Equipment selection for industrial process usually requires the extensive participation of industrial experts and technologists, which causes a serious waste of resources. This work presents an equipment selection kno...Equipment selection for industrial process usually requires the extensive participation of industrial experts and technologists, which causes a serious waste of resources. This work presents an equipment selection knowledge base system for industrial styrene process(S-ESKBS) based on the ontology technology. This structure includes a low-level knowledge base and a top-level interactive application. As the core part of the S-ESKBS, the low-level knowledge base consists of the equipment selection ontology library, equipment selection rule set and Pellet inference engine. The top-level interactive application is implemented using S-ESKBS, including the parsing storage layer, inference query layer and client application layer. Case studies for the industrial styrene process equipment selection of an analytical column and an alkylation reactor are demonstrated to show the characteristics and implementability of the S-ESKBS.展开更多
Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyet...Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowl- edge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized.展开更多
Sustainable engineering becomes a fast growing field of research and education.It aims at designing and operating systems of various scales such that they can use energy and resources in a sustainablemanner.Needless t...Sustainable engineering becomes a fast growing field of research and education.It aims at designing and operating systems of various scales such that they can use energy and resources in a sustainablemanner.Needless to say,this is one of the most challenging engineering problem types that needs scientists,researchers,engineers,and practitioners to collaboratively work for solutions.展开更多
Aiming at the limitations of the existing knowledge representations in intelligent detection,a novel extension-based knowledge representation(EKR) is proposed.The definitions,grammar rules,and formal semantics of EKR ...Aiming at the limitations of the existing knowledge representations in intelligent detection,a novel extension-based knowledge representation(EKR) is proposed.The definitions,grammar rules,and formal semantics of EKR are presented.A rhombus solving strategy(RSS) based on EKR is discussed in detail,including creation of the problem oriented model,extension operator,the solution formation of contradictions problem and extended inference of matter-element.A knowledge base system based on EKR and RSS is developed,which is applied in intelligent detection in the Dendrobium huoshanense photosynthesis process(DHPP).More reasonable results are obtained than traditional rule-based system.The EKR is feasible in intelligent detection to solve the limitations of traditional knowledge representations.展开更多
Indigenous cultures prescribed a means of maximizing the benefits they produced and enjoyed in their relationship with each other and the environment-based on their understanding of the nature of existence and how to ...Indigenous cultures prescribed a means of maximizing the benefits they produced and enjoyed in their relationship with each other and the environment-based on their understanding of the nature of existence and how to live in harmony with the forces shaping the nature of existence.The emergence of civilization introduced the claim that rational abilities superseded indigenous knowledge.This was followed by positivism and the claim that knowledge passed through three stages:mythological,philosophical,and scientific.This impacted indigenous cultures in ways that reached a height when postcolonial development experts convinced national leaders that progress required adopting advances in science.A failure to modernize was regarded as holding back progress.With the development paradigm now regarded as inadequate for achieving its goals and with the rise of the sustainability discourse,there is appreciation for indigenous knowledge.This article describes an indigenous cultural knowledge system that reflects the insight and wisdom of the world’s most respected scientific and philosophical traditions.The beliefs of the Bodo people of Northeast India are used as an example of an indigenous worldview that portrays insight proven to have value that is comparable to the natural sciences,plus theories of natural law and political philosophy.展开更多
Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpret...Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM.展开更多
Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images...Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled Retine (DDR) method, an unsupervised approach that integrates denoising priors into a Retinex-based training framework. By explicitly incorporating denoising, the DDR method effectively addresses the challenges of noise and artifacts in low-light images, thereby enhancing the performance of the Retinex framework. The model achieved a PSNR of 19.82 dB on the LOL dataset, which is comparable to the performance of supervised methods. Furthermore, by applying knowledge distillation, the DDR method optimizes the model for real-time processing of low-light images, achieving a processing speed of 199.7 fps without incurring additional computational costs. While the DDR method has demonstrated superior performance in terms of image quality and processing speed, there is still room for improvement in terms of robustness across different color spaces and under highly resource-constrained conditions. Future research will focus on enhancing the model’s generalizability and adaptability to address these challenges. Our rigorous testing on public datasets further substantiates the DDR method’s state-of-the-art performance in both image quality and processing speed.展开更多
