Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926850
Xiaolin Guo, R. Chi, Na Lin, Yang Liu
In this paper, a Taylor expansion linearization-based partial-form model-free adaptive control (TELPF-MFAC) method is proposed, which provides a new way to solve complex nonlinear nonaffine systems. The unknown nonlinear nonaffine system is transformed into a new linear data model (LDM) with a nonlinear residual term. Unknown parameters in LDM are estimated by an adaptive updating mechanism. By utilizing ad-ditional control knowledge in both the control and the parameter updating law, the performance of the proposed method can be improved consequently. Simulation study shows the effectiveness of the proposed TELPF-MFAC.
{"title":"Taylor Expansion Linearization-Based Partial-Form Model-Free Adaptive Control","authors":"Xiaolin Guo, R. Chi, Na Lin, Yang Liu","doi":"10.1109/ICIST55546.2022.9926850","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926850","url":null,"abstract":"In this paper, a Taylor expansion linearization-based partial-form model-free adaptive control (TELPF-MFAC) method is proposed, which provides a new way to solve complex nonlinear nonaffine systems. The unknown nonlinear nonaffine system is transformed into a new linear data model (LDM) with a nonlinear residual term. Unknown parameters in LDM are estimated by an adaptive updating mechanism. By utilizing ad-ditional control knowledge in both the control and the parameter updating law, the performance of the proposed method can be improved consequently. Simulation study shows the effectiveness of the proposed TELPF-MFAC.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130563493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926822
Pan Zhang, Ming Cui, Yuhao Chen, Wei Zhang
The current airline baggage handling is mainly by manual, which exist serious problems such as crucial handling, baggage loss, low efficiency, high human labor cost, and so on. To solve these problems, an automatic baggage handling process is more and more needed within current airport operation. To this end, high-accuracy classification and high-precision measurement of airline baggage are essential. In this paper, three works are reported: a baggage classification recognition method based on Convolutional Neural Network (CNN) model, a baggage measurement algorithm using a combination of two-dimensional(2D) image and three-dimensional(3D) point cloud, and their realizations in an embedded platform. Firstly, gray feature of image of an airline baggage was fused with height and gradient features of point cloud of the same baggage to construct a baggage information sample. Two thousand fused baggage information samples were fed into two CNNs (vgg16 and mobilenetv3) for training. The best one was selected as the final predictor. Secondly, three-dimensional size, centroid point position and deflection angle of a baggage were measured in 3D point cloud with help of edge information extracted from the 2D image of the same baggage by Scharr operator. Finally, the proposed recognition method and measurement algorithm were transplanted into an embedded platform for efficiency purpose. Experimental results show that average classification accuracy of the proposed 2D image and 3D point cloud fused baggage information CNN model increased 10% at the best shot compared to former reported models. The proposed 2D-3D combined measurement algorithm also obtained comparable precision versus three former jobs. Most importantly, total processing time of the proposed classification and measurement program takes 86 milliseconds, which is one fifth to one tenth of the best result of former works. Plus, a lightweight version in an embedded platform took 54 milliseconds, 200 times faster than PC terminal's 13 seconds including time of data transmission. Considering a distance of dozens of kilometers in airport remote baggage handling system, the proposed embedded platform version of classification and measurement program is promising in the future's automatic scenarios, such as baggage self-service check-in, baggage tracking, automatic baggage palletization, and so on.
