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Improved time-optimal B-spline feedrate scheduling for NURBS tool paths in CNC machining

Advances in Manufacturing 2023年第1期11卷 111-129页

作者： Yang Li Fu-Sheng Liang Lei Lu Cheng Fan School of Mechanic Engineering Northeast Electric Power UniversityJilin132012JilinPeople’s Republic of China Jiangsu Provincial Key Laboratory of Advanced Robotics and Collaborative Innovation Center of Suzhou Nano Science and Technology College of Mechanical and Electrical EngineeringSoochow UniversitySuzhou215021JiangsuPeople’s Republic of China

Feedrate scheduling in computer numerical control(CNC)machining is of great importance to fully develop the capabilities of machine tools while maintaining the motion stability of each *** and time-optimal feedrate scheduling plays a critical role in improving the machining efficiency and precision of complex surfaces considering the irregular curvature characteristics of tool paths and the limited drive capacities of machine *** study develops a general feedrate scheduling method for non-uniform rational B-splines(NURBS)tool paths in CNC machining aiming at minimizing the total machining time without sacrificing the smoothness of feed *** feedrate profile is represented by a B-spline curve to flexibly adapt to the frequent acceleration and deceleration requirements of machining along complex tool *** time-optimal B-spline feedrate is produced by continuously increasing the control points sequentially from zero positions in the bidirectional scanning and sampling *** required number of knots for the time-optimal B-spline feedrate can be determined using a progressive knot insertion *** improve the computational efficiency,the B-spline feedrate profile is divided into a series of independent segments and the computation in each segment can be performed *** proposed feedrate scheduling method is capable of dealing with not only the geometry constraints but also high-order drive constraints for any complex tool path with little computational *** and machining experiments are conducted to verify the effectiveness and superiorities of the proposed method.

关键词： B-spline feedrate Non-uniform rational B-splines(NURBS)tool path Knot insertion Bidirectional scanning computer numerical control(CNC)machining

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Research on Robot Path Planning Algorithm for Filter Plate Cleaning of Plate and Frame Filter Press 4

Research on Robot Path Planning Algorithm for Filter Plate C...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Xu, Jie Cui, Long Zhao, Yongguo Tian, Xincheng Center for Robotics School of Control Science and Engineering Shandong University Jinan China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan China Institute of Automation Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (纸本)9798331541729

Plate and frame filter press is an important solid-liquid separation equipment that plays an important role in many fields. The filter plate is one of the core components of the frame filter press, and its main function is to support the filter cloth and form a filter chamber. After the filtration process of the filter press is completed, a large number of impurities are often attached to the filter plate, so it is of great value to study the impurity cleaning problem of the filter plate of the plate and frame filter press. In this paper, aiming at the cleaning process of the filter plate of the plate and frame filter press, the image information of the filter plate is obtained by visual means, and then the image is analyzed and processed to obtain the position information of the impurities of the filter plate, the path planning of the obtained position information is carried out, and finally the robot is made to complete the cleaning task efficiently and accurately according to the planned path, which has certain practical value. © 2024 IEEE.

关键词： Robot programming

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A review of multimodal learning for text to images

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Multimedia Tools and Applications 2025年第10期84卷 8205-8245页

作者： Chen, Wei Yang, Yuqing Tian, Zijian Chen, Qiteng Liu, Jueting School of Computer Science & Technology China University of Mining and Technology Xuzhou221116 China Engineering Research Center of Mine Digitization Ministry of Education China University of Mining and Technology Xuzhou221116 China Beijing100083 China

Information exists in various forms in the real world, and the effective interaction and fusion of multimodal information plays a key role in the research of computer vision and deep learning. Generating an image that matches a given text description is one of the multimodal tasks that requires a strong generative model and cross-modal understanding. This paper provided a comprehensive analysis of recent advances in text-generated images and a taxonomy based on model architecture and characteristics. We introduced the classification of text generated image based on different frames, including text generated image method based on generation adversarial network, transformer and diffusion model. This paper introduced the network structure, advantages and disadvantages of each method, the benchmark data set and corresponding evaluation index, and summarized the application progress and experimental results according to different classification methods. Finally, we provided insights into current research challenges and possible future research directions and applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

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computers, Materials & Continua 2024年第2期78卷 2245-2267页

作者： Ashraf S.Mashaleh Noor Farizah Binti Ibrahim Mohammad Alauthman Mohammad Almseidin Amjad Gawanmeh School of Computer Sciences Universiti Sains MalaysiaPenangMalaysia Department Computer Center Al-Balqa Applied UniversitySaltJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Department of Computer Science Tafila Technical UniversityTafilaJordan College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic *** IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection *** paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT *** logic addresses IoT threat uncertainties and ambiguities *** component settings are optimized using PSO to improve *** methodology allows for more complex thinking by transitioning from binary to continuous *** of expert inputs,PSO data-driven tunes rules and membership *** study presents a complete IoT botnet risk assessment *** methodology helps security teams allocate resources by categorizing threats as high,medium,or low *** study shows how CICIoT2023 can assess cyber *** research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

关键词： IoT botnet detection risk assessment fuzzy logic particle swarm optimization(PSO) cybersecurity interconnected devices

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A Perspective of Mango Pest Classification Using Wrapper-Based Feature Selection Algorithm

A Perspective of Mango Pest Classification Using Wrapper-Ba...

