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On Abstract Sciences: From data, Information, Knowledge to Intelligence Sciences 21

On Abstract Sciences: From Data, Information, Knowledge to I...

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21st IEEE international conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022

作者： Wang, Yingxu Intl. Inst. of Cogn. Informatics and Cognitive Computing Visiting Professor: Stanford Univ. MIT School of Engineering and Hotchkiss Brain Institute University of Calgary Dept. of Electrical and Software Engineering Schulich CalgaryABT2N 1N4 Canada

ISBN: (纸本)9781665490849

The emergence of abstract sciences as a counterpart of classic concrete sciences is presented in this work. The framework of abstract sciences encompasses data, information, knowledge, and intelligence sciences from the bottom up. It is found that intelligence is the ultimate level of cognitive objects generated in human brains aggregated from data (sensory), information (cognition), and knowledge (comprehension). However, there is a lack of rigorous studies and coherent theories towards the theoretical framework of abstract sciences as the counterpart of classical concrete sciences. This paper explores the cognitive and mathematical models of abstract mental objects in the brain. The taxonomy and cognitive foundations of them are explored. A set of mathematical models of data, information, knowledge, and intelligence is formally created in intelligent mathematics. Based on the cognitive and mathematical models of the cognitive objects, formal properties and relationship of contemporary data, information, knowledge, and intelligence sciences are rigorously explained. © 2022 IEEE.

关键词： Concretes

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Method of forming an integrated automated control system for intelligent objects 2

Method of forming an integrated automated control system for...

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2nd international Scientific and Practical conference "Information Technologies and intelligent Decision Making Systems", ITIDMS-II-2021

作者： Safiullin, Ravil Epishkin, Alexander Safiullin, Ruslan Haotian, Tian St. Petersburg Mining University 2 21st Line St Petersburg199106 Russia St. Petersburg State University of Architecture and Civil Engineering 2-Ya Krasnoarmeyskaya Ulitsa 4 St Petersburg190005 Russia St. Petersburg Mining University 2 21st Line St Petersburg199106 Russia

The sequence of constructing the scientific and methodological apparatus of the monitoring system by the energy networks of modern intelligent buildings and structures is presented. The program for monitoring and control of energy networks of intelligent buildings and structures based on the use of intelligent automated systems has been substantiated. The types of support for the control system of energy networks of intelligent buildings and structures, when creating a local automated system for monitoring energy consumption, are determined, which are important in the formation of many tasks that are intended to perform functions at different levels and in various aspects. The result of the study is algorithms for creating an intelligent energy management system during the operation of engineering systems and electrical equipment of buildings and structures, monitoring it in real time and predicting the energy consumption of buildings, which will provide the accumulation of data necessary for optimal planning with energy resources, which will increase the validity of the adoption solutions. Copyright © 2021 for this paper by its authors.

关键词： Energy utilization

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A Physics-Informed Integrated Modeling Method for Lithium-ion Batteries 21

A Physics-Informed Integrated Modeling Method for Lithium-io...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Fan, Yunsheng Huang, Zhiwu Guan, Kaifu Shu, Boyu Liu, Yongjie Zhu, Zeyu He, Peinan Li, Shuo Central South University School of Automation Changsha China Central South University School of Computer Science and Engineering Changsha China Changsha University of Science and Technology School of Electronic Information and Electrical Engineering Changsha China

ISBN: (纸本)9798350329223

Battery models play a crucial role in battery management systems, describing the inner workings of batteries. However, offline models cannot adapt to the battery parameter degradation. To address this issue, a physics-informed integrated battery model combines equivalent electrical circuits and a neural network regression model is proposed. Electrical circuits capture essential battery electricity behaviors while a regression model with the recurrent neural network is employed to simulate the nonlinear open circuit voltage (OCV). The regression model is trained using unlabeled learning and the parameters of the electrical circuit are identified online. Finally, the proposed battery model is validated with a cubature Kalman filter. Extensive experiments confirmed that the proposed modeling method outperforms other methods with Less than 1.66% RMSE in SOC estimation. © 2023 IEEE.

关键词： Kalman filters

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Current status and Hotspots of stEM Education Research Revealed in Open Vision

Current Status and Hotspots of STEM Education Research Revea...

