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arXiv 2021年

作者： Wang, Shengbo Wen, Shiping Shi, Kaibo Zhu, Song Huang, Tingwen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China School of Mathematics China University of Mining and Technology Xuzhou221116 China Science Program Texas A & M University at Qatar Doha23874 Qatar

The optimal control input for linear systems can be solved from algebraic Riccati equation (ARE), from which it remains questionable to get the form of the exact solution. In engineering, the acceptable numerical solutions of ARE can be found by iteration or optimization. Recently, the gradient descent based numerical solutions has been proven effective to approximate the optimal ones. This paper introduces this method to tracking problem for heterogeneous linear systems. Differently, the parameters in the dynamics of the linear systems are all assumed to be unknown, which is intractable since the gradient as well as the allowable initialization needs the prior knowledge of system dynamics. To solve this problem, the method named dynamic regressor extension and mix (DREM) is improved to estimate the parameter matrices in finite time. Besides, a discounted factor is introduced to ensure the existence of optimal solutions for heterogeneous systems. Two simulation experiments are given to illustrate the effectiveness. © 2021, CC BY-NC-ND.

关键词： Gradient methods

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Exploring Deep Learning based Object Detection Architectures: A Review

Exploring Deep Learning based Object Detection Architectures...

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International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Hassan Muhammad Saddique Ahsan Raza Zain Ul Abideen Shah Nawaz Khan National University of Computer and Emerging Sciences Peshawar Pakistan Faculty of Electronic Engineering Ghulam Ishaq Khan Institute of Engineering Science and Technology Swabi Pakistan University of Engineering and Technology Peshawar Pakistan

ISBN: (数字)9781728146768

ISBN: (纸本)9781728146775

As one of the hottest fields of computer vision, object detection is employed in many useful applications, including face recognition, autonomous cars and human behaviour analysis. CNN's have played a vital role in the progression of computer vision, especially object detection. In this paper, a review is provided on object detection methods which utilize deep learning to render outstanding results, and different object detection frameworks are discussed and analyzed with respect to their differences amongst each other. The comparisons are carried out based upon the criterion of accuracy and frames per second. The review starts with the explanation of CNNs and their advantages. Then we focus our attention on two types of object detection architectures and dive deep into their respective details. In the end, we draw a conclusion based on the facts detailed in the review.

关键词： Object detection Proposals computer architecture Deep learning Feature extraction Training Detectors

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Robust Adaptive Safety-Critical Control for Unknown Systems with Finite-Time Element-Wise Parameter Estimation

arXiv

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arXiv 2021年

作者： Wang, Shengbo Lyu, Bo Wen, Shiping Shi, Kaibo Zhu, Song Huang, Tingwen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China School of Mathematics China University of Mining and Technology Xuzhou221116 China Science Program Texas A & M University at Qatar Doha23874 Qatar

Safety is always one of the most critical principles for a system to be controlled. This paper investigates a safety-critical control scheme for unknown structured systems by using the control barrier function (CBF) method. Benefited from the dynamic regressor extension and mixing (DREM), an extended element-wise parameter identification law is utilized to dismiss the uncertainty. On the one hand, it is shown that the proposed control scheme can always guarantee the safety in the identification process with noised signal injection excitation, which was not considered in the previous study. On the other hand, the element-wise estimation process in DREM can minimize conservatism of the safe adaptive process compared to other existing adaptive CBF algorithms. The stability as well as the forward invariance of the presented safe control-estimation scheme is proved. Furthermore, the robustness of the scheme under bounded disturbances is analyzed, where a robust CBF with modest conditions is used to ensure safety. The framework is illustrated by simulations on adaptive cruise control, where the slope resistance of the following vehicle is robustly estimated in finite time against small disturbances and the potential crash risk is avoided by the proposed safe control scheme. © 2021, CC BY-NC-ND.

关键词： Parameter estimation

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Predictive Analytics Model Based on Multiclass Classification for Asthma Severity by Using Random Forest Algorithm

Predictive Analytics Model Based on Multiclass Classificatio...

