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Global Energy Conference (GEC)

作者： Özay Can Davut Izci Serdar Ekinci Raymond Ghandour Mohammad Salman Department of Electronics and Automation Recep Tayyip Erdogan University Rize Turkey Department of Computer Engineering Batman University Batman Turkey College of Engineering and Technology American University of the Middle East Kuwait

ISBN: (数字)9798331532611

ISBN: (纸本)9798331532628

As a result of the rising usage of renewable energy sources (RES) and the altering load profile, it is becoming increasingly difficult to strike a balance between production and demand. This is because renewable energy sources are becoming more prevalent. For this reason, it is of the utmost importance to put the process of load frequency control (LFC) into action. Taking this into consideration, the objective of this study is to optimize the adjustment of the parameters of the proportionalintegral-derivative with filter (PID-F) controller for the LFC process in a two-area power system that consists of photovoltaic (PV) panels and wind energy systems simultaneously. During the past few years, a number of innovative algorithms have been developed. These algorithms include the mountain gazelle optimizer (MGO), the hippopotamus optimizer (HO), and the puma optimizer (PO). The parameters of the PID-F controller were determined with the help of these algorithms, which were applied accordingly. The findings of the simulation reveal that all three algorithms managed to achieve results that were successful and comparable to one another in the LFC process. These results were attained by all three algorithms.

关键词： Photovoltaic systems Renewable energy sources Time-frequency analysis Wind energy Process control Production Wind power generation Power systems Frequency control Testing

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Adapting Segment Anything Model for Unseen Object Instance Segmentation

arXiv

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arXiv 2024年

作者： Cao, Rui Song, Chuanxin Yang, Biqi Wang, Jiangliu Heng, Pheng-Ann Liu, Yun-Hui Department of Mechanical and Automation Engineering The Chinese University of Hong Kong New Territories Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong New Territories Hong Kong

Unseen Object Instance Segmentation (UOIS) is crucial for autonomous robots operating in unstructured environments. Previous approaches require full supervision on large-scale tabletop datasets for effective pretraining. In this paper, we propose UOIS-SAM, a data-efficient solution for the UOIS task that leverages SAM's high accuracy and strong generalization capabilities. UOIS-SAM integrates two key components: (i) a Heatmap-based Prompt Generator (HPG) to generate class-agnostic point prompts with precise foreground prediction, and (ii) a Hierarchical Discrimination Network (HDNet) that adapts SAM's mask decoder, mitigating issues introduced by the SAM baseline, such as background confusion and over-segmentation, especially in scenarios involving occlusion and texture-rich objects. Extensive experimental results on OCID, OSD, and additional photometrically challenging datasets including PhoCAL and HouseCat6D, demonstrate that, even using only 10% of the training samples compared to previous methods, UOIS-SAM achieves state-of-the-art performance in unseen object segmentation, highlighting its effectiveness and robustness in various tabletop scenes. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Prediction and Analysis of Power Consumption and Power Loss at Industrial Facilities

Prediction and Analysis of Power Consumption and Power Loss ...

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2023 International Conference on Clean Electrical Power, ICCEP 2023

作者： Gracheva, Elena Ivanovna Eduard Elsovich, Ibatullin Sinyukova, Tatiana Valtchev, Stanimir Miceli, Rosario Campagna, Nicola Kazan State Power Engineering University Departm. of Power Supply of Industrial Enterprises Kazan Russia Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia University Nova of Lisbon Faculty Fct and Uninova -CTS Portugal University of Palermo Department of Engineering Italy

ISBN: (纸本)9798350348378

In this article, a study of radial and trunk circuits of networks was carried out and power losses were determined using the proposed coefficients. Equivalent resistances of the investigated circuits were calculated considering the changes in circuit and mode parameters. Results of calculations are presented, considering equipment layout and technical parameters. © 2023 IEEE.

