In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilizati...In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.展开更多
A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and know...A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and knowledge base) cooperating mechanism. With all new features, ESKD may form a new research direction and provide a great probability for solving the wealth of knowledge in the knowledge base. The general structural frame of ESKD and some sub-systems among ESKD have been described, and the dynamic knowledge base based on double-base cooperating mechanism has been emphased on. According to the result of demonstrative experi- ment, the structure of ESKD is effective and feasible.展开更多
Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault char...Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault characteristic in the incipient stage can result in misdiagnosis, it is a challenge for fault-tolerant control to ensure system safety and reliability. Therefore, to address this issue, a fault diagnosis and fault-tolerant control with a knowledge transfer strategy(KT-FDFTC) is proposed in this paper. First, a knowledge reasoning diagnosis strategy using multi-source transfer learning is designed to distinguish the similar characteristic of incipient faults. Then, the multi-source knowledge can assist in the diagnosis strategy to strengthen the fault information for fault-tolerant control. Second, a knowledge adaptive compensation mechanism, which makes knowledge and data coupled into the output trajectory regarded as an objective function, is employed to dynamically compute the control law. Then, KT-FDFTC can ensure the stable operation to adapt to various fault conditions. Third, the Lyapunov function is established to demonstrate the stability of KT-FDFTC. Then, the theoretical basis can offer the successful application of KTFDFTC. Finally, the proposed method is validated through a real WWTP and a simulation platform. The experimental results confirm that KT-FDFTC can provide good diagnosis performance and fault tolerance ability.展开更多
文摘In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.
文摘A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and knowledge base) cooperating mechanism. With all new features, ESKD may form a new research direction and provide a great probability for solving the wealth of knowledge in the knowledge base. The general structural frame of ESKD and some sub-systems among ESKD have been described, and the dynamic knowledge base based on double-base cooperating mechanism has been emphased on. According to the result of demonstrative experi- ment, the structure of ESKD is effective and feasible.
基金supported by the National Natural Science Foundation of China (Grant Nos.62125301,62021003,62303024,U24A20275,62522302,62473011,92467205)the National Key Research and Development Project (Grant Nos.2022YFB3305800-5,2024YFE0212400)+2 种基金the Youth Beijing Scholars Program (Grant No.037)the Beijing Nova Program (Grant Nos.20240484694,20250484938)the Beijing Natural Science Foundation (Grant No.L253010)。
文摘Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault characteristic in the incipient stage can result in misdiagnosis, it is a challenge for fault-tolerant control to ensure system safety and reliability. Therefore, to address this issue, a fault diagnosis and fault-tolerant control with a knowledge transfer strategy(KT-FDFTC) is proposed in this paper. First, a knowledge reasoning diagnosis strategy using multi-source transfer learning is designed to distinguish the similar characteristic of incipient faults. Then, the multi-source knowledge can assist in the diagnosis strategy to strengthen the fault information for fault-tolerant control. Second, a knowledge adaptive compensation mechanism, which makes knowledge and data coupled into the output trajectory regarded as an objective function, is employed to dynamically compute the control law. Then, KT-FDFTC can ensure the stable operation to adapt to various fault conditions. Third, the Lyapunov function is established to demonstrate the stability of KT-FDFTC. Then, the theoretical basis can offer the successful application of KTFDFTC. Finally, the proposed method is validated through a real WWTP and a simulation platform. The experimental results confirm that KT-FDFTC can provide good diagnosis performance and fault tolerance ability.
