Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926895
Xiaoyu Du, Lv Lin, Zhijie Han, Changtao Zhang
Intrusion detection can monitor network transmis-sion in real-time. It is an active security protection technology, which plays a great role in network security. In this paper, a method based on a hybrid autoencoder and decision tree is proposed to conduct intrusion detection. The autoencoder is trained through positive sample data to make its parameters fit the normal flow. The gap between normal samples and abnormal samples is distinguished by calculating the loss value, and the gap is standardized as a newly generated feature. This method can not only avoid the information loss caused by dimensionality reduction of high-dimensional data but also ensure speed and accuracy. The intrusion detection algorithm with hybrid auto encoder and decision tree obtained by the method proposed in this paper is stronger than using decision tree alone and many common machine learning methods. For example, compare the decision tree method 1.74 % better in accuracy, 2.16% better in precision, 1.47% better in recall, 1.81 % better in fscore.
{"title":"An Intrusion Detection Algorithm Based on Hybrid Autoencoder and Decision Tree","authors":"Xiaoyu Du, Lv Lin, Zhijie Han, Changtao Zhang","doi":"10.1109/ICIST55546.2022.9926895","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926895","url":null,"abstract":"Intrusion detection can monitor network transmis-sion in real-time. It is an active security protection technology, which plays a great role in network security. In this paper, a method based on a hybrid autoencoder and decision tree is proposed to conduct intrusion detection. The autoencoder is trained through positive sample data to make its parameters fit the normal flow. The gap between normal samples and abnormal samples is distinguished by calculating the loss value, and the gap is standardized as a newly generated feature. This method can not only avoid the information loss caused by dimensionality reduction of high-dimensional data but also ensure speed and accuracy. The intrusion detection algorithm with hybrid auto encoder and decision tree obtained by the method proposed in this paper is stronger than using decision tree alone and many common machine learning methods. For example, compare the decision tree method 1.74 % better in accuracy, 2.16% better in precision, 1.47% better in recall, 1.81 % better in fscore.","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":"117147254","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/ICIST55546.2022.9926907
Yanping Mu, Xiaofeng Zhang, Meng Zhang, Huimin Wang
Epilepsy is a disease caused by abnormal discharge of neurons, which can seriously endanger people's life. Therefore, it is necessary to predict the occurrence of epilepsy timely. The main content of epilepsy prediction is to distinguish the preictal and interictal periods of electroencephalography (EEG) signals. The transfer entropy (TE) and phase transfer entropy (PTE) of EEG signals is computed and sliced to form the features of the EEG signals. Then, these features are inputted to a depthwise separable convolutional neural network (DSC-CNN), which has a small amount of parameters and computation, to classify the two periods of EEG signals. The CHB-MIT scalp EEG dataset are used to evaluate the performance of this scheme. The computation results are also compared to other state-of-the-art algorithms to verify its advantages. Experimental results show that the prediction time with this method reaches 22.6 minutes. The prediction specificity is 99.26% for the prediction time of 15 minutes, and it is 99.52% for the prediction time of 22.6 minutes. Moreover, the DSC-CNN has a small amount of parameters and short running time.
{"title":"Epilepsy prediction based on PTE and TE of EEG signals using DSC-CNN","authors":"Yanping Mu, Xiaofeng Zhang, Meng Zhang, Huimin Wang","doi":"10.1109/ICIST55546.2022.9926907","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926907","url":null,"abstract":"Epilepsy is a disease caused by abnormal discharge of neurons, which can seriously endanger people's life. Therefore, it is necessary to predict the occurrence of epilepsy timely. The main content of epilepsy prediction is to distinguish the preictal and interictal periods of electroencephalography (EEG) signals. The transfer entropy (TE) and phase transfer entropy (PTE) of EEG signals is computed and sliced to form the features of the EEG signals. Then, these features are inputted to a depthwise separable convolutional neural network (DSC-CNN), which has a small amount of parameters and computation, to classify the two periods of EEG signals. The CHB-MIT scalp EEG dataset are used to evaluate the performance of this scheme. The computation results are also compared to other state-of-the-art algorithms to verify its advantages. Experimental results show that the prediction time with this method reaches 22.6 minutes. The prediction specificity is 99.26% for the prediction time of 15 minutes, and it is 99.52% for the prediction time of 22.6 minutes. Moreover, the DSC-CNN has a small amount of parameters and short running time.","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":"126186484","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/ICIST55546.2022.9926800
Wing W. Y. Ng, Yongzhi Xu, Xing Tian, Yuxiang Yang, Haotian Wu, Ying Gao
A huge volume of images is uploaded to the mobile cloud environment every day with rapid growth of mobile devices. Different from retrieving image using a personal computer, infor-mation retrieval on mobile devices requires higher efficiency due to limitations in data transmission and memory cost. Therefore, an efficient scalable image retrieval model is proposed in this paper which consists of a two-layer bloom filter. The first layer of the proposed bloom filter generated from the asymmetric cyclical hashing (ACH) is used for primary image existence verification. In order to reduce the false positive rate commonly happening in bloom filters, the second layer of the bloom filter based on secure hashing is applied for verification in the second stage. The proposed model realizes approximated nearest neighbor retrieval with limited cost of storage and protects the stored data from illegal access simultaneously. Experimental results show that the proposed model yields significant better retrieval accuracy than other retrieval methods with more than 6 times faster in retrieval time and a smaller space requirement.
