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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Mehta, Deepak Kumar, Ritesh Shah, Pradeep Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Everlasting Magnet Synchronous vehicles (PMSMs) are becoming increasingly famous in electric-powered power structures due to their great efficiency and overall performance traits. PMSMs are synchronous motors that use everlasting magnets to supply torque. They provide easy and coffee-cost control answers and can be used for a spread of programs along with electric-powered motors, domestic appliances, and commercial automation. PMSMs are, in particular, appealing to electric pressure system programs due to their better electricity density, higher efficiency, better velocity variety, and less complicated manipulation than different synchronous vehicles. The precept in the back of the PMSM is that the rotor, which contains everlasting magnets, interacts with the rotating magnetic area created via the stator windings. The stator windings are energized via an outside strength digital supply, and the ensuing alternating cutting-edge creates a magnetic area, which in flip produces torque on the rotor. This torque causes the rotor to rotate and produce mechanical power. The torque of the PMSM can be managed through various amplitudes and segments of the stator modern-day. This adaptability makes PMSMs perfect for use in variable-speed power applications consisting of electric automobiles. © 2024 IEEE.

关键词： Everlasting Magnetic Synchronous Windings

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Smart Parking System using YOLO, ANPR and MQTT with Artificial Intelligence Features 3

Smart Parking System using YOLO, ANPR and MQTT with Artifici...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yadav, Krrish Roy, Sritama School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798331527549

Smart parking systems leverage advanced technologies to optimize parking space utilization and enhance user experience. This research paper explores the design, implementation, and evaluation of a smart parking system aimed at improving accessibility and security in urban parking facilities. It utilizes real-time sensor data to provide accurate parking availability information and empowers users to book slots through a secure mobile web app. The app integrates user-friendly navigation to guide drivers to their booked spots. OTP generation bolsters security, preventing unauthorized parking occupancy. One of the most significant and difficult problems in image-based sequence recognition is scene text recognition, which is the focus of this study's investigation. A brand-new neural network architecture is put forth that unifies transcription, sequence modelling, and feature extraction into a single framework. It performs admirably in both lexicon-free and lexicon-based scene text recognition tasks and is not restricted to any predefined lexicon. It produces a much more manageable and effective model, making it more suitable for real-world application scenarios. © 2025 IEEE.

关键词： App development Neural network OTP generation Smart parking text recognition

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Leveraging hierarchical semantic‐emotional memory in emotional conversation generation

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CAAI Transactions on Intelligence technology 2023年第3期8卷 824-835页

作者： Min Yang Zhenwei Wang Qiancheng Xu Chengming Li Ruifeng Xu Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhenChina School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaUSA School of Intelligent Systems Engineering Sun Yat‐Sen UniversityGuangzhouChina School of Computer Science Harbin Institute of Technology(Shenzhen)ShenzhenChina

Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable ***,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic *** this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training *** learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation *** experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual *** reproducibility,we release the code and data publicly at:https://***/siat‐nlp/HSEMEC‐code‐data.

关键词： deep learning emotional conversation generation semantic‐emotional memory

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Enhanced YOLOv5s-Based Apple Detection for Harvesting Robots Using a Multi-Rotated Box Algorithm

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第18期49卷 191-204页

作者： Feng, Wen Jian Zhao, Yi Lin Xie, Yongsheng Wei, Linbing Guangxi Eco-engineering Vocational & Technical College Liuzhou Guangxi 545004 China School of Mathematics and Computer Science Guangxi Science & Technology Normal University Laibin546199 China

