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Research on Single Bottleneck Optimization Based on Intelligent Manufacturing Mode

Research on Single Bottleneck Optimization Based on Intellig...

引用

2023 ieee international conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Hu, Han Cao, Jun Hui, Guan Wuhan Vocational College of Software and Engineering Wuhan China Huazhong University of Science and Technology State Key Lab of Digital Manufacturing Equipment and Technology Hust-Sany Joint Lab of Advanced Manufacturing School of Mechanical Science and Engineering Wuhan China Wuhan Railway Vocation College of Technology Wuhan China Northwest A&F University College of Languages and Culture Xianyang China

ISBN: (纸本)9798350399028

More and more manufacturing companies are adopting a multi-variety, low-volume production model, and there are bottlenecks in the workshop due to the different demands on machine capacity for different products. The presence of the bottleneck in the workshop will severely limit the daily output of the workshop, reducing productivity and profits. In this paper, an improved Jaya algorithm based on problem characteristics is proposed to solve the single bottleneck optimization problem within intelligent manufacturing mode. Firstly, under the single bottleneck of intelligent manufacturing mode, the flexible factor of the manufacturing machine is considered, which effectively solves the problem of machine load distribution in the workshop. Secondly, based on the basic JAYA algorithm, a two-layer encoding mechanism and decoding mechanism based on problem characteristics are proposed, as well as the improvement of the similarity method. Finally, the effectiveness of the improved algorithm in this problem is demonstrated by comparing it with other algorithms. © 2023 ieee.

关键词： Scheduling algorithms

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Sentiment Analysis using Speech Recognition for Improved Customer Operations Using Convolutional Neural Networks 2

Sentiment Analysis using Speech Recognition for Improved Cus...

引用

2nd international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Hema, D. Deva Sujith, N. Rithika, P.J. Bhavesh, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Dept. of Computer Science SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

This paper presents the application of advanced speech recognition technologies to transcribe and analyze customer interactions, enhancing both business efficiency and customer experience. Motivated by the need for human-like interaction in automated systems, our work introduces "Humanization through automation," a system designed to retain personal touches while optimizing operational efficiency. The approach involves capturing high-quality audio, followed by noise reduction and normalization to facilitate effective feature extraction using Spectrogram Conversion and Mel-Frequency Cepstral Coefficients (MFCC). The use of Convolutional, Pooling, and Dropout Layers refines detail detection and reduces overfitting, while Fully Connected Layers integrate and analyze features, using activation functions for accurate classification and sentiment analysis. By embedding sentiment detection within Interactive Voice Response (IVR) systems via Convolutional Neural Networks (CNNs), our system can interpret emotional tones, allowing dynamic responses and connecting customers with suitable representatives based on emotional state. This integration of speech recognition and sentiment analysis not only elevates interaction quality but also strengthens customer relationships, optimizes sales strategies, and fosters organizational growth, transitioning from traditional customer service models to solutions emphasizing efficiency and personalization. © 2024 ieee.

关键词： Sales

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Optimizing Skin Lesion Segmentation with UNet and Attention-Guidance Utilizing Test Time Augmentation 6

Optimizing Skin Lesion Segmentation with UNet and Attention-...

引用

6th international conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Ahamed, Md. Faysal Hossain, Md. Munawar Mary, Mekhala Mariam Islam, Md. Rabiul Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh International Standard University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350385779