{"title":"Improved Bloom Filter for Efficient Image Retrieval on Mobile Device","authors":"Wing W. Y. Ng, Yongzhi Xu, Xing Tian, Yuxiang Yang, Haotian Wu, Ying Gao","doi":"10.1109/ICIST55546.2022.9926800","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926800","url":null,"abstract":"A huge volume of images is uploaded to the mobile cloud environment every day with rapid growth of mobile devices. Different from retrieving image using a personal computer, infor-mation retrieval on mobile devices requires higher efficiency due to limitations in data transmission and memory cost. Therefore, an efficient scalable image retrieval model is proposed in this paper which consists of a two-layer bloom filter. The first layer of the proposed bloom filter generated from the asymmetric cyclical hashing (ACH) is used for primary image existence verification. In order to reduce the false positive rate commonly happening in bloom filters, the second layer of the bloom filter based on secure hashing is applied for verification in the second stage. The proposed model realizes approximated nearest neighbor retrieval with limited cost of storage and protects the stored data from illegal access simultaneously. Experimental results show that the proposed model yields significant better retrieval accuracy than other retrieval methods with more than 6 times faster in retrieval time and a smaller space requirement.","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":"114594058","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/ICIST55546.2022.9926890
Fei Gao, Lu Zhang, Zhi Weng
This paper proposes a new nonlinear mapping to address the output constraint problem. We transform the constrained tracking error into an equivalent unconstrained one. Then adaptive neural network (NN) control with predefined constraints is studied for nonlinear systems. The proposed scheme guarantees that all the signals in the closed-loop system are bounded and the system output asymptotically tracks the reference trajectory without the violation of the predefined constraints. Finally, we give a numerical example to show effectiveness of the proposed scheme.
{"title":"Control of nonlinear systems with predefined constraints using neural networks","authors":"Fei Gao, Lu Zhang, Zhi Weng","doi":"10.1109/ICIST55546.2022.9926890","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926890","url":null,"abstract":"This paper proposes a new nonlinear mapping to address the output constraint problem. We transform the constrained tracking error into an equivalent unconstrained one. Then adaptive neural network (NN) control with predefined constraints is studied for nonlinear systems. The proposed scheme guarantees that all the signals in the closed-loop system are bounded and the system output asymptotically tracks the reference trajectory without the violation of the predefined constraints. Finally, we give a numerical example to show effectiveness of the proposed scheme.","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":"131292513","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/ICIST55546.2022.9926905
Zhenhai Miao, Meng Li, Zepei Sun, Yong Chen
In this paper, the control problem of remote motor in cyber physical system (CPSs) under external disturbance is studied. An adaptive sliding mode reaching law and a double-threshold event-triggered strategy is proposed. Firstly, to reduce the chattering of the sliding mode surface, a new adaptive reaching law is designed, and the reachability is confirmed. In addition, a double-threshold event triggered strategy is proposed in order to effectively lower the system's consumption of communication resources. Finally, the stability of the system is proved. Furthermore, the effectiveness of the method is verified using a servo motor system.
{"title":"Adaptive Sliding Mode Control for Motor Cyber Physical System","authors":"Zhenhai Miao, Meng Li, Zepei Sun, Yong Chen","doi":"10.1109/ICIST55546.2022.9926905","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926905","url":null,"abstract":"In this paper, the control problem of remote motor in cyber physical system (CPSs) under external disturbance is studied. An adaptive sliding mode reaching law and a double-threshold event-triggered strategy is proposed. Firstly, to reduce the chattering of the sliding mode surface, a new adaptive reaching law is designed, and the reachability is confirmed. In addition, a double-threshold event triggered strategy is proposed in order to effectively lower the system's consumption of communication resources. Finally, the stability of the system is proved. Furthermore, the effectiveness of the method is verified using a servo motor system.","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":"116022493","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/ICIST55546.2022.9926837
Jinwu Li, Yan Zhang
In order to effectively evaluate the time series with high dimensionality and uncertainty, a time series segmentation and clustering method integrating cloud model analysis is proposed. The entropy and super entropy of the cloud model are used to identify the subsequence with the worst stability, further divide the subsequence, and dynamically realize segmentation to form a cloud model sequence. At the same time, the effectiveness evaluation indicator of segmented aggregation is constructed by the cloud model to determine the optimal number of segments. For different cloud model sequences, the cloud models are matched through the relationship of time window, and the similarity measures are given. The results of experiments indicate that the proposed method can effectively determine the number of time series segments, and greatly compress the original sequence, retain the basic characteristics of the data and improve the clustering efficiency of time series.
