), were used to estimate the proposed model’s performance in the application of mineral mapping. The predicted application capability of the proposed model is consistent with the theoretical analysis.
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Sun, Haidong, Zhengtao Zhang, and Peng Li. "Design and Analysis of Bridge Inspection System Based on Wireless Communication and Internet of Things Technology." Wireless Communications and Mobile Computing 2021 (October 13, 2021): 1–5. http://dx.doi.org/10.1155/2021/3410844.
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The continuous development of information technology and various electronic devices has accelerated the process of informatization and digitization, enabling the development and application of the emerging technology of wireless communication and the Internet of Things. Since the continuous occurrence of vicious bridge collapse accidents in China in recent years, the problem of bridge inspection has become a hot topic among the people. At the same time, how to apply wireless communication and the Internet of Things technology to bridge inspection systems has also become a new research topic. This article mainly studies the design and analysis of bridge detection systems based on wireless communication and Internet of Things technology. In order to expand the field of bridge detection and standard management and improve the credibility and reliability of safety problem prediction and evaluation, the bridge detection system will integrate IoT sensing, internet, remote communication, digital signal analysis and processing, big data knowledge mining, big data prediction and other technologies, design and analysis of the main structure of roads and bridges, and other multifaceted knowledge fields and build a professional intelligent digital network based on bridge inspection data collection, monitoring, analysis, evaluation, and early warning. From design to use and maintenance of the bridge, a digital neural network spanning time and space throughout the life cycle is constructed to construct a digital brain with bridge sensing points as neurons. This paper uses high-power infrared sensor equipment, satellite positioning systems, sensor equipment, and other technical equipment to achieve the purpose of data communication and exchange and realize intelligent positioning, identification, supervision, tracking, and other functions, making the wireless communication and Internet of Things reliable transmission, comprehensive perception, intelligent processing, and other capabilities very effective in the field of bridge inspection. Through the research and analysis of this article, there are more and more bridge inspection systems developed by the Internet of Things and wireless communication technology in China, and the percentage of related equipment used can reach more than 90%. The functions of the bridge inspection system are becoming more and more complete, and the results of the inspection data are also increasing.
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Liu, Kangying. "IT Audit of Cloud Accounting Platform Based on Big Data." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012024. http://dx.doi.org/10.1088/1742-6596/2066/1/012024.
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Abstract So far, the level of science and technology in China has experienced unprecedented development, at the same time, the computer level of our country has improved significantly. At present, more and more Big data technologies are gradually entering the field of vision of people, in most industries of our country they are dedicated to the research and application of big data technology. Big data, these can provide people with more and more reliable information.At the same time, it also brings great convenience to people’s daily life and brings creativity to enterprises.With the development of the times and the progress of science and technology, the application of modern computer technology can achieve the digitization of information technology audit. That is, the audit project data ion of digital IT are kept and stored according to actual needs. According to the relationship between different electronic documents, link and combine them to improve the efficiency of IT audit and minimize audit costs. In recent years, Our country’s informatization process has become deeper and deeper, which has brought great convenience to all walks of life. In traditional IT auditing, it has been unable to meet the current development needs. This requires redesigning IT auditing thinking, establishing a new framework, and creating digital IT auditing to meet the needs of the times and improve IT auditing efficiency. In this article, we will study all kinds of data in the digital IT audit project, and we will discuss the application audit of the cloud-based computing platform.Big Data, mainly the audit of cloud computing platform data, and whether it is effective in the input, processing and output process, in order to achieve the effect of high speed and high quality.Finally, summarize the research results.The efficiency of IT verification has improved significantly.
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Shang, Dinghui, Rufu Qin, Huiping Xu, Changwei Xu, Kelin Sun, and Yusheng Zhou. "Variation of Suspended Particles in the Bottom Layer of the East China Sea with Data from Seafloor Observatory." Sensors 19, no. 23 (November 25, 2019): 5156. http://dx.doi.org/10.3390/s19235156.
