Intelligent manufacturing(IM),a driving force behind the fourth industrial revolution,is reshaping the manufacturing sector by enhancing productivity,efficiency,and sustainability.Despite the rapid technological advan...Intelligent manufacturing(IM),a driving force behind the fourth industrial revolution,is reshaping the manufacturing sector by enhancing productivity,efficiency,and sustainability.Despite the rapid technological advancements in IM,comprehensive bibliometric reviews remain limited.This article systematically reviews the latest research in IM,addressing emerging hotspots,key technologies,and their applications across the entire product manufacturing cycle.Bibliometric analysis is employed to identify research trends visualize publication volume,collaboration patterns,research domains,co-citations,and emerging areas of interest.The article then examines key technologies supporting IM,including sensors,the Internet of Things(IoT),big data analytics,cloud computing,artificial intelligence(AI),digital twins,and virtual reality(VR)/augmented reality(AR).Furthermore,it explores the application of these technologies throughout the manufacturing cycle-from intelligent reliability design,material transportation and tracking,to intelligent planning and scheduling,machining and fabrication,monitoring and maintenance,quality inspection and control,warehousing and management,and sustainable green manufacturing—through specific case studies.Lastly,the article discusses future research directions,highlighting the increasing global market and the need for enhanced interdisciplinary collaboration,technological integration,computing power upgrades,and attention to security and privacy in IM.This study provides valuable insights for scholars and serves as a guide for future research and strategic investment decisions,offering a comprehensive view of the IM field.展开更多
Generative Artificial Intelligence(GenAI)systems have achieved remarkable capabilities across text,code,and image generation;however,their outputs remain prone to errors,hallucinations,and biases.Users often overtrust...Generative Artificial Intelligence(GenAI)systems have achieved remarkable capabilities across text,code,and image generation;however,their outputs remain prone to errors,hallucinations,and biases.Users often overtrust these outputs due to limited transparency,which can lead to misuse and decision errors.This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability.We introduce a methodology that adjusts explanations dynamically according to user expertise,model output confidence,and contextual risk factors,providing guidance that is informative but not overwhelming.The framework was evaluated using outputs from OpenAI’s Generative Pretrained Transformer 4(GPT-4)for text and code generation and Stable Diffusion,a deep generative image model,for image synthesis.The evaluation covered text,code,and visual modalities.A dataset of 5000 GenAI outputs was created and reviewed by a diverse participant group of 360 individuals categorized by expertise level.Results show that adaptive explanations improve error detection rates,reduce the mean squared trust calibration error,and maintain efficient decision making compared with both static and no explanation conditions.Theframework increased error detection by up to 16% across expertise levels,a gain that can provide practical benefits in high stakes fields.For example,in healthcare it may help identify diagnostic errors earlier,and in law it may prevent reliance on flawed evidence in judicial work.These improvements highlight the framework’s potential to make Artificial Intelligence(AI)deployment safer and more accountable.Visual analyses,including trust accuracy plots,reliability diagrams,and misconception maps,show that the adaptive approach reduces overtrust and reveals patterns of misunderstanding across modalities.Statistical results confirmthe robustness of thesefindings across novice,intermediate,and expert users.The study offers insights for designing explanations that balance completeness and simplicity to improve trust calibration and cognitive load.The approach has implications for safe and transparent GenAI deployment and can inform both AI interface design and policy development for responsible AI use.展开更多
