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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Rajanish Kumar Kaushal Himani Pandey G. Kalaiarasi Sujata Arya Devyani Rawat Natrayan L Department of Electrical Engineering Chandigarh University Mohali Punjab India Department of Applied Science and Humanities ITM (SLS) Baroda University Vadodara Gujarat India Department of Advanced Computer Science and Engineering Vignan's Foundation for Science Technology and Research (Deemed to be university) Guntur Andhra Pradesh India Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India Department of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Air pollution has caused widespread concern, particularly in developing nations such as India and China. Despite the availability of expensive or inaccurate methods such as sensor-based or community network-based approaches, image-based air pollution evaluation still requires significant improvement. In response to this pressing issue, our research develops an accurate Deep Learning (DL) model for estimating air quality from environmental images. We employed Visual Geometry Group 19 (VGG-19) and Residual Network 50 (ResNet-50) as DL models. The air quality dataset was collected from the internet and grouped into three categories: Good, Moderate, and Severe, based on the Particulate Matter (PM) values. Image pre-processing techniques such as resizing and rescaling were used. The DL models were evaluated using accuracy, loss, and time metrics. ResNet-50 was identified as the best model for identifying air quality based on the images. The achieved accuracy level by ResNet-50 is 97.87%, and it takes 45.02 seconds to analyze the test data count of 90 images. This shows the effectiveness of the ResNet-50 model in air quality prediction.

关键词： Deep learning Training Visualization Accuracy Atmospheric modeling Urban areas Web pages Air pollution Testing Residual neural networks

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Deep Reinforcement Learning for Quantum Hamiltonian engineering

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Physical Review Applied 2022年第2期18卷 024033-024033页

作者： Pai Peng (彭湃) Xiaoyang Huang Chao Yin Linta Joseph Chandrasekhar Ramanathan Paola Cappellaro Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics and Astronomy Dartmouth College Hanover New Hampshire 03755 USA Department of Nuclear Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

engineering desired Hamiltonians in quantum many-body systems is essential for applications such as quantum simulation, computation, and sensing. Many widely used quantum Hamiltonian engineering sequences are designed using human intuition based on perturbation theory, which may not describe the optimal solution and is unable to accommodate complex experimental imperfections. Here we numerically search for Hamiltonian engineering sequences using deep reinforcement learning (DRL) techniques and experimentally demonstrate that they outperform celebrated decoupling sequences on a solid-state nuclear magnetic resonance quantum simulator. As an example, we aim at decoupling strongly interacting spin-1/2 systems. We train DRL agents in the presence of different experimental imperfections and verify robustness of the output sequences both in simulations and experiments. Surprisingly, many of the learned sequences exhibit a common pattern that had not been discovered before, to our knowledge, but has a meaningful analytical description. We can thus restrict the searching space based on this control pattern, allowing us to search for longer sequences, ultimately leading to sequences that are robust against dominant imperfections in our experiments. Our results not only demonstrate a general method for quantum Hamiltonian engineering, but also highlight the importance of combining black-box artificial intelligence with an understanding of the physical system in order to realize experimentally feasible applications.

关键词： Quantum control Learning Machine learning Nuclear magnetic resonance

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Performance optimization of microbial electrolysis cell (MEC) for palm oil mill effluent (POME) wastewater treatment and sustainable Bio-H2 production using response surface methodology (RSM)

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第34期47卷 15464-15479页

作者： Chandrasekhar, Abudukeremu Kadier ab Junying Wang ab K. Wang, Junying Chandrasekhar, K. Abdeshahian, Peyman Islam, M. Amirul Ghanbari, Farshid Bajpai, Mukul Katoch, Surjit Singh Bhagawati, Prashant Basavaraj Li, Hui Kalil, Mohd Sahaid Hamid, Aidil Abdul Abu Hasan, Hassimi Ma, Peng-Cheng Laboratory of Environmental Science and Technology The Xinjiang Technical Institute of Physics and Chemistry Key Laboratory of Functional Materials and Devices for Special Environments Chinese Academy of Sciences Urumqi 830011 China Center of Material and Opto-electronic Research University of Chinese Academy of Sciences Beijing 100049 China School of Civil and Environmental Engineering Yonsei University Seoul 03722 Republic of Korea Department of Biotechnology Engineering School of Lorena University of São Paulo São Paulo 12602-810 Brazil Laboratory for Quantum Semiconductors and Photon-based BioNanotechnology Department of Electrical and Computer Engineering Faculty of Engineering Université de Sherbrooke Sherbrooke Québec J1K 2R1 Canada Research Center for Environmental Contaminants (RCEC) Abadan University of Medical Sciences Abadan Iran Environmental Engineering Lab Civil Engineering Department National Institute of Technology Hamirpur Hamirpur Himachal Pradesh 177005 India Faculty of Engineering Department of Civil Engineering Annasaheb Dange College of Engineering and Technology Ashta Maharashtra 416301 India Department of Chemical and Process Engineering Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia School of Biosciences and Biotechnology Faculty of Science and Technology National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia Research Centre for Sustainable Process Technology Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia

