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TechRxiv 2023年

作者： Bożejko, Wojciech Klempous, Ryszard Pempera, Jaroslaw Rozenblit, Jerzy Smutnicki, Czeslaw Uchroński, Mariusz Wodecki, Mieczyslaw The Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Poland University of Arizona United States Department of Computer Engineering Wroclaw University of Science and Technology Poland Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Poland Department of Telecommunications and Teleinformatics Wroclaw University of Science and Technology Poland

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. The main disadvantage of calculations on quantum computers is their non-determinism. For optimization problems, of course, it is possible to get surprisingly good results but without a guarantee of the actual optimality of the result, i.e., an assurance that there exists no better solution which could potentially be found by the quantum machine. In this paper, we propose a novel approach that provides such a guarantee of optimality. We generate a solution that is optimal in the strict mathematical sense, without probabilistic considerations. To accomplish this, we use the D-Wave quantum machine working as a sampler implementing quantum annealing – an approach considered a hardware metaheuristic – to obtain upper and lower bounds on the value of the objective function of the problem under consideration. Then, we use the Branch and Bound scheme controlled by quantum annealing. This allows us to obtain very quickly – in constant time – the boundaries of the considered subproblems. Our approach is a combination of calculations realized on a quantum machine controlled by procedures implemented on a classical computer, allowing us to generate optimal solutions to the NP-hard problems of task scheduling on a single machine with a total weighted tardiness criterion. The proposed quantum algorithm shows a significant advantage over the classical CPU approach both in terms of calculation time and the number of generated solution tree nodes. © 2023, CC BY.

关键词： Quantum computers

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Two-Stage Incentive Mechanism Based on a Cooperative Mode: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Wang, Long Wang, Yingjie Teng, Haojun Jiao, Xiuzhen Sun, Meimei Yang, Jishen Cai, Zhipeng Yantai University School of Computer and Control Engineering Yantai264005 China China Judicial Big Data Research Institute Co. Ltd China Naval Aviation University China Georgia State University Department of Computer Science AtlantaGA30303 United States

With the explosive growth of mobile data, Mobile Crowd Sensing (MCS) has become a popular paradigm for large-scale data collection. The difficulty of data collection and the gaps in workers’ sensing capabilities are key factors to consider in worker recruitment and task assignment. To address these issues, we designed a two-stage cooperative incentive mechanism. In the first stage, a shelving level is introduced to assess task difficulty. Tasks are divided into high-quality and low-quality groups based on quality scores, while workers are categorized into high-ability and low-ability groups based on their historical performance. The Improved Chaotic Particle Swarm Optimization (ICPSO) algorithm is then applied to generate optimal task combinations. In the second stage, we address benefit distribution among cooperating workers by introducing time-dependent rewards and employing Stackelberg game theory to analyze the optimal completion time for both high-ability and low-ability workers. This analysis determines the optimal reward distribution and ability value allocation, with proof of the existence and uniqueness of a Nash equilibrium. Comparative experiments conducted on real-world datasets demonstrate that our cooperative task mechanism outperforms existing task bundling methods, validating its rationality and effectiveness. © 2014 IEEE.

关键词： Nash equilibrium

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Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

arXiv

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arXiv 2023年

作者： Westny, Theodor Oskarsson, Joel Olofsson, Björn Frisk, Erik Department of Electrical Engineering Linköping University Sweden Department of Computer and Information Science Linköping University Sweden Department of Automatic Control Lund University Sweden

Given their adaptability and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neuralnetwork-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex ones, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun’s can significantly improve predictions. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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Absolute longitudinal distance measurement verification of a standard polystyrene nanoparticle near a surface in water by means of multi-wavelength evanescent field 22

Absolute longitudinal distance measurement verification of a...

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22nd International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2022

作者： Blattler, Aran Khajornrungruang, Panart Suzuki, Keisuke Baba, Akiyoshi Goto, Daiki Graduate School of Computer Science and System Engineering Mechanical Information Science and Technology Course Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Advanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan Center of Microelectronic System Kyushu Institute of Technology Fukuoka Japan School of Computer Science and System Engineering Advanced Mechanical Course Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781998999118

In the wet semiconductor process, abrasive nanoparticles are widely used in the chemical mechanical polishing (CMP) process to polish wafer surfaces for high planarization. During polishing, nanoparticles move with a high-speed three-dimensional (3D) position in the solution. In the 3D particles's moving positions, Z is a perpendicular longitudinal distance between the surface to the nanoparticle that plays a significant role in polishing, which could not be measured by general microscopy without longitudinal scanning. To understand nanoparticle phenomena near-surface, we have proposed and developed the apparatus with real-time fastmotion tracking on optical multi-wavelength evanescent fields microscopy to measure the 3D motion of the nanoparticle in water without scanning. In euspen's 2020, we could verify the relative distance ?Z of nanoparticles in water, but the absolute distance Z is unknown yet. In this manuscript, we have therefore developed an invisible nano-step height in water to verify the absolute height Z of an individual nanoparticle. The invisible nano-step height in water is made from an optical resin with a refractive index of 1.35 that is close to a refractive index of water to avoid unnecessary light during experimental verification. The nano-step height was less than 100 nm on a glass surface, fabricated using nanoimprint lithography. Polystyrene f100 nm standard particles in water were used to verify the height Z. The results of height Z of nanoparticles could be measured less than 100 nm with the measurement uncertainty ±10 nm (2s), which is related to the interferometer result of the nano-step height. © European Society for Precision engineering and Nanotechnology, Conference Proceedings - 22nd International Conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： Nanoparticles

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Analysis and Comparison of Optimization Method for Comparator Design

Analysis and Comparison of Optimization Method for Comparato...

