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Ono, Yumie, Naoko Kasai, Atsushi Ishiyama, T. Miyashita i Y. Terada. "A basic study on algorithm for automatic diagnosis by magnetocardiography". Physica C: Superconductivity 368, nr 1-4 (marzec 2002): 45–49. http://dx.doi.org/10.1016/s0921-4534(01)01138-8.
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Harbi, Zainab. "Automatic Diagnosis of Ovarian Cancer Based on Relative Entropy and Neural Network". Wasit Journal for Pure sciences 2, nr 3 (30.09.2023): 108–18. http://dx.doi.org/10.31185/wjps.172.
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Ovarian Cancer is one of the most common causes of death for women in developing countries. Screening and early diagnoses of OC are urgently needed. Early diagnosis would help in consequence procedures and treatment. Mass spectrometry (MS) data is been used as an effective component of cancer diagnosis tools. However, these valuable data have a large number of dimensions that can affect the learning process in addition to time-consuming considerations. Feature selection plays an important role in reducing information redundancy, and deals with the invalidation that occurs in basic classification algorithms when there are too many features and huge datasets. To improve the automatic system diagnosis accuracy, entropy-based selection features are proposed. These features are combined with the novel learning capabilities of neural networks to achieve higher diagnostic accuracy. Experiments have been performed using different feature selection algorithms and machine learning classification approaches. Experimental results have proved that the proposed system performs better based on the measure of accuracy.
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Wang, Zhi Song, Li Wei Tang, Wen Wen Yu i Jin Hua Cao. "Antiaircraft Gun Automatic Fusion Diagnosis Based on D-S Evidence Theory". Applied Mechanics and Materials 241-244 (grudzień 2012): 288–92. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.288.
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Antiaircraft gun automatic is irrotational machine, its motion presents characteristic of stage, so we proposed a fault diagnosis fusion model based on Dempster-Shafer (D-S) evidence theory. At first, feature parameters are extracted from test data of multi-sensor, then, we propose a revised Minkowski distance to create evidences. Finally, we fuse basic belief assignments according to Dempster combination rule, and the analysis result verifies the effectiveness and feasibility of proposed fault diagnosis method.
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Tang, Gong Min, Fu Jun Liu i Xiang Bin Sun. "Research and Design on SOA-Based Equipment ATS Architecture". Applied Mechanics and Materials 513-517 (luty 2014): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.403.
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By analyzing the current problems in equipment automatic test system, we describe the concept, the basic working principle and advantages of service-oriented architecture (SOA), then introduce the web service architecture and the standards of establishing service-oriented architecture. The architecture model of automatic test system on SOA-based equipment was designed, and was used to realize the unified description of equipment automatic test (including fault diagnosis) information. This effectively improves the equipment's capability in performance detection and maintenance support.
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Xu, Bo, Li Wei Guo i Jin Song Yu. "Software Platform for General Purpose Test and Diagnosis". Applied Mechanics and Materials 241-244 (grudzień 2012): 284–87. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.284.
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This paper centers on the software reuses of Automatic Test Systems (ATS) and the integration of test and diagnosis to reduce maintenance costs. Based on the research into the basic framework, data services, packages and definition of interfaces, we present an integrated software platform for test and diagnosis system. The platform achieves the separation between the user interface and test logic, the combination of fault modeling and diagnostic reasoning, and the integration of test and diagnosis.
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Lu, Zhi Li, Shi Guang Hu, Tai Yong Wang, Dong Xiang Chen i Qing Jian Liu. "Remote Monitoring and Intelligent Fault Diagnosis Technology Research Based on Open CNC System". Advanced Materials Research 819 (wrzesień 2013): 234–37. http://dx.doi.org/10.4028/www.scientific.net/amr.819.234.
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In modem manufacturing,the various and complex requirement of industry makes the CNC machine tools more and more automatic and networking.While a remote monitoring and intelligent fault diagnosis system is the basic and indispensable unit for automatic and networking machine tools.This paper is focused on open CNC system,the condition monitoring, and fault diagnosis technology are researched of open CNC system. Integration achieved the CNCmachine tools' status remote monitoring and intelligent fault diagnosis, and detailed analysis of the key technologies for the components of the system. Through effectively integration of the computer technology, Fault Tree Analysis method, or other technologies to enhance the automation, networking and intelligent level of the open CNC system. Keywords: open CNC system; remote monitoring; intelligent fault diagnosis ;Fault Tree Analysis Method;
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Meng, Fan Jun, Li Hong Liu, Li Guo Lai, Fei He, Yun He Zhang, Yi He i Hui Jun Chao. "Intrinsically Safe for Continuous Automated Production Safety Technology of Unitary Detonating Powder". Applied Mechanics and Materials 496-500 (styczeń 2014): 1568–73. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1568.
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Production of unitary detonating powder is great danger, to protect the operation of the safety of workers, reducing labor intensity, no one is reaching a dangerous place to operate, this paper used IPC, PLC, field bus technology, industrial television monitoring, fault diagnosis techniques for successful implementation of unitary detonating powder continuous automated production safety. This paper discussed the key technological breakthrough situation such as continuous automated production safety technology of unitary detonating powder, security technology, precise control of many parameters of the batching technology, compounded many-parameter control technology, on-line automatic screening, weighing, cartoning technology, explosion-proof robot automatic transmission technology, the following explosion and explosion-proof technology, and intrinsically safe technology. This paper realized production process of unitary detonating powder automation total volume of feed, multi-parameter control compound, pressure washing, online drying, automatic cartoning screening measurement and liquid, automatic transmission solid logistics. This paper developed the production line applied to unitary detonating powder production as a basic intrinsically safe for continuous automated production safety technology, implemented human isolation, hazardous processes unmanned operation, minimized the accident rate, greatly and improved production consistency and product quality. Production lines have been long-term security, stability reliably applied to military production, improved the technological level of our industry, and have had enormous economic, social and military effectiveness Key words: continuous automated production safety, parameter control, intrinsically safe for negative pressure vacuum pumping technology, stirring up tilt compounded material technology; negative pressure online drying technology
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Peng, Dong Hui, Fei Ye, Xin Wang i Chuan Hai Jiao. "The Research of the Grass-Roots Level Radar Equipment Maintenance and Detecting Expert System Model". Applied Mechanics and Materials 602-605 (sierpień 2014): 1793–96. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1793.
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In view of the grass-roots level radar equipment maintenance and testing difficulty is big, the efficiency is low, limited technical conditions, etc, put forward a kind of intelligent fault diagnosis expert system model suitable for the radar equipment, and focus on the basic structure of the model, knowledge acquisition and the relevant reasoning mechanism. According to the characteristics of the grass-roots level radar fault diagnosis, the system combines automatic test technology and expert system and can improve the efficiency and reliability of fault diagnosis.
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GK, Srushti, i Sumana K R. "Comparative Study of Prediction of IDBP values for Hemodialysis using Deep Learning". International Journal for Research in Applied Science and Engineering Technology 10, nr 7 (31.07.2022): 4749–53. http://dx.doi.org/10.22214/ijraset.2022.46077.
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Abstract: The field of medicine is expanding rapidly as new diseases appear regularly, necessitating the development of appropriate treatment options. A precise and efficient method of operation is necessary for correct diagnosis and treatment. Blood pressure (BP) is a vital sign that provides basic information about patients' health. During the clinical operation of hemodialysis, blood pressure (BP) variability affects significant global risks and secondary complications associated with adverse mortality. In patients with hypertension, continuous BP monitoring is important. If the scheme is automated, it can be very useful. Consequently, the implementation of an effective automatic medical diagnostic scheme could be very beneficial for all stratifications involved in this process.
