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Preliminary Study on Unexploded Ordnance Classification in Underwater Environment Based on the Raw Magnetometry data 24th

Preliminary Study on Unexploded Ordnance Classification in...

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24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Blachnik, Marcin Ściegienka, Piotr DĄbrowski, Daniel Department of Industrial Informatics Silesian University of Technology Gliwice44-100 Poland SR Robotics Sp. z o.o. Lwowska 38 Katowice40-389 Poland

ISBN: (纸本)9783031482311

Unexploded ordnance (UXO) dumped in water reservoirs pose a serious environmental and human safety hazard. Various ways of economically solving this problem are being sought. One of them is the use of machine learning methods for the automatic classification of dangerous objects based on the recorded signals. the paper presents the preliminary results on the use of machine learning methods applied to raw magnetometry data generated in a virtual environment based on the concept of a digital twin. this introduces a different approach to a standard approach, which is based on the inverse problem, where the signals are mapped to the magnetic dipole model. Conducted research points out that the highest performance can be obtained with neural networks, and a direct classification based on the raw signals allows to achieve accuracy of up to 93% when no remanent magnetization is present. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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Active intelligent Reflecting Surface Assisted Secure Computation of Wireless Powered MEC 11

Active Intelligent Reflecting Surface Assisted Secure Comput...

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11th IEEE international conference on Big data Security on Cloud, BigdataSecurity 2025

作者： Hu, Wenjing Chen, Wen Wang, Peng Donghua University College of Information Science and Technology Shanghai China

ISBN: (纸本)9798331595104

intelligent reflecting surfaces (IRS) have been extensively employed to enhance physical layer security. However, passive IRS face limitations in enhancing secrecy performance due to the multiplicative attenuation effect. To address this limitation, we propose an innovative design leveraging active IRS to enhance the secure computing performance of the mobile edge computing (MEC) system in the presence of eavesdropper. Furthermore, wireless power transfer (WPT) technology is integrated to furnish a continuous energy supply to edge devices, thereby facilitating the execution of computational tasks. In the proposed framework, energy signals are initially transmitted by the base station (BS) to charge multiple users. the harvested energy is subsequently used by the users for local computation and partial task offloading in the presence of eavesdropper. Simultaneously, active IRS is deployed to enhance energy harvesting efficiency and secure task offloading. To maximize the secure computation rate, we jointly optimize the time allocation for WPT and task offloading, the energy and receive beamforming of the BS, the reflection coefficients of the active IRS, the user's local computation frequency, and the transmit power. the resulting non-convex optimization problem is addressed through an iterative algorithm based on block coordinate descent (BCD). Simulation results indicate that the proposed active IRS-based scheme achieves substantial improvements in secure computation rate compared to existing benchmark methods. © 2025 IEEE.

关键词： active intelligent reflecting surface Mobile edge computing physical layer security secure computation wireless power transfer

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Feature-Based Transfer learning as a Means for Colorectal Cancer Diagnosis 1

引用

11th international conference on Robot Intelligence Technology and Applications, RiTA 2023

作者： Chibeze, Precious Xian, Qixin Teh, Szehong Hu, Bintao Li, Guojie Isa, Wan Hasbullah Mohd Majeed, Anwar P. P. Abdul School of Robotics Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China School of IoT Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China Department of Information Engineering Hebei University of Environmental Engineering Hebei300401 China Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia

ISBN: (数字)9783031706875

ISBN: (纸本)9783031706868

Colorectal cancer remains a significant global health concern, necessitating accurate and efficient diagnostic tools for early detection. Transfer learning, specifically feature-based transfer learning, has shown promise in enhancing the performance of medical image analysis tasks. In this study, we investigate the effectiveness of feature-based transfer learning, leveraging the InceptionV3 architecture, for the early detection of colorectal cancer. A dataset of 800 histological images containing eight classes of different tissue types was utilized in a ratio of 70:15:15 for training, validation and testing. the pre-trained InceptionV3 convolutional neural network (CNN) model was used to extract discriminative features from the images. three classifiers were used, namely k-Nearest Neighbors (kNN), Support Vector Machine (SVM) and Logistic Regression (LR), to classify images based on the extracted features. the findings from the present study suggest that the InceptionV3 + LR pipeline was able to discern the classes well. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Enhancing Cyber Security in DC Microgrids with intelligent Control Against False data Injection Attacks 11

Enhancing Cyber Security in DC Microgrids with Intelligent C...

