信息工程最新文献

{"title":"Corrigendum to “Dynamic exponent market maker: personalized portfolio manager and one pool to trade them all”","authors":"Wittawat Kositwattanarerk","doi":"10.1016/j.bcra.2025.100358","DOIUrl":"10.1016/j.bcra.2025.100358","url":null,"abstract":"","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100358"},"PeriodicalIF":5.6,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models for PHM: a review of optimization techniques and applications","authors":"Tingyi Yu,&nbsp;Junya Tang,&nbsp;Qingyun Yu,&nbsp;Li Li,&nbsp;Ying Liu,&nbsp;Raul Poler","doi":"10.1007/s43684-025-00100-5","DOIUrl":"10.1007/s43684-025-00100-5","url":null,"abstract":"<div><p>The rapid advancement of Large Language Models (LLMs) has created unprecedented opportunities for industrial automation, process optimization, and decision support systems. As industries seek to leverage LLMs for industrial tasks, understanding their architecture, deployment strategies, and fine-tuning methods becomes critical. In this review, we aim to summarize the challenges, key technologies, current status, and future directions of LLM in Prognostics and Health Management(PHM). First, this review introduces deep learning for PHM. We begin by analyzing the architectural considerations and deployment strategies for industrial environments, including acceleration techniques and quantization methods that enable efficient operation on resource-constrained industrial hardware. Second, we investigate Parameter Efficient Fine-Tuning (PEFT) techniques that allow industry-specific adaptation without prohibitive computational costs. Multi-modal capabilities extending LLMs beyond text to process sensor data, images, and time-series information are also discussed. Finally, we explore emerging PHM including anomaly detection systems that identify equipment malfunctions, fault diagnosis frameworks that determine root causes, and specialized question-answering systems that empower workers with instant domain expertise. We conclude by identifying key challenges and future research directions for LLM deployment in PHM. This review provides a timely resource for researchers, engineers, and decision-makers navigating the transformative potential of language models in industry 4.0 environments.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00100-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing predictive maintenance and mission assignment to enhance fleet readiness under uncertainty","authors":"Ryan O’Neil,&nbsp;Abdelhakim Khatab,&nbsp;Claver Diallo","doi":"10.1007/s43684-025-00104-1","DOIUrl":"10.1007/s43684-025-00104-1","url":null,"abstract":"<div><p>In many industrial settings, fleets of assets are required to operate through alternating missions and breaks. Fleet Selective Maintenance (FSM) is widely used in such contexts to improve the fleet performance. However, existing FSM models assume that upcoming missions are identical and require only a single system configuration for completion. Additionally, these models typically assume that all missions must be completed, overlooking resource constraints that may prevent readying all systems within the available break duration. This makes mission prioritization and assignment a necessary consideration for the decision-maker. This work proposes a novel FSM model that jointly optimizes system to mission assignment, component and maintenance level selection, and repair task allocation. The proposed framework integrates analytical models for standard components and Deep Neural Networks (DNNs) for sensor-monitored ones, enabling a hybrid reliability assessment approach that better reflects real-world multi-component systems. To account for uncertainties in maintenance and break durations, a chance-constrained optimization model is developed to ensure that maintenance is completed within the available break duration with a specified confidence level. The optimization model is reformulated using two well-known techniques: Sample Average Approximation (SAA) and Conditional Value-at-Risk (CVaR) approximation. A case study of military aircraft fleet maintenance is investigated to demonstrate the accuracy and added value of the proposed approach.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00104-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning to trade autonomously in stocks and shares: integrating uncertainty into trading strategies","authors":"Yuyang Li,&nbsp;Minghui Liwang,&nbsp;Li Li","doi":"10.1007/s43684-025-00101-4","DOIUrl":"10.1007/s43684-025-00101-4","url":null,"abstract":"<div><p>Machine learning, a revolutionary and advanced technology, has been widely applied in the field of stock trading. However, training an autonomous trading strategy which can effectively balance risk and Return On Investment without human supervision in the stock market with high uncertainty is still a bottleneck. This paper constructs a Bayesian-inferenced Gated Recurrent Unit architecture to support long-term stock price prediction based on characteristics of the stock information learned from historical data, augmented with memory of recent up- and-down fluctuations occur in the data of short-term stock movement. The Gated Recurrent Unit architecture incorporates uncertainty estimation into the prediction process, which take care of decision-making in an ever-changing dynamic environment. Three trading strategies were implemented in this model; namely, a Price Model Strategy, a Probabilistic Model Strategy, and a Bayesian Gated Recurrent Unit Strategy, each leveraging the respective model’s outputs to optimize trading decisions. The experimental results show that, compared with the standard Gated Recurrent Unit models, the modified model exhibits a huge tremendous/dramatic advantage in managing volatility and improving return on investment Return On Investment. The results and findings underscore the significant potential of combining Bayesian inference with machine learning to operate effectively in chaotic decision-making environments.