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Research on Key Technologies of data Acquisition and Analysis of Integrated Energy Equipment Based on Power Internet of Things 4

Research on Key Technologies of Data Acquisition and Analysi...

引用

4th international conference on applied machine learning, ICAML 2022

作者： Zhang, Shuai Wang, Ruiqi Li, Hongquan Ding, Hao Shang, Helong Liu, Qingye State Grid Shandong Integrated Energy Services Co. LTD. Shandong China Shandong Electric Electric Group New Energy Technology Co.LTD. Shandong China

ISBN: (纸本)9781665462655

In electric power construction promotion in the Internet of things, will reveal distributed power grid architecture design, a new type of composite, such as power electronic characteristics, will not only change the previous system structure, also puts forward the development direction of new, so we need personnel reasonable use of advanced technology such as artificial intelligence theory, a comprehensive optimization power iot systems. In order to better break through the difficulties faced by the development of the current power industry, it is necessary to fully integrate the power system and the traditional Internet of Things technology. Only in this way can we master the key technologies and solve the data acquisition problem of integrated energy equipment. On the basis of understanding the concept and technical framework of power Internet of Things, this paper clarified the architecture diagram and actual data collection method of multi-energy equipment data collection, analyzed the data collection and analysis performance of integrated energy equipment with power Internet of Things as the core, and comprehensively grasped the key technology of system operation. © 2022 IEEE.

关键词： data acquisition

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Sign Language Digit Detection with MediaPipe and machine learning Algorithm 12

Sign Language Digit Detection with MediaPipe and Machine Lea...

引用

12th IEEE international conference on Control System, Computing and Engineering, ICCSCE 2022

作者： Salim, Safyzan Jamil, Muhammad Mahadi Abdul Ambar, Radzi Roslan, Rozaini Kamardan, Muhammad Ghazali Research Group Universiti Tun Hussein Onn Malaysia Faculty of Electric and Electronic Engineering Batu Pahat Johor 86400 Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Applied Sciences and Technology Department of Mathematics and Statistics Muar Johor 84600 Malaysia

ISBN: (数字)9781665483391

ISBN: (纸本)9781665483391

A major challenge when developing machine learning (ML) sign language recognition using wearable is how to efficiently translate the gestures based on the acquired sensors data. Conventional method utilizes data fusion based on the obtained sensors' information by producing mapping/lookup table for creating classification model of gestures corresponding sensor value. Although this method is effective, it increases programming complexity. Therefore, emerging technology that can improve the simplicity and provide accuracy of gestures' data processing is needed. This work experiments the artificial intelligence approach of the development of American Sign Language (ASL) detection using MediaPipe, a ready-to-use cross-platform machine learning framework for computer vision works and Google Teachable machine a free web tool of machine learning model creation. © 2022 IEEE.

关键词： machine learning

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FL-DP: Differential Private Federated Neural Network 4th

FL-DP: Differential Private Federated Neural Network

引用

4th EAI international conference on Security and Privacy in New Computing Environments, SPNCE 2021

作者： Irfan, Muhammad Maaz Wang, Lin Ali, Sheraz Jing, Shan Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering Jinan China

ISBN: (纸本)9783030967901

The rapid development of the Internet and machine learning has brought convenience and comfort to users’ lives. However, due to various attacks and sensitive data leaks, the large amount of data used in machine learning training has made the issue of personal privacy a growing concern as well. In the era of big data, anyone’s information can be stolen, which makes many people feel uneasy. We propose a new approach called FL-DP (Federated learning Based on Differential Privacy). Based on differential privacy, this approach can effectively restrict the adversary’s access to the client model, which has the ability to limit data leakage. In this framework, we use the DP Laplace mechanism. It is ensured that all operations (including the server-side aggregation process) are secure and do not leak any information about the training data. Also, we consider adding multiple noises to the preprocessing process so that the client-side data is secure during the training process. Our approach not only provides client-level privacy, but also balances efficiency and privacy. After evaluation, our approach is highly scalable and can be applied to most machine learning based applications. © 2022, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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Effective data Selection and Replay for Unsupervised Continual learning 40

Effective Data Selection and Replay for Unsupervised Continu...