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International Conference on Electrical, Communication, and computer engineering (ICECCE)

作者： Wasif Akbar Wei-Ping Wu Muhammad Faheem Sehrish Saleem Arslan Javed Muhammad Asim Saleem School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China National College of Business Administration & Economics Lahore Pakistan MNS University of Engineering and Technology Multan Multan Pakistan Faculty of Electronic Information and Electrical Engineering School of Computer Science Dalian University of Technology Liaoning China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728171166

ISBN: (纸本)9781728171173

In modern life, health status prediction has become very crucial. Big data analysis plays a vital role to predict this perfectly. Asthma is a severe chronic disease with severe symptoms. Asthma disease is a chronic disease that leads to death. Researchers have focused on this for better decision making to predict the disease timely use of predictive analysis. This study proposes a Predictive Analytic Model for Asthma prediction using Random Forest (PAM-RF). Data of patients suffering from Asthma has been trained by a random forest approach which predicts to classify the data. Experiments are performed on Hadoop-spark which predicts the future health state of patients. The proposed approach has attained an accuracy of 98.80 percent to predict the asthma disease.

关键词： Big Data Diseases Forestry Predictive models Respiratory system

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Convergent adaptive control based prescribed-time synchronization of switched fuzzy competitive network systems with time-varying delays

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Neural networks : the official journal of the International Neural Network Society 2025年 190卷

作者： Dongdong Gao Fanchao Kong Tingwen Huang College of Mathematics and Computer Science Tongling University Tongling Anhui 244000 China Anhui Engineering Research Center of Intelligent Manufacturing of Copper-based Materials Tongling University Tongling 244000 China. Electronic address: 225410@***. School of Mathematics and Statistics Anhui Normal University Wuhu Anhui 241000 China. Electronic address: fanchaokong88@***. Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen 518055 China. Electronic address: huangtingwen@***.

This paper addresses the prescribed-time control problem for discontinuous fuzzy neutral-type competitive neural networks (FNTCNNs) featuring switchings and time-varying delays. Notably, FNTCNNs constitute a generalized class of singularly perturbed Filippov systems. The establishment of a prescribed-time stability lemma for time-varying delay singularly perturbed systems remains a critical yet unresolved challenge. To address this, we first develop a novel prescribed-time stability lemma for singularly perturbed Filippov systems using adjustment functions, the comparison principle, and inequality techniques. This is achieved through the application of the one-norm and the introduction of a new stability definition for such systems. Considering the switching law inherent in FNTCNNs, we achieve prescribed-time stabilization control by designing adaptive prescribed-time control strategies, employing differential inclusion theory and Filippov's solution framework. The proposed adaptive control strategies demonstrate convergence properties, ensuring that both the control strategies and system state variables converge to zero within the same prescribed-time interval. These newly developed strategies offer significant advantages over existing approaches. Finally, we validate our principal results through numerical simulations of second-order multi-agent systems subject to discontinuous disturbances.

关键词： Adaptive control Competitive neural networks Convergent control Prescribed-time control Singularly perturbed Filippov systems

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Room temperature charge-to-spin conversion from q-2DEG at SrTiO3-based interfaces

arXiv

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arXiv 2022年

作者： Shashank, Utkarsh Deka, Angshuman Fukuma, Yasuhiro Ye, Chen Wang, X. Renshaw Gupta, Surbhi Rawat, Rajdeep Singh Medwal, Rohit Asada, Hironori Department of Physics and Information Technology Faculty of Computer Science and System Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka820-8502 Japan Birck Nanotechnology Center School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu808-0196 Japan School of Physical and Mathematical Sciences Nanyang Technological University 637616 Singapore School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Natural Science and Science Education National Institute of Education Nanyang Technological University 637616 Singapore Department of Physics Indian Institute of Technology Kanpur Kanpur208016 India Graduate School of Sciences and Technology for Innovation Yamaguchi University 2-16-1 Tokiwadai Ube755-8611 Japan

