This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal...This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective.展开更多
The textile process planning is a knowledge reuse process in nature, which depends on the expert’s knowledge and experience. It seems to be very difficult to build up an integral mathematical model to optimize hundre...The textile process planning is a knowledge reuse process in nature, which depends on the expert’s knowledge and experience. It seems to be very difficult to build up an integral mathematical model to optimize hundreds of the processing parameters. In fact, the existing process cases which were recorded to ensure the ability to trace production steps can also be used to optimize the process itself. This paper presents a novel knowledge-reuse based hybrid intelligent reasoning model (HIRM) for worsted process optimization. The model architecture and reasoning mechanism are respectively described. An applied case with HIRM is given to demonstrate that the best process decision can be made, and important processing parameters such as for raw material optimized.展开更多
To use reasoning knowledge accurately and efficiently,many reasoning methods have been proposed. However,the differences in form among the methods may obstruct the systematical analysis and harmonious integration of t...To use reasoning knowledge accurately and efficiently,many reasoning methods have been proposed. However,the differences in form among the methods may obstruct the systematical analysis and harmonious integration of them.In this paper,a common reasoning model JUM(Judgement Model)is introduced. According to JUM,a common knowledge representation form is abstracted from different reasoning methods and its limitation is reduced.We also propose an algorithm for transforming one type of JUMs into another.In some cases,the algorithm can be used to resolve the key problem of integrating different types of JUM in one system.It is possible that a new architecture of knowledge-based system can be realized under JUM.展开更多
Natural language processing(NLP)is a subfield of artificial intelligence that focuses on enabling computers to understand and process human languages.In the last five years,we have witnessed the rapid development of N...Natural language processing(NLP)is a subfield of artificial intelligence that focuses on enabling computers to understand and process human languages.In the last five years,we have witnessed the rapid development of NLP in tasks such as machine translation,question-answering,and machine reading comprehension based on deep learning and an enormous volume of annotated and unannotated data.In this paper,we will review the latest progress in the neural network-based NLP framework(neural NLP)from three perspectives:modeling,learning,and reasoning.In the modeling section,we will describe several fundamental neural network-based modeling paradigms,such as word embedding,sentence embedding,and sequence-to-sequence modeling,which are widely used in modern NLP engines.In the learning section,we will introduce widely used learning methods for NLP models,including supervised,semi-supervised,and unsupervised learning;multitask learning;transfer learning;and active learning.We view reasoning as a new and exciting direction for neural NLP,but it has yet to be well addressed.In the reasoning section,we will review reasoning mechanisms,including the knowledge,existing non-neural inference methods,and new neural inference methods.We emphasize the importance of reasoning in this paper because it is important for building interpretable and knowledgedriven neural NLP models to handle complex tasks.At the end of this paper,we will briefly outline our thoughts on the future directions of neural NLP.展开更多
As an important part of medical and health field, nursing safety management plays a vital role in ensuring patients’ life safety. Swiss cheese model is an efficient system security management strategy, covering four ...As an important part of medical and health field, nursing safety management plays a vital role in ensuring patients’ life safety. Swiss cheese model is an efficient system security management strategy, covering four dimensions: organizational influence, poor supervision, potential unsafe behavior and unsafe operation behavior. Through in-depth analysis of the risk factors of accidents, this model constructs a multi-level defense system from the above four dimensions in order to prevent accidents. In order to continuously improve nursing safety management and nursing service quality, and promote the sustainable development of nursing work, this study mainly summarized and analyzed the Swiss cheese model and its application progress in the field of nursing safety, aiming at providing theoretical reference for subsequent researchers’ research and clinical practice.展开更多
Case-Based Reasoning （CBR） is an AI approach and been applied to many areas. However, one area - geography - has not been investigated systematically and thus has been identified as the focus for this study. This pa...Case-Based Reasoning （CBR） is an AI approach and been applied to many areas. However, one area - geography - has not been investigated systematically and thus has been identified as the focus for this study. This paper intends to further extend current CBR to a geographic CBR （Geo-CBR）. First, the concept of Geo-CBR is proposed. Second, a representation model for geographic cases has been established based on the Tesseral model and on a further extension in spatio-temporal dimensions for geographic cases. Third, a reasoning model for Geo-CBR is developed by considering the spatio-temporat characteristics and the uncertain and limited information of geographic cases. Finally, the Geo-CBR model is applied to forecasting the production of ocean fisheries to demonstrate the applicability of the developed Geo-CBR in solving problems in the real world. According to the experimental results, Geo-CBR is an effective and easy-to-implement approach for predicting geographic cases quantitatively.展开更多