关键词： Equivalent circuits

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Learning Fine-Grained Alignment for Aerial Vision-Dialog Navigation 39

Learning Fine-Grained Alignment for Aerial Vision-Dialog Nav...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Su, Yifei An, Dong Chen, Kehan Yu, Weichen Ning, Baiyang Ling, Yonggen Huang, Yan Wang, Liang School of Artificial Intelligence University of Chinese Academy of Sciences China MAIS Institute of Automation of Chinese Academy of Sciences China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Electrical and Computer Engineering Department Carnegie Mellon University United States Robotics X Tencent Shenzhen China

ISBN: (纸本)157735897X

Aerial Vision-Dialog Navigation (AVDN) is a new task that requires drones to navigate to a target location based on human-robot dialog history. This paper focuses on the critical fine-grained cross-modal alignment problem in AVDN, requiring the drone to align language entities with visual landmarks in top-down views. To achieve this, we first construct a Fine-Grained AVDN (FG-AVDN) dataset via a semi-automatic annotation pipeline, providing diverse multimodal annotations at the entity-landmark level. Based on this, a novel Fine-grained Entity-Landmark Alignment (FELA) method is proposed to learn the cross-modal alignment explicitly. Concretely, FELA first boosts the drone’s visual understanding with a precise semantic grid representation, which captures the environmental semantics and spatial structure simultaneously. Subsequently, to learn the entity-landmark alignment, we devise cross-modal auxiliary tasks from three perspectives, including grounding, captioning, and contrastive learning. Extensive experiments demonstrate that our explicit entity-landmark alignment learning is beneficial for AVDN. As a result, FELA achieves leading performance with 3.2% SR and 4.9% GP improvements over prior arts. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Robots

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Modeling replicator dynamics in stochastic games using Markov chain method 20

Modeling replicator dynamics in stochastic games using Marko...

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20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021

作者： Deng, Chuang Rong, Zhihai Wang, Lin Wang, Xiaofan Department of Automation Shanghai Jiao Tong University School of Computer Science and Engineering University of Electronic Science and Technology of China School of Mechatronic Engineering and Automation Shanghai University

ISBN: (纸本)9781713832621

In stochastic games, individuals need to make decisions in multiple states and transitions between states influence the dynamics of strategies significantly. In this work, by describing the dynamic process in stochastic game as a Markov chain and utilizing the transition matrix, we introduce a new method, named state-transition replicator dynamics, to obtain the replicator dynamics of a stochastic game. Based on our proposed model, we can gain qualitative and detailed insights into the influence of transition probabilities on the dynamics of strategies. We illustrate that a set of unbalanced transition probabilities can help players to overcome the social dilemmas and lead to mutual cooperation in a cooperation back state, even if the stochastic game has the same social dilemmas in each state. Moreover, we also present that a set of specifically designed transition probabilities can fix the expected payoffs of one player and make him lose the motivation to update his strategies in the stochastic game. © 2021 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Dynamics

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Simulation of the Functional Characteristics of Low-Voltage Switching Devices, Based on the Example of Automatic Circuit Breakers

Simulation of the Functional Characteristics of Low-Voltage ...

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2023 International Conference on Clean Electrical Power, ICCEP 2023

作者： Petrov, Almaz Radikovich Ivanovna Gracheva, Elena Sinyukova, Tatiana Valtchev, Stanimir Miceli, Rosario Rahman, Aqeel Ur Kazan State Power Engineering University Departm. of Power Supply of Industrial Enterprises Kazan Russia Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia Cts University Nova Faculty Fct and Uninova Lisbon Portugal University of Palermo Department of Engineering Italy

ISBN: (纸本)9798350348378

The article discusses the main features of mechanical, chemical, thermal, and electrical processes occurring in the contact zones of the surfaces of contact connections of electrical devices. The parameters determining the value of power losses and power consumption in low voltage switching equipment are analyzed. The main features of the characteristics of the topology and technical condition in the operating conditions of the equipment of shop low-voltage industrial power supply networks are shown. A comparative analysis of the technical characteristics of automatic circuit breakers. Using catalog data, the dependences of active power losses in circuit breakers on rated currents are established. Algorithms have been developed and the obtained dependencies have been modeled using approximating functions. The standard deviation of the compiled approximating functions is calculated. Analytical expressions of the dynamics of power losses per pole in the studied machines of some companies of the function of the rated current are presented. The graphical dependencies of the investigated parameters of low-voltage equipment are presented. The developed models are recommended to be used to increase the reliability of the assessment and refinement of the amount of active power and electricity losses in low-voltage electrical networks of industrial power supply systems. © 2023 IEEE.

关键词： Electric circuit breakers

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PID Controller Design for DFIG-based Wind Turbine via Reptile Search Algorithm

PID Controller Design for DFIG-based Wind Turbine via Reptil...

