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Iranian Conference on electrical engineering (ICEE)

作者： Arash Iranfar Mohammad Soleymannejad Behzad Moshiri Hamid D. Taghirad School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.

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Unified Temporal Erasable Itemset Mining with a Lower-Bound Strategy

Unified Temporal Erasable Itemset Mining with a Lower-Bound ...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Hong, Tzung-Pei Li, Jia-Xiang Tsai, Yu-Chuan Huang, Wei-Ming National University of Kaohsiung Department of Computer Science and Information Engineering Kaohsiung Taiwan National Sun Yat-Sen University Department of Computer Science and Engineering Kaohsiung Taiwan National University of Kaohsiung Library and Information Center Kaohsiung Taiwan China Steel Inc Department of Electrical and Control Kaohsiung Taiwan

ISBN: (纸本)9781665480451

Erasable itemset mining has been a valuable mining problem for manufacturers. It can extract less profitable materials from a product dataset and provide managers with good decision-making and a trade-off between cost and profit. However, the traditional erasable itemset mining methods seldom consider the time factor. For time-sensitive industries such as agro-processors, the time range is important in determining which materials are less profitable. Hong et al. first proposed the concept of temporal erasable itemset mining and seven lifespan options. They also proposed a unified temporal erasable (UTE) mining algorithm for getting incomplete temporal erasable itemsets. Howerever, the UTE algorithm does not satisfy the property of downward closure. In this work, we propose an improved algorithm to improve the performance of the UTE algorithm based on a lower-bound strategy and satisfying the property of downward closure. The proposed algorithm uses a hash table to store information that will be reused during the mining process to avoid scanning a dataset multiple times. The designed lower-bound strategy can preserve the downward closure property, narrowing the search space of candidate itemsets. In numerical experiments, we compare the performance using several metrics between the proposed method and the previous work. From the results of experiments, our proposed method outperforms the existing method on various metrics, such as execution time and the number of candidate erasable itemsets. © 2022 IEEE.

关键词： Data mining

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Convergence of energy-based learning in linear resistive networks

arXiv

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arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

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Exploiting Hankel–Toeplitz Structures for Fast Computation of Kernel Precision Matrices

arXiv

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arXiv 2024年

作者： Viset, Frida Kullberg, Anton Wesel, Frederiek Solin, Arno Delft Center for Systems and Control Delft University of Technology Netherlands Department of Electrical Engineering Linköping University Sweden Department of Computer Science Aalto University Finland

The Hilbert–space Gaussian Process (hgp) approach offers a hyperparameter-independent basis function approximation for speeding up Gaussian Process (gp) inference by projecting the gp onto M basis functions. These properties result in a favorable data-independent O(M3) computational complexity during hyperparameter optimization but require a dominating one-time precomputation of the precision matrix costing O(NM2) operations. In this paper, we lower this dominating computational complexity to O(NM) with no additional approximations. We can do this because we realize that the precision matrix can be split into a sum of Hankel–Toeplitz matrices, each having O(M) unique entries. Based on this realization we propose computing only these unique entries at O(NM) costs. Further, we develop two theorems that prescribe sufficient conditions for the complexity reduction to hold generally for a wide range of other approximate gp models, such as the Variational Fourier Feature (vff) approach. The two theorems do this with no assumptions on the data and no additional approximations of the gp models themselves. Thus, our contribution provides a pure speed-up of several existing, widely used, gp approximations, without further approximations. © 2024, CC BY.

关键词： Gaussian distribution

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Infrastructure-dependent ramp-metering control for METANET-s

Infrastructure-dependent ramp-metering control for METANET-s

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IEEE Conference on Decision and control

作者： Ayda Kamalifar Carlo Cenedese Michele Cucuzzella Antonella Ferrara Department of Electrical Computer Science and Biomedical Engineering University of Pavia Pavia Italy Department of Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland Center for Systems and Control ENTEG Faculty of Science and Engineering (FSE) University of Groningen (UG) Nijenborgh 4 Groningen the Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we propose a novel infrastructure-dependent ramp-metering control for the recently proposed METANET with service station (METANET-s) model, i.e., a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. We study the effect of a ramp-metering control scheme on a highway stretch with a service station and show that it is capable of actively regulate internal traffic demand attempting to exit the service station via its on-ramp, on top of contributing to decrease the traffic congestion on the mainstream. In fact, the proposed control scheme effectively prevents the backlog of vehicles attempting to merge back onto the mainstream. This dynamic control mechanism is further endowed by a route guidance control strategy increasing the share of vehicles stopping at the service station during main-stream congestion periods, e.g. via incentives. The combined effect of our control schemes allows to take full advantage of the presence of service stations, reducing the overall traffic congestion. Simulation results demonstrate the effectiveness of the proposed control strategies.

关键词： Simulation Merging Numerical models Vehicle dynamics Traffic congestion

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Hidden Multidimensional Modulation Side Channels in Quantum Protocols

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Physical Review Letters 2025年第13期134卷 130802-130802页

作者： Amita Gnanapandithan Li Qian Hoi-Kwong Lo Department of Electrical and Computer Engineering University of Toronto Toronto Canada Center for Quantum Information and Quantum Control University of Toronto Toronto Canada Quantum Bridge Technologies Inc. 108 College Street Toronto Canada

Quantum protocols including quantum key distribution and blind quantum computing often require the preparation of quantum states of known dimensions. Here, we show that, rather surprisingly, hidden multidimensional modulation is often performed by practical devices. This violates the dimensional assumption in quantum protocols, thus creating side channels and security loopholes. Our work has important impacts on the security of quantum cryptographic protocols.

