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New Power System knowledge Tracking Based on GRU Model

New Power System Knowledge Tracking Based on GRU Model

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Clean Energy and Electric Power engineering (ICCEPE), international conference on

作者： Na Bu Xiufan Ma Yuxi Liu Ying He Qian Zheng Linxin Wu Beijing China-Power Information Technology Co. Ltd. Beijing China North China Electric Power University Beijing China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

This paper presents a new forecasting model for hot words in power system. In order to explore the problem of knowledge tracking in the field of new power systems, this paper obtains literature related to new power systems in recent 4 years based on domestic literature databases, constructs data sets, and uses Gated Cycle Unit (GRU) in deep learning to predict future hot words. Through the above methods, the research hotspots and development trends in the field of new power systems can be tracked, and the complexity and dynamic changes of knowledge in the field of new power systems can be more efficiently dealt with. The research not only points out the hot words of each year and the words with high correlation with other keywords, but also predicts the ten hot words that may appear in 2025, and provides the direction for future research in the field of new power systems.

关键词： knowledge engineering Deep learning Power system dynamics Neurons Predictive models Logic gates Market research Low carbon economy data models Carbon

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An Empirical Study of Image based Steganography and Watermarking

An Empirical Study of Image based Steganography and Watermar...

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knowledge engineering and Communication systems (ICKECS), international conference on

作者： R Padma Vamsidhar Yendapalli A P Jyothi Computer Science and Engineering GITAM School of Technology GITAM University Bengaluru India Computer Science and Engineering Ramaiah University of Applied Sciences Bengaluru India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

In the current technological landscape, the transmis-sion of data through communication channels has experienced significant advancements. The rapid growth in communication technology has brought about improved outcomes. However, challenges arise in ensuring the security and integrity of the transmitted data. It becomes crucial to safeguard from unauthorized access or users without proper authorization, it is important to implement various security parameters and best practices. This need has led to the development of various data hiding techniques. The objective of this paper is to conduct a comprehensive study on the taxonomy of Steganography and Watermarking types, as well as the taxonomy of techniques employed in Steganography and Watermarking. The paper aims to provide an overview and classification of different types of Steganography and Watermarking techniques. It also focuseson analyzing the advantages and disadvantages associated with these *** examining the taxonomy and assessing the pros and cons of Steganography and Watermarking techniques, this paper aims to contribute to a better understanding of their applications and implications in data security. The findings of this study can serve as a valuable resource for researchers and practitioners working in the field of data hiding and information security.

关键词： knowledge engineering Steganography Technological innovation Wavelet domain Frequency-domain analysis Taxonomy Information security

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FAIRifying STEM data Ecosystem to Enhance data Reuse 53

FAIRifying STEM Data Ecosystem to Enhance Data Reuse

引用

53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Namgay, Phub Wangdi, Pema Thinley, Sangay Namgyel, Thinley Uppsala University Department of Informatics and Media Uppsala Sweden School of Data Science and Data Analytics Sherubtse College Royal University of Bhutan Bhutan

ISBN: (纸本)9798350336429

In the science, technology, engineering, and mathematics (STEM) community, academic and research workflows and work practices are increasingly mediated and informed by data. However, making digital materials and resources findable, accessible, interoperable, and reusable (FAIR) for teaching, learning, and research is an under-researched area. Thus, it is vital to examine the current data practices of STEM students and faculties and acquaint them with the FAIR data concept. FAIR data Principles is a set of guidelines that underscore metadata, vocabularies, licences, and standards to enhance data reuse. A study was conducted among students and faculties in the STEM community of the Royal University of Bhutan (RUB) to unpack their current data practices and explore areas for improvement using the FAIR data Principles. The STEM students and faculties of the RUB share and reuse digital materials and resources for teaching, learning, and research. Nevertheless, their data practice is not as widespread or desired in the literature on optimum data reuse. Moreover, the compliance of current data practices to the tenets of FAIR data Principles is not satisfactory. A pragmatic solution is complementing data practices with policies and infrastructural systems that underscore FAIR data Principles. A sensitisation programme such as seminars and hands-on exercises on data FAIRification is crucial to familiarise people with the essentialness of FAIR data, and doing so will provide a platform to develop their repertoire for FAIRifying data and encourage systematic sharing and reuse of data. An in-depth account of the FAIRifying STEM data ecosystem in the study contributes to the growing knowledge base on adopting FAIR data Principles in other areas of data-informed work and life. © 2023 IEEE.

关键词： Bhutan data Reuse FAIR data Principles FAIRifying data STEM data Ecosystem

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Research on Cross-Domain Text2SQL Enhancing database Schema Awareness 5th

Research on Cross-Domain Text2SQL Enhancing Database Schema ...