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2022 International Conference on Mechanisms and Robotics, ICMAR 2022

作者： Fan, Zhipeng Yang, Zeheng Zhang, Fei School of Electrical Engineering and Computer Science The Pennsylvania State University University Park United States School of Measurement & Control Technology and Instrument Tianjin University Tianjin China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781510657328

The comparator is essential in analog-to-digital converters (ADC). This paper analyzes different improvements in the designs of comparators. In order to improve the energy efficiency, the dynamic bias comparator was developed, and it also reduces the input-referred noise. A floating reservoir design was then adopted to solve some weaknesses of the dynamic bias comparator and further increases energy efficiency. On-chip inductors and charge pumps were also implemented to perfect the performance of the comparator. These two changes improve delay performance, and the first one reduces power consumption simultaneously. The last update was an energy-efficient ring oscillator collapse-based comparator. It adjusts the performance of the comparator without any external control and then makes the energy consumption achieve a minimum value without energy scaling. At the same time, this comparator has a wider resolution tuning capability. © 2022 SPIE.

关键词： Comparators (optical)

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Empirical Based Irrigation Model Using Predicted Soil Moisture for Durian Plantation 3rd

Empirical Based Irrigation Model Using Predicted Soil Moistu...

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3rd International Conference on control, Instrumentation and Mechatronics engineering, CIM 2022

作者： Ramli, Muhammad Shahrul Azwan Abidin, Mohamad Shukri Zainal Hean, Pui Boon Rahman, Mohd Amiruddin Abd Perumal, Thinagaran Reba, Mohd Nadzri Md Division of Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai Malaysia IoT Department MIE Industrial Sdn. Bhd Selangor Puchong Malaysia Department of Physics Faculty of Science Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Skudai Malaysia

ISBN: (纸本)9789811939228

It is vital to the agricultural activities to have sufficient water supply for its operation and maintenance mainly for cultivation to keep it in good condition Therefore, it is important to determine the soil moisture levels existing while designing a precise irrigation system. Installing soil moisture sensors in each tree is complicated or excessively expensive. Forecasting the value using climate data is a viable solution in this scenario. Climate data are used to forecast soil moisture and then utilized in this irrigation model. This study uses an Artificial Neural Network (ANN) to forecast soil moisture values. The statistical method is used to determine the predicted values’ correctness. After the process, the irrigation volume and schedule are calculated based on the most accurate prediction findings. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Soil moisture

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New Crowd Sensing Computing in Space-Air-Ground Integrated Networks

New Crowd Sensing Computing in Space-Air-Ground Integrated N...

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2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021

作者： Wang, Yingjie Wang, Mingze Meng, Lingkang Zhang, Qi Tong, Xiangrong Cai, Zhipeng School of Computer and Control Engineering Yantai University Shandong264005 China Georgia State University Department of Computer Science AtlantaGA30303 United States

ISBN: (纸本)9781665400114

With the development of Space-Air-Ground Inte-grated Networks (SAGINs), how to design a distributed task cooperation mechanism with low-latency computing, high data rates, low delay and high coverage is an important research content. Crowd Sensing computing could provide the effective solutions for SAGINs. This paper first considers the combination of SAGINs with crowd sensing computing for improving the task completion efficiency in SAGINs. The crowdsoucing methods are considered into the SAGINs in order to solve the complex and large-scale sensing tasks. In this paper, based on Blockchain and Multi-access Edge Computing (MEC), the traditional centralized application service architecture is transferred into a lightweight and distributed network architecture. Under this lightweight and distributed network architecture, this paper proposes a Q-learning based Task Cooperation (QTC) mechanism through dividing the three-dimensional space based on Voronoi diagram. We conduct the comparison experiments for the proposed QTC mechanism on real dataset. The performances of the proposed QTC mechanism on rate of task completion and total income of task execution are evaluated by the comparison experiments. © 2021 IEEE.

关键词： Blockchain

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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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Boundary Iterative Learning control for Repetitive Spatio-Temporal Processes

Boundary Iterative Learning Control for Repetitive Spatio-Te...

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IEEE Conference on Decision and control

作者： Maciej Patan Kamil Klimkowicz Krzysztof Patan Eric Rogers Institute of Control and Computation Engineering University of Zielona Góra Poland Institute of Control Electronic and Electric Engineering University of Zielona Góra Poland Department of Electronics and Computer Science University of Southampton UK

Iterative learning control for lumped processes is well established. Therefore, there is strong interest in developing designs that would produce similar flexibility for classes of distributed parameter systems. This paper develops a design for application to examples described by partial differential equations of convection-diffusion type in the multidimensional spatial domain, which have many applications, such as heat transfer problems. The system response is measured, and then the control is applied via specific boundary conditions using a sensor/actuator network, i.e., boundary control, as opposed to designs that require sensing and actuating application over the domain the dynamics are defined over. The convergence properties of the design are established in conjunction with rules for tuning its parameters for performance enhancement. Finally, the new design is applied to a laser heating problem in wafer staging, which requires boundary control.

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