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检索条件"机构=Dept. of Computer and Electrical Engineering and Computer Science"

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

引用

22nd International Learning and Technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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Frequency Response Deterioration due to Surface Ridge Abrasion of Tactile Sensors 13

Frequency Response Deterioration due to Surface Ridge Abrasi...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Yanwari, Muhammad Irwan Okamoto, Shogo Polines Dept. Computer Engineering Technology Japan Tokyo Metropolitan University Dept. Computer Science Tokyo Japan

ISBN: (纸本)9798350355079

Epidermal ridges are crucial for tactile sensing in both human fingers and synthetic sensors. These ridges are made of compliant materials and susceptible to abrasion damage after iterative use. This study focuses on examining the impact of sensor ridge damage on the acquired signal, which encompasses a broad range of frequency components. We compared the frequency responses of a tactile sensor scanning sandpaper with both intact and damaged sensors. The degradation of signal levels was evident across the 0-1,000 Hz range, with particularly prominent suppression of the peak frequency component. This study demonstrates the frequency-dependent influence of ridge abrasion. © 2024 IEEE.

关键词： Friction

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An Edge AI and Adaptive Embedded System Design for Agricultural Robotics Applications 25

An Edge AI and Adaptive Embedded System Design for Agricultu...

引用

30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Huang, Chun-Hsian Chen, Zhi-Rui Hsu, Huai-Shu Dept. Electrical Engineering National Changhua University of Education Changhua Taiwan Dept. Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (纸本)9798400706356

This work presents the AgrBot, an agricultural robot designed to intelligently estimate and predict crop pest and disease severity (PDS). The AgrBot incorporates two binarized neural network (BNN) hardware modules for recognizing target crops and estimating their PDS. In a resource-constrained FPGA-based design, these BNN hardware modules can be configured on-demand, showcasing system adaptivity. Furthermore, a multimodal model that integrates crop images, sensor data, and time features is presented for predicting PDS. Employing edge artificial intelligence (AI) through the BNN hardware modules and the multimodal model enables the AgrBot to determine if biological agents are applied to protect crops from pests and diseases, creating a comprehensive agricultural cyber-physical system (CPS). Experimental results demonstrate accuracies of 76.3% for recognizing target crops, 65.3% for estimating PDS, and 67% for predicting PDS. In comparison to existing microprocessor-based design methods, the AgrBot's BNN hardware modules improve frames per second (FPS) by a factor of 790, while the multimodal model reduces processing time by up to 50.9%. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Modeling of Weight Perception Based on Subjectivity by Considering Individual Differences 13

Modeling of Weight Perception Based on Subjectivity by Consi...

引用

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kushida, Daisuke Nakajima, Kei Tottori University Faculty of Engineering Dept. Electrical Engineering and Computer Science Tottori Japan Graduate School of Sustainability Science Tottori University Dept. Engineering Tottori Japan

ISBN: (纸本)9798350373332

The minimum amount of perception that humans can perceive is called the stimulus threshold. This study aims to clarify the stimulus threshold of weight perception for the biceps brachii muscle based on a method of constant stimuli. In general, the stimulus threshold depends on the individual. The authors consider that physical characteristics are related to individual differences, construct a model of the relationship between physical characteristics and stimulus thresholds, and attempt to estimate stimulus thresholds from physical characteristics. The constructed model is evaluated via cross-validation and found to be able to estimate stimulus thresholds from physical characteristics with an 85% accuracy. © 2024 IEEE.