The apple picking robot makes use of a number of technologies, one of which is the apple target identification algorithm. When it comes to automated apple picking, the robots' optical systems are crucial. Generally speaking, it finds ripe apples by taking photographs of its environment, processing them, and then analyzing the findings. The inability of traditional vision algorithms to process complex backdrops hinders the efficiency of harvesting robots. The continuous development and refining of the CNN have led to a substantial improvement in its efficacy in target identification during the last several years. The current crop of apple recognition algorithms struggles to tell the difference between partially obscured apples and ones entirely concealed by tree branches. Direct use of the algorithm endangers the harvesting robot's mechanical arm, apples, as well as gripping end-effector. In response to this real-world issue, we provide a lightweight apple targets identification approach for picking robots based on enhanced YOLOv5s. This method can automatically identify which apples in an apple tree picture are graspable and which ones are not. This method is able to circumvent the impact of light transformation, in contrast to the conventional segmentation approach. When there is a lot of resemblance between the fruit and the backdrop, though, it becomes more challenging to get strong recognition results. With a recall rate of 98%, a detection speed of 47 f/s, and a mAP (mean Average Precision) of apple detection of 98.13%, the findings demonstrate that the YOLO v5 network has perfect properties. The YOLO v5 is able to simultaneously fulfill the accuracy and speed criteria of apple identification, in contrast to more conventional network models like Faster R-CNN and YOLO v4. The experiment culminates with the employment of the apple-harvesting robot that the researcher developed themselves. Results demonstrate that the robot has a harvesting success rate of 99.2% i

关键词： Fruits

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Cooperative Traffic Signal Control with Bus Priority: A Multi-Agent Reinforcement Learning Approach 24

Cooperative Traffic Signal Control with Bus Priority: A Mult...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Congyu Wang, Chong Li, Yueqi Ye, Guoqiang Bao, Licheng Wen, Ming Zhang, Jian School of Software Nanjing Univ. of Information Science and Technology Nanjing China School of Computer Science Nanjing Univ. of Information Science and Technology Nanjing China School of Transportation Southeast Univ. Nanjing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Intelligent traffic signal control plays a crucial role in reducing the escalating problem of traffic congestion. However, traditional methods of traffic signal control struggle to effectively adapt to the ever-changing road conditions and demands in complex urban traffic environments. This becomes particularly problematic when considering the punctuality of buses, as it can lead to traffic congestion for other vehicles. To address this issue, this paper introduces a multi-agent reinforcement learning method that prioritizes bus punctuality while minimizing travel time for regular vehicles. We established the SUMO simulation environment with dedicated bus lanes and designed a reward function based on bus arrival and vehicle waiting times. By employing a deep reinforcement learning approach, we aimed to reduce average travel time. The simulation results demonstrate significant improvements, including a 74.44% reduction in vehicle waiting time at intersections and a 78.06% decrease in bus delay time. Notably, even when focusing solely on optimizing vehicle travel time, our approach enhances bus punctuality, resulting in a 57.03% reduction in bus delay time. Thus, we believe that our approach offers a viable solution for addressing bus priority policy challenges. © ASCE.

关键词： Travel time

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A Case Study on the Effects of Auditory Cues in Influencing Spatial Memory and Representation for a Person who is Blind 1

A Case Study on the Effects of Auditory Cues in Influencing ...

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1st International Workshop on Brain-computer Interfaces (BCI) for Multimedia Understanding, BCIMM 2024, co-located with ACM Multimedia 2024

作者： Nguyen, Minh Zhu, Howe Sun, Huiyuan Nguyen, Vincent Singh, Avinash Jin, Craig Lin, Chin-Teng School of Computer Science University of Technology Sydney Sydney Australia School of Electrical and Information Engineering The University of Sydney Sydney Australia Graduate School of Health University of Technology Sydney Sydney Australia

ISBN: (纸本)9798400711893

Navigation is a coordinated and goal-oriented movement through the environment, in which vision is an integral part of acquiring spatial information. People with blindness and vision impairment rely on alternative senses to navigate in daily life. Auditory cues are critical in building a spatial representation and effective navigation. Our work investigates the neurophysiological response to explore how auditory cues can shape a person’s spatial representation and memory of an environment. In our experiment, a person who is blind was presented with verbal and non-verbal cues, either at close-reaching distance or globally. Electroencephalography, walking trajectories and tactile drawing data were collected to evaluate spatial representation and memory formed by different auditory cues and the role of theta oscillations in such relationships. Our results indicated a consistent increase in theta power at turning and starting points, a higher cognitive load with verbal strategies, and enhanced path recollection with global strategies. These findings emphasise the role of theta as a key indicator of cognitive workload and spatial memory processing. The preliminary results contribute to a broader understanding of the underlying processing mechanism of auditory information processing to support spatial navigation. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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Open Text Classification Based on Dynamic Boundary Balance 9th

Open Text Classification Based on Dynamic Boundary Balance

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Xu, Ganlin Feng, Jianzhou Wei, Qikai School of Information Science and Engineering Yanshan University Qinhuangdao066000 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100000 China