引用

5th international conference on Artificial Intelligence in China, AIC 2023

作者： Lei, Jianyun Wang, Zishan Sun, Chong Zhang, Zejin Cheng, Yong College of Computer Science South-Central Minzu University Wuhan430074 China Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan430074 China Beijing Qingtai Data Technology Co. Ltd. Beijing100000 China

ISBN: (纸本)9789819975440

Text2SQL has become an important research direction of natural language processing because of its abundant practical scenarios and research value. RAT-SQL is a Text2SQL method based on relational self-attention, which solves the domain generalization problem by improving the schema encoding, schema linking and feature representations in the encoder. However, the approach only focuses on adding the known relation representation into self-attention to achieve alignment between entities and schema items, which cannot well fuse external heterogeneous information. The ability of the parser to generalize to invisible target domains remains limited. To address this problem, this paper proposes a new approach for Text2SQL tasks that injects factual relations within domain knowledge and extends some of the capabilities of RAT-SQL by jointly encoding natural language questions, database schemas and exogenous knowledge. Experiments show that the accuracy of EDSA-SQL is significantly improved compared to RAT-SQL on the publicly available dataset Spider. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rats

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Learning Skill-based Industrial Robot Tasks with User Priors 18

Learning Skill-based Industrial Robot Tasks with User Priors

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18th IEEE international conference on Automation Science and engineering, CASE 2022

作者： Mayr, Matthias Hvarfner, Carl Chatzilygeroudis, Konstantinos Nardi, Luigi Krueger, Volker Department of Computer Science LundSE 221 00 Sweden Greece Stanford University Department of Computer Science and Electrical Engineering CA94305 United States

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Nevertheless, given their prior experience, robot operators should be able to help the learning process further by providing educated guesses about where in the parameter space potential optimal solutions could be found. Interestingly, such prior knowledge is not exploited in current robot learning frameworks. We introduce an approach that combines user priors and Bayesian optimization to allow fast optimization of robot industrial tasks at robot deployment time. We evaluate our method on three tasks that are learned in simulation as well as on two tasks that are learned directly on a real robot system. Additionally, we transfer knowledge from the corresponding simulation tasks by automatically constructing priors from well-performing configurations for learning on the real system. To handle potentially contradicting task objectives, the tasks are modeled as multi-objective problems. Our results show that operator priors, both user-specified and transferred, vastly accelerate the discovery of rich Pareto fronts, and typically produce final performance far superior to proposed baselines. © 2022 IEEE.

关键词： Learning systems

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Construction of a Learning Engagement Evaluation Model Based on Multi-modal data Fusion

Construction of a Learning Engagement Evaluation Model Based...

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2022 IEEE international conference on knowledge engineering and Communication systems, ICKES 2022

作者： Chen, Jingchun Parameshachari, D. School of English & International Studies Xi'an Fanyi University Shaanxi Xi'an710105 China Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9781665456371

Learning engagement has become an important indicator affecting learning outcome in universities. It can not only reflect learners' learning process, but also fully resonate 'learner-centered' educational concept. Accurate evaluation of learning engagement is an important task. Multi-modal data fusion can extract and fuse dynamic and multi-dimensional input, which is of great value in characterizing learners' learning process. But there are some problems in traditional evaluation methods, such as poor real-time evaluation, low evaluation effect of single modal data, and social approval response bias. Based on multi-modal data fusion, an evaluation model of learners' engagement was constructed and its predictive effect was verified. In this study, OpenCV region extraction method was applied and an automatic evaluation method was proposed based on multi-modal data fusion calculation. Questionnaires were collected to explore factors impacting learning engagement from university students in mainland China. Results showed (a) the evaluation model of learning engagement, i.e., behavioral engagement, cognitive engagement and emotional engagement has good reliability and validity;(b) It demonstrates that learner engagement has a significant predictive effect on academic achievement. The study finds that improving learning engagement will significantly improve learners' overall academic gains and the learning engagement evaluation model can accurately assess learning engagement. © 2022 IEEE.

关键词： Modal analysis

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Credible System for Industrial IoT Networks Using Block Chain Technology 5

Credible System for Industrial IoT Networks Using Block Chai...

引用

5th international conference on Energy, Power, and Environment, ICEPE 2023

作者： Kottaimani, Dharani Manikandan, M.S.K. Thiagarajar College of Engineering Dept. of Ece Madurai India

ISBN: (纸本)9798350313123

The industrial Internet of things specifies the interconnected network of instruments, sensors, and devices used in industrial applications, such as manufacturing and energy management. One technology that has the potential to improve IIoT systems is block chain, which can provide redundancy, immutable storage, and encryption. Another way to enhance IIoT security is to use data science techniques to detect and prevent malicious activities. This study aims to address the problem of malicious devices in industrial settings that can degrade network performance and compromise the security of legitimate IoT devices. To establish trust between IoT devices and safeguard the network from malicious entities, the coordinator's IoT device is used. While some open-source ciphers may still be vulnerable to attack, organizations can benefit from the scrutiny of numerous users, making it difficult for centralized entities or third parties to make malicious modifications. Additionally, integrating a block chain-based data model can ensure data transparency. The proposed framework is experimentally evaluated using MATLAB to measure a variety of security metrics such as the likelihood of false authentication, message tampering, and attack intensity. Based on the simulation results, it is observed that the suggested approach effectively improves the security of industrial IoT networks by identifying and mitigating malicious attacks. © 2023 IEEE.

