IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-04-26 DOI: 10.1016/j.rcim.2025.103030

Wanqing Xia, Hao Zheng, Weiliang Xu, Xun Xu

{"title":"Large vision-language models enabled novel objects 6D pose estimation for human-robot collaboration","authors":"Wanqing Xia, Hao Zheng, Weiliang Xu, Xun Xu","doi":"10.1016/j.rcim.2025.103030","DOIUrl":"10.1016/j.rcim.2025.103030","url":null,"abstract":"<div><div>Six-Degree-of-Freedom (6D) pose estimation is essential for robotic manipulation tasks, especially in human-robot collaboration environments. Recently, 6D pose estimation has been extended from seen objects to novel objects due to the frequent encounters with unfamiliar items in real-life scenarios. This paper presents a three-stage pipeline for 6D pose estimation of previously unseen objects, leveraging the capabilities of large vision-language models. Our approach consists of vision-language model-based object detection and segmentation, mask selection with pose hypothesis generated from CAD models, and refinement and scoring of pose candidates. We evaluate our method on the YCB-Video dataset, achieving a state-of-the-art Average Recall (AR) score of 75.8 with RGB-D images, demonstrating its effectiveness in accurately estimating 6D poses for a diverse range of objects. The effectiveness of each operation stage is investigated in the ablation study. To validate the practical applicability of our approach, we conduct case studies on a real-world robotic platform, focusing on object pick-up tasks by integrating our 6D pose estimation pipeline with human intention prediction and task analysis algorithms. Results show that the proposed method can effectively handle novel objects in our test environments, as demonstrated through the YCB dataset evaluation and case studies. Our work contributes to the field of human-robot collaboration by introducing a flexible, generalizable approach to 6D pose estimation, enabling robots to adapt to new objects without requiring extensive retraining—a vital capability for advancing human-robot collaboration in dynamic environments. More information can be found in the project GitHub page: <span><span>https://github.com/WanqingXia/HRC_DetAnyPose</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"95 ","pages":"Article 103030"},"PeriodicalIF":9.1,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A simple and reliable semi-supervised anomaly detection network for detecting crack in stamped parts 一种用于冲压件裂纹检测的简单可靠的半监督异常检测网络

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2025-04-26 DOI: 10.1016/j.compind.2025.104301

Xingjun Dong , Changsheng Zhang , Shuaitong Liu , Dawei Wang

{"title":"A simple and reliable semi-supervised anomaly detection network for detecting crack in stamped parts","authors":"Xingjun Dong , Changsheng Zhang , Shuaitong Liu , Dawei Wang","doi":"10.1016/j.compind.2025.104301","DOIUrl":"10.1016/j.compind.2025.104301","url":null,"abstract":"<div><div>Stamped parts play a crucial role in industrial manufacturing, and it is particularly important to automatically inspect their surface cracks. Since crack is rare and diverse, supervised defect detection methods lack sufficient data and cannot achieve ideal results. Unsupervised anomaly detection algorithms, which do not require crack data, can identify unknown cracks. However, they tend to have high rates of missed detections and false positives when dealing with complex backgrounds in stamped parts. To address these problems, this paper proposes a network called simple and reliable semi-supervised anomaly detection, considering the presence of a small number of anomalous data in actual production. This network uses a large number of normal samples and a small number of anomalous samples to detect surface cracks in stamped parts. Firstly, a pre-trained feature extractor is used for feature extraction, coupled with a designed feature adaptation network to reduce domain bias. Secondly, by extracting normal features from normal images, adding noise to these normal features to generate abnormal features, and extracting abnormal features from abnormal images at multiple scales, a feature space is constructed. Finally, by training a simplified discriminator based on the constructed feature space, computational efficiency is enhanced, and the deployment process is simplified. In the experiments, we collaborated with a multinational company, using an actual production dataset for verification. The proposed algorithm can achieve the score of area under the receiver operating characteristic curve of 98.2% for detection and 97.9% for localization at a processing speed of 19 frames per second.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"169 ","pages":"Article 104301"},"PeriodicalIF":8.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Horizontal-to-tilted conversion of solar radiation data using machine learning algorithms 利用机器学习算法对太阳辐射数据进行水平到倾斜的转换

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-26 DOI: 10.1016/j.engappai.2025.110951

Ali Naci Celik , Bahadır Sarman , Kemal Polat

{"title":"Horizontal-to-tilted conversion of solar radiation data using machine learning algorithms","authors":"Ali Naci Celik , Bahadır Sarman , Kemal Polat","doi":"10.1016/j.engappai.2025.110951","DOIUrl":"10.1016/j.engappai.2025.110951","url":null,"abstract":"<div><div>Solar radiation is the main input of system design algorithms in solar energy engineering. Solar radiation is usually measured on horizontal surfaces. However, in majority of solar energy applications such as photovoltaics, surfaces are either fixed at certain angles or continuously track the sun for maximizing energy input. Therefore, converting solar radiation data from horizontal to tilted surfaces is essential. Conventionally, conversion of solar radiation from horizontal to tilted is carried out using analytical methods. As with many other disciplines in science and technology, machine learning has recently been successfully applied also to solar radiation modelling to solve various problems such as in-advance forecasting of solar radiation. In the present article, solar radiation collected on horizontal surface is converted to tilted surface by machine learning algorithms and compared to solar radiation measured at a tilted surface. Eight different machine learning algorithms have been presently used for the conversion of solar radiation data. Accuracy of the models has been assessed based on a total of seven statistical metrics commonly used in literature. Overall, extra trees algorithm led to the best results as indicated by the statistical metrics used, for example, the mean absolute error of 7.3219 and coefficient of determination 0.9964. Based on the results presently obtained, it is demonstrated that machine learning led to an improved prediction when compared to the analytical models. The present research highlights the crucial significance of such advanced techniques, emphasizing their potential to drive a paradigm shift in solar energy engineering.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"153 ","pages":"Article 110951"},"PeriodicalIF":7.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Policy optimization of finite-horizon Kalman filter with unknown noise covariance 未知噪声协方差的有限视界卡尔曼滤波策略优化