This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electroca...This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.展开更多
In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Exis...In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands;(2) overlooking the syntactic dependencies between Chinese word phrases and the underlying impact of sentiment scores between word phrases on sentiment inclination determination. To tackle these challenges, we propose the External Knowledge-enhanced Cross-Attention Fusion model, CITSA. Specifically, in the Cross Infusion Layer, we fuse consumer comment information and tobacco fundamental information through interactive attention mechanisms. In the Textual Attention Enhancement Layer, we introduce an emotion-oriented syntactic dependency graph and incorporate sentiment-syntactic relationships into consumer comments through a graph convolution network module. Subsequently, the Textual Attention Layer is introduced to combine these two feature representations. Additionally, we compile a Chinese-oriented tobacco sentiment analysis dataset, comprising 55,096 consumer reviews and 2074 tobacco fundamental information entries. Experimental results on our self-constructed datasets consistently demonstrate that our proposed model outperforms state-of-the-art methods in terms of accuracy, precision, recall, and F1-score.展开更多
This study tested a multilevel model of the workplace territorial behaviors and employees’knowledge sharing relationship,with team identification serving as a mediator and task interdependence as a moderator.Data wer...This study tested a multilevel model of the workplace territorial behaviors and employees’knowledge sharing relationship,with team identification serving as a mediator and task interdependence as a moderator.Data were collected from 253 employees(females=128,mean age=28.626,SD=6.470)from 40 work teams from different industries in China.Path analysis results indicated that workplace territorial behaviors were associated with lower employee knowledge sharing.Team identification enhanced employee knowledge sharing and partially mediated the relationship between workplace territorial behaviors and employee knowledge sharing.Task interdependence enhanced knowledge sharing and strengthened the relationship between team identification and knowledge sharing.Thesefindings extend the proposition of social information processing theory by revealing the mediating role of team identification in the relationship between workplace territorial behaviors and knowledge sharing,and clarifying the boundary conditions of team identification.Practical implications of thesefindings include a need for managers to foster collaborative atmospheres,design interdependent tasks,and mitigate territorial behaviors to enhance team identification and knowledge sharing.展开更多
Understanding definitions and differences between the processes,knowledge processes and business processes is the first step of the integration of knowledge processes into management systems of an organization.The nex...Understanding definitions and differences between the processes,knowledge processes and business processes is the first step of the integration of knowledge processes into management systems of an organization.The next step is to understand throughout the company why the processes should be introduced and continuously maintained.The knowledge is one of the most valuable assets of the company,relevant part of the intellectual capital.The management of the knowledge and its lifecycle can give a market advantage for the organization.In the nuclear industry this is the vital requirement to maintain the safe and reliable operation of a nuclear facility,or radiation safety activates.Those companies who already implemented an integrated management system were following international standards,or good practices(like ISO 9001,EFQM,Standard Nuclear Performance Model developed by Nuclear Energy Institute(NEI)and others).This article focuses on nuclear industry organizations,approaches and methods,and how to integrate the knowledge processes into management system.This is the last step of the knowledge management implementation in an organization.When it was done,we can say that the knowledge processes are embedded into organization’s day-to-day life and the knowledge managed in the organization as all other resources.展开更多
