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Links Between HX-Groups and Hypergroups

Algebra Colloquium 2021年第3期28卷 441-452页

作者： Irina Cristea Michal Novák Babatunde Oluwaseun Onasanya Centre for Information Technologies and Applied Mathematics University of Nova Gorica Slovenia Faculty of Electrical Engineering and Communication Brno University of Technology Czech Republic Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing 404000China Department of Mathematics University of IbadanNigeria

The concept of an HX-group is an upgrade of the concept of a group,in which a new operation is defined on the family of non-empty subsets of a *** this new support set together with the new operation is a group,then we call it an *** the other hand,a hyperoperation is a mapping having the same codomain as the operation of an HX-group,i.e.,the family of non-empty subsets of the initial set,but a different domain-the set *** could be(and was indeed)a source of confusion,which is clarified in this ***,HX-groups naturally lead to constructions of *** links between these two algebraic concepts are presented,with the aim of reviving the old notion of an HX-group in the current research on algebraic *** of such existing links and one newly established link are also discussed.

关键词： HX-group hyperstructure theory Chinese hypergroupoid EL-hyperstructure power set

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Knowledge Graph Completion with Low-Dimensional Gated Hierarchical Hyperbolic Embedding

SSRN

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SSRN 2024年

作者： Fang, Yan Liu, Xiaodong Lu, Wei Pedrycz, Witold Lang, Qi Yang, Jianhua School of Control Science and Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6R 2V4 AB Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia School of Computer Science and Information Technology Northeast Normal University Changchun130117 China

Knowledge Graph Embedding aims to advance the learning performance of knowledge graphs by deliberating the complexity of hierarchical data structures in a finite-dimensional space. Most traditional Euclidean models struggle to accurately describe and navigate the intricate hierarchical relationships between nodes. The hyperbolic embedding methods, particularly in conjunction with graph neural networks, offer promise in effectively modeling complex hierarchies in a limited-dimensional space. These methods can accommodate more information about nodes under the same conditions but are also more susceptible to interference from complex neighborhood features. This paper introduces HyperGatE, which novelty combines a hyperbolic graph hierarchical neighbor structure based on a gated filter and a hyperbolic isometric rotation model for knowledge graph completion. By analyzing the hierarchical structure of the knowledge base and using a gated mechanism to form multi-layer embedding networks of entity-relational paths, the gated filter selects diverse uneven multi-hop relations within the entity neighborhood and measures them through graph attention networks. Furthermore, the simplified hyperbolic isometric model allows for uncovering hierarchical structures in the data. HyperGatE experimentally surpasses the state-of-the-art Euclidean and hyperbolic space models in high and low dimensions across four well-known data, namely WN18RR, FB15k-237, NELL-995, and Kinship. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Deep learning-based segmentation of breast masses using convolutional neural networks 12

Deep learning-based segmentation of breast masses using conv...

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12th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023

作者： Anghelache Nastase, I.-N. Moldovanu, S. Moraru, L. The Modelling & Simulation Laboratory Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Emil Racovita Theoretical Highschool 12-14 Regiment 11 Siret Street Galati800332 Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Department of Chemistry Physics & Environment Faculty of Sciences and Environment Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania

Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.61

关键词： Graphic methods

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Fast optimize-and-sample method for differentiable Galerkin approximations of multi-layered Gaussian process priors

Fast optimize-and-sample method for differentiable Galerkin ...

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International Conference on information Fusion

作者： Muhammad F. Emzir Niki A. Loppi Zheng Zhao Syeda S. Hassan Simo Sä rkkä Control and Instrumentation Engineering Department KFUPM Saudi Arabia NVIDIA Finland Department of Information Technology Uppsala University Sweden Department of Electrical Engineering and Automation Aalto University Finland

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

Multi-layered Gaussian process (field) priors are non-Gaussian priors, which offer a capability to handle Bayesian inference on both smooth and discontinuous functions. Previ-ously, performing Bayesian inference using these priors required the construction of a Markov chain Monte Carlo sampler. To converge to the stationary distribution, this sampling technique is computationally inefficient and hence the utility of the approach has only been demonstrated for small canonical test problems. Furthermore, in numerous Bayesian inference applications, such as Bayesian inverse problems, the uncertainty quantification of the hyper-prior layers is of less interest, since the main concern is to quantify the randomness of the process/field of interest. In this article, we propose an alternative approach, where we optimize the hyper-prior layers, while inference is performed only for the lowest layer. Specifically, we use the Galerkin approximation with automatic differentiation to accelerate optimization. We validate the proposed approach against several existing non-stationary Gaussian process methods and demonstrate that it can significantly decrease the execution time while maintaining comparable accuracy. We also apply the method to an X-ray tomography inverse problem. Due to its improved performance and robustness, this new approach opens up the possibility for applying the multi-layer Gaussian field priors to more complex problems.

