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Intelligent system for online Hand Gesture Prediction

Intelligent system for online Hand Gesture Prediction

引用

2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gnanapriya, S. Kavinaya, N. Jayasri, P.S. Amirthaa, R.S. Nandhakumar, V. Department of Information Technology Easwari Engineering College Chennai India

ISBN: (纸本)9798350305708

Humans may engage with computers in a natural way by using hand gestures to carry out a variety of tasks. However, elements like the hand's complexity, Gesture structures, differences in hand size, posture, and ambient illumination can all have an impact on how well hand gesture recognition systems perform. Due to recent advancements in deep learning, picture recognition algorithms now perform significantly better. Real-time picture segmentation is essential for many uses of autonomous vehicles. Due to recent advancements in deep learning, picture recognition algorithms now perform significantly better. The Deep Convolutional Neural Network has shown improved performance in visual representation and classification, especially when compared to traditional machine learning methods. In this study, a Convolutional Neural Network (CNN) for hand gesture detection is proposed. In order to quantitatively increase the size of the dataset and provide the resilience required for a deep learning approach, data augmentation is initially utilized, which randomly moves photos both horizontally and vertically to an extent of 20% of the original dimensions. A gesture can be used to convey both an activity or an emotion. Also included are sign language and body language. The static and the dynamic are the two different categories of gestures. The former use nonverbal cues like a raised hand or body language to communicate. The latter can communicate certain ideas through body language and hand gestures. In order to determine the user's intent, the gesture or movement of the body or body components is recognized. © 2023 IEEE.

关键词： CNN Deep Learning Hand Gesture Recognition Hand Landmarks Neural Networks

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From PID control to fuzzy control through evolutive optimization techniques in floating offshore wind turbines 5th

From PID control to fuzzy control through evolutive optimiza...

引用

5th international conference on Renewable Energies Offshore, RENEW 2022

作者： Hernández-Hernández, C. Sierra-García, J. Santos, M. National University of Education at Distance - UNED Madrid Spain Electromechanical Engineering Department University of Burgos Burgos Spain Institute of Knowledge Technology Complutense University of Madrid Madrid Spain

ISBN: (纸本)9781032420035

Floating offshore wind turbines (FOWT) are complex systems subject to strong disturbances such as wind, waves and adverse weather conditions, which makes the control task difficult. Several control techniques have been applied to find efficient solutions to improve the productivity of these renewable energy systems. In this work we propose the tuning of a PI control and a novel data-driven optimisation process that combines particle swarm optimisation and pattern search techniques to tune the parameters of a fuzzy controller of the pitch angle of a FOWT. Both controllers were applied to NREL’s 5 MW barge-type floating wind turbine considering different wind and wave conditions, and have been compared with the integrated controller in FAST giving better results in terms of rated power error, as well as lower tower top displacement and platform pitch oscillations. © 2023 the Author(s).

关键词： Fuzzy control

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TSFN: an Effective Time Series Anomaly Detection Approach via Transformer-based Self-feedback Network 26

TSFN: an Effective Time Series Anomaly Detection Approach vi...

引用

26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Hongwei Yang, Rong Qing, Huang Liu, Kedong Jiang, Zhuojun Li, Yangxi Zhang, Hong Institute of Information Engineering Chinese Academy of Science Beijing China National Engineering Research Center of Information Security Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China China Coal Research Institute Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (纸本)9798350331684

As the scale of data on the Internet continues to increase, the management and monitoring of time series data are facing significant challenges. Efficient and stable time-series data anomaly detection methods are necessary for fields such as traffic detection, power grid operation and maintenance, financial stock market, and industry. However, there are fewer abnormal data labels in time series data, and the labeling cost is high. Traditional expert knowledge-based supervised methods have been difficult to adapt to large-scale data metric management and timely abnormal alarms. At the same time, the way based on the new neural network has an extensive time overhead when faced with massive data, and it isn't easy to apply it in a real-time industrial environment. Therefore, we propose the TSFN model in this paper, an unsupervised method of a transformer-based self-feedback network. Which can capture timing dependencies, learn normal data distribution and improve the self-feedback ability for sensitive areas, and can be used to detect anomalies in multidimensional time series more quickly. Our experimental research on five public datasets shows that our method has fast training speed, good stability, excellent anomaly detection ability, and good generalization ability compared with the baseline method. © 2023 IEEE.

