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Yan, Yilin, Jonathan Chen y Mei-Ling Shyu. "Efficient Large-Scale Stance Detection in Tweets". International Journal of Multimedia Data Engineering and Management 9, n.º 3 (julio de 2018): 1–16. http://dx.doi.org/10.4018/ijmdem.2018070101.
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Stance detection is an important research direction which attempts to automatically determine the attitude (positive, negative, or neutral) of the author of text (such as tweets), towards a target. Nowadays, a number of frameworks have been proposed using deep learning techniques that show promising results in application domains such as automatic speech recognition and computer vision, as well as natural language processing (NLP). This article shows a novel deep learning-based fast stance detection framework in bipolar affinities on Twitter. It is noted that millions of tweets regarding Clinton and Trump were produced per day on Twitter during the 2016 United States presidential election campaign, and thus it is used as a test use case because of its significant and unique counter-factual properties. In addition, stance detection can be utilized to imply the political tendency of the general public. Experimental results show that the proposed framework achieves high accuracy results when compared to several existing stance detection methods.
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Ghimire, Niroj y Surendra Shrestha. "Fake News Stance Detection using Deep Neural Network". Journal of Lumbini Engineering College 4, n.º 1 (7 de diciembre de 2022): 49–53. http://dx.doi.org/10.3126/lecj.v4i1.49366.
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With the advancement of technology, fake news is more widely exposed to users. Fake news may be found on the Internet, news sources and social media platforms. The spread of the fake news has harmed both individuals and society. The way to observe fake news using the stance detection technique is the focus of this paper. Given a set of news body and headline pairs, stance detection is the task of automatic detection of relationships among pieces of text. Pre-trained GloVe word embedding is used for the word to vector representation as it can capture the inter-word semantic information. The LSTM neural network had been shown efficient in deep learning applications because it can capture sequential information of input data. In this paper, it is found that the LSTM-based encoding decoding model using pre-trained GloVe word embedding achieved 93.69% accuracy on the FNC-1 dataset.
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Willemsen, A. T. M., F. Bloemhof y H. B. K. Boom. "Automatic stance-swing phase detection from accelerometer data for peroneal nerve stimulation". IEEE Transactions on Biomedical Engineering 37, n.º 12 (1990): 1201–8. http://dx.doi.org/10.1109/10.64463.
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Martínez, Rubén Yáñez, Guillermo Blanco y Anália Lourenço. "Spanish Corpora of tweets about COVID-19 vaccination for automatic stance detection". Information Processing & Management 60, n.º 3 (mayo de 2023): 103294. http://dx.doi.org/10.1016/j.ipm.2023.103294.
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Stede, Manfred. "Automatic argumentation mining and the role of stance and sentiment". Journal of Argumentation in Context 9, n.º 1 (4 de mayo de 2020): 19–41. http://dx.doi.org/10.1075/jaic.00006.ste.
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Abstract Argumentation mining is a subfield of Computational Linguistics that aims (primarily) at automatically finding arguments and their structural components in natural language text. We provide a short introduction to this field, intended for an audience with a limited computational background. After explaining the subtasks involved in this problem of deriving the structure of arguments, we describe two other applications that are popular in computational linguistics: sentiment analysis and stance detection. From the linguistic viewpoint, they concern the semantics of evaluation in language. In the final part of the paper, we briefly examine the roles that these two tasks play in argumentation mining, both in current practice, and in possible future systems.
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Lidstone, Daniel E., Louise M. Porcher, Jessica DeBerardinis, Janet S. Dufek y Mohamed B. Trabia. "Concurrent Validity of an Automated Footprint Detection Algorithm to Measure Plantar Contact Area During Walking". Journal of the American Podiatric Medical Association 109, n.º 6 (1 de noviembre de 2019): 416–25. http://dx.doi.org/10.7547/17-118.
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Background: Monitoring footprints during walking can lead to better identification of foot structure and abnormalities. Current techniques for footprint measurements are either static or dynamic, with low resolution. This work presents an approach to monitor the plantar contact area when walking using high-speed videography. Methods: Footprint images were collected by asking the participants to walk across a custom-built acrylic walkway with a high-resolution digital camera placed directly underneath the walkway. This study proposes an automated footprint identification algorithm (Automatic Identification Algorithm) to measure the footprint throughout the stance phase of walking. This algorithm used coloration of the plantar tissue that was in contact with the acrylic walkway to distinguish the plantar contact area from other regions of the foot that were not in contact. Results: The intraclass correlation coefficient (ICC) demonstrated strong agreement between the proposed automated approach and the gold standard manual method (ICC = 0.939). Strong agreement between the two methods also was found for each phase of stance (ICC > 0.78). Conclusions: The proposed automated footprint detection technique identified the plantar contact area during walking with strong agreement with a manual gold standard method. This is the first study to demonstrate the concurrent validity of an automated identification algorithm to measure the plantar contact area during walking.
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Houliston, B. R., A. F. Merry y D. T. Parry. "TADAA: Towards Automated Detection of Anaesthetic Activity". Methods of Information in Medicine 50, n.º 05 (2011): 464–71. http://dx.doi.org/10.3414/me11-02-0001.
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SummaryBackground: Task analysis is a valuable research method for better understanding the activity of anaesthetists in the operating room (OR), providing evidence for designing and evaluating improvements to systems and processes. It may also assist in identifying potential error paths to adverse events, ultimately improving patient safety. Human observers are the current ‘gold standard’ for capturing task data, but they are expensive and have cognitive limitations.Objectives: Towards Automated Detection of Anaesthetic Activity (TADAA) – aims to produce an automated task analysis system, employing Radio Frequency Identification (RFID) technology to capture anaesthetists’ location, orientation and stance (LOS). This is the first stage in a scheme for automatic detection of activity.Methods: Active RFID tags were attached to anaesthetists and various objects in a high fidelity OR simulator, and anesthetic procedures performed. The anaesthetists’ LOSs were calculated using received signal strength (RSS) measurements combined with machine learning tools including Self-Organizing Maps (SOMs). These LOSs were compared to those derived from video recordings.Results: SOM clustering was effective at determining anaesthetists’ LOS from RSS data for each procedure. However cross-procedure comparison was less reliable, probably because of changes in the environment.Conclusions: Active RFID tags provide potentially useful information on LOS at a low cost and with minimal impact on the work environment. Machine learning techniques may be employed to handle the variable nature of RFID’s radio signals. Work on mapping LOS data to activities will involve integration with other sensors and task analysis techniques.
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Omero, Paolo, Massimiliano Valotto, Riccardo Bellana, Ramona Bongelli, Ilaria Riccioni, Andrzej Zuczkowski y Carlo Tasso. "Writer’s uncertainty identification in scientific biomedical articles: a tool for automatic if-clause tagging". Language Resources and Evaluation 54, n.º 4 (11 de junio de 2020): 1161–81. http://dx.doi.org/10.1007/s10579-020-09491-8.