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Płudowski, Jakub, i Jan Mulawka. "Machine Learning in Recognition of Basic Pulmonary Pathologies". Applied Sciences 12, nr 16 (12.08.2022): 8086. http://dx.doi.org/10.3390/app12168086.
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Nowadays, during the diagnosis process, the doctor is able to obtain access to much information describing the patient’s condition using appropriate tools. However, there are always two sides to the coin. The doctor has certain limitations regarding the amount of data they can process at once. Information technology comes to the rescue, which with the help of computers is able to quickly and effectively separate important information from redundant information and support the doctor in making a diagnosis. In this work, a decision-making system was created to diagnose common lung pathologies in digital radiography images. Here, we consider four basic pulmonary diseases: pneumothorax, pneumonia, pulmonary consolidation, and lung lesions. Our objective is to develop a new automatic detection method of lung pathologies on chest X-ray radiographs using python programming language and its libraries. The approach uses solutions in the field of artificial intelligence, such as deep learning, convolutional neural network and segmentation to make a diagnosis that aims to help the radiologist at work. In the first sections, this work describes the fundamentals of the present form of diagnosis, a proposal to improve this process, the method of operation of the algorithms used, data acquisition, segmentation and processing methods. Then, the results of the operation of four different models and their implementation in a practical window program were presented. The best model, which detects pulmonary consolidation, achieves accuracy higher than 91%, which is a satisfactory result because they are not intended to replace radiologists but to improve their work. In the future, this type of program can be further developed by adding models that recognize other conditions.
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Oh, Seok, Young-Jae Kim, Young-Taek Park i Kwang-Gi Kim. "Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach". Sensors 22, nr 1 (30.12.2021): 245. http://dx.doi.org/10.3390/s22010245.
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The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS images. We employed the Attention U-Net model for automatic PCL segmentation. The Attention U-Net was compared with the Basic U-Net, Residual U-Net, and U-Net++ models. The Attention U-Net showed a better dice similarity coefficient (DSC) and intersection over union (IoU) scores than the other models on the internal test. Although the Basic U-Net showed a higher DSC and IoU scores on the external test than the Attention U-Net, there was no statistically significant difference. On the internal test of the cross-over study, the Attention U-Net showed the highest DSC and IoU scores. However, there was no significant difference between the Attention U-Net and Residual U-Net or between the Attention U-Net and U-Net++. On the external test of the cross-over study, all models showed no significant difference from each other. To the best of our knowledge, this is the first study implementing segmentation of PCL on EUS images using a deep-learning approach. Our experimental results show that a deep-learning approach can be applied successfully for PCL segmentation on EUS images.
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Mohd Asri, Muhammad Firdaus, Norashikin Yahya i Irraivan Elamvazuthi. "AUTOMATIC ARRHYTHMIA DETECTION ALGORITHM USING STATISTICAL AND AUTOREGRESSIVE MODEL FEATURES". Platform : A Journal of Engineering 4, nr 1 (28.02.2020): 41. http://dx.doi.org/10.61762/pajevol4iss1art5898.
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Human heart healthiness is one of the major components to determine a person’s overall healthiness. Automatic arrhythmia detection is important in a remote area where there is a lack of experienced cardiologists. In this work, an automatic arrhythmia detection algorithm is developed using statistical and autoregressive features to assist medical officers in the diagnosis of arrhythmia diseases. Basic statistical components namely mean, energy, standard deviation, mean absolute deviation, fractal, inter-quartile range and min/max value, were calculated. Alongside with statistical features, 10th order auto-regressive model parameters are used as input features to support vector machine (SVM). All features are calculated using an electrocardiogram (ECG) signals windowed into per beat manner. The proposed algorithm is able to classify normal ECG beat and five types of abnormal ECG beat; paced beat, right & left bundle branch block beat, premature ventricular contraction beat and aberrated atrial premature beat. By using SVM with quadratic and cubic kernel function, the proposed algorithm achieved the best accuracy of 95.8%.
Keywords: ECG, cascade-SVM, AR model, statistical model, heart condition, computer-aided diagnosis
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Yan, Jing, Tingliang Liu, Xinyu Ye, Qianzhen Jing i Yuannan Dai. "Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network". PLOS ONE 16, nr 8 (26.08.2021): e0256287. http://dx.doi.org/10.1371/journal.pone.0256287.
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The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks are constructed through basic elements such as spatial separable convolutions with the aim to effectively reduce model parameters. Secondly, the LCNN model for the intelligent fault diagnosis is constructed via lightweight convolution blocks instead of the tradi-tional convolution operation. Finally, to address the “black box” problem, the entire network is visualized through Tensorboard and t-distribution stochastic neighbor embedding. The results demonstrate that when the number of lightweight convolutional blocks reaches 6, the diagnosis accuracy of the LCNN model exceeds 99.9%. And the proposed model has become the most robust with parameters significantly decreasing. Furthermore, the proposed LCNN model has realized accurate, automatic, and robust fault diagnosis of rotating machinery, which makes it more suitable for deployment under the IIoT context.
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Adeshina, Adekunle. "Automated Medical Visualization Application of Supervised Learning to Clinical Diagnosis, Disease and Therapy Management.docx". SLU Journal of Science and Technology 5, nr 1&2 (29.12.2022): 104–14. http://dx.doi.org/10.56471/slujst.v5i.311.
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The rapid advancement and development in high performance computing, ultrafast computing, autonomous technologies and complexity of biomedical data for visualization and image guidance play a significant role in modern surgery to help surgeons perform their surgical procedures. Brain tumour diagnosis requires an enhanced, effective as well as accurate 3-D visualization system for navigation, reference, diagnosis as well as documentation. The automatic and effective 3-D high performance artificial intelligence-enabled medical visualization framework was designed and implemented using automated machine learning (AutoML) to take the advantage of complexity in the underlying datasets to help specialists in identifying the most appropriate regions of interest and their associated hyper parameters for optimizing performance, whilst simultaneously attempting to maximize the reliability of resulting predictions. C# and Compute Unified Device Architecture (CUDA) in Microsoft.NET environment in comparison side by side with visual basic studio was used for the implementation. The framework was evaluated for rendering processing speed with brain datasets obtained from the department of surgery, University of North Carolina, United States. Interestingly, our framework achieves 3-D visualization of the human brain, reliable enough to detect and locate possible brain tumor with high interactive speed and accuracy.
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Liang, Fei, Rou Feng, Simeng Gu, Shan Jiang, Xia Zhang, Naling Li, Minghong Xu, Yiyuan Tang i Fushun Wang. "Neurotransmitters and Electrophysiological Changes Might Work as Biomarkers for Diagnosing Affective Disorders". Disease Markers 2021 (18.09.2021): 1–12. http://dx.doi.org/10.1155/2021/9116502.
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Affective disorders are the leading causes of human disability worldwide; however, the diagnosis is still hard to define, because emotion is the least study subjects in psychology. Recent emotional studies suggest that human emotions are developed from basic emotions, which are evolved for fundamental human lives. Even though most psychologists agree upon the idea that there are some basic emotions, there is little agreement on how many emotions are basic, which emotions are basic, and why they are basic. In our previous papers, we suggested that there are three basic emotions: joy, fear, and disgust. These basic emotions depend on the peptides and monoamines: dopamine-joy (peptides-reward), norepinephrine-fear (anger), and serotonin-disgust (sadness). Further tests with event-related potentials (ERP) found that joy, fear, and disgust showed the fastest response compared with other emotions, suggesting that they are fast automatic responses, which confirmed that these three emotions are prototypical emotions. Other basic emotions, anger and sadness, are due to object induced behaviors instead of sensation of object, so they developed secondary to prototypical emotions. Thus, we concluded that only joy, fear, and disgust are prototypical emotions, which can mix into other emotions, like the primary colors. In all, the neural substrates for all emotions, including the affections, are possibly monoamine neuromodulators: joy-dopamine (peptides), fear (anger)–norepinephrine, and disgust-serotonin. We hope these basic emotional studies will offer some neural mechanisms for emotional processing and shed lights on the diagnosis of affective disorders.
