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检索条件"任意字段=Proceedings of the 9th Knowledge-Based Software Engineering Conference"

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Improvement proposal to increase the OEE in a mechanical press in the metalworking sector through SMED and TPM 9

Improvement proposal to increase the OEE in a mechanical pre...

引用

9th International conference on Innovation and Trends in engineering, CONIITI 2023

作者： Zegarra-Silva, Joaquin Andres Caceres-Larco, Mauricio Anibal Flores-Perez, Alberto Universidad de Lima Facultad de Ingeniería Lima Peru

the metalworking sector is one of the industries that has more impact on Peru, representing 25.9% of the manufacturing industry9;s workforce. therefore, it is crucial for them to approach the biggest availability p... 详细信息

ISBN: (纸本)9798350369465

the metalworking sector is one of the industries that has more impact on Peru, representing 25.9% of the manufacturing industry's workforce. therefore, it is crucial for them to approach the biggest availability possible for its machines. In this case study, it was identified that a company that belonged to this sector had an OEE (Overall Equipment Efficiency) of 56.39%. this resulted in a monthly economic loss of 55,892.85, caused by numerous machine failures and a deficient set-up process. A model that combined TPM (Total Productive Maintenance) and SMED (Single-Minute Exchange of Die) methodologies was proposed to solve the issue, supported by the 5S tool. As a result, the OEE increased to 88.45%, and the set-up time was reduced from an initial value of 24.09 to 12.85 minutes. Additionally, the availability was increased by 40.58%. Furthermore, it is important to note that an economic evaluation was done, showing that after one year, a Net Present Value (NPV) of 99,286.89 was achieved, with an Internal Rate of Return (IRR) of 67% and a payback period of 4 months and 13 days. Finally, it is worth mentioning that the proposed model was validated using a simulation through Arena software. © 2023 IEEE.

关键词： Computer software

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Short-term Wind Power Prediction based on DLinear-CrossFormer Considering Error Correction 9

Short-term Wind Power Prediction Based on DLinear-CrossForme...

引用

9th Asia conference on Power and Electrical engineering, ACPEE 2024

作者： Chen, Yanxu Zhao, Yongning Pan, Shiji Ye, Lin Pei, Ming China Agricultural University College of Information and Electrical Engineering Beijing China

ISBN: (纸本)9798350309638

Predicting wind power accurately is a vital method for mitigating challenges in integrating large-scale wind power into the grid, leading to enhanced security and cost-effectiveness in power system functionality. A new hybrid wind power forecasting framework is proposed to enhance accuracy of this paper's short-term wind power projection. Firstly, the original wind power series undergoes decomposition into trend components and remaining detail components by singular spectrum analysis (SSA). Considering the model characteristics, the DLinear is employed to forecast the series trend, and the CrossFormer predicts the remaining detail components containing nonlinearities and periodicities, and the initial prediction value is obtained by linear superposition. the Light Gradient Boosting Machine (LightGBM) model predicts the error series to correct and compensate the initial prediction value. To validate the predictive capabilities of the model, experiments were carried out using wind power data from four stations exhibiting high correlation. the findings demonstrate that the hybrid model outperforms a single deep learning model in terms of accuracy and stability when compared to existing single prediction models, which also suggest that the hybrid prediction framework is a robust and dependable approach for wind power prediction. © 2024 IEEE.

关键词： Error correction

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ZK-CPABE: Blockchain-based CP-ABE Scheme Via Zero-knowledge Proof 7

ZK-CPABE: Blockchain-based CP-ABE Scheme Via Zero-Knowledge ...

引用

7th International conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2024

作者： Zhao, Jinyi Zhu, Jie Yin, Peng Yu, Yimin Xie, Long School of Logistics and Management Engineering Yunnan University of Finance and Economics Kunming China Division of Science & Technology Administration Yunnan University of Finance and Economics Kunming China Lianjia Primary School Wuhua District Kunming China School of Information Yunnan University of Finance and Economics Kunming China

ISBN: (纸本)9781510681866

the integration of blockchain technology with Ciphertext-Policy Attribute-based Encryption (CP-ABE) has addressed key challenges in distributed systems, such as mutual trust and collusion, while enhancing auditability of key management. However, existing blockchain-based CP-ABE models face a trilemma, unable to achieve decentralization, scalability, and security concurrently. therefore, we propose ZK-CPABE, which merges Zero-knowledge Proofs with blockchain-based CP-ABE, significantly improving verifiability and scalability. this system combines a secure private key distribution and zero-knowledge proofs to construct off-chain computation and scalable transactions for CP-ABE. Our Ethereum-based ZK-CPABE prototype demonstrates its effectiveness and potential for secure, scalable data sharing and access control. © 2024 SPIE.

关键词： Blockchain

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Effect of Al-Si Coating Weight on the Heat Transfer Coefficient in Hot Stamping of 22MnB5 Steel 9

Effect of Al-Si Coating Weight on the Heat Transfer Coeffici...