引用

40th IEEE international conference on data Engineering, ICDE 2024

作者： Liu, Hanmo Di, Shimin Li, Haoyang Li, Shuangyin Chen, Lei Zhou, Xiaofang South China Normal University Guangzhou China Guangzhou China The Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9798350317152

Recently, continual learning (CL) has attracted much attention due to its widespread applications in the real world. Given a set of data sets sequentially, continual learning aims to achieve good performance on the new data sets while avoiding deterioration in performance on the old data sets. Despite the success, most CL models follow the supervised setting, which limits their potential in data scarcity cases. Thus, some pioneering works study unsupervised CL (UCL) to discuss what CL tricks suit the unsupervised setting. However, their advancements lack in-depth analysis of the characteristics of UCL, especially the lack of attention to the use of old data. We identify that using old data sets is essential for improving the UCL model performance while existing works ignore them. Unfortunately, given a limited data storage budget, it is a nontrivial task to select representative data and effectively replay them without label assistance. To further improve the UCL performance, we present a new method in this paper, named Effective data Selection and Replay (EDSR) for UCL. Specifi-cally, we analyze that entropy can be an effective data selection metric, where representative data usually exhibit the highest entropy in the representation space. Then, to balance the model stability for old data and the plasticity for new data, we adopt a strategy of replaying those stored representative data with a noise-enhanced knowledge distillation process. The empirical study demonstrates the outstanding performance of EDSR on benchmark computer vision data sets. Especially, EDSR shows strong resistance to forgetting old data knowledge while maintaining high accuracy. The implementation is publicly available at https://***/LeeJarvis996/edsr-project/tree/main/EDSR. © 2024 IEEE.

关键词： Entropy

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Robotic Packaging Optimization with Reinforcement learning 19

Robotic Packaging Optimization with Reinforcement Learning

引用

19th IEEE international conference on Automation science and Engineering, CASE 2023

作者： Drijver, Eveline Pérez-Dattari, Rodrigo Kober, Jens Santina, Cosimo Della Ajanovic, Zlatan Delft University of Technology Cognitive Robotics Department Delft2628 CD Netherlands

ISBN: (纸本)9798350320695

Intelligent manufacturing is becoming increasingly important due to the growing demand for maximizing productivity and flexibility while minimizing waste and lead times. This work investigates automated secondary robotic food packaging solutions that transfer food products from the conveyor belt into containers. A major problem in these solutions is varying product supply which can cause drastic productivity drops. Conventional rule-based approaches, used to address this issue, are often inadequate, leading to violation of the industry's requirements. Reinforcement learning, on the other hand, has the potential of solving this problem by learning responsive and predictive policy, based on experience. However, it is challenging to utilize it in highly complex control schemes. In this paper, we propose a reinforcement learning framework, designed to optimize the conveyor belt speed while minimizing interference with the rest of the control system. When tested on real-world data, the framework exceeds the performance requirements (99.8% packed products) and maintains quality (100% filled boxes). Compared to the existing solution, our proposed framework improves productivity, has smoother control, and reduces computation time. © 2023 IEEE.

关键词： Reinforcement learning

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Artificial Intelligence in Diabetes Management in Last Two Decades: A Bibliometric Analysis on Web of science 3

Artificial Intelligence in Diabetes Management in Last Two D...

引用

3rd international conference on Intelligent Computing, Communication and Convergence, ICI3C 2023

作者： Ji, Yaya Zhu, Zhu Zheng, Yujia Wu, Huiqun Medical School of Nantong University Department of Medical Informatics Nantong China

ISBN: (纸本)9798350343922

Objectives: To conduct a bibliometric analysis to analyze the landscape of artificial intelligence(AI) in diabetes management in the past two ***: The literatures on AI in diabetes management during 2003 to 2021 were respectively retrieved from Web of science database. CiteSpace software was applied to perform analysis of frequency distribution and co-occurrence of sub-keyword terms, frequency distribution analysis of reference citations, and analysis of burst detection methods for keyword terms and cited references, in order to create a knowledge map to reveal diabetes management hotspots and cutting-edge directions.(p/)(p)Results: A total of 1,242 articles on diabetes AI managements were retrieved. The results showed that the number of papers increased year by year, and the number of papers published was the largest in 2019-2021, suggesting that in the field of diabetes management, digital technology was widely adopted in the daily management of diabetes and was conducive in early screening for diabetes complications. In the field of AI in diabetes, various machine learning models including latest deep learning have been proposed and investigated. In the literatures on diabetes digital interventions, the keywords with higher frequency were management, intervention, diabetes, care and blood sugar control. Comparatively, the keywords with the highest frequency in diabetes AI are machine learning, management, diabetes, systems and ***: Publication outputs of the application of AI in DM management have been rising over last two decades. Some hotspots such as mobile medicine, telemedicine, deep learning, and digital therepeutics are of great significance for future research. © 2023 IEEE.