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Khan, Muhammad Ali, Ahmed Farooq Cheema, Sohaib Zia Khan i Shafiq-ur-Rehman Qureshi. "Image based portable wear debris analysis tool". Industrial Lubrication and Tribology 67, nr 4 (8.06.2015): 389–98. http://dx.doi.org/10.1108/ilt-11-2014-0127.
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Purpose – The purpose of this paper is to show the development of an image processing-based portable equipment for an automatic wear debris analysis. It can analyze both the qualitative and quantitative features of machine wear debris: size, quantity, size distribution, shape, surface texture and material composition via color. Design/methodology/approach – It comprises hardware and software components which can take debris in near real-time from a machine oil sump and process it for features diagnosis. This processing provides the information of the basic features on the user screen which can further be used for machine component health diagnosis. Findings – The developed system has the capacity to replace the existing off-line methods due to its cost effectiveness and simplicity in operation. The system is able to analyze debris basic quantitative and qualitative features greater than 50 micron and less than 300 micron. Originality/value – Wear debris basic features analysis tool is developed and discussed. The portable and near real-time analysis offered by the discussed work can be more technically effective as compared to the existing off-line and online techniques.
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Liu, Peng, Qinghua Wang, Yanli Luo, Zhiguo He i Wei Luo. "Study on a New Transient Productivity Model of Horizontal Well Coupled with Seepage and Wellbore Flow". Processes 9, nr 12 (14.12.2021): 2257. http://dx.doi.org/10.3390/pr9122257.
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Digital transformation has become one of the major themes of the development of the global oil industry today. With the development of digital transformation, on-site production will surely achieve further automated management, that is, on-site production data automatic collection, real-time tracking, diagnosis and optimization, and remote control of on-site automatic adjustment devices. In this process, the realization of real-time optimization work based on massive data collection needs to be carried out combined with oil and gas well transient simulation. Therefore, research of the horizontal well capacity prediction transient model is one of the important basic works in the work of oil and gas digital transformation. In this paper, the method and process of establihing the transient calculation model of single-phase flow in horizontal wells are introduced in detail from three aspects: reservoir seepage, horizontal wellbore flow (taking one kind of flow as an example), and the coupling model of two flows. The model is more reliable through the verification of pressure recovery data from multiple field logs. The transient model of single-phase seepage in horizontal wells will lay the foundation for the establishment of transient models of oil-gas two-phase seepage and oil-gas-water three-phase seepage.
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Li, Guo Ping, i Qing Wei Zhang. "Hydraulic Fault Diagnosis Expert System of Excavator Based on Fault Tree". Advanced Materials Research 228-229 (kwiecień 2011): 439–46. http://dx.doi.org/10.4028/www.scientific.net/amr.228-229.439.
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The constituting of diagnosis expert system and the basic theory of fault tree analysis were introduced at first. A model of combining fault tree with expert system was analyzed. It focused on the hydraulic excavator PC220-7 for Komatsu according to the hydraulic system breakdown of law and order was particularity. A method combined the fault tree analysis with an expert system based rules was adopted. It was expressed systematically expert knowledge by building a fault tree. According to the fault tree, a database of diagnostic rule can be automatically generated. Automatic diagnosis knowledge acquisition has become reality. A hydraulic system fault of excavator was given as an example to design an expert system with the function of complex reasoning and explanation. It provides a reference for hydraulic excavator of the use of management and fault maintenance personnel to solve excavator problems. It shows that this method has a good application in the future.
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Fei, Liguo, Jun Xia, Yuqiang Feng i Luning Liu. "A novel method to determine basic probability assignment in Dempster–Shafer theory and its application in multi-sensor information fusion". International Journal of Distributed Sensor Networks 15, nr 7 (lipiec 2019): 155014771986587. http://dx.doi.org/10.1177/1550147719865876.
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Multi-sensor information fusion occurs in a vast variety of applications, including medical diagnosis, automatic drive, speech recognition, and so on. Often these problems can be modeled by Dempster–Shafer theory. In Dempster–Shafer theory, the most primary processing unit is the basic probability assignment, which is a description of objective information in the real world. How to make this description more effective is a vital but open issue. A novel basic probability assignment generation model is proposed in this article whose objective is to provide perspective with respect to how basic probability assignment can be determined based on learning algorithms. First, the basic probability assignment generation model is constructed based on clustering idea using K-means method, which is employed to determine basic probability assignment with the proposed basic probability assignment generation method. Moreover, the proposed basic probability assignment generation method is extended by K–nearest neighbor (K-NN) algorithm. The detailed implementation of the proposed method is demonstrated by several numerical examples. As an extension, a classifier called KKC is constructed according to the developed approach, and its classification effect is compared with several famous classification algorithms. Experiments manifest desirable results with regard to classification accuracy, which illustrates the applicability of the proposed method to determine basic probability assignment.
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Feng, Lixiao, Guorong Chen i Jun Peng. "An Ontology-Based Cognitive Model for Faults Diagnosis of Hazardous Chemical Storage Devices". International Journal of Cognitive Informatics and Natural Intelligence 12, nr 4 (październik 2018): 101–14. http://dx.doi.org/10.4018/ijcini.2018100106.
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Due to high temperature, high pressure, high corrosion, and many other factors, the hazardous chemical device is facing more severe security challenges than other industries. Now, the monitoring methods have been very mature, which play a basic monitoring role, not a predictive fault diagnosis. In this article, the hazardous chemical device's status data will be collected from the existing industrial monitoring network, the real-time data will be preprocessed and then stored in a database, and the data will be imported to the real-time data into the ontology cognitive model; the data will be performed by big data processing and automatic reasoning so that real-time status of hazardous chemical device and the warning of security risks predict are easily obtained at any time. The model is proposed to solve the problem of knowledge representation and reasoning of the hazardous chemical device based on ontology. The model is analyzed and implemented in Protégé software.
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Li, Xibai, Yan Sun, Juyang Jiao, Haoyu Wu, Chunxi Yang i Xubo Yang. "Automatic Discoid Lateral Meniscus Diagnosis from Radiographs Based on Image Processing Tools and Machine Learning". Journal of Healthcare Engineering 2021 (20.04.2021): 1–7. http://dx.doi.org/10.1155/2021/6662664.
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The aim of the present study is to build a software implementation of a previous study and to diagnose discoid lateral menisci on knee joint radiograph images. A total of 160 images from normal individuals and patients who were diagnosed with discoid lateral menisci were included. Our software implementation includes two parts: preprocessing and measurement. In the first phase, the whole radiograph image was analyzed to obtain basic information about the patient. Machine learning was used to segment the knee joint from the original radiograph image. Image enhancement and denoising tools were used to strengthen the image and remove noise. In the second phase, edge detection was used to quantify important features in the image. A specific algorithm was designed to build a model of the knee joint and measure the parameters. Of the test images, 99.65% were segmented correctly. Furthermore, 97.5% of the tested images were segmented correctly and their parameters were measured successfully. There was no significant difference between manual and automatic measurements in the discoid ( P = 0.28 ) and control groups ( P = 0.15 ). The mean and standard deviations of the ratio of lateral joint space distance to the height of the lateral tibial spine were compared with the results of manual measurement. The software performed well on raw radiographs, showing a satisfying success rate and robustness. Thus, it is possible to diagnose discoid lateral menisci on radiographs with the help of radiograph-image-analyzing software (BM3D, etc.) and artificial intelligence-related tools (YOLOv3). The results of this study can help build a joint database that contains data from patients and thus can play a role in the diagnosis of discoid lateral menisci and other knee joint diseases in the future.