引用

2023 international conference on Information Education and Artificial Intelligence, ICIEAI 2023

作者： Gou, Pingzhang Hu, Xinyue College of Computer Science and Engineering Northwest Normal University Gansu Lanzhou730070 China

ISBN: (纸本)9798400716157

stEM education is regarded as the key to cultivating core competencies in the 21st century, and is of great significance for achieving science and technology innovation and talent cultivation. Using bibliometric method and visual analysis, the stEM education-related literature from 2013-2022 included in the Web of Science core collection is used as the data source. With the help of CiteSpace, Bicomb, and SPSS, the current status of international stEM education research is analyzed by detecting burst words in co-cited literature;and the hot topics of stEM education research are revealed by processing high-frequency keywords and conducting cluster analysis. To summarize the research experience of stEM education in the international perspective, some insights are drawn: promoting the deep learning of stEM education, deepening technology empowerment in stEM education, and attaching importance to the humanistic factors of stEM education. It is hoped that these insights will help to contribute to the localization and innovation of stEM education, meet the needs of comprehensive science and technology innovation talent cultivation, and promote high-quality development of education modernization. © 2023 ACM.

关键词： Deep learning

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Household Waste Classification Algorithms Based on Various Convolutional Neural Networks: A Review of Performance Optimization 21

Household Waste Classification Algorithms Based on Various C...

引用

21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Renxiang, Huang Jia, Chen Jianping, Li School of Artificial Intelligence and Big Data Sichuan University of Arts and Science Chengdu635002 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper reviews the research progress of deep learning-based household waste classification algorithms. It first introduces the importance of household waste classification and the application value of deep learning in this field. Next, it outlines the theoretical foundations of deep learning technology in waste classification, including the principles of convolutional neural networks and their applications in image recognition. The article provides a detailed analysis of the application of different deep learning models in waste classification, such as MobileNetV2, ShuffleNetV1, EfficientNetV1, and YOLOV5, as well as their optimizations and innovations in practical applications. Furthermore, it discusses optimization and innovation methods for deep learning models, including learning rate adjustment, optimizer selection, attention mechanisms, data augmentation, and lightweight network design. Finally, it summarizes the current research shortcomings and challenges, and looks forward to future research directions. This review provides a systematic perspective for a deeper understanding of the application of deep learning in waste classification and offers directions for future research. © 2024 IEEE.

关键词： Convolutional neural networks

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automated Face Selection and Censoring on Image and Video using Artificial intelligent

Automated Face Selection and Censoring on Image and Video us...

引用

international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Ratchai Thipbumrung Panyanat Aonpong Department of Computing Faculty of Science Silpakorn University Nakhon Pathom Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

The Personal data Protection Act (PDPA) was created to prevent the breach of personal information of users of computer systems without the data owner's consent. One type of data that frequently has problems with privacy violations is images and videos. Because of difficult control, as a result, there are often extraneous people in the frame instead of just the intended subject. If the person caught in the frame does not want this information published, there will be a problem with that video. This causes the identity of the person to be concealed so that they can be identified. Although doing this is an acceptable method, censorship is labor-intensive and time-consuming. For these reasons, we proposed the automated Face Selection and Censoring on Image and Video System using Multi-Task Cascaded Convolutional Neural Networks (MTCNN) Model to automatically detect the face and censor only unwanted persons. Furthermore, our proposed method also executes an automated system that can sense and ignore some frames that are not essential or redundant with other nearby frames to reduce complex processing and time-consumption.

关键词： Computer science Image recognition Face recognition data protection Multitasking Censorship Convolutional neural networks

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Clustered Federated learning via Gradient-based Partitioning 41

Clustered Federated Learning via Gradient-based Partitioning

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41st international conference on Machine learning, ICML 2024

作者： Kim, Heasung Kim, Hyeji de Veciana, Gustavo Department of Electrical and Computer Engineering The University of Texas AustinTX United States

Clustered Federated learning (CFL) is a promising distributed learning framework that addresses data heterogeneity issues across multiple clients by grouping clients and providing a shared generalized model for each group. However, under privacy-preserving federated learning protocols where there is no direct sharing of clients' local datasets, existing approaches often fail to find optimal client groupings resulting in sub-optimal performance. In this paper, we propose a novel CFL algorithm that achieves robust clustering and learning performance. Conceptually, our algorithm groups clients that exhibit similarity in their model updates by periodically accumulating and clustering the gradients that clients compute for various models. The proposed algorithm is shown to achieve a near-optimal error rate for stochastic convergence to optimal models under mild conditions. We present a detailed analysis of the algorithm along with an evaluation on several CFL benchmarks demonstrating that it outperforms existing approaches in terms of convergence speed, clustering accuracy, and task performance. Copyright 2024 by the author(s)

关键词： Federated learning

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Calibrating the Dark Count Rate of Single Photon Avalanche Diodes Using Linear Regression 21

Calibrating the Dark Count Rate of Single Photon Avalanche D...

