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Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

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Design of Signal Conditioner for Sensor Equipment of Human Body Surrogate for Forensic Crash Testing

Design of Signal Conditioner for Sensor Equipment of Human B...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Peter Vestenický Eduard Kolla Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Žilina Slovakia Institute of Forensic Research and Education University of Žilina Žilina Slovakia

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

This paper describes design and measurements of signal conditioner used for amplification of signals from accelerometers intended to use in human body surrogate for forensic crash testing. The design consists of instrumentation amplifier, Bessel low pass antialiasing filter and final gain stage. The characteristics of conditioner (frequency response, impulse response) are measured and compared with professional amplifier Endevco 136. Final testing of the conditioner together with sensor Endevco 7264H-2K is performed on the drop weight impact tester and the results are compared with Endevco 136, too.

关键词： Accelerometers Costs Forensics Instruments Low-pass filters Keyboards Vehicle crash testing Production Gyroscopes Piezoresistance

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WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equivalent Permittivity

WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equi...

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2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Hu, Pengming Yang, Weidong Wang, Xuyu Mao, Shiwen Shen, Erbo Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Knight Foundation School of Computing and Information Sciences Florida International University FL33199 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States

ISBN: (纸本)9781665494274

Moisture content is one of the important indexes of food storage security. The existing detection methods are time-consuming and high cost such that it is difficult to realize online moisture detection. In this paper, according to the dielectric properties of wheat, we propose a wheat moisture content detection system with commercial Wi-Fi devices, termed WiWm-EP. First, we introduce the relationship between the moisture content of wheat and its dielectric constant. Then, we establish an equivalent permittivity (EP) model to characterize wheat moisture content, where the EP can be calculated from channel state information (CSI) of the dual antenna model. Besides, we build the fitting function between the EP and the moisture content as the wheat moisture detection model. Finally, we evaluate the performance of the system through different experiments. The average relative error of the detection results of five wheat samples with different moisture contents is less than 3%. © 2023 IEEE.

关键词： Permittivity

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Actively tunable dual-broadband graphene-based terahertz metamaterial absorber

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Chinese Physics B 2021年第12期30卷 495-502页

作者： Dan Hu Tian-Hua Meng Hong-Yan Wang Mai-Xia Fu School of Physics and Electrical Engineering Anyang Normal UniversityAnyang 455000China Department of Physics and Electronics Science Shanxi Datong UniversityDatong 037009China School of Education Information Technology and Communication Anyang Normal UniversityAnyang 455000China College of Information Science and Engineering Henan University of TechnologyKey Laboratory of Grain Information Processing and ControlMinistry of EducationZhengzhou 450001China

A tunable metamaterial absorber(MA)with dual-broadband and high absorption properties at terahertz(THz)frequencies is designed in this *** MA consists of a periodic array of flower-like monolayer graphene patterns at top,a SiO_(2)dielectric spacer in middle,and a gold ground plane at the *** simulation results demonstrate that the designed MA has two wide absorption bands with an absorption of over 90%in frequency ranges of 0.68 THz-1.63 THz and 3.34 THz-4.08 THz,and the corresponding relative bandwidths reach 82.3%and 20%,*** peak absorptivity of the absorber can be dynamically controlled from less than 10%to nearly 100%by adjusting the graphene chemical potential from 0 eV to 0.9 ***,the designed absorber is polarization-insensitive and has good robustness to incident *** a high-performance MA has broad application prospects in THz imaging,modulating,filtering,etc.

关键词： metamaterial graphene absorber terahertz

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Deep Learning-based Source Code Classification and Visualization of Decision Rationales

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Procedia Computer Science 2024年 246卷 2753-2761页

作者： Shinichi Oeda Seiya Mitsumori Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan

In programming education, it is crucial to provide instruction that is tailored to students’ proficiency levels. For this purpose, an objective evaluation of each student’s coding ability is essential. Additionally, it is necessary to understand the characteristics of source code written by both beginners and advanced students. Previous research has successfully converted the structural information of source code into graphs and assessed coding skills using deep learning, achieving high accuracy. However, it remains unclear which specific structural elements significantly influence these assessments. This study addresses this gap by transforming source code into abstract syntax trees and developing a model that uses Graph Convolutional Networks to categorize code as either beginner or advanced users based on learned structural information. Furthermore, we apply Integrated Gradients to visualize the decision-making basis of our model and elucidate the structural characteristics distinguishing source code written by beginner and advanced users.

关键词： Abstract Syntax Tree Deep Learning Explainable AI Graph Convolutional Networks Integrated Gradients Source Code Classification

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Constrained Multi-Objective Optimal Combined Economic Emission Dispatch

Constrained Multi-Objective Optimal Combined Economic Emissi...

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2022 International Conference on Electrical engineering and Sustainable Technologies, ICEEST 2022

作者： Ali, Mazhar Khan, Muhammad Ahmad Zain Yousaf, Muhammad Tahir, Muhammad Faizan Haleem Shah, Sayyed Qaboos, Abdullah Tianjin University School of Electrical and Electronic Information Control Engineering Tianjin China Tianjin University School of Electrical and Information Engineering Tianjin China Hubei University of Automotive Technology School of Electrical and Information Engineering Shiyan China South China University of Technology School of Electric Power Guangzhou China COMSATS University Islamabad Department of Electrical and Computer Engineering Rawalpindi Pakistan

ISBN: (纸本)9781665462099

In this paper, researchers adopt multi-objective particle swarm optimization to tackle the issue of multi-objective combined economic emission dispatch (CEED). The equality and inequality restrictions in this optimization process make it non-linear. In most cases, it divides up the total amount of power generated by all the units in a given system. Primary goals include lowering fuel costs and minimizing environmental impact within prescribed constraints. Economic dispatch is often formulated as a nonlinear system, and the resulting solutions often fall behind expectations. However, these methods are not suitable for solving the economic dispatch issue because of their non-convex character. Several AI methods are used for the economic dispatch issue due to its complexity and the fact that it is neither linear nor smooth. The conventional form of the meta-heuristic approach, particle swarm optimization (PSO), has difficulty dealing with such difficulties, yet it is still widely utilized. In order to solve this issue, this work presents an updated form of particle swarm optimization. The efficiency of the customized version of specific swarm optimization (PSO) is verified in the MATLAB Simulink environment. Finding a Pareto-optimal, industrially, the multi-objective solution to the emission dispatch issue using multi-objective particle swarm optimization is shown to be a viable option. © 2022 IEEE.

