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Motion Sickness Alleviation Based on a Mindfulness Brain-computer Interface 16

Motion Sickness Alleviation Based on a Mindfulness Brain-Com...

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16th International Conference on Advanced Computational Intelligence, ICACI 2024

作者： Zhu, Jiawei Bao, Xiaoyu Huang, Qiyun Wang, Tao Huang, Li Han, Yupeng Li, Kendi Chen, Di Xu, Kailin Wang, Zijian Jiang, Ya Li, Yuanqing Pazhou Laboratory Dept. Research Center for Brain-Computer Interface Guangzhou China Huizhou Desay SV Automotive Co. Ltd. Huizhou China South China University of Technology Dept. School of Automation Science and Engineering China Pazhou Laboratory Dept. Research Center for Brain-Computer Interface China South China Brain-Computer Interface Technology Co. Ltd. Guangzhou China

ISBN: (纸本)9798350308501

Motion sickness is a common affliction that affects nearly half of the global population and poses challenges to comfortable travel experiences, necessitating diverse intervention strategies. Pharmacological interventions have shown promise but present limitations due to side effects. To date, there is no non- pharmacological alleviation method for motion sickness that has been proven effective in a large population. In this study, we developed a novel mindfulness brain-computer interface (mindfulness BCI) for alleviation of motion sickness. Thirty-one subjects who were susceptible to motion sickness participated in our experiments in a real-world car motion environment. The experimental results demonstrated that the proposed mindfulness BCI can effectively alleviate motion sickness symptoms, where more than 90% of subjects reported the effectiveness of the mindfulness BCI system and a significant reduction of Motion Sickness Susceptibility Questionnaire (MISC) scores was obtained when comparing the BCI-based meditation state with the resting state for the subjects (t30=4.968, P © 2024 IEEE.

关键词： Brain computer interface

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Transfer and Bayesian Learning Approaches for Land Use and Land Cover Classification

Transfer and Bayesian Learning Approaches for Land Use and L...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Zainab Malik Aisha Sir Elkhatem Gokhan Bilgin Dept. of Computer Engineering Yildiz Technical University Istanbul Turkey Dept. of Control and Automation Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Land use and land cover (LULC) classification is essential for understanding the impact of both human activities and natural processes on the Earth’s surface. This classification plays a critical role in deforestation, urban planning, and damage assessment. However, traditional machine learning techniques often face challenges in achieving consistent accuracy in diverse datasets. In this study, we implement transfer learning and Bayesian learning approaches to enhance the accuracy and robustness of LULC classification. Transfer learning leverages knowledge from previous classification problems, while Bayesian learning addresses uncertainties in the classification process. Using the Remote Sensing Image Classification Benchmark (RSI-CB128) dataset, the study evaluates the performance of Bayesian Convolutional Neural Networks (CNN), Mobile Neural Networks (MobileNet), Inception Neural Networks (InceptionNet), Densely Connected Convolutional Networks (DenseNet) and Efficient Neural Networks (EfficientNet). The study also investigates the impact of varying the Bayesian layers on model performance, finding that a medium number of layers optimally captures model uncertainty. The results indicate that the transfer learning models, particularly MobileNet, achieved the highest accuracy of 97. 48% compared to 90. 33% for Bayesian CNN. These findings suggest that transfer learning techniques are highly effective for LULC classification, providing a reliable method for practical applications in remote sensing.

关键词： Deforestation Accuracy Uncertainty Computational modeling Transfer learning Neural networks Urban planning Land surface Bayes methods Convolutional neural networks

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency 5

Intelligent Operational Decision Support System for Sugar Fa...

引用

5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Jingyao Yuan, Haitao Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios. © 2024 IEEE.

关键词： Mobile edge computing

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A Deep Learning Model for More Descriptive Image Captioning

A Deep Learning Model for More Descriptive Image Captioning

引用

Decision Aid Sciences and Application (DASA), International Conference on

作者： Tunahan Doner Buket Kaya Dept. of Computer Engineering Firat University Elazig Turkiye Dept. of Electronics and Automation Firat University Elazig Turkiye

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Describing images with natural and fluent sentences is recognized as a challenging problem in computer vision and natural language processing. The limited number of studies in this area, especially in the Turkish language, and the fact that the existing datasets usually contain generalized descriptions made this research necessary. In this study, a rich dataset in Turkish language was created and this dataset was developed by optimizing both the natural structure of the language and the sentence lengths. An accuracy rate of 36% was achieved in the training process and 31% in the testing process. As a result, the generated dataset aims to contribute to Turkish image recognition models to produce more natural and detailed sentences.

