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arXiv 2022年

作者： Benisty, Hadas Song, Alexander Mishne, Gal Charles, Adam S. Yale Neuroscience New HavenCT06510 United States Max Planck Institute for Intelligent Systems Stuttgart Germany Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. In this review, we cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges. © 2022, CC BY-NC-ND.

关键词： Fluorescence microscopy

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Weak Electron-Phonon Coupling Contributing to High Thermoelectric Performance in Gete

SSRN

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SSRN 2023年

作者： Jin, Yang Wang, Dongyang Zhu, Yingcai Su, Lizhong Wang, Ziyuan Ge, Zhen-Hua Qiu, Yuting Zhao, Li-Dong School of Materials Science and Engineering Beihang University Beijing100191 China Henan Key Laboratory of Diamond Optoelectronic Devices and Systems Key Laboratory of Material Physics Ministry of Education School of Physics Zhengzhou University Zhengzhou450052 China Faculty of Materials Science and Engineering Kunming University of Science and Technology Kunming650093 China Beihang School Beihang University Beijing100191 China Key Laboratory of Intelligent Sensing Materials and Chip Integration Technology of Zhejiang Province Hangzhou Innovation Institute Beihang University Hangzhou310051 China

Thermoelectric materials can achieve the interconversion of electricity and heat, providing potential for power generation. It is a challenge to elevate conversion efficiency due to the coupled thermoelectric parameters. Herein, decoupling electron-phonon transport is successfully achieved in GeTe-based thermoelectric materials by single doping trivalent dopant M (M=In, Sb, Bi). The high conversion efficiency of ~10.3% and power density of ~ 0.6 W cm-2 under a temperature difference of 419 K are realized in a segmented single-leg device. And the remarkable hardness of ~ 2.92 GPa in M-doped GeTe provides a prospect for device application. The advanced performance in M-doped GeTe is mainly ascribed to its high ZT value through manipulating deformation potential. The three trivalent dopants facilitate band convergence due to the enhanced symmetry, which increases effective mass without scarifying carrier mobility. Compared with In and Sb, Bi element can be the most effective dopant to decrease deformation potential from 25.3 eV to 17.2 eV. The low deformation potential by doping with Bi, indicating weakening electron-phonon coupling, leads to high weighted mobility, further optimizing the power factor. The In-doped GeTe exhibits low lattice thermal conductivity at ambient temperature because of the softened phonons, while the lattice thermal conductivity of Bi-doped GeTe is significantly decreased at high temperature due to the larger atomic mass and size of Bi than that of the other two series. Consequently, the highest ZT of ~1.9 at 723 K is attained in Ge0.94Bi0.06Te which outperforms other single-doped GeTe-based thermoelectric materials. © 2023, The Authors. All rights reserved.

关键词： Germanium alloys

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How Can Businesses, Low-Tech Businesses in Particular, Benefit from the Metaverse? A Delphi Perspective from Academics

How Can Businesses, Low-Tech Businesses in Particular, Benef...

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International Conference on Marketing and Technologies, ICMarkTech 2023

作者： Lopes, Teresa Sofia Amorim Carreira, Rui Carlos, Vera Silva Pereira, Rui Humberto Alves, Carlos Pratas, Joaquim dos Santos, José Paulo Marques Department of Business Administration University of Maia Maia Portugal NIPE—Centre for Research in Economics and Management School of Economics and Management University of Minho Braga Portugal Polytechnic Institute of Maia Maia Portugal LIACC—Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal NECE—Research Center for Business Sciences Dep. of Management and Economics University of Beira Interior Covilhã Portugal CEOS.PP—Centre for Organizational and Social Studies Polytechnic of Porto Porto Portugal Algoritmi Research Centre University of Minho Guimarães Portugal LASI—Intelligent Systems Associate Laboratory Guimarães Portugal Porto Accounting and Business School Polytechnic Institute of Porto São Mamede de Infesta Portugal