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Burriel-Valencia, Jordi, Ruben Puche-Panadero, Javier Martinez-Roman, Angel Sapena-Bano, Manuel Pineda-Sanchez, Juan Perez-Cruz i Martin Riera-Guasp. "Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems". Electronics 8, nr 1 (21.12.2018): 6. http://dx.doi.org/10.3390/electronics8010006.
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Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine’s condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.
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Cui, Jingyu, i Yuze Xia. "Diagnosability verification of discrete event systems". Applied and Computational Engineering 6, nr 1 (14.06.2023): 324–30. http://dx.doi.org/10.54254/2755-2721/6/20230801.
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Fault diagnosis is one of the key topics in the study of computer program operation and automatic control of large complex systems these days. It can be used in wide-spanning areas. As early as the last century, researchers started to study diagnosis structures and made a series of progress. Moreover, the problem of diagnosability verification of a system received much attention from many researchers. Therefore, in this paper, a discrete event system (DES) is proposed and a diagnoser is constructed as an automaton model to verify the diagnosability of a given system. A method is proposed to test if a given system is diagnosable under the discrete event system structure. The states of a system are classified into three categories, and a diagnoser structure with basic algorithms and functions is defined to verify diagnosability. The proposed diagnoser structure can better capture the behavior of the system and verify its diagnosability.
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Kim, Changgyun, Donghyun Kim, HoGul Jeong, Suk-Ja Yoon i Sekyoung Youm. "Automatic Tooth Detection and Numbering Using a Combination of a CNN and Heuristic Algorithm". Applied Sciences 10, nr 16 (13.08.2020): 5624. http://dx.doi.org/10.3390/app10165624.
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Dental panoramic radiography (DPR) is a method commonly used in dentistry for patient diagnosis. This study presents a new technique that combines a regional convolutional neural network (RCNN), Single Shot Multibox Detector, and heuristic methods to detect and number the teeth and implants with only fixtures in a DPR image. This technology is highly significant in providing statistical information and personal identification based on DPR and separating the images of individual teeth, which serve as basic data for various DPR-based AI algorithms. As a result, the mAP(@IOU = 0.5) of the tooth, implant fixture, and crown detection using the RCNN algorithm were obtained at rates of 96.7%, 45.1%, and 60.9%, respectively. Further, the sensitivity, specificity, and accuracy of the tooth numbering algorithm using a convolutional neural network and heuristics were 84.2%, 75.5%, and 84.5%, respectively. Techniques to analyze DPR images, including implants and bridges, were developed, enabling the possibility of applying AI to orthodontic or implant DPR images of patients.
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Nuryadi, Nanang, Aziz Setyawan Hidayat, Felix Wuryo Handono, Ayuni Asistyasari i Yosep Nuryaman. "Expert System for Diagnosing Damage to Automatic Motorcycle Engines Using the Forward Chaining Method". INTECOMS: Journal of Information Technology and Computer Science 7, nr 3 (9.06.2024): 860–71. http://dx.doi.org/10.31539/intecoms.v7i3.10394.
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Damage to the matic engine due to negligence in treatment. The new vehicle owners aware of the damage after the vehicle can not operate properly. Therefore, the use of vehicles likely to require regular maintenance. By way of detecting damage to what is happening on the vehicle. For example, if the vehicle sound noisy and have no idea why this happens, it is this which encourages the development of an expert system to identify / diagnose matic damage to the engine. Submission of information was carried out using visual basic applications that have been made. By running the application diagnosis expert system engine matic damage to the computer, it will be processed in the system then the results will be displayed on the computer screen. This system is expected to provide optimal information from the user and the system of reciprocity.This study is expected to provide all information related to the engine damage problem quickly and efficiently on a reciprocal basis between the user and the system.
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Dong, Tao. "Design Consideration of a Health-Information-Technology-Supported Intelligent Urinalysis System". Advanced Materials Research 989-994 (lipiec 2014): 1077–81. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1077.
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Urinalysis is not only widely employed in medical diagnosis but also suitable for household daily monitoring of personal health conditions. However, current urinalysis methods and instruments require more professional knowledge, while the sampling and treatment of urine samples are manual and inconvenient. In this work, a new-concept automatic urinalysis system is proposed to provide personal urinalysis services for home users. The system forms an eco-friendly intelligent toilet, which is of great significances in the future healthcare network. The basic strategy is to design a fixed supporting platform and various disposable urinalysis lab-on-chips with great expansibility and high flexibility. The intelligent device has multiple functions of automatic urine sampling, rapid on-chip detecting, auto-decontaminating and personalized health information technology (HIT) supporting, thus to provide a low-cost solution of automatic urinalysis services for both inpatients in hospitals and home-users. The structure of the urinalysis system logically resembles the frame with fixed smart cellphones and various mobile application programs. Besides, a biological lighting module is also designed to harvest the energy in wasted urine by continuous culturing vibrio fischeri, a luminescent bacterium. The integrated urinalysis system could create the possibility of remote medical services for home users, and meanwhile generates a new branch in the field of microsystem, which is entitled as ‘HIT-oriented lab-on-chips’.
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Bogdan, T. V., V. O. Onishchenko, V. V. Bogdan i O. V. Savchenko. "The effect of L-arginine on the balance of essential amino acids in plasma of the patients with stable angina". Likarska sprava, nr 7-8 (30.12.2020): 25–30. http://dx.doi.org/10.31640/jvd.7-8.2020(3).
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Background. Despite the significant achievements of clinical medicine in the prevention, diagnosis and treatment of coronary heart disease, the levels of morbidity, disability and mortality among the population of Ukraine from this pathology remain consistently high. The purpose. To improve the treatment of patients with stable angina by studying the effect of L-arginine on the balance of essential amino acids in blood plasma. Material and methods. It was examined 67 patients with stable angina. They were divided into two groups: group Ipatients received antianginal basic therapy, group II patients received basic antianginal therapy and L-arginine. The amino acid spectrum of patients' blood plasma was studied by ion-exchange liquid column chromatography, using an automatic amino acid analyzer T-339 Microtechna (Czech Republic, Prague). Results and discussion. In patients with stable angina who received basic therapy and L-arginine, in contrast to patients who received only basic therapy, plasma levels of arginine became normalized, which probably contributes to the synthesis of NO. The level of valine, leucine and isoleucine, which provide the synthesis of acyl-CoA and succinyl-CoA, became also normalized. Conclusion. Administration of L-arginine to patients with stable angina together with antianginal therapy helps to correct plasma amino acid imbalances, which is likely to effectively affect the course of the disease and prognosis.
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Chamarczuk, Michał, Michał Malinowski, Yohei Nishitsuji, Jan Thorbecke, Emilia Koivisto, Suvi Heinonen, Sanna Juurela, Miłosz Mężyk i Deyan Draganov. "Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider". GEOPHYSICS 84, nr 3 (1.05.2019): Q13—Q25. http://dx.doi.org/10.1190/geo2018-0504.1.