Interfacial two-dimensional electron gas (2DEG), especially the SrTiO3-based ones at the unexpected interface of insulators, have emerged to be a promising candidate for efficient charge-spin current interconversion. In this article, to gain insight into the mechanism of the charge-spin current interconversion at the oxide-based 2DEG, we focused on conducting interfaces between insulating SrTiO3 and two types of aluminium-based amorphous insulators, namely SrTiO3/AlN and SrTiO3/Al2O3, and estimated their charge-spin conversion efficiency, θcs. The two types of amorphous insulators were selected to explicitly probe the overlooked contribution of oxygen vacancy to the θcs. We proposed a mechanism to explain results of spin-torque ferromagnetic resonance (ST-FMR) measurements and developed an analysis protocol to reliably estimate the θcs of the oxide based 2DEG. The resultant θcs/t, where t is the thickness of the 2DEG, were estimated to be 0.244 nm-1 and 0.101 nm-1 for the SrTiO3/AlN and SrTiO3/Al2O3, respectively, and they are strikingly comparable to their crystalline counterparts. Furthermore, we also observe a large direct current modulation of resonance linewidth in SrTiO3/AlN samples, confirming its high θcs and attesting an oxygen-vacancy-enabled charge-spin conversion. Our findings emphasize the defects' contribution to the charge-spin interconversion, especially in the oxide-based low dimensional systems, and provide a way to create and enhance charge-spin interconversion via defect engineering. Copyright © 2022, The Authors. All rights reserved.

关键词： Aluminum compounds

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A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis

A five-layer deep convolutional neural network with stochast...

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作者： Zhang, Yu-Dong Satapathy, Suresh Chandra Liu, Shuaiqi Li, Guang-Run School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia School of Computer Engg KIIT Deemed To University Bhubaneswar India College of Electronic and Information Engineering Hebei University BaodingHebei071002 China Department of Imaging Jinhu People’s Hospital No. 160 Jinhu Shenhua Street Huai’an China

Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements: (i) We introduced stochastic pooling to replace average pooling and max pooling;(ii) We combined conv layer with batch normalization layer and obtained the conv block (CB);(iii) We combined dropout layer with fully connected layer and obtained the fully connected block (FCB). Our algorithm achieved a sensitivity of 93.28% ± 1.50%, a specificity of 94.00% ± 1.56%, and an accuracy of 93.64% ± 1.42%, in identifying COVID-19 from normal subjects. We proved using stochastic pooling yields better performance than average pooling and max pooling. We compared different structure configurations and proved our 3CB + 2FCB yields the best performance. The proposed model is effective in detecting COVID-19 based on chest CT images. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Attitudes toward the use of voice-assisted technologies among people with Parkinson disease: Findings from a web-based survey

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JMIR Rehabilitation and Assistive Technologies 2021年第1期8卷 e23006页

作者： Duffy, Orla Synnott, Jonathan McNaney, Roisin Zambrano, Paola Brito George Kernohan, W. Institute of Nursing and Health Research Faculty of Life and Health Sciences Ulster University Newtownabbey United Kingdom School of Computing Faculty of Computing Engineering and the Built Environment Ulster University Newtownabbey United Kingdom Department of Human Centered Computing Faculty of Information Technology Monash University Clayton Australia School of Computer Science Electrical and Electronic Engineering and Engineering Maths Faculty of Engineering University of Bristol Bristol United Kingdom

Background: Speech problems are common in people living with Parkinson disease (PD), limiting communication and ultimately affecting their quality of life. Voice-assisted technology in health and care settings has shown some potential in small-scale studies to address such problems, with a retrospective analysis of user reviews reporting anecdotal communication effects and promising usability features when using this technology for people with a range of disabilities. However, there is a need for research to establish users' perspectives on the potential contribution of voice-assisted technology for people with PD. Objective: This study aims to explore the attitudes toward the use of voice-assisted technology for people with PD. Methods: A survey was approved for dissemination by a national charity, Parkinson's UK, to be completed on the web by people living with the condition. The survey elicited respondent demographics, PD features, voice difficulties, digital skill capability, smart technology use, voice-assisted technology ownership and use, confidentiality, and privacy concerns. Data were analyzed using descriptive statistics and summative content analysis of free-text responses. Results: Of 290 participants, 79.0% (n=229) indicated that they or others had noticed changes in their speech or voice because of the symptoms of their condition. Digital skills and awareness were reported on 11 digital skills such as the ability to find a website you have visited before. Most participants (n=209, 72.1%) reported being able to perform at least 10 of these 11 tasks. Similarly, of 70.7% (n=205) participants who owned a voice-assisted device, most of them (166/205, 80.9%) used it regularly, with 31.3% (52/166) reporting that they used the technology specifically to address the needs associated with their PD. Of these 166 users, 54.8% (n=91) sometimes, rarely, or never had to repeat themselves when using the technology. When asked about speech changes since they started us