关键词： Monte Carlo methods Uncertainty Inverse problems Gaussian processes X-ray tomography Markov processes Robustness

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Optimization of Crane Scheduling Time for Steel Plate Outbound Operations Based on Backtracking Algorithm

Optimization of Crane Scheduling Time for Steel Plate Outbou...

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Chinese automation Congress (CAC)

作者： Jiang Xiang Zhezhuang Xu Jinlong Wang Boyu Chen Qingdong Zhang Xiuren Wu Yuxiong Xia College of Electrical Engineering and Automation Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China Sansteel Minguang Co. Ltd. Sanming China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Steel plate is a highly customized product, and steel plates from the same order may be stored in different stacks. However, during the outbound process, the steel plates of the next order can only be dispatched from the warehouse once all plates from the preceding order have been fully completed. In this case, the outbound scheduling of steel plates is a challenging problem since its efficiency and complexity are affected by the shuffling of steel plates. In order to address the challenge, this paper formulates the steel plate outbound scheduling as a dynamic programming model with the aim of minimizing the crane operation time. To solve the problem, an decision tree-based backtracking search algorithm (DTBSA) is developed, in which the search tree is pruned through lower bound functions and dominance rules, so as to reduce the search range of the solution space. Experimental results prove that the proposed method can obtain optimal scheduling results within a reasonable time range.

关键词： Backtracking Lower bound Cranes Heuristic algorithms Optimal scheduling Search problems Dynamic scheduling Dynamic programming Steel Decision trees

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Compensation control of PMSMs Based on a High-order Sliding Mode Observer with Inertia Identification

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Li, Ke Ding, Jie Sun, Xiaodong Lei, Gang Dianov, Anton Demidova, Galina Prakht, Vladimir Jiangsu University School of Electrical and Information Engineering Zhenjiang212013 China Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China ITMO University Faculty of Control Systems and Robotics Petersburg197101 Russia Ural Federal University Department of Electrical Engineering Ekaterinburg620062 Russia

This article proposes a new sliding mode control (NSMC) reaching law to improve the speed tracking and anti-interference performance of permanent magnet synchronous motors (PMSMs). The NSMC improves the sliding mode control (SMC) in approaching speed and reducing chattering. Firstly, considering that the external load interference will adversely affect the control system, a high-order sliding mode observer (HOSMO) is designed to observe the load torque, and the observed value is compensated to the output of the speed controller. Then, combined with the Landau algorithm, a variable adaptive gain algorithm is introduced to decrease the impact of inertia mismatch on the system control performance. The algorithm identifies the rotational inertia online, reduces the effect of load disturbance on the identification performance, and updates the identification results to the observation in processors and controllers. Experimental results show that the proposed control method reduces the speed and current fluctuations of the system when load torque and inertia vary and improves the system's robustness. © 2013 IEEE.

关键词： Permanent magnets

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Dynamic Event-Triggered Model Predictive control for Multiple Platoons under Non-Ideal Communications

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IEEE Internet of Things Journal 2025年

作者： Han, Qiaoni Xu, Chengfei Wang, Xiaocheng Yang, Hongjiu Zuo, Zhiqiang Yang, Bo Guan, Xinping Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China Tianjin Normal University Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin300387 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

The uncertainty of wireless communication seriously affects the control performance of the platoon. In this paper, a model predictive control (MPC) method based on dynamic event-triggering is proposed to address the impact of non-ideal communications on multi-vehicle systems. Firstly, a hierarchical platoon architecture combining backbone layer and sub-platoon layers is proposed to achieve efficient coordination among multiple platoons, and a communication-aware control mechanism is designed for each vehicle to effectively mitigate the adverse effects of non-ideal communication scenarios. Secondly, a dynamic event-triggering mechanism is established for each vehicle, which is related to time-varying delay and random packet loss parameters, so as to effectively reduce the frequency of MPC solutions and data transmission. Additionally, based on different communication topologies, distributed model predictive controllers are suggested for backbone layer and sub-platoon layer to achieve precise and comfortable tracking. Then, sufficient conditions are provided to ensure the recursive feasibility of the MPC algorithm and the stability of the platoon system. Finally, extensive simulations are conducted, and the results demonstrate the effectiveness of the proposed algorithm in terms of control performance and low computational complexity under non-ideal communication conditions. © 2014 IEEE.