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Abstract In a previous study, we manually identified seven categories (verbs, non-verbs, modal verbs in the simple present, modal verbs in the conditional mood, if, uncertain questions, and epistemic future) of Uncertainty Markers (UMs) in a corpus of 80 articles from the British Medical Journal randomly sampled from a 167-year period (1840–2007). The UMs detected on the base of an epistemic stance approach were those referring only to the authors of the articles and only in the present. We also performed preliminary experiments to assess the manual annotated corpus and to establish a baseline for the UMs automatic detection. The results of the experiments showed that most UMs could be recognized with good accuracy, except for the if-category, which includes four subcategories: if-clauses in a narrow sense; if-less clauses; as if/as though; if and whether introducing embedded questions. The unsatisfactory results concerning the if-category were probably due to both its complexity and the inadequacy of the detection rules, which were only lexical, not grammatical. In the current article, we describe a different approach, which combines grammatical and syntactic rules. The performed experiments show that the identification of uncertainty in the if-category has been largely double improved compared to our previous results. The complex overall process of uncertainty detection can greatly profit from a hybrid approach which should combine supervised Machine learning techniques with a knowledge-based approach constituted by a rule-based inference engine devoted to the if-clause case and designed on the basis of the above mentioned epistemic stance approach.
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Karande, Hema, Rahee Walambe, Victor Benjamin, Ketan Kotecha y TS Raghu. "Stance detection with BERT embeddings for credibility analysis of information on social media". PeerJ Computer Science 7 (14 de abril de 2021): e467. http://dx.doi.org/10.7717/peerj-cs.467.
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The evolution of electronic media is a mixed blessing. Due to the easy access, low cost, and faster reach of the information, people search out and devour news from online social networks. In contrast, the increasing acceptance of social media reporting leads to the spread of fake news. This is a minacious problem that causes disputes and endangers the societal stability and harmony. Fake news spread has gained attention from researchers due to its vicious nature. proliferation of misinformation in all media, from the internet to cable news, paid advertising and local news outlets, has made it essential for people to identify the misinformation and sort through the facts. Researchers are trying to analyze the credibility of information and curtail false information on such platforms. Credibility is the believability of the piece of information at hand. Analyzing the credibility of fake news is challenging due to the intent of its creation and the polychromatic nature of the news. In this work, we propose a model for detecting fake news. Our method investigates the content of the news at the early stage i.e., when the news is published but is yet to be disseminated through social media. Our work interprets the content with automatic feature extraction and the relevance of the text pieces. In summary, we introduce stance as one of the features along with the content of the article and employ the pre-trained contextualized word embeddings BERT to obtain the state-of-art results for fake news detection. The experiment conducted on the real-world dataset indicates that our model outperforms the previous work and enables fake news detection with an accuracy of 95.32%.
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Briggs, Eloise V. y Claudia Mazzà. "Automatic methods of hoof-on and -off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass". PLOS ONE 16, n.º 7 (26 de julio de 2021): e0254813. http://dx.doi.org/10.1371/journal.pone.0254813.
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Detection of hoof-on and -off events are essential to gait classification in horses. Wearable sensors have been endorsed as a convenient alternative to the traditional force plate-based method. The aim of this study was to propose and validate inertial sensor-based methods of gait event detection, reviewing different sensor locations and their performance on different gaits and exercise surfaces. Eleven horses of various breeds and ages were recruited to wear inertial sensors attached to the hooves, pasterns and cannons. Gait events detected by pastern and cannon methods were compared to the reference, hoof-detected events. Walk and trot strides were recorded on asphalt, grass and sand. Pastern-based methods were found to be the most accurate and precise for detecting gait events, incurring mean errors of between 1 and 6ms, depending on the limb and gait, on asphalt. These methods incurred consistent errors when used to measure stance durations on all surfaces, with mean errors of 0.1 to 1.16% of a stride cycle. In conclusion, the methods developed and validated here will enable future studies to reliably detect equine gait events using inertial sensors, under a wide variety of field conditions.
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Kiran, Sirra Kanthi, M. Shashi y K. B. Madhuri. "Multi-stage Transfer Learning for Fake News Detection Using AWD-LSTM Network". International Journal of Information Technology and Computer Science 14, n.º 5 (8 de octubre de 2022): 58–69. http://dx.doi.org/10.5815/ijitcs.2022.05.05.
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In the recent decades, the automatic veracity verification of rumors is essential, since online social media platforms allow users to post news item or express opinion towards a circulating piece of information without much restriction. The intention of fake news is to make the readers believe in inaccurate information, where the detection of fake news by using content is a difficult task. So, the auxiliary information: user profile, social engagement of the users, and other user’s comments are useful in the detection of fake news. In this manuscript, a novel multi-stage transfer learning approach is introduced for an effective fake news detection, where it utilizes user’s comments as auxiliary information to detect whether the given tweet is true or false. The stances of the response tweets contain opinions on news/rumors are often used for verifying the veracity of the circulating information. In order to devastate the effects of the specific rumors at the earliest, the multi-stage transfer learning approach automatically predict veracity of rumors jointly with the stances of their response tweets. The proposed multi-stage transfer learning is an inductive transfer learning variation that is used to forecast the stance of responses, then to identify fake news. The proposed model’s effectiveness is evaluated on the two-benchmark datasets: semEval-2017 task 8 and PHEME. The proposed model outperformed the existing approaches by obtaining a classification accuracy of 64.30% and 65.30%, an F-measure of 65.95% and 63.90% on semEval-2017 task 8, and PHEME on event-wise datasets.
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Pluym, Liesbet M., Dominiek Maes, Jürgen Vangeyte, Koen Mertens, Jeroen Baert, Stephanie Van Weyenberg, Sam Millet y Annelies Van Nuffel. "Development of a system for automatic measurements of force and visual stance variables for objective lameness detection in sows: SowSIS". Biosystems Engineering 116, n.º 1 (septiembre de 2013): 64–74. http://dx.doi.org/10.1016/j.biosystemseng.2013.06.009.
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Hatrisse, Chloé, Claire Macaire, Marie Sapone, Camille Hebert, Sandrine Hanne-Poujade, Emeline De Azevedo, Frederic Marin, Pauline Martin y Henry Chateau. "Stance Phase Detection by Inertial Measurement Unit Placed on the Metacarpus of Horses Trotting on Hard and Soft Straight Lines and Circles". Sensors 22, n.º 3 (18 de enero de 2022): 703. http://dx.doi.org/10.3390/s22030703.
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The development of on-board technologies has enabled the development of quantification systems to monitor equine locomotion parameters. Their relevance among others relies on their ability to determine specific locomotor events such as foot-on and heel-off events. The objective of this study was to compare the accuracy of different methods for an automatic gait events detection from inertial measurement units (IMUs). IMUs were positioned on the cannon bone, hooves, and withers of seven horses trotting on hard and soft straight lines and circles. Longitudinal acceleration and angular velocity around the latero-medial axis of the cannon bone, and withers dorso-ventral displacement data were identified to tag the foot-on and a heel-off events. The results were compared with a reference method based on hoof-mounted-IMU data. The developed method showed bias less than 1.79%, 1.46%, 3.45% and −1.94% of stride duration, respectively, for forelimb foot-on and heel-off, and for hindlimb foot-on and heel-off detection, compared to our reference method. The results of this study showed that the developed gait-events detection method had a similar accuracy to other methods developed for straight line analysis and extended this validation to other types of exercise (circles) and ground surface (soft surface).