关键词： Training Deep learning computer vision Accuracy Image recognition Transformers Natural language processing Testing

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Contrastive Self-Supervised Learning for Cocoa Disease Classification

Contrastive Self-Supervised Learning for Cocoa Disease Class...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Usman Khalid Buket Kaya Mehmet Kaya Dept. of Computer Engineering Firat University Elaziğ Turkey Dept. of Electronics and Automation Firat University Elaziğ Turkey

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Self-supervised learning has shown significant improvement within several disciplines including the health sectors and areas where acquisition of annotated data is difficult. This study focuses on the application of self-supervised learning in tackling cocoa disease detection and classification challenges by taking cocoa swollen shoot virus disease (CSSVD) and anthracnose. Self-supervised learning has proven to offer resilience against both OOD and adversarial attacks and as an alternative to the cost involved in the annotation process of the vast amount of data. This work leverages a contrastive self-supervised learning pretext model trained on cocoa leaves and pods, a remarkable performance was shown when we fine-tuned and pre-trained on a downstream task. Several works are being done on the subject of self-supervised learning and fully-supervised learning. In order to get the best out of these two techniques, we propose a hybrid approach which will combine both SLL and fully supervised learning. Thus in any deep learning project regardless of the amount of annotated dataset around, a pretext model should be developed from the dataset which can then be fined-tuned using the entire dataset at hand. Three models were developed and the best come in as a result of fine-tuning the learnt features using the entire dataset. After performing the downstream task (classification of cocoa diseases) a validation accuracy of 87%, 72% and 69% for the fine-tune, fully-supervised and pre-trained respectively were obtained. This approach holds promise for enhancing the productivity in crop management, particularly in regions like Ghana and Ivory Coast where their GDP relies on cocoa production

关键词： Productivity Deep learning Costs Economic indicators Supervised learning Crops Self-supervised learning Viruses (medical) Diseases Resilience

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods 4

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

引用

4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Zaiets, Nataliia Vlasenko, Lidiia Lutska, Nataliia Zhyltsov, Andrii Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350395532

The paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis;binary classification decision tree;feedforward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes. © 2023 IEEE.

关键词： Decision trees

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Pulling Figure of Dielectric Resonator for 3D Filament Defectoscopy 34

Pulling Figure of Dielectric Resonator for 3D Filament Defec...

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34th International Scientific Symposium Metrology and Metrology Assurance, MMA 2024

作者： Miroshnyk, Maryna Zaichenko, Olga Miroshnyk, Anatolii Hapon, Nataliia Pakhomov, Yurii Klymenko, Liubov V. N. Karazin Kharkiv National University Dept. of Theoretical and Applied System Engineering Kharkov Ukraine Kharkiv National University of Radioelectronics Dept. of Design and Operation of Electronic Devices Kharkiv Ukraine Kharkiv National University of Radioelectronics Dept. of Design Automation Kharkiv Ukraine O.M. Beketov National University of Urban Economy in Kharkiv Dept. of Computer Science and Information Technologies Kharkiv Ukraine Ukrainian State University of Railway Transport Dept. of Specialized Computer Systems Kharkiv Ukraine

ISBN: (纸本)9798350352832

The research aim is to derive connection between the parameters of dielectric resonators frequency shift and the warp defect of 3D printing filament. Object of investigation are the processes of interaction of electromagnetic wave of a dielectric resonator with filament for 3D printing. The proposed model describes the connection between the frequency shift and defect warp of filament frequency pulling depending on the load. © 2024 IEEE.

关键词： Q factor measurement

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Arithmetic Optimization Algorithm based Controller Design for Automatic Voltage Regulator System

Arithmetic Optimization Algorithm based Controller Design fo...

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2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022

作者： Izci, Davut Ekinci, Serdar Cetin, Haluk Batman University Dept. of Electronics & Automation Batman Turkey Batman University Dept. of Computer Engineering Batman Turkey Institute of Postgraduate Studies Batman University Batman Turkey

ISBN: (数字)9781665488945

ISBN: (纸本)9781665488945

The promise of a contemporary artificial intelligence optimization technique named arithmetic optimization algorithm is demonstrated in this work for automatic voltage regulator control. In this regard, a real proportional-integral-derivative (PID) plus second order derivative (PIDD2) controller is employed. The parameters of the real PIDD2 controller are tuned with the arithmetic optimization algorithm in order to reach a better control mechanism for the automatic voltage regulator. The performance index of integral of time squared error is used as an objective function for minimization. The results from the proposed method are compared with previously reported methods such as PIDD2 controller tuned with particle swarm optimization, fractional order PID controller tuned with slime mould algorithm, PID controller tuned with sine cosine algorithm and PID acceleration controller tuned with whale optimization algorithm. The time and frequency domain analyses confirm the more promising performance of reported method in this work for the AVR system compared to other available ones with good capability. © 2022 IEEE.

关键词： Controllers

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Multi-Frequency State-Feedback Current Control for Active Power Filters

Multi-Frequency State-Feedback Current Control for Active Po...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Lidia Sánchez David Reigosa Tuure Nurminen Rayane Mourouvin Marko Hinkkanen Fernando Briz Dept. of Elect. Computer & System Engineering University of Oviedo Gijón Spain Dept. of Electrical Engineering and Automation Aalto University Espoo Finland

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Feedback-based methods combining proportional-integral and/or resonant controllers are the most common option for Active Power Filter (APF) control. Alternatively to classical feedback-based control, state-feedback current control could be used, potentially enabling better dynamic performance. State-feedback current control has been previously proposed for grid-connected converters; however, reference tracking response of multiple harmonic components of the controlled current has not been reported. This is highly relevant to comply with grid codes. This paper analyses the design and implementation of state-feedback current control for harmonic current compensation in APFs.

关键词： Current control Power filters Sensitivity Passive filters Active filters Harmonic analysis Disturbance observers MATLAB Power harmonic filters Standards

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