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2020 International Conference on computer, control, Electrical, and Electronics engineering, ICCCEEE 2020

作者： Alzain, Omar Busati Liu, Xiangjie Ali, Ayman North China Electric Power Univ. School of Control and Computer Eng. Beijing China International Univ. of Africa Department of Electrical Engineering Khartoum Sudan

ISBN: (纸本)9781728191119

This work offers developed control of the rotor outlet current relying on the online gains adaptation of Sliding Mode control (SMC) with resonant function regulator using discrete adapted Bird Swarm Optimization (ABSA) for the Doubly Fed Induction Generator (DFIG) based wind power system (WPS) during distorted main voltage. The resonant function involved in SMCR-ABSA handle (5th-and 7th-grade) harmonics on the quantities system with different regulation targets without an extra account for the harmonics items succession. The simulated SMCR-ABSA dynamic reaction computational analysis evinces that the ABSA algorithm gave highly preferential results mitigating the harmonic ratio associated with quantities compared to the reported SMCR-BAT, SMC, PI+R and PI controllers. Furthermore, the ABSA algorithm improved the global solutions search and convergence within 1e-2 tolerance range during iterations compared to ordinary BAT algorithm. © 2021 IEEE.

关键词： Birds

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A Hybrid Deep Learning Approach for Software Just-in-Time Defects Prediction

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International Journal of Network Security 2025年第3期27卷 476-486页

作者： Yu, Yingting Chen, Mingliang Zeng, Chuanhan Xie, Guoqiang Pan, Benren Cui, Liu Sun, Heyang Li, Yuancheng State Grid Jiangxi Electric Power Co. Ltd. Research Institute Jiangxi Province Nanchang330096 China Xi'an Jiaotong University Shaanxi Province Xi'an710049 China School of Control and Computer Engineering North China Electric Power University Changping District Beijing102206 China

To address the challenges of high maintenance costs, time-consuming processes, and delayed defect repairs in software projects, we introduce a Just-In-Time (JIT) Defect Prediction method tailored for software. Initially, we established an enriched code change metrics system. Building upon existing change metrics, we incorporated two novel dimensions—time and review—to mitigate errors and reduce data deviations during feature extraction. Recognizing the prevalent imbalance where defect-free samples in open-source software outweigh defective ones, we innovatively utilize the Boundary Equilibrium Generative Adversarial Networks (BEGAN) model. This strategy aims to produce synthetic defect data, rectifying the dataset imbalance. Concluding our methodology, we present the CBGRU model for JIT defect prediction. This model synergistically integrates the Convolutional Neural Network (CNN) for data properties and the Bidirectional Gated Recurrent Unit (BGRU) to capture temporal correlations within diverse data sets. Empirical evaluations across three open-source software platforms reveal that BEGAN surpasses conventional imbalance-handling techniques. Remarkably, our proposed CBGRU model enhances Precision by 17% and F-measure by 9.6% compared to prior methodologies. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Open source software

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One Solution for Deterministic Scheduling on GPU for Automotive Algorithms

One Solution for Deterministic Scheduling on GPU for Automot...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Kenjic, Dusan Milosevic, Milena Antic, Marija Teslic, Nikola University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia RT-RK Institute for Computer Based Systems Novi Sad Serbia

ISBN: (纸本)9781665404174

In order for higher levels of autonomous driving to be achieved, the demanding environmental perception and decision-making algorithms are developed. Meeting real-time system requirements is a mandatory task for all of these algorithms to be properly utilized. In order to meet these requirements, GPUs are widely used, and one of the biggest challenges is providing the deterministic scheduling of different tasks on GPU based on their priority classes. The complete determinism which ensures the absolute control of processes on GPU has still not been achieved. In this paper, we propose and evaluate one method that allows scheduling of tasks with different types of time constraints. © 2021 IEEE.