关键词： Anomaly detection

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IoT Based Smart Plug

IoT Based Smart Plug

引用

2023 international conference on Innovations in engineering and Technology, ICIET 2023

作者： Anilkumar, Anandhu George, Anie Aswin, M.R. Devika, G. Jyothy, Anu St. Joseph's College of Engineering & Technology Department of Electronics & Communication Engineering Kerala Kottayam686579 India

ISBN: (纸本)9798350309980

This study focuses on implementation of a system to make users aware of the usage of IoT based applications and promote sustainable living, energy saving, and behaviour change. In this context, the usage of smart plugs in the electrical system's distribution side is gaining popularity. Electrical gadgets called smart plugs can link a regular appliance to the internet. Additionally, it enables users to keep tabs on their energy use and so modify it. The study found that compared to ordinary houses, households with the proposed system installed used energy roughly less than 5 %. Participants' energy usage dropped when they were happier with obtaining automated systems and the associated knowledge. Results show that this programme can help people consume less energy, which is good for smart homes and smart communities. In recent decades, energy and energy management have emerged as significant research subjects. Making people aware of their energy usage habits can help to cut down on energy use dramatically. Using IoT-based techniques and smart plugs to collect data that is sent to the central database via the wireless-gateway, we apply a smart and sustainable technology in this project. When not in use, the Smart plugs can be turned on and off from the main server thanks to the system's design. This capability has led to the creation of a mechanism to turn off the smart plugs after business hours. In addition, we thoroughly examined the reason behind the lack of demand for the existing home automation systems in the market and found that most people are not ready to switch to a new system just to enjoy one or two features. Hence, the proposed system is integrated with multiple features which would attract more users and help to expand the practical implementation. © 2023 IEEE.

关键词： Energy conservation

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Embracing the Future: The Evolution of Flexible Reconfigurable manufacturing systems

Embracing the Future: The Evolution of Flexible Reconfigurab...

引用

systems, Advanced Technologies and knowledge (ISSATK), international Symposium of

作者： Salah Hammedi Jalloul Elmeliani Lotfi Nabli National School of Engineers of Monastir Networked Objects Control and Communication Systems (NOCCS) ENISo University of Sousse Tunisia National Engineering School of Bizerte Networked Objects Control and Communication Systems (NOCCS) ENISo University of Sousse Tunisia

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

Flexible Reconfigurable manufacturing systems (FRMS) are a radical approach to modern manufacturing, focusing on adaptability and efficiency to respond to dynamic market demands. The fundamental principles and advantages of FRMS are discussed in this article. These systems offer a versatile solution to traditional, inflexible manufacturing setups thanks to their modularity, automation, reconfigurability and data-driven decision-making. The key benefits include the ability to adapt to changing production requirements, cost efficiency, improved product quality, and resource optimization. FRMS are found in various industries, such as automotive, electronics, aerospace, and pharmaceuticals, and they meet the needs of customization, precision, and rapid production shifts. FRMS’s future looks promising, as emerging trends like AI integration, sustainability, customization, and interconnectivity are poised to further enhance their capabilities. Manufacturers can excel in an ever-evolving industrial landscape thanks to FRMS’s position in meeting the challenges of an increasingly dynamic global market as they continue to evolve.

关键词： Adaptation models Scalability Decision making Market research Aerodynamics Software systems Security Vehicle dynamics Sustainable development manufacturing systems

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Gain Property and data Analysis for Diagnosing Failures in a High-Efficiency Induction Motor 7

Gain Property and Data Analysis for Diagnosing Failures in a...

引用

7th international conference on Information systems engineering (ICISE)

作者： Bryan, Asimbaya William, Onate Gustavo, Caiza Univ Politecn Salesiana Quito Ecuador

ISBN: (纸本)9781450397889

Induction motors are the most commonly used in the industrial market, corresponding to 90% in areas such as manufacturing, pharmaceutical, machines and tools;this is due to its robustness compared to other types of machines. Due to the main role they play in large scale production, they should not stop due to failures. From this perspective, it is intended to diagnose any type of malfunction that occurs in these traction machines, before a production stop takes place. These situations give rise to the proposition of a variety of time-domain and frequency-domain methods to make a successful diagnosis of the failures. This paper proposes the Gain Property method, which relates the currents and voltages (C/V) supplied to a high-efficiency induction motor;the results obtained by such method are stated in two ways: using statistical tools (gray correlation, average deviation and quadratic deviation) and a Bayesian probabilistic tool, in order to analyze the behavior of the results and obtain a favorable diagnosis. In a testbench the motor was subject to four types of incipient failures, and after processing the data of the gains in the three supply lines it was concluded that, depending on the techniques used as statistical tool, the effectiveness of the diagnosis changes, approximating its results in 35%;on the other hand, the Bayesian probabilistic method exhibited a significant improvement for failure diagnosis.