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The in situ scattering and transmissometry laser (LISST-100X), equipped with an acoustic wave and current (AWAC) meter and conductivity, temperature, and depth (CTD) instruments over the seabed in the East China Sea, was used to monitor the variation in suspended particles in the bottom sea layer, including particle size distribution (PSD) and volume concentration. The power law approximation was tested to describe the variability in PSD based on the field data. The results show that the power law was robust in processing continuous data, accompanied with the same optimal reference particle size (~63 μm) and little change in the corresponding exponent (~3.4) in both periods. Suspended particles were divided into three types: macroflocs (>133 μm), microflocs (36–133 μm), and single grains (<36 μm). Particle sizes were coarse during the two seasons, with macroflocs representing more than 60% of all the suspended particles, especially in February, when the particle size spectra were usually open-ended. Results from the harmonic analysis method indicate that tidal-induced resuspension and advection are the major reasons for the diurnal dynamics of sediments. Due to the tidal asymmetry in the region, we only found one mode in volume concentration at the moment of maximum velocity. However, the ratios of macroflocs were bimodal, with maximum floods and ebbs in one tidal cycle in February, when the higher mode at the maximum ebbs may be contributed to by the flocculation of finer particles considering the decreasing ratios of finer particles. Due to the enhanced stratification and the clean barrier built up by the Taiwan Warm Current in the southeast corner, the significant daily variation in suspended particles observed in February weakened in September. The influence of waves was uncertain, although the correlation coefficient between significant wave height and volume concentration was about 64% in February.
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Ye, Fengchun, Zhihong Liu, and Longgong Xia. "Materials and Energy Balance of E-Waste Smelting—An Industrial Case Study in China." Metals 11, no. 11 (November 11, 2021): 1814. http://dx.doi.org/10.3390/met11111814.
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The application of Nerin Recycling Technologies (NRT) in electronic waste (E-waste) smelting was introduced in this study, and the material and energy balance was calculated based on the practical data with the METSIM software (METSIM International, USA). The main results are as follows: (1) the optimized processing parameters in the NRT smelting practice were the E-waste feeding rate of 5.95 t/h, oxidation smelting duration of 3.5 h, reduction smelting duration of 0.5 h, oxygen enrichment of 21–40 vol.%, oxygen consumption of 68.06 Nm3/ton raw material, slag temperature of 1280 °C, slag composition: Fe/SiO2 mass ratio of 0.8–1.4, CaO, 15–20 wt.%, Cu in crude copper ≥ 95 wt.%, Cu in slag, 0.5 wt.%, recovery of Cu, Au, and Ag ≥ 98%; (2) 98.49% Au, 98.04% Ag, 94.11% Ni, and 79.13% Sn entered the crude copper phase in the smelting process, 76.73% Pb and 67.22% Zn volatilized to the dust phase, and all halogen elements terminated in the dust and off-gas; (3) total heat input of the process was 79,480 MJ/h, the energy released by chemical reactions accounted for 69.94% of the total, and heat from fuels burning accounted for 33.04%. The energy brought away by the off-gas was 38,440 MJ/h, which was the largest part in heat output. The heat loss with the smelting slag accounted for 28.47% of the total.
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Huang, Xia, Shunyi Zheng, and Li Gui. "Automatic Measurement of Morphological Traits of Typical Leaf Samples." Sensors 21, no. 6 (March 23, 2021): 2247. http://dx.doi.org/10.3390/s21062247.
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It is still a challenging task to automatically measure plants. A novel method for automatic plant measurement based on a hand-held three-dimensional (3D) laser scanner is proposed. The objective of this method is to automatically select typical leaf samples and estimate their morphological traits from different occluded live plants. The method mainly includes data acquisition and processing. Data acquisition is to obtain the high-precision 3D mesh model of the plant that is reconstructed in real-time during data scanning by a hand-held 3D laser scanner (ZGScan 717, made in Zhongguan Automation Technology, Wuhan, China). Data processing mainly includes typical leaf sample extraction and morphological trait estimation based on a multi-level region growing segmentation method using two leaf shape models. Four scale-related traits and six corresponding scale-invariant traits can be automatically estimated. Experiments on four groups of different canopy-occluded plants are conducted. Experiment results show that for plants with different canopy occlusions, 94.02% of typical leaf samples can be scanned well and 87.61% of typical leaf samples can be automatically extracted. The automatically estimated morphological traits are correlated with the manually measured values EF (the modeling efficiency) above 0.8919 for scale-related traits and EF above 0.7434 for scale-invariant traits). It takes an average of 196.37 seconds (186.08 seconds for data scanning, 5.95 seconds for 3D plant model output, and 4.36 seconds for data processing) for a plant measurement. The robustness and low time cost of the proposed method for different canopy-occluded plants show potential applications for real-time plant measurement and high-throughput plant phenotype.
