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24th EEEIC International Conference on Environment and electrical engineering and 8th I and CPS Industrial and Commercial Power systems Europe, EEEIC/I and CPS Europe 2024

作者： Datsios, Zacharias G. Patsalis, Diamantis G. Mikropoulos, Pantelis N. Tsovilis, Thomas E. School of Electrical & Computer Engineering Aristotle University of Thessaloniki High Voltage Laboratory Thessaloniki Greece

ISBN: (纸本)9798350355185

This study investigates the effects of the AC power-frequency operating voltage instantaneous value when lightning strikes to an overhead transmission line (OHTL) on the computed fast-front overvoltages across insulation, the current flowing through line surge arresters (LSAs), and the energy absorbed by LSAs. The investigation is performed via ATP-EMTP simulations of two typical 150 kV and 400 kV OHTLs with two circuits per tower, equipped with non-gapped line surge arresters (NGLAs). Two NGLA configurations are studied: 3 and 6 NGLAs installed at all towers. Simulation of lightning strikes to phase conductors (shielding failure) and to towers are carried out for negative first return strokes. The tower ground resistance at low current and low frequency is a parameter in simulations;ground resistance values are selected among those commonly encountered in practice. It is found that there is a significant variation of NGLA absorbed energy depending on the power-frequency voltage value at lightning strike time instant. An enhanced variation is observed for the 400 kV OHTL. Hence, the worst-case conditions in terms of power-frequency voltage should be considered for NGLA energy withstand capability selection. © 2024 IEEE.

关键词： Overhead lines

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From Bricks to Bots: Automated Collision-Aware Sequence Planning for LEGO Reconstruction with a Two-Finger Gripper 10

From Bricks to Bots: Automated Collision-Aware Sequence Plan...

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Barghi, Arman Pasiar, Navid Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331509972

An automated, vision-guided assembly method is proposed for reconstructing LEGO arrangements using a 3 DOF Delta Parallel Robot with a two-finger gripper. Assembly sequence planning, an NP-complete problem, focuses on determining the optimal sequence of bricks for constructing a target structure. The complexity increases when robotic con-straints-such as avoiding collisions between the gripper and the partially constructed structure-Are integrated into the planning. To address this, the Arrangement Planning Process is introduced, combining target arrangement identification with collision-Aware sequence planning. This process includes precise detection techniques that merge instance segmentation with stud detection. Notable innovations include an assembly-by-disassembly algorithm for efficient planning and experimental techniques such as the Safe Approach and the Wiggling Technique for accurate brick placement. By implementing a homing method, positioning accuracy improved approximately twofold compared to previous work. The results demonstrate the feasibility and precision of the proposed method in automated LEGO reconstruction. © 2024 IEEE.

关键词： Grippers

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Classifying Bicycle Infrastructure Using On-Bike Street-Level Images 27

Classifying Bicycle Infrastructure Using On-Bike Street-Leve...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Backman, Kal Beck, Ben Kulic, Dana School of Public Health and Preventive Medicine Monash University Department of Electrical and Computer Systems Engineering Australia

ISBN: (纸本)9798331505929

While cycling offers an attractive option for sus-tainable transportation, many potential cyclists are discouraged from taking up cycling due to the lack of suitable and safe infrastructure. Efficiently mapping cycling infrastructure across entire cities is necessary to advance our understanding of how to provide connected networks of high-quality infrastructure. Therefore we propose a system capable of classifying available cycling infrastructure from on-bike smartphone camera data. The system receives an image sequence as input, temporally analyzing the sequence to account for sparsity of signage. The model outputs cycling infrastructure class labels defined by a hi-erarchical classification system. Data is collected via participant cyclists covering 7,006Km across the Greater Melbourne region that is automatically labeled via a GPS and OpenStreetMap database matching algorithm. The proposed model achieved an accuracy of 95.38%, an increase in performance of 7.55% compared to the non-temporal model. The model demonstrated robustness to extreme absence of image features where the model lost only 6.6% in accuracy after 90% of images being replaced with blank images. This work is the first to classify cycling infrastructure using only street-level imagery collected from bike-mounted mobile phone cameras, while demonstrating robustness to feature sparsity via long temporal sequence analysis. © 2024 IEEE.

关键词： Bicycles

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Small Object Detection Without Attention for Aerial Surveillance 13th

Small Object Detection Without Attention for Aerial Surveil...