引用

11th IEEE international conference on Power Electronics, Drives and Energy Systems, PEDES 2024

作者： MacHina, Venkata Siva Prasad Koduru, Sriranga Suprabhath Madichetty, Sreedhar Mishra, Sukumar Ecole Centrale School of Engineering Mahindra university Electrical and Computer Engineering Hyderabad India IIT(ISM) Dhanbad Department of Electrical Engineering Dhanbad India

Cyber security plays a major role in operation and control of the cyber physical systems (CPS). Various methods have been proposed in literature for securing the CPS from cyber-attacks. the adversary who has better knowledge of the system formulates an attack that manipulates the defence mechanism to achieve the objectives of adversary. these type of attacks are called as stealthy local overt attacks (SLCAs). they are far more dangerous as they are hard to detect. Any further delay in the detection and mitigation of these SLCAs lead to catastrophic failure of the system. In this article, an intelligent control mechanism is developed to detect and mitigate the stealthy local covert attack that is caused by false data injection (FDI) attack. the proposed algorithm is tested on a DC-DC boost converter which is an integral part of a DC microgrid. the proposed algorithm consists of two feed-forward-back propagation networks where one controller uses voltage information and the other controller uses the current information are modelled to generate the respective duties for the DC-DC buck converter. A decision block is placed in the path of the plant and controller that ensures correct duty is given to the converter that is free from the attack. the proposed control mechanism is initially tested in the MATLAB platform and later implemented on a real-time hardware setup. © 2024 IEEE.

关键词： Cyber-attacks Deep learning False data injection attacks Stealthy local covert attacks

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Ensuring data Security and Routing Efficiency in Warehouse Sensor Networks Using Wireless Blockchain Technology 11th

Ensuring Data Security and Routing Efficiency in Warehouse S...

引用

11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Anitha Rajakumari, P. Karthick, S. Mahapatra, Rajendra Prasad Parwekar, Pritee Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad India

ISBN: (纸本)9789819717231

the integration of wireless sensor networks (WSNs) and blockchain technology has gained significant attention in recent years due to its potential in addressing security and privacy concerns in various applications. In this paper, we propose blockchain technology with wireless-based secure warehouse system (BCWS2) that leverages wireless blockchain technology along with an efficient routing policy to ensure data integrity, authenticity, and confidentiality in a warehouse environment. the proposed system consists of a network of battery-powered wireless sensors deployed within the warehouse premises to monitor various environmental parameters such as temperature, humidity, and light levels. these sensors collect data and transmit it securely to a blockchain network using cryptographic techniques to prevent unauthorized access and tampering. the blockchain network, powered by smart contracts, acts as a decentralized ledger, ensuring the immutability and transparency of the collected sensor data. Each sensor node contributes to the blockchain by forming a consensus on the validity of the data through a distributed consensus protocol. this enables secure and tamper-proof storage of the collected data, eliminating the need for a central authority or trusted intermediary. In order to enhance routing efficiency within the warehouse sensor network, we present a highly efficient routing policy that leverages machine learning algorithms. Our proposed routing policy aims to optimize the path selection process, ensuring effective and streamlined communication within the network. the policy dynamically adjusts the routing paths based on the current network conditions, such as node congestion, battery levels, and signal strength. the proposed routing approach significantly enhances network performance, minimizes energy consumption, and guarantees prompt delivery of sensor data to the blockchain network. this system effectively tackles various challenges encountered in conventiona

关键词： Blockchain

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HABA: A High-Speed Animal Behavior Acquisition and Analysis System 13

HABA: A High-Speed Animal Behavior Acquisition and Analysis ...