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Yang, Huaiyuan, Yucheng Li, Hua Zhou, Yili Zhao, and Lei Song. "A Research on the Sharing Platform of Wild Bird Data in Yunnan Province Based on Blockchain and Interstellar File System." Sensors 22, no. 18 (September 14, 2022): 6961. http://dx.doi.org/10.3390/s22186961.
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Sharing scientific data is an effective means to rationally exploit scientific data and is vital to promote the development of the industrial chain and improve the level of science and technology. In recent years, the popularity of the open data platform has increased, but problems remain, including imperfect system architecture, unsound privacy and security, and non-standardized interaction data. To address these problems, the blockchain’s decentralization, smart contracts, distributed storage, and other features can be used as the core technology for open data systems. This paper addresses the problems of opening, allocation-right confirmation, sharing, and rational use of wild-bird data from Yunnan Province, China. A data storage model is proposed based on the blockchain and interstellar file system and is applied to wild-bird data to overcome the mutual distrust between ornithology institutions in the collaborative processing and data storage of bird data. The model provides secure storage and secure access control of bird data in the cloud, thereby ensuring the decentralized and secure storage of wild-bird data for multiple research institutions.
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Liu, Yiju, and E. F. Avdokushin. "Forming the Foundations of the “Digital Silk Road”." World of new economy 13, no. 4 (December 4, 2019): 62–71. http://dx.doi.org/10.26794/2220-6469-2019-13-4-62-71.
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The article focuses on the analysis of the innovative component of the Chinese initiative “One Belt, One Road” and its positioning as a “Digital Silk Road”. The Chinese initiative “One Belt, One Road” as its practical implementation demonstrates its new features and peculiarities. The article analyses the practice of introducing new technologies into production in Chinese enterprises, as well as interaction with Russian and Kazakhstan companies. The article is based on the problem-factual principle of presentation of the material using the method of factor analysis of information obtained in the course of economic and statistical data collection and processing. The primary sources used in the present article are mainly the original works of Chinese authors, as well as publications in Russian scientific journals and the media. Electronic commerce, as well as several new technologies used on the routes of the new silk road like the Internet, artificial intelligence, big data, cloud computing, blockchain, are becoming a basis of new Silk Road. The transition to the digitalisation of the new Silk Road is determined by the significant achievements of China in the development of electronic commerce, artificial intelligence, FinTech. The concept of digitalisation includes the main imperatives of the “Fourth industrial revolution”, which are beginning to be introduced along the routes of the economic belt of the Silk Road.
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Luo, Chuan, Xin Luo, and Ning Ma. "Exploring the determinants of information adoption." Nankai Business Review International 10, no. 4 (November 4, 2019): 618–34. http://dx.doi.org/10.1108/nbri-11-2018-0065.
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Purpose The purpose of this study is to explore the effects of review group’s content-related and environment-associated attributes on information adoption intention of information readers. In addition, this study further investigates the effects of these determinants in different websites with different commercial attributes (i.e. online stores and third party forums). Design/methodology/approach This study used a structured online survey to collect data from an online store and a third party forum in China; totally 302 responses were collected. Findings The empirical results confirm that all of the five determinants significantly affect the information adoption intention of information readers. Furthermore, the authors found that four of the determinants have distinct effects in different websites. Originality/value The findings of this study validate the significant moderating role of website attributes in readers’ information processing. Information readers use distinct criteria to evaluate the received review information; electronic word-of-mouth determinants have varying effects on different websites with different commercial attributes.
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Li, Haifeng, Xin Dou, Chao Tao, Zhixiang Wu, Jie Chen, Jian Peng, Min Deng, and Ling Zhao. "RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data." Sensors 20, no. 6 (March 12, 2020): 1594. http://dx.doi.org/10.3390/s20061594.
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Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications.
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Li, Yunkun, and Gabriela Mogos. "Digital forensics on Tencent QQ-instant messaging service in China." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (January 1, 2022): 412. http://dx.doi.org/10.11591/ijeecs.v29.i1.pp412-420.