关键词： Pareto principle

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Latent State Estimation Method for Students Using Knowledge Tracing Combining with IRT 26

Latent State Estimation Method for Students Using Knowledge ...

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26th International Conference on Knowledge-Based and Intelligent information and engineering Systems, KES 2022

作者： Oeda, Shinichi Kakizaki, Toma Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan

The Intelligent Tutoring Systems (ITSs) are a system that provides an efficient learning environment by assigning questions that are suitable to the learner's skill state. To improve the performance of ITSs, conventional studies have proposed student models that can estimate skill states with high accuracy. However, these models were based on the assumption that the log data was correct. In other words, they do not take into account the possibility of students cheating or memorizing answers, assuming that the test was conducted fairly and impartially. In recent years, many examinations have been conducted online as a countermeasure against COVID-19 (coronavirus) infections, and students may be able to obtain hints for the examinations using the internet or books. In this way, it is important to know how students approach learning in order to estimate their skill state. In this study, we propose a method for estimating the latent state of learners using a model that combines Knowledge Tracing, the de-facto standard for student modeling method, and Item Response Theory. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent information & engineering Systems (KES 2022)

关键词： Students

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TFD:Trust-Based Fraud Detection in SIoT With Graph Convolutional Networks

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Jiang, Nan Gu, Weihao Li, Lang Zhou, Fengqi Qiu, Sen Zhou, Tianqing Chen, Honglong School of Information Engineering East China Jiaotong University Nanchang China Hainan University Hainan China Department of Information and Computational Science East China Jiaotong University Nanchang China School of Control Science and Engineering Dalian University of Technology Dalian China College of Control Science and Engineering China University of Petroleum Beijing China

With the rapid development of Artificial Intelligence (AI) technology, its application in areas such as Social Internet of Things (SIoT) and e-commerce has become increasingly widespread. However, there are many uncertainties and potential risks in the widespread deployment of AIoT, and miscreants take advantage of these vulnerabilities to commit fraud on SIoT, which raises serious public concerns about security and privacy. Our work is dedicated to distinguishing fraudsters from consumers through fraud. Given the superior ability of Graph Neural Networks (GNN) in modeling network environments, it has become an important tool in the field of fraud detection. In this work, we propose a fraud detection method based on potential trust between users in SIoT, which combines the graph structure of SIoT with pairwise trust relationships between users. Specifically, we construct an end-to-end framework that utilizes Graph Convolutional Networks (GCNs) to learn and mine potential trust patterns in different social relationships. Further, a trust-aware neighborhood difference aggregation method is introduced by us to significantly distinguish normal consumers from fraudsters. To cope with the class imbalance problem, we employ an imbalance-aware loss function for optimization. Extensive experiments on two datasets confirm that our model is very effective compared to existing techniques. IEEE

关键词： Convolution

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Mitigating Polarization in Recommender Systems via Network-aware Feedback Optimization

arXiv

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arXiv 2024年

作者： Chandrasekaran, Sanjay De Pasquale, Giulia Belgioioso, Giuseppe Dörfler, Florian The Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Physikstrasse 3 Zürich8092 Switzerland

We consider a recommender system that takes into account the interaction between recommendations and the evolution of user interests. Users opinions are influenced by both social interactions and recommended content. We leverage online feedback optimization to design a recommender system that trades-off between maximizing engagement and minimizing polarization. The recommender system is agnostic about users’ opinion, clicking behavior, and social interactions, and solely relies on clicks. We establish optimality and closed-loop stability of the resulting feedback interconnection between the social platform and the recommender system. We numerically validate our algorithm when the user population follows an extended Friedkin–Johnsen model. We observe that network-aware recommendations significantly reduce polarization without compromising user engagement. © 2024, CC BY.

关键词： Convex optimization

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Data Preprocessing with IRM for skill extraction using NMF 26

Data Preprocessing with IRM for skill extraction using NMF

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26th International Conference on Knowledge-Based and Intelligent information and engineering Systems, KES 2022

作者： Oeda, Shinichi Iizumi, Shunsuke Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan

To provide examinees with appropriate question items, identifying and evaluating users' latent and acquired skills is important in e-learning systems. These latent skills are defined in terms of a Q-matrix. Constructing Q-matrices manually is relatively labor-intensive, because doing so requires expert insight as well as verbal examination to identify which skills students have mastered. Methods to extract a Q-matrix from examination results automatically using nonnegative matrix factorization (NMF) have been explored, and improved methods such as online NMF have been proposed, which extracts an immutable Q-matrix from time-series data. In this study, we propose a data preprocessing method to improve the accuracy of NMF using a co-clustering method called an infinite relational model (IRM). We also describe experiments conducted using synthetic data, in which learners'skills and the bias in the number of their responses were evaluated, and show that the results demonstrate the efficacy of the proposed approach. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent information & engineering Systems (KES 2022)

关键词： Non-negative matrix factorization

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