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De Magistris, Giorgio, Samuele Russo, Paolo Roma, Janusz T. Starczewski y Christian Napoli. "An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19". Information 13, n.º 3 (7 de marzo de 2022): 137. http://dx.doi.org/10.3390/info13030137.
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Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure.
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Verlekar, Tanmay, Luís Soares y Paulo Correia. "Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System". Sensors 18, n.º 9 (21 de agosto de 2018): 2743. http://dx.doi.org/10.3390/s18092743.
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Systemic disorders affecting an individual can cause gait impairments. Successful acquisition and evaluation of features representing such impairments make it possible to estimate the severity of those disorders, which is important information for monitoring patients’ health evolution. However, current state-of-the-art systems perform the acquisition and evaluation of these features in specially equipped laboratories, typically limiting the periodicity of evaluations. With the objective of making health monitoring easier and more accessible, this paper presents a system that performs automatic detection and classification of gait impairments, based on the acquisition and evaluation of biomechanical gait features using a single 2D video camera. The system relies on two different types of features to perform classification: (i) feet-related features, such as step length, step length symmetry, fraction of foot flat during stance phase, normalized step count, speed; and (ii) body-related features, such as the amount of movement while walking, center of gravity shifts and torso orientation. The proposed system uses a support vector machine to decide whether the observed gait is normal or if it belongs to one of three different impaired gait groups. Results show that the proposed system outperforms existing markerless 2D video-based systems, with a classification accuracy of 98.8%.
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Ramadan, Mohamed K., Aliaa A. A. Youssif y Wessam H. El-Behaidy. "Detection and Classification of Human-Carrying Baggage Using DenseNet-161 and Fit One Cycle". Big Data and Cognitive Computing 6, n.º 4 (6 de octubre de 2022): 108. http://dx.doi.org/10.3390/bdcc6040108.
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In recent decades, the crime rate has significantly increased. As a result, the automatic video monitoring system has become increasingly important for researchers in computer vision. A person’s baggage classification is essential in knowing who has abandoned baggage. This paper proposes a model for classifying humans carrying baggage. Two approaches are used for comparison using a deep learning technique. The first approach is based on categorizing human-containing image regions as either with or without baggage. The second approach classifies human-containing image regions based on the human position direction attribute. The proposed model is based on the pretrained DenseNet-161 architecture. It uses a "fit-one-cycle policy" strategy to reduce the training time and achieve better accuracy. The Fastai framework is used for implementation due to its super computational ability, simple workflow, and unique data cleansing functionalities. Our proposed model was experimentally validated, and the results show that the process is sufficiently precise, faster, and outperforms the existing methods. We achieved an accuracy of between 96% and 98.75% for the binary classification and 96.67% and 98.33% for the multi-class classification. For multi-class classification, the datasets, such as PETA, INRIA, ILIDS, and MSMT17, are re-annotated with one’s direction information about one’s stance to test the suggested approach’s efficacy.
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Chung, Wei Der, Woon Ki Na, Shih Chieh Shie, Hsin Pei Chen y Xiao Hu. "Multi-Sensor Based Autonomous Planning for Robotic Manufacturing Systems". Advanced Materials Research 791-793 (septiembre de 2013): 826–30. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.826.
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Current trends in precision machinery include increased adaptability, speed and reliability. This, combined with the development of artificially-intelligent automatic sensors can lead to the establishment of highly-reliable and systematic manufacturing systems. During the automation process, equipment process parameters frequently need to be adjusted to match the requirements of different processes. Thus how to best maintain normal equipment operation and stable quality through these frequent adjustments is a key issue for manufacturers. Therefore, high-quality automated production systems allowing for fast-changeover and real-time automatic detection and performance monitoring are effectively needed.
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Briene, Petra, Olga Szczodry, Pieterjan De Geest, Stephanie Van Weyenberg, Annelies Van Nuffel, Jürgen Vangeyte, Sam Millet, Bart Ampe, Frank A. M. Tuyttens y Jarissa Maselyne. "Testing the potential of the Sow Stance Information System (SowSIS) based on a force plate system built into an electronic sow feeder for on-farm automatic lameness detection in breeding sows". Biosystems Engineering 204 (abril de 2021): 270–82. http://dx.doi.org/10.1016/j.biosystemseng.2021.01.024.
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Zhang, Chuan Wei, Guang Fan Hu, Xiang Chao Qu y Li Ping Zhang. "Top Charging Car Automatic Control System Based on PLC". Applied Mechanics and Materials 552 (junio de 2014): 192–95. http://dx.doi.org/10.4028/www.scientific.net/amm.552.192.
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In view of the furnace top charging car ore bin automatic distributing problems, This article puts forward a kind of high reliability, stable performance of automatic material distributing control system ,The system uses wireless communication technology、Ore bin material level detection technology and High reliability of PLC control technology, Achieving material distributing car accords to the need of ore bin for charging, Making material distributing more rational and effective, reduce labor intensity and improve the level of automation for production.`
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Ajayi, Oluibukun Gbenga, Ifeanyi Jonathan Nwadialor, Ifeanyi Chukwudi Onuigbo y Olurotimi Adebowale Kemiki. "PRELIMINARY INVESTIGATION OF THE ROBUSTNESS OF MAXIMALLY STABLE EXTREMAL REGIONS (MSER) MODEL FOR THE AUTOMATIC REGISTRATION OF OVERLAPPING IMAGES". Geoplanning: Journal of Geomatics and Planning 5, n.º 1 (25 de abril de 2018): 63. http://dx.doi.org/10.14710/geoplanning.5.1.63-74.
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Various researchers in Digital Image processing have developed keen interest in the automation of object detection, description and extraction process used for various applications and this has led to the development of series of Feature detection and extraction models one of which is the Maximally Stable Extremal Regions Feature Algorithm (MSER). This paper investigates the robustness of MSER algorithm (a blob-like and affine-invariant feature detector) for the detection and extraction of corresponding features used for the automatic registration of series of overlapping images. The robustness investigation was carried out in three different registration campaigns using overlapping images extracted from google earth and image pair acquired from an Unmanned Aerial Vehicle (UAV). Sum of Square Difference (SSD) and Bilinear interpolation models were used to establish the similarity measure between the images to be registered, resampling of the pixel-values and computation of non-integer coordinates respectively while Random Sampling Consensus (RANSAC) algorithm was used to exclude the outliers and to compute the transformation matrix using affine transformation function. The results obtained from this preliminary investigation shows that the processing speed of MSER is quite high for Automatic Image Registration with a relatively high accuracy. While an accuracy of 61.54% was obtained from the first campaign with a processing time of 11.92 seconds, the second campaign gave an accuracy of 52.02% with a processing time of 11.20 seconds and the third campaign produced an accuracy of 55.62% with a processing time of 3.27 seconds. The obtained speed and accuracy shows that MSER is a very robust model and as such, can be deployed as the feature detection and extraction model in the development of an automatic image registration scheme.