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-04-26 DOI: 10.1016/j.automatica.2025.112320

Haoran Li, Yuan-Hua Ni

引用次数: 0

Reconstructing networks from the algebraic model of networked evolutionary games 基于网络进化博弈代数模型的网络重构

IF 3.7 2区计算机科学

Nonlinear Analysis-Hybrid Systems Pub Date : 2025-04-26 DOI: 10.1016/j.nahs.2025.101603

Fei Wang , Jun-e Feng , Biao Wang

引用次数: 0

Cross-Polarisation Suppression in Self-Diplexing Antennas 自双工天线中的交叉极化抑制

IF 1.1 4区计算机科学

Iet Microwaves Antennas & Propagation Pub Date : 2025-04-26 DOI: 10.1049/mia2.70020

Yitong Yao, Gang Dong

引用次数: 0

High-precision and high-speed flow rate control with nonlinear disturbance compensation using valve body position feedback 采用阀体位置反馈非线性扰动补偿的高精度高速流量控制

IF 3.1 3区计算机科学

Mechatronics Pub Date : 2025-04-26 DOI: 10.1016/j.mechatronics.2025.103317

Koki Hattori, Wataru Ohnishi, Takafumi Koseki

引用次数: 0

Set-membership estimator design with privacy-preserving for sensor networks: A state-decomposition-based approach 基于状态分解的传感器网络隐私保护集隶属度估计器设计

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-04-26 DOI: 10.1016/j.automatica.2025.112347

Yuhan Xie , Sanbo Ding , Nannan Rong

{"title":"Set-membership estimator design with privacy-preserving for sensor networks: A state-decomposition-based approach","authors":"Yuhan Xie , Sanbo Ding , Nannan Rong","doi":"10.1016/j.automatica.2025.112347","DOIUrl":"10.1016/j.automatica.2025.112347","url":null,"abstract":"<div><div>Set-membership estimation over open sensor networks is a current research hotspot. It remains a risk of data leakage under highly open network channels. This paper focuses on establishing set-membership estimator design strategy under a privacy-preserving framework. Firstly, with the aid of state decomposition methods, a new group of set-membership estimators with privacy-preserving are originally established. Wherein the real measurement is divided into two parts for information interaction and performance compensation respectively. The proposed estimator structure contributes to the security of private data by confusing eavesdroppers. Secondly, a sufficient condition is established to guarantee that the ellipsoidal estimation sets are updated properly. Compared with the existing results on set-membership estimation, the developed one is less conservative. Thirdly, a rigorous theoretical analysis is proposed to justify that the proposed estimator is sufficiently robust to confuse eavesdroppers. Lastly, a simulation example is given to evaluate the estimation and privacy-preserving performance of the proposed technique.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"177 ","pages":"Article 112347"},"PeriodicalIF":4.8,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Differentially private and communication-efficient distributed nonconvex optimization algorithms 差分私有和通信高效的分布式非凸优化算法

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-04-26 DOI: 10.1016/j.automatica.2025.112338

Antai Xie , Xinlei Yi , Xiaofan Wang , Ming Cao , Xiaoqiang Ren

引用次数: 0

DFAT: A web-based toolkit for estimating demand flexibility in building-to-grid integration DFAT：一个基于web的工具包，用于估算从建筑到电网集成的需求灵活性

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-04-26 DOI: 10.1016/j.softx.2025.102131

Michael Leong , Medha Mahanta , Clara Yin , Timothy Jairus Garcia , Zach Tan , Anand Krishnan Prakash , Doug Black , Rongxin Yin

{"title":"DFAT: A web-based toolkit for estimating demand flexibility in building-to-grid integration","authors":"Michael Leong , Medha Mahanta , Clara Yin , Timothy Jairus Garcia , Zach Tan , Anand Krishnan Prakash , Doug Black , Rongxin Yin","doi":"10.1016/j.softx.2025.102131","DOIUrl":"10.1016/j.softx.2025.102131","url":null,"abstract":"<div><div>Demand Flexibility Assessment Tool (DFAT) is an open source web-based tool that estimates the demand flexibility potential of common control strategies in commercial buildings. The toolkit features a demand flexibility estimation tool that contains two calculators, basic and advanced, based on the level of input of customer data. The basic version calculates demand shed metrics for the control strategy “global temperature adjustment” and “cycle on/off compressors” using customer building information, local weather data, and electrical meter data. The advanced version, which uses detailed HVAC equipment data, calculates demand flexibility metrics for control strategies such as static pressure reset, global temperature adjustment, and cycle on/off compressors. In addition to the demand flexibility estimation tool, this toolkit offers a benchmarking tool that helps facility operators, aggregators, and utility resource managers assess demand flexibility opportunities, quantify/verify performance, and compare their performance against that of their peers.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102131"},"PeriodicalIF":2.4,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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