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convergence and Divergence: A New Paradigm for Pedestrian Detection 1

引用

20th international conference on Intelligent computing (ICIC)

作者： Zhu, Yueyan Huang, Hai Yue, Shan Zhang, Shu Chen, Aoran Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Minist Culture & Tourism Key Lab Interact Technol & Experience Syst Beijing 100876 Peoples R China

ISBN: (数字)9789819756001

ISBN: (纸本)9789819755998;9789819756001

Complex backgrounds, scale and occlusion variance have long limited the accuracy of pedestrian detection. In this paper, we propose a pedestrian detector named convergence and Divergence (CADNet). In "convergence" network, we propose a cross-scale semantic alignment block (CSAB). CSAB effectively mitigates the background interference and resolves scale variance through multi-scale global contexts aggregation, without extensive computational overhead. In "Divergence" network, we propose a receptive field differentiation block (RFDB) to tackle the challenges of scale and occlusion variance. RFDB generates discriminative features with varying receptive fields, effectively capturing pedestrians across different scales and occlusion conditions. Due to the effectiveness of the proposed components, CADNet achieves an excellent performance of 8.47% and 2.16% MR-2 on a Reasonable subset of CityPersons and Caltech, respectively. Extensive experiments demonstrate the robustness and efficiency of CADNet, ensuring its superior performance in various scenarios.

关键词： Object Detection Pedestrian Detection Cross-scale Semantic Alignment Receptive Field Differentiation

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Analysis of Recent IIoT Security technology Trends in a Smart Factory Environment 5

Analysis of Recent IIoT Security Technology Trends in a Smar...

引用

5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Kim, Jihye Park, Jaehyoung Lee, Jong-Hyouk Sejong Univ Dept Comp & Informat Secur Seoul South Korea Sejong Univ Protocol Engn Lab Seoul South Korea Sejong Univ Dept Comp & Informat Secur Seoul South Korea Sejong Univ Convergence Engn Intelligent Drone Seoul South Korea

ISBN: (纸本)9781665456456

Recently, the manufacturing industry is changing into a smart manufacturing era with the development of 5G, artificial intelligence, and cloud computing technologies. As a result, Operational technology (OT), which controls and operates factories, has been digitized and used together with Information technology (IT). Security is indispensable in the smart manufacturing industry as a problem with equipment, facilities, and operations in charge of manufacturing can cause factory shutdown or damage. In particular, security is required in smart factories because they implement automation in the manufacturing industry by monitoring the surrounding environment and collecting meaningful information through Industrial IoT (IIoT). Therefore, in this paper, IIoT security proposed in 2022 and recent technology trends are analyzed and explained in order to understand the current status of IIoT security technology in a smart factory environment.

关键词： Smart Factory Industrial IoT Security

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Controlled Face Generation Using Conditional GAN

Controlled Face Generation Using Conditional GAN

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Srujana, S.N. Veena, N. Karthik, S.A. Vijaya Shetty, S. Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Manipal Institute of Technology Manipal Academy of Higher Education Department of Information Technology Bengaluru India Nitte Meenakshi Institute of Technology Department of Cse Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

The Conditional GAN generates the human faces with certain conditions applied to its features. In Conditional GANs, the generator and discriminator networks are extended to take in additional conditional information during the training process. The generator network synthesizes faces with specific attributes. The discriminator is also conditioned on the same external information which helps it learn not only to differentiate between real and fake samples but also to recognize and potentially control specific attributes in the generated data. By altering the conditionally labelled data supplied to the discriminator and generator, the characteristics of the created faces can be modified. The conditional attributes include hair color, gender, face shape, expression, and age. Applying the conditions while keeping the random noise constant leads to generating faces that exhibit those specific attributes. The convergence occurs while the discriminator can no longer reliably distinguish between real and fake images. This point indicates that the generator has successfully learned to produce realistic-looking images that resemble the real data distribution. The trained generator network can be used to create new, artificial faces when the GAN has converged. © 2025 IEEE.

关键词： Discriminators

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UAVS Task Assignment Based on Hybrid Swarm Intelligence Algorithm 8

UAVS Task Assignment Based on Hybrid Swarm Intelligence Algo...