关键词： Internet of things

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Zero-knowledge Range Proofs and Blockchain Integration for Enhancing SCADA data Security

Zero-Knowledge Range Proofs and Blockchain Integration for E...

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data Science, Agents & Artificial Intelligence (ICDSAAI), international conference on

作者： Vikram P S Manisha J Nene Department of Computer Science and Engineering Defence Institute of Advanced Technology Pune India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Traditional Supervisory Control and data Acquisition (SCADA) are prone to cyber attacks which makes it difficult in keeping data secure. As the traditional architecture relies on centralized storage, which makes them vulnerable to unauthorized access and manipulation. To tackle these security concerns, the study demostrates the integration of blockchain technology and Bulletproof, a zero-knowledge range proof technique into SCADA systems to securely store aggregated sensor value proof in the ledger. The study uses Hyperledger fabric, creating a transparent and tamper-proof record for audits and providing a more secure and trustworthy system. This approach reduces storage and computational overhead while ensuring that data remains private and within valid ranges without exposing sensitive details. By combining blockchain with zero-knowledge range proofs, the proposed solution enhances both security and efficiency in SCADA systems. It ensures that sensor data remains protected from tampering while keeping blockchain resources optimized. This makes industrial systems more secure, reliable, and ready for the future of automation.

关键词： Privacy Energy consumption Distributed ledger Scalability data security SCADA systems Fabrics Blockchains Reliability Cyberattack

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MFD-Net: Mechanical Fault Detection Using Deep Feature Extraction for manufacturing Industries

MFD-Net: Mechanical Fault Detection Using Deep Feature Extra...

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Integrated Intelligence and Communication systems (ICIICS), international conference on

作者： Anjani Pothuri S. Nagarajan Department of Computer Science and Engineering Shri Vishnu Engineering Co llege for Women Bhimavaram India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

In manufacturing industries, mechanical faults contribute to nearly 40% of equipment downtime, with annual losses exceeding billions globally. Despite efforts to implement preventive maintenance, many current fault detection systems struggle with real-time accuracy and adaptability across different fault types. This study introduces a novel Mechanical Fault Detection Network (MFD-Net) that enhances fault detection precision and efficiency in manufacturing environments. MFD- Net integrates advanced data preprocessing with a deep learning convolutional neural network (DLCNN) for effective feature extraction, paired with a machine learning-based Categorical Boosting (MLCB) classifier for optimal classification. Leveraging the Stator with Permanent Magnet Synchronous Machines (PMSM) dataset, which includes two classes (Fault and Normal), the model achieved remarkable performance metrics: an accuracy of 99.95%, precision of 99.93%, recall of 99.95%, and F-score of 99.94%. These results affirm MFD-Net's capability to deliver high accuracy and reliability, marking a substantial advancement in MFD technology for complex industrial environments.

关键词： manufacturing industries Adaptation models Accuracy Fault detection data preprocessing Feature extraction Boosting Real-time systems manufacturing Reliability

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Inductive Machine Learning for Classifying Breast Lesions from Imbalanced MRI-B data 14th

Inductive Machine Learning for Classifying Breast Lesions fr...

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14th international conference on Information, Intelligence, systems, and Applications, IISA 2023

作者： Karampotsis, Evangelos Panourgias, Evangelia Dounias, Georgios School of Engineering University of the Aegean 41 Kountouriotou Street Chios82100 Greece Medical School National and Kapodistrian University of Athens 76 V. Sofias Athens11523 Greece

ISBN: (纸本)9783031674259

This paper presents a hybrid machine-learning approach for modelling the diagnostic process of breast cancer by analyzing data derived from medical observations of magnetic mammograms. Specifically, magnetic mammography data MRI-data) collected in collaboration with medical experts, are utilized. The proposed methodology combines different stages of data pre-processing (value grouping of variables, filling-in missing values, etc.) in combination to expert knowledge and guidance. The range of the involved diagnostic variables is identified with the aid of descriptive statistics (frequencies), as well as the number of training instances for all the decision classes. Fifteen decision variables are used, both nominal and numerical, corresponding to imaging data and clinical observations. Two different decision problems are defined and solved, (i) a 2-class problem corresponding to the discrimination between healthy and cancerous situations, and (ii) a 6-class problem corresponding to the separation between different specialized cancerous diagnoses. A new data balancing technique is then applied, adapted to the special requirements of the specific application domain, aiming at the best possible distribution of training instances based on entropy information criteria. In addition, boosting techniques are also applied in the phase of rule induction. Feature selection techniques can also assist the whole process by reducing possible noise existing in the data through removal of confusing variables. Diagnostic rules are finally induced either in the form of decision trees or in the form of rule sets, which obtain higher classification accuracy than standard ML approaches, while comprehensibility of the outcome remains high. Specifically, results are compared with the use of known standard metrics (k-fold cross-validation, leave-one-out and use-test-set approaches) as well as by related medical experts. The classification performance is very satisfactory (exceeds 95%) and the

关键词： Diseases

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