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Data packet analysis targeting instant messaging (IM) applications has become one of the most mentioned case examples in the digital forensic industry, considering that the forensic engineers can extract valuable information by analysing the data packets used by the IM software. The crucial part of this process is to accomplish a series of research and investigation, in addition to correctly implement the related forensics tools. This paper is intended to use QQ, a popular IM software in China, as an experiment example, in cooperation with various tools from Kali Linux, a digital forensics-oriented Linux distribution, to present the complete process of the data packet analysis operation. The result concludes from the experiment may be able to provide constructive suggestions to other related digital forensics cases.
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Luo, L., Z. Jia, Z. Shen, Y. Zhang, and S. Liu. "Study on time measurement for CSA-based readout electronics in STCF ECAL." Journal of Instrumentation 17, no. 02 (February 1, 2022): P02034. http://dx.doi.org/10.1088/1748-0221/17/02/p02034.
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Abstract Super Tau-Charm Facility (STCF) will provide unique support for particle physics research in China owing to its high luminosity and large dynamic range. Its electromagnetic calorimeter (ECAL) is designed as a new type of calorimeter which can perform high-precision energy and time measurement at the same time. Considering the high event rate and pile up induced by high luminosity, and the large channel number scale of ECAL, the readout electronics needs to adopt a high-precision, unconventional, and real-time processing method to reduce the data amount in transmission. Based on the charge-sensitive front-end electronics in energy measurement, an online time measurement method based on waveform fitting algorithm has been studied and implemented in this work. The parameters which would affect the time resolution are analyzed according to simulation and experimental test. After the optimization of electronics, a time resolution of 165 ps at the equivalent deposition energy of 1 GeV is achieved, which indicates that the waveform fitting method based on charge-sensitive readout electronics can meet the future requirements.
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Rao, Pinzeng, Linjiang Lou, Weiguo Jiang, Yicheng Wang, Xiaoya Wang, and Xiayu Cao. "Continuous Dynamics Monitoring of Multi-Lake Water Extent Using a Spatial and Temporal Adaptive Fusion Method Based on Two Sets of MODIS Products." Sensors 19, no. 22 (November 8, 2019): 4873. http://dx.doi.org/10.3390/s19224873.
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Due to the widespread presence of noise, such as clouds and cloud shadows, continuous, high spatiotemporal-resolution dynamic monitoring of lake water extents is still limited using remote sensing data. This study aims to take an approach to mapping continuous time series of highly-accurate lake water extents. Four lakes from diverse regions of China were selected as cases. In order to reduce the impact of noise and ensure high spatial and temporal resolution of the final results, two sets of MODIS products (including MOD09A1 and MOD13Q1) are used to extract water bodies. This approach mainly comprises preliminary classification, post processing and data fusion. The preliminary classification used the Random Forest (RF) classifier to efficiently and automatically obtain the initial classification results. Post-processing is implemented to repair the classification results affected by noise as much as possible. The processed results of the two sets of products are fused by using the Homologous Data-Based Spatial and Temporal Adaptive Fusion Method (HDSTAFM), which reduces the effect of noise and also improve the temporal and spatial resolution for the final water results. We determined the accuracy using Landsat-based water results, and the values of overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficients (KC) are mostly greater than 0.9. Good correlation was achieved for a time series of water area and altimetry data, obtained by multiple satellites, and also for water-level data selected from hydrological stations.
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Chen, Yunzhu, and Xingwei Xue. "Advances in the Structural Health Monitoring of Bridges Using Piezoelectric Transducers." Sensors 18, no. 12 (December 7, 2018): 4312. http://dx.doi.org/10.3390/s18124312.
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With the rapid development of the world’s transportation infrastructure, many long-span bridges were constructed in recent years, especially in China. However, these bridges are easily subjected to various damages due to dynamic loads (such as wind-, earthquake-, and vehicle-induced vibration) or environmental factors (such as corrosion). Therefore, structural health monitoring (SHM) is vital to guarantee the safety of bridges in their service lives. With its wide frequency response range, fast response, simple preparation process, ease of processing, low cost, and other advantages, the piezoelectric transducer is commonly employed for the SHM of bridges. This paper summarizes the application of piezoelectric materials for the SHM of bridges, including the monitoring of the concrete strength, bolt looseness, steel corrosion, and grouting density. For each problem, the application of piezoelectric materials in different research methods is described. The related data processing methods for four types of bridge detection are briefly summarized, and the principles of each method in practical application are listed. Finally, issues to be studied when using piezoelectric materials for monitoring are discussed, and future application prospects and development directions are presented.
