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arXiv 2024年

作者： Wang, Li Liu, Yiping Fu, Xiangzheng Ye, Xiucai Shi, Junfeng Yen, Gary G. Zeng, Xiangxiang The College of Computer Scienceand Electronic Engineering Hunan University Hunan Changsha China The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States The School of System Information and Engineering University of Tsukuba Ibaraki Japan Neher’s Biophysics Laboratory for Innovative Drug Discovery State Key Laboratory of Quality Research in Chinese Medicine Macau Institute for Applied Research in Medicine and Health Macau University of Science and Technology 999078 China

Antimicrobial peptides (AMPs) have exhibited unprecedented potential as biomaterials in combating multidrugresistant bacteria. Despite the increasing adoption of artificial intelligence for novel AMP design, challenges pertaining to conflicting attributes such as activity, hemolysis, and toxicity have significantly impeded the progress of researchers. This paper introduces a paradigm shift by considering multiple attributes in AMP design. Presented herein is a novel approach termed Hypervolume-driven Multi-objective Antimicrobial Peptide Design (HMAMP), which prioritizes the simultaneous optimization of multiple attributes of AMPs. By synergizing reinforcement learning and a gradient descent algorithm rooted in the hypervolume maximization concept, HMAMP effectively expands exploration space and mitigates the issue of pattern collapse. This method generates a wide array of prospective AMP candidates that strike a balance among diverse attributes. Furthermore, we pinpoint knee points along the Pareto front of these candidate AMPs. Empirical results across five benchmark models substantiate that HMAMP-designed AMPs exhibit competitive performance and heightened diversity. A detailed analysis of the helical structures and molecular dynamics simulations for ten potential candidate AMPs validates the superiority of HMAMP in the realm of multiobjective AMP design. The ability of HMAMP to systematically craft AMPs considering multiple attributes marks a pioneering milestone, establishing a universal computational framework for the multi-objective design of AMPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Prototype Development of Graphical Pattern Security System on Raspberry Pi 1

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2nd International Conference on Innovative Technology, engineering and Sciences, iCITES 2020

作者： Gunawan, Teddy Surya Nasir, Fatin Nabilah Kartiwi, Mira Ismail, Nanang Electrical and Computer Engineering Department International Islamic University Malaysia Gombak Malaysia School of Electrical Engineering and Telecommunications UNSW Sydney Australia Information Systems Department International Islamic University Malaysia Gombak Malaysia Electrical Engineering Department UIN Sunan Gunung Djati Bandung Indonesia

ISBN: (数字)9783030709174

ISBN: (纸本)9783030709167

Password is a primary security mechanism that consists of a secret phrase created using alphabetic, numeric, alphanumeric, and symbolic characters, or a combination. It is used to restrict access to a system, application, or service to only those users who have the authorization to use the system or device. Because of increased computing power, modern passwords must be very long and complicated, which will make them hard to remember. Some research studies show that it is easier for people to remember visual passwords instead of textual ones. Therefore, this research aims to enhance the authentication process's security using the graphical pattern unlock approach on Raspberry Pi. The grid size of 5 × 5 was selected in the implementation, as it provides the tradeoff between security strength and an easy pattern to remember. The proposed system could be connected to the smart home system, which can enhance their security. Prototype validation revealed that the prototype is working as intended, and the authentication process took around 1.2 s to complete. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Authentication

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Bilateral Contract for Load Frequency and Renewable Energy Sources Using Advanced Controller

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computers, Materials & Continua 2022年第11期73卷 3165-3180页

作者： Krishan Arora Gyanendra Prasad Joshi Mahmoud Ragab Muhyaddin Rawa Ahmad H.Milyani Romany F.Mansour Eunmok Yang School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwara144001PunjabIndia School of Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia King Abdullah City for Atomic and Renewable Energy(K.A.CARE) RiyadhSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoul02707Korea

Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated ***,the collaboration of various manufacturing agencies,autonomous power manufacturers,and buyers have created complex installation *** regular active load and inefficiency of best measures among varied associates is a huge *** sudden load deviation will give rise to immediate amendment in frequency and tie-line power *** is essential to deal with every zone’s frequency and tie-line power within permitted confines followed by fluctuations within the ***,it can be proficient by implementing Load Frequency Control under the Bilateral case,stabilizing the power and frequency distinction within the interrelated power *** the net deviation in multiple areas is possible by minimizing the unbalance of Bilateral Contracts with the help of proportional integral and advanced controllers like Harris Hawks *** proposed the advanced controller Harris Hawk optimizer-based model and validated it on a test *** experiment results show that the delay time is 0.0029 s and the settling time of 20.86 s *** model can also be leveraged to examine the decision boundaries of the Bilateral case.