引用

8th international conference on Image, Vision and computing, ICIVC 2023

作者： Yuan, De-Ping The 20th Research Institute of China Electronics Technology Group Corporation Xi'an China

ISBN: (纸本)9798350335231

Aiming at the contradiction between the globalism and the convergence speed of the UAVS task assignment method, a hybrid swarm intelligence algorithm is proposed, which combines the wolf colony algorithm and simulated annealing algorithm. Firstly, based on the analysis of UAV cluster operation application and task allocation modeling, the basic wolf colony algorithm is improved, the fixed step size of walking, summoning and rounding up behavior in the wolf colony algorithm is changed to the adaptive step size to improve the convergence speed and search accuracy;Secondly, the initial results of the improved wolf colony algorithm are taken as the input of the simulated annealing algorithm, and a fast search is carried out to further improve the convergence speed of the algorithm;Finally, in the same application scenario, through simulation and comparison with particle swarm optimization algorithm and basic wolf swarm optimization algorithm, it is verified that the proposed algorithm has great improvement in distribution accuracy and convergence speed. © 2023 IEEE.

关键词： Simulated annealing

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Modified Grey Wolf Optimization using Refraction Learning and Differential Evolution Mutation 5

Modified Grey Wolf Optimization using Refraction Learning an...

引用

5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Bajaj, Pranshu Dhingra, Siddharth Aggarwal, Karan Kaur, Preeti Netaji Subhas University of Technology New Delhi India

ISBN: (纸本)9798331522100

An enhanced variant of the Grey Wolf Optimisation (GWO) technique is presented in this paper, addressing a number of issues, including reduced population diversity, slow convergence and vulnerability to local optima. The proposed variant incorporates innovative techniques, including Opposition Learning, Refraction Learning (RL), Differential Evolution Mutation and a non-linear time-varying hyperparameter tuning mechanism. Combining these strategies greatly enhances the standard GWO's performance, leading to a more streamlined and effective search procedure. This enhancement leads to better performance in multi-modal landscapes, facilitates escaping local optima and allows the search space to be explored more extensively. Extensive testing across 23 benchmark functions demonstrates that the new variant outperforms the original GWO as well as other contemporary optimization methods. The results indicate substantial improvements in convergence speed, global search capability and solution accuracy, demonstrating how effective this method can be as an optimisation tool for solving challenging real-world issues. © 2025 IEEE.

关键词： Swarm intelligence

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Latency-Optimized Task Scheduling for Dispersed computing 2

Latency-Optimized Task Scheduling for Dispersed Computing

引用

2nd international conference on Electronic Information Engineering and Computer Communication, EIECC 2022

作者： Ma, Jun Xia, Geming Yu, Chaodong Chen, Jian College of Computer Science and Technology National University of Defense Technology Hunan Changsha410003 China

ISBN: (纸本)9781510663121

A new task scheduling scheme based on dispersed computing is proposed to address the problem of poor utilization of idle computing resources in the network by the traditional computing paradigm. The scheme designs a dispersed computing task scheduling model to reduce the load pressure on NCPs in a recursive manner by combining the idea of multiple hops. For the characteristics of geographic dispersion and network dynamics in the dispersed computing environment, the time delay is selected as the optimization objective, and the task scheduling decision is made in a distributed manner by combining it with the artificial fish swarm algorithm. At the same time, the scheduling algorithm is designed mainly by setting variable step size and field of view so that the algorithm can guarantee the convergence speed while the solution accuracy is improved. The experiments show that the algorithm meets the high requirements of dispersed computing on time delay and achieves the expected goal of the system. © 2023 SPIE.

关键词： Scheduling algorithms

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Joint Computation Offloading and Task Caching Strategy for MEC-Enabled IIoT 20th

Joint Computation Offloading and Task Caching Strategy for M...

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20th international conference on Intelligent computing (ICIC)

作者： Deng, Yunfeng Sun, Haifeng Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang Sichuan Peoples R China

ISBN: (纸本)9789819756742;9789819756759

Industrial Internet of Things (IIoT), as a key link in the transformation of traditional manufacturing to digitalization, can be paired with Multi-access Edge computing (MEC) technology to satisfy the low-latency environment required by industry. Nonetheless, the system contends with uncertain environmental factors such as dynamic changes in channel state and random task generation. Motivated by these, this paper designs an intelligent offloading and task caching strategy to reduce the overall execution latency of tasks. The interaction process within system is modeled as an Markov Decision Process (MDP), and we introduce a low-latency scheduling strategy leveraging Deep Reinforcement Learning (DRL), termed DDPG-LL. Besides, the proposed strategy is tailored for optimizing the task queue of the MEC server. By considering factors such as priority, waiting time, and completion expectations, queue adjustments are dynamically made at each time slot. Simulation results demonstrate that the proposed strategy achieves rapid and stable convergence, and effectively reduces the completion latency of tasks compared to the baseline strategies.