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Fan, Yufeng, Xiaodong Zhu, Hulin Sui, Haotai Sun, and Zhongming Wang. "Design and Application of Toxic and Harmful Gas Monitoring System in Fire Fighting." Sensors 19, no. 2 (January 17, 2019): 369. http://dx.doi.org/10.3390/s19020369.
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In recent years, fire accidents in petrochemical plant areas and dangerous goods storage ports in China have shown a trend of frequent occurrence. Toxic and harmful gases are diffused in the scenes of these accidents, which causes great difficulties for fire fighting and rescue operations of fire fighting forces, and consequently, casualties of firefighters often occur. In order to ensure the safety of firefighters in such places, this paper designs a monitoring system of toxic and harmful gases specially used in fire fighting and rescue sites of fire forces, and establishes the transmission network, monitoring terminal and data processing software of the monitoring system of toxic and harmful gases, establishing the danger model of the monitoring area of toxic and harmful gas-monitoring terminal, and the danger model of fire fighters’ working area, fusing the field toxic and harmful gas data, terminal positioning data, and field environmental data, designing the data structure of the input data set and the network structure of the RNN cyclic neural network model, and realizing the dynamic early warning of toxic and harmful gases on site.
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Shen, Shiguang, Chaoyang Zhu, Chenjing Fan, Chengcheng Wu, Xinran Huang, and Lin Zhou. "Research on the evolution and driving forces of the manufacturing industry during the “13th five-year plan” period in Jiangsu province of China based on natural language processing." PLOS ONE 16, no. 8 (August 18, 2021): e0256162. http://dx.doi.org/10.1371/journal.pone.0256162.
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The development of China’s manufacturing industry has received global attention. However, research on the distribution pattern, changes, and driving forces of the manufacturing industry has been limited by the accessibility of data. This study proposes a method for classifying based on natural language processing. A case study was conducted employing this method, hotspot detection and driving force analysis, wherein the driving forces industrial development during the “13th Five-Year plan” period in Jiangsu province were determined. The main conclusions of the empirical case study are as follows. 1) Through the acquisition of Amap’s point-of-interest (POI, a special point location that commonly used in modern automotive navigation systems.) data, an industry type classification algorithm based on the natural language processing of POI names is proposed, with Jiangsu Province serving as an example. The empirical test shows that the accuracy was 95%, and the kappa coefficient was 0.872. 2) The seven types of manufacturing industries including the pulp and paper (PP) industry, metallurgical chemical (MC) industry, pharmaceutical manufacturing (PM) industry, machinery and electronics (ME) industry, wood furniture (WF) industry, textile clothing (TC) industry, and agricultural and food product processing (AF) industry are drawn through a 1 km× 1km projection grid. The evolution map of the spatial pattern and the density field hotspots are also drawn. 3) After analyzing the driving forces of the changes in the number of manufacturing industries mentioned above, we found that manufacturing base, distance from town, population, GDP per capita, distance from the railway station were the significant driving factors of changes in the manufacturing industries mentioned above. The results of this research can help guide the development of manufacturing industries, maximize the advantages of regional factors and conditions, and provide insight into how the spatial layout of the manufacturing industry could be optimized.
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Xiu, Xiaolei, Qing Qian, and Sizhu Wu. "Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study." JMIR Medical Informatics 8, no. 10 (October 7, 2020): e18287. http://dx.doi.org/10.2196/18287.