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21st international System-on-Chip Design conference, ISOCC 2024

作者： Cho, Youngmin Burm, Jinwook Sogang University Dept. of Electronics Engineering Seoul121-742 Korea Republic of

ISBN: (纸本)9798350377088

The Dark Count Rate (DCR) is an inherent characteristic of Single Photon Avalanche Diodes (SPADs). Despite the use of an Active Quenching Active Reset (AQAR) scheme to reduce DCR at the circuit level, the counter connected to the quenching circuit often registers an increase, even in the absence of intended light input. Consequently, an external method is required for accurate calibration of the DCR. This paper proposes the use of linear regression, implemented in a Python program, as a method for such calibration. © 2024 IEEE.

关键词： Particle beams

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Enhancing Road Infrastructure Maintenance Using Deep learning Approach 23th

Enhancing Road Infrastructure Maintenance Using Deep Learnin...

引用

23rd international conference on intelligent Systems Design and Applications, ISDA 2023

作者： Vinothkumar, S. Dhanushya, S. Guhan, S. Krisvanth, P. Kongu Engineering College TamilNadu Erode638060 India

ISBN: (纸本)9783031648359

The physical gathering of info on road damage can be time-consuming and dangerous to people's safety, yet preserving the transportation network is essential for providing secure and lasting transport systems. To address these challenges, this paper presents a novel approach that leverages Unmanned Aerial Vehicles (UAVs) and deep learning approaches can greatly improve the effectiveness and precision of road damage identification. The proposed approach employs the YOLOv8 algorithm, cutting-edge object detection and localization model, for detecting road damage in UAV images. The YOLOv8 model offers improved accuracy compared to previous versions, namely YOLOv4, YOLOv5, and YOLOv7, by using UAVs equipped with high-resolution cameras, info on road damage can be captured efficiently and remotely. On a synthesis of the Indian roadway data and the RDD2022 dataset, this method was trained and tested. The trained YOLOv8 model was used to identify and locate different types of road degradation such cracks, potholes, and surface degradation. The combination of UAV imagery and deep learning algorithms has shown great potential to detect automated road deterioration. This research highlights the advantages of using YOLOv8 for improved accuracy in identifying road damages. This work lays the path for further developments in the field, enabling safer transport systems and more effective road maintenance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Object detection

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Efficient Giant Graph Unlearning via Push-Pull Tuning 21

Efficient Giant Graph Unlearning via Push-Pull Tuning

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作者： Chen, Fahao Li, Peng Yu, Shui The University of Aizu School of Computer Science and Engineering Japan University of Technology School of Computer Science Sydney Australia

ISBN: (纸本)9798350329223

The advent of deep learning applications for data collection has elicited concerns pertaining to privacy, particularly regarding the potential exposure of data through various vulnerabilities, such as membership inference attacks. In response to these concerns, several machine unlearning techniques have been proposed, which can effectively eliminate specific data from a trained model. However, it is important to note that existing methods primarily concentrate on Euclidean data, leaving non-Euclidean data structures, such as graphs, unexplored. Our preliminary experiments reveal that directly applying these techniques to graph data yields suboptimal results, especially on large graphs. In this work, we shift our focus towards addressing the unlearning challenge within the context of graph data trained by graph neural networks (GNNs). We propose a simple yet effective approach termed push-tuning, which sophistically manipulates loss values associated with the data intended for unlearning. This manipulation redirects the model's predictions and facilitates the unlearning process. Moreover, we also propose a pull-tuning method to recover accuracy on the remaining data, which is degraded by the unlearning process. To jointly consider data removal and accuracy recovery, we further introduce an alternation method. To assess the effectiveness of our proposed method, we conduct comprehensive experiments using five popular benchmark datasets. The experimental results demonstrate that our approach can effectively unlearn the target nodes while preserving model's accuracy on the remaining nodes. © 2023 IEEE.

关键词： Graph neural networks

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