A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional de...A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.展开更多
In this paper,the ideas of universal logic is introduced into fuzzy systems.After giving the definitions of the softened fuzzy reasoning models based on Schweizer-Sklar t-norms and Schweizer-Sklar implications,i.e.,α...In this paper,the ideas of universal logic is introduced into fuzzy systems.After giving the definitions of the softened fuzzy reasoning models based on Schweizer-Sklar t-norms and Schweizer-Sklar implications,i.e.,α-models andβ-models,we give the sufficient and necessary conditions for these models to be continuous,and discuss the continuity of some commonly used models.We also prove that when anα-model or aβ-model is used as a fuzzy controller,it has universal property with respect to function approximation.The results we obtained show thatα-models andβ-models are more flexible than the existing models in applications.展开更多
How the recent progress of reasoning large language models(LLMs),especially the new open-source model DeepSeek-R1,can benefit financial services is an underexplored problem.While LLMs have ignited numerous application...How the recent progress of reasoning large language models(LLMs),especially the new open-source model DeepSeek-R1,can benefit financial services is an underexplored problem.While LLMs have ignited numerous applications within the financial sector,including financial news analysis and general customer interactions.展开更多
In-context learning(ICL), which teaches a large language model(LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong paradigm for using LLMs. While ear...In-context learning(ICL), which teaches a large language model(LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong paradigm for using LLMs. While early studies primarily used a fixed or random set of demonstrations for all test queries, recent research suggests that retrieving semantically similar demonstrations to the input from a pool of available demonstrations results in better performance. This work expands the applicability of retrieval-based ICL approaches along several dimensions. We extend the success of retrieval-based ICL to instructionfinetuned LLMs as well as Chain-of-Thought(CoT) prompting. While the prior work utilizes general Large Language Models(LLMs), such as GPT-3, we find that retrieved demonstrations also enhance instructionfinetuned LLMs. This insight implies that training data, despite being exposed during the fine-tuning phase, can still be effectively used through retrieval and in-context demonstrations during testing, resulting in superior outcomes when compared to utilizing no demonstrations or selecting them at random. For CoT, when the demonstrations contain reasoning chains, we get improvements by retrieving based on such chains. Finally, we train a task-specific demonstration retriever that outperforms off-the-shelf retrievers.展开更多
基金supported by the National Key Research&Development Program of China(2021YFB3301100)the National Natural Science Foundation of China(52004014)the Fundamental Research Funds for the Central Universities(ZY2406).
文摘This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective.
基金This research was supported by technology innovation fund of the national economy and trade committee , People s Republic of China ,under contract number 02LJ 14 05 01
文摘The textile process planning is a knowledge reuse process in nature, which depends on the expert’s knowledge and experience. It seems to be very difficult to build up an integral mathematical model to optimize hundreds of the processing parameters. In fact, the existing process cases which were recorded to ensure the ability to trace production steps can also be used to optimize the process itself. This paper presents a novel knowledge-reuse based hybrid intelligent reasoning model (HIRM) for worsted process optimization. The model architecture and reasoning mechanism are respectively described. An applied case with HIRM is given to demonstrate that the best process decision can be made, and important processing parameters such as for raw material optimized.
文摘To use reasoning knowledge accurately and efficiently,many reasoning methods have been proposed. However,the differences in form among the methods may obstruct the systematical analysis and harmonious integration of them.In this paper,a common reasoning model JUM(Judgement Model)is introduced. According to JUM,a common knowledge representation form is abstracted from different reasoning methods and its limitation is reduced.We also propose an algorithm for transforming one type of JUMs into another.In some cases,the algorithm can be used to resolve the key problem of integrating different types of JUM in one system.It is possible that a new architecture of knowledge-based system can be realized under JUM.
