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2024 International Conference on computational intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Kaliappan, P. Sudha, S. Shankar, D. Metering and Utility Automation Division Central Power Research Institute Bengaluru India

ISBN: (纸本)9798350381887

The power sector is one of the most essential infrastructures globally. Digital transformation is underway in the power sector, making it more vulnerable to cyberattacks. The power sector is experiencing an increase in cyber incidents day by day. To protect power sector infrastructure against cyber attacks, technology, procedures, and people must work together;only then can prevention be achieved. The power sector needs to adhere to standards, regulations, and guidelines available for the effective implementation of a cyber security framework. The power sector has gained more attention and focus in the last five years. Numerous standards, including the NIST Cybersecurity Framework, IEC 62443, ieee 1686, IEC 62351, and IEC 62056, tackle cybersecurity concerns within control systems. Every standard covers and focuses on different domains and parts of the overall system. This paper presents applicable international standards for cyber security in the power sector. © 2024 ieee.

关键词： Cyber attacks

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Local Search Enhanced Multi-Objective Evolutionary Algorithm for Fuzzy Flexible Job Shop Scheduling

Local Search Enhanced Multi-Objective Evolutionary Algorithm...

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Zhang, Xuwei Zhao, Ziyan Liu, Shixin College of Information Science and Engineering Northeastern University The State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

ISBN: (纸本)9781665430654

The uncertainty in actual manufacturing systems often manifests as uncertain processing times, especially in flexible manufacturing systems. This work proposes a Decomposition-based Evolutionary Algorithm with Local Search (DLSEA) to solve flexible scheduling with fuzzy processing times by minimizing makespan and total machine workload. Considering the different scales of objectives, two normalization methods are employed on subpopulations, respectively, aiming to mitigate the potential detrimental effects of a single normalization method. This work also introduces a local search method to enhance the performance of DLSEA. The proposed DLSEA is compared with four state-of-the-art algorithms on two series of cases. The experimental results show that DLSEA exhibits superior search capabilities. © 2023 ieee.

关键词： Fuzzy flexible job shop scheduling Local search Multi-objective evolutionary algorithm Normalization

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Using Weka API for creating a custom classification application 17

Using Weka API for creating a custom classification applicat...

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17th ieee International symposium on Applied computational intelligence and Informatics, SACI 2023

作者： Robu, Raul Arseni-Ailoi, Paul Ungureanu-Anghel, Dan Politehnica University of Timisoara Faculty of Automation and Computers Department of Automation and Applied Informatics Timisoara Romania

ISBN: (纸本)9798350321104

In real world applications, classification models can be built by repeatedly going through the steps of data preprocessing, building classification models with different dedicated algorithms, testing the resulted models, until building a good enough classification model is achieved. The model built in this way can be used in order to perform predictions on new data, with a degree of trust resulted by testing the model. All the operations described above can be done with Weka, which is a very powerful machine learning tool. The authors consider that after going through the previous steps, for many real world applications, it would be beneficial to go through a supplementary step, of developing a custom application for data classification, specific to the realized study. The proposed application will use the Weka API, which will allow it to rebuild the selected classification model, save and load it in/from a binary file and then make predictions with the help of a user interface perfectly adapted to the data set used. The proposed application makes it easier to make predictions for a final beneficiary. The paper presents how such a custom classification application can be developed, using the Weka API and Java programming language. In order to validate the proposed solution, the authors have built a custom application for the classification of data of patients who may have heart diseases, starting from the Cleveland heart disease data set, obtained from the UCI Machine Learning Repository. The proposed application allows saving and loading the classification model through serialization and deserialization, so that building it in order to make predictions is necessary only once. © 2023 ieee.