We develop a neuro-knowledge-based expert system (NKBES) frame in this work. The system mainly concerns with decision of gating system and die casting machine based on a neuro-inference engine launched under the MATLA...We develop a neuro-knowledge-based expert system (NKBES) frame in this work. The system mainly concerns with decision of gating system and die casting machine based on a neuro-inference engine launched under the MATLAB software environment. For enhancement of reasoning agility, an error back-propagation neural network was applied. A rapidly convergent adaptive learning rate (ALR) and a momentum-based error back-propagation algorithm was used to conduct neuro-reasoning. The working effect of the system was compared to a conventional expert system that is based on a two-way (forward and backward) chaining inference mechanism. As the reference, the present paper provided the neural networks sum-squared error (S5E) and ALR vs iterative epoch curves of process planning case mentioned above. The study suggests that the neuro-modeling optimization application to die casting process design has good feasibility, and based on that a novel and effective intelligent expert system can be launched at low cost.展开更多
The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines ...The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines the utilization of reinforcement learning as a control strategy, with a particular focus on its deployment in pivotal stages of the product development lifecycle, specifically between system architecture and system integration and verification. A controller based on reinforcement learning was developed and evaluated in comparison to traditional proportional-integral controllers in dynamic and fault-prone environments. The results illustrate the superior adaptability, stability, and optimization potential of the reinforcement learning approach, particularly in addressing dynamic disturbances and ensuring robust performance. The study illustrates how reinforcement learning can facilitate the transition from conceptual design to implementation by automating optimization processes, enabling interface automation, and enhancing system-level testing. Based on the aforementioned findings, this paper presents future directions for research, which include the integration of domain-specific knowledge into the reinforcement learning process and the validation of this process in real-world environments. The results underscore the potential of artificial intelligence-driven methodologies to revolutionize the design and deployment of intelligent mechatronic systems.展开更多
The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of ...The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.展开更多
An expert system skeleton tool of sintering process was constructed using object-oriented method, which can actualize two functions, i. e. , the shell function and the program function. The skeleton tool offered a pla...An expert system skeleton tool of sintering process was constructed using object-oriented method, which can actualize two functions, i. e. , the shell function and the program function. The skeleton tool offered a platform to build a prototype system, to program class code, and to develop the expert system. Four branch expert systems were developed using the skeleton tool including the control of chemical composition, the control of sintering process state, the control of expended energy, and the diagnosis of abnormity. It is found that the performance of all systems is satisfactory in practice.展开更多
Purpose:This work aims to normalize the NLPCONTRIBUTIONS scheme(henceforward,NLPCONTRIBUTIONGRAPH)to structure,directly from article sentences,the contributions information in Natural Language Processing(NLP)scholarly...Purpose:This work aims to normalize the NLPCONTRIBUTIONS scheme(henceforward,NLPCONTRIBUTIONGRAPH)to structure,directly from article sentences,the contributions information in Natural Language Processing(NLP)scholarly articles via a two-stage annotation methodology:1)pilot stage-to define the scheme(described in prior work);and 2)adjudication stage-to normalize the graphing model(the focus of this paper).Design/methodology/approach:We re-annotate,a second time,the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising:contribution-centered sentences,phrases,and triple statements.To this end,specifically,care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme.Findings:The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences,4,702 contribution-information-centered phrases,and 2,980 surface-structured triples.The intra-annotation agreement between the first and second stages,in terms of F1-score,was 67.92%for sentences,41.82%for phrases,and 22.31%for triple statements indicating that with increased granularity of the information,the annotation decision variance is greater.Research limitations:NLPCONTRIBUTIONGRAPH has limited scope for structuring scholarly contributions compared with STEM(Science,Technology,Engineering,and Medicine)scholarly knowledge at large.Further,the annotation scheme in this work is designed by only an intra-annotator consensus-a single annotator first annotated the data to propose the initial scheme,following which,the same annotator reannotated the data to normalize the annotations in an adjudication stage.However,the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly articles.This would entail a larger initiative of enlisting multiple annotators to accommodate different worldviews into a“single”set of structures and relationships as the final scheme.Given that the initial scheme is first proposed and the complexity of the annotation task in the realistic timeframe,our intraannotation procedure is well-suited.Nevertheless,the model proposed in this work is presently limited since it does not incorporate multiple annotator worldviews.This is planned as future work to produce a robust model.Practical implications:We demonstrate NLPCONTRIBUTIONGRAPH data integrated into the Open Research Knowledge Graph(ORKG),a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge,as a viable aid to assist researchers in their day-to-day tasks.Originality/value:NLPCONTRIBUTIONGRAPH is a novel scheme to annotate research contributions from NLP articles and integrate them in a knowledge graph,which to the best of our knowledge does not exist in the community.Furthermore,our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.展开更多
