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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 37467 条记录，以下是781-790 订阅

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Aligning the Representation of knowledge Graph and Large Language Model for Causal Question Answering

Aligning the Representation of Knowledge Graph and Large Lan...

引用

2024 IEEE international conference on Big data, Bigdata 2024

作者： Zeng, Zefan Cheng, Qing Hu, Xingchen Liu, Zhong Shen, Jingke Zhang, Yahao Laboratory for Big Data and Decision National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350362480

Causal Question Answering (CQA) is essential for knowledge discovery, focusing on the intricate dynamics between events and entities without predefined contexts. Despite advancements of CQA models through knowledge Graphs (KGs) and Pre-Trained Language Models (PLMs), existing approaches are hindered by knowledge conflict, insufficient capacity, and limitations in information fusion. Large Language Models (LLMs) have significantly improved natural language understanding and reasoning but often suffer from causal hallucinations. To address these challenges, we introduce KLop, a framework that aligns representations of Causal knowledge Graph (CKG) and Large Language Models for CQA. KLop pre-trains a graph embedding model for entity embedding and uses a frozen LLM for text embedding. The main components of KLop are the descriptor module and the aligner module. The descriptor leverages descriptive texts generated by LLMs to create training data for knowledge alignment, while the aligner utilizes self-attention to train query tokens for modality alignment. Experiments on public CQA datasets validate that KLop outperforms various advanced baselines in reasoning accuracy, as well as achieving causal knowledge integration and joint reasoning. © 2024 IEEE.

关键词： knowledge graph

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A Fuzzy Neural Network-Based Time Series Analysis Model for Condition Monitoring of Work Equipment 4

A Fuzzy Neural Network-Based Time Series Analysis Model for ...

引用

4th international conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Chen, Shizhe Dong, Wei Fang, Lei Qian, Weini Hangzhou Electric Power Equipment Manufacturing Co. Ltd Hangzhou China Hangzhou Electric Power Equipment Manufacturing Co. Ltd Jiande Guanyuan Complete Electrical Manufacturing Branch Hangzhou China

ISBN: (纸本)9798331531409

This paper aims to overcome the shortcomings of existing technologies and provide a fuzzy neural network-based time series analysis model for tool status monitoring, which has good thermal insulation effect, long service life of sound insulation materials, and good sound insulation effect. Its basic principle is as follows. Fuzzy neural network (FNN) is a comprehensive combination of fuzzy logic (FL) and neural network (NN), which is based on the principle that fuzzy inference systems can be represented by multi-level feedforward neural networks. Compared with end-to-end neural network deep learning algorithms, it has faster processing speed, lower hardware cost requirements, and more accurate data detection accuracy. © 2024 IEEE.

关键词： Thermal insulation

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Branchy-TEE: Deep Learning Security Inference Acceleration Using Trusted Execution Environment 35

Branchy-TEE: Deep Learning Security Inference Acceleration U...

引用

35th international conference on Software engineering and knowledge engineering, SEKE 2023

作者： Wang, Yulong Deng, Kai Meng, Fanzhi Chen, Zhi Yin, Mingyong Yang, Run Institute of Computer Application China Academy of Engineering Physics Mianyang China School of Cyberspace Science Harbin Institute of Technology Harbin China

Deep Learning as a Service (DLaaS) has become a remarkable trend in modern data-driven online services. Both data holders and service providers need to build on trust in third-party cloud infrastructure platforms. However, once the trust is broken, data holders’ sensitive data and service providers’ intellectual property rights will face significant security and privacy risks. In this paper, we propose a secure and efficient inference framework for deep learning in untrustworthy cloud platforms, termed Branchy-TEE, which aims to protect the confidentiality and integrity of data and models of multiple participating actors throughout the inference process using the Trusted Execution Environment (TEE). Branchy-TEE dynamically loads the inference network into the TEE on-demand based on early-exit mechanism, expecting to break the hardware performance bottleneck of the TEE. Moreover, a joint training method based on knowledge distillation for multi-exit networks is proposed, by flowing"knowledge" from the final exit with high accuracy to the early branch exit with lower accuracy. Finally, the effectiveness and efficiency of Branchy-TEE are verified through extensive experiments in real environments, while achieving an optimal balance between performance and hardware resources. © 2023 knowledge systems Institute Graduate School. All rights reserved.

关键词： Distillation

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Characterizing Complex Network Properties of knowledge Graphs 15

Characterizing Complex Network Properties of Knowledge Graph...