{"title":"Time Series Segmentation and Clustering Method Based on Cloud Model","authors":"Jinwu Li, Yan Zhang","doi":"10.1109/ICIST55546.2022.9926837","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926837","url":null,"abstract":"In order to effectively evaluate the time series with high dimensionality and uncertainty, a time series segmentation and clustering method integrating cloud model analysis is proposed. The entropy and super entropy of the cloud model are used to identify the subsequence with the worst stability, further divide the subsequence, and dynamically realize segmentation to form a cloud model sequence. At the same time, the effectiveness evaluation indicator of segmented aggregation is constructed by the cloud model to determine the optimal number of segments. For different cloud model sequences, the cloud models are matched through the relationship of time window, and the similarity measures are given. The results of experiments indicate that the proposed method can effectively determine the number of time series segments, and greatly compress the original sequence, retain the basic characteristics of the data and improve the clustering efficiency of time series.","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":"123947319","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/ICIST55546.2022.9926783
Kun Zhang, Mingkai Zheng, Qing Yu, Yi Liu
Pavement disease is an important factor threatening road safety. Most traditional disease recognition methods often rely on manual detection, which is time-consuming and inefficient. In this work, by introducing the object detection and segmentation into the detection of pavement diseases, a multi-class pavement disease detection method is proposed. First, diseases are located based on YOLOv4. CSPDarknet53 is used as the backbone network. The feature extraction performance is further improved by spatial pyramid pooling. Then, on the basis of pavement disease location, the pyramid scene parsing network (PSPNet) is employed to extract the pixel of the disease area to realize the accurate analysis of the anomaly. The feasibility of the proposed method is verified by a pavement disease detection experiment using the actual road dataset collected from a province in eastern China, including seven common diseases.
{"title":"Multi-Class Pavement Disease Recognition Using Object Detection and Segmentation","authors":"Kun Zhang, Mingkai Zheng, Qing Yu, Yi Liu","doi":"10.1109/ICIST55546.2022.9926783","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926783","url":null,"abstract":"Pavement disease is an important factor threatening road safety. Most traditional disease recognition methods often rely on manual detection, which is time-consuming and inefficient. In this work, by introducing the object detection and segmentation into the detection of pavement diseases, a multi-class pavement disease detection method is proposed. First, diseases are located based on YOLOv4. CSPDarknet53 is used as the backbone network. The feature extraction performance is further improved by spatial pyramid pooling. Then, on the basis of pavement disease location, the pyramid scene parsing network (PSPNet) is employed to extract the pixel of the disease area to realize the accurate analysis of the anomaly. The feasibility of the proposed method is verified by a pavement disease detection experiment using the actual road dataset collected from a province in eastern China, including seven common diseases.","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":"125948155","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/ICIST55546.2022.9926908
Zhongcai Lyu, Jie Zhu
Personalized speech synthesis aims to transfer speech style with a few speech samples from the target speaker. However, pretrain and fine-tuning techniques are required to overcome the problem of poor performance for similarity and prosody in a data-limited condition. In this paper, a zero-shot style transfer framework based on multi-scale control is presented to handle the above problems. Firstly, speaker embedding is extracted from a single reference speech audio by a specially designed reference encoder, with which Speaker-Adaptive Linear Modulation (SALM) could generate the scale and bias vector to influence the encoder output, and consequently greatly enhance the adaptability to unseen speakers. Secondly, we propose a prosody module that includes a prosody extractor and prosody predictor, which can efficiently predict the prosody of the generated speech from the reference audio and text information and achieve phoneme-level prosody control, thus increasing the diversity of the synthesized speech. Using both objective and subjective metrics for evaluation, the experiments demonstrate that our model is capable of synthesizing speech of high naturalness and similarity of speech, with only a few or even a single piece of data from the target speaker.