The paper describes an efficient direct method to solve an equation Ax = b, where A is a sparse matrix, on the Intel®Xeon PhiTM coprocessor. The main challenge for such a system is how to engage all available ...The paper describes an efficient direct method to solve an equation Ax = b, where A is a sparse matrix, on the Intel®Xeon PhiTM coprocessor. The main challenge for such a system is how to engage all available threads (about 240) and how to reduce OpenMP* synchronization overhead, which is very expensive for hundreds of threads. The method consists of decomposing A into a product of lower-triangular, diagonal, and upper triangular matrices followed by solves of the resulting three subsystems. The main idea is based on the hybrid parallel algorithm used in the Intel®Math Kernel Library Parallel Direct Sparse Solver for Clusters [1]. Our implementation exploits a static scheduling algorithm during the factorization step to reduce OpenMP synchronization overhead. To effectively engage all available threads, a three-level approach of parallelization is used. Furthermore, we demonstrate that our implementation can perform up to 100 times better on factorization step and up to 65 times better in terms of overall performance on the 240 threads of the Intel®Xeon PhiTM coprocessor.展开更多
This paper describes a method of calculating the Schur complement of a sparse positive definite matrix A. The main idea of this approach is to represent matrix A in the form of an elimination tree using a reordering a...This paper describes a method of calculating the Schur complement of a sparse positive definite matrix A. The main idea of this approach is to represent matrix A in the form of an elimination tree using a reordering algorithm like METIS and putting columns/rows for which the Schur complement is needed into the top node of the elimination tree. Any problem with a degenerate part of the initial matrix can be resolved with the help of iterative refinement. The proposed approach is close to the “multifrontal” one which was implemented by Ian Duff and others in 1980s. Schur complement computations described in this paper are available in Intel®Math Kernel Library (Intel®MKL). In this paper we present the algorithm for Schur complement computations, experiments that demonstrate a negligible increase in the number of elements in the factored matrix, and comparison with existing alternatives.展开更多
With the increasing importance of cloud services worldwide, the cloud infrastructure and platform management has become critical for cloud service providers. In this paper, a novel architecture of intelligent server m...With the increasing importance of cloud services worldwide, the cloud infrastructure and platform management has become critical for cloud service providers. In this paper, a novel architecture of intelligent server management framework is proposed. In this framework, the communication layer is based on the Extensible Messaging and Presence Protocol (XMPP), which was developed for instant messaging and has been proven to be highly mature and suitable for mobile and large scalable deployment due to its extensibility and efficiency. The proposed architecture can simplify server management and increase flexibility and scalability when managing hundreds of thousands of servers in the cloud era.展开更多
Artificial intelligence(AI)algorithms achieve outstanding results in many applicationdomains such as computer vision and natural language processing The performance ofAl models is the outcome of complex and costly mod...Artificial intelligence(AI)algorithms achieve outstanding results in many applicationdomains such as computer vision and natural language processing The performance ofAl models is the outcome of complex and costly model architecture design and trainingprocesses.Hence,it is paramount for model owners to protect their AI models frompiracy-model cloning,illegitimate distribution and use.IP protection mechanisms havebeen applied to Al models,and in particular to deep neural networks,to verify themodel ownership.State-of-the-art AI model ownership protection techniques have beensurveyed.The pros and cons of Al model ownership protection have been reported.The majonity of previous works are focused on watermarking,while more advancedmethods such fingerprinting and attestation are promising but not yet explored indepth.This study has been concluded by discussing possible research directions in thearea.展开更多
Recent advances have positioned artificial intelligence(AI)as a transformative force to accelerate experiments and enhance decision-making in biological research.Yet scientific progress relies on exploring uncharted i...Recent advances have positioned artificial intelligence(AI)as a transformative force to accelerate experiments and enhance decision-making in biological research.Yet scientific progress relies on exploring uncharted intellectual territories,while AI was trained by history.This inherent limitation underscores its role as a collaborator rather than a successor to humanity.展开更多