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13th International Conference on Information Technology and its Applications, CITA 2024

作者： Choi, Jehwan Nguyen, Duy-Linh Vo, Xuan-Thuy Jo, Kang-Hyun Intelligent Systems Laboratory Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan44610 Korea Republic of

ISBN: (纸本)9783031741265

This paper introduces the development of an essential deep-learning model for surveillance systems utilizing high-mounted CCTV or drones. Objects seen from elevated angles often look smaller and may appear at different angles compared to ground-level observations. To improve the detection of small objects, we propose a network incorporating an element-wise multiplication module based on the vanilla Vision Transformer (ViT) architecture. However, traditional transformer models need significant computational resources, which may not be practical for edge devices like CCTV cameras or drones. Therefore, we apply the Attention-Free Transformer (AFT) to reduce computational requirements enabling real-time operation on low-capacity devices. We validate the performance by combining ViT and AFT with the YOLOv5 real-time object detection model. Practical applicability is confirmed by implementing it on the low-capacity device named ODROID H3+. Validation datasets include Autonomous Driving Drone, VisDrone, AerialMaritime, and PKLot, all containing numerous small-sized objects. Experimental results on the VisDrone dataset show that YOLOv5 nano + AFT reduces parameter count by 4.6% while increasing accuracy by 1%, making it an efficient network. The model size is suitable for edge device implementation at 3.7 MB. Similarly, Aerial Maritime and PKLot datasets indicate a decreased amount of parameters and increased accuracy. Hence, the proposed deep learning model is applicable for aerial surveillance systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Drones

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Autonomous Rearrangement Planning Using Object Similarity Learning 12

Autonomous Rearrangement Planning Using Object Similarity Le...

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12th RSI International Conference on Robotics and Mechatronics, ICRoM 2024

作者： Jabari, Parmida Babi, Zeynab Ezzati Ghasemi, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad School of Electrical and Computer Engineering University of Tehran Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9798331529734

This study investigates the integration of artificial intelligence in robotics assembly systems, focusing on enhancing flexibility and adaptability in dynamic environments where the robot might not be given the precise objects of the intended construction. To this end, a developed approach is presented which is based on object detection, similarity assessment, and sequence planning for autonomous construction tasks. The system utilizes a YOLOv8 model for object detection and a Siamese network architecture, which facilitates object replacement. It processes two input images, one representing the target structure and another showing the available objects, extracting attributes such as bounding boxes, centers, and orientations. An exact object-matching step is performed before a two-stage similarity assessment. The two-stage similarity assessment prioritizes single-object matches and employs a multi-object similarity network only when necessary. This multi-stage approach is designed to maintain high similarity to the intended design while minimizing possible object shortcomings. During the construction phase, motion planning is performed using a 3-degrees-of-freedom Delta parallel robot, and a 2-finger gripper is utilized for robotics grasping. The results of this study, conducted on wooden bricks of varying shapes and sizes, demonstrate the effectiveness of the YOLOv8 model for object detection, achieving over 80% accuracy across 18 classes with a classification loss of approximately 0.25. Additionally, the Siamese network performed robustly, with a loss of less than 0.01 for both single and multiple object comparisons. © 2024 IEEE.

关键词： Robotic assembly

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Lightning impulse flashover modelling of a typical MV porcelain insulator using ATP-EMTP: Application to a MV overhead distribution line 14

Lightning impulse flashover modelling of a typical MV porcel...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Mikropoulos, Pantelis N. Tsouris, Petros P. High Voltage Laboratory School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9781837242689

Flashover prediction of medium voltage overhead line insulators is of great importance in insulation coordination studies. In electromagnetic transient simulations insulator flashover is modelled using experimentally obtained flashover volt-time curves, or the integration method with parameters appropriately adjusted based on experimental flashover results. However, these modelling approaches, being specifically related to the reference experimental results, cannot be easily generalised to apply to the great variety of utilized distribution line insulators, differing in geometry and material, as well as to the variability in the waveshape and polarity of the lightning surge overvoltages. In this study a modelling approach for lightning impulse flashover of typical MV overhead line insulators is introduced, based on the CIGRE leader progression model however with its model parameters appropriately adjusted. An excellent agreement between experimental and modelling flashover results has been achieved by expressing model parameters as a function of the average steepness of the applied voltage at leader inception. The developed model is implemented in ATP-EMTP environment to evaluate insulation flashover of an overhead line of the Hellenic distribution system due to direct positive lightning strikes. The main advantage of developed leader progression model is its ability to accurately describe the dynamics of discharge growth over insulators, starting from leader inception up to flashover. © The Institution of engineering & Technology 2024.