引用

9th International conference on Hot Sheet Metal Forming of High-Performance Steel, CHS2 2024

作者： Singh, Arpan Bhattacharya, Ardhendu S. Butcher, Cliff Daun, Kyle Department of Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave. West WaterlooONN2L 3G1 Canada

ISBN: (纸本)9780930767303

this work presents experimental tests and physics-based modelling to elucidate how Al-Si coating weight affects the heat transfer coefficient (HTC) during hot stamping of aluminized steel sheets. the experiments consisted of an industrially-relevant press apparatus equipped with a flat die, which was used to quench furnace-heated Al-Si coated blanks. the measured blank and subsurface die temperatures were processed through an inverse heat conduction algorithm to infer the time-resolved HTC. It was observed that blanks with a coating weight of 150 g/m2 had time-averaged HTCs 16.2% lower than the HTCs of blanks with a coating weight of 80 g/m2. these observations were explained using a physics-based heat transfer model that accounts for the coating thickness and contact area between the blank and the die caused by the surface roughness. © CHS2 2024 . All rights reserved.

关键词： Surface roughness

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Intelligent Control of Power Converters using Reinforcement Learning 9

Intelligent Control of Power Converters using Reinforcement ...

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9th International conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Krishna, R. Sathya Barna Sudharani, R. Devi, S. Rukmani Kulkarni, Pramodkumar H. Rohini, G. Sindhu, M. Sathyabama Institute of Science And Technology Department of Computer Science Engineering Tamil Nadu Chennai600119 India Panimalar Engineering College Department of Mathematics Tamil Nadu Poonamallee600123 India Saveetha College of Liberal Arts And Sciences Deemed To Be University SIMATS Department of Computer Science Tamil Nadu Chennai600124 India Dr. D. Y. Patil Institute of Technology Department of Electronics And Telecommunications Maharashtra Pimpri411018 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Thiruverkadu600077 India R.M.K. Engineering College Department of Computer Science And Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798350365092

Power converter control techniques must evolve to accommodate power system complexity and renewable energy integration. Traditional control methods may be difficult to adapt to these dynamic and uncertain systems. Reinforcement learning (RL) can build optimal control policies in complex and unpredictable situations, making it a promising intelligent control solution in this scenario. this research examines how reinforcement learning can control power converters in energy systems. Advanced reinforcement learning methods, notably deep reinforcement learning, are used to improve power converter efficiency in varied operational scenarios. this research examines the integration of reinforcement learning (RL)-based controllers into power converter systems, focusing on their autonomous learning and adaptation. the explored method uses Reinforcement Learning (RL) flexibility to address power system time-varying conditions, non-linearities, and uncertainties. this research considers practical concerns such RL-based control's effects on real-time execution, processing requirements, and safety. the report concludes with suggestions for future research including expanding RL-based intelligent control systems to power system applications. Reinforcement learning keywords include stability, efficiency, reliability, renewable energy, deep reinforcement learning, power converters, intelligent control, and energy systems. © 2024 IEEE.

关键词： Power converters

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OGCP: Experimental Evaluation of Epilepsy Disease Prediction Using Optimized Gradient Classification Principle 9

OGCP: Experimental Evaluation of Epilepsy Disease Prediction...

引用

9th International conference on Communication and Electronics Systems, ICCES 2024

作者： Prasad, Himayavardhini Jagath Ramkumar, G. Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Epilepsy is a neurological disorder characterized by recurrent seizures, and accurate, real-time prediction of seizures is critical for timely interventions. this study presents an Optimized Gradient Classification Principle (OGCP) with a Hybrid Deep Learning (DL) Model for predicting epilepsy from EEG signals. the proposed model was evaluated using the Epilepsy Seizure dataset from Kaggle and compared against nine well-established models, including CNN, LSTM, GRU, ResNet50, XGBoost, and SVM. the performance of the models was evaluated based on key metrics such as accuracy, precision, F1-score, recall, inference time, and model size. the proposed model outperformed all other models, achieving an impressive accuracy of 96.89%, precision of 94.56%, and F1-score of 95.78%. the model's inference time was 43.4 ms, making it suitable for real-time applications. Additionally, the model's relatively moderate size of 57.9 MB ensures efficient deployment in memory-constrained devices. these results demonstrate the superiority of the OGCP-based Hybrid DL model for epilepsy prediction, making it a promising approach for medical applications where accuracy and real-time performance are crucial. Future work will focus on improving inference time and incorporating additional physiological signals to enhance predictive performance. © 2024 IEEE.

关键词： Prediction models

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Cancer Region Segmentation in Pre-Processed Breast Ultrasound Image Using VGG16 based UNet/SegNet 9

Cancer Region Segmentation in Pre-Processed Breast Ultrasoun...