ISBN: (纸本)9789819715510

Two years ago, Meta was born, accompanied by the announcement of the Metaverse. The Metaverse would be a collection of 3D immersive ecosystems for people’s socialization. This innovative idea should allow one to live in the Metaverse as in real life without physical barriers. Attending shows, concerts, virtual travel, and caring for one’s health or education should be possible. In sum, a new marketplace is born. Amid such newness, the question of how businesses may benefit from the Metaverse arises. Albeit tech businesses must be comfortable and adapted to the potentialities of the new marketplace, the entrance of the low-techs may be more problematic. The study brings together a panel of academic experts to reflect on these questions. They agree that there is a myriad of potentialities in the new marketplace, not only for B2C but also for B2B. Low-techs may be less adapted to benefit from the Metaverse. A following-the-wave strategy must be avoided. On the contrary, investing in understanding the new paradigm and careful planning is essential. The Metaverse has no physical barriers, which tend to limit low-techs’ actions. Their nonexistence creates new opportunities and must be where customers are, after all. The Metaverse, however, still needs development to gain critical mass. Hardware easy to use, its cost, speedy and cheap Internet, and mainly, trustable and recognized currencies are still required. Nonetheless, the Metaverse may be a new world full of opportunities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Commerce

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Reconstruction of Helmholtz Plane to Stabilize Zinc Metal Anode/Electrolyte Interface

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SSRN 2023年

作者： Jiao, Shangqing Fu, Jimin Yin, Qifang Yao, Haimin Hu, Haibo School of Materials Science and Engineering Key Laboratory of Structure and Functional Regulation of Hybrid Materials Ministry of education Anhui University Hefei230601 China Research Institute for Intelligent Wearable Systems School of Fashion and Textiles The Hong Kong Polytechnic University Hung Hom Kowloon 999077 Hong Kong Faculty of Engineering China University of Geosciences Wuhan430074 China Department of Mechanical Engineering The Hong Kong Polytechnic University Hung Hom Kowloon 999077 Hong Kong

Dendrites proliferation and parasitic side reactions on zinc metal anodes (ZMAs) severely weaken the energy efficiencies of aqueous zinc metal batteries (AZMBs). Herein, an effective Helmholtz plane (HP) reconstruction mechanism on ZMAs in ZnSO4 electrolyte activated by zinc pyrrolidone carboxylate (PCA-Zn) additive is proposed. Systematical experiments together with numerical simulations identified that the dissociated PCA− anions featuring bis-zincophilic-terminal preferentially adhere to the ZMAs surface with carboxyls serving as anchors to replace H2O and SO42−, reshaping an H2O/SO42−-repellent HP to restrain water-triggered side reactions. Meanwhile, the other free zincophilic carbonyl terminals of PCA− anions endow the restructured HP with spatial confinement effect on Zn2+, effectively suppressing their rampant in-plane diffusion and homogenizing ions flux during Zn deposition. Moreover, the HP exhibits a self-repair behavior against unexpected electrode surface damage upon cycling. Benefiting from the multifunctional HP that enables more stable ZMAs/electrolyte interphasial chemistry, an excellent Zn||Zn symmetric electroplating/stripping cyclic durability exceeding 480 h under a depth of discharge up to 20% (10 mA cm−2/10 mAh cm−2) is achieved, accounting for significantly boosted rechargeability (89.7% capacity holding ratio after 5000 cycles) of the carbon-cloth@polyaniline||ZMA full AZMBs compared to that without PCA-Zn additive (malfunction after only 960 cycles). © 2023, The Authors. All rights reserved.

关键词： Electrolytes

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Online Attentive Kernel-Based Temporal Difference Learning

arXiv

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arXiv 2022年

作者： Yang, Guang Chen, Xingguo Yang, Shangdong Wang, Huihui Dong, Shaokang Gao, Yang The the Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications National Engineering Laboratory for Agri-Product Quality Traceability Beijing Technology and Business University China The State Key Laboratory for Novel Software Technology Nanjing University China The PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference, creating difficulty for the deep neural network to be applied to an online RL algorithm in a fully online setting. Therefore, a simpler and more adaptive approach is introduced to evaluate value function with the kernel-based model. Sparse representations are superior at handling interference, indicating that competitive sparse representations should be learnable, non-prior, non-truncated and explicit when compared with current sparse representation methods. Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA. In this paper, we propose an Online Attentive Kernel-Based Temporal Difference (OAKTD) algorithm using two-timescale optimization and provide convergence analysis of our proposed algorithm. Experimental evaluations showed that OAKTD outperformed several Online Kernel-based Temporal Difference (OKTD) learning algorithms in addition to the Temporal Difference (TD) learning algorithm with Tile Coding on public Mountain Car, Acrobot, CartPole and Puddle World tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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Implementation of a low-cost current perturbation-based improved PO MPPT approach using Arduino board for photovoltaic systems