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The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent surface-wave sources. Additionally, long recordings provide large data volumes that require robust and efficient processing methods. We address these problems by developing a two-step wavefield evaluation and event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal characteristics of noise recordings by simultaneous analysis of adjacent receiver lines. We test our method on synthetic data representing transient ambient-noise sources at the surface and in the deeper subsurface. We discriminate between basic types of seismic events by using three adjacent receiver lines. Subsequently, we apply TWEED to 600 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h noise panel. Using TWEED, we successfully detect 1093 body-wave events in the full data set. To increase the computational efficiency, we use slowness parameters derived from the first step of TWEED as input to a support vector machine (SVM) algorithm. Using this approach, we detect 94% of the TWEED-evaluated body-wave events indicating the possibility to limit the illumination analysis to only one step, and therefore increase the time efficiency at the price of lower detection rate. However, TWEED on a small volume of the recorded data followed by SVM on the rest of the data could be efficiently used for a quick and robust (real-time) scanning for body-wave energy in large data volumes for subsequent application of SI for retrieval of reflections.
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Fonollà, Roger, Quirine E. W. van der Zander, Ramon M. Schreuder, Ad A. M. Masclee, Erik J. Schoon, Fons van der Sommen i Peter H. N. de With. "A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities". Applied Sciences 10, nr 15 (22.07.2020): 5040. http://dx.doi.org/10.3390/app10155040.
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Colorectal polyps are critical indicators of colorectal cancer (CRC). Blue Laser Imaging and Linked Color Imaging are two modalities that allow improved visualization of the colon. In conjunction with the Blue Laser Imaging (BLI) Adenoma Serrated International Classification (BASIC) classification, endoscopists are capable of distinguishing benign and pre-malignant polyps. Despite these advancements, this classification still prevails a high misclassification rate for pre-malignant colorectal polyps. This work proposes a computer aided diagnosis (CADx) system that exploits the additional information contained in two novel imaging modalities, enabling more informative decision-making during colonoscopy. We train and benchmark six commonly used CNN architectures and compare the results with 19 endoscopists that employed the standard clinical classification model (BASIC). The proposed CADx system for classifying colorectal polyps achieves an area under the curve (AUC) of 0.97. Furthermore, we incorporate visual explanatory information together with a probability score, jointly computed from White Light, Blue Laser Imaging, and Linked Color Imaging. Our CADx system for automatic polyp malignancy classification facilitates future advances towards patient safety and may reduce time-consuming and costly histology assessment.
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Kim, Kwang Baek, Hyun Jun Park, Doo Heon Song i Sang-suk Han. "Developing an Intelligent Automatic Appendix Extraction Method from Ultrasonography Based on Fuzzy ART and Image Processing". Computational and Mathematical Methods in Medicine 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/389057.
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Ultrasound examination (US) does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer diameter of the appendix is most important. Therefore, clear delineation of the appendix on US images is essential. In this paper, we propose a new intelligent method to extract appendix automatically from abdominal sonographic images as a basic building block of developing such an intelligent tool for medical practitioners. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply fuzzy ART learning algorithm to the organ area in order to extract appendix accurately. The experiment verifies that the proposed method is highly accurate (successful in 38 out of 40 cases) in extracting appendix.
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Han, Xianjing, i Guoxin Liang. "Echocardiographic Features of Patients with Coronary Heart Disease and Angina Pectoris under Deep Learning Algorithms". Scientific Programming 2021 (13.11.2021): 1–8. http://dx.doi.org/10.1155/2021/8336959.
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Based on the VGG19-fully convolutional network (FCN) (VGG19-FCN) and U-Net model in the deep learning algorithms, the left ventricle in the ultrasonic cardiogram was segmented automatically. In addition, this study evaluated the value of ultrasonic cardiogram features after segmentation by the optimized algorithm in diagnosing patients with coronary heart disease (CHD) and angina pectorisody; patients with arrhythmia; and pa. In this study, 30 patients with confirmed CHD and 30 normal people without CHD from the same hospital in a certain area were selected as the research objects. Firstly, the VGG19-FCN and U-Net model algorithms were selected to automatically segment the left ventricular part of the apical four-chamber static image, which was realized through the weights of the fine-tune basic model algorithm. Subsequently, the experimental subjects were divided into a normal group and a CHD group, and the data were obtained through the ultrasonic cardiogram feature analysis of automatic segmentation by the algorithm. The differences in the ejection fraction (EF), left ventricular fractional shortening (FS), and E/A values (in early and late of the diastolic phase) of the left ventricle for patients in the two groups were compared. In addition, the ultrasonic cardiogram left ventricular segmentation results of normal people and patients with CHD were compared. A comprehensive analysis suggested that the U-Net model was more suitable for the practical application of automatic ultrasonic cardiogram segmentation. According to the analyzed data results, the global systolic function parameters (EF, FS, and E/A values) of the left ventricle for patients showed statistically obvious differences ( P < 0.05 ). In summary, deep learning algorithms can effectively improve the efficiency of ultrasonic cardiogram left ventricular segmentation, show a great role in the diagnosis of CHD patients, and provide a reliable theoretical basis and foundation research on the subsequent CHD imaging diagnosis. The comprehensive analysis showed that the U-Net model was more suitable for the practical application of echocardiographic automatic segmentation, and this study can effectively improve the efficiency of echocardiographic left ventricular segmentation, which played an important role in the diagnosis of coronary heart disease, providing a reliable theoretical basis and foundation for subsequent CHD imaging research.
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Hu, Jin, Hui Pin Lin, Qun Wang i Zheng Yu Lu. "A Smart Remote Controlled Multi-Functional Lighting System". Applied Mechanics and Materials 635-637 (wrzesień 2014): 1187–93. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1187.
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Recently, remote control technology attracted wide attentions and began to be used in lighting applications with computer and internet, power electronics, automatic control technologies together. Most conventional remote controlled lighting systems realized basic lighting functions, i.e. turning on/off and dimming. However, modern remote controlled lighting system needs to have multi-functions including line switching control, line anti-theft, illuminance detection, metering electrical parameters, ballast’s fault-condition detection, data analysis and statistics, abnormal-conditions alert and diagnosis. These functions will make the system more intelligent and practical. This paper proposes such a system comprising intelligent ballast, line switcher, meter, illuminance detector, server, website, etc. The system integration method is illustrated by analyzing the system’s working principle and functions. An experimental system is set up as a demonstration and the test results verify the proposal.
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Lisitsyna, Liubov, Marina Senchilo i Sergei Teleshev. "CREATING RLCP-COMPATIBLE VIRTUAL LABORATORIES FOR TRAINING BASIC ALGORITHMS ON NEURAL NETWORKS". Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2021, nr 3 (30.07.2021): 134–42. http://dx.doi.org/10.24143/2072-9502-2021-3-134-142.
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The article describes the principles of developing RLCP-compatible virtual laboratories. There are build two virtual laboratories based on these principles for mastering the basic algo-rithms on neural networks: Algorithm for Sequential Signal Propagation in Perceptron and Algorithm for Training Perceptron Using Method of Backward Error Propagation. Virtual laboratories consist of two independent modules – a virtual stand and an RLCP server. The virtual stand implements a visual display of the task's data and provides the listener with tools for forming and editing intermediate solutions and responses. Since the virtual laboratories were assumed for the first acquaintance with neural networks, the simplest neural network architectures in the form of single-layer perceptrons were used as the initial data. And the algorithm of sequential propagation of signals in a neural network (VL1) and the algorithm of training a neural network with a teacher based on the method of inverse error propagation (VL2) are used as the basic algorithms. For automatic generation of equally complex and valid tasks there have been proposed algorithms with high efficiency (the average time for generating an individual task on the VL2 stand for a student was no longer than 3 seconds). It was found out experimentally that such virtual laboratories should be created in two modes: the mode of training and mode of certification. The training shop works for solving problems using the studied algorithms on the stands of virtual laboratories in the training mode with the diagnosis of admitted errors significantly increase the effectiveness of students' results
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Kimura, Satoru, Takahiro Emoto, Yoshitaka Suzuki, Mizuki Shinkai, Akari Shibagaki i Fumio Shichijo. "Novel Approach Combining Shallow Learning and Ensemble Learning for the Automated Detection of Swallowing Sounds in a Clinical Database". Sensors 24, nr 10 (11.05.2024): 3057. http://dx.doi.org/10.3390/s24103057.