关键词： Bilateral contract load frequency control optimization harris hawks optimizer

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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Synthesis and Design of Near-Ideal Elliptical Filtering Frequency Selective Surfaces

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IEEE Transactions on Antennas and Propagation 2025年

作者： Jiang, Hao Qian, Mei Zhang, Yinghao Liu, Peiqin Lyu, Yanhe Chen, Zhi Ning Xue, Quan South China University of Technology Guangdong Provincial Key Laboratory of Millimeter-Wave and Terahertz Guangdong-Hong Kong-Macao Joint Laboratory for Millimeter-Wave and Terahertz School of Electronics and Information Engineering Guangdong Guangzhou510640 China National University of Singapore Department of Electrical and Computer Engineering Queenstown 117583 Singapore

This work presents the design and synthesis of near-ideal elliptical filtering frequency selective surfaces (FSSs). We propose a symmetrical composite circuit topology with four cascaded resonators interleaved with three dielectric layers. Series resonance in the central dual-layer resonators induces capacitive and inductive couplings, generating an elliptical response in the lower sideband. Meanwhile, cascaded parallel circuits within these resonators produce another elliptical response in the upper sideband. By cascading these resonant behaviors in an improved higher-order topology, the proposed FSS achieves five in-band transmission poles (TPs) and multiple transmission zeros (TZs) on both sidebands. Both simulation and measurement results demonstrate that the proposed FSS exhibits a wide 1-dB passband, an ultra-thin profile ( © 1963-2012 IEEE.

关键词： Frequency selective surfaces (FSSs) ideal elliptical filtering low profile wide 1-dB passband

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Validating a Chaos Based PRBG Under Different Chaotic Maps

Validating a Chaos Based PRBG Under Different Chaotic Maps

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Moysis, Lazaros Lawnik, Marcin Volos, Christos Baptista, Murilo S. Fragulis, George F. Goudos, Sotirios K. University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece Aristotle University of Thessaloniki Laboratory of Nonlinear Systems - Circuits and Complexity Physics Department Thessaloniki Greece University of Western Macedonia Department of Mechanical Engineering Kozani Greece Silesian University of Technology Faculty of Applied Mathematics Department of Mathematics Applications and Methods for Artificial Intelligence Gliwice44-100 Poland Supa University of Aberdeen Institute for Complex Systems and Mathematical Biology Department of Physics AberdeenAB24 3UX United Kingdom Aristotle University of Thessaloniki ELEDIA@AUTH Physics Department Thessaloniki Greece

ISBN: (纸本)9798350318845

Chaos-based random bit generators are abundantly used in chaos-based encryption and security applications, as a fast, deterministic source of randomness, to perform actions like permutation and substitution. The chaotic map used as a source of randomness though may heavily affect the bit generator's statistical randomness, key space, and speed. Thus, any given bit generator may not be equally efficient for all the chaotic maps used as a seed. Motivated by this, the present work considers a bit generator that utilizes a common modulo-based hashing technique, and studies its performance under a plethora of different chaotic maps. For each map, the generator is evaluated with respect to randomness, key space, and operations per bit ratio. This analysis can serve as a guideline for random bit generator usage in future chaos-based encryption designs. © 2024 IEEE.

关键词： Cryptography

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Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds

arXiv

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arXiv 2023年

作者： Xu, Zhenghao Ji, Xiang Chen, Minshuo Wang, Mengdi Zhao, Tuo H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Policy gradient methods equipped with deep neural networks have achieved great success in solving high-dimensional reinforcement learning (RL) problems. However, current analyses cannot explain why they are resistant to the curse of dimensionality. In this work, we study the sample complexity of the neural policy mirror descent (NPMD) algorithm with deep convolutional neural networks (CNN). Motivated by the empirical observation that many high-dimensional environments have state spaces possessing low-dimensional structures, such as those taking images as states, we consider the state space to be a d-dimensional manifold embedded in the D-dimensional Euclidean space with intrinsic dimension d D. We show that in each iteration of NPMD, both the value function and the policy can be well approximated by CNNs. The approximation errors are controlled by the size of the networks, and the smoothness of the previous networks can be inherited. As a result, by properly choosing the network size and hyperparameters, NPMD can find an ϵ-optimal policy with Oe(ϵ− α d −2) samples in expectation, where α ∈ (0, 1] indicates the smoothness of environment. Compared to previous work, our result exhibits that NPMD can leverage the low-dimensional structure of state space to escape from the curse of dimensionality, explaining the efficacy of deep policy gradient algorithms. © 2023, CC BY.