{"title":"Airline baggage classification/recognition and measurement based on computer vision","authors":"Pan Zhang, Ming Cui, Yuhao Chen, Wei Zhang","doi":"10.1109/ICIST55546.2022.9926822","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926822","url":null,"abstract":"The current airline baggage handling is mainly by manual, which exist serious problems such as crucial handling, baggage loss, low efficiency, high human labor cost, and so on. To solve these problems, an automatic baggage handling process is more and more needed within current airport operation. To this end, high-accuracy classification and high-precision measurement of airline baggage are essential. In this paper, three works are reported: a baggage classification recognition method based on Convolutional Neural Network (CNN) model, a baggage measurement algorithm using a combination of two-dimensional(2D) image and three-dimensional(3D) point cloud, and their realizations in an embedded platform. Firstly, gray feature of image of an airline baggage was fused with height and gradient features of point cloud of the same baggage to construct a baggage information sample. Two thousand fused baggage information samples were fed into two CNNs (vgg16 and mobilenetv3) for training. The best one was selected as the final predictor. Secondly, three-dimensional size, centroid point position and deflection angle of a baggage were measured in 3D point cloud with help of edge information extracted from the 2D image of the same baggage by Scharr operator. Finally, the proposed recognition method and measurement algorithm were transplanted into an embedded platform for efficiency purpose. Experimental results show that average classification accuracy of the proposed 2D image and 3D point cloud fused baggage information CNN model increased 10% at the best shot compared to former reported models. The proposed 2D-3D combined measurement algorithm also obtained comparable precision versus three former jobs. Most importantly, total processing time of the proposed classification and measurement program takes 86 milliseconds, which is one fifth to one tenth of the best result of former works. Plus, a lightweight version in an embedded platform took 54 milliseconds, 200 times faster than PC terminal's 13 seconds including time of data transmission. Considering a distance of dozens of kilometers in airport remote baggage handling system, the proposed embedded platform version of classification and measurement program is promising in the future's automatic scenarios, such as baggage self-service check-in, baggage tracking, automatic baggage palletization, and so on.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125002210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926961
Hongzong Li, Jun Wang
In this article, Sudoku is formulated as a quadratic unconstrained binary optimization, and a variables reduction algorithm is proposed based on given elements. Collaborative neurodynamic optimization algorithms based on discrete Hopfield networks or Boltzmann machines are developed for solving the formulated optimization problem. A population of discrete Hopfield networks or Boltzmann machines operating concurrently are employed for scatter search. A particle swarm optimization rule is used to re-initialize the initial states of discrete Hopfield networks or Boltzmann machines upon their local convergence. Experimental results on five Sudoku instances are elaborated to demonstrate the efficacy of the proposed collaborative neurodynamic optimization algorithms for solving Sudoku puzzles.
{"title":"Collaborative Neurodynamic Algorithms for Solving Sudoku Puzzles","authors":"Hongzong Li, Jun Wang","doi":"10.1109/ICIST55546.2022.9926961","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926961","url":null,"abstract":"In this article, Sudoku is formulated as a quadratic unconstrained binary optimization, and a variables reduction algorithm is proposed based on given elements. Collaborative neurodynamic optimization algorithms based on discrete Hopfield networks or Boltzmann machines are developed for solving the formulated optimization problem. A population of discrete Hopfield networks or Boltzmann machines operating concurrently are employed for scatter search. A particle swarm optimization rule is used to re-initialize the initial states of discrete Hopfield networks or Boltzmann machines upon their local convergence. Experimental results on five Sudoku instances are elaborated to demonstrate the efficacy of the proposed collaborative neurodynamic optimization algorithms for solving Sudoku puzzles.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117245866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926780
Hangjun Che, Jun Wang, A. Cichocki
In this paper, sparse signal reconstruction is for-mulated a q-ratio minimization problem subjecting to linear underdetermined equations. In view of the nonconvexity of the objective function, the q-ratio formulation with $q=2$ is approximately reformulated as an iteratively reweighted convex optimization problem in the majorization-minimization frame-work. A neurodynamic optimization approach is introduced to solve the formulated problem iteratively. The experimental results on sparse signal reconstruction are discussed to demonstrate the performance of the proposed approach.