To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new lig...To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.展开更多
A predictive parallel search algorithm,the fuzzy match inference strategy,is implemented ina prototype expert system.Selection of separation technologies and sequencing of separators are beingapproached in an integrat...A predictive parallel search algorithm,the fuzzy match inference strategy,is implemented ina prototype expert system.Selection of separation technologies and sequencing of separators are beingapproached in an integrated manner.The fuzzy match mechanism results in a relatively smaller subsetof favored schemes,constituting a hyperstructure for further quantitative evaluation and combinationoptimization.An industrial application example of aromatics extraction separation is presented.展开更多
Due to the increasing amount and complexity of knowledge in product design, the know-ledge map based on design process is presented as a tool to reuse product design process, promote the product design knowledge shari...Due to the increasing amount and complexity of knowledge in product design, the know-ledge map based on design process is presented as a tool to reuse product design process, promote the product design knowledge sharing. The relationship between design task flow and knowledge flow is discussed; A knowledge organizing method based on design task decomposition and a visualization method to support the knowledge retrieving and sharing in product design are proposed. And a knowledge map system to manage the knowledge in product design process is built with Visual C＋＋ and SVG. Finally, a brief case study is provided to illustrate the construction and application of knowledge map in fuel pump design.展开更多
A multi agent computer aided assembly process planning system (MCAAPP) for ship hull is presented. The system includes system framework, global facilitator, the macro agent structure, agent communication language, ag...A multi agent computer aided assembly process planning system (MCAAPP) for ship hull is presented. The system includes system framework, global facilitator, the macro agent structure, agent communication language, agent oriented programming language, knowledge representation and reasoning strategy. The system can produce the technological file and technological quota, which can satisfy the production needs of factory.展开更多
基金Supported by the National Science Foundation China(61422303)National Key Technology R&D Program(2015BAF22B02)the Development Fund for Shanghai Talents
文摘Equipment selection for industrial process usually requires the extensive participation of industrial experts and technologists, which causes a serious waste of resources. This work presents an equipment selection knowledge base system for industrial styrene process(S-ESKBS) based on the ontology technology. This structure includes a low-level knowledge base and a top-level interactive application. As the core part of the S-ESKBS, the low-level knowledge base consists of the equipment selection ontology library, equipment selection rule set and Pellet inference engine. The top-level interactive application is implemented using S-ESKBS, including the parsing storage layer, inference query layer and client application layer. Case studies for the industrial styrene process equipment selection of an analytical column and an alkylation reactor are demonstrated to show the characteristics and implementability of the S-ESKBS.
文摘Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowl- edge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized.
文摘Sustainable engineering becomes a fast growing field of research and education.It aims at designing and operating systems of various scales such that they can use energy and resources in a sustainablemanner.Needless to say,this is one of the most challenging engineering problem types that needs scientists,researchers,engineers,and practitioners to collaboratively work for solutions.
基金the National Natural Science Founda-tion of China(No.60974038)
文摘Aiming at the limitations of the existing knowledge representations in intelligent detection,a novel extension-based knowledge representation(EKR) is proposed.The definitions,grammar rules,and formal semantics of EKR are presented.A rhombus solving strategy(RSS) based on EKR is discussed in detail,including creation of the problem oriented model,extension operator,the solution formation of contradictions problem and extended inference of matter-element.A knowledge base system based on EKR and RSS is developed,which is applied in intelligent detection in the Dendrobium huoshanense photosynthesis process(DHPP).More reasonable results are obtained than traditional rule-based system.The EKR is feasible in intelligent detection to solve the limitations of traditional knowledge representations.