引用

15th international conference on knowledge engineering and Ontology Development, KEOD 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Rossanez, Anderson da Silva Torres, Ricardo dos Reis, Julio Cesar Institute of Computing University of Campinas SP Campinas Brazil Agricultural Biosystems Engineering Wageningen Data Competence Center Wageningen University & Research Wageningen Netherlands Department of ICT and Natural Sciences Norwegian University of Science and Technology Ålesund Norway

ISBN: (纸本)9789897586712

knowledge Graphs have been established as one the most relevant representations to encode knowledge, with relevant applications in the public and private sectors. One common research direction concerning the analysis of created knowledge graphs relies on the assumption that their intrinsic properties and structure are similar to what is observed in complex networks. However, studies concerning identifying typical complex network structures in knowledge graphs are lacking in the literature. This paper bridges this gap by analyzing commonly and recently used knowledge graphs in the semantic web field, seeking to demonstrate their complex network properties. Evaluation involving DBpedia and Wikidata data confirms the occurrence of intrinsic complex network structures in their respective knowledge graphs. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0).

关键词： Complex networks

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Intelligent interface of the machine based on knowledge graph 20

Intelligent interface of the machine based on knowledge grap...

引用

20th IEEE international conference on Mechatronics and Automation, ICMA 2023

作者： Ren, Huanrong Jiang, Pingyu Xi'an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Shanxi Xi'an710049 China

ISBN: (纸本)9798350320831

The machine intelligent interface is a user interface that involves some aspect of artificial intelligence or computational intelligence to support the device sense, communication, and processing data for autonomy. This article proposed the implementation of an intelligent interface for the machine. It is based on the knowledge graph (KG) technology, integrating AI algorithms, and service behaviors to users, which we argue are three critical factors for an advanced interface. As for implementation, firstly, we introduced the construction of the machine KG and the definition of user request modes. Defining user request modes could restrict interact scope into domain area and in case access beyond the domain. Then, we combine semantic parsing and information retrieval to process multiple user request modes. Our implemented intelligent interface has the advantages of ensuring accuracy and interpretability oriented to core definition requests and flexibility oriented to random complex requests. © 2023 IEEE.

关键词： knowledge graph

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ghosh, Tiash Rasheed, Razeen A. Singh, Sanjai Kumar Jenamani, Mamata Routray, Aurobinda Dept. of Electrical Engineering Indian Institute of Technology Kharagpur Kharagpur India GEOPIC Oil & Natural Gas Corporation Dehradun India Dept. of Industrial & Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9798350368741

Seismic Fault Detection is a crucial aspect of oil exploration. While traditional deep learning methods struggle to handle complex seismic data patterns, training a deep learning model solely on synthetic seismic data may not yield satisfactory results. This research paper, involves utilizing a pre-trained vision-based transformer to extract relevant features from seismic data. By leveraging the knowledge learned from a different but related task, the model can capture general fault patterns in field data. In this framework, a portion of the pre-trained model architecture is employed and trained using a Structural Similarity-based loss function to learn fault-related features. This allows the model to adapt to the faulted structures of a geological dataset and improve fault detection performance in field data applications. In comparison to the state-of-the-art method, the proposed method yields improved results on real field dataset. © 2025 IEEE.

关键词： knowledge transfer Seismic fault detection Structural similarity loss Transformer architecture

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Processing of Nonlinear Heterogeneous data in Information and Control systems

Processing of Nonlinear Heterogeneous Data in Information an...

引用

2024 international Russian Smart Industry conference, SmartIndustryCon 2024

作者： Shevnina, Yu.S. Tsarapkin, S.F. Sin, Si Thu Thant Institute of System and Software Engineering and Information Technologies National Research University Miet Moscow Zelenograd Russia

ISBN: (纸本)9798350395044

The article presents a method for cluster analysis of heterogeneous data using the provisions of fuzzy logic. Heterogeneous are data of various formats and collected from various sources. In addition, such data is usually incomplete and inaccurate, which makes it difficult to process and cluster them. The article presents mathematical models for representing a candidate for a vacant position, which is characterized by heterogeneous data. The apparatus of algebraic systems was used to develop mathematical models. A method for determining the membership function of fuzzy sets using a probabilistic approach, as the most effective when working with heterogeneous data, is described in detail. An example of the formation of a base of logical rules for the formation and selection of classification features in a set of heterogeneous data of the personnel reserve of a manufacturing enterprise is given. The selected classification features allow for further accurate and efficient verification and evaluation of information about candidates for a vacant position. In addition, the proposed method of cluster analysis of heterogeneous data can be applied in various subject areas that involve the use of incomplete and inaccurate data, for example, socio-economic, technical, biological systems. © 2024 IEEE.