{"title":"Neurodynamics-based Iteratively Reweighted Convex Optimization for Sparse Signal Reconstruction","authors":"Hangjun Che, Jun Wang, A. Cichocki","doi":"10.1109/ICIST55546.2022.9926780","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926780","url":null,"abstract":"In this paper, sparse signal reconstruction is for-mulated a q-ratio minimization problem subjecting to linear underdetermined equations. In view of the nonconvexity of the objective function, the q-ratio formulation with $q=2$ is approximately reformulated as an iteratively reweighted convex optimization problem in the majorization-minimization frame-work. A neurodynamic optimization approach is introduced to solve the formulated problem iteratively. The experimental results on sparse signal reconstruction are discussed to demonstrate the performance of the proposed approach.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114402690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926802
Jiasen Wang, Jun Wang
In this paper, a continuous-time dual neural network model for linear assignment is presented. The model is based on a dual formulation of the primal linear assignment problem. Global convergence of the dual neural network is ensured under given conditions and assumptions. The dual neural network is compact in the sense that its number of neurons is the same as the number of agents. Simulation results on selecting communication paths with deterministic delay and jitter quality of services in networks and assigning multiple vehicles to formation targets are presented to substantiate the efficacy of the dual neural network model.
{"title":"A Dual Assignment Network with Applications in Deterministic Communication Path Selection and Multi-Vehicle Target Assignment","authors":"Jiasen Wang, Jun Wang","doi":"10.1109/ICIST55546.2022.9926802","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926802","url":null,"abstract":"In this paper, a continuous-time dual neural network model for linear assignment is presented. The model is based on a dual formulation of the primal linear assignment problem. Global convergence of the dual neural network is ensured under given conditions and assumptions. The dual neural network is compact in the sense that its number of neurons is the same as the number of agents. Simulation results on selecting communication paths with deterministic delay and jitter quality of services in networks and assigning multiple vehicles to formation targets are presented to substantiate the efficacy of the dual neural network model.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121030335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926857
Sufang Zhou, Jianing Fan, Xiaoyu Du, Baojun Qiao, Zhi Qiao
With the development of the Internet, there are more and more sensitive information on medical data, and direct use will result in the leakage of relevant information. These privacy issues largely limit the development of the medical industry, and online medical diagnosis services can break the time and region restrictions. In response to the existing privacy requirements, we use the random forest of machine learning to train the classifier. Compared with other classification models, the random forest classifier has higher accuracy and can process large-scale medical data. In the process of interaction between medical service providers and medical users, SHE (symmetric homomorphic encryption) method and Boneh-Lynn-Shacham(BLS) short signature algorithm are used to ensure the privacy and non-tampering of data during the interaction. Since both the random forest and the user query vector is in the state of ciphertext, we design a security comparison algorithm to ensure that the comparison can be completed without revealing privacy. Futhermore, a disease risk list can be obtained, which can achieve multi-disease diagnosis. We also prove that the proposed protocol is secure and efficient by security analysis and efficiency analysis.
{"title":"Efficient Multi-disease Privacy-Preserving Medical Pre-Diagnosis Based on Partial Homomorphic Encryption","authors":"Sufang Zhou, Jianing Fan, Xiaoyu Du, Baojun Qiao, Zhi Qiao","doi":"10.1109/ICIST55546.2022.9926857","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926857","url":null,"abstract":"With the development of the Internet, there are more and more sensitive information on medical data, and direct use will result in the leakage of relevant information. These privacy issues largely limit the development of the medical industry, and online medical diagnosis services can break the time and region restrictions. In response to the existing privacy requirements, we use the random forest of machine learning to train the classifier. Compared with other classification models, the random forest classifier has higher accuracy and can process large-scale medical data. In the process of interaction between medical service providers and medical users, SHE (symmetric homomorphic encryption) method and Boneh-Lynn-Shacham(BLS) short signature algorithm are used to ensure the privacy and non-tampering of data during the interaction. Since both the random forest and the user query vector is in the state of ciphertext, we design a security comparison algorithm to ensure that the comparison can be completed without revealing privacy. Futhermore, a disease risk list can be obtained, which can achieve multi-disease diagnosis. We also prove that the proposed protocol is secure and efficient by security analysis and efficiency analysis.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132096123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926781
Libao Deng, Yuanzhu Di, Zhe Yang, Chunlei Li, Xianxin Mao
As the globalization continues to advance, the econ-omy of countries all over the world is greatly influenced. At the same time, the increasing level of customization leads to smaller production batches, more frequent changes, and higher material losses in manufacturing industry. As a result, lot streaming is widely used in production and manufacture. This article address-es the flexible job-shop scheduling problem with lot streaming (FJSP-LS). A self-adaptive differential evolution algorithm based on model transformation (SDEA-MT) is presented. First, in order to generate diverse population with high quality, two heuristics are employed cooperatively for hybrid initialization. Second, the mathematical model is converted into continuous mode based on a specially designed transformation scheme. Third, a probability-based mutation method and a problem-specific crossover strategy are designed cooperatively to generate better solutions. Forth, a local search method is implemented to balance the exploration and exploitation. The effects of parameter setting is investigated through extensive computational tests. The competitive results demonstrate the effectiveness of every special design and the efficiency of SDEA-MT.
