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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

共 761 条记录，以下是711-720 订阅

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Robustness Investigation of FOC of PMSG Based Variable-Speed Wind Turbine Connected on Grid

Robustness Investigation of FOC of PMSG Based Variable-Speed...

引用

international conference on Advanced Electrical Engineering (ICAEE)

作者： Mohammed AOUMRI Abdelkader HARROUZ Ibrahim YAICHI Seddik BAALA Patrice WIRA Laboratoire de Développement Durable et Informatique (LDDI) University of Ahmed Draïa Adrar Algeria Institute for Research in Computer Science Mathematics Automation and Signal University of Haute Alsace Mulhouse France

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

Wind energy has emerged as an increasingly viable renewable energy source, driven by rising fossil fuel costs, energy security concerns, and environmental considerations. In 2022, wind energy production grew by 9% compared to 2021, maintaining an annual rate exceeding 12% since 2012. This study focuses on the application of Field Oriented Control (FOC), a prevalent control strategy in wind power generation systems, to a permanent magnet synchronous generator (PMSG) in a variable speed wind turbine configuration. The research methodology involved implementing FOC and conducting robustness analyses through PMSG parameter variations. The primary objectives were to maximize wind energy capture efficiency while ensuring sustained control performance. MATLAB/Simulink was employed to simulate and evaluate the various components of the wind turbine system (WTS), facilitating comprehensive analysis of the results.

关键词： Wind energy generation Systematics Wind speed Robustness Synchronous generators Permanent magnets Stability analysis Wind turbines Security Wind energy conversion

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A Cost-Effective Thermal Imaging Safety Sensor for Industry 5.0 and Collaborative Robotics 3rd

A Cost-Effective Thermal Imaging Safety Sensor for Industry...

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3rd international conference on Intelligent Edge processing in the IoT Era, Edge-IoT 2022, and the 4th international conference on Smart Governance for Sustainable Smart Cities, SmartGov 2022

作者： Barros, Daniel Fraga-Lamas, Paula Fernández-Caramés, Tiago M. Lopes, Sérgio Ivan ADiT-Lab Instituto Politécnico de Viana do Castelo Viana do Castelo Portugal Department of Computer Engineering Faculty of Computer Science Universidade da Coruña A Coruña15071 Spain Centro de Investigación CITIC Universidade da Coruña A Coruña15071 Spain CiTin - Centro de Interface Tecnológico Industrial Arcos de Valdevez Portugal IT - Instituto de Telecomunicações Campus de Santiago Aveiro3810-193 Portugal

ISBN: (纸本)9783031359811

The Industry 5.0 paradigm focuses on industrial operator well-being and sustainable manufacturing practices, where humans play a central role, not only during the repetitive and collaborative tasks of the manufacturing process, but also in the management of the factory floor assets. Human factors, such as ergonomics, safety, and well-being, push the human-centric smart factory to efficiently adopt novel technologies while minimizing environmental and social impact. As operations at the factory floor increasingly rely on collaborative robots (CoBots) and flexible manufacturing systems, there is a growing demand for redundant safety mechanisms (i.e., automatic human detection in the proximity of machinery that is under operation). Fostering enhanced process safety for human proximity detection allows for the protection against possible incidents or accidents with the deployed industrial devices and machinery. This paper introduces the design and implementation of a cost-effective thermal imaging Safety Sensor that can be used in the scope of Industry 5.0 to trigger distinct safe mode states in manufacturing processes that rely on collaborative robotics. The proposed Safety Sensor uses a hybrid detection approach and has been evaluated under controlled environmental conditions. The obtained results show a 97% accuracy at low computational cost when using the developed hybrid method to detect the presence of humans in thermal images. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Cost effectiveness

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Data model for bigdata system for multimedia 2021

Data model for bigdata system for multimedia

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3rd Asia Pacific Information Technology conference, APIT 2021

作者： Bobulski, Janusz Kubanek, Mariusz Department of Computer Science Czestochowa University of Technology Czestochowa Poland

ISBN: (纸本)9781450388108

Big Data systems are characterized by the processing of data that is created in real time, has a different complexity, is variable and diverse and, most importantly, is huge. They require the application of innovative technologies, tools and methods in order to extract new and useful knowledge from them. Effective data acquisition and processing will play a key role in the global and local economy, social policy and large corporations. Big Data is a new challenge and information opportunities. The article presents a project of a system for multimedia data analysis with the use of artificial intelligence. © 2021 ACM.

