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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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A weblab control experiment using the ball and beam system and multiobjective optimization 5

A weblab control experiment using the ball and beam system a...

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5th Experiment at International Conference, exp.at 2019

作者： Kagami, Ricardo Massao Kovalski Da Costa, Guinther Mendes, Luciano Antonio Freire, Roberto Zanetti Industrial and Systems Engineering Graduate Program-PPGEPS Pontifícia Universidade Católica Do Paraná-PUCPR Curitiba Brazil Department of Control and Automation Engineering Pontifícia Universidade Católica Do Paraná-PUCPR Curitiba Brazil

ISBN: (纸本)9781728136370

y associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with possibilities of increasing both students' knowledge and skills. control engineering is an area where educational laboratories are usually costly, thus the use of virtual laboratories for remote experimentation became an interesting approach. In this work, it is presented a remote experimentation procedure for the ball and beam system, which is a nonlinear and unstable open loop process. Considering the advanced control topic, multiobjective optimization was introduced in the experiment in order to tune Proportional-Integral-Derivative (PID) controllers. Through the description of the system model, the multiobjective optimization technique, and the experiment configuration, students can simulate and test their controllers remotely in a real plant. © 2019 IEEE.

关键词： Multiobjective optimization

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A Machine Learning Method for Vehicle Classification by Inductive Waveform Analysis ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13928-13932页

作者： Bruno R. Vasconcellos Marcelo Rudek Marcelo de Souza Pontifícia Universidade Católica do Paraná - PUCPR Control and Automation Egineering Curitiba PR 80215-901 Pontifícia Universidade Católica do Paraná - PUCPR Industrial and Systems Engineering Graduate Program Curitiba PR 80215-901 Fiscaltech Curitiba PR 81290-270

The classification of vehicles is a matter of great importance for traffic control and management, helping with traffic surveillance as well as in statistical data collection. Among the several vehicular classification techniques, the most popular uses inductive loop sensors, because they achieve high accuracy rate at low cost. This paper proposes 5 different vehicle classification models by inductive waveform analysis: KNN, SVC, Decision Tree, Random Forest, and Voting Classifier. A brief introduction to the mathematical basis of these models and the main forms of vehicle detection are also presented. The obtained results reached an accuracy of 94% and showed how inductive waveform analysis is still a valid option for vehicle classification.

关键词： Machine learning Intelligent transportation systems Information processing Decision support

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Emerging contaminants:A One Health perspective

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The Innovation 2024年第4期5卷 140-170,139页

作者： Fang Wang Leilei Xiang Kelvin Sze-Yin Leung Martin Elsner Ying Zhang Yuming Guo Bo Pan Hongwen Sun Taicheng An Guangguo Ying Bryan WBrooks Deyi Hou Damian EHelbling Jianqiang Sun Hao Qiu Timothy MVogel Wei Zhang Yanzheng Gao Myrna JSimpson Yi Luo Scott XChang Guanyong Su Bryan MWong Tzung-May Fu Dong Zhu Karl JJobst Chengjun Ge Frederic Coulon Jean Damascene Harindintwali Xiankui Zeng Haijun Wang Yuhao Fu Zhong Wei Rainer Lohmann Changer Chen Yang Song Concepcion Sanchez-Cid Yu Wang Ali El-Naggar Yiming Yao Yanran Huang Japhet Cheuk-Fung Law Chenggang Gu Huizhong Shen Yanpeng Gao Chao Qin Hao Li Tong Zhang Natàlia Corcoll Min Liu Daniel SAlessi Hui Li Kristian KBrandt Yolanda Pico Cheng Gu Jianhua Guo Jianqiang Su Philippe Corvini Mao Ye Teresa Rocha-Santos Huan He Yi Yang Meiping Tong Weina Zhang Fidèle Suanon Ferdi Brahushi Zhenyu Wang Syed AHashsham Marko Virta Qingbin Yuan Gaofei Jiang Louis A.Tremblay Qingwei Bu Jichun Wu Willie Peijnenburg Edward Topp Xinde Cao Xin Jiang Minghui Zheng Taolin Zhang Yongming Luo Lizhong Zhu Xiangdong Li DamiàBarceló Jianmin Chen Baoshan Xing Wulf Amelung Zongwei Cai Ravi Naidu Qirong Shen Janusz Pawliszyn Yong-guan Zhu Andreas Schaeffer Matthias C.Rillig Fengchang Wu Gang Yu James M.Tiedje State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China Department of Chemistry Hong Kong Baptist UniversityHong KongChina HKBU Institute of Research and Continuing Education Shenzhen Virtual University ParkShenzhenChina Technical University of Munich TUM School of Natural SciencesInstitute of Hydrochemistry85748 GarchingGermany School of Resources&Environment Northeast Agricultural UniversityHarbin 150030China Climate Air Quality Research UnitSchool of Public Health and Preventive MedicineMonash UniversityMelbourneVICAustralia Faculty of Environmental Science&Engineering Kunming University of Science&TechnologyKunming 650500China Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria College of Environmental Science and EngineeringNankai UniversityTianjin 300350China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory of Environmental Theoretical Chemistry South China Normal UniversityGuangzhouGuangdong 510006China Department of Environmental Science Baylor UniversityWacoTXUSA Center for Reservoir and Aquatic Systems Research(CRASR) Baylor UniversityWacoTXUSA School of Environment Tsinghua UniversityBeijing 100084China School of Civil and Environmental Engineering Cornell UniversityIthacaNY 14853USA Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province Zhejiang University of TechnologyHangzhou 310014China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Laboratoire d’Ecologie Microbienne Universite Claude Bernard Lyon 1UMR CNRS 5557UMR INRAE 1418VetAgro Sup69622 VilleurbanneFrance Department of Pla