关键词： Scheduling

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Image Enhancement using linear filters for Rectal Cancer Diagnosis using MR Images

Image Enhancement using linear filters for Rectal Cancer Dia...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Sonal Patreena D’Almeida Surekha Kamath K.V Rajagopal K.N Manjunath Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Radiodiagnosis and Imaging Kasturba Medical College Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

computer-aided diagnosis (CAD) is an efficient tool in analyzing medical images for the diagnosis and staging of rectal cancer. Magnetic Resonance Imaging (MRI) plays a crucial role in the detection and evaluation of rectal cancer due to its high-resolution imaging capabilities. This paper discusses the pre-processing methods for MR images of rectal cancer, which facilitate further image analysis for diagnosing the stage of cancer. Pre-processing is a critical step for MR images, aiding in subsequent steps of segmentation and classification. This paper investigates the impact of widely used pre-processing techniques. Initially, pelvic DICOM images are provided as input. By combining three filters—mean, median, and adaptive median filters—the noise is removed from the image. Image enhancement techniques such as Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to enhance the quality of the image. Statistical parameters like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Structural Similarity Index Measure (SSIM) are used to evaluate performance. Results indicate that CLAHE provides superior outcomes.

关键词： Image quality Maximum likelihood detection PSNR Magnetic resonance imaging Noise Nonlinear filters computer aided diagnosis Medical diagnostic imaging Image enhancement Cancer

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A Born-Again Takagi-Sugeno-Kang Fuzzy Classifier with Decoupled Fuzzy Dark Knowledge Distillation

SSRN

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SSRN 2024年

作者： Zhang, Xiongtao Yin, Zezong Jiang, Yunliang Jiang, Yizhang Sun, Danfeng Liu, Yong School of Information Engineering Huzhou University Huzhou31300 China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China School of Computer Science Hangzhou Dianzi University Hangzhou310018 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In order to enpower the hight performance as well as interpretability of low-order TSK fuzzy classifier, a born-again TSK fuzzy classier embedded with decoupled fuzzy dark knowledge distillation called HTSK-LLM-DKD is proposed in this study. HTSK-LLM DKD achieves the following distinctive characteristics: 1) It takes high-order TSK as teacher model and low-order TSK fuzzy classifier as student model, and leverages the proposed Least Learning Machine based Decoupling Knowledge Distillation to distill the fuzzy dark knowledge from high-order TSK fuzzy classifier to low-order TSK fuzzy classifier, which resulting in low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to high-order TSK fuzzy classifier;specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it computes teacher/student soft labels by the softmax function with distilling temperature parameter;3) By reformulating the Kullback Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model, which taking HTSK-LLM-DKD with outstanding performance and relatively fast running speed. Extensive experiment results on UCI benchmarking datasets and real-world dataset Cleveland Heart Disease demonstrate the effectiveness of the proposed HTSK-LLM-DKD. © 2024, The Authors. All rights reserved.

关键词： Students

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Deep Learning for Content-Based Personalized Viewport Prediction of 360-Degree VR Videos

IEEE Networking Letters

引用

IEEE Networking Letters 2020年第2期2卷 81-84页

作者： Chen, Xinwei Kasgari, Ali Taleb Zadeh Saad, Walid Department of Control Science and Engineering Zhejiang University Hangzhou310058 China Electrical and Computer Engineering Department Wireless at VT Virginia Tech BlacksburgVA United States

In this letter, the problem of head movement prediction for virtual reality videos is studied. In the considered model, a deep learning network is introduced to leverage position data as well as video frame content to predict future head movement. For optimizing data input into this neural network, data sample rate, reduced data, and long-period prediction length are also explored for this model. Simulation results show that the proposed approach yields 16.1% improvement in terms of prediction accuracy compared to a baseline approach that relies only on the position data. © 2019 IEEE.

关键词： Virtual reality

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Scalable Distributed Organizational Structure and Self-Organization Mechanisms for Unmanned System Cluster 9

Scalable Distributed Organizational Structure and Self-Organ...

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9th IEEE International Conference on computer Science and Network Technology, ICCSNT 2021

作者： Liu, Haibo Lin, Yang Shen, Jing Wang, Xing Shi, Changting Zhang, Jiayu College of Computer Science And Technology Harbin Engineering University Harbin150001 China Science And Technology On Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781665443647

Reasonable control organizational structure can help an unmanned system cluster cooperate more effectively to complete tasks. Previous research of existing organizational structures has problems implementing the task in a natural environment, such as the cluster's lack of scalability. This paper proposes a three-layer scalable distributed organizational structure and dynamic self-organization mechanisms, which overcomes the lack of scalability and ensures that the cluster can still perform stably after the cluster size changes. The simulation results show that the organizational structure realizes information sharing and cross-layer control, which enhance the flexibility and stability of unmanned system clusters. Comparison results show that our achievements can make a good performance, contributing to this area of future research. © 2021 IEEE.

