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Android based Integrated Parking System for Real-Time Parking 1

Android based Integrated Parking System for Real-Time Parkin...

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1st IEEE international conference on automation, Computing and Renewable Systems, ICACRS 2022

作者： Raji, C.G. Aboobacker, Adil Bin Muhammad, Anfas Jamshidha, K. Shemeem, Jishana Department of Computer Science Assumption Autonomous College Kottayam Kerala Changanacheri India Computer Science & Engineering MEA Engineering College Kerala Perinthalmanna India

ISBN: (纸本)9781665460842

The lack of parking spaces is getting worse as the number of vehicles on the road rises daily. Finding a parking spot can be difficult, especially in big cities or places where athletic or cultural events are planned. An integrated parking system is suggested as a solution to this problem. Despite the fact that there has been a tonne of research on the creation of smart parking systems, much of it does not deal with the problem of in-the-moment detection of incorrect parking and automatic collection of parking fees. The proposed system combined a real-time parking reservation system with a smart payment method for the beneficial to the society. © 2022 IEEE

关键词： Reservation systems

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Data Transfer Optimization Scheme Based on dSpace High-Fidelity LiDAR Model 4

Data Transfer Optimization Scheme Based on dSpace High-Fidel...

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4th international conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Xue, Xianbin Tan, Beihai Chen, Siyang Hu, Ziwei Guangdong University of Technology School of Automation Guangzhou China Guangdong University of Technology School of Integrated Circuit Guangzhou China

ISBN: (纸本)9798350384062

Validating intelligent algorithms within the realm of Autonomous driving Domain Controllers (ADC) using virtual simulation platforms has become a foundational industry paradigm. Modeling the perception system of autonomous driving on a simulation platform, especially sensor modeling, has become a crucial aspect for validating intelligent algorithms. To accurately simulate real sensors, it is imperative to construct sensor models reasonably and ensure accurate data transmission between the sensor model and the ADC. This paper, based on the high-fidelity dSpace simulation platform, employs a LiDAR model to simulate a real LiDAR within the perception system. Post-processing operations, such as time synchronization and data validation, are applied to the LiDAR model to ensure the accuracy of the sensor model's data link. Addressing issues of mismatched data transmission frequencies and inconsistent data packet quantities between the LiDAR model and the ADC, we propose a solution named DCD-FIFO based on a First-In-First-Out (FIFO) queue. In this solution, the LiDAR model actively manages frame dropping, complements missing data packets, and delays sending data packets to the ADC to resolve data transmission issues. Experimental results demonstrate that the high-fidelity LiDAR simulation model in the dSpace platform accurately simulate a real LiDAR. Moreover, using the proposed DCD-FIFO solution, reliable data transmission is achieved between the LiDAR model and the ADC. © 2024 IEEE.

关键词： Autonomous vehicles

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A Design of an Energy Regulation Network Framework of Central Air Conditioning System Based on LSTM Prediction Model 4

A Design of an Energy Regulation Network Framework of Centra...

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4th international conference on Advances in Electrical engineering and computer Applications, AEECA 2023

作者： Lu, Jie Song, Linze Yuan, Yuan Wang, Jianping Wan, Qixuan Zheng, Xuan Ding, Tan China Construction Third Engineering Bureau The First Construction and Installation Co. Ltd Shenzhen518109 China Wuchang Shouyi University School of Mechatronics and Automation Wuhan430064 China

ISBN: (纸本)9798350308082

With the development of the economy, more buildings are established for people to live in. To save power consumed by central air condition systems (CACS) in buildings and make CACS control easily, network technology is employed in this field which greatly improve the level of the control system of CACS. Nevertheless, when complex control network technology such as Cloud-Edge-Device architecture are used, it is more difficult to be implemented in practical engineering. To solve the problem, a novel control Network framework is proposed in this article, in which an energy management and system adjustment strategy based on Short Term Memory networks (LSTM) model is used to predict load of CACS and adjust the control system. The specific work is as follows: firstly, a novel control network framework is proposed and used to control CACS. Then, based on the network framework, an energy management and adjustment strategy based on LSTM model is used to adjust the control system by the prediction value of CACS obtained by LSTM model. Finally, to prove that the accuracy of LSTM model proposed above is greatly affected by its structure parameters, the two models with different parameters are respectively used to predict load of CACS based on the same group of samples. The experiment results prove that more complex structure may improve prediction accuracy of LSTM model. Therefore, the network structure and prediction model may promote the application of complex network technology in practical engineering. © 2023 IEEE.

关键词： Long short-term memory

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Process Improvement of Taping for an Assembly Electrical Wiring Harness 11th

Process Improvement of Taping for an Assembly Electrical Wir...

