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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Nakagawa, Masato Hasegawa, Keisuke The University of Tokyo Graduate School of Information Science and Technology Department of Information Physics and Computing Tokyo Japan Saitama University Graduate School of Science and Engineering Department of Mechanical Science Saitama Japan

ISBN: (纸本)9798350346459

In order to expand the applications of electronically controllable intense mid-air ultrasonic fields generated with airborne ultrasonic phased arrays (AUPA), a precise spatial calibration method between multiple AUPAs is in great demand. However, the conventional methods, such as manual fine-tuning and acoustic beam scanning, take a long time to complete. Here, we propose a quicker calibration method using thermal imaging. By placing an acoustically translucent screen in the AUPAs' sound field containing narrow and straight sound beams, acoustic energy in the field is converted into a two-dimensional heat map. By shooting several images with a thermal camera changing the beam position, information about the relative spatial information between the screen and the AUPAs is promptly visualized. We developed the algorithm to estimate the postures of AUPAs from the obtained image. We conducted a preliminary experiment about thermal imaging and a numerical simulation of the AUPA posture estimation algorithm and verified the effectiveness of our proposed method. © 2023 IEEE.

关键词： Calibration

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Abnormal Clustering and Cross Slicing Transformer for Insect Fine-Grained Image Classification 2

Abnormal Clustering and Cross Slicing Transformer for Insect...

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2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Mei, Aokun Huo, Hua Big Data and Computing Intelligence Engineering Technology Research Center School of Information Engineering Henan University of Science and Technology Big Data Analysis Laboratory of Henan Medical Luoyang471023 China

ISBN: (纸本)9798350390254

Insect fine-grained image classification is an application scenario in fine-grained image classification. It not only has the characteristics of small inter-class differences and large intra-class differences, but also has the difficulty that some categories have multiple life-stage forms, which makes the general fine-grained image classification model difficult to play a role in insect scenes. To this end, based on the Vision Transformer, we design a fine-grained classification network for insect images based on abnormal clustering and cross slicing, called ACCS-Trans. In the first stage of the model, we use segmentation and clustering operations to distinguish the special morphology of insects in those few-shot life stages. The model can avoid the interference of few-sample abnormal morphology on the class feature extraction of the current class during training. The second stage is the cross slicing module, which uses the anchor box of the image sample segmentation region in the first stage to cut the original sample image to form the main target image, which is used as the information supplement area of the original image. Finally, the image is divided into doubling patch groups by two vertical patch operations. In the third stage, we concatenate the multiplication patch group and input it into the Vision Transformer network for class feature extraction. We fully experiment with ACCS-Trans on two insect image datasets. Compared with the current mainstream fine-grained image classification models, ACCS- Trans achieves state-of-the-art effects on both datasets, and we do ablation experiments for each module. The effect of each module on our ACCS- Trans is analyzed. The excellent performance of our ACCS- Trans in insect scenes is verified in these experiments, these provide new ideas for the task of fine-grained classification of insect images. © 2024 IEEE.

关键词： Image segmentation

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ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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An Efficient Detection of Floating Marine Debris using Deep Learning Techniques

An Efficient Detection of Floating Marine Debris using Deep ...

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2024 IEEE Bangalore Humanitarian technology Conference, B-HTC 2024

作者： Pandey, Prachi Guru Prasad, M.S. Mayank, Kumar Gowda, Mahima M Prashanth Kumar, S.P. Karthik, D.U. Dept. of Computer Science and Engg. Dehradun India REVA University School of Computing and Information Technology Bengaluru India Acharya Institute of Technology Dept. of Computer Science and Engg. Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Information Science and Engg. Bengaluru India

ISBN: (纸本)9798350344509

Growing concerns about the environmental consequences of floating debris in aquatic ecosystems have underscored the need for the development of efficient and automated methods for debris classification and monitoring. This research paper presents a comprehensive investigation into the application of neural networks, specifically Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and CNN-LSTM architectures, for this purpose. Our study entails the creation of a diverse dataset encompassing various debris types and environmental conditions, ensuring its real-world relevance and generalizability. Thorough exploration is conducted of the three neural network models to evaluate their effectiveness in classifying floating debris from images captured in aquatic environments. CNNs are chosen for their established image recognition capabilities, while LSTMs are incorporated to capture sequential information and potential temporal dependencies within debris trajectories. The outcomes of our study unveil the strengths and limitations of CNN, CNN-LSTM, and LSTM architectures in the context of floating debris classification. Proposed work provides insights into the suitability of each model in various real-world scenarios, including riverine systems, oceans, and coastal regions. Furthermore, we discuss the implications of our findings for environmental monitoring, debris removal strategies, and policy development. Throughout our research, each model's performance is assessed in terms of accuracy, precision, recall, and F1-score, considering the unique challenges posed by the inherently noisy and dynamic nature of aquatic environments. This research contributes to the evolving body of knowledge regarding the application of neural networks in environmental monitoring, with a particular focus on the critical domain of floating debris classification. Our findings are expected to guide the development of automated systems that can aid in mitigating the environmental i

关键词： Debris

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Research on Resource Scheduling of computing Power Network Based on Deep Q-Network Algorithm 3

Research on Resource Scheduling of Computing Power Network B...