关键词： Quantum cryptography

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Research of Improved TD3 Robotic Arm Path Planning using Evolutionary Algorithm

Research of Improved TD3 Robotic Arm Path Planning using Evo...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Feng Zhang Jianqiang Xiong Shuai Yuan Ke Wen School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

Nowadays, application of automated intelligent robot arm devices to improve industrial production efficiency has become a popular research field in the world. The previous off-line path planning method of robotic arm has the inadequacies of low efficiency and slow speed. Although the deep reinforcement learning has accomplished many achievements in the path planning of control manipulator, there are still some problems such as long training time and low planning accuracy. To solve the abovementioned issues, we propose an improved Twin Delayed Deep Deterministic policy gradient (TD3) algorithm (Improved Cross-Entropy Method-TD3: ICEM-TD3) for the path planning of the robotic arm. First, this paper combines evolutionary strategies with TD3 to generate action networks. Then the exploration of TD3 algorithm in the action space is replaced by the exploration in the parameter space. In addition, this paper designs a new reward function to weaken the redundancy of planning and accelerate the convergence speed of training. Finally, the Gazebo simulator is adopted to verify the proposed algorithm, and the results illustrate that the proposed algorithm can greatly improve the accuracy of the path planning of the manipulator using deep reinforcement learning.

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control of Reaction-Diffusion Processes Under Communication Delays

Control of Reaction-Diffusion Processes Under Communication ...

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European control Conference (ECC)

作者： Luca Ballotta Juncal Arbelaiz Vijay Gupta Luca Schenato Mihailo R. Jovanović Delft Center for Systems and Control Delft University of Technology Delft The Netherlands Department of Mechanical and Aerospace Engineering Center for Statistics and Machine Learning Princeton University Princeton NJ USA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Information Engineering University of Padova Padova Italy Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper we investigate the design of optimal spatially distributed controllers for a linear and spatially invariant reaction-diffusion process over the real line. The controller receives state measurements from different spatial locations with non-negligible delays. In this set-up and for the class of proportional spatially invariant state feedback controllers, the optimal control synthesis problem is equivalent to a feedback gain optimization for a spatially distributed delay system. We show that the spatial locality of optimal feedback gains is affected not only by diffusion and reaction coefficients, but also by the parameter representing communication time-delay that causes a sharp flattening of the control gains. In the expensive control regime, the optimal controller is solved analytically, yielding some practical design guidelines.

关键词： State feedback Delay systems Design methodology Process control Optimal control Europe Delays

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Deep Q-learning Sampling Based on Advantages 5

Deep Q-learning Sampling Based on Advantages

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5th International Conference on Intelligent Robotics and control engineering, IRCE 2022

作者： Xie, Ming Ren, Xinrui Yu, Jianbo Shu, Feng Shanghai Engineering Research Center of Ultra-Precision Motion Control and Measurement Academy for Engineering and Technology Fudan University Shanghai China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Microelectronics Fudan University State Key Laboratory of ASIC and System Shanghai200433 China

ISBN: (数字)9781665469951

ISBN: (纸本)9781665469951

Deep Q-learning (DQN) has shown recent success on a wide range of complicated sequential decision-making issues, especially in the classic control area. However, in most DQN training, the sampling policies, particularly the ϵ-greedy policy and its derivation rules, produce noise erroneously due to the unsteadiness and unreliability of Q values utilized for searching for optimum actions. We begin with a fundamental hypothesis: complete advantages-based exploitation may be employed to help in successful exploration, and the gap between advantages on outstanding and poor actions tends to extend during the early phases of training before stabilizing. We then present a novel DQN architecture with a superior sampling policy that is totally and directly based on advantage values. This new DQN is named A-sampling DQN, and it requires minor code changes based on the original DQN codes. In most cases, A-sampling DQN and its derivatives outperformed the traditional DQN method. © 2022 IEEE.

关键词： Deep learning

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Virtually coupled train set control subject to space-time separation:A distributed economic MPC approach with emergency braking configuration

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High-Speed Railway 2024年第3期2卷 143-152页

作者： Xiaolin Luo Tao Tang Le Wang Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China Department of Electrical and Computer Engineering University of CaliforniaRiversideCA 92521USA Beijing Zongheng Electro-Mechanical Technology Development Co. Ltd.Beijing 100081China School of Automation and Intelligence Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe *** guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB *** this case,the minimal safety distance is usually numerically calculated without an analytic ***,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing *** solve this problem,we propose a Distributed Economic Model Predictive control(DEMPC)approach with computation efficiency and theoretical ***,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved *** theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient ***,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among ***,experimental results demonstrate the performance and advantages of the proposed approaches.

关键词： Virtually coupled train set Space-time separation Economic model predictive control Distributed model predictive control Emergency braking configuration

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