关键词： Multi-access Edge computing Task Offloading Task Caching Deep Reinforcement Learning

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Research on Multi-source Data Security Fusion Analysis technology Based on Federated Learning 14

Research on Multi-source Data Security Fusion Analysis Techn...

引用

14th IEEE international conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Liu, Donglan Zhao, Fuhui Sun, Lili Zhang, Fangzhe Sun, Mengqian Yao, Honglei Zhang, Hao Wang, Rui Liu, Xin State Grid Shandong Electric Power Research Institute Shandong Province Jinan250003 China

ISBN: (纸本)9798350361889

In this paper, we study the multi-source data security fusion analysis technology based on federated learning. A rapid model convergence technique based on data distribution difference evolution comparison is proposed to explore the data distribution law among participants and accelerate the model convergence speed. In view of the difference of data distribution among participants, the difference between local data distribution and global data distribution is obtained by analyzing the evolution law of data distribution of participants. Then, the weight is assigned according to the similarity between the data distribution of each participant, and it is used to guide the rapid convergence of the global global model, and improve the convergence speed of the machine learning model based on the federated learning framework. A collaborative optimization technology based on cooperative game is proposed, which takes into account the performance of individual modeling and global modeling, and improves the accuracy of the model. The accuracy of the global model and the accuracy of each participant's local personalized model are considered for scenarios that need to meet both the participant's personalized modeling and global modeling requirements. Through the cooperative game model, the accuracy optimization of the global model and the local personalized model of each participant is solved, and the coordination of the accuracy of the global model and the local personalized model is realized. © 2024 IEEE.

关键词： Game theory

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R&D of Single-Chip Microcomputer Automatic Control System Based on AI technology 3

R&D of Single-Chip Microcomputer Automatic Control System Ba...

引用

3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Wang, Renjun Li, Xiaojiang Tianjin University of Technology Zhonghuan Information College Department of Electronic and Information Engineering Tianjin China

ISBN: (纸本)9798350343922

With the rapid development of the economy and industrial production, industrial automation technology continues to improve, and the application of single-chip microcomputer automatic control systems in the industrial field is also becoming more and more widespread. The application of a single-chip microcomputer automatic control system can achieve automated control of industrial production processes and equipment, improve production efficiency and product quality, and reduce costs. However, there are still problems in current industrial production, such as low efficiency in factories and inability to fully complete production tasks according to requirements, which need to be further addressed. Therefore, this article studied the application of AI (artificial intelligent) technology in single-chip microcomputer automatic control systems and evaluated the production efficiency based on AI technology through data analysis. The research results indicate that compared to traditional single-chip microcomputer automatic control systems, AI technology based single-chip microcomputer automatic control systems have higher production efficiency and can better adapt to different environments, thus making adjustments and improving production efficiency. © 2023 IEEE.

关键词： Hydraulic mining

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A Novel Voltage Based In-Memory computing Architecture using STT-MRAM 8

A Novel Voltage Based In-Memory Computing Architecture using...

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8th IEEE international conference for convergence in technology, I2CT 2023

作者： Sai Sumukha, K.V. Prajwal Raju, R. Gowda, Sinchana Sunita, M.S. PES University Bengaluru India

ISBN: (纸本)9798350334012

This paper deals with In-Memory computing (IMC), where computations are performed within the memory. IMC is a memory architecture to overcome the performance gap between processors and memory. In this work, A novel voltage-based sensing mechanism has been used to implement IMC. The architecture has been built using an 8x8 array of Spin Transfer Torque Magneto-resistive RAM (STT-MRAM) cells with voltage-based sense circuits to perform logical operations- NOT, AND, OR, and XOR. Further, an 8-bit Ripple Carry Adder has been built in the architecture to demonstrate the addition operation. The simulations are performed in Cadence Virtuoso 6.1.7-64b, 2018 using 45nm technology transistors. Delay and Energy were determined for each operation and it was observed that NOT operation had least delay and OR operation consumed the least energy. © 2023 IEEE.

关键词： MRAM devices

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