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Cervical auscultation is a simple, noninvasive method for diagnosing dysphagia, although the reliability of the method largely depends on the subjectivity and experience of the evaluator. Recently developed methods for the automatic detection of swallowing sounds facilitate a rough automatic diagnosis of dysphagia, although a reliable method of detection specialized in the peculiar feature patterns of swallowing sounds in actual clinical conditions has not been established. We investigated a novel approach for automatically detecting swallowing sounds by a method wherein basic statistics and dynamic features were extracted based on acoustic features: Mel Frequency Cepstral Coefficients and Mel Frequency Magnitude Coefficients, and an ensemble learning model combining Support Vector Machine and Multi-Layer Perceptron were applied. The evaluation of the effectiveness of the proposed method, based on a swallowing-sounds database synchronized to a video fluorographic swallowing study compiled from 74 advanced-age patients with dysphagia, demonstrated an outstanding performance. It achieved an F1-micro average of approximately 0.92 and an accuracy of 95.20%. The method, proven effective in the current clinical recording database, suggests a significant advancement in the objectivity of cervical auscultation. However, validating its efficacy in other databases is crucial for confirming its broad applicability and potential impact.
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He, Chao, i Pingping Liu. "Deep Learning Based Recognition of Lepidoptera Insects". International Journal of Advanced Network, Monitoring and Controls 8, nr 4 (1.12.2023): 20–28. http://dx.doi.org/10.2478/ijanmc-2023-0073.
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Abstract The successful application of cutting-edge computer vision technology to automatic insect classification has long been a focus of research in insect taxonomy. The results of this research have a wide range of applications in areas such as environmental monitoring, pest diagnosis and epidemiology. However, there is still a gap between the current techniques used in automatic insect classification and the latest computer vision techniques. The research in this paper is conducted on Lepidoptera, a class of insects that are widely infested, including butterflies and moths. The study focuses on the application of deep learning algorithms in image processing of Lepidoptera insects. In order to improve the recognition rate for Lepidoptera insect recognition, this paper uses a detection model based on deep neural networks to realize the recognition of Lepidoptera insects in complex environments. Specifically, the yolov7 algorithm is adopted as the basic model for this experiment, and the reasons for using this model are explained in terms of the splicing of network modules, loss function, positive sample allocation strategy, and the merging of convolution and normalization, respectively. Through experiments, it is proved that the algorithm can effectively improve the gesture recognition rate, the recognition accuracy reaches 79.5%, and the recognition speed is as high as 33.08it/s.
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Zhang, Jieni, Kun Yang, Zhufu Shen, Shengbo Sang, Zhongyun Yuan, Runfang Hao, Qi Zhang i Meiling Cai. "End-to-End Automatic Classification of Retinal Vessel Based on Generative Adversarial Networks with Improved U-Net". Diagnostics 13, nr 6 (17.03.2023): 1148. http://dx.doi.org/10.3390/diagnostics13061148.
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The retinal vessels in the human body are the only ones that can be observed directly by non-invasive imaging techniques. Retinal vessel morphology and structure are the important objects of concern for physicians in the early diagnosis and treatment of related diseases. The classification of retinal vessels has important guiding significance in the basic stage of diagnostic treatment. This paper proposes a novel method based on generative adversarial networks with improved U-Net, which can achieve synchronous automatic segmentation and classification of blood vessels by an end-to-end network. The proposed method avoids the dependency of the segmentation results in the multiple classification tasks. Moreover, the proposed method builds on an accurate classification of arteries and veins while also classifying arteriovenous crossings. The validity of the proposed method is evaluated on the RITE dataset: the accuracy of image comprehensive classification reaches 96.87%. The sensitivity and specificity of arteriovenous classification reach 91.78% and 97.25%. The results verify the effectiveness of the proposed method and show the competitive classification performance.
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Bynagari, Naresh Babu, i Takudzwa Fadziso. "Theoretical Approaches of Machine Learning to Schizophrenia". Engineering International 6, nr 2 (22.12.2018): 155–68. http://dx.doi.org/10.18034/ei.v6i2.568.
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Machine learning techniques have been successfully used to analyze neuroimaging data in the context of disease diagnosis in recent years. In this study, we present an overview of contemporary support vector machine-based methods developed and used in psychiatric neuroimaging for schizophrenia research. We focus in particular on our group's algorithms, which have been used to categorize schizophrenia patients and healthy controls, and compare their accuracy findings to those of other recently published studies. First, we'll go over some basic pattern recognition and machine learning terms. Then, for each study, we describe and discuss it independently, emphasizing the key characteristics that distinguish each approach. Finally, conclusions are reached as a result of comparing the data obtained using the various methodologies presented to determine how beneficial automatic categorization systems are in understanding the molecular underpinnings of schizophrenia. The primary implications of applying these approaches in clinical practice are then discussed.
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Abdul-Jabbar, Safa S., Alaa K. Farhan, Abdelaziz A. Abdelhamid i Mohamed E. Ghoneim. "Razy: A String Matching Algorithm for Automatic Analysis of Pathological Reports". Axioms 11, nr 10 (12.10.2022): 547. http://dx.doi.org/10.3390/axioms11100547.
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Pathology reports are necessary for specialists to make an appropriate diagnosis of diseases in general and blood diseases in particular. Therefore, specialists check blood cells and other blood details. Thus, to diagnose a disease, specialists must analyze the factors of the patient’s blood and medical history. Generally, doctors have tended to use intelligent agents to help them with CBC analysis. However, these agents need analytical tools to extract the parameters (CBC parameters) employed in the prediction of the development of life-threatening bacteremia and offer prognostic data. Therefore, this paper proposes an enhancement to the Rabin–Karp algorithm and then mixes it with the fuzzy ratio to make this algorithm suitable for working with CBC test data. The selection of these algorithms was performed after evaluating the utility of various string matching algorithms in order to choose the best ones to establish an accurate text collection tool to be a baseline for building a general report on patient information. The proposed method includes several basic steps: Firstly, the CBC-driven parameters are extracted using an efficient method for retrieving data information from pdf files or images of the CBC tests. This will be performed by implementing 12 traditional string matching algorithms, then finding the most effective ways based on the implementation results, and, subsequently, introducing a hybrid approach to address the shortcomings or issues in those methods to discover a more effective and faster algorithm to perform the analysis of the pathological tests. The proposed algorithm (Razy) was implemented using the Rabin algorithm and the fuzzy ratio method. The results show that the proposed algorithm is fast and efficient, with an average accuracy of 99.94% when retrieving the results. Moreover, we can conclude that the string matching algorithm is a crucial tool in the report analysis process that directly affects the efficiency of the analytical system.
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Ding, Minghu, Xiaowei Zou, Qizhen Sun, Diyi Yang, Wenqian Zhang, Lingen Bian, Changgui Lu, Ian Allison, Petra Heil i Cunde Xiao. "The PANDA automatic weather station network between the coast and Dome A, East Antarctica". Earth System Science Data 14, nr 11 (15.11.2022): 5019–35. http://dx.doi.org/10.5194/essd-14-5019-2022.