关键词： Reinforcement learning

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Toward Building Human-Like Sequential Memory Using Brain-Inspired Spiking Neural Models

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IEEE Transactions on Neural Networks and Learning systems 2025年第6期36卷 10143-10155页

作者： Malu Zhang Xiaoling Luo Jibin Wu Ammar Belatreche Siqi Cai Yang Yang Haizhou Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things Sichuan University of Science and Engineering Yibin China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Computer and Information Sciences Faculty of Engineering and Environment Northumbria University Newcastle upon Tyne U.K. Department of Electrical and Computer Engineering National University of Singapore Lower Kent Ridge Rd Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong (CUHK) Shenzhen China

The brain is able to acquire and store memories of everyday experiences in real-time. It can also selectively forget information to facilitate memory updating. However, our understanding of the underlying mechanisms and coordination of these processes within the brain remains limited. However, no existing artificial intelligence models have yet matched human-level capabilities in terms of memory storage and retrieval. This study introduces a brain-inspired spiking neural model that integrates the learning and forgetting processes of sequential memory. The proposed model closely mimics the distributed and sparse temporal coding observed in the biological neural system. It employs one-shot online learning for memory formation and uses biologically plausible mechanisms of neural oscillation and phase precession to retrieve memorized sequences reliably. In addition, an active forgetting mechanism is integrated into the spiking neural model, enabling memory removal, flexibility, and updating. The proposed memory model not only enhances our understanding of human memory processes but also provides a robust framework for addressing temporal modeling tasks.

关键词： Neurons Biological system modeling Computational modeling Brain modeling Dendrites (neurons) Oscillators Neural activity Membrane potentials Encoding Adaptation models

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Multi-Objective Spiking Neural Network for Optimal Wind Power Prediction Interval

Multi-Objective Spiking Neural Network for Optimal Wind Powe...

引用

IEEE International Symposium on Circuits and systems (ISCAS)

作者： Yinsong Chen Samson Yu Jason K. Eshraghian Chee Peng Lim School of Engineering Deakin University Melbourne VIC Australia Department of Electrical and Computer Engineering University of California Santa Cruz CA USA Institute for Intelligent Systems Research and Innovation Deakin University Melbourne VIC Australia

Precise and reliable measurement of wind power uncertainty plays a significant role in the economic operation and real-time control of the smart grid. In this paper, a novel spiking neural network (SNN) architecture is proposed for solving regression tasks, and a multi-objective gradient descent (MOGD) algorithm is employed to generate high-quality wind power prediction intervals (PIs). SNNs improve upon conventional artificial neural networks (ANNs) by encoding interneuron communication into temporally-distributed spikes, which reduce memory access frequency and data communication, and therefore, the computational power requirements of deep learning workloads. This becomes exceedingly important for continual data analysis in remote geographic regions which often lack reliable cloud access and power supply, where many wind power farms are stationed. Given that neuron spikes are all stereotypically treated to be identical, they are a natural fit for tasks that may conflict in a common network architecture, such as multimodal data or where multiple, potentially competing, objectives are being optimized for. This paper proposes an SNN architecture that achieves comparable performance with its ANN counterpart on a complex regression task, i.e., wind power interval prediction. The resulting multi-objective SNN demonstrates superior performance as compared with those from state-of-art ANNs in wind power interval prediction.

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LDNet-RUL: Lightweight Deformable Neural Network for Remaining Useful Life Prognostics of Lithium-ion Batteries

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IEEE Transactions on Power Electronics 2025年

作者： Wu, Xiankui Li, Penghua Deng, Zhongwei Liu, Zhitao Kurboniyon, Mekhrdod S. Xiang, Sheng Yin, Gang Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Chongqing University of Science and Technology School of Computer Science and Engineering Chongqing401331 China University of Electronic Science and Technology School of Mechanical and Electrical Engineering Chengdu611731 China Zhejiang University Institute of Cyber-Systems and Control Zhejiang 310058 China National Academy of Sciences of Tajikistan Center of Innovative Development of Science and New Technologies Dushanbe734025 Tajikistan Chongqing University College of Resource and Safety Engineering Chongqing400044 China

Deep neural networks (DNNs)-based battery remaining useful life (RUL) prognostics models suffer from high computational costs, long inference times, and a tendency to overfit, posing significant challenges for deployment on memory-constrained edge devices. To address this, LDNet-RUL, a lightweight yet high-performance model, is proposed for lithiumion battery lifetime prognostics. Specifically, a deformable depthwise convolution unit is designed that efficiently adapts to variations in input features with relatively low computational cost, which leverages fewer parameters and an adaptive receptive field to capture battery degradation dynamics. A multi-scale feature extraction method is introduced to extract degradation features from both short-term and long-term data during the battery charging process, which makes the method applicable to various battery charging protocols. To address overfitting and accelerate model inference, an adaptive channel selector, an adaptive residual connector, and a progressive dimensionalitygrouped bottleneck filter are incorporated to refine feature selection and reduce the model's complexity, boosting the model's overall performance. Experiments on vehicle lithium-ion battery datasets demonstrate that LDNet-RUL achieves state-of-the-art (SOTA) performance, with average RMSE of 0.23 and Score of 0.49. The model is lightweight, with only 7.92K parameters, achieves inference times of 3.94ms on GPU and CPU, and 5.39ms on the NVIDIA Jetson TX2. © 1986-2012 IEEE.

关键词： Convolution

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