文摘Indigenous cultures prescribed a means of maximizing the benefits they produced and enjoyed in their relationship with each other and the environment-based on their understanding of the nature of existence and how to live in harmony with the forces shaping the nature of existence.The emergence of civilization introduced the claim that rational abilities superseded indigenous knowledge.This was followed by positivism and the claim that knowledge passed through three stages:mythological,philosophical,and scientific.This impacted indigenous cultures in ways that reached a height when postcolonial development experts convinced national leaders that progress required adopting advances in science.A failure to modernize was regarded as holding back progress.With the development paradigm now regarded as inadequate for achieving its goals and with the rise of the sustainability discourse,there is appreciation for indigenous knowledge.This article describes an indigenous cultural knowledge system that reflects the insight and wisdom of the world’s most respected scientific and philosophical traditions.The beliefs of the Bodo people of Northeast India are used as an example of an indigenous worldview that portrays insight proven to have value that is comparable to the natural sciences,plus theories of natural law and political philosophy.
基金Supported in part by Science Center for Gas Turbine Project(Project No.P2022-DC-I-003-001)National Natural Science Foundation of China(Grant No.52275130).
文摘Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM.
基金support by the Guangxi Natural Science Foundation(Grant No.2024GXNSFAA010484)the NationalNatural Science Foundation of China(No.62466013),this work has been made possible.
文摘Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled Retine (DDR) method, an unsupervised approach that integrates denoising priors into a Retinex-based training framework. By explicitly incorporating denoising, the DDR method effectively addresses the challenges of noise and artifacts in low-light images, thereby enhancing the performance of the Retinex framework. The model achieved a PSNR of 19.82 dB on the LOL dataset, which is comparable to the performance of supervised methods. Furthermore, by applying knowledge distillation, the DDR method optimizes the model for real-time processing of low-light images, achieving a processing speed of 199.7 fps without incurring additional computational costs. While the DDR method has demonstrated superior performance in terms of image quality and processing speed, there is still room for improvement in terms of robustness across different color spaces and under highly resource-constrained conditions. Future research will focus on enhancing the model’s generalizability and adaptability to address these challenges. Our rigorous testing on public datasets further substantiates the DDR method’s state-of-the-art performance in both image quality and processing speed.
文摘This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.
基金supported by the Global Research and Innovation Platform Fund for Scientific Big Data Transmission(Grant No.241711KYSB20180002)National Key Research and Development Project of China(Grant No.2019YFB1405801).
文摘In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands;(2) overlooking the syntactic dependencies between Chinese word phrases and the underlying impact of sentiment scores between word phrases on sentiment inclination determination. To tackle these challenges, we propose the External Knowledge-enhanced Cross-Attention Fusion model, CITSA. Specifically, in the Cross Infusion Layer, we fuse consumer comment information and tobacco fundamental information through interactive attention mechanisms. In the Textual Attention Enhancement Layer, we introduce an emotion-oriented syntactic dependency graph and incorporate sentiment-syntactic relationships into consumer comments through a graph convolution network module. Subsequently, the Textual Attention Layer is introduced to combine these two feature representations. Additionally, we compile a Chinese-oriented tobacco sentiment analysis dataset, comprising 55,096 consumer reviews and 2074 tobacco fundamental information entries. Experimental results on our self-constructed datasets consistently demonstrate that our proposed model outperforms state-of-the-art methods in terms of accuracy, precision, recall, and F1-score.