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13th IEEE international conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2023

作者： Zhou, Lijun Xuan, Wenqiang Hao, Shuang Meng, Lu College of Information Science and Engineering Northeastern University Shenyang China College of Life and Health Sciences Northeastern University Shenyang China

ISBN: (纸本)9798350315196

Dopamine (DA) is an important neurotransmitter that plays several critical roles in central nervous system, as the initiation, speed and fluidity of voluntary movement. Motor dysfunction induced by several neurological disorders are associated with dopamine depletion. Using head-fixed behavior task already achieved the monitoring of dynamic changes of DA in motor skilling learning process, however, it is difficult to establish the correlation between DA and movement initiation in freely moving status, due to the limitations of video and brain signals acquisition of the current systems. Here, we propose an intelligent High-speed Animal Behavior Acquisition and Analysis(HABA) system that integrates the mouse behavior collection and artificial intelligence analysis in a freely moving behavior task. HABA can not only focus on record the mouse movement at a high-frame rate, but also capture real-time DA changes via DA probe. And then, the designed software automatically analyzes the correlation between behavioral parameters and DA via defined stimulation and velocity. Experiments have demonstrated that the system has the capability to capture data related to mice in a highly detailed manner and can further analyze it in a completely automated fashion, thus promoting the advancement of relevant neurobiological theories in a significant manner. © 2023 IEEE.

关键词： Neurophysiology

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Vulnerability Analysis of Satellite Products 11th

Vulnerability Analysis of Satellite Products

引用

11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Liu, Zhibin Liu, Zhe Guo, Liang Kong, Xiaoyao Fan, Qifan Chu, Chenlei Beijing Institute of Spacecraft Environment Engineering Beijing China Beijing Engineering Research Center of the Intelligent Assembly Technology and Equipment for Aero-Space Product Beijing China

ISBN: (纸本)9789819721191

through the analysis of satellite vulnerability characteristics and the statistics of damage accidents, this paper obtains four categories of risk factors, and further expounds the four aspects of "human error", "environmental factors", "satellite products’ own energy source", "assembly test equipment, real-time, tool characteristics", reflecting the characteristics of satellite products. Considering both universality and pertinence (according to the characteristics of satellite assembly activities), it can provide necessary basis for subsequent safety analysis and evaluation. Finally, the analytic hierarchy process and fuzzy evaluation method are used to analyze the data examples and determine the judgment criteria. the corresponding matrix is obtained through the primary and secondary parameters. According to the maximum membership principle, the risk level corresponding to the largest of the five values corresponding to the membership of the top-level parameters is the result of the comprehensive risk assessment. the results show that the method is suitable for the vulnerability analysis of satellite products, and can be used for risk assessment. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Risk assessment

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ML-Driven Audit Risk Assessment with Differential Privacy 11

ML-Driven Audit Risk Assessment with Differential Privacy

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11th international conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Zhang, Zikun Mohi-Ud-Din, Ghulam Xiong, Yang Zhang, Xu Lin, Xia Ling Ai, Chen Min, Hu Nanchang Hangkong University School of Information Engineering Nanchang China Nanchang University School of Software Nanchang China Jiangxi Normal University College of Computer and Information Engineering Nanchang China Jiangxi University of Traditional Chinese Medicine Network and Information Technology Center Nanchang China

ISBN: (纸本)9798331518127

In contemporary auditing practices, the ability to effectively identify and assess potential fraudulent companies is crucial for maintaining market fairness and transparency. Previous research has leveraged various machine learning techniques to detect fraudulent activities in financial data. However, these models often lack robustness and fail to protect the privacy of the financial information used for training. Here we introduces a novel approach by incorporating differential privacy into the training process, ensuring data protection without compromising model performance. To enhance model performance, we employed interquartile range filtering for outlier removal and optimized feature selection using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Principal Component Analysis (PCA). For model training, we employed Logistic Regression, Random Forest, and Decision Trees, with hyperparameters meticulously fine-tuned through grid search and Bayesian optimization techniques to achieve optimal performance. To address privacy concerns, we implemented differential privacy using the Laplace mechanism, effectively safeguarding sensitive financial data. Our empirical results demonstrate that the enhanced models maintain high accuracy and robustness, providing a reliable tool for fraud detection while ensuring financial data privacy. © 2024 IEEE.