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Abstract. This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network, which includes 11 automatic weather stations (AWSs) across the Prydz Bay–Amery Ice Shelf–Dome A area from the coast to the summit of the East Antarctic Ice Sheet. The ∼ 1460 km transect from Zhongshan to Panda S follows roughly along ∼ 77∘ E longitude and covers all geographic units of East Antarctica. Initial inland observations, near the coast, started in the 1996/97 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1 h intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real time via the Argos system. The data quality is generally very reliable, and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the PANDA AWS network. This dataset, which is publicly available, is planned to be updated on a near-real-time basis and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at https://doi.org/10.11888/Atmos.tpdc.272721 (Ding et al., 2022b).
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Schmidt, Christian, Dorothea Kesztyüs, Martin Haag, Manfred Wilhelm i Tibor Kesztyüs. "Proposal of a Method for Transferring High-Quality Scientific Literature Data to Virtual Patient Cases Using Categorical Data Generated by Bernoulli-Distributed Random Values: Development and Prototypical Implementation". JMIR Medical Education 9 (9.03.2023): e43988. http://dx.doi.org/10.2196/43988.
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Background Teaching medicine is a complex task because medical teachers are also involved in clinical practice and research and the availability of cases with rare diseases is very restricted. Automatic creation of virtual patient cases would be a great benefit, saving time and providing a wider choice of virtual patient cases for student training. Objective This study explored whether the medical literature provides usable quantifiable information on rare diseases. The study implemented a computerized method that simulates basic clinical patient cases utilizing probabilities of symptom occurrence for a disease. Methods Medical literature was searched for suitable rare diseases and the required information on the respective probabilities of specific symptoms. We developed a statistical script that delivers basic virtual patient cases with random symptom complexes generated by Bernoulli experiments, according to probabilities reported in the literature. The number of runs and thus the number of patient cases generated are arbitrary. Results We illustrated the function of our generator with the exemplary diagnosis “brain abscess” with the related symptoms “headache, mental status change, focal neurologic deficit, fever, seizure, nausea and vomiting, nuchal rigidity, and papilledema” and the respective probabilities from the literature. With a growing number of repetitions of the Bernoulli experiment, the relative frequencies of occurrence increasingly converged with the probabilities from the literature. For example, the relative frequency for headache after 10.000 repetitions was 0.7267 and, after rounding, equaled the mean value of the probability range of 0.73 reported in the literature. The same applied to the other symptoms. Conclusions The medical literature provides specific information on characteristics of rare diseases that can be transferred to probabilities. The results of our computerized method suggest that automated creation of virtual patient cases based on these probabilities is possible. With additional information provided in the literature, an extension of the generator can be implemented in further research.
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Ghafourian, Ehsan, Farshad Samadifam, Heidar Fadavian, Peren Jerfi Canatalay, AmirReza Tajally i Sittiporn Channumsin. "An Ensemble Model for the Diagnosis of Brain Tumors through MRIs". Diagnostics 13, nr 3 (3.02.2023): 561. http://dx.doi.org/10.3390/diagnostics13030561.
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Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method.
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Banerjee, Sunetra, Juan Lyu, Zixun Huang, Hung Fat Frank Leung, Timothy Tin-Yan Lee, De Yang, Steven Su, Yongping Zheng i Sai-Ho Ling. "Light-Convolution Dense Selection U-Net (LDS U-Net) for Ultrasound Lateral Bony Feature Segmentation". Applied Sciences 11, nr 21 (30.10.2021): 10180. http://dx.doi.org/10.3390/app112110180.
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Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement.
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Yang, Juan, Haijing Sui, Ronghong Jiao, Min Zhang, Xiaohui Zhao, Lingling Wang, Wenping Deng i Xueyuan Liu. "Random-Forest-Algorithm-Based Applications of the Basic Characteristics and Serum and Imaging Biomarkers to Diagnose Mild Cognitive Impairment". Current Alzheimer Research 19, nr 1 (styczeń 2022): 76–83. http://dx.doi.org/10.2174/1567205019666220128120927.
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Background: Mild cognitive impairment (MCI) is considered a s the early stage of Alzheimer's Disease (AD). The purpose of our study was to analyze the basic characteristics andserum and imaging biomarkers for the diagnosis of MCI patients as a more objective and accurate approach. Methods: The Montreal Cognitive Test was used to test 119 patients aged ≥65. Such serum bio-markers were detected as preprandial blood glucose, triglyceride, total cholesterol, Aβ1-40, Aβ1-42, and P-tau. All the subjects were scanned with 1.5T MRI (GE Healthcare, WI, USA) to obtain DWI, DTI, and ASL images. DTI was used to calculate the anisotropy fraction (FA), DWI was used to calculate the apparent diffusion coefficient (ADC), and ASL was used to calculate the cerebral blood flow (CBF). All the images were then registered to the SPACE of the Montreal Neurological Institute (MNI). In 116 brain regions, the medians of FA, ADC, and CBF were extracted by automatic anatomical labeling. The basic characteristics included gender, education level, and previous disease history of hypertension, diabetes, and coronary heart disease. The data were randomly divided into training sets and test ones. The recursive random forest algorithm was applied to the diagnosis of MCI patients, and the recursive feature elimination (RFE) method was used to screen the significant basic features and serum and imaging biomarkers. The overall accuracy, sensitivity, and specificity were calculated, respectively, and so were the ROC curve and the area under the curve (AUC) of the test set. Results: When the variable of the MCI diagnostic model was an imaging biomarker, the training accuracy of the random forest was 100%, the correct rate of the test was 86.23%, the sensitivity was 78.26%, and the specificity was 100%. When combining the basic characteristics, the serum and imaging biomarkers as variables of the MCI diagnostic model, the training accuracy of the random forest was found to be 100%; the test accuracy was 97.23%, the sensitivity was 94.44%, and the specificity was 100%. RFE analysis showed that age, Aβ1-40, and cerebellum_4_6 were the most important basic feature, serum biomarker, imaging biomarker, respectively. Conclusion: Imaging biomarkers can effectively diagnose MCI. The diagnostic capacity of the basic trait biomarkers or serum biomarkers for MCI is limited, but their combination with imaging biomarkers can improve the diagnostic capacity, as indicated by the sensitivity of 94.44% and the specificity of 100% in our model. As a machine learning method, a random forest can help diagnose MCI effectively while screening important influencing factors.
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Hishida, Hirotoshi, Koichi Tokuuye, Keiko Hishida, Hayato Tojo, Yasuhiro Hishida i Tomomi Koide. "Basic Research on the Development of an Automatic Heart Sound Diagnosis System - Analysis of Heart Sounds for Learning Policy and Experiment for the Prototype of the Auscultation Part -". Journal of Systemics, Cybernetics and Informatics 21, nr 2 (kwiecień 2023): 55–63. http://dx.doi.org/10.54808/jsci.21.02.55.
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A design policy was established for a specific data flow and learning method for the automatic heart-sound diagnosis system under development. The production of each part becomes possible, and auscultation and learning begin. It can be used over clothes as long as it is applied well to the skin surface of the chest. It would be nice to be able to set multiple auscultation positions, but there is a limit to what ordinary people can be asked to do, so this should be considered while having AI learn. We analyzed normal heart sounds to explore learning strategies. Sounds I and II are considered to be important anchor information sources for identifying other heart sounds. Abnormal heart sounds may not be heard at every beat and the rhythm may be abnormal. AI refers to the multiple beats of heart sounds during auscultation. Heartbeat analysis is a multidimensional information analysis related to time and space, and heart sounds are factored if normal and abnormal heart sounds can be organized based on the score. For pitch that tends to depend on individuals and devices, a relative discussion would be more appropriate.
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Shimotsuura, Yasuhiro, Hiroyuki Maezawa i Yoshiaki Omura. "Experimental Production of the Bi-Digital O-Ring Test Muscular Power Evaluation Device Using an Air-type Automatic Analysis System". Acupuncture & Electro-Therapeutics Research 45, nr 1 (24.08.2020): 15–30. http://dx.doi.org/10.3727/036012920x15958782196808.