关键词： Diseases

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Real-Time Emotion Detection System using Emotive and ESP-32 20

Real-Time Emotion Detection System using Emotive and ESP-32

引用

20th IEEE India Council international conference, INDICON 2023

作者： Janapati, Ravichander Bhattacharya, Sandip Gorre, Srinivas Rao Sreedhar, K. Verma, Bhavesh K Adupa, Chakradhar Desai, Usha Bikshalu, K. Sr University Department of Ece Warangal India Vasavi College of Engg Department of It Hyderabad India Sr University Cognitive Science Department Warangal India Sea College of Engg & Tech Department of Ece Bengaluru India Kakatiya University Department of Ece Warangal India

ISBN: (纸本)9798350305593

This paper aims to develop an emotion detection system using Emotive and ESP-32. The system will use Emotive, an EEG headset that measures brain activity, to detect emotions in real-time. The data obtained from the Emotive will be transmitted wirelessly to an ESP-32 microcontroller, which will process the data and classify the emotion using machine learning algorithms. The ESP-32 will be programmed using the Arduino IDE and will use a wireless communication protocol such as Wi-Fi or Bluetooth to transmit the data. The machine learning algorithms will be trained using a dataset of EEG signals and corresponding emotions and will be optimized to classify emotions accurately. The system will have various applications in fields such as mental health, education, and entertainment. It could be used to detect the emotional state of a person and provide appropriate support or feedback. For example, in a classroom setting, the system could be used to detect when a student is feeling frustrated or confused and provide additional resources to help them understand the material better. Overall, this work has the potential to create a more empathetic and responsive environment, by detecting and responding to human emotions in real-time. © 2023 IEEE.

关键词： Brain activity Brain Computer Interface (BCI) EEG headset Emotion detection(ED) ESP

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Prediction Algorithm of Guiding Effectiveness based on Fuzzy data Network Structure Specification with Character Color Class 5

Prediction Algorithm of Guiding Effectiveness based on Fuzzy...

引用

5th international conference on Inventive Computation Technologies (ICICT)

作者： Yuan, Zhenping Zhanjiang Presch Educ Coll Zhanjiang 524000 Guangdong Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

This paper summarizes the short-term market forecast of teaching effectiveness and the theoretical basis of fuzzy theory and deep belief network, studies the learning algorithm of DBN model, and constructs a fuzzy deep learning network model for stock price forecasting through experiments. For the first time, the deep learning network was applied to the market prediction of teaching effectiveness, and the model effect was tested and analyzed, and the DBN was determined through experiments to have the most structure. The abnormal teaching evaluation data was eliminated by using the improved cosine dissimilarity algorithm, and the normalization method was adopted. The standardization method standardizes the evaluation data, and uses the traditional K-modes algorithm to perform a preliminary cluster analysis on the evaluation data, and points out three problems.

关键词： Real-time Prediction Guiding Effectiveness Fuzzy data Structure Specification

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Nested Random Forest: A Personalized Imputation Method for Missing data 8

Nested Random Forest: A Personalized Imputation Method for M...

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8th international conference on Big data and Information Analytics, BigDIA 2022

作者： Wang, Ke Luo, Menghua Deng, Mingsen Chen, Haiwen Information College Guizhou University of Finance and Economics Guiyang China Big Data Application and Economics Colllege Guizhou University of Finance and Economics Guiyang China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

In the field of data science, the imputation of missing data is a common and important problem. Accompanied by the development of artificial intelligence, the data analysis methods of machine learning have received unprecedented attention. As one kind of the important algorithms of machine learning, Random Forest has been sought after by researchers for its excellent practical effect and reasonable interpretation. In this paper, a Nested Random Forest imputation method is proposed to deal with the missing data problem. To ensure the authenticity of the filling data, this method not only considers the overall distribution of the data but also pays attention to the personalized characteristics of the data. From the experimental results, the Nested Random Forest algorithm achieves better effects than the seven mainstream baseline methods in three real-world datasets of different application fields. © 2022 IEEE.

关键词： machine learning

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Scalable Random Forest with data-Parallel Computing 29th

Scalable Random Forest with Data-Parallel Computing

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29th international conference on Parallel and Distributed Computing (Euro-Par)

作者： Vazquez-Novoa, Fernando Conejero, Javier Tatu, Cristian Badia, Rosa M. Barcelona Supercomp Ctr BSC CNS Barcelona Spain

ISBN: (纸本)9783031396977;9783031396984

In the last years, there has been a significant increment in the quantity of data available and computational resources. This leads scientific and industry communities to pursue more accurate and efficient machine learning (ML) models. Random Forest is a well-known algorithm in the ML field due to the good results obtained in a wide range of problems. Our objective is to create a parallel version of the algorithm that can generate a model using data distributed across different processors that computationally scales on available resources. This paper presents two novel proposals for this algorithm with a data-parallel approach. The first version is implemented using the PyCOMPSs framework and its failure management mechanism, while the second variant uses the new PyCOMPSs nesting paradigm where the parallel tasks can generate other tasks within them. Both approaches are compared between them and against MLlib Apache Spark Random Forest with strong and weak scaling tests. Our findings indicate that while the MLlib implementation is faster when executed in a small number of nodes, the scalability of both new variants is superior. We conclude that the proposed data-parallel approaches to the Random Forest algorithm can effectively generate accurate and efficient models in a distributed computing environment and offer improved scalability over existing methods.

关键词： Random Forest PyCOMPSs COMPSs Parallelism Distributed Computing Dislib machine learning HPC

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