文摘This study tested a multilevel model of the workplace territorial behaviors and employees’knowledge sharing relationship,with team identification serving as a mediator and task interdependence as a moderator.Data were collected from 253 employees(females=128,mean age=28.626,SD=6.470)from 40 work teams from different industries in China.Path analysis results indicated that workplace territorial behaviors were associated with lower employee knowledge sharing.Team identification enhanced employee knowledge sharing and partially mediated the relationship between workplace territorial behaviors and employee knowledge sharing.Task interdependence enhanced knowledge sharing and strengthened the relationship between team identification and knowledge sharing.Thesefindings extend the proposition of social information processing theory by revealing the mediating role of team identification in the relationship between workplace territorial behaviors and knowledge sharing,and clarifying the boundary conditions of team identification.Practical implications of thesefindings include a need for managers to foster collaborative atmospheres,design interdependent tasks,and mitigate territorial behaviors to enhance team identification and knowledge sharing.
文摘Understanding definitions and differences between the processes,knowledge processes and business processes is the first step of the integration of knowledge processes into management systems of an organization.The next step is to understand throughout the company why the processes should be introduced and continuously maintained.The knowledge is one of the most valuable assets of the company,relevant part of the intellectual capital.The management of the knowledge and its lifecycle can give a market advantage for the organization.In the nuclear industry this is the vital requirement to maintain the safe and reliable operation of a nuclear facility,or radiation safety activates.Those companies who already implemented an integrated management system were following international standards,or good practices(like ISO 9001,EFQM,Standard Nuclear Performance Model developed by Nuclear Energy Institute(NEI)and others).This article focuses on nuclear industry organizations,approaches and methods,and how to integrate the knowledge processes into management system.This is the last step of the knowledge management implementation in an organization.When it was done,we can say that the knowledge processes are embedded into organization’s day-to-day life and the knowledge managed in the organization as all other resources.
文摘We develop a neuro-knowledge-based expert system (NKBES) frame in this work. The system mainly concerns with decision of gating system and die casting machine based on a neuro-inference engine launched under the MATLAB software environment. For enhancement of reasoning agility, an error back-propagation neural network was applied. A rapidly convergent adaptive learning rate (ALR) and a momentum-based error back-propagation algorithm was used to conduct neuro-reasoning. The working effect of the system was compared to a conventional expert system that is based on a two-way (forward and backward) chaining inference mechanism. As the reference, the present paper provided the neural networks sum-squared error (S5E) and ALR vs iterative epoch curves of process planning case mentioned above. The study suggests that the neuro-modeling optimization application to die casting process design has good feasibility, and based on that a novel and effective intelligent expert system can be launched at low cost.
文摘The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines the utilization of reinforcement learning as a control strategy, with a particular focus on its deployment in pivotal stages of the product development lifecycle, specifically between system architecture and system integration and verification. A controller based on reinforcement learning was developed and evaluated in comparison to traditional proportional-integral controllers in dynamic and fault-prone environments. The results illustrate the superior adaptability, stability, and optimization potential of the reinforcement learning approach, particularly in addressing dynamic disturbances and ensuring robust performance. The study illustrates how reinforcement learning can facilitate the transition from conceptual design to implementation by automating optimization processes, enabling interface automation, and enhancing system-level testing. Based on the aforementioned findings, this paper presents future directions for research, which include the integration of domain-specific knowledge into the reinforcement learning process and the validation of this process in real-world environments. The results underscore the potential of artificial intelligence-driven methodologies to revolutionize the design and deployment of intelligent mechatronic systems.
基金supported by the National Science Foundation of China(No.51575264)the Jiangsu Province Science Foundation for Excellent Youths under Grant BK20121011the Fundamental Research Funds for the Central Universities(No.NS2015050)
文摘The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.
基金National Natural Science Foundation of China (50374080)Innovation Project of Postgraduate Education of Central South University (042310011)
文摘An expert system skeleton tool of sintering process was constructed using object-oriented method, which can actualize two functions, i. e. , the shell function and the program function. The skeleton tool offered a platform to build a prototype system, to program class code, and to develop the expert system. Four branch expert systems were developed using the skeleton tool including the control of chemical composition, the control of sintering process state, the control of expended energy, and the diagnosis of abnormity. It is found that the performance of all systems is satisfactory in practice.