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00101-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning monocular face reconstruction from in the wild images using rotation cycle consistency","authors":"Xinrong Hu,&nbsp;Kaifan Yang,&nbsp;Ruiqi Luo,&nbsp;Tao Peng,&nbsp;Junping Liu","doi":"10.1016/j.vrih.2022.08.014","DOIUrl":"10.1016/j.vrih.2022.08.014","url":null,"abstract":"<div><div>With the popularity of the digital human body, monocular three-dimensional (3D) face reconstruction is widely used in fields such as animation and face recognition. Although current methods trained using single-view image sets perform well in monocular 3D face reconstruction tasks, they tend to rely on the constraints of the a priori model or the appearance conditions of the input images, fundamentally because of the inability to propose an effective method to reduce the effects of two-dimensional (2D) ambiguity. To solve this problem, we developed an unsupervised training framework for monocular face 3D reconstruction using rotational cycle consistency. Specifically, to learn more accurate facial information, we first used an autoencoder to factor the input images and applied these factors to generate normalized frontal views. We then proceeded through a differentiable renderer to use rotational consistency to continuously perceive refinement. Our method provided implicit multi-view consistency constraints on the pose and depth information estimation of the input face, and the performance was accurate and robust in the presence of large variations in expression and pose. In the benchmark tests, our method performed more stably and realistically than other methods that used 3D face reconstruction in monocular 2D images.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 4","pages":"Pages 379-392"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychological and physiological model of tactile rendering fidelity using combined electro and mechanical vibration","authors":"Rui Song ,&nbsp;Xiaoying Sun ,&nbsp;Dangxiao Wang ,&nbsp;Guohong Liu ,&nbsp;Dongyan Nie","doi":"10.1016/j.vrih.2023.10.006","DOIUrl":"10.1016/j.vrih.2023.10.006","url":null,"abstract":"<div><div>High-fidelity tactile rendering offers significant potential for improving the richness and immersion of touchscreen interactions. This study focuses on a quantitative description of tactile rendering fidelity using a custom-designed hybrid electrovibration and mechanical vibration (HEM) device. An electrovibration and mechanical vibration (EMV) algorithm that renders 3D gratings with different physical heights was proposed and shown to achieve 81% accuracy in shape recognition. Models of tactile rendering fidelity were established based on the evaluation of the height discrimination threshold, and the psychophysical-physical relationships between the discrimination and reference heights were well described by a modification of Weber’s law, with correlation coefficients higher than 0.9. The physiological-physical relationship between the pulse firing rate and the physical stimulation voltage was modeled using the Izhikevich spiking model with a logarithmic relationship.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 4","pages":"Pages 344-366"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bidirectional projective sampling for physics-based differentiable rendering","authors":"Ruicheng Gao ,&nbsp;Yue Qi","doi":"10.1016/j.vrih.2025.05.001","DOIUrl":"10.1016/j.vrih.2025.05.001","url":null,"abstract":"<div><h3>Background</h3><div>Physics-based differentiable rendering (PBDR) aims to propagate gradients from scene parameters to image pixels or vice versa. The physically correct gradients obtained can be used in various applications, including inverse rendering and machine learning. Currently, two categories of methods are prevalent in the PBDR community: reparameterization and boundary sampling methods. The state-of-the-art boundary sampling methods rely on a guiding structure to calculate the gradients efficiently. They utilize the rays generated in traditional path-tracing methods and project them onto the object silhouette boundary to initialize the guiding structure.</div></div><div><h3>Methods</h3><div>In this study, we propose an augmentation of previous projective-sampling-based boundary-sampling methods in a bidirectional manner. Specifically, we utilize the rays spawned from the sensors and also employ the rays emitted by the emitters to initialize the guiding structure.</div></div><div><h3>Results</h3><div>To demonstrate the benefits of our technique, we perform a comparative analysis of differentiable rendering and inverse rendering performance. We utilize a range of synthetic scene examples and evaluate our method against state-of-the-art projective-sampling-based differentiable rendering methods.</div></div><div><h3>Conclusions</h3><div>The experiments show that our method achieves lower variance gradients in the forward differentiable rendering process and better geometry reconstruction quality in the inverse-rendering results.