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary *** global efforts to mitigate legacy pollutants,the continuous introduction of new substances remains a major threat to both people and the *** response,global initiatives are focusing on risk assessment and regulation of emerging contaminants,as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental Science-Policy Panel on Chemicals,Waste,and Pollution *** review identifies the sources and impacts of emerging contaminants on planetary health,emphasizing the importance of adopting a One Health *** for monitoring and addressing these pollutants are discussed,underscoring the need for robust and socially equitable environmental policies at both regional and international *** actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

关键词： pollution planet contaminants

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Robot Hand Interaction Using Plastic Deformation control with Inner Position Loop

Robot Hand Interaction Using Plastic Deformation Control wit...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Kenichi Murakami Koki Ishimoto Taku Senoo Masatoshi Ishikawa Data Science Research Division Information Technology Center The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Department of Information Physics and Computing The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Electrical Systems and Control Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima City Hiroshima Japan

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

In this study, the inner position loop addition for plastic deformation control of the error robustness of the model and actual parameters is proposed. The inner position loop is a type of position feedback control based on the estimation of model parameters and dynamics of robots. This position control system with proportional and derivative gains modifies the trajectory of the end effector close to the desired trajectory. The robustness of the proposed control scheme is evaluated by comparing it with plastic deformation control without an inner position loop in both simulation and actual environments. Finally, the stable reaction with the proposed control is demonstrated for the case where a human pushes the robot finger.

关键词： Strain Plastics Force Deformable models Robots Friction Trajectory

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A WebLab control Experiment using the Ball and Beam System and Multiobjective Optimization

A WebLab Control Experiment using the Ball and Beam System a...

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Experiment@ International Conference (exp.at)

作者： Ricardo Massao Kagami Guinther Kovalski da Costa Luciano Antônio Mendes Roberto Zanetti Freire Industrial and Systems Engineering Graduate Program – PPGEPS Pontifícia Universidade Católica do Paraná – PUCPR Curitiba Brazil Department of Control and Automation Engineering Pontifícia Universidade Católica do Paraná - PUCPR Curitiba Brazil

By associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with possibilities of increasing both students' knowledge and skills. control engineering is an area where educational laboratories are usually costly, thus the use of virtual laboratories for remote experimentation became an interesting approach. In this work, it is presented a remote experimentation procedure for the ball and beam system, which is a nonlinear and unstable open loop process. Considering the advanced control topic, multiobjective optimization was introduced in the experiment in order to tune Proportional-Integral-Derivative (PID) controllers. Through the description of the system model, the multiobjective optimization technique, and the experiment configuration, students can simulate and test their controllers remotely in a real plant.

关键词： Laboratories Mathematical model PD control Optimization Process control PI control

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Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical systems

Learning Deep Neural Network Representations for Koopman Ope...

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American control Conference

作者： Enoch Yeung Soumya Kundu Nathan Hodas Biomolecular Science and Engineering Graduate Program Center for Dynamical Systems Control and Computation University of California Santa Barbara Energy and Environment Directorate Pacific Northwest National Laboratory National Security Directorate Pacific Northwest National Laboratory

ISBN: (纸本)9781538679012;9781538679265

The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by the computational complexity of extended dynamic mode decomposition;this requires a combinatorially large basis set to adequately describe many nonlinear systems of interest, e.g. cyber-physical infrastructure systems, biological networks, social systems, and fluid dynamics. Often the dictionaries generated for these problems are manually curated, requiring domain-specific knowledge and painstaking tuning. In this paper we introduce a computational framework for learning Koopman operators of nonlinear dynamical systems using deep learning. We show that this novel method automatically selects efficient deep dictionaries, requiring much lower dimensional dictionaries while outperforming state-of-the-art methods. We benchmark this method on partially observed nonlinear systems, including the glycolytic oscillator and show it is able to predict on test data quantitatively 100 steps into the future, using only a single timepoint as an initial condition, and quantitative oscillatory behavior 400 steps into the future.

关键词： Nonlinear dynamical systems Dictionaries as Topic DYNAMICAL systems System analyses INITIAL CONDITIONS Hydrodynamics complexity classes Learning

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Design and Implementation of OLED Driving and OPD Readout Circuitry for an Optical Vibration Sensor 17

Design and Implementation of OLED Driving and OPD Readout Ci...

