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International Conference on control, automation and Systems ( ICCAS)

作者： Sitthiphat Promchaiwattana David Banjerdpongchai Department of Electrical Engineering Faculty of Engineering Intelligent Control Automation of Process Systems Research Unit Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok Thailand

This paper presents the design of supervisory model predictive control for HVAC systems with consideration of peak demand shaving and thermal comfort. In this work, we analyze the operating points with the thermal comfort requirement. Afterwards, we linearize the nonlinear dynamic model to a linear time-varying model. In particular, we specify that the operating point of the open-loop HVAC system satisfies the thermal comfort condition. This makes the predicted mean vote (PMV) equal to zero or nearly to zero. Subsequently, we calculate the zone temperature, the zone humidity ratio, and the volumetric flow rate. When considering the time-varying disturbance profiles, it appears that the operating points are time-varying. We observe that the coefficient of performance (COP) and the operating points are time-varying which lead to time-varying model. Moreover, we design supervisory control (SC) and model predictive control (MPC) for linear time-varying model. In the SC design, we aim to find the optimal reference zone temperature which is used as the reference signal for MPC. In the MPC design, the objectives are twofold. First, the zone temperature tracks the reference signal. Second, both electrical energy cost and deviation of desired thermal comfort are minimized. The computer simulation shows that MPC of time-varying HVAC system yields the best reference tracking. In addition, it reduces total electrical energy cost the most. We conclude that the optimal temperature reference can be effectively applied to the building HVAC systems while the occupants feel comfortable and the total electrical energy cost is the lowest.

关键词： HVAC Costs Input variables Buildings Predictive models Supervisory control Thermal analysis

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FixGPT: A Novel Three-Tier Deep Learning Model for Automated Program Repair 8

FixGPT: A Novel Three-Tier Deep Learning Model for Automated...

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Ye, Wenhao Xia, Jun Feng, Shuo Zhong, Xiangyu Yuan, Shuai Guan, Zhitao State Grid Hunan Electric Power Company Limited Changsha China School of Control and Computer Engineering North China Electric Power University Beijing China Hunan Sgit Technology CO. LTD. Changsha China Brock University Department of Finance Operations and Information Systems ON Canada

ISBN: (纸本)9798350331035

Automatic program repair plays a crucial role in the software development and implementation. While deep learning-based approaches have made significant progress, one inherent challenge is the inefficiency in code representation, which hampers accurate patch generation. Furthermore, the training data used by these data-driven approaches may be limited, and they may not be able to capture the subtle differences between vulnerabilities and patches. To address these issues, FixGPT, we propose a three-tier deep learning model in the study. Specifically, a generative pre-trained transformer model is designed in the first tier to capture code characteristics and programming patterns. The second tier integrates a generation model based on the structure of neural machine translation, for the purpose of generating potential patches. Finally, a contrast model is introduced in the last tier to differentiate between the vulnerability and the patch. We also incorporate the Byte Pair Encoding approach to reduce the search space by converting identifiers into subwords. Detailed experimental studies have been carried out to evaluate the performance of FixGPT on two well-known benchmarking datasets: QuixBugs and Defects4J. The results demonstrated significant improvements in the effectiveness and accuracy in comparison with existing solutions. We complement these findings through the analysis of two case studies. © 2023 IEEE.

关键词： deep learning program repair programming language model vulnerability

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A modular microfluidic system based on a multilayered configuration to generate large-scale perfusable microvascular networks

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Microsystems & Nanoengineering 2021年第1期7卷 63-75页