关键词： Differential privacy

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High-Performance Implementation Architecture for Additive Homomorphic Encryption Algorithms on FPGA 11

High-Performance Implementation Architecture for Additive Ho...

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11th IEEE international conference on Big data Security on Cloud, BigdataSecurity 2025

作者： Zhang, Yuxuan Guo, Hua Chen Chen, Junxin Liu, Wenmao Beihang University School of Cyber Science and Technology BeiJing China Ministry of Education Key Laboratory of Data and Intelligent System Security Tianjin China Nsfocus Technologies Group Co. Ltd. BeiJing China

ISBN: (纸本)9798331595104

Additive homomorphic encryption is one of the key technologies for ensure data privacy in federated learning. To accelerate these algorithms, several studies have deployed homomorphic encryption algorithms on parallel platforms, such as FPGAs. Although many works have demonstrated good acceleration effects, an efficient and generalizable implementation architecture remains lacking. In this paper, we propose an efficient FPGA implementation architecture compatible with various partially homomorphic encryption algorithms. First, an efficient modular multiplier which supports dynamic modulus length is designed. We introduce the redundant number system and design an iteration cycle control module to reduce computing cost and to fit the modulus with different length, respectively. Secondly, a high-performance FPGA implementation architecture compatible with multiple additive homomorphic encryption algorithms is proposed. Based on the processes of algorithms, we propose a customized multifunctional multiplier and a top-level control module to realize a full reuse without any performance loss. According to implementation results on Kintex UltraScale XCKU060, the encrypt speed of Paillier and OU achieves 473.79 OP/s and 88.96 OP/s under 128-bit security, respectively. Compared with existing similar works, our architecture achieves 8.4 × speedup, demonstrating its performance and functional advantages. © 2025 IEEE.

关键词： FPGA hardware implementation Homomorphic encryption algorithms Montgomery modular multiplication Okamoto-Uchiyama algorithm Paillier algorithm

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Study on Impedance Coefficient Identification of Heating Pipe Network based on Reinforcement learning Improved Differential Evolution 11

Study on Impedance Coefficient Identification of Heating Pip...

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11th Joint international Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Zhao, Ming Guo, Zhengjun Li, Hui Liu, Yang Li, He School of Computer and Information Engineering Harbin University of Commence Harbin China Heilongjiang ZN Control Engineering Co. Ltd. Harbin China

ISBN: (纸本)9798350333664

With the improvement of process level and the development of intelligent technology, it becomes possible to obtain the operating parameters of the heating pipe network in real time, and the heating effect can be optimized by real-time analysis and adjustment of the operating parameters. However, this adjustment may affect the impedance coefficient of the heat supply pipe network. In this paper, an improved differential evolution algorithm based on reinforcement learning is proposed based on the hydraulic calculation model of the heating pipe network and applied to optimize the impedance coefficient. In addition, this paper also adopts a variety of other optimization algorithms, such as genetic algorithm, generalized inverse matrix method and differential evolution algorithm, respectively, to carry out the optimization identification of the model and conduct a comparative verification analysis. the results show that the optimized identification model proposed in this paper has high identification accuracy, can meet the process requirements of pipe network impedance parameter identification, and is suitable for the identification of complex pipe network impedance parameters. © 2023 IEEE.

关键词： differential evolution hydraulic modeling of heating network impedance parameter identification reinforcement learning

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