ISBN: (纸本)9783031466700

Open classification is the problem where there exist some unseen/unknown classes in the test set, i.e., these unknown/unseen classes don’t appear when the model is trained. Existing work often maps samples to high-dimensional space to make decisions, which leads to unobservable and inexplicable results. To address the issue, we shift perspectives to two-dimensional space and put forward a two-stage learning method built on the dynamic decision boundaries balance. We refer it to open classification with dynamic boundary balance (OCD2B). First, we construct a vanilla classifier via known classes with BERT model. Then, we use the prior knowledge of known classes to dynamically determine the decision boundaries between known classes and unknown classes in low-dimensional space. We propose a novel boundary loss function as a boundary balance strategy to reduce open space risk and empirical risk. Experimental results on two standard datasets show that our method achieves performance gain over existing methods, providing easily observable results. In particular, the larger the ratio of unseen classes is, the more obvious the performance advantage the model achieves. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

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computers, Materials & Continua 2024年第4期79卷 983-1003页

作者： Xiangyan Tang Chengchun Ruan Xiulai Li Binbin Li Cebin Fu School of Computer Science and Technology Hainan UniversityHaikou570228China Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China School of Cyberspace Security(School of Cryptology) Hainan UniversityHaikou570228China

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion *** enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial *** build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small *** replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small ***,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple *** module enhances the perception of spatial contextual features and the utilizationof multiscale feature *** the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

关键词： Small object detection YOLOv7 multi-scale attention spatial context

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MTOClus: Multi-Type Objects Clustering in Heterogeneous information Networks 3

MTOClus: Multi-Type Objects Clustering in Heterogeneous Info...

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3rd International Conference on Communications, information System, and Data science, CISDS 2024

作者： Liang, Yongjie Hu, Wujie Wu, Junjie Wu, Jinzhao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Electric Engineering Guangxi University Nanning China Guilin University of Aerospace Technology Guilin China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

Clustering is crucial for analyzing heterogeneous information networks (HINs). Mainly state-of-the-art algorithms often focus on single-type node clustering, overlooking the clustering of multiple node types and requiring manual setting of cutoff distances and clustering centers. This paper introduces a clustering algorithm integrating multi-type objects, named MTOClus, which generates multiple similarity matrices for different node types and assigns weights to each matrix to indicate their significance. By aggregating weighted similarity matrices, it creates a distance matrix. MTOClus automates cluster center selection through node sorting and mitigates the impact of outliers by utilizing the K-Nearest Neighbors approach to compute the cutoff distance. Experimental results across four datasets consistently show that MTOClus outperforms six other algorithms, underscoring its superior clustering efficacy. © 2025 SPIE.

关键词： Clustering algorithms

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Feature Preserving Parameterization for Quadrilateral Mesh Generation Based on Ricci Flow and Cross Field

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computer Modeling in engineering & sciences 2023年第10期137卷 843-857页

作者： Na Lei Ping Zhang Xiaopeng Zheng Yiming Zhu Zhongxuan Luo International School of Information Science and Engineering Dalian University of TechnologyDalian116000China School of Mathematical Sciences Dalian University of TechnologyDalian116000China School of Software Technology Dalian University of TechnologyDalian116000China

We propose a newmethod to generate surface quadrilateralmesh by calculating a globally defined parameterization with feature *** the field of quadrilateral generation with features,the cross field methods are wellknown because of their superior performance in feature *** methods based on metrics are popular due to their sound theoretical basis,especially the Ricci flow *** cross field methods’major part,the Poisson equation,is challenging to solve in three dimensions *** it comes to cases with a large number of elements,the computational costs are expensive while the methods based on metrics are on the *** addition,an appropriate initial value plays a positive role in the solution of the Poisson equation,and this initial value can be obtained from the Ricci flow *** we combine the methods based on metric with the cross field *** use the discrete dynamic Ricci flow algorithm to generate an initial value for the Poisson equation,which speeds up the solution of the equation and ensures the convergence of the *** experiments show that our method is effective in generating a quadrilateral mesh for models with features,and the quality of the quadrilateral mesh is reliable.

关键词： Quadrilateral mesh feature preserving Ricci flow cross field

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