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2022 IEEE Global Energy Conference, GEC 2022

作者： Izci, Davut Ekinci, Serdar Budak, Cafer Gider, Veysel Batman University Department of Electronics and Automation Batman Turkey Batman University Department of Computer Engineering Batman Turkey Rectorate Batman University Distance Education Application and Research Center Batman Turkey Dicle University Department of Electric-Electronic Engineering Diyarbakir Turkey

ISBN: (数字)9781665497510

ISBN: (纸本)9781665497510

This paper presents a new design procedure for a doubly fed induction generator (DFIG) based wind energy conversion (WEC) system in a wind turbine (WT) using a proportional-integral-derivative (PID) controller and a recent metaheuristic approach known as reptile search algorithm (RSA). As the control scheme has a significant role on the efficiency and reliability of DFIG-based WEC system, we aim to propose the RSA tuned PID controller as the most efficient approach to operate this system. To demonstrate the efficiency and reliability of the proposed design method, previously reported design schemes such as gravitational search algorithm, bacterial foraging optimization and particle swarm optimization based PID controller approaches were used for comparisons. The obtained results showed that the proposed reptile search algorithm tuned PID controller with 6th order transfer function model of doubly fed induction generator enhances the transient performance considerably compared to other reported design approaches for wind energy conversion system. © 2022 IEEE.

关键词： Three term control systems

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Constrained Residual Race: An Efficient Hybrid Controller for Autonomous Racing

Constrained Residual Race: An Efficient Hybrid Controller fo...

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Chinese automation Congress (CAC)

作者： Mengmeng Zou Yiwen Lu Hanxiao Liu Yilin Mo Xiaoqiang Ren School of Computer Engineering and Science School of Institute of Artificial Intelligence School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Automation and BNRist Tsinghua University Beijing China

Deep reinforcement learning (DRL) has shown promise in solving the autonomous racing problem without requiring an accurate dynamics model, but random sampling of actions can lead to inefficient exploration. This paper proposes an efficient racing method, Constrained Residual Race (CRR), which combines a geometry-based reactive obstacle avoidance algorithm, Follow-The-Gap Method (FTG), with DRL. CRR generates safe guided actions with FTG and complementary actions with DRL, which are then fused into a final control action through a constraint equation. Using FTG as a prior controller enhances exploration efficiency and produces safe driving actions. Constraining the DRL policy output ensures that it does not overwrite the prior policy during early stages of exploration. Additionally, the DRL policy improves over the performance of the prior controller during training, reducing the lap time of the car. Validation on the F1TENTH simulator shows that CRR achieves lower lap times in most cases and exhibits excellent generalization capability.

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Reliability and Overload Capacity of Power Transformers

Reliability and Overload Capacity of Power Transformers

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2023 International Conference on Clean Electrical Power, ICCEP 2023

作者： Gracheva, Elena Ivanovna Petrova, Renata Maratovna Sinyukova, Tatiana Valtchev, Stanimir Miceli, Rosario Caruso, Massimo Kazan State Power Engineering University Departm. of Power Supply of Industrial Enterprises Kazan Russia Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia University Nova of Lisbon Faculty Fct and Uninova -CTS Portugal University of Palermo Department of Engineering Italy

ISBN: (纸本)9798350348378

The article describes the reliability of transformers, the failure rate and uptime to the first failure, the durability and maintainability of transformers and their service life, the allowable temperature rises and the dependence of steady-state overheating on the load factor of the transformer. An assessment of the effect of ambient temperature on the rated power of the transformer is shown. Dependences of heating on the changing load of transformers are given. The heating time constant is calculated depending on the power and cooling system of the transformer. The winding heating temperature is considered for real daily load schedules of a 33/11 kV transformer, the maximum actual overheats are compared when loaded with full power with allowable overheats. These studies can be used in transformers 33/11 kV and others with subsequent interpolation of values. © 2023 IEEE.

关键词： Failure rate

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A QoE-Aware Adaptive Energy-Efficient Transmission Scheduling Method 30

A QoE-Aware Adaptive Energy-Efficient Transmission Schedulin...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Yuan, Yankun Wang, Lin Xiao, Chonghui Liu, Zijuan Dang, Fan Wang, Xu Zhao, Haitian Miao, Xin Yanshan University School of Information Science and Engineering China National Computer Network Emergency Response Technical Team/Coordination Center of China China Tsinghua University Department of Automation and BNRist China Tsinghua University Global Innovation Exchange China Tsinghua University School of Software and BNRist China

ISBN: (纸本)9798331515966

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications. © 2024 IEEE.

关键词： Adaptive algorithm Anomaly detection Energy conservation IoT LSTM Models QoE

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