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3rd International Conference on Cloud computing, Big Data Application and Software Engineering, CBASE 2024

作者： Wang, Zhijun Zhou, Jingbo High Performance Computing Center Changchun Normal University Changchun China School of Electronic Information Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331506582

The resource scheduling of computing power networks is a complex project, and the scheduling strategies used will directly affect the efficiency of subsequent processing of computing tasks. This article focuses on the resource scheduling problem of computing power networks, using the DQN algorithm in deep reinforcement learning to optimize resource utilization, resource mobilization rate, and reduce task loss rate, providing a solution. © 2024 IEEE.

关键词： Scheduling algorithms

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Converting Voice Signal to Visual Indian Sign Language 9

Converting Voice Signal to Visual Indian Sign Language

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9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Sudhakaran, Pradeep Preetha, M. Kataria, Anshul Sindhu, V. Rajveer Mathur, S. School of Computing Srm Institute of Science And Technology Department of Computing Technologies Chengalpattu District Tamil Nadu Kattankulathur603203 India Career Development Centre Srm Institute of Science And Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Sri Krishna College of Engineering And Technology Department of Information Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350365092

Deaf people rely heavily on sign language as their primary means of communication since it is a sort of symbolic representation that they use. In contrast to sound patterns that are transferred by acoustic methods, sign language involves the employment of body posture and physical communication in order to assist the efficient presentation of a person's own phrases. When talking with those who have trouble speaking vocally, as well as with others who do not have hearing impairments, it is possible to use this method. Using developing technologies such as internet applications, machine learning, and natural language processing, the purpose of this initiative is to close the gap that exists between those who are deaf or hard of hearing or who have hearing impairments and the general community. This will be accomplished by bridging the gap between the two groups. To provide aid to those who are deaf or hard of hearing, the major aim of this project is to design a software or interface that transforms audio and speech into the proper sign language. This will be accomplished in order to provide support to such individuals. Hand forms, posture, and body actions are all in sync with one another whenever they occur at the same time. The method is made up of two distinct components: first, it converts speech into text by utilizing the voice-to-text application programming interface (API);second, it represents the text by utilizing Parse Trees and employs Natural Language Processing semantics (NLTK in particular) for the purpose of lexical analysis of Sign Language Grammar. Both of these components are a part of the method. In this work, the regulations of Indian Sign Language (ISL) are adhered to, and the work develops upon those norms while also adhering to the grammatical requirements of ISL. © 2024 IEEE.

关键词： Semantics

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Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process 30

Prompting Explicit and Implicit Knowledge for Multi-hop Ques...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Huang, Guangming Long, Yunfei Luo, Cunjin Shen, Jiaxing Sun, Xia School of Computer Science and Electronic Engineering University of Essex United Kingdom Department of Computing and Decision Sciences Lingnan University Hong Kong School of Information Science and Technology Northwest University China

ISBN: (纸本)9782493814104

Pre-trained language models (PLMs) leverage chains-of-thought (CoT) to simulate human reasoning and inference processes, achieving proficient performance in multi-hop QA. However, a gap persists between PLMs' reasoning abilities and those of humans when tackling complex problems. Psychological studies suggest a vital connection between explicit information in passages and human prior knowledge during reading. Nevertheless, current research has given insufficient attention to linking input passages and PLMs' pre-training-based knowledge from the perspective of human cognition studies. In this study, we introduce a Prompting Explicit and Implicit knowledge (PEI) framework, which uses prompts to connect explicit and implicit knowledge, aligning with human reading process for multi-hop QA. We consider the input passages as explicit knowledge, employing them to elicit implicit knowledge through unified prompt reasoning. Furthermore, our model incorporates type-specific reasoning via prompts, a form of implicit knowledge. Experimental results show that PEI performs comparably to the state-of-the-art on HotpotQA. Ablation studies confirm the efficacy of our model in bridging and integrating explicit and implicit knowledge. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Implicit Knowledge Multi-hop QA Prompt

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Smart Energy Management System Using Machine Learning

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Computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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