To properly diagnose and screen for skin disorders, precise segmentation of the affected area is required. Segmenting skin lesions is difficult because of factors such as the form of the lesion, its proximity to surrounding skin, and the presence of hair. To effectively tackle this matter, we present a pioneering technique that utilizes deep learning to achieve comprehensive automation in skin lesion segmentation. This approach incorporates advanced preprocessing methodologies to enhance the overall performance. The architecture Attention-UNet is employed for the purpose of selectively segmenting images of skin lesions. The high detection rate of this design allows for precise melanoma segmentation in real-time. Finally, in the post-processing phase, we applied test-time augmentation (TTA) to further enhance segmentation precision. Both the ISIC 2017 and ISIC 2018 skin lesion datasets were used for training and testing the suggested technique. The model's mDSC, mIoU, recall, and precision were all 91.93%, 88.15%, 92.72%, and 91.15%, respectively, when trained independently on the ISIC 2017 datasets. Separate training on the ISIC 2018 datasets achieved similar results: 92.17% mDSC, 90.26% mIoU, 92.34% recall, and 92.01% precision. After being trained on a dataset consisting ISIC data from both 2017 and 2018, the proposed method achieved mDSC scores of 92.30% percent, mIoU scores of 90.06% percent, recall rates of 93.91% percent, and precision rates of 90.74% percent. Our model demonstrates superior FPS scores compared to other models, indicating effective real-time performance. Due to its scalability and reliability, the suggested methodological framework may be utilized to create a completely automated computer-aided skin lesion diagnostic system. © 2024 ieee.

关键词： Deep learning

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High-Resolution Image Anomaly Detection via Spatiotemporal Consistency Incorporated Knowledge Distillation 19

High-Resolution Image Anomaly Detection via Spatiotemporal C...

引用

19th ieee international conference on automation science and engineering, CASE 2023

作者： Cao, Yunkang Zhang, Yiheng Shen, Weiming School of Mechanical Science and Engineering Huazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan China

ISBN: (纸本)9798350320695

This paper introduces a new, practical, and challenging scenario of high-resolution (HR) image anomaly detection. Anomaly detection methods cooperated with sliding windows are typical solutions for this task, but they fail to capture long-term dependencies. This paper proposes a Spatiotemporal Consistency Incorporated Knowledge Distillation (STCIKD) method, which translates HR images into video sequences and exploits spatial and temporal consistency between them to capture both local spatial and long-term dependencies. STCIKD consists of a teacher network and two student networks. Among the two students, a spatial student network captures spatial consistency by reconstructing the current video frame, and another temporal student network learns temporal consistency by predicting the future frame. This paper benchmarks several state-of-the-art image anomaly detection methods and evaluates STCIKD for HR image anomaly detection. The results show that by incorporating spatial and temporal consistency, STCIKD significantly outperforms other methods. © 2023 ieee.

关键词： Distillation

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Intelligent Energy Management Energy Storage Systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

引用

2024 international conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 ieee.

关键词： Machine learning

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Classification of Bacteria from Agar Plate Using Deep Learning and Image Processing

Classification of Bacteria from Agar Plate Using Deep Learni...

引用

2024 international conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Sunanda Inchara, B.K. Disha, V. Divya Shree, K. Sai Manusha, G.C. Dayananda Sagar College Of Engineering Computer Science Department Karnataka Bengaluru India

ISBN: (纸本)9798350364828

This A key component of illness diagnosis is the classification of microorganisms. This is an all-inclusive method for automatically classifying bacteria on agar plates by combining image processing techniques with deep learning methods. Numerous characteristics, including batch size, edges, shape, and color, support the model's categorization and enhance its capacity to discriminate between groups. The research makes use of a unique dataset known as the Agar Dataset, which is spread throughout five different types of bacteria: Pseudomonas aeruginosa (P. aeruginosa), Bacillus subtilis (B. subtilis), Candida albicans (C. albicans), Staphylococcus aureus (S. aureus), and Escherichia coli (E. coli). The suggested model uses a deep convolutional neural network, together with GoogLeNet, AlexNet, VGG-16, SqueezeNet, and DenseNet-161, to extract and learn hierarchical features from photos of bacteria. The most accurate model among the others is GoogLeNet. This signifies a noteworthy progression in the fields of microbiology and laboratory automation. This ground-breaking method uses artificial intelligence to improve and expedite the labor- intensive process of bacterial identification © 2024 ieee.

关键词： Algae

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Research on Output Characteristics of Switched Reluctance Generator Based on Finite Element Model 8

Research on Output Characteristics of Switched Reluctance Ge...