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11th international conference on Industrial engineering and Applications - Europe (ICIEA - Europe)

作者： Jebbor, Ikhlef Raouf, Youssef Benmamoun, Zoubida Hachimi, Hanaa Ibn Tofail Univ Natl Sch Appl Sci Lab Adv Syst Engn Kenitra Morocco Liwa Coll Technol Fac Engn Abu Dhabi 31009 U Arab Emirates

ISBN: (纸本)9783031581120;9783031581137

The production of automotive wire harnesses now requires a significant amount of manual labor. Even so, a greater level of automation is required due to present and future application demands such as the miniaturization of electronic components, the monitoring of process parameters, the growing need for paperwork for processes, and the rise in payments. Technology helps manufacturing organizations in the most important aspects of the design-to-manufacturing process. This is relevant to the wire harness sector, which is a crucial part of industrial automotive manufacturing. The automotive wiring harness suppliers designed computer-Aided Design technologies (CAD) to support wire harness assembly operations and design work. Even with the application of these techniques, engineers will still have to do trial-and-error-work to find effective assembly techniques. This study presents a new approach to optimize the wire harness assembly procedures without focusing on trial and error or the experience of experienced engineers to develop operational assembly processes. The most crucial and challenging step in the assembly process sequence is taping routed cables. The taping process's complexity is mostly determined by how the jig is set up on the workstation and the tape method. As a result, the suggested technique models and optimizes the tape direction and jig arrangement.

关键词： Electrical wiring harness Industrial engineering Lean Manufacturing Layout jigs Optimization of taping

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Comparative Analysis for the Maintenance Phase of Depth of Anesthesia: Decentralized or Decoupled? 4

Comparative Analysis for the Maintenance Phase of Depth of A...

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4th international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Hegedus, Erwin T. Mihai, Marcian D. Othman, Ghada Ben Copot, Dana Birs, Isabela R. Muresan, Cristina I. University of Cluj-Napoca Dept. of Automation Technical Cluj-Napoca Romania DySC Research Group Ghent University Ghent Belgium

ISBN: (纸本)9798350391183

General anesthesia requires precise control of both anesthetic depth and hemodynamic stability to ensure patient safety. The interactions between anesthetic and hemodynamic drugs can complicate this process, making it crucial to optimize control strategies. This paper presents a comparative analysis between two control strategies-decentralized and decoupled approaches-for the maintenance phase of general anesthesia, focusing on reducing interactions between the subsystems controlling the depth of anesthesia and hemodynamic parameters. A fractional-order PID controller is employed to implement the decoupled strategy, aiming to minimize cross-effects between anesthetic and hemodynamic drugs. The performance of this approach is evaluated against the decentralized strategy using a benchmark model and patient data from 24 virtual subjects. Results indicate that the decoupled approach offers superior disturbance rejection and control stability. The findings suggest that this strategy can enhance patient safety and surgical outcomes by improving the precision of anesthetic administration. © 2024 IEEE.

关键词： Disturbance rejection

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Towards Enabling Data Security: NFT Generator automation for Blockchain User Experience 3

Towards Enabling Data Security: NFT Generator Automation for...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Kishan, Makadiya Madhu, Shukla Krunal, Vaghela Marwadi University Department of Computer Engineering Rajkot India Marwadi University Department of AI ML and DS Rajkot India

ISBN: (纸本)9798350354171

Non-fungible tokens (NFTs) have become increasingly more popular in recent years as a manner to represent particular virtual assets on the blockchain. However, the creation of NFTs can be a complicated and technical process that calls for an understanding of blockchain technology and programming. To make the procedure more accessible to a wider target audience, NFT turbines have been developed to enable customers to create their very own NFTs without the want for technical knowhow. This paper pursues to discover the consumer experience of NFT mills and their capability effect on the adoption of NFTs. We conducted a literature evaluation to study present studies on NFTs and case studies of NFT generator structures and remarks from popular NFT generator systems to gain insights into the person revealed. Our analysis revealed that NFT turbines can substantially simplify the procedure of making and selling NFTs. By supplying person-pleasant interfaces and automation of technical duties, NFT turbines can allow a much broader audience to take part in the NFT marketplace. There also are demanding situations that want to be addressed to make sure a fine user enjoy. These encompass issues around the protection, transparency, and authenticity of NFTs. We identified numerous key layout concerns for NFT turbines, which include ease of use, customization alternatives, and assistance for a couple of blockchains. we discovered that Mintable gives a streamlined and consumer-friendly revel in for developing and selling NFTs, with several customization options and aid for multiple blockchains. NFT generators will possibly play an increasingly vital function in permitting users to take part in this emerging economy. Overall, this research highlights the significance of personal experience within the design of NFT generators and gives insights and hints for designers looking to create NFT generator systems that are consumer-friendly, on-hand, and secure. © 2024 IEEE.