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International Conference on Advanced Communications and Machine Intelligence, MICA 2023

作者： Muthaiah U Sonai, Veeramani Roy, Ram Vinod Banerjee, Sayan Ramanathan, C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Vengal Tamil Nadu Chennai601103 India Department of Computer Science and Engineering School of Engineering Shiv Nadar University Kalavakkam Tamil Nadu Chennai603110 India Peggy and Charles Stephenson Cancer Center University of Oklahoma Health Science Center OKlahoma CityOK73104 United States

ISBN: (纸本)9789819762217

The presence of pests on mango fruits reduces productivity and increases the need for pesticide usage. Therefore, early pest identification can significantly impact productivity. However, identifying various types of pests from images is a challenging and time-consuming *** article introduces a feature selection technique using a wrapper approach to improve classification accuracy. The method involves adapting the basic random forest classifier by choosing a subset of high-accuracy features to input into the classifier. Our proposed modified random forest (MRF) classifier achieves a classification accuracy of 99.8%, representing a substantial improvement of 7.19%, 13.53%, and 7.65% compared to the random forest, k-nearest neighbor, and Ada Boost classifiers, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ada Boost classifiers Feature Selection K-nearest neighbor Mango Pests Random Forest Wrapper-based feature selection

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Advances of triboelectric and piezoelectric nanogenerators toward continuous monitoring and multimodal applications in the new era

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International Journal of Extreme Manufacturing 2025年第1期7卷 174-205页

作者： Jianlong Hong Xiao Wei Huiyun Zhang Yukun Xiao Chongguang Meng Yuqi Chen Jiahui Li Ling Li Sanghoon Lee Qiongfeng Shi Jun Wu Interdisciplinary Research Center School of Electronic Science and EngineeringSoutheast UniversityNanjing 211189People’s Republic of China Department of Robotics&Mechatronics Engineering Daegu Gyeongbuk Institute of Science&TechnologyDaegu 42899Republic of Korea

Benefiting from the widespread potential applications in the era of the Internet of Thing and metaverse,triboelectric and piezoelectric nanogenerators(TENG&PENG)have attracted considerably increasing *** outstanding characteristics,such as self-powered ability,high output performance,integration compatibility,cost-effectiveness,simple configurations,and versatile operation modes,could effectively expand the lifetime of vastly distributed wearable,implantable,and environmental devices,eventually achieving self-sustainable,maintenance-free,and reliable ***,current triboelectric/piezoelectric based active(***-powered)sensors still encounter serious bottlenecks in continuous monitoring and multimodal applications due to their intrinsic limitations of monomodal kinetic response and discontinuous transient *** work systematically summarizes and evaluates the recent research endeavors to address the above challenges,with detailed discussions on the challenge origins,designing strategies,device performance,and corresponding diverse ***,conclusions and outlook regarding the research gap in self-powered continuous multimodal monitoring systems are provided,proposing the necessity of future research development in this field.

关键词： triboelectric piezoelectric continuous monitoring multimodal sensing internet of thing

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PVDM-YOLOv8l: a solution for reliable pedestrian and vehicle detection in autonomous vehicles under adverse weather conditions

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Multimedia Tools and Applications 2024年 1-26页

作者： Tahir, Noor Ul Ain Zhang, Zuping Asim, Muhammad Iftikhar, Sundas A. Abd El-Latif, Ahmed School of Computer Science and Engineering Central South University Changsha410083 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Gianforte School of Computing Montana State University Bozeman59717 United States Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown remarkable advancements, accurately identifying pedestrians and vehicles in adverse weather conditions remains a challenging task. Adverse weather introduces image quality degradation, leading to issues such as low contrast, reduced visibility, blurred edges, false detection, misdetection of tiny objects, and other impediments that further complicate the accuracy of detection. This paper introduces a novel Pedestrian and Vehicle Detection Model under adverse weather conditions, denoted as PVDM-YOLOv8l. In our proposed model, we first incorporate the Swin-Transformer method, which is designed for global extraction of feature of small objects to identify in poor visibility, into the YOLOv8l backbone structure. To enhance detection accuracy and address the impact of inaccurate features on recognition performance, CBAM is integrated between the neck and head networks of YOLOv8l, aiming to gather crucial information and obtain essential data. Finally, we adopted the loss function Wise-IOU v3. This function was implemented to mitigate the adverse effects of low-quality instances by minimizing negative gradients. Additionally, we enhanced and augmented the DAWN dataset and created a custom dataset, named DAWN2024, to cater to the specific requirements of our study. To verify the superiority of PVDM-YOLOV8l, its performance was compared against several commonly used object detectors, including YOLOv3, YOLOv3-tiny, YOLOv3-spp, YOLOv5, YOLOv6, and all the versions of YOLOv8 (n, m, s, l, and x) and some traditional models. The experimental results demonstrate that our proposed model achieved a 6.6%, 5.4%, 6%, and 5.1% improvement in precision, recall, F1-score and mean Average Precision (mAP) on the custom DAWN2024 dataset. This substantial improvement in accuracy ind