Microbial electrolysis cells (MECs) are a new bio-electrochemical method for converting organic matter to hydrogen gas (H-2). Palm oil mill effluent (POME) is hazardous wastewater that is mostly formed during the crude oil extraction process in the palm oil industry. In the present study, POME was used in the MEC system for hydrogen generation as a feasible treatment technology. To enhance biohydrogen generation from POME in the MEC, an empirical model was generated using response surface methodology (RSM). A central composite design (CCD) was utilized to perform twenty experimental runs of MEC given three important variables, namely incubation temperature, initial pH, and influent dilution rate. Experimental results from CCD showed that an average value of 1.16 m(3) H-2/m(3) d for maximum hydrogen production rate (HPR) was produced. A second-order polynomial model was adjusted to the experimental results from CCD. The regression model showed that the quadratic term of all variables tested had a highly significant effect (P < 0.01) on maximum HPR as a defined response. The analysis of the empirical model revealed that the optimal conditions for maximum HPR were incubation temperature, initial pH, and influent dilution rate of 30.23 ?, 6.63, and 50.71%, respectively. Generated regression model predicted a maximum HPR of 1.1659 m(3) H-2/m(3) d could be generated under optimum conditions. Confirmation experimentation was conducted in the optimal conditions determined. Experimental results of the validation test showed that a maximum HPR of 1.1747 m(3) H-2/m(3) d was produced. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Microbial electrolysis cell (MEC) Hydrogen production Palm oil mill effluent (POME) Wastewater Optimization Response surface methodology (RSM)

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Multi-Objective Optimization-Based Waveform Design for Multi-User and Multi-Target MIMO-ISAC Systems

arXiv

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arXiv 2024年

作者： Wang, Peng Han, Dongsheng Cao, Yashuai Ni, Wanli Niyato, Dusit the School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Baoding071003 China the Department of Electronic Engineering Tsinghua University Beijing100084 China the School of Computer Science and Engineering Nanyang Technological University Singapore

Integrated sensing and communication (ISAC) opens up new service possibilities for sixth-generation (6G) systems, where both communication and sensing (C&S) functionalities co-exist by sharing the same hardware platform and radio resource. In this paper, we investigate the waveform design problem in a downlink multi-user and multi-target ISAC system under different C&S performance preferences. The multi-user interference (MUI) may critically degrade the communication performance. To eliminate the MUI, we employ the constructive interference mechanism into the ISAC system, which saves the power budget for communication. However, due to the conflict between C&S metrics, it is intractable for the ISAC system to achieve the optimal performance of C&S objective simultaneously. Therefore, it is important to strike a tradeoff between C&S objectives. By virtue of the multi-objective optimization theory, we propose a weighted Tchebycheff-based transformation method to re-frame the C&S trade-off problem as a Pareto-optimal problem, thus effectively tackling the constraints in ISAC systems. Finally, simulation results reveal the trade-off relation between C&S performances, which provides insights for the flexible waveform design under different C&S performance preferences in MIMO-ISAC systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Multi-IRS Deployment Optimization for Enhanced Wireless Coverage: A Performance-Cost Trade-off

Multi-IRS Deployment Optimization for Enhanced Wireless Cove...

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IEEE International Conference on Communications (ICC)

作者： Weidong Mei Rui Zhang National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering National University of Singapore The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between the base station (BS) and multiple users via tunable single/multiple signal reflections, thereby significantly enhancing the BS's coverage performance. To achieve this goal, we present an optimization framework for multi-IRS deployment in this paper and study its efficient design. In particular, we assume that a set of candidate locations for deploying IRSs are given in an area of interest, and show that there exists a fundamental trade-off between maximizing the BS's coverage in the area and minimizing the total cost in multi-IRS deployment design. Specifically, the more IRSs deployed over those candidate locations, the smaller number of IRS reflections on average required for achieving a LoS link between the BS and any user location in the area, which helps reduce the cascaded path loss and thus enhance the communication performance. To optimally characterize this trade-off, we formulate the multi-IRS deployment problem based on graph theory and propose a new successive removal algorithm to efficiently solve this problem by iteratively removing IRSs from the candidate locations while satisfying a given communication performance constraint. Simulation results are provided to show the efficacy of the proposed design approach and algorithm for multi-IRS deployment.