作者： Tao Yue Da Zhao Duc T.T.Phan Xiaolin Wang Joshua Jonghyun Park Zayn Biviji Christopher C.W.Hughes Abraham P.Lee Department of Biomedical Engineering University of CaliforniaIrvineCAUSA School of Mechatronic Engineering and Automation Shanghai UniversityShanghaiChina Department of Molecular Biology and Biochemistry University of CaliforniaIrvineCAUSA Department of Micro/Nano Electronics Shanghai Jiao Tong UniversityShanghaiChina National Key Laboratory of Science and Technology for Micro/Nano Fabrication Shanghai Jiao Tong UniversityShanghaiChina Key Laboratory for Thin Film and Micro Fabrication of the Ministry of Education Shanghai Jiao Tong UniversityShanghaiChina Department of Electrical Engineering and Computer Science University of CaliforniaIrvineCAUSA. Department of Applied Mathematics-Biology Brown UniversityProvidenceRIUSA. Department of Mechanical and Aerospace Engineering University of CaliforniaIrvineCAUSA

The vascular network of the circulatory system plays a vital role in maintaining homeostasis in the human *** this paper,a novel modular microfluidic system with a vertical two-layered configuration is developed to generate largescale perfused microvascular networks in *** two-layer polydimethylsiloxane(PDMS)configuration allows the tissue chambers and medium channels not only to be designed and fabricated independently but also to be aligned and bonded *** method can produce a modular microfluidic system that has high flexibility and scalability to design an integrated platform with multiple perfused vascularized tissues with high *** medium channel was designed with a rhombic shape and fabricated to be semiclosed to form a capillary burst valve in the vertical direction,serving as the interface between the medium channels and tissue *** and anastomosis at the vertical interface were successfully achieved by using different combinations of tissue chambers and medium *** large-scale microvascular networks were generated and quantified in terms of vessel length and *** leakage of the perfused 70-kDa FITC-dextran confirmed the lumenization of the microvascular networks and the formation of tight vertical interconnections between the microvascular networks and medium channels in different structural *** platform enables the culturing of interconnected,large-scale perfused vascularized tissue networks with high density and scalability for a wide range of multiorgan-on-a-chip applications,including basic biological studies and drug screening.

关键词： interconnection networks configuration

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A Workplace Equality Workshop for the control engineering Classroom

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IFAC-PapersOnLine 2022年第17期55卷 97-102页

作者： Margret Bauer Frida Heskebeck University of Applied Sciences Hamburg Department of Automatic Control Lund University Sweden Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

engineering, in general, is a non-diverse profession around the globe. Women are one group of minorities in engineering. Despite the fact that control engineering is heavily based on mathematics, which has a larger number of female students, it has the same proportion of women as other engineering disciplines. To address the issue of low female participation in engineering disciplines both at university and, more importantly, in career, we have developed a workshop that can be carried out in a 90-minute-lecture in the classroom. In this paper, we first explain the context and need for such a workshop and then present the contents of the developed material as a resource for colleagues in the control engineering community.

关键词： control engineering education diversity inclusion gender equality

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Unsupervised Behavior Extraction via Random Intent Priors

arXiv

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arXiv 2023年

作者： Hu, Hao Yang, Yiqin Ye, Jianing Mai, Ziqing Zhang, Chongjie Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Automation Tsinghua University China Department of Computer Science & Engineering Washington University in St. Louis United States

Reward-free data is abundant and contains rich prior knowledge of human behaviors, but it is not well exploited by offline reinforcement learning (RL) algorithms. In this paper, we propose UBER, an unsupervised approach to extract useful behaviors from offline reward-free datasets via diversified rewards. UBER assigns different pseudo-rewards sampled from a given prior distribution to different agents to extract a diverse set of behaviors, and reuse them as candidate policies to facilitate the learning of new tasks. Perhaps surprisingly, we show that rewards generated from random neural networks are sufficient to extract diverse and useful behaviors, some even close to expert ones. We provide both empirical and theoretical evidence to justify the use of random priors for the reward function. Experiments on multiple benchmarks showcase UBER’s ability to learn effective and diverse behavior sets that enhance sample efficiency for online RL, outperforming existing baselines. By reducing reliance on human supervision, UBER broadens the applicability of RL to real-world scenarios with abundant reward-free data. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education

The Emergence of Artificial Intelligence in Education and it...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Imre Farkas Attila Kovari Mónika Rajcsanyi-Molnar Department of Computer Systems and Control Technology Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Economics and Management Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes.