为提升设施农业的环境感知与预警能力,同时兼顾连续可靠与成本可控,以Arduino UNO R 3为主控,构建了多模态感知与预警平台。集成温湿度和光照等多传感器,电路系统采用三层叠板结构,上位机实现阈值警告与可视化操作界面。基于能量守恒与...为提升设施农业的环境感知与预警能力,同时兼顾连续可靠与成本可控,以Arduino UNO R 3为主控,构建了多模态感知与预警平台。集成温湿度和光照等多传感器,电路系统采用三层叠板结构,上位机实现阈值警告与可视化操作界面。基于能量守恒与水汽守恒提出名义预测环境模型,并结合辐照度分解、传热与换气,推导出温室参数的名义解析表达,并在六个经纬度测试点采集多组数据进行工况验证。实验结果表明,在五档通风条件下,光照校准共180组样本,回归斜率接近1%;温湿度实测与模型预测高度吻合,通风增强可将稳态温升由56—64℃区间显著拉低,并将相对湿度稳定在约35%—50%RH之间;多模态联动测试中,火焰、超声、CO_(2)与可燃气体通道的响应灵敏度分别为99%、89%、95%、91%,实现了从透过辐射到室内多模态响应的闭环集成,为面向不同温室环境测量的推广与应用提供了可行的技术路径。展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.52375447,52305477 and 52105457)the Shandong Provincial Natural Science Foundation of China(Grant Nos.ZR2023QE057,ZR2024QE100 and ZR2024ME255)+2 种基金Qingdao Municipal Science and Technology Planning Park Cultivation Plan(Grant No.23-1-5-yqpy-17-qy)Shandong Provincial Science and Technology SMEs Innovation Capacity Improvement Project(Grant No.2022TSGC1115)the Special Fund of Taishan Scholars Project。
文摘Intelligent manufacturing(IM),a driving force behind the fourth industrial revolution,is reshaping the manufacturing sector by enhancing productivity,efficiency,and sustainability.Despite the rapid technological advancements in IM,comprehensive bibliometric reviews remain limited.This article systematically reviews the latest research in IM,addressing emerging hotspots,key technologies,and their applications across the entire product manufacturing cycle.Bibliometric analysis is employed to identify research trends visualize publication volume,collaboration patterns,research domains,co-citations,and emerging areas of interest.The article then examines key technologies supporting IM,including sensors,the Internet of Things(IoT),big data analytics,cloud computing,artificial intelligence(AI),digital twins,and virtual reality(VR)/augmented reality(AR).Furthermore,it explores the application of these technologies throughout the manufacturing cycle-from intelligent reliability design,material transportation and tracking,to intelligent planning and scheduling,machining and fabrication,monitoring and maintenance,quality inspection and control,warehousing and management,and sustainable green manufacturing—through specific case studies.Lastly,the article discusses future research directions,highlighting the increasing global market and the need for enhanced interdisciplinary collaboration,technological integration,computing power upgrades,and attention to security and privacy in IM.This study provides valuable insights for scholars and serves as a guide for future research and strategic investment decisions,offering a comprehensive view of the IM field.
文摘Generative Artificial Intelligence(GenAI)systems have achieved remarkable capabilities across text,code,and image generation;however,their outputs remain prone to errors,hallucinations,and biases.Users often overtrust these outputs due to limited transparency,which can lead to misuse and decision errors.This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability.We introduce a methodology that adjusts explanations dynamically according to user expertise,model output confidence,and contextual risk factors,providing guidance that is informative but not overwhelming.The framework was evaluated using outputs from OpenAI’s Generative Pretrained Transformer 4(GPT-4)for text and code generation and Stable Diffusion,a deep generative image model,for image synthesis.The evaluation covered text,code,and visual modalities.A dataset of 5000 GenAI outputs was created and reviewed by a diverse participant group of 360 individuals categorized by expertise level.Results show that adaptive explanations improve error detection rates,reduce the mean squared trust calibration error,and maintain efficient decision making compared with both static and no explanation conditions.Theframework increased error detection by up to 16% across expertise levels,a gain that can provide practical benefits in high stakes fields.For example,in healthcare it may help identify diagnostic errors earlier,and in law it may prevent reliance on flawed evidence in judicial work.These improvements highlight the framework’s potential to make Artificial Intelligence(AI)deployment safer and more accountable.Visual analyses,including trust accuracy plots,reliability diagrams,and misconception maps,show that the adaptive approach reduces overtrust and reveals patterns of misunderstanding across modalities.Statistical results confirmthe robustness of thesefindings across novice,intermediate,and expert users.The study offers insights for designing explanations that balance completeness and simplicity to improve trust calibration and cognitive load.The approach has implications for safe and transparent GenAI deployment and can inform both AI interface design and policy development for responsible AI use.