关键词： Scalability

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A Learning-Based Adaptive Compliance Method for Symmetric Bi-Manual Manipulation

SSRN

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SSRN 2024年

作者： Cao, Yuxue Zhao, Wenbo Wang, Shengjie Zheng, Xiang Ma, Wenke Zhao, Jiawei Zhang, Tao Beijing Institute of Control Engineering China Department of Automation Tsinghua University China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science City University of Hong Kong Hong Kong Qian Xuesen Laboratory of Space Technology China Department of Automatic Control Xi’an Research Institute of Hi-tech China

Symmetric bi-manual manipulation is an essential skill in on-orbit operations due to its potent load capacity. Previous works have applied compliant control to maintain the stability of manipulations. However, traditional methods have viewed motion planning and compliant control as two separate modules, which can lead to conflicts with the simultaneous change of the desired trajectory and impedance parameters in the presence of external forces and disturbances. Additionally, the joint usage of these two modules requires experts to manually adjust parameters. To achieve high efficiency while enhancing adaptability, we propose a novel Learning-based Adaptive Compliance algorithm (LAC) that improves the efficiency and robustness of symmetric bi-manual manipulation. Specifically, the algorithm framework integrates desired trajectory generation and impedance-parameter adjustment under a unified framework to mitigate contradictions and improve efficiency. Second, we introduce a centralized Actor-Critic framework with LSTM networks preprocessing the force states, enhancing the synchronization of bi-manual manipulation. When evaluated in dual-arm cooperative handling and peg-in-hole assembly experiments, our method outperforms baseline algorithms in terms of optimality and robustness. © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence

arXiv

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arXiv 2023年

作者： Rikos, Apostolos I. Grammenos, Andreas Kalyvianaki, Evangelia Hadjicostis, Christoforos N. Charalambous, Themistoklis Johansson, Karl H. The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom The Alan Turing Institute London United Kingdom The Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia1678 Cyprus

We propose two distributed iterative algorithms that can be used to solve, in finite time, the distributed optimization problem over quadratic local cost functions in large-scale networks. The first algorithm exhibits synchronous operation whereas the second one exhibits asynchronous operation. Both algorithms share salient features. Specifically, the algorithms operate exclusively with quantized values, which means that the information stored, processed and exchanged between neighboring nodes is subject to deterministic uniform quantization. The algorithms rely on event-driven updates in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage. Finally, once the algorithms converge, nodes distributively terminate their operation. We prove that our algorithms converge in a finite number of iterations to the exact optimal solution depending on the quantization level, and we present applications of our algorithms to (i) optimal task scheduling for data centers, and (ii) global model aggregation for distributed federated learning. We provide simulations of these applications to illustrate the operation, performance, and advantages of the proposed algorithms. Additionally, it is shown that our proposed algorithms compare favorably to algorithms in the current literature. Quantized communication and asynchronous updates increase the required time to completion, but finite-time operation is maintained. © 2023, CC BY.

关键词： Cost functions

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computer Vision-control-Based CNN-PID for Mobile Robot

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computers, Materials & Continua 2021年第7期68卷 1065-1079页

作者： Rihem Farkh Mohammad Tabrez Quasim Khaled Al jaloud Saad Alhuwaimel Shams Tabrez Siddiqui College of Engineering Muzahimiyah BranchKing Saud UniversityP.O.Box 2454Riyadh11451Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar UniversityTunisTunisia

With the development of artificial intelligence technology,various sectors of industry have *** them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively *** on the use of image-based deep learning to monitor autonomous driving systems have recently been *** this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified *** robot should be able to follow the trajectory with speed *** controllers were used simultaneously to achieve *** neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed *** study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.

关键词： Autonomous car pid control deep learning convolutional neural network differential drive system raspberry pi

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