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Background With the increasing incidences and mortality of digestive system tumor diseases in China, ways to use clinical experience data in Chinese electronic medical records (CEMRs) to determine potentially effective relationships between diagnosis and treatment have become a priority. As an important part of artificial intelligence, a knowledge graph is a powerful tool for information processing and knowledge organization that provides an ideal means to solve this problem. Objective This study aimed to construct a semantic-driven digestive system tumor knowledge graph (DSTKG) to represent the knowledge in CEMRs with fine granularity and semantics. Methods This paper focuses on the knowledge graph schema and semantic relationships that were the main challenges for constructing a Chinese tumor knowledge graph. The DSTKG was developed through a multistep procedure. As an initial step, a complete DSTKG construction framework based on CEMRs was proposed. Then, this research built a knowledge graph schema containing 7 classes and 16 kinds of semantic relationships and accomplished the DSTKG by knowledge extraction, named entity linking, and drawing the knowledge graph. Finally, the quality of the DSTKG was evaluated from 3 aspects: data layer, schema layer, and application layer. Results Experts agreed that the DSTKG was good overall (mean score 4.20). Especially for the aspects of “rationality of schema structure,” “scalability,” and “readability of results,” the DSTKG performed well, with scores of 4.72, 4.67, and 4.69, respectively, which were much higher than the average. However, the small amount of data in the DSTKG negatively affected its “practicability” score. Compared with other Chinese tumor knowledge graphs, the DSTKG can represent more granular entities, properties, and semantic relationships. In addition, the DSTKG was flexible, allowing personalized customization to meet the designer's focus on specific interests in the digestive system tumor. Conclusions We constructed a granular semantic DSTKG. It could provide guidance for the construction of a tumor knowledge graph and provide a preliminary step for the intelligent application of knowledge graphs based on CEMRs. Additional data sources and stronger research on assertion classification are needed to gain insight into the DSTKG’s potential.
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Pan, Yinan, Yuan Li, and Jing Jin. "Design and Implementation of Fully Convolutional Network Algorithm in Landscape Image Processing." Wireless Communications and Mobile Computing 2022 (February 26, 2022): 1–9. http://dx.doi.org/10.1155/2022/7387363.
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With the gradual improvement of the quality of life, taste, and ecological and environmental awareness of urban residents in China, the environmental landscape of residential areas has gradually become a hot spot. At present, the level of the residential environmental landscape has become a necessary means for real estate developers to publicize products and improve economic benefits. Although many residential areas have invested a high cost in constructing environmental landscapes, there are always some deficiencies and defects in the design and implementation of environmental landscapes in residential areas due to various reasons. Therefore, to ameliorate the low efficiency and high cost of manual processing of landscape images, a Fully Convolutional Network (FCN) model based on the traditional Convolutional Neural Network (CNN) is designed for semantic segmentation of landscape images to deal with the excessive amount of landscape elements in landscape image processing. The deconvolution method is utilized to realize pixel-level semantic segmentation. Besides, the image preprocessing method enhances the data to prevent overfitting from commonly occurring in FCN. Moreover, the model two-stage training method ameliorates long training time and complex convergence in deep learning. Finally, three upsampling network structures, i.e., FCN-32s, FCN-16s, and FCN-8s, are selected for a comparative experiment to determine the most suitable network. The experimental results demonstrate that the FCN-8s upsampling network structure is the most prominent; it attains a pixel accuracy of more than 90%, an average accuracy of 88%, and an average Image Understanding of 75%. The three values are the highest among the three upsampling structures, indicating that the FCN-8s can realize accurate landscape image processing. Besides, the recognition accuracy of FCN for landscape elements reaches 90%, 25% higher than that of CNN. This method is effective and accurate in classifying landscape elements, improves the classification accuracy intelligently, and significantly reduces the cost of landscape element classification, which is feasible.
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Rong, Yu, Zhong Zhuang, Zhengwei He, and Xuming Wang. "A Maritime Traffic Network Mining Method Based on Massive Trajectory Data." Electronics 11, no. 7 (March 23, 2022): 987. http://dx.doi.org/10.3390/electronics11070987.
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Intelligent ships are the future direction of maritime transportation. Route design and route planning of intelligent ships require high-precision, real-time maritime traffic network information, which changes dynamically as the traffic environment changes. At present, there is a lack of high-precision and accurate information extraction methods for maritime traffic networks. Based on the massive trajectory data of vessels, the adaptive waypoint extraction model (ANPG) is proposed to extract the critical waypoints on the traffic network, and the improved kernel density estimation method (KDE-T) is constructed to mine the spatial–temporal characteristics of marine lanes. Then, an automatic traffic network generation model (NNCM), based on the pix2pix network, is put forward to reconstruct the maritime traffic network. NNCM has been tested on the historical trajectory data of Humen waters and Dongping waters in China, the experimental results show that the NNCM model improves the extraction accuracy by 13% and 33% compared to the geometric analysis method and density clustering method. It is of great significance to improve the navigation accuracy of intelligent ships. This method can also provide important technical support for waterway design and monitoring and maritime traffic supervision.