基金This work was co-funded by the European Research Council for the project ScienceGRAPH(Grant agreement ID:819536)by the TIB Leibniz Information Centre for Science and Technology.
文摘Purpose:This work aims to normalize the NLPCONTRIBUTIONS scheme(henceforward,NLPCONTRIBUTIONGRAPH)to structure,directly from article sentences,the contributions information in Natural Language Processing(NLP)scholarly articles via a two-stage annotation methodology:1)pilot stage-to define the scheme(described in prior work);and 2)adjudication stage-to normalize the graphing model(the focus of this paper).Design/methodology/approach:We re-annotate,a second time,the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising:contribution-centered sentences,phrases,and triple statements.To this end,specifically,care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme.Findings:The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences,4,702 contribution-information-centered phrases,and 2,980 surface-structured triples.The intra-annotation agreement between the first and second stages,in terms of F1-score,was 67.92%for sentences,41.82%for phrases,and 22.31%for triple statements indicating that with increased granularity of the information,the annotation decision variance is greater.Research limitations:NLPCONTRIBUTIONGRAPH has limited scope for structuring scholarly contributions compared with STEM(Science,Technology,Engineering,and Medicine)scholarly knowledge at large.Further,the annotation scheme in this work is designed by only an intra-annotator consensus-a single annotator first annotated the data to propose the initial scheme,following which,the same annotator reannotated the data to normalize the annotations in an adjudication stage.However,the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly articles.This would entail a larger initiative of enlisting multiple annotators to accommodate different worldviews into a“single”set of structures and relationships as the final scheme.Given that the initial scheme is first proposed and the complexity of the annotation task in the realistic timeframe,our intraannotation procedure is well-suited.Nevertheless,the model proposed in this work is presently limited since it does not incorporate multiple annotator worldviews.This is planned as future work to produce a robust model.Practical implications:We demonstrate NLPCONTRIBUTIONGRAPH data integrated into the Open Research Knowledge Graph(ORKG),a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge,as a viable aid to assist researchers in their day-to-day tasks.Originality/value:NLPCONTRIBUTIONGRAPH is a novel scheme to annotate research contributions from NLP articles and integrate them in a knowledge graph,which to the best of our knowledge does not exist in the community.Furthermore,our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.
基金support provided by the National Natural Science Foundation of China(22122802,22278044,and 21878028)the Chongqing Science Fund for Distinguished Young Scholars(CSTB2022NSCQ-JQX0021)the Fundamental Research Funds for the Central Universities(2022CDJXY-003).
文摘To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.
基金Supported in parts by the National Natural Science Foundation of China, the state Commission of Education of China
文摘A predictive parallel search algorithm,the fuzzy match inference strategy,is implemented ina prototype expert system.Selection of separation technologies and sequencing of separators are beingapproached in an integrated manner.The fuzzy match mechanism results in a relatively smaller subsetof favored schemes,constituting a hyperstructure for further quantitative evaluation and combinationoptimization.An industrial application example of aromatics extraction separation is presented.
基金This project is supported by National Basic Research Program of China (973 Program, No. 2003CB317005)Shuguang Program of Shanghai Municipal Educational Committee, China (No. 05SG15).
文摘Due to the increasing amount and complexity of knowledge in product design, the know-ledge map based on design process is presented as a tool to reuse product design process, promote the product design knowledge sharing. The relationship between design task flow and knowledge flow is discussed; A knowledge organizing method based on design task decomposition and a visualization method to support the knowledge retrieving and sharing in product design are proposed. And a knowledge map system to manage the knowledge in product design process is built with Visual C＋＋ and SVG. Finally, a brief case study is provided to illustrate the construction and application of knowledge map in fuel pump design.
文摘A multi agent computer aided assembly process planning system (MCAAPP) for ship hull is presented. The system includes system framework, global facilitator, the macro agent structure, agent communication language, agent oriented programming language, knowledge representation and reasoning strategy. The system can produce the technological file and technological quota, which can satisfy the production needs of factory.