{"title":"A Self-Adaptive Differential Evolution Algorithm Based on Model Transformation for Flexible Job-Shop Scheduling Problem with Lot Streaming","authors":"Libao Deng, Yuanzhu Di, Zhe Yang, Chunlei Li, Xianxin Mao","doi":"10.1109/ICIST55546.2022.9926781","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926781","url":null,"abstract":"As the globalization continues to advance, the econ-omy of countries all over the world is greatly influenced. At the same time, the increasing level of customization leads to smaller production batches, more frequent changes, and higher material losses in manufacturing industry. As a result, lot streaming is widely used in production and manufacture. This article address-es the flexible job-shop scheduling problem with lot streaming (FJSP-LS). A self-adaptive differential evolution algorithm based on model transformation (SDEA-MT) is presented. First, in order to generate diverse population with high quality, two heuristics are employed cooperatively for hybrid initialization. Second, the mathematical model is converted into continuous mode based on a specially designed transformation scheme. Third, a probability-based mutation method and a problem-specific crossover strategy are designed cooperatively to generate better solutions. Forth, a local search method is implemented to balance the exploration and exploitation. The effects of parameter setting is investigated through extensive computational tests. The competitive results demonstrate the effectiveness of every special design and the efficiency of SDEA-MT.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123598973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Atmospheric visibility is one of the critical indicators for meteorological characterization and environmental quality evaluation. This paper studies the influence of different meteorological parameters on atmospheric visibility, including seven main factors: temperature, humidity, wind speed, and atmospheric pressure. To establish a regression model of visibility calculation under the influence of multiple factors, this paper proposes a method named Rashomon principal component optimization regression. This paper specifically introduces the modeling and implementation of this method. The key is to solve the Rashomon coefficient, the uncertainty influence coefficient, and the regression dimension coefficient. This method employs principal component analysis to establish a loop algorithm that effectively selects different feature spaces. The main purpose is to reflect the multi-scale characteristics of the sample data, and not only consider the overall or local characteristics to deviate from the actual situation. In addition, the interaction between different factors is considered, and the analytic network process (ANP) model is used to reflect the uncertainty in the modeling. The proposed method benefits the future analysis and prediction of visibility based on meteorological data. Meanwhile, it provides theoretical support for big data problems under multiple factors.