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Tsamis, Konstantinos I., Prokopis Kontogiannis, Ioannis Gourgiotis, Stefanos Ntabos, Ioannis Sarmas y George Manis. "Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning". Bioengineering 8, n.º 11 (10 de noviembre de 2021): 181. http://dx.doi.org/10.3390/bioengineering8110181.
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Recent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeuropathy and decision making about carpal tunnel syndrome. The study included 38 volunteers, examined prospectively. The purpose was to investigate the possibility of automatically detecting the median nerve mononeuropathy based on common electrodiagnostic criteria, used in everyday clinical practice, as well as new features selected based on physiology and mathematics. Machine learning techniques were used to combine the examined characteristics for a stable and accurate diagnosis. Automatic electrodiagnosis reached an accuracy of 95% compared to the standard neurophysiological diagnosis of the physicians with nerve conduction studies and 89% compared to the clinical diagnosis. The results show that the automatic detection of carpal tunnel syndrome is possible and can be employed in decision making, excluding human error. It is also shown that the novel features investigated can be used for the detection of the syndrome, complementary to the commonly used ones, increasing the accuracy of the method.
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Liu, Cheng-Chien, Yu-Cheng Zhang, Pei-Yin Chen, Chien-Chih Lai, Yi-Hsin Chen, Ji-Hong Cheng y Ming-Hsun Ko. "Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation". Remote Sensing 11, n.º 2 (10 de enero de 2019): 119. http://dx.doi.org/10.3390/rs11020119.
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Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals that dominate the results of change detection if they are not removed completely from imagery. As various architectures of deep learning (DL) have been proposed and advanced quickly, their potential in perceptual tasks has been widely accepted and successfully applied to many fields. A comprehensive survey of DL in RS has been reviewed, and the RS community has been suggested to be leading researchers in DL. Based on deep residual learning, semantic image segmentation, and the concept of atrous convolution, we propose a new DL architecture, named CloudNet, with an enhanced capability of feature extraction for classifying cloud and haze from Sentinel-2 imagery, with the intention of supporting automatic change detection in LULC. To ensure the quality of the training dataset, scene classification maps of Taiwan processed by Sen2cor were visually examined and edited, resulting in a total of 12,769 sub-images with a standard size of 224 × 224 pixels, cut from the Sen2cor-corrected images and compiled in a trainset. The data augmentation technique enabled CloudNet to have stable cirrus identification capability without extensive training data. Compared to the traditional method and other DL methods, CloudNet had higher accuracy in cloud and haze classification, as well as better performance in cirrus cloud recognition. CloudNet will be incorporated into the Open Access Satellite Image Service to facilitate change detection by using Sentinel-2 imagery on a regular and automatic basis.
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Li, Bin, Jin Ping Hong y Zheng Feng Cao. "The Manipulator Cleared the Surface of Fluid Pipelines". Advanced Engineering Forum 2-3 (diciembre de 2011): 330–33. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.330.
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Manipulator cleared the surface of fluid pipelines is the necessary pipeline engineering construction equipment. Developing an advanced automatic robotic pipeline construction and making it industry can satisfy the needs of pipeline engineering in the country improve mechanization level and work efficiency and reduce labor intensity. According to the actual situation, the paper conform the principle of the robotic work. Analysis the work process of the manipulator, the design gives the optical detection system. This manipulator adopts automatic rotary and feeding, intelligent control and automatic detecting effects and feedback. The movement of the manipulator can been regulated and controlled timely by the photoelectric detection device of the manipulator. It is suitable for cleaning the surface of various pipelines in the complex condition field. This paper studies the revolution，feeding and feedback of the pipeline construction of the manipulator. Finally, study and design a stable running, flexible movement, accurate positioning, the effect is automatically detected and reliable intelligent robot which compatible with the subject. This manipulator can well meet the requirements of operation.
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Wang, Su Yu, Yi Bo Du y Miao Wu. "Remote Control Techniques and Monitoring System for Roadheader". Advanced Materials Research 791-793 (septiembre de 2013): 878–83. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.878.
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The system which could be described as "trinity, seven subsystems" has realized automatic remote control of the machine working process with orientation, localization and formalization. At local place, it realized automatic position and pose online detection, automatic orientation tunneling, automatic section cutting and forming and adaptive control of cutting arm swaying speed. It was the first time that realized absolute measuring of roadheader frame position and pose error under coal mine working condition in our country which could avoid error accumulation in relative measuring and guarantee the high precision of orientation tunneling. In underground remote, it realized remote monitoring, wireless remote control and video surveillance, thus realized remote control with multiple angles, visualization and one-button control under the condition of dustiness and vibration. At any network access point on ground using mining ring network, it realized real-time monitoring and displaying of roadheader running state parameters, and realized the functions of remote fault diagnosis and alarming. The whole system has been tested many times underground. The application results show that it is stable and reliable. With the development of automation, intellectualization, and informatization and other high technology, this system will be more and more suitable for fully mechanized excavation face.
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Abdullah, Saeed, Mark Matthews, Ellen Frank, Gavin Doherty, Geri Gay y Tanzeem Choudhury. "Automatic detection of social rhythms in bipolar disorder". Journal of the American Medical Informatics Association 23, n.º 3 (14 de marzo de 2016): 538–43. http://dx.doi.org/10.1093/jamia/ocv200.
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Objective To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones. Methods Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app. Results We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0–7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score <3.5) states with high accuracy (precision: 0.85 and recall: 0.86). Conclusions Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.
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Shao, Faming, Xinqing Wang, Fanjie Meng, Ting Rui, Dong Wang y Jian Tang. "Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs". Sensors 18, n.º 10 (21 de septiembre de 2018): 3192. http://dx.doi.org/10.3390/s18103192.
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Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.
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Li, Pengfei, Zhihao Yun, Kaihang Gao, Laiqiang Si y Xinwu Du. "Design and Test of A Force Feedback Seedling Pick-Up Gripper for An Automatic Transplanter". Agriculture 12, n.º 11 (10 de noviembre de 2022): 1889. http://dx.doi.org/10.3390/agriculture12111889.
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Aiming at the problems of seedling injury and planting leakage due to the lack of seeding clamping force detection and real-time control in vegetable transplanting, a force feedback gripper was developed based on the linear Hall element. The mechanical properties of the stem of pepper cavity seedlings were first analyzed to provide a basis for the design of the gripper. A linear Hall sensor, a magnet, an elastic actuator, and an Arduino Uno development board make up the grasping force detecting system. Upon picking up a seedling, the elastic actuator, which is connected to the magnet, bends like a cantilever beam. As a result of the micro-displacement created by the elastic actuator, the Hall sensor’s voltage changes and can be used to determine the clamping force. Detection avoids direct contact between the sensor and the cavity seedlings, reducing the risk of sensor damage. Finite element method (FEM) simulations were used to determine the initial spacing between the magnet and Hall sensor and the effect of the elastic actuator. Control commands are sent to the servo based on the gripping force collected by the Arduino Uno board. Finally, the functions of accurate measurement, display, storage, and control of the clamping force of the cavity tray seedlings are realized, so that the damage rate of the cavity tray seedlings is reduced. In order to explore the influence of the elastic actuators on the clamping force detection system and the performance of the force feedback gripper, a calibration test of the clamping force detection system and a test of the indoor transplantation of pepper seedlings were carried out. Based on the calibration test, the clamping force detection system has a sensitivity of 0.0693 V/N, linearity of 3.21%, an average linear coefficient of determination of 0.986, and a range of 10 N, which fully meet the clamping force detection accuracy requirements during transplantation. Indoor tests showed that the force feedback gripper was stable and adaptable. This study can provide a reference for detecting and controlling clamping forces during transplantation.
