Knowledge-Based Fault Diagnosis in Industrial Internet of Things: A Survey

Chi, Yuanfang; Dong, Yanjie; Wang, Jane; Yu, F. Richard; Leung, Victor C. M.

doi:10.1109/jiot.2022.3163606

Cited by 86 publications

(18 citation statements)

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“…Early approaches were predominantly rule-based, where predefined logical frameworks were applied to data processing [7,8]. Subsequent research had evolved into more dynamic models that utilized principles of both deductive and inductive reasoning to adapt to new information [8,9,10]. Hybrid models, combining statistical methods with rule-based logic, were developed and demonstrated increased flexibility in handling ambiguous data, and they allowed AI systems not only to execute tasks but also to explain their reasoning processes, an essential step towards transparent AI [11,12].…”

Section: Logical Reasoning In Aimentioning

confidence: 99%

Empower LlaMa 2 for Advanced Logical Reasoning in Natural Language Understanding

Watanabe,

Kinasaka,

Nakamura

2024

Preprint

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Enhancing LlaMa 2 through the integration of a rule-based reasoning layer and a logic inference engine, collectively referred to as LlaMa+, has markedly improved its capabilities in natural language understanding. The performance enhancements were rigorously evaluated using the sophisticated MMLU and LegalBench benchmark datasets, where LlaMa+ showed significant increases in precision, accuracy, recall, and F1-score. These improvements are indicative of the model's advanced ability to interpret and reason with complex texts, reflecting a substantial progression towards AI systems that can engage with human language in a contextually aware and cognitively profound manner. This research not only underscores the enhanced analytical abilities of LlaMa+ but also explores the implications for practical applications in fields requiring nuanced language comprehension such as legal analysis, academic research, and technical problem-solving. Future directions involve optimizing the computational efficiency of these enhancements to ensure wider applicability and exploring autonomous methods for updating the reasoning mechanisms to keep pace with evolving data and applications.

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Section: Logical Reasoning In Aimentioning

confidence: 99%

Empower LlaMa 2 for Advanced Logical Reasoning in Natural Language Understanding

Watanabe,

Kinasaka,

Nakamura

2024

Preprint

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“…However, it is not considered what will be the result of the proposed method in large-scale IIoT systems. Y. Chi et al [15] and H. Chen and J. J. Rodrigues et al [16] consider the features of the architecture of IIoT systems and determine the need to use the distributed algorithms for data processing, as well as for wireless and cloud data processing technologies. C. Ma et al offer a special in-network computation-oriented node placement (INP) algorithm for efficient distributed data processing in the IIoT [17].…”

Section: Related Workmentioning

confidence: 99%

“…Because IIoT systems are distributed, data analysis algorithms must be adapted to certain types of systems. Therefore, it is necessary to provide effective data collection from different nodes, confidentiality, protection against external interference, and reliable communication between end and control devices [15,16].…”

Section: Features Of Iiot and Smart Grid Systemsmentioning

confidence: 99%

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Modified Masking-Based Federated Singular Value Decomposition Method for Fast Anomaly Detection in Smart Grid Systems

et al. 2023

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The digitalization of production in smart grids entails challenges related to data collection, coordination, privacy protection, and anomaly detection. Machine learning techniques offer effective tools for processing Big Data, but identifying critical system states amidst vast amounts of data remains a challenge. To expedite data analysis, preprocessing through machine learning algorithms becomes essential. This paper introduces the advanced FedSVD algorithm, utilizing Singular Value Decomposition (SVD), which efficiently decomposes large datasets, establishes relationships, and identifies irrelevant data. The algorithm operates in federated machine learning systems, enabling local data processing on private devices while sharing only results with the global learning model. This approach enhances information processing confidentiality and facilitates the exchange of anomaly detection outcomes among network devices. The results of the study demonstrate that the modified FedSVD processing is 5 ms faster on average in comparison to the non-modified one. The proposed FedSVD algorithm calculates anomaly detection with higher accuracy by an average of 1–3% compared to the non-modified FedSVD and SVD ones. The advanced FedSVD algorithm proves to be a decentralized, confidential, and efficient solution for anomaly detection in smart grid systems.

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“…A Sugeno system's defuzzification process is more computationally efficient than a Mamdani system because it employs a weighted average of a few data points rather than computing the centroid of a twodimensional area. Because each rule is linearly dependent on the input variables, the Sugeno approach is excellent for acting as an interpolating supervisor of several linear controllers that will be applied to distinct operating states of a nonlinear dynamic system [27]. For example, a PV's performance might vary dramatically depending on the light intensity and the temperature.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

Samavat

Nazari

Ghalehnoie

et al. 2023

International Journal of Energy Research

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With the maturation of nonlinear systems, considerable endeavors have been made to provide valid and high-speed controllers to supervise superior and more complex systems. Artificial intelligence has been remembered as the head topic among designers in the last decade. One of the popular control techniques is fuzzy logic, which is known to provide a controller that simulates the behavior of an expert operator. On the other hand, due to the necessity of change in human energy sources and the popularity of solar energy, attention to the greatest utilization of this category of green resources has significantly increased. Maximum power point tracking (MPPT) in solar systems is a headed topic, with innovative methods being presented every day despite numerous articles. However, the less discussed topic is the choice of a fuzzy inference system. In this article, the two classes of Mamdani and Sugeno are discussed to introduce the best controller for extracting more power from a solar system by implementing both types and gaining an understanding of their differences. In addition, the influence of the number of input membership functions on the controller performance is investigated. Therefore, two different input membership functions are given to each fuzzy system model. It should be noted that fuzzy system setup has been done by genetic algorithm to respond to the mortal desire to automate various processes, which is a subset of artificial intelligence. Accordingly, four different fuzzy systems have been designed and implemented on a solar system. The results were tested and summarized in various radiations in MATLAB Simulink.

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Knowledge-Based Fault Diagnosis in Industrial Internet of Things: A Survey

Cited by 86 publications

References 0 publications

Empower LlaMa 2 for Advanced Logical Reasoning in Natural Language Understanding

Empower LlaMa 2 for Advanced Logical Reasoning in Natural Language Understanding

Modified Masking-Based Federated Singular Value Decomposition Method for Fast Anomaly Detection in Smart Grid Systems

A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

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