关键词： Vehicle detection

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User story clustering in agile development:a framework and an empirical study

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Frontiers of computer science 2023年第6期17卷 43-59页

作者： Bo YANG Xiuyin MA Chunhui WANG Haoran GUO Huai LIU Zhi JIN School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry Oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijing 100083China School of Information Science and Technology North China University of TechnologyBeijing 100144China College of Computer Science and Technology Inner Mongolia Normal UniversityHohhot 010020China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthorn VIC 3122Australia Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationBeijing 100871China Institute of Software School of Computer SciencePeking UniversityBeijing 100871China

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development *** stories are the primary means of requirements collection and elicitation in the agile development.A project can involve a large amount of user stories,which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis,effective mapping to developed features,and efficient ***,the current user story clustering is mainly conducted in a manual manner,which is time-consuming and subjective to human *** this paper,we propose a novel approach for clustering the user stories automatically on the basis of natural language ***,the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story *** similarity of user stories is calculated,which can be used to generate the connected graph as the basis of automatic user story *** evaluate the approach based on thirteen datasets,compared against ten baseline *** results show that our clustering approach has higher accuracy,recall rate and F1-score than these *** is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile *** study also highlights promising research directions for more accurate requirements elicitation.

关键词： user story agile development user story mapping clustering

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Body Shape Control of Magnetic Soft Continuum Robots with PID Controller 11

Body Shape Control of Magnetic Soft Continuum Robots with PI...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Sangsefidi, Naeimeh Korayem, Habibnejad Iran University of Science and Technology Robotics Research Laboratory Center of Excellence in Experimental Solid Mechanics and Dynamics School of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798350308105

Magnetically guided soft robots have emerged as a promising technology in minimally invasive surgery due to their ability to adapt to complex environments. However, one of the main challenges in this field is damage to the vascular structure caused by unwanted stress on the vessel wall and deformation of the vessel due to improper control of the shape of the robot body during surgery. Therefore, this article proposes an approach for controlling the form of a magnetic soft continuous robot body using a PID controller. The magnetic soft continuous robot is modelled using Cosserat theory in static mode and solved numerically. The designed controller adjusts the position of each part of the robot to match the desired shape. The PID controller is considered to minimize the robot's contact with the vessel wall and prevent unwanted vessel deformation. The simulation results confirmed the accuracy of the numerical solution of the static Cosserat model. Also, they showed the effectiveness of the proposed control method in achieving the desired shape with a maximum error of about 0.3 millimeters. © 2023 IEEE.

关键词： Controllers

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CSE-NER: A Category Semantic Enhanced Multi-Task Learning Framework for Named Entity Recognition 5

CSE-NER: A Category Semantic Enhanced Multi-Task Learning Fr...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Dai, Dongbo Zhang, Guangjie Li, Shengzhou Song, Na Xu, Yubao Tang, Zheng Zhang, Huiran School of Computer Engineering and Science Shanghai University Shanghai China University of Tsukuba Department of Computer Science Tsukuba Japan Shanghai University Research Center of Nanoscience and Nanotechnology Shanghai China University of Toyama Faculty of Engineering Toyama Japan

ISBN: (纸本)9798350385557

Named Entity Recognition (NER) is a fundamental and crucial task in natural language processing. Currently, the use of large pre-trained models has become the mainstream for NER. However, when there is a distributional difference between pre-training data and downstream task data, such extensive prior training may lead to issues like 'knowledge hallucination' or 'domain shift' in the downstream task. To address this, we propose a Category Semantic Enhanced framework for Named Entity Recognition (CSE-NER) in this paper. By introducing more downstream task information during the fine-tuning phase, we enhance the model's adaptability. Specifically, we incorporate a Category Semantic Embedding layer, providing category semantic information distinct from context embeddings. Additionally, we introduce an auxiliary task, Entity Type Classification, to help the model effectively utilize the added category semantic information. Experiments on five datasets demonstrate the effectiveness of our proposed framework, outperforming various state-of-the-art methods. © 2024 IEEE.

关键词： Semantics

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