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 4","pages":"Pages 367-378"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating models of real aboveground scene and underground geological structures at an open pit mine","authors":"Biao Dong ,&nbsp;Wenjun Tan ,&nbsp;Weichao Chang ,&nbsp;Baoting Li ,&nbsp;Yanliang Guo ,&nbsp;Quanxing Hu ,&nbsp;Guangwei Liu","doi":"10.1016/j.vrih.2023.08.004","DOIUrl":"10.1016/j.vrih.2023.08.004","url":null,"abstract":"<div><h3>Background</h3><div>As information technology has advanced and been popularized, open pit mining has rapidly developed toward integration and digitization. The three-dimensional reconstruction technology has been successfully applied to geological reconstruction and modeling of surface scenes in open pit mines. However, an integrated modeling method for surface and underground mine sites has not been reported.</div></div><div><h3>Methods</h3><div>In this study, we propose an integrated modeling method for open pit mines that fuses a real scene on the surface with an underground geological model. Based on oblique photography, a real-scene model was established on the surface. Based on the surface-stitching method proposed, the upper and lower surfaces and sides of the model were constructed in stages to construct a complete underground three-dimensional geological model, and the aboveground and underground models were registered together to build an integrated open pit mine model.</div></div><div><h3>Results</h3><div>The oblique photography method used reconstructed a surface model of an open pit mine using a real scene. The surface-stitching algorithm proposed was compared with the ball-pivoting and Poisson algorithms, and the integrity of the reconstructed model was markedly superior to that of the other two reconstruction methods. In addition, the surface-stitching algorithm was applied to the reconstruction of different formation models and showed good stability and reconstruction efficiency. Finally, the aboveground and underground models were accurately fitted after registration to form an integrated model.</div></div><div><h3>Conclusions</h3><div>The proposed method can efficiently establish an integrated open pit model. Based on the integrated model, an open pit auxiliary planning system was designed and realized. It supports the functions of mining planning and output calculation, assists users in mining planning and operation management, and improves production efficiency and management levels.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 4","pages":"Pages 406-420"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic load balancing for real-time multiview path tracing on multi-GPU architectures","authors":"Erwan Leria,&nbsp;Markku Makitalo,&nbsp;Julius Ikkala,&nbsp;Pekka Jääskeläinen","doi":"10.1016/j.vrih.2022.08.013","DOIUrl":"10.1016/j.vrih.2022.08.013","url":null,"abstract":"<div><div>Stereoscopic and multiview rendering are used for virtual reality and the synthetic generation of light fields from three-dimensional scenes. Because rendering multiple views using ray tracing techniques is computationally expensive, the utilization of multiprocessor machines is necessary to achieve real-time frame rates. In this study, we propose a dynamic load-balancing algorithm for real-time multiview path tracing on multi-compute device platforms. The proposed algorithm was adapted to heterogeneous hardware combinations and dynamic scenes in real time. We show that on a heterogeneous dual-GPU platform, our implementation reduces the rendering time by an average of approximately 30%–50% compared with that of a uniform workload distribution, depending on the scene and number of views.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 4","pages":"Pages 393-405"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-robot collaboration integrated with virtual reality in construction and manufacturing industries: A systematic review","authors":"Ehsan Shourangiz,&nbsp;Fatemeh Ghafari,&nbsp;Chao Wang","doi":"10.1016/j.vrih.2024.08.004","DOIUrl":"10.1016/j.vrih.2024.08.004","url":null,"abstract":"<div><div>The integration of Human-Robot Collaboration (HRC) into Virtual Reality (VR) technology is transforming industries by enhancing workforce skills, improving safety, and optimizing operational processes and efficiency through realistic simulations of industry-specific scenarios. Despite the growing adoption of VR integrated with HRC, comprehensive reviews of current research in HRC-VR within the construction and manufacturing fields are lacking. This review examines the latest advances in designing and implementing HRC using VR technology in these industries. The aim is to address the application domains of HRC-VR, types of robots used, VR setups, and software solutions used. To achieve this, a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology was conducted on the Web of Science and Google Scholar databases, analyzing 383 articles and selecting 53 papers that met the established selection criteria. The findings emphasize a significant focus on enhancing human-robot interaction with a trend toward using immersive VR experiences and interactive 3D content creation tools. However, the integration of HRC with VR, especially in the dynamic construction environment, presents unique challenges and opportunities for future research, including developing more realistic simulations and adaptable robot systems. This paper offers insights for researchers, practitioners, educators, industry professionals, and policymakers interested in leveraging the integration of HRC with VR in construction and manufacturing industries.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 4","pages":"Pages 317-343"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}