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Triple Point Masking

arXiv

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arXiv 2024年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an710071 China Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. Our code and models are available at https://***/liujia99/TPM. © 2024, CC BY.

关键词： Signal encoding

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The more the merrier: Adding hidden measurements to secure industrial control systems 20

The more the merrier: Adding hidden measurements to secure i...

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7th Symposium on Hot Topics in the science of Security, HotSoS 2020

作者： Giraldo, Jairo Urbina, David Tang, CheeYee Cardenas, Alvaro A. Electrical Engineering Department University of Utah United States Computer Science Department University of Texas at Dallas United States Engineering Laboratory National Institute of Standards and Technology Computer Science and Engineering Department University of California United States

ISBN: (纸本)9781450375610

Industrial Control Systems (ICS) collect information from a variety of sensors throughout the process, and then use that information to control some physical components. Control engineers usually have to pick which measurements they are going to use and then they purchase sensors to take these measurements. However, in most cases they only need a small subset of all possible measurements that can be used. Economic and efficiency reasons motivate engineers to use only a small number of sensors for controlling a system;however, as attacks against industrial systems continue to increase, we need to study a systematic way to add sensors to the system to identify potentially malicious attacks. We propose the addition of hidden sensor measurements to a system to improve its security. Hidden sensor measurements are by our definition measurements that were not considered in the original design of the system, and are not used for any operational reason. We only add them to improve the security of the system and using them in anomaly detection and mitigation. We show the addition of these new, independent, but correlated measurements to the system makes it harder for adversaries to launch false-data injection stealthy attacks and, even if they do, it is possible to limit the impact caused by those attacks. When an attack is detected, we replace the compromised sensor measurements with estimated ones from the new sensors improving the risky open-loop simulations proposed by previous work. © 2020 ACM.

关键词： Anomaly detection

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End-to-End Delay Performance for Mobile Cyber-Physical Systems with Edge Computing 11

End-to-End Delay Performance for Mobile Cyber-Physical Syste...

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11th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Wu, Yan Yang, Qinghai Qiao, Feiyu Kwak, K.S. Leung, C.M. Pan, Guangchuan State Key Laboratory of ISN School of Telecommunications Engineering Xidian University No.2 South Taibai Road Shaanxi Xi'an710071 China Communication Engineering Inha University #100 Inha-Ro Nam-gu Incheon22212 Korea Republic of College of Computer Science and Software Engineering Shenzhen University 518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Xidian University No.2 South Taibai Road Shaanxi Xi'an710071 China

ISBN: (纸本)9781450398039

In this letter, we investigate the end-to-end (E2E) delay performance for mobile cyber-physical systems (M-CPS) with edge computing. The traditional delay analysis is based on flow conservation law, whereas the flow is not conserved in the M-CPS with edge computing due to the existence of edge computing node and remote controller. To tackle this problem, we introduce two scaling factors to ensure the flow conservation, then the E2E delay bound of M-CPS with edge computing is derived via stochastic network calculus. In addition, to cater for the delay demand for M-CPS stability, the stable-aware delay bound is obtained. Numerical results show the gap between the analytic bound and simulation can be narrower as the average signal-to-noise (SNR) growing. © 2022 ACM.

关键词： Cyber Physical System

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Median Filtering Detection Based on Quaternion Convolutional Neural Network

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computers, Materials & Continua 2020年第10期65卷 929-943页

作者： Jinwei Wang Qiye Ni Yang Zhang Xiangyang Luo Yunqing Shi Jiangtao Zhai Sunil Kr Jha Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Department of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing100093China Department of Electrical Computer Engineering New Jersey Institute of TechnologyNew JerseyNJ07102USA

Median filtering is a nonlinear signal processing technique and has an advantage in the field of image ***,more attention has been paid to the forensics research of median *** this paper,a median filtering forensics method based on quaternion convolutional neural network(QCNN)is *** median filtering residuals(MFR)are used to preprocess the *** the output of MFR is expanded to four channels and used as the input of *** QCNN,quaternion convolution is designed that can better mix the information of different channels than traditional *** quaternion pooling layer is designed to evaluate the result of quaternion *** is proposed to features well combine the three-channel information of color image and fully extract forensics *** show that the proposed method has higher accuracy and shorter training time than the traditional convolutional neural network with the same convolution depth.

关键词： Median filtering forensics quaternion convolution layer quaternion pooling layer color image

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