关键词： Forecasting

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Automatic multimodal registration of cone-beam computed tomography and intraoral scans: a systematic review and meta-analysis

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CLINICAL ORAL INVESTIGATIONS 2025年第2期29卷 1-13页

作者： Zheng, Qianhan Wu, Yongjia Chen, Jiahao Wang, Xiaozhe Zhou, Mengqi Li, Huimin Lin, Jiaqi Zhang, Weifang Chen, Xuepeng Zhejiang Univ Stomatol Hosp Canc CtrKey Lab Oral Biomed Res Zhejiang Prov Clin Res Ctr Oral Dis Zhejiang ProvSch MedSch St Hangzhou 310006 Peoples R China Zhejiang Univ Social Med & Hlth Affairs Adm Hangzhou 310058 Zhejiang Peoples R China

ObjectivesTo evaluate recent advances in the automatic multimodal registration of cone-beam computed tomography (CBCT) and intraoral scans (IOS) and their clinical significance in *** comprehensive literature search was conducted in October 2024 across the PubMed, Web of Science, and ieee Xplore databases, including studies that were published in the past decade. The inclusion criteria were as follows: English-language studies, randomized and nonrandomized controlled trials, cohort studies, case-control studies, cross-sectional studies, and retrospective *** the 493 articles identified, 22 met the inclusion criteria. Among these, 14 studies used geometry-based methods, 7 used artificial intelligence (AI) techniques, and 1 compared the accuracy of both approaches. Geometry-based methods primarily utilize two-stage coarse-to-fine registration algorithms, which require relatively fewer computational resources. In contrast, AI methods leverage advanced deep learning models, achieving significant improvements in automation and *** advances in CBCT and IOS registration technologies have considerably increased the efficiency and accuracy of 3D dental modelling, and these technologies show promise for application in orthodontics, implantology, and oral surgery. Geometry-based algorithms deliver reliable performance with low computational demand, whereas AI-driven approaches demonstrate significant potential for achieving fully automated and highly accurate registration. Future research should focus on challenges such as unstable registration landmarks or limited dataset diversity, to ensure their stability in complex clinical scenarios.

关键词： Cone-beam computed tomography Intraoral scan Registration Artificial intelligence ICP

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Adaptive weighted broad learning system for imbalanced classification 6

Adaptive weighted broad learning system for imbalanced class...

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6th ieee Advanced Information Management, Communicates, Electronic and automation control Conference, IMCEC 2024

作者： Li, Yanting Gao, Yiping Jin, Junwei Chen, C. L. Philip Zhengzhou University of Light Industry College of Computer Science and Technology Zhengzhou China Henan University of Technology College of Artificial Intelligence and Big Data Zhengzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350316520

Broad Learning System (BLS) perform well in classification tasks with good computational efficiency. However, its effectiveness decreases when faced with imbalanced data distribution. The traditional BLS cannot solve this problem effectively. In this paper, we introduce an adaptive weighted method for BLS, aiming to improve its classification performance on imbalanced data. This method can assign different weights according to the distribution characteristics of samples. There exists a closed-form solution to the problem and the transformation matrix can be solved efficiently by pseudo-inverse. Experimental results on several imbalanced datasets demonstrate that the proposed method surpasses standard BLS and many advanced imbalanced classification methods. © 2024 ieee.

关键词： Classification (of information)

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Bayesian inference for neural network based high-precision modeling

Bayesian inference for neural network based high-precision m...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Morales, Jorge Yu, Wen CINVESTAV IPN Natl Polytech Inst Dept Control Automat Mexico City DF Mexico CINVESTAV IPN Dept Control Automat Mexico City DF Mexico

ISBN: (纸本)9781665487689

In this paper we implement neural network structure and Bayesian inference in order to improve performance black-box modeling for unknonw nonlinear systems. This kind of structure works in batch form passing both the identification and the statistical training. Two nonlinear systems and two data sets of seismic information from regions of Italy and Mexico are used to evaluate the methods. The results are satisfied.

关键词： neural networks Bayesian inference highprecision modeling

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Data Augmentation for Cardiovascular Time Series Data Using WaveNet

Data Augmentation for Cardiovascular Time Series Data Using ...