关键词： Intelligent systems

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Quantum Computing and Machine Learning Algorithms - A Review

Quantum Computing and Machine Learning Algorithms - A Review

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Innovations in computer science & Software Engineering (ICONICS), international conference on

作者： Iram Manan Faisal Rehman Hanan Sharif Naveed Riaz Muhammad Atif Muhammad Aqeel Department of Information Technology Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Computer Science & Information Technology Lahore Leads University Lahore Pakistan Department of Computer Science & Information Technology NUST Islamabad Pakistan

The goal of machine learning (ML) is to develop models that automatically learn from the past without being explicitly programmed. ML has numerous applications, such as pattern recognition, forecasting upcoming trends, and judgement. It can handle massive amounts of huge vectors and tensors representing multidimensional data. To handle these processes on traditional computers, enormous time and computational resources are required. Unlike traditional computers, which use binary bits to compute, quantum computers (Q.C.) use qubits, which can simultaneously hold 0 and 1 combinations through superposition and entanglement. This makes *** an excellent choice for implementing ML algorithms because they are skilled at handling and post-processing large tensors. Although many of the models used for ML on quantum computers are based on concepts from their classical computing counterparts, the use of quantum computers has made them the better of the two. This paper assess the speed and complexity benefits of using quantum computers and gives a general overview of the state of knowledge regarding the use of ML on Q.C.

关键词： computers Quantum computing Tensors Quantum entanglement Computational modeling Qubit Machine learning

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Evaluation of Subjective Answers Using Machine Learning

Evaluation of Subjective Answers Using Machine Learning

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international conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： Sneha G Shreya Shree G Tina Babu Rekha R Nair Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bengaluru India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India

Negative methods are currently used to evaluate subjective writing. The evaluation of the subjective responses is an essential responsibility. When a human analyses anything, the evaluation's quality can change depending on the person's emotions. All outcomes in machine learning are solely dependent upon the user's input data. To address this issue, our suggested method combines machine learning (ML) and natural language processing (NLP). To analyse the subjective response, our algorithm performs tasks including tokenizing words and phrases, classifying parts of speech, chunking and chinking, lemmatizing words, and word netting. Our suggested approach also offers the context's semantic meaning. There are two modules in our system. Extracting data from scanned photos is the initial step. Then arranging it properly, and the second is using ML and NLP to analyse the text obtained in the previous phase and assigning grades to it.

关键词： Machine learning algorithms Semantics Machine learning Writing Natural language processing Classification algorithms Data mining

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Lung Cancer Detection Using Convolutional Neural Network

Lung Cancer Detection Using Convolutional Neural Network

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international conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： Dhyanendra Jain Prashant Singh Amit Kumar Pandey Mayank Singh Harikesh Singh Amarjeet Singh Department of CSE-AIML ABES Engineering College Ghaziabad India Department of computer science & engineering Sunder Deep Engineering College Ghaziabad India Department of CSE-DS ABES Engineering College Ghaziabad India Department of computer science & engineering JSS Academy of Technical Education Noida India

Automatic detection of defects and abnormalities in CT scan images has gain lot of importance in the recent times as because of the deteriorating quality of air almost everyone is facing one or the other type of lung related issues thus it is of esteem importance to study the computed tomography (CT) images of the pulmonary nodules to efficiently treat the diseases like lung cancer. The use of machine learning is an efficient way to distribute the work of doctors and process the large amount of data to produce accurate results on the go. Three phases of CT image pre-processing, Deep Learning, and Convolutional Neural Network use make up the diagnosis approach. The pre-processing converts raw data into usable form and deep learning algorithm assigns weight to the data, in the last stage CNN is use to conclude the health status of the lung, i.e. normal or abnormal.

关键词： Deep learning Computed tomography Lung Lung cancer Medical services Convolutional neural networks Tumors

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Epileptic Seizure Detection using short time Fourier transform and Convolutional Neural Network

Epileptic Seizure Detection using short time Fourier transfo...