关键词： Overhead lines

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Dual Adaptive Representation Alignment for Cross-Domain Few-Shot Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11720-11732页

作者： Zhao, Yifan Zhang, Tong Li, Jia Tian, Yonghong Peking University The School of Computer Science Beijing100871 China Beihang University The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China The Pengcheng Laboratory Guangdong Shenzhen518055 China Peking University Shenzhen Granduate School The School of Electronic and Computer Engineering Guangdong Shenzhen518055 China

Few-shot learning aims to recognize novel queries with limited support samples by learning from base knowledge. Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains, which are usually infeasible for realistic applications. Toward this issue, we propose to address the cross-domain few-shot learning problem where only extremely few samples are available in target domains. Under this realistic setting, we focus on the fast adaptation capability of meta-learners by proposing an effective dual adaptive representation alignment approach. In our approach, a prototypical feature alignment is first proposed to recalibrate support instances as prototypes and reproject these prototypes with a differentiable closed-form solution. Therefore feature spaces of learned knowledge can be adaptively transformed to query spaces by the cross-instance and cross-prototype relations. Besides the feature alignment, we further present a normalized distribution alignment module, which exploits prior statistics of query samples for solving the covariant shifts among the support and query samples. With these two modules, a progressive meta-learning framework is constructed to perform the fast adaptation with extremely few-shot samples while maintaining its generalization capabilities. Experimental evidence demonstrates our approach achieves new state-of-the-art results on 4 CDFSL benchmarks and 4 fine-grained cross-domain benchmarks. © 1979-2012 IEEE.

关键词： Alignment

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Model-Free Dynamic Control of Five-bar Mechanism via Subchain Assembly and Reinforcement Learning 12

Model-Free Dynamic Control of Five-bar Mechanism via Subchai...

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12th RSI International Conference on Robotics and Mechatronics, ICRoM 2024

作者： Sedghi, Soheil Tale Masouleh, Mehdi Kalhor, Ahmad School of Electrical and Computer Engineering University of Tehran Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9798331529734

Parallel robots are versatile but challenging to control due to their complex dynamics and the need for precise modeling. This research presents a novel approach to modular control of parallel robots, utilizing reinforcement learning to dynamically assemble and control complex arbitrary systems. By treating each sub chain of a parallel robot as an individual agent, the proposed method offers a flexible and adaptable framework for various control tasks. The study compares the learning performance of different RL algorithms and proposed decision modeling on the tracking control of a five-bar robot with a 55 cm link length, aiming to achieve an error of less than 5 cm in the PyBullet simulator. In single-agent reinforcement learning, three algorithms (DDPG, PPO, and SAC) were employed, with PPO and SAC effectively solving the problem. In multi-agent reinforcement learning, two algorithms (FACMAC and MAPPO) were utilized. While MAPPO successfully learned the problem, FACMAC exhibited difficulties in grasping the problem. The results obtained from comparing the proposed RL algorithms indicated that single-agent RL learns the control problem better and faster than multi-agent reinforcement learning. In short, the paper focuses on formulating an RL solution which can effectively control parallel robots and can potentially be used for pre-training subchains. © 2024 IEEE.

关键词： Reinforcement learning

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PNSS: Unknown Face Presentation Attack Detection with Pseudo Negative Sample Synthesis

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computers, Materials & Continua 2025年第5期83卷 3097-3112页

作者： Hongyang Wang Yichen Shi Jun Feng Zitong Yu Zhuofu Tao School of Information Science and Technology Shijiazhuang Tiedao UniversityShijiazhuang050043China Shijiazhuang Key Laboratory of Artificial Intelligence Shijiazhuang Tiedao UniversityShijiazhuang050043China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai200240China School of Computer Science and Technology Great Bay UniversityDongguan523808China School of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

Face Presentation Attack Detection(fPAD)plays a vital role in securing face recognition systems against various presentation *** supervised learning-based methods demonstrate effectiveness,they are prone to overfitting to known attack types and struggle to generalize to novel attack *** studies have explored formulating fPAD as an anomaly detection problem or one-class classification task,enabling the training of generalized models for unknown attack ***,conventional anomaly detection approaches encounter difficulties in precisely delineating the boundary between bonafide samples and unknown *** address this challenge,we propose a novel framework focusing on unknown attack detection using exclusively bonafide facial data during *** core innovation lies in our pseudo-negative sample synthesis(PNSS)strategy,which facilitates learning of compact decision boundaries between bonafide faces and potential attack ***,PNSS generates synthetic negative samples within low-likelihood regions of the bonafide feature space to represent diverse unknown attack *** overcome the inherent imbalance between positive and synthetic negative samples during iterative training,we implement a dual-loss mechanism combining focal loss for classification optimization with pairwise confusion loss as a *** architecture effectively mitigates model bias towards bonafide samples while maintaining discriminative *** evaluations across three benchmark datasets validate the framework’s superior ***,our PNSS achieves 8%–18% average classification error rate(ACER)reduction compared with state-of-the-art one-class fPAD methods in cross-dataset evaluations on Idiap Replay-Attack and MSU-MFSD datasets.

关键词： Face presentation attack detection pseudo negative sample anomaly detection one-class classification

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