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8th IEEE Information Technology International Seminar, ITIS 2022

作者： Rahajoe, Ani Dijah Agussalim Setyaningsih, Emy Mandyartha, Eka Prakarsa Akbar, Fawwaz Muttaqin, Faisal Universitas Pembangunan Nasional Veteran Jawa Timur Department of Computer Science Surabaya Indonesia Institut Sains & Teknologi Akprind Department of Computer System Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350398199

Feature selection is one part of preprocessing which aims to reduce data dimensions. This study aims to produce optimal performance of the best feature selection method implemented on low- and high-dimensional data. The feature selection methods used are Sequential Forward Selection (SFwS) and Sequential Backward Selection (SBwS) algorithms. Meanwhile, to test the results of the best feature selection algorithm using the classification algorithm Logistic Regression, Linear Regression, k-Nearest Neighbor (kNN). The low dimensional data test results show that the SFS algorithm with the kNN classifier and the Regression group has the best average score. The SFwS algorithm with Logistic Regression has the best average score in the high dimensional data set. The minimal number of feature selections resulted from the SFwS algorithm rather than the SBwF algorithm. Although it should have the same accuracy or number of selected features, in this study, the results were different except for SFS-Linear Regression. The highest average accuracy score for low dimensional data is the Wine dataset of 0.994 (SFS-kNN), and for high dimensional data is the Parkinson's disease dataset of 1 (SFS-LGR). The least number of feature selections obtained from the SFwS-Logistic Regression algorithm is one feature with an accuracy score of 0.8083. The Sequential Backward Selection algorithm generally has a longer running time than the Sequential Forward Selection. © 2022 IEEE.

关键词： Feature Selection

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Prior Knowledge-Driven Hybrid Prompter Learning for RGB-Event Tracking

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Mianzhao Shi, Fan Cheng, Xu Chen, Shengyong Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Key Laboratory of Computer Vision and System School of Computer Science and Engineering Tianjin300384 China Technical University of Denmark Department of Technology Management and Economics Kongens Lyngby Denmark

Event data can asynchronously capture variations in light intensity, thereby implicitly providing valuable complementary cues for RGB-Event tracking. Existing methods typically employ a direct interaction mechanism to fuse RGB and event data. However, due to differences in imaging mechanisms, the representational disparity between these two data types is not fixed, which can lead to tracking failures in certain challenging scenarios. To address this issue, we propose a novel prior knowledge-driven hybrid prompter learning framework for RGB-Event tracking. Specifically, we develop a frame-event hybrid prompter that leverages prior tracking knowledge from the foundation model as intermediate modal support to mitigate the heterogeneity between RGB and event data. By leveraging its rich prior tracking knowledge, the intermediate modal reduces the gap between the dense RGB and sparse event data interactions, effectively guiding complementary learning between modalities. Meanwhile, to mitigate the internal learning disparities between the lightweight hybrid prompter and the deep transformer model, we introduce a pseudo-prompt learning strategy that lies between full fine-tuning and partial fine-tuning. This strategy adopts a divide-and-conquer approach to assign different learning rates to modules with distinct functions, effectively reducing the dominant influence of RGB information in complex scenarios. Extensive experiments conducted on two public RGB-Event tracking datasets show that the proposed HPL outperforms state-of-the-art tracking methods, achieving exceptional performance. © 1991-2012 IEEE.

关键词： Object detection

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DMDMN: distraction mining and dual mutual network for curvilinear structure segmentation

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Applied Intelligence 2025年第11期55卷 1-14页

作者： Wu, Qingbo Meng, Shuofei Huang, Wei Chen, Shengyong Engineering Research Center of Learning-Based Intelligent System (Ministry of Education) Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

Curvilinear Structure Segmentation (CSS) aims to predict the binary masks of curvilinear objects in photographs, such as blood vessels and road cracks. Despite impressive segmentation results, current CSS methods often struggle with disconnected segmentations and inaccurate edges, essentially caused by the distractions in the feature domain, which are from image domain-specific challenges, and by the imbalance of scales of thicker and thinner branches. To address these problems, we propose Distraction Mining and Dual Mutual Network (DMDMN) based on reverse attention and mutual learning to improve CSS. Specifically, DMDMN first extracts various levels of features from an input image using a plain network. Then, a Reverse Attention Module (RAM) is designed at each level to enhance the extracted features by identifying and removing false positive and negative distractions. Next is a Three-Head Fusion Module (THFM) at a separate level, which serves as an exchanger to mutually integrate the features from the branch segmentation head with those from the heads of skeleton extraction and edge detection. With the segmented results flowing back to RAMs, the previous two steps alternate several times till the final prediction of a high-quality segmentation, which is topologically connective along branches and pixel-wise accurate at edges. In addition, extensive experiments on five public datasets have demonstrated the superiority of the proposed DMDMN over state-of-the-art approaches both qualitatively and quantitatively.