个性化语音合成的目的是利用目标说话者的少量语音样本来转移语音风格。然而，需要预训练和微调技术来克服在数据有限的条件下相似性和韵律性能差的问题。针对上述问题，本文提出了一种基于多尺度控制的零弹式迁移框架。首先，通过专门设计的参考编码器从单个参考语音音频中提取扬声器嵌入，通过扬声器自适应线性调制(speaker - adaptive Linear Modulation, SALM)产生影响编码器输出的尺度和偏置向量，从而大大提高了对未知扬声器的适应性。其次，我们提出了包含韵律提取器和韵律预测器的韵律模块，该模块可以有效地从参考音频和文本信息中预测生成语音的韵律，实现音素级韵律控制，从而增加合成语音的多样性。实验结果表明，我们的模型能够在仅使用少量甚至单个目标说话人数据的情况下，合成出高度自然度和相似度的语音。
{"title":"Enriching Style Transfer in multi-scale control based personalized end-to-end speech synthesis","authors":"Zhongcai Lyu, Jie Zhu","doi":"10.1109/ICIST55546.2022.9926908","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926908","url":null,"abstract":"Personalized speech synthesis aims to transfer speech style with a few speech samples from the target speaker. However, pretrain and fine-tuning techniques are required to overcome the problem of poor performance for similarity and prosody in a data-limited condition. In this paper, a zero-shot style transfer framework based on multi-scale control is presented to handle the above problems. Firstly, speaker embedding is extracted from a single reference speech audio by a specially designed reference encoder, with which Speaker-Adaptive Linear Modulation (SALM) could generate the scale and bias vector to influence the encoder output, and consequently greatly enhance the adaptability to unseen speakers. Secondly, we propose a prosody module that includes a prosody extractor and prosody predictor, which can efficiently predict the prosody of the generated speech from the reference audio and text information and achieve phoneme-level prosody control, thus increasing the diversity of the synthesized speech. Using both objective and subjective metrics for evaluation, the experiments demonstrate that our model is capable of synthesizing speech of high naturalness and similarity of speech, with only a few or even a single piece of data from the target speaker.","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":"129431593","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/ICIST55546.2022.9926963
Guan Huang, Zhuo Zhang, Weisheng Yan
This paper studies the distributed leader-following optimal control problem for multi-agent systems (MASs). In particular, a class of linear MASs with leader's bounded unknown control input is considered. We propose a dynamical sliding mode control protocol to address the presence of nonzero control input of leader. Meanwhile, we design a distributed optimal nominal control protocol to achieve leader-following consensus control of MASs. Under the nominal control protocol, the energy cost performance of the MASs can be optimized by solving the algebraic Riccati equation (ARE). Finally, a numerical simulation of multi spacecraft consensus tracking control example is provided to verify the effectiveness of the proposed algorithm.
{"title":"Distributed Leader-Following Optimal Control for Linear Multi-Agent Systems with Nonzero Leader's Control Input","authors":"Guan Huang, Zhuo Zhang, Weisheng Yan","doi":"10.1109/ICIST55546.2022.9926963","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926963","url":null,"abstract":"This paper studies the distributed leader-following optimal control problem for multi-agent systems (MASs). In particular, a class of linear MASs with leader's bounded unknown control input is considered. We propose a dynamical sliding mode control protocol to address the presence of nonzero control input of leader. Meanwhile, we design a distributed optimal nominal control protocol to achieve leader-following consensus control of MASs. Under the nominal control protocol, the energy cost performance of the MASs can be optimized by solving the algebraic Riccati equation (ARE). Finally, a numerical simulation of multi spacecraft consensus tracking control example is provided to verify the effectiveness of the proposed algorithm.","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":"131250379","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/ICIST55546.2022.9926865
Wenhui Dou, Shihong Ding, Chen Ding
This paper considers the practical adaptive event-triggered finite-time stabilization problem for a class of second-order systems. First, by using the adding a power integrator (API) technique, a novel adaptive event-triggered control method is proposed to assure the practical finite-time stability of the closed-loop system. Under the constructed adaptive law, the control gain changes dynamically according to whether the state enters or leaves the predefined domain. In addition, via utilizing the Lyapunov method, the practical finite-time stability (PFTS) of the closed-loop system is proved, and the control system does not exist in the Zeno behavior. Finally, the effectiveness of the designed algorithm is verified by the simulation results.
{"title":"Practical Adaptive Event-triggered Finite-time Stabilization for A Class of Second-order Systems","authors":"Wenhui Dou, Shihong Ding, Chen Ding","doi":"10.1109/ICIST55546.2022.9926865","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926865","url":null,"abstract":"This paper considers the practical adaptive event-triggered finite-time stabilization problem for a class of second-order systems. First, by using the adding a power integrator (API) technique, a novel adaptive event-triggered control method is proposed to assure the practical finite-time stability of the closed-loop system. Under the constructed adaptive law, the control gain changes dynamically according to whether the state enters or leaves the predefined domain. In addition, via utilizing the Lyapunov method, the practical finite-time stability (PFTS) of the closed-loop system is proved, and the control system does not exist in the Zeno behavior. Finally, the effectiveness of the designed algorithm is verified by the simulation results.","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":"114233975","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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