关键词： Predictive control systems

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A Subordinate Multi-Swarm Particle Swarm Optimization Algorithm based on the Dynamic Random Cooperative Learning Strategy 22

A Subordinate Multi-Swarm Particle Swarm Optimization Algori...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Li, Mi Wang, YuanYuan Lu, ShengFu Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781450396110

Multi-swarm particle swarm optimization algorithms have received much attention in recent years due to their effectiveness in maintaining the diversity of population. However, during the evolution process, the algorithm often suffers from the aggregation of particles within the sub-swarm which could make the particles fall into the local optimum. In order to solve this problem, we propose a new multi-swarm particle swarm optimization algorithm: A Subordinate Multi-Swarm Particle Swarm Optimization Algorithm based on the Dynamic Cooperative Learning Strategy Algorithm (SMS-DCLS-PSO). In the SMS-DCLS-PSO algorithm, we use the number of stagnant local optimums in the sub-swarms as the aggregation flag. When the sub-swarms reach the aggregation condition, the dynamic random cooperative learning strategy is adopted to realize the adaptive periodic information exchange between sub-swarms. To further enhance the diversity of the population, the best particle of the main population and the worst particle of the sub-swarms are respectively mutated. When the sub-swarms don't meet the aggregation conditions, the sub-swarms evolve independently which means the particles only exchange the information within their own sub-swarm. The main-swarm evaluates the local optimal position of each sub-swarm and its own local optimal position in each iteration to update the velocity. The results of the comparison with SMS-DCLS-PSO and other PSO algorithms on the latest public test suite demonstrate that SMS-DCLS-PSO has a better performance on optimization. © 2022 ACM.

关键词： Particle swarm optimization (PSO)

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Particle Swarm Optimization Algorithm with Mirror-image Inertia Weight 22

Particle Swarm Optimization Algorithm with Mirror-image Iner...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Lu, ShengFu Han, JiXin Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781450396110

So far, no matter what kind of inertia weighting algorithm, its value changes within the positive range (0, 1.2). To further improve the optimization performance of particles, this paper proposes a mirror-image inertia weight (miIW) strategy, which extends the value range of inertia weight from the positive domain (0, 1.2) to the negative domain (-1.2, 0). The CEC2014 function was used to test the optimization performance of different inertia weight algorithms in the PSO algorithm using three strategies of positive inertia weight (PIW), negative inertia weight (NIW), and mirror-image inertia weight (miIW), The results show that the optimization performance of miIW is better than PIW and NIW. To verify the actual optimization ability of the mirror-image inertial weight (miIW), we compared the effects of PSO with miIW, PSO with PIW, and PSO with NIW in the optimization of the classification model parameters. The results show that the PSO with miIW optimization result is the best. These research results show that the miIW strategy increases the flight distance of the particles, which expands the search range of the particles, improves the overall development capabilities of the particles, and increases the search density of the particles. The expansion of the search range and the increase of the search density work together to promote a significant improvement in the optimization performance of the PSO algorithm. © 2022 ACM.

关键词： Mirrors

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An Optimizing Method Based on Combining Edge Computer and Long Short-Term Memory Networks Applied to Solar Power Forecasting

An Optimizing Method Based on Combining Edge Computer and Lo...

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2021 IEEE International Future Energy Electronics Conference, IFEEC 2021

作者： Nguyen, Minh-Ngoc Pham, Manh-Hai Wu, Yuan-Kang Nguyen, Ngoc-Trung Phenikaa University Faculty of Electrical and Electronic Engineering Hanoi Viet Nam Electric Power University Faculty of Energy Technology Hanoi Viet Nam National Chung-Cheng University Department of Electrical Engineering Chiayi Taiwan Electric Power University Control and Automation Faculty Hanoi Viet Nam

ISBN: (纸本)9781665434485

Along with the rapid development of solar energy sources on the global power system,the challenges related to the weather instability of this power type are also of increasing concern. Intending to solve these challenges,the study of solar power forecasting has become one of the most important problems in the power system field. Most of the published forecast algorithms aim to minimize prediction errors. However,in reality,convenience and data processing time are two necessary goals to commercialize a forecasting software. Recently,IoT is emerging as an effective solution to solve the above multi-objective problem. Especially,in the current trend of real-time forecasting,the application of Edge Computer in the IoT environment is bringing new opportunities for the goal of reducing data processing time while ensuring acceptable forecast errors. Moreover,Edge Computer also reduces the risk of network congestion due to its distributed processing characteristics. This paper introduces a new methodology using Edge Computer combined with the LSTM algorithm applied to the solar power forecasting problem. Real-time calculation results show that the accuracy can be improved by the good choice of the converted data type while the execution time is halved compared to without using Edge Computer. © 2021 IEEE.

关键词： Solar energy

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