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Jiang, Yu’e, and Jiaxiang Liu. "Health Monitoring System for Nursing Homes with Lightweight Security and Privacy Protection." Journal of Electrical and Computer Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1360289.
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With the rapid growth of aged population in China, it is urgent to design a safe and effective monitoring system for the nursing homes. An optimized scheme and high performance security and privacy protection for monitoring system have already become the focus studied especially. So this paper proposed a health monitoring system with lightweight security and privacy protection for nursing homes. Dual-band RFID, virtual routing location algorithm, and diet and exercise data collection based on RFID were adopted to obtain the location and health information. And that fused a mobile authentication protocol based on Hash function to realize security access and privacy protection, which can improve security and reduce the complexity of calculation and the implementation cost compared with the typical authentication protocols. The experiment results show that the ratio of relative network delay is below 35%. The system has strong real-time, high security, more comprehensive data, and lower cost of computation and communication. It can satisfy the requirements of health monitoring for nursing homes.
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Du, Jian, Ting Chen, and Luxia Zhang. "Measuring the Interactions Between Health Demand, Informatics Supply, and Technological Applications in Digital Medical Innovation for China: Content Mapping and Analysis." JMIR Medical Informatics 9, no. 7 (July 6, 2021): e26393. http://dx.doi.org/10.2196/26393.
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Background There were 2 major incentives introduced by the Chinese government to promote medical informatics in 2009 and 2016. As new drugs are the major source of medical innovation, informatics-related concepts and techniques are a major source of digital medical innovation. However, it is unclear whether the research efforts of medical informatics in China have met the health needs, such as disease management and population health. Objective We proposed an approach to mapping the interplay between different knowledge entities by using the tree structure of Medical Subject Headings (MeSH) to gain insights into the interactions between informatics supply, health demand, and technological applications in digital medical innovation in China. Methods All terms under the MeSH tree parent node “Diseases [C]” or node “Health [N01.400]” or “Public Health [N06.850]” were labelled as H. All terms under the node “Information Science [L]” were labelled as I, and all terms under node “Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]” were labelled as T. The H-I-T interactions can be measured by using their co-occurrences in a given publication. Results The H-I-T interactions in China are showing significant growth and a more concentrated interplay were observed. Computing methodologies, informatics, and communications media (such as social media and the internet) constitute the majority of I-related concepts and techniques used for resolving the health promotion and diseases management problems in China. Generally there is a positive correlation between the burden and informatics research efforts for diseases in China. We think it is not contradictory that informatics research should be focused on the greatest burden of diseases or where it can have the most impact. Artificial intelligence is a competing field of medical informatics research in China, with a notable focus on diagnostic deep learning algorithms for medical imaging. Conclusions It is suggested that technological transfers, namely the functionality to be realized by medical/health informatics (eg, diagnosis, therapeutics, surgical procedures, laboratory testing techniques, and equipment and supplies) should be strengthened. Research on natural language processing and electronic health records should also be strengthened to improve the real-world applications of health information technologies and big data in the future.
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Wang, Zheyu, Haoce Huang, Liping Cui, Juan Chen, Jiye An, Huilong Duan, Huiqing Ge, and Ning Deng. "Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System." JMIR Medical Informatics 8, no. 4 (April 23, 2020): e17642. http://dx.doi.org/10.2196/17642.
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Background Health education emerged as an important intervention for improving the awareness and self-management abilities of chronic disease patients. The development of information technologies has changed the form of patient educational materials from traditional paper materials to electronic materials. To date, the amount of patient educational materials on the internet is tremendous, with variable quality, which makes it hard to identify the most valuable materials by individuals lacking medical backgrounds. Objective The aim of this study was to develop a health recommender system to provide appropriate educational materials for chronic disease patients in China and evaluate the effect of this system. Methods A knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP). Results The constructed ontology contained 40 classes, 31 object properties, 67 data properties, and 32 individuals. A total of 80 SWRL rules were defined to implement the semantic logic of mapping patient original data to the ontology vector space. The recommender system was implemented as a separate Web service connected with patients' smartphones. According to the evaluation results, our system can achieve a macro precision up to 0.970 for the top 1 recommendation and an overall MAP score up to 0.628. Conclusions This study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.