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Yu, Jian Wu, Yi Jian Deng, Wen Yi Zou y Gong Fa Zhang. "A New Automatic Backlash Adjustment Method for Lapping of Spiral Bevel Gear". Advanced Materials Research 565 (septiembre de 2012): 307–11. http://dx.doi.org/10.4028/www.scientific.net/amr.565.307.
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Based on the lapping principle of spiral bevel gears and hypoid gears, this paper focuses on a new automatic backlash adjustment method for lapping process, which includes on-line detection and measurement of backlash, automatic backlash control and software etc. The experimental results shows that this on-line automatic backlash control method is efficiency and stable in lapping process, and it can improve gear surface finishing and reduce transmission noise apparently.
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Huang, Fan, Behdad Dashtbozorg, Jiong Zhang, Erik Bekkers, Samaneh Abbasi-Sureshjani, Tos T. J. M. Berendschot y Bart M. ter Haar Romeny. "Reliability of Using Retinal Vascular Fractal Dimension as a Biomarker in the Diabetic Retinopathy Detection". Journal of Ophthalmology 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/6259047.
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The retinal fractal dimension (FD) is a measure of vasculature branching pattern complexity. FD has been considered as a potential biomarker for the detection of several diseases like diabetes and hypertension. However, conflicting findings were found in the reported literature regarding the association between this biomarker and diseases. In this paper, we examine the stability of the FD measurement with respect to (1) different vessel annotations obtained from human observers, (2) automatic segmentation methods, (3) various regions of interest, (4) accuracy of vessel segmentation methods, and (5) different imaging modalities. Our results demonstrate that the relative errors for the measurement of FD are significant and FD varies considerably according to the image quality, modality, and the technique used for measuring it. Automated and semiautomated methods for the measurement of FD are not stable enough, which makes FD a deceptive biomarker in quantitative clinical applications.
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Chen, Haibin, Liwen Chen, Tao Tong, Haohan Zhen, Lei Yu, Peigang Shen y Yu Wu. "Research on automatic detection method of secondary circuit connection of the measuring device mutual inductor". AIP Advances 13, n.º 2 (1 de febrero de 2023): 025335. http://dx.doi.org/10.1063/5.0133142.
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Due to the influence of the connection distance between the mutual inductor primary circuit and the metering device secondary circuit, the detection induced current of the traditional method is extremely small, which cannot accurately detect the authentic situation of the wiring, resulting in long detection time and low detection accuracy. Therefore, this paper proposes the automatic detection method of the secondary circuit connection of the metering device mutual inductor. Through the voltage sampling circuit, the secondary circuit measurement data are obtained. Using the pulse signal shaping function and the ADE7753 chip to process the secondary circuit measurement data, the secondary circuit connection fault judgment is determined. According to the judgment, the electric energy meter is used, combined with the secondary circuit measurement data, and the automatic detection of the secondary circuit connection is achieved by calculation. The experimental results show that compared with the traditional detection method, the automatic detection method of the secondary circuit connection of the metering device mutual inductor designed in this paper achieves normal and stable detection induction current. There is no inductive current with an extremely small condition, the detection accuracy is high, and the detection time is short, indicating that the method is suitable for application in actual detection projects.
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Wenjie, Zhang, Li Wei, Jin Chengming, Chen Shuo y Zheng shanqi. "Construction of Backup Center for Enterprise Electronic Files Based on Cloud Platform". E3S Web of Conferences 136 (2019): 04006. http://dx.doi.org/10.1051/e3sconf/201913604006.
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The development in information technologies lies such as cloud platform provide good ways to improve the intelligence of present electrical enterprises. However, the massive data from the power stations and systems should be properly stored to avoid the sudden breakdowns of the system. Therefore, it is meaningful to construct the data backup center for all the data in the enterprise. The main objective to the properly manage and analyze these data to find and solve the potential threats in the enterprise system. This paper analyses the main functions of the data backup center and provides some potential ways to build it with high intelligence and automation level. By using stable facilitations, intelligent information classification modules and automatic anomaly detection tools, the data backup center could help the smooth operating of the whole enterprise.
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Sun, Rong Xia, Lei Gao, Jian Kang, Pan Pan Huang, Yi Tian y Xiao Feng Chen. "Automatic On-Line Detection and Sorting System Design and Implementation for Paper-Making Printing". Advanced Materials Research 383-390 (noviembre de 2011): 3–5. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.3.
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Transforms the papermaking production line to spurt the code automatic detection separation system is the production of impending rapid requirement. This design using the original paper cutter to cut the paper separating function is bad, choose the trigger for separating door action by photoelectric sensor signal, triggering bad signal detection equipments, and complete code identification papers in the test paper separating bad logo. In printing, the PLC control high-speed counter acquisition traction, printing roll encoder pulse signal range. This system includes color standard sensor signal detection, Siemens S7-200 PLC controller, etc. Also gives the system installation problems and presents the corresponding countermeasures. The paper realize online automatic testing equipments, satisfying the function of separation, stable, zero leakage rate of selected control of the production requirement.
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German, Tim P., Jeffrey L. Niehaus, Meghan P. Roarty, Barry Giesbrecht y Michael B. Miller. "Neural Correlates of Detecting Pretense: Automatic Engagement of the Intentional Stance under Covert Conditions". Journal of Cognitive Neuroscience 16, n.º 10 (diciembre de 2004): 1805–17. http://dx.doi.org/10.1162/0898929042947892.
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Typically developing children begin to produce and understand pretend play between 18 and 24 months of age, and early pretense has been argued to be a candidate “core” capacity central to the deployment of representations of other peoples' mental states—“theory of mind.” In a functional magnetic resonance imaging study, 16 healthy adult volunteers were imaged while watching short (5 sec) clips of actors who either performed simple everyday actions or pretended to perform a similar set of actions, under covert conditions (e.g., participants were not directed to attend to actors' mental states). There was increased activity in the medial prefrontal areas (Brodmann's areas [BA] 9/6/32, 9, and 10), inferior frontal gyrus bilaterally (BA 44, 47), temporo-parietal regions (BA 21 and 22), and parahippocampal areas, including the amygdala, when subjects viewed pretend actions as compared with real actions. This result suggests that at least some areas previously implicated in making explicit mental state judgments are also strongly activated in response to actions that call for mental state interpretation (e.g., pretense) even when there is no explicit instruction for “mind reading.” This outcome is discussed in terms of accounts that propose “theory of mind” to be underwritten by automatic specialized mechanisms for the interpretation of the behavior of social agents.
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Lin, Shuai, Cheng Xu, Lipei Chen, Siqi Li y Xiaohan Tu. "LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning". Sensors 20, n.º 8 (14 de abril de 2020): 2212. http://dx.doi.org/10.3390/s20082212.