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 10卷

作者： Chellakhi, Abdelkhalek Beid, Said El Marghichi, Mouncef El Bouabdalli, El Mahdi Harrison, Ambe Abouobaida, Hassan Laboratory of Engineering Sciences for Energy (LabSIPE) National School of Applied Sciences of El Jadida Chouaib Doukkali University El Jadida 24000 Morocco CISIEV Team Cadi Ayyad University Marrakech 40160 Morocco Intelligent Systems Design Laboratory (ISDL) Faculty of Science Abdelmalek Essaadi University Tetouan 93000 Morocco Department of Electrical and Electronics Engineering College of Technology (COT) University of Buea PO Box Buea 63 Cameroon

In photovoltaic (PV) systems, the conversion of solar energy into electrical energy by the PV module is influenced by various factors, including sunlight intensity and temperature. To achieve optimal performance, it is crucial to accurately track the maximum power point (MPP) of the PV module. Among the numerous MPP tracking (MPPT) techniques that have been developed, the perturbation and observation (PO) method has gained significant attention due to its simplicity and reliability. However, the fixed perturbation step size used in the traditional PO algorithm can result in poor tracking capability, excessive ripple, and drift, leading to high power loss and low tracking efficiency. To address these limitations, this paper proposes an improved version of the PO strategy that introduces an adaptable step-magnitude mechanism, utilizing current perturbation instead of the voltage perturbation typically employed in the classical PO method. This enhanced indirect PO approach maintains low complexity and can be easily implemented on a cost-effective Arduino Uno board. A streamlined C++ code integrates the improved PO MPPT strategy with a Proportional Integral Derivative (PID) controller, eliminating the need for separate PID blocks and further reducing system complexity. To evaluate the effectiveness of the proposed technique, comparative analyses are conducted against the traditional PO algorithm, particle swarm optimization (PSO), fuzzy logic control (FLC), and a recently introduced approach, the zone voltage (ZV) method. Simulation results using Proteus software demonstrate that the improved PO approach outperforms the other techniques in various aspects, including achieving the highest static and dynamic tracking efficiencies of 99.38% and 99.88%, respectively, negligible power loss and fluctuations, the fastest convergence speed, and the shortest tracking time of 0.19 seconds. © 2024 The Author(s)

关键词： Adaptable step magnitude Arduino Uno board C++ code Maximum power point tracking (MPPT) Perturbation and observation (PO) Photovoltaic system Proteus software

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Evaluation Metrics for Collaborative Fault Detection and Diagnosis in Cyber-Physical systems

Evaluation Metrics for Collaborative Fault Detection and Dia...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Luis Piardi André Oliveira Pedro Costa Paulo Leitão Research Center in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Bragança Bragança Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal Universidade Tecnológica Federal do Paraná (UTFPR) Curitiba-PR Brazil Technology and Science (INESC TEC) Institute for Systems and Computer Engineering Porto Portugal Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragança Bragança Portugal

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Cyber-physical systems (CPS) rapidly expand within industrial contexts in a new era of digitalization, processing power, and inter-device communication capabilities. These advancements integrate technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud and edge computing, granting processes and operations a high degree of autonomy. In addition, these interconnections foster collective intelligence arising from information exchange and collaboration between components, often outperforming individual capabilities. This collective intelligence manifests in fault detection and diagnosis (FDD) tasks within CPS, as it significantly improves the flexibility, performance, and scalability. However, the inherent complexity of CPS poses challenges in determining the best configuration of the collaboration parameters, such as when and how to collaborate, wherein incorrect adjustments may lead to decision errors and compromise the system's performance. With this in mind, this paper proposes seven metrics to evaluate collaboration performance for fault detection and diagnosis in multi-agent systems (MAS)-based CPS, evaluating when the collaboration is beneficial or when the collaboration parameters need to be adjusted. The experiments focus on collaborative fault detection in temperature and humidity sensors within warehouse racks, where the proposed evaluation metrics point out the impact of collaboration on the detection task, as well as possible actions to be adopted to improve the agent's performance.