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Zhang, Lei, Lun Xie, Zhiliang Wang, and Chen Huang. "Cascade Parallel Random Forest Algorithm for Predicting Rice Diseases in Big Data Analysis." Electronics 11, no. 7 (March 29, 2022): 1079. http://dx.doi.org/10.3390/electronics11071079.
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Experts in agriculture have conducted considerable work on rice plant protection. However, in-depth exploration of the plant disease problem has not been performed. In this paper, we find the trend of rice diseases by using the cascade parallel random forest (CPRF) algorithm on the basis of relevant data analysis in the recent 20 years. To confront the problems of high dimensions and imbalanced data distributions in agricultural data. The proposed method diminishes the dimensions and the negative effect of imbalanced data by cascading several random forests. For experimental evaluation, we utilize the Spark platform to analyze botanic data from several provinces of China in the past 20 years. Results for the CPRF model of plant diseases that affect rice yield, as well as results for samples by using random forest, CRF, and Spark-MLRF are presented, and the accuracy of CPRF is 96.253%, which is higher than that of the other algorithms. These results indicate that the CPRF and the utilization of big data analysis are beneficial in solving the problem of plant diseases.
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Jones, Lewis C. R., Nicholas Goffin, Jinglei Ouyang, Nazanin Mirhossein, Jiaji Xiong, Yufeng Li, Lin Li, et al. "Laser specific energy consumption: How do laser systems compare to other manufacturing processes?" Journal of Laser Applications 34, no. 4 (November 2022): 042029. http://dx.doi.org/10.2351/7.0000790.
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Laser material interactions are routinely praised for their selective processing and high processing rates. However, this does not guarantee that the total manufacturing system has a low energy intensity compared to conventional manufacturing processes. This paper presents the results of a collaborative UK and China research project to improve the comprehension of the total energy consumption and carbon emissions for laser-based manufacturing. A range of individual laser cutting, welding, and cleaning processes were studied to assess their energy efficiency, including the laser and its ancillary subsystems (e.g., cooling and extraction). The project developed a systematic analysis method, adapted from BS ISO 14955-1:2017, which incorporated time and subsystem level studies to quantify all energy consumption components of a laser system. Previous research has identified that the laser system's most significant contributor to the total energy consumption are the auxiliary or supporting subsystems, not the laser emission. This identified that using only the absorbed radiation to evaluate manufacturing efficiency is misleading. All the processes evaluated followed a negative correlation between processing rate (kg/h) and specific energy consumption (J/kg). The new data conclude that laser processes have a relatively high energy intensity compared to conventional manufacturing alternatives. The results can be used to identify where the most significant improvements to individual laser systems can be made. The comprehensive comparison of processes allows manufacturers to select processes to improve environmental impact.
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Tang, Ke, Haiwen Yuan, Jianxun Lv, and Fengchen Chen. "Developing an Intelligent Monitoring Technology for Airport Stone Column Machines." Sensors 20, no. 11 (May 28, 2020): 3050. http://dx.doi.org/10.3390/s20113050.
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Most of the construction machinery for vibro-sinking stone columns, which are widely used in China, needs to be improved in terms of degree of automation. Engineering quality control is mainly carried out post-inspection; consequently, it is difficult to control the construction quality in real time. According to the construction characteristics of traditional stone column machines, we established the theory and model for the real-time monitoring of stone column construction, as well as put forward an intelligent monitoring method for stone column machines. With the comprehensive application of critical technologies such as the Global Navigation Satellite System (GNSS) measurement technology, laser ranging sensors, and massive data processing, an intelligent data acquisition technique and associated monitoring equipment for stone column construction machines are developed. The data acquisition and storage of crucial construction parameters, such as pile depth, pile point co-ordinates, bearing layer current, and reverse insertion times, are realized. A large number of actual construction data are collected and the construction quality parameters of stone column machines are obtained. By comparison with third-party detection data, it is verified that the intelligent monitoring technique for stone column machines proposed in this paper is feasible.