文摘The paper describes an efficient direct method to solve an equation Ax = b, where A is a sparse matrix, on the Intel®Xeon PhiTM coprocessor. The main challenge for such a system is how to engage all available threads (about 240) and how to reduce OpenMP* synchronization overhead, which is very expensive for hundreds of threads. The method consists of decomposing A into a product of lower-triangular, diagonal, and upper triangular matrices followed by solves of the resulting three subsystems. The main idea is based on the hybrid parallel algorithm used in the Intel®Math Kernel Library Parallel Direct Sparse Solver for Clusters [1]. Our implementation exploits a static scheduling algorithm during the factorization step to reduce OpenMP synchronization overhead. To effectively engage all available threads, a three-level approach of parallelization is used. Furthermore, we demonstrate that our implementation can perform up to 100 times better on factorization step and up to 65 times better in terms of overall performance on the 240 threads of the Intel®Xeon PhiTM coprocessor.
文摘This paper describes a method of calculating the Schur complement of a sparse positive definite matrix A. The main idea of this approach is to represent matrix A in the form of an elimination tree using a reordering algorithm like METIS and putting columns/rows for which the Schur complement is needed into the top node of the elimination tree. Any problem with a degenerate part of the initial matrix can be resolved with the help of iterative refinement. The proposed approach is close to the “multifrontal” one which was implemented by Ian Duff and others in 1980s. Schur complement computations described in this paper are available in Intel®Math Kernel Library (Intel®MKL). In this paper we present the algorithm for Schur complement computations, experiments that demonstrate a negligible increase in the number of elements in the factored matrix, and comparison with existing alternatives.
文摘With the increasing importance of cloud services worldwide, the cloud infrastructure and platform management has become critical for cloud service providers. In this paper, a novel architecture of intelligent server management framework is proposed. In this framework, the communication layer is based on the Extensible Messaging and Presence Protocol (XMPP), which was developed for instant messaging and has been proven to be highly mature and suitable for mobile and large scalable deployment due to its extensibility and efficiency. The proposed architecture can simplify server management and increase flexibility and scalability when managing hundreds of thousands of servers in the cloud era.
基金supported by the European Union Horizon 2020 research and innovation program under CPSoSAware project(grant no.871738)by Science Foundation Ireland,grant no.12/RC/2289-P2,Insight Centre for Data Analytics。
文摘Artificial intelligence(AI)algorithms achieve outstanding results in many applicationdomains such as computer vision and natural language processing The performance ofAl models is the outcome of complex and costly model architecture design and trainingprocesses.Hence,it is paramount for model owners to protect their AI models frompiracy-model cloning,illegitimate distribution and use.IP protection mechanisms havebeen applied to Al models,and in particular to deep neural networks,to verify themodel ownership.State-of-the-art AI model ownership protection techniques have beensurveyed.The pros and cons of Al model ownership protection have been reported.The majonity of previous works are focused on watermarking,while more advancedmethods such fingerprinting and attestation are promising but not yet explored indepth.This study has been concluded by discussing possible research directions in thearea.
文摘Recent advances have positioned artificial intelligence(AI)as a transformative force to accelerate experiments and enhance decision-making in biological research.Yet scientific progress relies on exploring uncharted intellectual territories,while AI was trained by history.This inherent limitation underscores its role as a collaborator rather than a successor to humanity.
文摘为提升设施农业的环境感知与预警能力,同时兼顾连续可靠与成本可控,以Arduino UNO R 3为主控,构建了多模态感知与预警平台。集成温湿度和光照等多传感器,电路系统采用三层叠板结构,上位机实现阈值警告与可视化操作界面。基于能量守恒与水汽守恒提出名义预测环境模型,并结合辐照度分解、传热与换气,推导出温室参数的名义解析表达,并在六个经纬度测试点采集多组数据进行工况验证。实验结果表明,在五档通风条件下,光照校准共180组样本,回归斜率接近1%;温湿度实测与模型预测高度吻合,通风增强可将稳态温升由56—64℃区间显著拉低,并将相对湿度稳定在约35%—50%RH之间;多模态联动测试中,火焰、超声、CO_(2)与可燃气体通道的响应灵敏度分别为99%、89%、95%、91%,实现了从透过辐射到室内多模态响应的闭环集成,为面向不同温室环境测量的推广与应用提供了可行的技术路径。