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The 3rd international conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： O. Alshaltone R. Shaikh N. Nasir F. Barneih M. Sameer T. Bonny M. A. Shabi A. A. Shammaa Research Institute of Science and Engineering University of Sharjah Sharjah UAE University of Mumbai Mumbai India National Institute of Technology Patna India

The progress of Artificial Intelligence (AI) has been the most remarkable in the 21st Century. However, how far AI must have gone, it works in healthcare remains a big challenge for great minds across the globe. Epilepsy is a neurological disorder that occurs in a human being at any stage in life. Thus, frequent to rare seizures are faced by an individual having epilepsy and sometimes lead to death. The electroencephalogram (EEG) signals help diagnose this ailment. However, often long EEG signal takes a day or more even for trained neurologists to detect this disorder and may even lead to human errors. Thus, it becomes imperative to develop a robust and computationally efficient system that can help detect this ailment without causing delays and human error. This paper aims to extract the time-frequency plane from time domain signals using Short-time Fourier transform (STFT) and develop a neural network model using CNN to classify seizures. This paper has achieved the highest classification accuracy of 100% for some of the experiments and 95-99% accuracy for a few of them, thus helping better classify health and seizures with less computation required.

关键词： electroencephalography Fourier transforms health care medical signal detection medical signal processing neural nets neurophysiology signal classification time-frequency analysis

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Research on patent classification based on hierarchical label semantics

Research on patent classification based on hierarchical labe...

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Education, Knowledge and Information Management (ICEKIM), international conference on

作者： Jie Chen Shu Zhao Fan Lu Feng Liu Yangping Zhang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9781665416672

Patent classification is an essential task in patent information management and knowledge mining. Most existing studies are based on the textual content of individual patent texts (e.g., titles and abstracts) for classification, but the patent also has label information with hierarchical structure and semantic description. However, the semantics correlation of patent texts and their labels have been largely ignored. In this paper, we propose a novel framework called HLSPC for patent classification by leveraging the hierarchical semantics correlation of patent texts and their labels. Specifically, we first apply a patent representation learning module for capturing the semantics representation of patent texts and hierarchical labels. Then, we design a label attention learning module to build the semantics correlation between patent texts and hierarchical labels, which enhances patent representation. Finally, we deploy a multi-level fusion module to get the refined category prediction for each patent which can preserve both local and global hierarchical prediction information. Extensive experimental results on two English patent datasets demonstrate the effective power of the HLSPC model.

关键词： Representation learning Patents Correlation Semantics Text categorization Education Information management

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Efficient Binarization of Ancient Handwritten Vattezhuthu Documents

Efficient Binarization of Ancient Handwritten Vattezhuthu Do...

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international conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： MP Ayyoob P. Muhamed Ilyas Department of Computer Science Sullamussalam Science College Kerala India

Vattezhuthu, mostly written on palm leaves, was one of the ancient scripts of India which existed from 8th century AD to 15th century AD. This was one of the degraded handwritten scripts due to many reasons. The main obstacles of binarizing these ancient handwritten texts are its variable background intensity, ink bleed, uneven illumination, blurred character and contrast issues, smear or smudge and shadows etc. Therefor an effective pre-processing method is required to binarize these degraded documents. In this paper, we propose a hybrid approach for binarization and enhancement of Vattezhuthu script. In the proposed technique pixel expansion is conducted on thin handwritten vattezhuthu script using super resolution. After that image enhancement is done using median filter. Noise is filtered out by finding out the contour of enhanced image by using contour approximation and area filter. Then the morphological operation, boundaries, is used on this result.

关键词： Filtering Superresolution Lighting Ink Image enhancement

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Design and optimization of MobileNet neural network acceleration system based on FPGA

Design and optimization of MobileNet neural network accelera...

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international conference on Electronics, Communications and Information Technology (CECIT)

作者： Wenqiang Qin Zhongcheng Wu Jun Zhang Fang Li Institute of Physical Science and Information Technology Anhui University Hefei China Center for High Magnetic Field Science Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China

Aiming at the problem of low computational efficiency when convolutional neural network is deployed in embedded devices, this work proposes a convolution neural network acceleration architecture based on FPGA, and designs and implements a MobileNet neural network acceleration system based on FPGA. Firstly, the method of dynamic fixed-point quantization and operator fusion is designed to optimize the model. Secondly, the method of convolution block, parallel convolution calculation and data stream optimization is designed based on FPGA to improve the efficiency of convolution operation and system throughput. The system is tested on Xilinx PYNQ-Z2 platform. The average frame rate of the system is 6 fps and the power consumption is 2.45w. The system has the characteristics of low power consumption and low delay, which can effectively improve the operation efficiency of convolutional neural network algorithm in embedded devices.

关键词： Power demand Quantization (signal) Convolution Neural networks Parallel processing Throughput Convolutional neural networks

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