引用

9th International conference on Communication and Electronics Systems, ICCES 2024

作者： Mohan, Ramya Narayanan, Mathiyazhagan Rajinikanth, Venkatesan Department of Computer Science and Engineering Center for Research and Innovation TN Chennai602105 India

ISBN: (纸本)9798350377972

Breast Cancer (BC) is a predominant cause of mortality among women globally, with its incidence progressively rising due to various causes. Early detection of BC is crucial to plan and execute appropriate treatment to cure the disease. the clinical level examination of the BC involves in;personal check by the doctor, screening using non-invasive medical imaging modality, and biopsy test to confirm the BC and its stage. Compared to the radiological imaging modalities, the breast-ultrasound (BU) based screening is widely preferred due to its simplicity. the abnormality recorded with BU is then examined to classify the tumor as benign/malignant, an essential process during BC detection. this research aims to propose a deep-learning (DL) tool to segment the tumor from the chosen BU-image. To enhance the segmentation accuracy, every image is preprocessed using tri-level thresholding with Tsallis's Entropy and Hummingbird-Algorithm (TE+HA). the performance of proposed UNet/SegNet is verified on both unprocessed and preprocessed images. the experimental outcome of this research confirms that the VGG-UNet offered an improved mean accuracy (>97%) when the preprocessed BU is considered for examination. © 2024 IEEE.

关键词： Ultrasonic imaging

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An intelligent car with automatic patrol 9

An intelligent car with automatic patrol

引用

9th International conference on Electromechanical Control Technology and Transportation, ICECTT 2024

作者： Jiang, Yangjiao Chen, Yong Li, Tianyu Ding, Boyu Ling, Huxian Department of Automotive Engineering College of Science and Technology Hubei Institute of Automotive Technology Hubei Shiyan China Department of Mechanical Engineering Hubei Institute of Automotive Technology Hubei Shiyan China

ISBN: (纸本)9781510682399

[Objective] based on the needs of highway inspection, this article aims to develop a fully functional intelligent inspection vehicle, which can improve the ability of highway supervision. [Method] the overall framework of the inspection vehicle was designed, the mechanical design of the inspection vehicle was carried out by three-dimensional software, and the circuit and interactive software were designed by the electrical system design software. [Results] the real vehicle was manufactured by 3D printing and other production methods and the actual vehicle was tested on the road, and the functions of autonomous driving, active obstacle avoidance, illegal photography, and remote communication of the inspection vehicle were realized. [Conclusion] the design of this intelligent inspection vehicle can complete the highway inspection task more efficiently and provide a practical and reliable solution for road safety and security. © 2024 SPIE.

关键词： Crime

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Design And Study the Characteristics Of Piezoelectric Power Ultrasonic Transducers Using the Finite Element Method 9

Design And Study The Characteristics Of Piezoelectric Power ...

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9th International conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： thinh, Nguyen Van On, Do Viet Ky, Phan Ngoc Nghia, Hoang Ba Dai Dinh, Le Phuoc Tung, Vo thanh Vuong, Le Dai University of Technology and Education University of Danang University of Sciences Hue University Danang Viet Nam University of Technology and Education University of Danang Electrical and Electronic Engineering Danang Viet Nam University of Sciences Hue University Electrical Electronic and Materials Technology Hue Viet Nam Hue University School of Engineering and Technology Hue Viet Nam

ISBN: (纸本)9798331505042

In this study, Comsol Multiphysics software is applied to solve differential equations and analyze parameters using the finite element method in the piezoelectric environment. the results of the study were to build a power ultrasonic transducer model incorporating six piezoelectric ceramic rings (PZT8) with head diameters varying from 20 to 50 mm and to design a power ultrasonic transducer model whose horn heads are cylindrical and conical. the diameter of the element head affects the characteristic parameters, including impedance, displacement, and resonance frequency, according to the frequency of the ultrasonic element. the result is that the tranducer has an optimal head diameter of 30 mm, suitable for applications at a frequency of 20 kHz. For both cylindrical and conical horns, a detailed analysis is conducted about the voltage distribution and deformation of the transducer's elements based on the natural frequency. Finding a low-cost design model appropriate for application goals is guided by the findings of this study. © 2024 IEEE.

关键词： Piezoelectricity

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Wireless Sensor Networks Deployment Strategy of 3D Surface based on Delaunay Triangulation and Enhanced Butterfly Optimization Algorithm 9

Wireless Sensor Networks Deployment Strategy of 3D Surface B...

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9th International conference on Communication, Image and Signal Processing, CCISP 2024

作者： Mao, Shun Gou, Pingzhang Zheng, Chao College of Computer Science and Engineering Northwest Normal University LanZhou China

ISBN: (纸本)9798350356656

Various complex challenges are often encountered in 3D coverage for wireless sensor networks (WSNs). Existing 3D surface deployment methods suffer from high coverage cost and low coverage efficiency. therefore, this study proposes a 3D surface WSN coverage optimization method based on Delaunay Triangulation and Enhanced Butterfly Optimization Algorithm (DTEBOA-3D). Firstly, a 3D surface probabilistic coverage model is established by digital elevation model and delaunay triangulation (DEM-DT) to enhance the perception effect;then, tent mapping is used in the algorithm to enhance the diversity of butterfly populations, and Lévy flight is used to enhance the exploration optimization function;and finally, the convergence is accelerated by the Virtual Force Algorithm (VFA) to improve the optimization accuracy. In a 3D surface deployment simulation experiment, DTEBOA-3D is compared with three other deployment methods. the experimental results show that the DTEBOA-3D algorithm is effective in 3D surface coverage. © 2024 IEEE.

关键词： Digital elevation model

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