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High-speed railways have been one of the most popular means of transportation all over the world. As an important part of the high-speed railway power supply system, the overhead catenary system (OCS) directly influences the stable operation of the railway, so regular inspection and maintenance are essential. Now manual inspection is too inefficient and high-cost to fit the requirements for high-speed railway operation, and automatic inspection becomes a trend. The 3D information in the point cloud is useful for geometric parameter measurement in the catenary inspection. Thus it is significant to recognize the components of OCS from the point cloud data collected by the inspection equipment, which promotes the automation of parameter measurement. In this paper, we present a novel method based on deep learning to recognize point clouds of OCS components. The method identifies the context of each single frame point cloud by a convolutional neural network (CNN) and combines some single frame data based on classification results, then inputs them into a segmentation network to identify OCS components. To verify the method, we build a point cloud dataset of OCS components that contains eight categories. The experimental results demonstrate that the proposed method can detect OCS components with high accuracy. Our work can be applied to the real OCS components detection and has great practical significance for OCS automatic inspection.
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Wang, Chi-Te, Ji-Yan Han, Shih-Hau Fang y Ying-Hui Lai. "Ambulatory Phonation Monitoring With Wireless Microphones Based on the Speech Energy Envelope: Algorithm Development and Validation". JMIR mHealth and uHealth 8, n.º 12 (3 de diciembre de 2020): e16746. http://dx.doi.org/10.2196/16746.
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Background Voice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application. Objective This study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios. Methods We designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system. Results The proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions. Conclusions This study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users.
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Zhu, Zhong Xiang, Yan He, Zhi Qiang Zhai, Jin Yi Liu y En Rong Mao. "Research on Cotton Row Detection Algorithm Based on Binocular Vision". Applied Mechanics and Materials 670-671 (octubre de 2014): 1222–27. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1222.
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As a relative locating method, machine vision is generally used for automatic navigation of cotton cultivator or cotton insecticide sprayer. However, it is difficult to achieve reliable and stable recognition of crop row with monocular stereo vision system, because it neither can access directly to the depth information of the image, which leads to massive time-consuming calculation, nor possess high-accuracy recognition or a good anti-noise property. This paper presents an algorithm for cotton row detection based on binocular stereo vision to be used for automatic navigation of cotton cultivator. The Zhang's plane calibration is used to obtain the internal and external parameters of the binocular stereo vision. Preprocessing means are applied to distinguish the cotton from soil, stereoscopic match is conducted according to the SIFT operators after the preprocessing of images, after which cotton space three-dimensional coordinates are acquired by parallax distance measuring method, with the elevation information combination of Hough transform, cotton lines are finally detected. The detection results indicate that this method has an accuracy higher than 90%, which primarily meets the need of automatic navigation for cotton cultivator.
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Chaibi, Sahbi, Tarek Lajnef, Abdelbacet Ghrob, Mounir Samet y Abdennaceur Kachouri. "A Robustness Comparison of Two Algorithms Used for EEG Spike Detection". Open Biomedical Engineering Journal 9, n.º 1 (31 de julio de 2015): 151–56. http://dx.doi.org/10.2174/1874120701509010151.
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Spikes and sharp waves recorded on scalp EEG may play an important role in identifying the epileptogenic network as well as in understanding the central nervous system. Therefore, several automatic and semi-automatic methods have been implemented to detect these two neural transients. A consistent gold standard associated with a high degree of agreement among neuroscientists is required to measure relevant performance of different methods. In fact, scalp EEG data can often be corrupted by a set of artifacts and are not always served as data of gold standard. For this reason, the use of intracerebral EEG data mixed with gaussian noise seems to best resemble the output of scalp EEG brain and serves as a consistent gold standard. In the present framework, we test the robustness of two important methods that have been previously used for the automatic detection of epileptiform transients (spikes and sharp waves). These methods are based respectively on Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT). Our purpose is to elaborate a comparative study in terms of sensitivity and selectivity changes via the decrease of Signal to Noise Ratio (SNR), which is ranged from 10 dB up to -10 dB. The results demonstrate that, DWT approach turns to be more stable in terms of sensitivity, and it successfully follows the detection of relevant spikes with the decrease of SNR. However, CWT-based approach remains more stable in terms of selectivity, so that, it performs well in terms of rejecting false spikes compared to DWT approach.
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Liu, Bing Tao y Peng Ju Ding. "Experimental Study on Determination of Total Iron in Water by Automatic Discrete Spectrophotometry". Advanced Materials Research 807-809 (septiembre de 2013): 223–26. http://dx.doi.org/10.4028/www.scientific.net/amr.807-809.223.
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A new method was introduced for rapid measurement of iron ions in drinking water by automatic discrete spectrophotometry. In the iron concentration range 0-1.5mg/L, the linear relationship was good and the correlation coefficient was 0.9998. The total iron detection of recovery efficiency was 99.94%±1.6% and detection limit was 0.006mg/L and precision of the RSD% was 0.35%. The results show that the method was direct, fast, stable operation and lower iron concentration in water can be accurately detected.There is no significant difference compared with the results of atomic absorption spectrometry methods.
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Yang, Xiao, Lilong Chai, Ramesh Bahadur Bist, Sachin Subedi y Zihao Wu. "A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor". Animals 12, n.º 15 (5 de agosto de 2022): 1983. http://dx.doi.org/10.3390/ani12151983.
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Real-time and automatic detection of chickens (e.g., laying hens and broilers) is the cornerstone of precision poultry farming based on image recognition. However, such identification becomes more challenging under cage-free conditions comparing to caged hens. In this study, we developed a deep learning model (YOLOv5x-hens) based on YOLOv5, an advanced convolutional neural network (CNN), to monitor hens’ behaviors in cage-free facilities. More than 1000 images were used to train the model and an additional 200 images were adopted to test it. One-way ANOVA and Tukey HSD analyses were conducted using JMP software (JMP Pro 16 for Mac, SAS Institute, Cary, North Caronia) to determine whether there are significant differences between the predicted number of hens and the actual number of hens under various situations (i.e., age, light intensity, and observational angles). The difference was considered significant at p < 0.05. Our results show that the evaluation metrics (Precision, Recall, F1 and mAP@0.5) of the YOLOv5x-hens model were 0.96, 0.96, 0.96 and 0.95, respectively, in detecting hens on the litter floor. The newly developed YOLOv5x-hens was tested with stable performances in detecting birds under different lighting intensities, angles, and ages over 8 weeks (i.e., birds were 8–16 weeks old). For instance, the model was tested with 95% accuracy after the birds were 8 weeks old. However, younger chicks such as one-week old birds were harder to be tracked (e.g., only 25% accuracy) due to interferences of equipment such as feeders, drink lines, and perches. According to further data analysis, the model performed efficiently in real-time detection with an overall accuracy more than 95%, which is the key step for the tracking of individual birds for evaluation of production and welfare. However, there are some limitations of the current version of the model. Error detections came from highly overlapped stock, uneven light intensity, and images occluded by equipment (i.e., drinking line and feeder). Future research is needed to address those issues for a higher detection. The current study established a novel CNN deep learning model in research cage-free facilities for the detection of hens, which provides a technical basis for developing a machine vision system for tracking individual birds for evaluation of the animals’ behaviors and welfare status in commercial cage-free houses.