关键词： individual difference modeling stimuli threshold subjectivity weight perception

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A Feedback-based Decision-Making Mechanism for Actor-Critic Deep Reinforcement Learning

A Feedback-based Decision-Making Mechanism for Actor-Critic ...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Yang, Guang Geng, Ziye Li, Jiahe Zhao, Yanxiao Abdelwahed, Sherif Luo, Changqing Virginia Commonwealth University Dept. of Computer Science RichmondVA United States Virginia Commonwealth University Dept. of Electrical and Computer Engineering RichmondVA United States

ISBN: (纸本)9798350362480

Deep reinforcement learning (DRL) has achieved remarkable success in solving sequential decision-making problems across various domains. However, a critical challenge is sample inefficiency, especially in real-world environments with high-dimensional solution spaces due to continuous state and action spaces. Although off-policy actor-critic algorithms have been proposed to mitigate this issue, the gains in sample efficiency remain limited, as decision-making in these algorithms relies solely on the policy function that might not always yield optimal actions. To bridge the gap, we design a novel feedback-based decision-making mechanism (FADA) that incorporates a feedback mechanism into the actor-critic framework to enhance decision-making robustness. Specifically, FADA utilizes feedback from the value function (critic) to calibrate the decisions produced by the policy function (actor). More concretely, FADA comprises four integrated modules: a decision-space expansion module (DEM) to produce a pool of candidate actions, a critic-guided evaluation module (CGEM) that estimates the efficacy of the candidate actions, an adaptive selection module (ASM) that adaptively selects a set of elite actions based on the estimated efficacy and samples the final action, and an iterative refinement module (IRM) that improves the quality of elite actions. We evaluate our approach on multiple tasks in the DeepMind Control Suite, and the experimental results demonstrate a significant improvement in sample efficiency. © 2024 IEEE.

关键词： Deep reinforcement learning

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A New Fault Diagnosis Approach Based on Extreme-Point Symmetric Modal Decomposition and Energy Operator Demodulation 5

A New Fault Diagnosis Approach Based on Extreme-Point Symmet...

引用

5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Fu, Sicheng Liu, Baqiao Chen, Zhongzhe Cao, Shuchen Syracuse University Dept. of Electrical Engineering & Computer Science SyracuseNY United States Uiuc Dept. of Computer Science Urbana United States School of Mechanical and Electrical Engineering Uestc Chengdu China Bayer Crop Science Chesterfield Missouri United States

ISBN: (纸本)9798350386943

In this paper, the Extreme-point Symmetric Modal Decomposition (ESMD) was firstly introduced to the fault diagnosis field of rotating machinery, and a new fault diagnosis approach of rotating machinery based on ESMD and Energy Operator Demodulation (EOD) is proposed. Firstly, the gear fault vibration signal is decomposed into the Finite Intrinsic Mode Function (IMF) components though ESMD. Secondly, these IMF components were demodulated by the EOD to form envelope signals. Lastly, the fault feature information is extracted by analyzing IMF components and envelope signals. The ESMD is employed to decompose a simulation signal and made comparison with the Empirical Mode Decomposition (EMD) and the Local Mean Decomposition (LMD). The result shows that the ESMD can overcome the problems of end effect of EMD. Moreover, it avoids low efficiency of LMD. The method is applied to the fault diagnosis of a gear with broken teeth. The results indicated that this gear fault can be effectively identified, which demonstrated that the proposed method is an effective and practical method for fault diagnosis of rotating machinery. © 2024 IEEE.

关键词： EOD ESMD Fault Diagnosis

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MLBERT: Multi-Level Fully Binarized BERT 1

MLBERT: Multi-Level Fully Binarized BERT

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Nasab, Mahdi Mohammadi Fakhire, Mahboube Salehi, Mostafa E. Modarresi, Mehdi University of Tehran College of Engineering Electrical and Computer Engineering Dept. Tehran Iran

