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International Conference on Data engineering Workshops

作者： Wei Guo Yan Lyu Xueyong Xu Weiwei Wu School of Computer Science and Engineering Southeast University Nanjing China North Information Control Research Academy Group Co. Ltd Nanjing China

ISBN: (数字)9798350384031

ISBN: (纸本)9798350384048

Dynamic pricing has become a prevalent strategy for balancing supply and demand in urban sharing economy platforms such as Uber and Airbnb. The dynamic pricing algorithms, however, are black-boxes and have encountered issues of discrimination. While existing studies have focused on group fairness within these algorithms, limited attention has been paid to individual fairness. The key challenge is in quantifying individual similarity within temporal-spatial dimensions and the inaccessibility of the algorithms. We propose a novel framework to assess and explain individual fairness of dynamic pricing algorithms. We define individual fairness by measuring individual similarity on latent temporal-spatial representation learned from relevant downstream tasks. We also introduce a triplet loss as a fairness constraint for fair representation. As the dynamic pricing algorithms are inaccessible, we proposed a sampling based Cohort Shapley explanation method to explain the discriminatory instances. We conduct experiments on datasets from both Uber ride-sharing and Airbnb pricing platforms. Our experimental results demonstrate that our proposed triplet loss approach strikes a balance between fairness and downstream task performance. Our case study illustrates that the proposed explanation method provides reasonable and clear explanations for instances of individual unfairness.

关键词： Supply and demand Heuristic algorithms Conferences Pricing Sharing economy Data engineering Task analysis

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Integration of a multi-rate electron density profile observer in the plasma control system of TCV 49

Integration of a multi-rate electron density profile observe...

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49th EPS Conference on Plasma Physics, EPS 2023

作者： Pastore, F. Felici, F. Sauter, O. Bosman, T.O.S.J. Galperti, C. Vincent, B. Pau, A. Poels, Y. Ravensbergen, T. Vu, N.M.T. Lausanne Switzerland DIFFER - Dutch Institute for Fundamental Energy Research Energy Systems and Control Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mechanical Engineering Control Systems Technology Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mathematics and Computer Science Data Mining Group Eindhoven Netherlands ITER Organization Route de Vinon-sur-Verdon St. Paul Lez Durance13067 Cedex France

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Hybrid Autonomous Underwater Glider (HAUG) Obstacle Detection and Avoidance

Hybrid Autonomous Underwater Glider (HAUG) Obstacle Detectio...

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11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Putra, A. A. Gde Jenana Trilaksono, Bambang Riyanto Hidayat, Egi Muhammad Idris School of Electrical Engineering and Informatics Institut Teknologi Bandung Control and Computer Systems Research Group Bandung Indonesia

ISBN: (纸本)9781665437660

Hybrid Autonomous Underwater Glider (HAUG) is a vehicle used for underwater missions such as monitoring and finding new underwater resources. HAUG has good endurance and maneuverability compared to conventional Autonomous Underwater Vehicle (AUV) and Autonomous Underwater Glider (AUG). It is because HAUG has two operational modes. They are AUV and AUG's operational mode. When HAUG is in some missions, it may be faced with an obstacle that can be a threat to the HUG's safety. Therefore, HAUG should have the ability to detect and avoid obstacles. Gemini 720 im Imaging Forward Looking Sonar (FLS) is used for obstacle detection in this work. The main issue of underwater obstacle detection is noisy data received by sonar. Therefore, by designing an obstacle detection, it will overcome those issues. Frost filter and local histogram entropy are used in the sonar data processing. The processed sonar data are provided in the local sonar frame then will be used by obstacle avoidance systems. BK-product fuzzy and reactive algorithms are used for obstacle avoidance. In this paper, we added some procedures to those obstacle avoidance algorithms to handle the huge or non-complex u-shaped obstacle. Both of the obstacle detection and avoidance simulations are in ROS (Robot Operating System). The obstacle detection simulation shows that the different sizes of obstacles can be detected with average errors of approximately 0.335 meters. The obstacle avoidance simulations are in AUV's mode with no ocean current applied. The obstacle avoidance simulated in this work is with two cases. Using simulated lidar as a sensor's output and using sonar's plugin provided by Gazebo. The obstacle avoidance using simulated lidar shows that the error's value is approximately 10.12 meters, 103.62 meters, and 354.4 meters respectively. The obstacle avoidance simulation with sonar's plugin shows that the error's value is 6.55 meters. © 2021 IEEE.