关键词： Failure Diagnosis Statistical Analysis Induction Motor Gain Property

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Privacy issues involved in data mining and countermeasures 2

Privacy issues involved in data mining and countermeasures

引用

2nd international conference on Mechanical engineering, Intelligent manufacturing and Automation Technology, MEMAT 2022

作者： Liu, Jing Law School Xi'an Jiaotong University 28 Xianning West Road Shaanxi Province Xi'an China

ISBN: (纸本)9783800757619

data mining can extract valuable information from large amounts of data and has matured in recent years as an important resource for productive life. This technology is a decision support process. It is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, database, visualization technology, etc. It is highly automated to analyse a company's data, make inductive inferences and extract potential patterns from it to help decision makers adjust their market strategies, reduce risk and make the right decisions. Technology is neutral and not entirely good or bad. However, with the rapid development of the Internet, the increasing popularity of embedded and location-based technologies and the widespread use of location-based social networks, the amount of personal privacy information has increased dramatically and it is very easy to violate personal privacy if data mining tools are applied directly to the data for comprehensive analysis. Thus, in order to be able to carry out data mining with privacy protection, this article suggests some solutions, including: 1) Establish a perfect data privacy protection assessment mechanism and legal means.;2) Develop a new theoretical framework for privacy protection;and 3) Efficient encryption algorithm. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Decision support systems

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Acoustic Emission Probe for Power Line Structure Diagnostic systems

Acoustic Emission Probe for Power Line Structure Diagnostic ...

引用

2022 international conference on Industrial engineering, Applications and manufacturing, ICIEAM 2022

作者： Bryakin, Ivan V. Bochkarev, Igor V. Khramshin, Vadim R. Institute of Mechanical Engineering and Automation of the National Academy of Science Bishkek Kyrgyzstan Kyrgyz State Technical University Named after I. Razzakov Bishkek Kyrgyzstan Nosov Magnitogorsk State Technical University Magnitogorsk Russia

ISBN: (纸本)9781665483698

The authors developed an acoustic emission probe (AEP), an electromagnetic converter using solid element inertia, to measure low-frequency dynamic processes based on a new operating principle. This probe lacks the excitation coil and uses a cylindrical electrode that is commonly used by capacitance couplers for excitation. This electrode together with the metering coils for an electrode-inductance vibration sensor with capacity coupling. The functional elements of the AEP form a four-arm measuring bridge that uses capacitors as auxiliary bridge arms. These are integral components of the probe, which assures high invariance against destabilizing factors. The resonance operation mode of the electric circuits of the bridge AEP provides the maximum voltage on the metering coils along with minimum operating currents, which ultimately results in the high responsivity of the suggested AEP design in general. The lack of ferromagnetic circuits, magnetized structural elements, and the quarter-phase transformation of the data signal assure the required level of interference protection. The authors provide a theoretical justification of the operability and efficiency of the suggested AEP along with a hardware implementation option. The authors give a generic layout of a monitoring system for the integrity of power line pylons using the AEP developed. © 2022 IEEE.

关键词： Electrodes

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Towards True Explainable Artificial Intelligence for Real World Applications 15

Towards True Explainable Artificial Intelligence for Real Wo...

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15th international Joint conference on Computational Intelligence, IJCCI 2023

作者： Hagras, Hani The Computational Intelligence Centre School of Computer Science and Electronics Engineering University of Essex Wivenhoe Park ColchesterCO43SQ United Kingdom

ISBN: (纸本)9789897586743

We are entering a new era which is characterized by huge amounts of data which are generated from almost every application in our everyday lives. It is getting easier to organise such huge amounts of data via efficient data bases and ever growing and cheaper data storage systems (which can nicely scaleup in cloud based solutions). Due to the huge sizes, high dimensionality and complex relationships of such data, Artificial Intelligence (AI) technologies are well placed to handle such data and generate new services, business opportunities and even provide breakthroughs to completely change our lives and realise new industrial revolution as anticipated. The vast majority of AI technologies employ what is called opaque box models (such as Deep learning, Random forests, support vector machines, etc) which produce very good accuracies but it is quite difficult to analyse, understand and augment such models with human experience/knowledge. Furthermore, it is equally difficult to understand, analyse and justify the outputs of such opaque AI models. Hence, there is a need for Explainable AI (XAI) models which could be easily understood, analysed and augmented by the users/stake holders. There is a need also for such XAI models outputs to be easily understood and analysed by the lay user. In this paper, we will review the current trends in XAI and argue the real-world need for true XAI which provides full transparency and clarity at the model and output level. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Support vector machines

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Using BERT and Word Definitions for Implicit Sentiment Analysis 24

Using BERT and Word Definitions for Implicit Sentiment Analy...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on data Science and systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Xianyong Li, Qizhi School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350319934

Implicit sentiment frequently appears in texts. To accurately classify the implicit sentiments of sentences, this paper uses search engines to retrieve definitions of words and incorporates knowledge into language models for implicit sentiment analysis. We propose a model using BERT and word definitions for implicit sentiment analysis (BDISA). Specifically, we use BERT and Word2Vec to represent tokens and word definitions, respectively. Then, by using a fusing layer, we fuse these two representations. Finally, the model outputs the predicted sentiment labels. The experimental results show that the macro-F1 value of the BDISA model is 9.56% and 0.46% higher than KG-MPOA (random and BERT) on the SMP2019-ECISA dataset. The ablation experiments illustrate that word definitions and the fusion layer are critical for the model. © 2022 IEEE.

关键词： Sentiment analysis

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