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Belief Combination of Classifiers for Incomplete Data

IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 652-667页

作者： Zuowei Zhang Songtao Ye Yiru Zhang Weiping Ding Hao Wang IEEE the Research Center for Optical Fiber Sensing Zhejiang Laboratory the School of Automation Northwestern Polytechnical University the School of Cyber Engineering Xidian University the Department of Computer Science CY Cergy Paris Université the School of Information Science and Technology Nantong University the School with of Cyber Engineering Xidian University the Department of Computer Science Norwegian University of Science and Technology

Data with missing values,or incomplete information,brings some challenges to the development of classification,as the incompleteness may significantly affect the performance of *** this paper,we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier(BCC)method based on the evidence *** proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by *** BCC,different attributes are regarded as independent sources,and the collection of each attribute is considered as a ***,multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query ***,these sub-classification results with different weights(discounting factors)are used to provide supplementary information to jointly determine the final classes of query *** weights consist of two aspects:global and *** global weight calculated by an optimization function is employed to represent the reliability of each classifier,and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern *** comparative experiments including seven methods on twelve datasets are executed,demonstrating the out-performance of BCC over all baseline methods in terms of accuracy,precision,recall,F1 measure,with pertinent computational costs.

关键词： Classifier fusion classification evidence theory incomplete data missing values

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Enhancing gait parameter analysis for Cerebral Palsy using Attention modules

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Gait & Posture 2024年 113卷 225-226页

作者： John Bosco Uroko Donging Gu Haider Raza Liang Hu University of Essex School of Computer Science and Electronic Engineering Essex United Kingdom Harbin Institute of Technology Department of Automation- School of Mechanical and Electrical Engineering and Automation Shenzhen China

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On the Convergence of Policy Gradient for Designing a Linear Quadratic Regulator by Leveraging a Proxy System 63

On the Convergence of Policy Gradient for Designing a Linear...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ye, Lintao Mitra, Aritra Gupta, Vijay Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China North Carolina State University Department of Electrical and Computer Engineering RaleighNC27695 United States Purdue University Elmore Family School of Electrical and Computer Engineering West LafayetteIN47906 United States

ISBN: (纸本)9798350316339

Policy gradient algorithms have been shown to converge to the optimal controller in a linear quadratic regulator (LQR) design problem. Calculating policy gradients using the true system such as for a robot may, however, be costly. We consider a formulation in which an ample number of gradient calculations from a proxy or approximate system such as a simulator are mixed with gradient calculations from the true system at a few time steps. Under the simplifying assumption of exact gradient calculation, we show that policy gradient in this framework can still converge to a neighborhood of the desired optimal controller. In this sense, it can reduce both the burden and cost of experimenting only on the true system and potentially obtain a less conservative controller than a purely robust control approach that relies only on the proxy system for designing the controller. © 2024 IEEE.

关键词： Robust control

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Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding

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Journal of automation and Intelligence 2023年第4期2卷 227-238页

作者： Sakhile Twala Xianming Ye Xiaohua Xia Lijun Zhang Department of Electrical Electronic and Computer EngineeringUniversity of PretoriaPretoria 0002South Africa School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China

In developing countries like South Africa,users experienced more than 1030 hours of load shedding outages in just the first half of 2023 due to inadequate power supply from the national *** homes that cannot afford to take actions to mitigate the challenges of load shedding are severely inconvenienced as they have to reschedule their demand *** study presents optimal strategies to guide households in determining suitable scheduling and sizing solutions for solar home systems to mitigate the inconvenience experienced by residents due to load *** start with,we predict the load shedding stages that are used as input for the optimal strategies by using the K-Nearest Neighbour(KNN)*** on an accurate forecast of the future load shedding patterns,we formulate the residents’inconvenience and the loss of power supply probability during load shedding as the objective *** solving the multi-objective optimisation problem,four different strategies to fight against load shedding are identified,namely(1)optimal home appliance scheduling(HAS)under load shedding;(2)optimal HAS supported by solar panels;(3)optimal HAS supported by batteries,and(4)optimal HAS supported by the solar home system with both solar panels and *** these strategies,appliance scheduling with an optimally sized 9.6 kWh battery and a 2.74 kWp panel array of five 550 Wp panels,eliminates the loss of power supply probability and reduces the inconvenience by 92%when tested under the South African load shedding cases in 2023.

关键词： Load shedding Inconvenience Optimal scheduling and sizing strategies K-Nearest Neighbour(KNN) Multi-objective mixed integer nonlinear optimisation

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Learning Robust Global-Local Representation From EEG for Neural Epilepsy Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5720-5732页

作者： Zhou, Xinliang Liu, Chenyu Yang, Ruizhi Zhang, Liangwei Zhai, Liming Jia, Ziyu Liu, Yang Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Carnegie Mellon University School of Computer Science PittsburghPA15213 United States Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Wuhan430079 China Central China Normal University School of Computer Science Wuhan430079 China Chinese Academy of Sciences Brainnetome Center Institute of Automation Beijing100190 China

Epilepsy is a life-threatening and challenging neurological disorder, and applying an electroencephalogram (EEG) is a commonly used clinical approach for its detection. Neuropsychological research indicates that epilepsy seizures are highly associated with distinct ranges of temporal EEG patterns. Although previous attempts to automatically detect epilepsy have achieved high classification performance, one crucial challenge still remains: how to effectively learn the robust global-local representation associated with epilepsy in the signals? To address the above challenge, we propose global-local neural epilepsy detection network (GlepNet), a novel architecture for automatic EEG epilepsy detection. We interleave the temporal convolution model together with the multihead attention mechanism within the GlepNet's encoder blocks to jointly capture the interlaced epilepsy seizure local and global features in EEG signals. Meanwhile, the interpretable method, gradient-weighted class activation mapping (Grad-CAM), is applied to visually confirm that the GlepNet acquires the ability to accord significant weight to EEG segments containing epileptiform abnormalities such as spike-wave complexes. Specifically, the Grad-CAM heatmaps are generated by backpropagating the gradients from the encoder blocks to highlight the epilepsy seizure-related parts. Extensive experiments show the superiority of the GlepNet over state-of-the-art methods on multiple EEG epilepsy datasets. The code will soon be open-sourced on GitHub. © 2024 IEEE.