关键词： Meters Laser radar Simulation Operating systems Sonar Robot sensing systems Systems engineering and theory

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TiO₂-CuO heterojunction nanoparticles synthesized by green chemistry supported on beach sand granules: Optical, morphological and structural characterization

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Nano-Structures and Nano-Objects 2023年 35卷

作者： Solano, Ricardo Maestre, David Mueses, Miguel Herrera, Adriana Engineering Doctorate Program Nanomaterials and Computer-Aided Process Engineering Research Group University of Cartagena Cartagena 130015 Colombia Faculty of Physics Department of Materials Physics Universidad Complutense de Madrid Madrid 28040 Spain School of Engineering Department of Chemical Engineering Modeling and Applications of Advanced Oxidation Technologies Research Group Photocatalysis & Solar Photoreactors Engineering University of Cartagena Cartagena 130015 Colombia School of Engineering Department of Chemical Engineering Nanomaterials and Computer Aided Process Engineering Research Group University of Cartagena Cartagena 130015 Colombia

This research has studied the optical, morphological/textural and structural properties of pulverized TiO2-CuO heterostructures and their immobilization on chemically purified beach sand granules to evaluate the poten... 详细信息

This research has studied the optical, morphological/textural and structural properties of pulverized TiO₂-CuO heterostructures and their immobilization on chemically purified beach sand granules to evaluate the potential of these materials for wastewater treatment. The preparation of TiO₂-CuO heterojunctions was developed by the ultrasound-assisted wet impregnation method in the presence of PEG-400. For this methodology, the TiO₂ and CuO nanoparticles were first synthesized by green chemistry using Moringa (Moringa Oleifera) leaves aqueous extract and an ultrasound bath. Then, the metal oxide nanoparticles were calcinated and immobilized on etched sand granules via immersion in a coating suspension. All materials were characterized by UV–Vis DRS, photoluminescence (PL), SEM-EDS, HRTEM-SAED, BET area, Raman spectroscopy, XRD and XPS. Optical characterization revealed that the CuO incorporation favors light adsorption in the visible spectrum and reduces the band gap energy from 3.14 to 1.28 eV. Morphological studies showed that the pulverized materials are agglomerated with an affinity for the high porosity regions of the beach sand granules. Also, TiO₂-CuO heterojunctions exhibited a broader size distribution (15.81 ± 7.45 nm) than pure TiO₂ nanoparticles (10.38 ± 1.24 nm). The structural analysis confirmed that the pure TiO₂ and CuO nanoparticles are formed exclusively in the anatase and tenorite phases. At the same time, the heterostructures’ process preparation favors the partial transition from CuO to Cu₂O. Finally, the purified sand support is mainly based on α-Quartz (trigonal);the as-prepared material is a potentially versatile and reusable candidate for pollutant photodegradation under UV or visible light sources. © 2023 Elsevier B.V.

关键词： Green chemistry Inorganic substrate Nanocomposite TiO2-cuO Type-II heterojunction

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Modeling and HIL Real-Time Simulation of a Series Hybrid Vehicle with Regenerative Breaking for Energy Management Algorithm Testing

Modeling and HIL Real-Time Simulation of a Series Hybrid Veh...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： William K. Moreira Alvin Huynh Cassiano Rech Sheldon S. Williamson Power Electronics and Control Research Group - GEPOC Federal University of Santa Maria Santa Maria RS Brazil Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

The objective of this paper is to present the design of a real-time Hardware in-the-loop (HIL) platform for testing different models of internal combustion engine (ICE), electric machines, energy management algorithms (EMAs) and power converter topologies in a series hybrid vehicle configuration with regenerative breaking and optimal fuel consumption. The article presents the modeling of the ICE and the electrical components, as well as the design of the controllers for each element of the powertrain. The engine model is composed by table data combined with lowpass filters to provide dynamic behavior. The proposed system is composed by two Permanent Magnet Synchronous Machines (PMSMs), one ICE and one battery pack. The electric machines are modeled in synchronous reference frame, and classical controllers are implemented for controlling the electric currents, DC bus voltage and the traction motor speed. The battery is modeled utilizing coulomb counting for SOC estimation and a Thevenin based battery model. The HIL platform is composed by and OPAL-RT OP 4510 and a Texas Instruments TMS32028379D.

关键词： Analytical models Traction motors Real-time systems Hardware Batteries State of charge Energy management

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State Derivative Normalization for Continuous-Time Deep Neural Networks

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IFAC-PapersOnLine 2024年第15期58卷 253-258页

作者： Jonas Weigand Gerben I. Beintema Jonas Ulmen Daniel Görges Roland Tóth Maarten Schoukens Martin Ruskowski Chair of Machine Tools and Control Systems RPTU Kaiserslautern and the German Research Center for Artificial Intelligence Kaiserslautern Germany Control Systems (CS) Group at the Department of Electrical Engineering Eindhoven University of Technology Netherlands. R. Tóth is also affiliated to the Systems and Control Laboratory at the Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature.