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Perimeter-Search-Based Semi-Join Query Optimization Algorithm in Data-Sharing Environment

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Chinese Journal of Electronics 2023年第4期18卷 593-598页

作者： Xiaoqing Wang Youyuan Fang Yanxing Zheng Jianjian Zong Tianyang Gu Department of Computer Science and Technology Tsinghua University Beijing China Beijing Institute of System Engineering Beijing China

The problem of SQO (Semi-join query optimization) in data-sharing environment is addressed and a Perimeter-search-based semi-join query optimization algorithm (PSSJA) is presented. The algorithm can determine optimal sequence of semi-join operations, minimize the data transmission cost, saves storage space, and reduce the search complexity in a costeffective manner. Experiment shows the efficiency of our algorithm.

关键词： Costs Query processing Soft sensors Distributed databases Aerospace electronics Complexity theory Data communication

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Improved Sparrow Search Algorithm with Support Vector Machine for Wind Turbine Fault Detection 2

Improved Sparrow Search Algorithm with Support Vector Machin...

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2nd International Conference on Data science and Information system, ICDSIS 2024

作者： Nagendar, Y. Alabdeli, Haider Shruthi, B.S. Kamesh, M.R. Sudha, N. Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University Department Of Computers Techniques Engineering College Of Technical Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Civil Engineering Bengaluru India Dayananda Sagar College Of Engineering Department Of Mechanical Engineering Bengaluru India Angel College Of Engineering And Technology Embedded System Technologies Tamil Nadu India

ISBN: (纸本)9798350381665

Wind turbines are widely used across the globe as a primary means of renewable energy by using the power of wind, it converts kinetic energy into electrical energy, contributing to generating electricity. However, turbines are positioned far from control centers, operating in environments that continuously change and have uncontrollable factors and conditions lead to wind turbine failures. The proposed method using an Improved Sparrow Search Algorithm (ISSA) optimizer along with also used a Support Vector Machine (SVM) classifier helps to detect the faults in turbines. This method uses the Supervisory Control and Data Acquisition (SCADA) dataset for wind turbine fault detection, the optimization using ISSA which is improved by Uniformity Diversity Orientation Strategy (UDOS) to optimize model's internal parameters. This helps to learn pattern during training period and helps to classify the normal and fault patterns. Then in the SVM classification process using the parameters this compares the obtained values with the current fitness value to classify patterns. The performance of the ISSA is analyzed by using accuracy, precision, f1-score, and recall. This shows that the proposed method outperforms existing methods such as K Nearest Neighbors (KNN), stacking classifier, and Feature Simplification Random Forest (FS-RF) in terms of accuracy 98.78%, precision 98.97%, recall 98.24%, and f1-score 98.00%. © 2024 IEEE.

关键词： Wind turbines

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Affinity-Based Power Flow Optimization in Reconfigurable Picogrid

Affinity-Based Power Flow Optimization in Reconfigurable Pic...

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2024 IEEE/SICE International Symposium on system Integration, SII 2024

作者： Kokubu, Ryota Oeda, Kaito Kawakita, Yuusuke Yokogawa, Shinji Tobe, Yoshito Ichikawa, Haruhisa Graduate School of Science and Engineering Aoyama Gakuin University Sagamihara Japan Kanagawa Institute of Technology Department of Information and Computer Sciences Atsugi Japan Info-Powered Energy System Research Center The University of Electro-Communications Chofu Japan

ISBN: (纸本)9798350312072

In recent years, there has been an increasing demand to adopt renewable energy resources to power grids for environmental issues and energy concerns. A virtual grid system, which is designed to utilize decentralized resources, is proposed as an approach. This system enables users to construct their power grids with virtual grid hubs (VG Hub) which interconnect via commercially available cables according to USB Power Delivery (USB PD). A prototype of VG Hub is capable of synthesizing and distributing DC power for connected devices, and the power flow control is managed by a microcontroller. Our previous study proposed a method called Minimum-Hop Power Path Routing (MHPPR). This method determines the power flow that uses the closest source in candidates which can fulfill the demand when a load connects to the network to minimize power loss due to transmission. However, this could result in unproductive power flow, such as a flow that consumes power stored in batteries prior to surplus power from generators. Therefore, we assumed that there are factors to value a source for a load other than demand and supply capacity, and inferred that the factor derives from the type of device. We defined this factor as the affinity between devices. The objective of this study is to solve the power path optimization problem while taking affinity into account. In this study, we discussed device classification and affinity between types and defined affinity coefficients. We then extended MHPPR by incorporating this affinity coefficient. The results have shown that the proposed method selected the sources following the priority defined by the affinity. © 2024 IEEE.