ISBN: (纸本)9798350348637

BERT models, which use deep learning approaches for natural language comprehension, greatly improve language processing tasks in embedded devices. Their use in such devices enables sophisticated capabilities such as real-time language translation and making interactions more natural and efficient. BERT models, with their high computational complexity and large parameter count, impose significant issues in terms of processing power and memory requirements, particularly in resource-limited devices. These problems frequently require specialized hardware or optimization approaches, such as model compression, to enable efficient deployment without sacrificing performance. Binary Neural Networks, which simplify the weights and activations to binary values, are crucial for significantly reducing computational complexity and memory usage. This makes them ideal for deployment in resource-constrained environments like embedded systems and mobile devices, enabling efficient execution of complex machine learning tasks. Multi-level binarization (MLB) in neural network models takes advantage of bit-wise operations and enhances accuracy while reducing computational and memory requirements. We propose an MLB-based framework to enhance binarized BERT models which supports MLB for embedding layers as well. The results indicate that the DynaBERT model, when compressed to 1-bit precision for both Transformer layers and the entire embedding layers, along with 3-bit MLB activations, has no accuracy loss for SST-2, loses only about 0.5% of accuracy for MRPC, and 3% accuracy drop for RTE task, which is compatible with state-of-the-art. © 2024 IEEE.

关键词： Deep neural networks

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Clustering Data Log CITA Blockchain Performance Monitoring with Mean Shift Method

Clustering Data Log CITA Blockchain Performance Monitoring w...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Ubaya, Huda Stiawan, Deris Suprapto, Bhakti Yudho Perdana, Muhammad Iqbal Universitas Sriwijaya Faculty of Computer Science Dept. of Computer Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Engineering Dept. of Electrical Engineering Palembang Indonesia

ISBN: (纸本)9798350307306

CITA is a high-performance enterprise blockchain system with six microservices (RPC, Auth, Consensus, Chain, Executor, and Network) that exchange information through Message Bus. There is a dataset in the form of performance logs from CITA in the format of CPU, Memory, Network, PReads, PWrite, and VMem taken in real-time using the Performance Monitoring Framework for Blockchain. This framework has the advantage of lower overhead, more details, and better scalability than other performance monitoring approaches, but there is no clustering feature in the monitoring results. Thus, it is necessary to include a clustering feature so that users know the performance directly without the need to analyze it more deeply. This research uses the Mean Shift algorithm, which does not require the number of clusters for the initial process but only determines the maximum distance of centroid displacement through calculation. The number of clusters estimated is 2 clusters, where CITA produces 7 cluster samples with cluster evaluation results with the highest average silhouette score from CITA, which is 0.555 in the Network-Received Network-Sent feature pair. So, it is known that the Mean Shift clustering algorithm produces goodness of fit results from clustering on the CITA Blockchain performance log, which means that the clusters are well separated and can be clearly distinguished. © 2023 IEEE.

关键词： Blockchain

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Performance Analysis of Deep Learning Models in Driver Behavior Recognition

Performance Analysis of Deep Learning Models in Driver Behav...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kapoor, Rameshwari Kuliyal, Rashmi Bhanot, Nilesh Bagla, Piyush Rawat, Ruchira Graphic Era Hill University Dept. of Computer Science and Engineering Uttarakhand Dehradun India University Dept. of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798331542344

The World Health Organization report states that nearly 1.25 million people lose their lives due to an increasing number of road accidents every year. The majority of accidents are reported due to driver errors, which include distractions like the use of mobile phones and fatigue, which causes drowsiness. This study aims to address these issues by performing a comparative analysis between different deep learning models representing two types of learning mechanisms: Traditional deep learning and transfer learning. This study portrays the use of two datasets to accomplish this task. These two datasets are the Driver Drowsiness Dataset DDD and the State Farm Distracted Drivers Dataset. The purpose of this investigative research is to explore the execution and efficiency differences between three deep learning algorithms, the traditional convolutional neural network, ResNet50, and VGG16, in classifying driver behavior by training these models individually on both datasets. This study also targets developing an alarm system that sounds an alarm based on a threshold by using an ensemble of all the models trained for comparative study and averaging their predictions to classify driver behavior. VGG16 achieved an accuracy of 99.74% on the driver drowsiness dataset, whereas Custom CNN Architecture achieved an accuracy of 98.70% on the driver distraction dataset. © 2024 IEEE.

关键词： Contrastive Learning

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