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17th IEEE SENSORS Conference, SENSORS 2018

作者： Wu, Yi-Cheng Kao, Yung-Hua Chao, Paul C.-P. Wey, Chin-Long Sauter, Thilo Eka, Fitrah P. Pandey, Rajeev Department of Electrical Engineering National Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Chiao Tung University Hsinchu City Taiwan EECS International Graduate Program National Chiao Tung University Hsinchu City Taiwan Department for Integrated Sensor Systems Danube University Krems Austria Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu City Taiwan

ISBN: (纸本)9781538647073

This study presents an organic light-emitting diode (OLED) drive circuit and an organic photo detector (OPD) readout circuits for a vibration sensor. Vibration sensor consist of micro-electro-mechanical systems (MEMS), which are OLED and OPD needs to be integrated for many applications in automation or consumer electronics. The light flux is generated by an OLED located on top of the structure and the modulated light flux is measured by an OPD. The OLED drive circuit contains pulse width modulation (PWM) driver to control PWM output for optimization of voltage operation is designed in order to minimize power consumption efficiency of device. The readout circuit includes a trans-impedance amplifier (TIA) to convert incident light of OPD to voltage output, a 14-bit analog-to-digital converter (ADC) to extract amplitude of vibration level, circuit to generate clock for time-to-digital converter (TDC) which detect frequency of vibration sensor. The designed circuit has been successfully fabricated by using TSMC 0.18 μm CMOS process, the chip area is 1.012 mm2. The power consumption of chip is about 6.2 mW. The measured frequency of PWM is about 200 kHz with 50% duty cycle. The frequency range of the vibration can be sensed well as about 10 Hz1.5 kHz, and resulting in high SNR at frequencies under 60 Hz. © 2018 IEEE.

关键词： Electric power utilization

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Model mismatch in multi-objective optimisation and preservation of trade-off order. ⁎

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IFAC-PapersOnLine 2019年第26期52卷 249-254页

作者： Gilberto Reynoso-Meza Alejandro Vignoni Yadira Boada Jesüs Picü Enric Picü Industrial and Systems Engineering Graduate Program - PPGEPS. Polytechnic School. Pontifcia Universidade Catülica do Paraná PUCPR. Rua Imaculada Conceição 1155 Zip code 80215-901 Curitiba PR Brazil Synthetic Biology and Biosystems Control Lab I.U. de Automática e Informática Industrial (ai2) Universitat Politècnica de Valencia 46022 Camino de Vera S/N Valencia Spain

Multi-objective optimisation is a valuable tool for tuning dynamical systems when simultaneous optimisation performance objectives are in conflict. When the goal is tuning the parameters of a synthetic biology device, mismatch between the model implemented in silico -a more or less coarse simplification of the real system- and the actual in vivo implementation might lead to a disagreement between the in silico and in vivo design objectives for a given solution from the Pareto front. Here, we propose an iterative closed-loop multi-objective optimisation approach where the new information provided by the difference between the in silico Pareto front and its in vivo implementation is used to improve the parametric model. This aims to minimise the discrepancies between in silico and in vivo performance objectives while preserving the trade-off order among solutions. As a proof-of-concept we consider the problem of tuning a synthetic gene circuit used as feedforward-feedback controller for the expression of a protein of interest. We use an extended parametric model of the gene synthetic circuit to represent the in vivo set up and a simplified one for the in silico one.

关键词： Multi-objective optimisation model mismatch synthetic biology synthetic gene circuit

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Effects of drug resistance in the tumour-immune system with chemotherapy treatment

arXiv

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arXiv 2020年

作者： Trobia, José Gabrick, Enrique C. Seifert, Evandro G. Borges, Fernando S. Protachevicz, Paulo R. Szezech, José D. Iarosz, Kelly C. Santos, Moises S. Caldas, Iberê L. Tian, Kun Ren, Hai-Peng Grebogi, Celso Batista, Antonio M. Graduate Program in Science - Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Department of Mathematics and Statistics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Department of Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Faculty of Telêmaco Borba FATEB Telêmaco BorbaPR84266-010 Brazil Graduate Program in Chemical Engineering Federal Technological University of Paraná Ponta Grossa Paraná84016-210 Brazil Shaanxi Key Lab of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Xi’an Technological University Xi’an710021 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom

Cancer is a term used to refer to a large set of diseases. The cancerous cells grow and divide and, as a result, they form tumours that grow in size. The immune system recognise the cancerous cells and attack them, though, it can be weakened by the cancer. One type of cancer treatment is chemotherapy, which uses drugs to kill cancer cells. Clinical, experimental, and theoretical research has been developed to understand the dynamics of cancerous cells with chemotherapy treatment, as well as the interaction between tumour growth and immune system. We study a mathematical model that describes the cancer growth, immune system response, and chemotherapeutic agents. The immune system is composed of resting cells that are converted to hunting cells to combat the cancer. In this work, we consider drug sensitive and resistant cancer cells. We show that the tumour growth can be controlled not only by means of different chemotherapy protocols, but also by the immune system that attacks both sensitive and resistant cancer cells. Furthermore, for all considered protocols, we demonstrate that the time delay from resting to hunting cells plays a crucial role in the combat against cancer cells. Copyright © 2020, The Authors. All rights reserved.

关键词： Chemotherapy

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