文摘Natural language processing(NLP)is a subfield of artificial intelligence that focuses on enabling computers to understand and process human languages.In the last five years,we have witnessed the rapid development of NLP in tasks such as machine translation,question-answering,and machine reading comprehension based on deep learning and an enormous volume of annotated and unannotated data.In this paper,we will review the latest progress in the neural network-based NLP framework(neural NLP)from three perspectives:modeling,learning,and reasoning.In the modeling section,we will describe several fundamental neural network-based modeling paradigms,such as word embedding,sentence embedding,and sequence-to-sequence modeling,which are widely used in modern NLP engines.In the learning section,we will introduce widely used learning methods for NLP models,including supervised,semi-supervised,and unsupervised learning;multitask learning;transfer learning;and active learning.We view reasoning as a new and exciting direction for neural NLP,but it has yet to be well addressed.In the reasoning section,we will review reasoning mechanisms,including the knowledge,existing non-neural inference methods,and new neural inference methods.We emphasize the importance of reasoning in this paper because it is important for building interpretable and knowledgedriven neural NLP models to handle complex tasks.At the end of this paper,we will briefly outline our thoughts on the future directions of neural NLP.
文摘As an important part of medical and health field, nursing safety management plays a vital role in ensuring patients’ life safety. Swiss cheese model is an efficient system security management strategy, covering four dimensions: organizational influence, poor supervision, potential unsafe behavior and unsafe operation behavior. Through in-depth analysis of the risk factors of accidents, this model constructs a multi-level defense system from the above four dimensions in order to prevent accidents. In order to continuously improve nursing safety management and nursing service quality, and promote the sustainable development of nursing work, this study mainly summarized and analyzed the Swiss cheese model and its application progress in the field of nursing safety, aiming at providing theoretical reference for subsequent researchers’ research and clinical practice.
文摘Case-Based Reasoning （CBR） is an AI approach and been applied to many areas. However, one area - geography - has not been investigated systematically and thus has been identified as the focus for this study. This paper intends to further extend current CBR to a geographic CBR （Geo-CBR）. First, the concept of Geo-CBR is proposed. Second, a representation model for geographic cases has been established based on the Tesseral model and on a further extension in spatio-temporal dimensions for geographic cases. Third, a reasoning model for Geo-CBR is developed by considering the spatio-temporat characteristics and the uncertain and limited information of geographic cases. Finally, the Geo-CBR model is applied to forecasting the production of ocean fisheries to demonstrate the applicability of the developed Geo-CBR in solving problems in the real world. According to the experimental results, Geo-CBR is an effective and easy-to-implement approach for predicting geographic cases quantitatively.
基金National "863" project (2001AA114140) the National Natural Science Foundation of China (60135020).
文摘A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.
基金This work was supported by the Youth Research Fund of Shantou University(No.YR08003)the National Natural Science Foundation of China(Grant No.10971125).
文摘In this paper,the ideas of universal logic is introduced into fuzzy systems.After giving the definitions of the softened fuzzy reasoning models based on Schweizer-Sklar t-norms and Schweizer-Sklar implications,i.e.,α-models andβ-models,we give the sufficient and necessary conditions for these models to be continuous,and discuss the continuity of some commonly used models.We also prove that when anα-model or aβ-model is used as a fuzzy controller,it has universal property with respect to function approximation.The results we obtained show thatα-models andβ-models are more flexible than the existing models in applications.
文摘How the recent progress of reasoning large language models(LLMs),especially the new open-source model DeepSeek-R1,can benefit financial services is an underexplored problem.While LLMs have ignited numerous applications within the financial sector,including financial news analysis and general customer interactions.
文摘In-context learning(ICL), which teaches a large language model(LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong paradigm for using LLMs. While early studies primarily used a fixed or random set of demonstrations for all test queries, recent research suggests that retrieving semantically similar demonstrations to the input from a pool of available demonstrations results in better performance. This work expands the applicability of retrieval-based ICL approaches along several dimensions. We extend the success of retrieval-based ICL to instructionfinetuned LLMs as well as Chain-of-Thought(CoT) prompting. While the prior work utilizes general Large Language Models(LLMs), such as GPT-3, we find that retrieved demonstrations also enhance instructionfinetuned LLMs. This insight implies that training data, despite being exposed during the fine-tuning phase, can still be effectively used through retrieval and in-context demonstrations during testing, resulting in superior outcomes when compared to utilizing no demonstrations or selecting them at random. For CoT, when the demonstrations contain reasoning chains, we get improvements by retrieving based on such chains. Finally, we train a task-specific demonstration retriever that outperforms off-the-shelf retrievers.