引用

8th international Symposium on computer science and Intelligent Control, ISCSIC 2024

作者： Li, Hao Wang, Honghua HoHai University School of Electrical and Power Engineering Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Nanjing Changzhou China HoHai University College of Artificial Intelligence and Automation Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Changzhou China

ISBN: (纸本)9798350380286

A finite element model of a switched reluctance generator is established in ANSYS software. The transient characteristics of the switched reluctance generator are simulated, and the influence of structural parameters such as stator embrace, rotor embrace, stator yoke thickness, and winding turns, as well as control parameters such as initial excitation voltage, speed, turn-on angle, and turn off angle on the output characteristics of the switched reluctance generator are analyzed. This provides a basis for parameter optimization and actual operation control of the switched reluctance generator. © 2024 ieee.

关键词： control parameters finite element model structural parameters switched reluctance generator

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Connection of Big Data Analytics & Artificial Intelligence

Connection of Big Data Analytics & Artificial Intelligence

引用

2023 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Soni, Rohit Baghel, Ananya Paliya, Sparsh Mamtani, Ritika Gupta, Lalita Lnct Group of Colleges Computer Science Engineering Madhya Pradesh Bhopal462021 India Vit Bhopal Computer Science Engineering Madhya Pradesh Bhopal466114 India Maulana Azad National Institute of Technology Electronics and Communication Engineering Madhya Pradesh Bhopal462003 India

ISBN: (纸本)9798350398748

Big data is emerging as an origin for providing various opportunities in the field of artificial intelligence. There are also many design considerations in the market where parallel processing frameworks are used effectively and in a more economical way. Big data and artificial intelligence work incredibly well together because they enable us to effectively identify a variety of trends and hidden patterns, which then enables us to make predictions about the future. With this combination, parallel processing can be worked upon at a level that was not possible to achieve earlier. It supports performance development at a very high level and works effectively and efficiently for the same. © 2023 ieee.

关键词： Big data

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Object Recognition and Tracking System to Assist Visually Impaired: A Neural Network-Based Deep RBM Technique 1

Object Recognition and Tracking System to Assist Visually Im...

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1st ieee international conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Patil, Sonal Ramagundam, Shashishekhar Sukumar, P. Khan, Mohiuddin Ali Patil, Nisha Deepak Nishant, Neerav G H Raisoni Institute of Engineering and Business Management HoD of CSE-IT Department Jalgaon India Software and Dev Engg4- SDET/ IT COMCAST MonroviaMD United States Department of Computer Science and Engineering Tamilnadu Erode India Jazan University Department of Computer & Network Engineering Jazan Saudi Arabia GCOERC Computer Engineering Nashik India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350371802

Being able to see is fundamental to almost every aspect of our everyday lives, thus those who are visually impaired confront enormous obstacles. Thanks to recent developments in computer vision and computing, a system that can provide auditory input to visually impaired individuals regarding objects and their environments has been designed. Sequencing is crucial for preprocessing, feature selection, and model training. Although extremely accurate data input is still required for object detection, preparation is getting computationally cheaper. We were partially successful in reducing the image size by employing Gaussian Blur, which imparts a smoother appearance to the photos. We use color descriptors and Fourier transforms for feature extraction. Deep RBM training necessitates precise control over the attributes. In comparison to this method, current RBM and DNN algorithms look antiquated. The results demonstrated a notable enhancement in accuracy, reaching 98.31%. © 2024 ieee.

关键词： Object detection

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Noise Pollution Detection and Notification Using Flet and Arduino

Noise Pollution Detection and Notification Using Flet and Ar...

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2024 international conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Kala, I. Swathy, S. Vishnu Priya, V. Harshavarthan, P.K. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350378177

This project seeks to identify urban, traffic, and industrial noise hazards. This is accomplished by Arduino, which uses sound sensors to recognize and differentiate between light and loud noise. With the help of the ThingSpeak server, the noise level data for different time is also tracked. The proj ect extends its benefits to the users by notifying users via mail if they are in an area with high noise levels. The Flet User Interface framework was used in the development of this web application. © 2024 ieee.

关键词： Background noise

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