关键词： Renewable energy

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FFCI: A Camera and IMU Sensors Based Multi-modal Neural Network for Activity Recognition in Smart Factory

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IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Yujue Niu, Xin Lv, Xianwei Yu, Chen Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

Worker activity recognition is an important aspect of the construction of smart factory. The development of deep neural networks and the widespread distribution of sensors in the smart factory have brought opportunities for the recognition of workers' activities. The existing methods based on camera and IMU sensors respectively have problems of visual occlusion and difficulty in recognition of similar activities, which result in the reduction of recognition accuracy. Therefore, we propose a feature-level fusion based on camera and IMU sensor (FFCI) for activity recognition in smart factory. The FFCI employs an optimized multi-modal fusion strategy to integrate the visual information and the action information of workers' activities. In order to verify the effectiveness of FFCI, we define a simple yet fine-grained assembly task and collect six common operations. Furthermore, we add visual occlusion and similar activity data to get closer to the smart factory. Finally, we evaluate the FFCI on the collected dataset and achieve a recognition accuracy of 98.17%, demonstrating its effectiveness in accurately classifying workers' activities. © 1975-2011 IEEE.

关键词： Factory automation

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Power Control and Interference Pricing Strategies for Heterogeneous Networks With Renewable Energy Sources

Power Control and Interference Pricing Strategies for Hetero...

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作者： Zhou, Chengcheng Xu, Haitao Tang, Runze Lin, Ruijie Guo, Chao Han, Zhu University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing Institute of Electronic System Engineering Beijing China China Academy of Information and Communications Technology Beijing China Beijing Electronics Science and Technology Institute Department of Electronic and Communication Engineering Beijing China University of Houston HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In this article, the power control and interference pricing problems between the macro base station (MBS) and small cell base stations (SBSs) in Heterogeneous networks (HetNets) are investigated. To optimally allocate power resources and effectively manage the power resources to reduce interference in HetNets, an innovative Stackelberg differential game approach is proposed. In the proposed Stackelberg game, the SBSs control their transmit power to achieve cost minimization based on the interference price offered by the MBS. Given the optimal power control strategies of SBSs, the MBS can adjust the interference price to realize the objective optimization. Renewable energy sources are introduced into the proposed HetNets, and differential equations formulate the SBSs' dynamic energy. Meanwhile, the MBS's state is given by the dynamic spectrum resources. Then the differential game is imported to achieve optimal power control and interference pricing. The open-loop Nash equilibrium solutions are first analyzed for the MBS and SBSs. Then the feedback Nash equilibrium solutions are given. Under the feedback pattern, the proposed model's performances in the non-cooperative and cooperative situations are discussed. Numerical simulations are given to show the convergence and effectiveness of the proposed game approach. © 1967-2012 IEEE.

关键词： Power control

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Agile Factor Flexible Industry 4.0 with MES Manufacturing Execution system Along with ERP Back-End Integration: Ready-Made Garments as a Case Study

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American Journal of Operations Research 2024年第4期14卷 137-150页

作者： Mohamed Mostafa Mohamed Abdulaziz Saleh Alraddadi AppSoft Co. Alexandria Egypt Department of Networks and Information System College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia

Industry 4.0, or the Fourth Industrial Revolution, is based on digitized the manufacturing process and makes use of all digital tools so its combination of various digital technologies computers, ERP software, IoT, machine learning and AI techniques, Manufacturing Execution systems (MES), and big data analytics to create a new, fully digitized manufacturing system. The Critical Success Factors (CSFs) of MES adoption are both a quantitative and qualitative measurement. We use the case of ready-made garments to improve each of the three Overall Equipment Efficiency (OEE) factors: Availability, Performance, and Quality. In this study, we adopt real-time management of production activities on the shop floor from order receipt to finished products, then measure the improvement.

关键词： Industry 4.0 ERP MES IOT

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Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp Pose Selection Skill 6

Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Lee, Sukhan Jang, Byungwoo Hyeon, Seokjong Lee, Soojin Lee, Jaesun Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Artificial Intelligence Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of AI System Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Regional Industry Innovation Korea Institute of Industrial Technology Ulsan44413 Korea Republic of

The edge-brain framework (EBF) is a S/W framework that allows manufacturers to easily integrate AI-empowered robot work skills into a legacy production system in an edge computing environment with minimal time and effort. For EBF-based smart manufacturing, is a skill-based task-level robot programming environment that plays a key role. In this paper, we present the grasp pose selection skill as a case study to demonstrate the proposed skill-based edge-brain smart manufacturing. The proposed grasp-pose selection skill determines the optimal 6D grasp pose associated with the target object in a cluttered workspace by taking into consideration grasp stability, object manipulability, and collision-free path efficacy. To achieve a real-time operation of optimal grasp pose selection skill, deep learning networks are designed to compute, in particular, the manipulability and collision-free path efficacy indices. Experiments demonstrate the effectiveness of the proposed real-time grasp pose selection skill developed for EBF-based smart manufacturing. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart manufacturing

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