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Feldhans, Robert Schulz, Alexander Kummert, Johannes Habigt, Moriz Stemmler, Maike Kohler, Christina Abel, Dirk Rossaint, Rolf Hammer, Barbara Bielefeld University Machine Learning Group Bielefeld Germany Rwth Aachen University Anaesthesiology Clinic Faculty of Medicine Aachen Germany Institute of Automatic Control Rwth Aachen University Aachen Germany

ISBN: (纸本)9781665430654

In this work we present a novel approach for generating cardiovascular data using a modified WaveNet architecture. This can enable further research in areas where data is scarce and hard to obtain. By generating additional time series data in a set of animal tests performance of existing models could be improved and more difficult approaches, that require substantial amounts of data, attempted. We validate our approach on a classification task and compare it to similar methods of data augmentation. © 2023 ieee.

关键词： Data Augmentation Medical Application Time Series

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Derivation and Numerics of Integral-aided Denoising Zhang Time Derivativers 16

Derivation and Numerics of Integral-aided Denoising Zhang Ti...

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16th International Conference on Advanced computational intelligence, ICACI 2024

作者： Zhang, Yunong An, Peng Yang, Wuyi Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Sun Yat-Sen University School of Artifical Intelligence Zhuhai China

ISBN: (纸本)9798350308501

The research on time derivativer (TD, a.k.a., tracking differentiator) is a hot topic in the field of industrial automation control. In this paper, a new kind of time derivativer (called integral-aided denoising Zhang time derivativer, IDZTD) is proposed with the aid of Zhang neural dynamics (ZND). The comparative study is conducted among Han tracking differentiator (HTD, also called classic tracking differentiator), standard Zhang time derivativer (ZTD), and IDZTD. The research indicates that the IDZTD has better performance and noise immunity in the numerical experiments with different initial activation functions and design parameters. In addition, to some extent, the larger the design parameters are, the better the performance of IDZTD is. Besides, the noise immunity of IDZTD with nonlinear activation is better than that with linear activation. © 2024 ieee.

关键词： Chemical activation

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Generating Cardiovascular Data to Improve Training of Assistive Heart Devices

Generating Cardiovascular Data to Improve Training of Assist...

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Kummert, Johannes Schulz, Alexander Feldhans, Robert Habigt, Moriz Stemmler, Maike Kohler, Christina Abel, Dirk Rossaint, Rolf Hammer, Barbara Bielefeld University Machine Learning Group Bielefeld Germany Rwth Aachen University Anaesthesiology Clinic Faculty of Medicine Aachen Germany Institute of Automatic Control Rwth Aachen University Aachen Germany

ISBN: (纸本)9781665430654

In many medical applications data is a scarce resource and can often only be obtained with invasive surgery. This is for instance the case for physiological cardiovascular data that is necessary to improve the functionality of assistive heart devices. In this work we explore the viability of a GAN architecture to generate cardiovascular data towards enriching a data set obtained in animal testing on which training of future applications can be improved which potentially reduces the need for further animal testing. We evaluate the usefulness of our synthesized data using a downstream task. © 2023 ieee.

关键词： Data Enrichment Data Generation GAN Medical Application

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UNCERTAINTY-DRIVEN control FOR A SELF-SUPERVISED LIFELONG LEARNING DRONE

UNCERTAINTY-DRIVEN CONTROL FOR A SELF-SUPERVISED LIFELONG LE...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Brown, Ryan Brna, Andrew Cook, Jared Park, Samuel Aguilar-Simon, Mario Teledyne Sci Co Intelligent Syst Lab 19 TW Alexander Dr Res Triangle Pk NC 27709 USA

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

It has been a long standing goal of artificial intelligence to develop algorithms that support adaptive automation that allow unmanned vehicles to operate safely and independently in real-world environments. Here, we summarize experiments that demonstrate how a novel algorithm for measuring uncertainty during operation by a drone can support self-supervised learning. Our uncertainty-modulated learning algorithm is inspired by neuromodulatory mechanisms in the brain that control both the flow of information in neural circuits and the computational properties of those circuits. Our algorithm suggests how uncertainty can be used as an internal measure of performance that can trigger adaptation and the execution of information-seeking behaviors. This results in emergent behaviors that enable a drone to continually learn and adapt to support robust performance in real-world changing environments.

关键词： Self-Supervised Learning Lifelong Learning Closed-Loop control Uncertainty Modulated Learning Neuromodulation

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