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As Bi-Digital O-Ring Test (originated and founded by Prof. Y. Omura in New York, 1997-2020; follow as BDORT)is a diagnosis method that is carried out on the basic theory of the physiological phenomenon called the decline of muscular power of fingers, the examiner, and patients (or mediator) are demanded to do BDORT by constant regular power. Namely BDORT is a diagnosis method that estimates the relative muscular decline of the patients, so there is such a view that the results of BDORT are reflected by consciousness of the examiner. The authors used the ORT tester by using air system to avoid the influence of electromagnetic wave and evaluated the decline of the muscle strength and open degree of the O-ring shaped by the patients. Patients of the Shimotsuura Clinic are subjected and checked by direct BDORT method. When the patients shapes the O-Ring, staff members stimulated the parts of the body by plastic stick and push foot switch. Decline of the muscle strength & open degree was evaluated. When the open degree was more than 20%, stimulated points were evaluated as abnormal. Opposite side arm of the O-Ring shaped arm was checked as control. The results of the direct BDORT method between ORT evaluation apparatus and the patient was consistent with the results of the indirect method of BDORT method between the doctor and the assistant. Even where the patients complain of ill, the muscle strength was declined and opened the O-Ring by using ORT evaluation apparatus. Especially in the parts of the strong response of Integrin α5β1 checked by the doctor, the muscle strength decreased and the open degree was much higher than other parts of the body. Patients could experience of BDORT by numeral objective evaluation of the decline of the muscle strength by using ORT evaluation apparatus.
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Sulema, Ye S., i A. I. Dychka. "Software system of automatic identification and distributed storage of patient medical data". System technologies 3, nr 146 (11.05.2023): 134–48. http://dx.doi.org/10.34185/1562-9945-3-146-2023-13.
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Due to the rapid development of information technologies, informatization in the medical industry is essential. The main component of electronic health care is medical information systems designed for the accumulation, processing, analysis and transmis-sion of medical data. In the medical field, specialized software products are used to per-form diagnostic studies, process the results of laboratory tests, and make decisions at the stage of establishing a diagnosis. The use of mobile devices in medical information systems is developing. However, the degree of automation of processes in the provision of medical services and the protection of the personal and medical data of patients is still insufficient. The purpose of the research is to create a basic architecture of a software system that would simplify the process of developing software for automated input, processing, search and confidential patient access to their medical data in a medical information system based on multi-color barcoding of information using mobile devices. The architecture of the software system is proposed, in which, based on the princi-ples of distribution, anonymization, and data ownership, a patient can provide access to medical personnel to their medical data by reading a multi-color interference-resistant barcode from one smartphone (patient’s) by the camera of another smartphone (doctor’s). It is shown that in order to ensure the reliability of such transmission, it is neces-sary to use an interference-resistant barcode, which would ensure the integrity of the data in the conditions of possible distortion of the barcode image (change in lighting, scanning angle, trembling of the operator's hand, blurring or skewing of the image, etc.). The use of mobile devices for the barcode method of transmission and processing of data allows providing the protected electronic co-operating of a patient and a doctor both directly and remotely. It guarantees high reliability and confidentiality of the ex-change of data. The proposed technical solutions make it possible to improve the quality of medi-cal care and strengthen the protection of the patient's medical data.
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Ismail, Ali Rida, Slavisa Jovanovic, Naeem Ramzan i Hassan Rabah. "ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring". Sensors 23, nr 3 (3.02.2023): 1697. http://dx.doi.org/10.3390/s23031697.
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Increased life expectancy in most countries is a result of continuous improvements at all levels, starting from medicine and public health services, environmental and personal hygiene to the use of the most advanced technologies by healthcare providers. Despite these significant improvements, especially at the technological level in the last few decades, the overall access to healthcare services and medical facilities worldwide is not equally distributed. Indeed, the end beneficiary of these most advanced healthcare services and technologies on a daily basis are mostly residents of big cities, whereas the residents of rural areas, even in developed countries, have major difficulties accessing even basic medical services. This may lead to huge deficiencies in timely medical advice and assistance and may even cause death in some cases. Remote healthcare is considered a serious candidate for facilitating access to health services for all; thus, by using the most advanced technologies, providing at the same time high quality diagnosis and ease of implementation and use. ECG analysis and related cardiac diagnosis techniques are the basic healthcare methods providing rapid insights in potential health issues through simple visualization and interpretation by clinicians or by automatic detection of potential cardiac anomalies. In this paper, we propose a novel machine learning (ML) architecture for the ECG classification regarding five heart diseases based on temporal convolution networks (TCN). The proposed design, which implements a dilated causal one-dimensional convolution on the input heartbeat signals, seems to be outperforming all existing ML methods with an accuracy of 96.12% and an F1 score of 84.13%, using a reduced number of parameters (10.2 K). Such results make the proposed TCN architecture a good candidate for low power consumption hardware platforms, and thus its potential use in low cost embedded devices for remote health monitoring.
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Choi, Choul Jun, Jae Yeol Kim i Seung Hyun Choi. "Evaluation of Adhesive Strength in the Bonded Area of Shoes by Using IR Camera". Key Engineering Materials 345-346 (sierpień 2007): 1149–52. http://dx.doi.org/10.4028/www.scientific.net/kem.345-346.1149.
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The Infrared Camera usually detects only Infrared waves emitted from the light in order to illustrate the temperature distribution. An Infrared diagnosis system can be applied to various fields. But the defect discrimination can be automatic or mechanized in the shoes total inspection system. The thermal images of the specimens were analyzed. In shoes, weak bonding due to the separation of the bonded parts delamination causes defects. The most serious defect occurs in the bonding between the outer covering of the shoe and the sole, and to up now, this defect has been detected only by inspection with the naked eye. This study introduces a method for special shoes nondestructive total inspection. Performance of the proposed method is shown through thermo-Image. In search of superior inspection methods, we evaluated an applicable non-destructive inspection method and also carried out basic research for developing an innovative nondestructive inspection system for shoes. The total inspection system using infrared thermal camera for special shoes, its applicability, and system configuration are introduced.
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Huang, Jianqing, Weiwei Cai, Yingchun Wu i Xuecheng Wu. "Recent advances and applications of digital holography in multiphase reactive/nonreactive flows: a review". Measurement Science and Technology 33, nr 2 (1.12.2021): 022001. http://dx.doi.org/10.1088/1361-6501/ac32ea.
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Abstract In various multiphase flows, the characterization of particle dynamics is important in the understanding of the interaction between particles and the surrounding flows. Digital holography (DH) is a versatile 3D imaging technique, which has shown great advantages in quantitative analysis and nonintrusive diagnosis of various particle fields. This review focuses on the advances and applications of DH in multiphase reactive/nonreactive flows in the last two decades. First, the basic principles of DH are introduced, including its mathematical background and representative experimental configurations. Then, the image processing algorithms for hologram reconstruction and automatic focusing are summarized, along with the methods for separating overlapping particles and tracking moving particles. As a prevailing and powerful tool, the recent applications of deep learning in processing holographic images is also included in this review. Furthermore, the applications of DH in the characterization of particle dynamics in multiphase reactive/nonreactive flows are surveyed in detail. Lastly, the review concludes with a discussion on the technical limits of DH and provides insights into its promising future research directions.
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Veronese, Elisa, Umberto Castellani, Denis Peruzzo, Marcella Bellani i Paolo Brambilla. "Machine Learning Approaches: From Theory to Application in Schizophrenia". Computational and Mathematical Methods in Medicine 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/867924.
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In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.