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Gong, Xiang Shan y Li Ya Cui. "Design of an Automatic Magnetic Balance System". Advanced Materials Research 765-767 (septiembre de 2013): 2012–16. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2012.
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Along with the fast development of high-technology fields in China, especially in aviation and aerospace, in order to meet the fast and accurate detection requirements of various magnetic material properties, to solve artificial measurement error and low efficiency on the current manual measuring way, a kind of automatic magnetic balance system was developed. The mechanism design, measurement & control system design and the main technical indicators were also described. The application results show that the automatic magnetic balance system can work stable and reliable, and the test capacity of the system is effectively expanded because of higher precision, higher measuring efficiency and more convenient operation, so the system can satisfy the testing requirements in the process such as scientific research, manufacture, teaching and etc.
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Wang, Yi Xiao, Ya Nan Li, Yi Wang, Xiao Dong Chen y Dao Yin Yu. "Automatic Monitoring System on Embedded Platform for Environmental Noise Detection". Advanced Materials Research 403-408 (noviembre de 2011): 1507–10. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1507.
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A novel system based on embedded platform for environmental noise monitoring is presented in this paper. The system is designed by ARM and DSP to implement data collection and processing respectively, as a substitute for conventional MCU and DSP structure. The DSP is used as the core processor to implement a high precision IIR filter for 1/3 octave band spectral analysis in real time. The noise data detected beyond the reference value can be processed and stored automatically, and the noise pollution sources can be identified in time by the images captured by an USB camera on the terminal. Then the data and images acquired by the system will be transmitted to the sever computer through GPRS , and the users can obtain the real time noise data and pollution sources images when they explore the website of noise monitoring center. All the properties of 1/3 octave filters can meet the International Electrotechnical Commission Standard IEC61260-2001 class I. The system has the capability of high accuracy and stable performance at all time compared with the conventional system, indicating that it has great significance to the control of a city’s noise pollution condition.
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Gao, Qing Min y Lei Gao. "Design of Library Temperature and Humidity Control System". Advanced Materials Research 760-762 (septiembre de 2013): 1038–42. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1038.
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This is a design based on the library temperature and humidity automatic measurement system and control system. Using modern information gathering techniques, computer data processing and control technology, through the choice of hardware devices and designing of software programs, it is feasible to achieve automatic detection of the library temperature and humidity parameters and control of air conditioning equipment. Demonstrated by engineering examples, the system structure is clear, stable and reliable. It has significantly improved the quality of preservation of books in the library, therefore, has a wide prospect in the field of library temperature and humidity control.
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Gao, Qiang, Wenjie Liu, Dong Li, Yalin Wang, Hongquan Gao y Tao Xue. "Research and Implementation of the Roll Position Automatic Adjustment System on the One-Time Forming of Cold-Formed Steel Unit". Journal of Advanced Manufacturing Systems 17, n.º 02 (11 de mayo de 2018): 231–48. http://dx.doi.org/10.1142/s0219686718500142.
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In order to solve the existing problems of inefficiency, instability, inaccuracy and low degree of automation, the roll position automatic adjustment system is designed for the one-time forming of cold-formed steel unit. The automatic control technology, sensor technology, communication technology and human–machine interface (HMI) configuration technology are applied to this system and the high-performance PLC controller is used as control core. Triple-phase asynchronous motors are controlled by PLC (S7-1500) to drive the gear connected with reducer and coupling so as to drive the rolls connected with the upper and lower horizontal axes to move. The osiswitch is used to calibrate zero position and the closed-loop control system is constructed by analyzing and feedbacking the signal of the sensor. In this way the system can improve the roll position adjustment accuracy. The practical applications show that the whole system is simple, stable and efficient and the maximum detection error of roller displacement is only [Formula: see text]2[Formula: see text]mm. The system can automatically call the corresponding roll position data according to the specification of the steel pipe. The system not only improves the adjustment speed and accuracy, but also avoids the waste of manpower so that the enterprise production efficiency can be greatly improved. Meanwhile, in order to carry out efficient management of the unit, the unit’s health management system was used to analyze and monitor the status of the unit in real-time.
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Ma, Xiaoya, Zhaoqian Gong, Feng Zhang, Shun Wang, Xiaojun Liu y Guangyou Fang. "An Automatic Drift-Measurement-Data-Processing Method with Digital Ionosondes". Remote Sensing 14, n.º 19 (21 de septiembre de 2022): 4710. http://dx.doi.org/10.3390/rs14194710.
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Drift detection is one of the important detection modes in a digital ionosonde system. In this paper, a new data processing method is presented for boosting the automatic and high-quality drift measurement, which is helpful for long-term ionospheric observation, and has been successfully applied to the Chinese Academy of Sciences, Digital Ionosonde (CAS-DIS). Based on Doppler interferometry principle, this method can be successively divided into four constraint steps: extracting the stable echo data; restricting the ionospheric detection region; extracting the reliable reflection cluster, including Doppler filtering and coarse clustering analysis; and calculating the drift velocity. Ordinary wave (O-wave) data extraction, complementary code pulse compression and other data preprocessing techniques are used to improve the signal-to-noise ratio (SNR) of echo data. For the purpose of eliminating multiple echoes, the ionospheric region is determined by combining the optimal height range and detection frequencies obtained from the ionogram. Successively, Doppler filtering and coarse clustering analysis extract reliable reflection clusters. Finally, the weighting factor is brought in, and then weighted least-squares (WLS) is used to fit the drift velocity. The entire data processing process can be implemented automatically without constantly changing parameter settings due to changes in external conditions. This is the first time coarse clustering analysis has been used to extract the paracentral reflection cluster to eliminate scattered reflection points and outer reflection clusters, which further reduces the impacts of external conditions on parameter settings and improves the ability of automatic drift measurement. Compared with the previous method possessed by Digisonde Protable Sounder 4D (DPS4D), the new method can achieve comparable drift detection precision and results even with fewer reflection points. In 2021–2022, several experiments on F region drift detection were carried out in Hainan, China. Results indicate that drift velocities fitted by the new method have diurnal variation and change more gently; the trends of drift velocities fitted by the new method and the previous method are semblable; and this new method can be widely applied to digital ionosondes.
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AlShemmary et al., Ebtesam. "Towards Accurate Pupil Detection Based on Morphology and Hough Transform". Baghdad Science Journal 17, n.º 2 (11 de mayo de 2020): 0583. http://dx.doi.org/10.21123/bsj.2020.17.2.0583.
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Automatic recognition of individuals is very important in modern eras. Biometric techniques have emerged as an answer to the matter of automatic individual recognition. This paper tends to give a technique to detect pupil which is a mixture of easy morphological operations and Hough Transform (HT) is presented in this paper. The circular area of the eye and pupil is divided by the morphological filter as well as the Hough Transform (HT) where the local Iris area has been converted into a rectangular block for the purpose of calculating inconsistencies in the image. This method is implemented and tested on the Chinese Academy of Sciences (CASIA V4) iris image database 249 person and the IIT Delhi (IITD) iris database v1 using MATLAB 2017a. This method has high accuracy in the center and radius finding reaches 97% for 2268 iris on CASIA V4 image and 99.77% for 2240 iris images on IITD, the speed is acceptable compared to the real-time detection speed and stable performance.