关键词： Ethics Power engineering Large language models Education Collaboration Learning (artificial intelligence) Reliability Artificial intelligence Monitoring Guidelines

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification

A Novel Attention-based Explainable Deep Learning Framework ...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Gladys W. Muoka Ding Yi Chiagoziem Chima Ukwuoma Moindi Duke Martin Ahmet Arif Aydin Mugahed A. Al-Antari School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China Department of Computer Science Unicaf University Lilongwe Lilongwe Malawi Department of Computer Engineering Faculty of Engineering İnönü University Turkey Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul Korea

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study’s primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics.

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Live Demo: E2P-Events to Polarization Reconstruction from PDAVIS Events

Live Demo: E2P-Events to Polarization Reconstruction from PD...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Delbruck, Tobi Wang, Zuowen Mei, Haiyang Haessig, Germain Joubert, Damien Haque, Justin Chen, Yingkai Milde, Moritz B. Gruev, Viktor Univ. of Zurich and ETH Zurich Sensors Group Institute of Neuroinformatics Switzerland Center for Vision Automation & Control High-Performance Vision Systems AIT Austrian Institute of Technology Vienna Austria Western Sydney University Intl. Centre for Neuromorphic Systems The MARCS Institute Sydney Australia University of Illinois at Urbana-Champaign Dept. of Electrical and Computer Engineering UrbanaIL United States

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [... 详细信息

ISBN: (纸本)9798350302493

关键词： Polarization

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Detection of Precipitation Cloud over the Tibet Based on the Improved U-Net

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computers, Materials & Continua 2020年第12期65卷 2455-2474页

作者： Runzhe Tao Yonghong Zhang Lihua Wang Pengyan Cai Haowen Tan School of Automation Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Chosun UniversityGwangju501759Korea

Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring.U-Net,an advanced machine learning(ML)method,is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A(FY-4A).First,in this algorithm,the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model(DEM)has been used as predictor variables for our ***,the efficiency of the feature was improved by changing the traditional convolution layer serial connection method of U-Net to residual ***,in order to solve the problem of the network that would produce semantic differences when directly concentrated with low-level and high-level features,we use dense skip pathways to reuse feature maps of different layers as inputs for concatenate neural networks feature layers from different ***,according to the characteristics of precipitation clouds,the pooling layer of U-Net was replaced by a convolution operation to realize the detection of small precipitation *** was experimentally concluded that the Pixel Accuracy(PA)and Mean Intersection over Union(MIoU)of the improved U-Net on the test set could reach 0.916 and 0.928,the detection of precipitation clouds over Tibet were well actualized.

关键词： U-net fy-4a precipitation cloud dense skip connections residual network

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Constrained Generative Sampling of 6-DoF Grasps

Constrained Generative Sampling of 6-DoF Grasps

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jens Lundell Francesco Verdoja Tran Nguyen Le Arsalan Mousavian Dieter Fox Ville Kyrki KTH Royal Institute of Technology Sweden Department of Electrical Engineering and Automation Intelligent Robotics Group School of Electrical Engineering Aalto University Finland NVIDIA Corporation USA Paul G. Allen School of Computer Science & Engineering University of Washington Seattle USA

Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable grasps is sufficient. However, for completing specific tasks, such as squeezing out liquid from a bottle, we want the grasp to be on a specific part of the object's body while avoiding other locations, such as the cap. This work presents a generative grasp sampling network, VCGS, capable of constrained 6-Degrees of Freedom (DoF) grasp sampling. In addition, we also curate a new dataset designed to train and evaluate methods for constrained grasping. The new dataset, called CONG, consists of over 14 million training samples of synthetically rendered point clouds and grasps at random target areas on 2889 objects. VCGS is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in simulation and on a real robot. The results demonstrate that VCGS achieves a 10-15% higher grasp success rate than the baseline while being 2–3 times as sample efficient. Supplementary material is available on our project website.

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