关键词： System Identification Continuous-Time Neural Ordinary Differential Equations Nonlinear State-Space

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Output Feedback control for Deterministic Unknown Dynamics Discrete-Time System using Deep Recurrent Q-Networks

TechRxiv

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TechRxiv 2023年

作者： Putri, Adi Novitarini Hidayat, Egi Mahayana, Dimitri Machbub, Carmadi Control System and Computer Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia Institut Teknologi Sains Bandung Bekasi Indonesia

The current application of control theory is commonly carried out in systems with a model or known system dynamics. However, in practice this is a formidable task to achieve as not all state information can be known. The use of the Output Feedback (OPFB) scheme in the field of control systems also possesses a weakness because it requires the use of an observer. This appears rather contradictory as the use of an observer requires system dynamics information. This research proposes an optimal control scheme using Deep Recurrent Q-Networks (DRQN) to generate an optimal control signal trajectory based on a collection of input and output data from the system itself. The approach proposed in this study is based on the Q-Learning method from the Reinforcement Learning (RL) scheme. The Long-Short Term Memory (LSTM) is used to approximate the Q-function and determine the control signals for a system without a known model. The method that we proposed in this study has been tested on four case studies. The control signal trajectory generated from our proposed algorithm, is much smaller than the control signal that generated from classical Q-Learning scheme. The results of this research are certainly relevant to the aim of OPFB, namely that the controller is designed to be able to regulate (bring the state trajectory to zero) and minimize control signal energy. It is empirically discovered that the same result is proven by the norm values resulting from the Q-function trajectory. The norm of Q-function trajectory for our proposed algorithm on the 1st, 2nd, 3rd, and 4th case studies are 2.11E-08, 3.15E-06, 3.79E-09, and 1.59E-13, respectively. © 2023, CC BY.

关键词： Trajectories

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Modeling a Single Camera System based on Predicted Bounding Box and Pan-Tilt Angles

Modeling a Single Camera System based on Predicted Bounding ...

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IEEE International Conference on System engineering and Technology

作者： Adi Novitarini Putri Fabiola Maria Egi Hidayat Dimitri Mahayana Carmadi Machbub Control and Computer Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Sains Bandung Bekasi Indonesia

The control schemes that are related to visual servoing systems are often done through trial and error. Based on the classical control concept, it is necessary to have a mathematical model of the plant before designing the controller. Therefore, this research focuses on producing a mathematical model of the pan-tilt system. We propose the combination of the computer vision method with the classic system identification method, namely N4SID to obtain the dynamics model of a pan-tilt system. In this research, the system identification procedure was successful in producing a state-space model of a visual servoing system using a pan-tilt system and a single camera. This study merged traditional system identification techniques with computer vision to create a discrete-time linear state-space model. When the system order value equals three, the approximation with the minimum MSE value is obtained.

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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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Autonomous Vehicle Drift With a Soft Actor-critic Reinforcement Learning Agent 20

Autonomous Vehicle Drift With a Soft Actor-critic Reinforcem...

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20th IEEE Jubilee World Symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Hunor Toth, Szilard Janos Viharos, Zsolt Bardos, Adam Institute of Computer Science and Control Research Laboratory on Engineering Management Intelligence Intelligent Processes Research Group Budapest Hungary John von Neumann University Faculty of Economics Kecskemét Hungary Budapest University of Technology and Economics Department of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665497046

Self-driving vehicles have become a more and more important field in recent years. Supported by the techniques of Artificial Intelligence (AI), the current tendency of positive results in applications is making it a promising area to focus further research. Additionally, Reinforcement Learning (RL) is already proved to be an efficient approach for complex problems, e.g. robots, industrial systems and also games (like chess, Go), *** is a driving technique at handling limits where the driver intentionally oversteers, with loss of traction, while driving the vehicle through the entirety of a corner. It is a very challenging control task and often results in an accident when it occurs on public roads, consequently, the efficient control of this motion is especially important in the safety of autonomous *** paper reports novel research results whose main goal is to develop a self-driving agent for drift motion control based on vehicle simulation by Matlab/Simulink. Longitudinal and lateral velocity together with the yaw rate formed the state representation of the vehicle. The agent action space consists of two continuous actuator values: pedal ratio and roadwheel angle. The goal of the agent is twofold: first, it has to jump into a drifting state, second, it has to keep the vehicle in *** simulation results show that the proposed Soft Actor-Critic (SAC) RL agent is capable of learning to approach a pre-determined drift equilibrium from cornering and staying in this drift situation as well. For the training, the solution excluded using any kind of prior data, it only works with information gained from the simulation model, which is a remarkable difference from the actual state-of-the-art RL-based solutions. © 2022 IEEE.

关键词： Training Roads Simulation Reinforcement learning Mathematical models Data models Artificial intelligence

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