关键词： Feature extraction

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Jingyao Yuan, Haitao Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios. © 2024 IEEE.

关键词： Mobile edge computing

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DPAL-BERT:A Faster and Lighter Question Answering Model

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computer Modeling in engineering & Sciences 2024年第10期141卷 771-786页

作者： Lirong Yin Lei Wang Zhuohang Cai Siyu Lu Ruiyang Wang Ahmed AlSanad Salman A.AlQahtani Xiaobing Chen Zhengtong Yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA College of Resources and Environmental Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering ***,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced *** study aims to minimize the inference time of such models while maintaining computational *** also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and *** research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge *** experimental results showed that the distilled models greatly outperform models trained from *** addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the *** demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

关键词： DPAL-BERT question answering systems knowledge distillation model compression BERT Bi-directional long short-term memory(BiLSTM) knowledge information transfer PAL-BERT training efficiency natural language processing

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Adaptive Prescribed-Time Neural Control of Nonlinear Systems via Dynamic Surface Technique

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4948-4958页

作者： Wang, Ping Yu, Chengpu Lv, Maolong Zhao, Zilong Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Air Force Engineering University College of Air Traffic Control and Navigation Xi'an710051 China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands

The adaptive practical prescribed-time (PPT) neural control is studied for multiinput multioutput (MIMO) nonlinear systems with unknown nonlinear functions and unknown input gain matrices. Unlike existing PPT design schemes based on backstepping, this study proposes a novel PPT control framework using the dynamic surface control (DSC) approach. First, a novel nonlinear filter (NLF) with an adaptive parameter estimator and a piecewise function is constructed to effectively compensate for filter errors and facilitate prescribed-time convergence. Based on this, a unified DSC-based adaptive PPT control algorithm, augmented with a neural networks (NNs) approximator, is developed, where NNs are used to approximate unknown nonlinear system functions. This algorithm not only addresses the inherent computational complexity explosion associated with traditional backstepping methods but also reduces the constraints on filter design parameters compared to the DSC algorithm that relies on linear filters. The simulation showcases the effectiveness and superiority of the devised scheme by employing a two-degree-of-freedom robot manipulator. © 2024 IEEE.

关键词： Flexible manipulators

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Adaptive Output Synchronization and Parameter Estimation for Heterogeneous Multi-Agent Systems with an Unknown Input of Leader 42

Adaptive Output Synchronization and Parameter Estimation for...

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42nd Chinese Control Conference, CCC 2023

作者： Qin, Zhen Wu, Huai-Ning Wang, Jin-Liang School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Computer Science and Technology Tiangong University Tianjin300387 China

ISBN: (纸本)9789887581543

In this study, the adaptive output synchronization and parameter estimation are investigated in a class of heterogeneous multi-agent systems (HMASs) with an unknown control input for the leader. First, a fully distributed observer is proposed for each follower to estimate the leader's state. Next, an adaptive law is developed for each follower to estimate the true value of leader's unknown feedback gain matrix, without requiring the persistent excitation (PE) condition. Then, output synchronization controllers for followers, based on the distributed observers, are designed to ensure all followers achieve output synchronization with the leader. Finally, a simulation example demonstrates the effectiveness of the proposed methods. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Multi agent systems

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Reducing the Complexity of Operational Domain Computation in Silicon Dangling Bond Logic 18

Reducing the Complexity of Operational Domain Computation in...

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18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023

作者： Walter, Marcel Drewniok, Jan Ng, Samuel Sze Hang Walus, Konrad Wille, Robert Technical University of Munich Design Automation Bavaria Munich Germany University of British Columbia Department of Electrical and Computer Engineering VancouverBC Canada Austria

ISBN: (纸本)9798400703256

Silicon Dangling Bonds (SiDBs) constitute a beyond-CMOS computational nanotechnology platform that enables higher integration density and lower power consumption than contemporary CMOS nodes. Recent manufacturing breakthroughs in the domain sparked the interest of academia and industry alike in the race for a green computation future at the nanoscale. However, as the fabrication of SiDBs requires atomic precision, SiDB logic systems are inherently susceptible to environmental defects and material variations, which inevitably occur. The Operational Domain is a methodology to evaluate the resilience of SiDB logic against physical parameter variations. However, state-of-the-art implementations require a quadratic number of exponentially complex physical simulator calls to assess the operational domain. This paper presents two novel algorithms to obtain operational domains in an efficient fashion: one based on flood fill, and one based on contour tracing. Experimental evaluations confirm that they reduce the number of required simulator calls by 70.87% and 95.29 %, respectively. Particularly contour tracing achieves the shift from a quadratic to a linear relation, thereby reducing the complexity and paving the way for realizing reliable SiDB-based computing systems. © 2023 ACM.

关键词： Silicon

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