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Asher, M., D. Toshkova y N. Lieven. "Automatic regime detection for Rotor Track and Balance using vibration only sensor data". Aeronautical Journal 124, n.º 1275 (8 de abril de 2020): 617–34. http://dx.doi.org/10.1017/aer.2020.17.
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ABSTRACTRotor Track and Balance (RTB) is an important part of regular helicopter maintenance. The ability to perform this service assessment during normal operations, rather than with a series of explicit RTB flights, would greatly reduce the time the vehicle is non-operational and the maintenance costs associated with these flights and adjustments. This paper presents a novel methodology for identifying the RTB-related flight regimes, using a minimal number of vibration signals and comparing these to repeatable and stable characteristic vibration profiles. The technique is stable, with an 81% success in correct identification of the flight regime, when applied to a whole flight with a number of unknown regime events. The method can be run in real time, making it an effective way of identifying periods of flight that are suitable for RTB measurements. A new technique for visually representing any real-time flight signal, such as vibration, is also presented.
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Zhao, Xinying y Kun Luo. "Automatic Calculation Method of Load Resonant Frequency of Photovoltaic Power Supply". International Journal of Circuits, Systems and Signal Processing 15 (12 de agosto de 2021): 945–52. http://dx.doi.org/10.46300/9106.2021.15.101.
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The resonance phenomenon of photovoltaic power supply load makes the output voltage of grid-connected photovoltaic power supply system difficult to keep stable, which brings trouble to power supply. Therefore, it is necessary to study the automatic calculation method of load resonance frequency of photovoltaic power supply, so as to detect the load resonance frequency of photovoltaic power supply in real time, thereby ensuring the normal operation of photovoltaic power supply system. The load resonance frequency of photovoltaic power supply is divided into steady-state load resonance frequency and dynamic load resonance frequency. The mathematical model of load resonant circuit of photovoltaic power supply is established by calculating algorithm of load resonant frequency of photovoltaic power supply in steady state, and load resonant frequency of photovoltaic power supply in steady state is calculated. The resonance detection algorithm based on wavelet transform and Hilbert-Huang transform is used to analyze and calculate the load resonance frequency after detecting the resonance signal of photovoltaic power system. The experimental results show that the resonant frequency of photovoltaic power supply load calculated by this method is not much different from the actual resonant frequency of photovoltaic power supply load, and the error range is between-0.30% and 0.49%. Therefore, this method can keep the load resonance frequency of the photovoltaic power supply the same as the RF transmission resonance frequency, and can ensure the normal operation of the photovoltaic power supply.
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Shojaedini, Seyed Vahab, Amir Salar Jafarpisheh, Nematollah Rouhbakhsh, Mohsen Vahedi y Negar Amirian. "A Novel Method for Automated Estimation of Effective Parameters of Complex Auditory Brainstem Response: Adaptive Processing Based on the Correntropy Concept". Iranian Rehabilitation Journal 20, n.º 1 (1 de junio de 2022): 19–32. http://dx.doi.org/10.32598/irj.20.1.568.1.
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Objectives: Automated Auditory Brainstem Responses (ABR) peak detection is a novel technique to facilitate the measurement of neural synchrony along the auditory pathway through the brainstem. Analyzing the location of the peaks in these signals and the time interval between them may be utilized either for analyzing the hearing process or detecting peripheral and central lesions in the human hearing system. Methods: In this paper, model-based signal processing is proposed to estimate the effective parameters of ABR signals. In this process, the biological parameters of the signal are assessed by utilizing a Finite Impulse Response (FIR) adaptive filter in which its adaptation procedure is performed based on the correntropy concept. The proposed method is applied on a set of ABR signals recorded in response to three stimuli of /da/, /ba/, and /ga/, and then its performances are compared with an existing state-of-the-art technique. Results: The results show that the proposed method can significantly increase the accuracy of estimating the parameters in stable stimulations (/da/, /ba/) for major positive and negative peaks. This improvement is more significant (up to 2-3 times) for /ba/ stimulus and especially in major positive peaks. However, in other peaks, the improvements also occurred in smaller amounts. However, for unstable stimuli (/ga/), no significant improvement was achieved. Discussion: Increasing the accuracy performance of the proposed method for detecting the stable stimuli (while its performance remains unchanged) for detecting unstable stimuli indicates its effectiveness in automated clinical analysis of ABR signals.
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Hung, Chang-Hung. "A Study of Automatic and Real-Time Table Tennis Fault Serve Detection System". Sports 6, n.º 4 (28 de noviembre de 2018): 158. http://dx.doi.org/10.3390/sports6040158.
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Calling a table tennis fault serve has never been easy for umpires, since they can only rely on their intuition. This study presents an algorithm that is able to automatically find the positions of the ball and racket in the images captured by high-speed camera. The trajectory of ball toss is analyzed and the result can be used as the objective basis for the umpire to decide if the serve is legal. This algorithm mainly consists of YCbCr color space processing, morphological processing method, circle Hough transform application, separation of moving and static components in an image sequence using the stable principal component pursuit method. The experiment results show that YCbCr color space provides better performance than HSV color space in recognizing the ball color close to skin tone. It is also demonstrated that the positions of the ball and racket can be successfully located by using the methods of color segmentation and stable principal component pursuit. Lastly, it is hoped that this study will provide more useful information regarding how to identify illegal ball toss in tennis ball game using image processing techniques to other researchers.
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Li, Dongjun, Guoying Meng, Zhiyuan Sun y Lili Xu. "Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector". Applied Sciences 12, n.º 1 (23 de diciembre de 2021): 107. http://dx.doi.org/10.3390/app12010107.
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In the coal mining process, various types of tramp materials will be mixed into the raw coal, which will affect the quality of the coal and endanger the normal operation of the equipment. Automatic detection of tramp materials objects is an important process and basis for efficient coal sorting. However, previous research has focused on the detection of gangue, ignoring the detection of other types of tramp materials, especially small targets. Because the initial Single Shot MultiBox Detector (SSD) lacks the efficient use of feature maps, it is difficult to obtain stable results when detecting tramp materials objects. In this article, an object detection algorithm based on feature fusion and dense convolutional network is proposed, which is called tramp materials in raw coal single-shot detector (TMRC-SSD), to detect five types of tramp materials such as gangue, bolt, stick, iron sheet, and iron chain. In this algorithm, a modified DenseNet is first designed and a four-stage feature extractor is used to down-sample the feature map stably. After that, we use the dilation convolution and multi-branch structure to enrich the receptive field. Finally, in the feature fusion module, we designed cross-layer feature fusion and attention fusion modules to realize the semantic interaction of feature maps. The experiments show that the module we designed is effective. This method is better than the existing model. When the input image is 300 × 300 pixels, it can reach 96.12% MAP and 24FPS. Especially in the detection of small objects, the detection accuracy has increased by 4.1 to 95.57%. The experimental results show that this method can be applied to the actual detection of tramp materials objects in raw coal.