关键词： EHealth Mobile phone Parkinson disease Telerehabilitation

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Optimization of institutional incentives for cooperation in structured populations

arXiv

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arXiv 2023年

作者： Wang, Shengxian Chen, Xiaojie Xiao, Zhilong Szolnoki, Attila Vasconcelos, Vítor V. School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu611731 China Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Institute of Technical Physics and Materials Science Centre for Energy Research P.O. Box 49 BudapestH-1525 Hungary Computational Science Lab Informatics Institute University of Amsterdam Amsterdam1098XH Netherlands Institute for Advanced Study University of Amsterdam Amsterdam1012 GC Netherlands

The application of incentives, such as reward and punishment, is a frequently applied way for promoting cooperation among interacting individuals in structured populations. However, how to properly use the incentives is still a challenging problem for incentive-providing institutions. In particular, since the implementation of incentive is costly, to explore the optimal incentive protocol, which ensures the desired collective goal at a minimal cost, is worthy of study. In this work, we consider the positive and negative incentives respectively for a structured population of individuals whose conflicting interactions are characterized by a prisoner’s dilemma game. We establish an index function for quantifying the cumulative cost during the process of incentive implementation, and theoretically derive the optimal positive and negative incentive protocols for cooperation on regular networks. We find that both types of optimal incentive protocols are identical and time-invariant. Moreover, we compare the optimal rewarding and punishing schemes concerning implementation cost and provide a rigorous basis for the usage of incentives in the game-theoretical framework. We further perform computer simulations to support our theoretical results and explore their robustness for different types of population structures, including regular, random, small-world, and scale-free networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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Dpb-Mopso: A Dynamic Pareto Bi-Level Multi-Objective Particle Swarm Optimization Algorithm

SSRN

引用

SSRN 2022年

作者： Aboud, Ahlem Rokbani, Nizar Fdhila, Raja Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Hussain, Amir Alimi, Adel M. University of Sousse ISITCom Sousse4011 Tunisia BP 1173 Sfax3038 Tunisia High Institute of Applied Science and Technology of Sousse University of Sousse Tunisia Department of Computer Science College of Computers and Information Technology Taif University P.O.Box. 11099 Taif21944 Saudi Arabia College of Applied Computer Science King Saud University Riyadh Saudi Arabia Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Particle Swarm Optimization (PSO) system based on the distributed architecture over multiple sub-swarms is very efficient for static multi-objective optimization, but has not been considered for solving dynamic multi-objective problems (DMOPs). Tracking the most effective solutions over time and ensuring good exploitation and exploration are the main challenges of solving DMOP. This study proposes a Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable a multiple sub-swarm’ subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. The DPb-MOPSO system is tested on a set of DMOPs with different types of time-varying Pareto Optimal Set (POS) and Pareto Optimal Front (POF). Inverted generational distance (IGD), mean inverted generational distance (MIGD), and hypervolume difference (HVD) metrics are used to assess the DPb-MOPSO performances. Quantitative results are analyzed using Friedman's analysis of variance, while stability is analyzed using Lyapunov's theorem. The DPb-MOPSO is more robust than several dynamic multi-objective evolutionary algorithms in solving 21 complex problems over a range of changes in both the POS and POF. On IGD and HVD, DPb-MOPSO can solve 8/13 and 7/13 of the 13 UDF and ZJZ functions with moderate changes. DPb-MOPSO is able to resolve 4/8 FDA and DMOP benchmarks with severe changes to the MIGD, and 5/8 with moderate changes. DPb-MOPSO assumes 4/8 of the solving function on IGD

关键词： Particle swarm optimization (PSO)

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