关键词： Membership functions

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Development of Middleware for data-Centric Digital Twin of Additive manufacturing

Development of Middleware for Data-Centric Digital Twin of A...

引用

2023 IEEE international conference on Smart Information systems and Technologies, SIST 2023

作者： Shaimergenova, Karina Ali, Md. Hazrat Shehab, Essam Nzarbayev Univeristy Mechanical & Aerospace Engineering Deprtment Astana Kazakhstan

ISBN: (纸本)9798350335040

Additive manufacturing (AM) is a layer-by-Iayer manufacturing process for different metals/alloys and polymers. Despite the various technological and other opportunities of production, one of the challenges is a Big data issue in data management of monitoring and controlling the AM manufacturing process. The data-centric Digital Twin (DT) technology should support the AM process. To improve the data performance and accuracy of data-centric DT in AM, the development of middleware was investigated. Middleware is a software system, which its leading role is data management and communication between simulation and physical modules of the DT. This paper introduces the implementation of the middleware in data-centric DT of AM. The aim of the research is to design and develop a data-centric middleware architecture model to enable the creation of DT for AM. © 2023 IEEE.

关键词： Middleware

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Hybrid LSTM-RNN and Lion Optimization Algorithm for IoT-based Proactive Healthcare data Management

Hybrid LSTM-RNN and Lion Optimization Algorithm for IoT-base...

引用

2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Kabila, R. Balaji, S. Sri Vikraam, V. Kumar, S. Rajesh Praveen, R. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India Research & Development Icu Medical India Llp Chennai600006 India

ISBN: (纸本)9798350359688

In the realm of healthcare, the integration of Internet of Things (IoT) technology has revolutionized patient monitoring, allowing for real-time data collection and analysis. This abstract introduces a novel approach to patient monitoring utilizing a hybrid Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) architecture combined with Lion Optimization for enhanced feature selection. The proposed system aims to predict and monitor patient health conditions, particularly focusing on cardiac health. By harnessing IoT devices for continuous data acquisition, vital signs such as heart rate, blood pressure, and oxygen saturation levels are collected in real-time. The hybrid LSTM-RNN model is employed to analyze this data, leveraging its ability to capture temporal dependencies and patterns in sequential data. Furthermore, Lion Optimization, inspired by the hunting behavior of lions, is utilized for feature selection to enhance the predictive accuracy of the model. This optimization technique intelligently selects the most relevant features from the collected data, thereby improving the efficiency and effectiveness of the predictive model. The combination of IoT-based patient monitoring, hybrid LSTM-RNN architecture, and Lion Optimization offers a accuracy of 99.99 %, precision of 97%, recall of 96.06%, and f1 measure of 96.53%. By providing early detection and prediction of health issues, this approach facilitates timely interventions and personalized treatment plans, ultimately enhancing patient outcomes and quality of life. © 2024 IEEE.

关键词： Long short-term memory

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Privacy Protection knowledge Distillation with Parameter Adaptation 14

Privacy Protection Knowledge Distillation with Parameter Ada...

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14th IEEE international conference on Cyber Technology in Automation, Control, and Intelligent systems, CYBER 2024

作者： Wang, Yanmei Liu, Baichen Shang, Zhenduo Han, Zhi Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798331506056

knowledge distillation(KD), as an implementation of network compression, transfers knowledge from sophisticated networks teacher to lightweight networks student. Due to privacy issues, few-shot and zero-shot KD comes into being. The existing zero-shot or data-free KD methods mostly use the teacher network to synthesize pseudo samples or data impressions to train student networks. However, the generation of pseudo samples is extremely time-consuming, and student network training with pseudo samples does not easily converge, which is usually achieved with a small learning rate over several thousand iterations to realize a satisfactory result. A variety of difficulties raised by zero-shot KD lead us to reconsider the need to use generated datasets to preserve user privacy. In this paper, drawing on the knowledge of privacy protection, we cut off the mapping between images and labels for privacy protection. In addition, we propose utilizing the structural similarity to realize parameter transfer between student and teacher networks. Based on the condition that parameter transfer provides a promising prior for training student networks, the proposed method achieves fast convergence and alleviates the overfitting properties of neural networks. We evaluate our framework on various datasets, and the experimental results demonstrate its effectiveness. © 2024 IEEE.

关键词： Differential privacy

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