{"title":"Visibility and Meteorological Parameter Model Based on Rashomon Regression Analysis","authors":"Chengyuan Zhu, Kaixiang Yang, Qinmin Yang, Yanyun Pu, Hao Jiang","doi":"10.1109/ICIST55546.2022.9926838","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926838","url":null,"abstract":"Atmospheric visibility is one of the critical indicators for meteorological characterization and environmental quality evaluation. This paper studies the influence of different meteorological parameters on atmospheric visibility, including seven main factors: temperature, humidity, wind speed, and atmospheric pressure. To establish a regression model of visibility calculation under the influence of multiple factors, this paper proposes a method named Rashomon principal component optimization regression. This paper specifically introduces the modeling and implementation of this method. The key is to solve the Rashomon coefficient, the uncertainty influence coefficient, and the regression dimension coefficient. This method employs principal component analysis to establish a loop algorithm that effectively selects different feature spaces. The main purpose is to reflect the multi-scale characteristics of the sample data, and not only consider the overall or local characteristics to deviate from the actual situation. In addition, the interaction between different factors is considered, and the analytic network process (ANP) model is used to reflect the uncertainty in the modeling. The proposed method benefits the future analysis and prediction of visibility based on meteorological data. Meanwhile, it provides theoretical support for big data problems under multiple factors.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117244144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926899
Yunong Zhang, Yining Zhang, Jielong Chen
Gross domestic product (GDP) is considered as a rational measure of comprehensive national power. Therefore, the forecast of GDP growth is a hot topic for scholars in economics and other fields. In this long paper, the authors (i.e., we) use a class of year-prediction (YP) algorithms so-called WZ (Weng-Zhang) algorithms to predict the occurrences of negative GDP growth of UK (United Kingdom). We conclude that around 2026, 2037, 2042, 2048, 2054, and 2068, the GDP growth of the UK has greater risks of becoming under 0.
{"title":"Three-Variable Weng-Zhang Algorithms with Subscript-Consistent Traversal Type Added as well as Five-Variable Ones Applied to UKGDPNG Year Forecast","authors":"Yunong Zhang, Yining Zhang, Jielong Chen","doi":"10.1109/ICIST55546.2022.9926899","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926899","url":null,"abstract":"Gross domestic product (GDP) is considered as a rational measure of comprehensive national power. Therefore, the forecast of GDP growth is a hot topic for scholars in economics and other fields. In this long paper, the authors (i.e., we) use a class of year-prediction (YP) algorithms so-called WZ (Weng-Zhang) algorithms to predict the occurrences of negative GDP growth of UK (United Kingdom). We conclude that around 2026, 2037, 2042, 2048, 2054, and 2068, the GDP growth of the UK has greater risks of becoming under 0.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134338937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926793
Siyuan Guo, Fan Zhang
Epilepsy is one of the most common psychiatric disorders in humans, and the sudden onset of seizures can seriously affect patients' lives. Predicting seizures can help prevent accidents and help physicians to intervene in treatment. Most studies on seizure prediction have chosen to customize prediction models for patients for high accuracy and sensitivity, which are difficult to adapt to the high variability between electroencephalogram (EEG) signals of different patients and cannot be applied to other patients and are difficult to use clinically. The main energy of EEG signal is concentrated in the low-frequency phase, which contains more detailed information, inspired by some methods in speech signal processing. The SPCNN, a patient-independent epilepsy prediction model, was constructed using convolutional neural networks by introducing more Mel-Frequency Cepstral Coefficients (MFCC) features concentrated in the low-frequency region, and obtained 93% accuracy, 91 % sensitivity, and 83% F1-score values in the CHB-MIT dataset.
{"title":"A SPCNN Model for Patient-Independent Prediction of Epilepsy Using MFCC Features","authors":"Siyuan Guo, Fan Zhang","doi":"10.1109/ICIST55546.2022.9926793","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926793","url":null,"abstract":"Epilepsy is one of the most common psychiatric disorders in humans, and the sudden onset of seizures can seriously affect patients' lives. Predicting seizures can help prevent accidents and help physicians to intervene in treatment. Most studies on seizure prediction have chosen to customize prediction models for patients for high accuracy and sensitivity, which are difficult to adapt to the high variability between electroencephalogram (EEG) signals of different patients and cannot be applied to other patients and are difficult to use clinically. The main energy of EEG signal is concentrated in the low-frequency phase, which contains more detailed information, inspired by some methods in speech signal processing. The SPCNN, a patient-independent epilepsy prediction model, was constructed using convolutional neural networks by introducing more Mel-Frequency Cepstral Coefficients (MFCC) features concentrated in the low-frequency region, and obtained 93% accuracy, 91 % sensitivity, and 83% F1-score values in the CHB-MIT dataset.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134381528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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