关键词： Measurement Temperature sensors Fault detection System performance Collaboration Cyber-physical systems Collective intelligence Sensors Internet of Things Artificial intelligence

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Decomposed Neural Architecture Search for image denoising

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Applied Soft Computing 2022年 124卷

作者： Li, Di Bai, Yunpeng Bai, Zongwen Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an710129 China Department of Computer Science Faculty of Business and Physical Sciences Aberystwyth University AberystwythSY233DB United Kingdom Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data School of Physics and Electronic Information Yan'an University Yan'an716000 China

In practical applications of deep learning, as the demand for the modeling capability increases, the network size may need to be massively enlarged in response. This may form a significant challenge in practice, especially when facing the dilemma of limited computational resources, making model compression indispensable. It can be time-consuming and interminable to obtain an appropriate network architecture through manual compression. In this paper, we propose an automated method for searching decomposed network architectures, named DNAS (standing for Decomposed Neural Architecture Search). It integrates both tasks of neural architecture search and tensor decomposition based model compression within a unified framework. The method is able to efficiently find a compact network with high performance for image denoising, with respect to memory and runtime. Particularly, using one single V100 GPU, it only takes about 1.5 h to obtain a denoising network on the BSD500 dataset. Experimental results demonstrate that compared with models developed using existing methods, DNAS consumes significantly less inference time and employs much fewer trainable parameters, outperforming existing approaches on both synthetic and real-world denoising datasets. © 2022 Elsevier B.V.

关键词： Network architecture

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Optimal mixed control of networked reaction-diffusion systems

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Physical Review Research 2025年第1期7卷 013262-013262页

作者： Xiaofeng Luo Runzi He Lifeng Hou Shupeng Gao Zhen Jin Gui-Quan Sun Lili Chang Ludovico Minati Stefano Boccaletti School of Mathematics North University of China Shanxi Taiyuan 030051 Peoples's Republic of China Sino-Europe Complex Science Center North University of China Shanxi Taiyuan 030051 Peoples's Republic of China School of Computer Science and Technology North University of China Shanxi Taiyuan 030051 Peoples's Republic of China Complex Systems Research Center Shanxi University Shanxi Taiyuan 030006 Peoples's Republic of China Tangshan Research Institute Beijing Institute of Technology Hebei Tangshan 063000 Peoples's Republic of China School of Mechanical Engineering North China University of Science and Technology Hebei Tangshan 063000 Peoples's Republic of China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi Taiyuan 030006 Peoples's Republic of China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 Peoples's Republic of China Center for Mind/Brain Sciences (CIMeC) University of Trento 38123 Trento Italy CNR-Institute of Complex Systems Via Madonna del Piano 10 50019 Sesto Fiorentino Italy Institute of Interdisciplinary Intelligent Science Ningbo University of Technology Ningbo 315211 Peoples's Republic of China

We introduce a control scheme able to steer the evolution of networked reaction-diffusion systems toward any intended dynamics. Specifically, we consider simplicial complexes of reaction-diffusion systems that can be collapsed into weighted complex networks by leveraging the linear relationship between the Laplacian matrix of the simplicial complex and the Laplacian matrix of each order simplex. The method integrates reaction and network-based diffusion controls and, as far as effectiveness and cost are concerned, features a significantly improved performance over traditional single-variable control methods. Numerical simulations using the FitzHugh-Nagumo and susceptible-infected-removed models demonstrate the resilience of the proposed mixed control across diverse systems, highlighting the potential of the method for complex system management and intervention strategies.

关键词： Laplace transforms

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Improving Classification Attacks in IOT Intrusion Detection System using Bayesian Hyperparameter Optimization 3

Improving Classification Attacks in IOT Intrusion Detection ...

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3rd International Seminar on Research of Information Technology and intelligent systems, ISRITI 2020

作者： Kunang, Yesi Novaria Nurmaini, Siti Stiawan, Deris Suprapto, Bhakti Yudho Universitas Bina Darma Computer Science Departement Palembang Indonesia Intelligent System Research Group Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Universitas Srwijaya Electrical Engineering Departement Palembang Indonesia Universitas Sriwijaya Computer Networking Information Systems Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9781728184067

The growth of the Internet of Things (IoT) presents challenges in the field of security. The Intrusion Detection System is an alternative to protecting the internet of things. In this study, we propose an intrusion detection system model that combines unsupervised algorithm and a deep neural network. Autoencoder as unsupervised learning algorithm has a function as a feature extractor that speeds up the learning process on a deep neural network. The performance of a deep learning model depends heavily on the selection of hyperparameters of neural network architecture. In this case, we used Bayesian Hyperparameter Optimization to perform hyperparameter tuning of deep learning models with various activation and weight initialization techniques. The accumulation result is useful to help determine the correct activation function and weight initialization and the hyperparameters that most influence the deep learning model. The results of this study show that Bayesian hyperparameter optimization can improve classification results significantly. Evaluation using the BoT-IoT dataset, the classification accuracy results in deep learning model can reach 99.99%. © 2020 IEEE.

关键词： Internet of things

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