关键词： Sales

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A Structure-Based Method for Prediction of Protein-Protein Interaction Sites Through Combining Local and Global Features 8th

A Structure-Based Method for Prediction of Protein-Protein...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhang, Hongyang Zhao, Yanpeng Liu, Ziyi He, Song Bo, Xiaochen Zan, Peng School of Mechatronics Engineering and Automation Shanghai Key Laboratory of Power Station Automation Technology Shanghai University Shanghai200444 China Department of Biotechnology Institute of Health Service and Transfusion Medicine Beijing100850 China

ISBN: (纸本)9789819601875

The identification of protein-protein interaction(PPI) sites is critical for drug discovery and analysis of protein function. So far, numerous computational methods based on artificial intelligence have been developed to accelerate the identification of PPI sites. However, those methods learned only sequential features or local structural features, leading to low predictive performance. Therefore, it is urgently necessary to develop a new method to improve the performance of predicting PPI sites. Herein, a new structure-based method for predicting the protein-protein interaction sites through combing the local and global encoder, SLGPPIS, was therefore proposed. Specifically, SLGPPIS utilizes the local and global encoder to extract the residue feature respectively and concatenating them to predict the PPI sites. Compared with other existing methods, SLGPPIS exhibited superior performance on prediction of PPI sites. Moreover, a visual interpretative experiment was conducted to systematically investigate the prediction process of SLGPPIS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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An Insight into Applications of Deep Learning in Cancer Classification 3

An Insight into Applications of Deep Learning in Cancer Clas...

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3rd international conference on Computation, automation and Knowledge Management, ICCAKM 2022

作者： Jyothi, B.N. Tejasvi, Koti Begum, Ruqqaiya Jabbar, M.A. Vardhaman College of Engineering Computer Science and Engineering Department Shamshabad India Vardhaman College of Engineering Computer Science and Engineering Department IEEE Shamshabad India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

Rapid enviro14nmental change has led to an increase in a number of diseases, including skin cancer, which is primarily brought on by sun exposure. There is always a need for ongoing study and enhancement of treatments or procedures available, even though there have been medicines for these types of disorders for decades. Despite being a proven therapeutic procedure, conventional techniques like biopsies, imaging tests, and scanning also have drawbacks like time commitment and accuracy in identifying the images. For the classification of clinical images, machine learning and deep learning approaches have been used to overcome these drawbacks. This paper highlights various machine learning and deep learning methods that are used to classify skin cancer diseases. This review will aid health care professionals and doctors in the diagnosis of skin cancer diseases. © 2022 IEEE.

关键词： Image classification

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Advancements in Crown Sheath Rot Detection using DenseNet121 and UNet in Rice Plant Leaf Analysis

Advancements in Crown Sheath Rot Detection using DenseNet121...

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2023 international conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Thilagavathy, R. Kumar, S.Senthil Deebalakshmi, R. Deepa, D. Kavitha, R. Kumar, L. Sathish Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur India School of Arts and Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Perundurai India Karpagam Academy of Higher Education Department of Computer Applications TN Coimbatore India Vit Bhopal University School of Computing Science and Engineering MP Kotrikalan India

ISBN: (纸本)9798350348910

This article aims to investigate the leaf spot disease in rice, a condition that has been observed to cause substantial reductions in crop yield, ranging from 10% to 50%. The disease is characterized by the appearance of circular lesions on the leaves of rice plants, which gradually expand and cover the entire leaf surface, leading to the eventual death of the affected leaves. In order to achieve automation in the detection and severity classification of the disease under investigation, a range of deep learning models were utilized. These models encompassed Faster DenseNet121 and UNet. These methods proposed in this study was trained and tested using a dataset consisting of 1120 images. The findings of the study indicate that the DenseNet121 approach that yielded the highest level of success demonstrated a noteworthy classification accuracy rate of 95.23%. The results obtained from the integration of image processing techniques with deep learning models demonstrated superior performance compared to the analysis solely based on deep learning models. The integration described in this study presents a novel approach to enhance the efficiency of diagnosing leaf spot disease on rice. By leveraging advanced technology, this integration aims to reduce the time required for diagnosis, minimize the occurrence of human errors, and facilitate the timely application of suitable pesticides to mitigate the detrimental effects of the disease. © 2023 IEEE.

关键词： Integration

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Diabetic Retinopathy Detection using Squeezenet 2

Diabetic Retinopathy Detection using Squeezenet

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Nanthini, K. Sivabalaselvamani, D. Sharvanthika, K.S. Ojha, Shubham Kumar Siva, M. Department of Computer Applications Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798350340235

Diabetic retinopathy (DR), which causes lesions on the retina and impairs vision, is a common complication of diabetes mellitus. It can cause blindness if it is not caught in time. With early identification and treatment of DR, the risk of vision loss can be considerably decreased. Ophthalmologists must manually diagnose DR retina fundus images, which is more expensive, time-consuming, and error-prone than computer-aided diagnosis technologies. Recently, deep learning has become one of the most well-liked techniques forenhancing performance, especially in classifying and interpreting medical pictures. Deep learning models can detect specific lesions or abnormalities associated with diabetic retinopathy such as microaneurysms, hemorrhages, exudates, and neovascularization. By training CNNs on annotated retinal images with labeled lesions, these models can learn to identify and localize the presence of these abnormalities, aiding in early detection and diagnosis. The images have been converted to grayscale using an image batch processor and the images are resized are the pre-processing steps. Using deep learning algorithms like Squeezenet, diabetic retinopathy images are classified into No DR indications, mild NPDR, moderate NPDR, Severe NPDR, extremely severe NPDR, PDR, and Advanced PDR cases. Squeezenet produced an accuracy of 94.7% without augmentation and had an accuracy of 97 percent. Then the pre-processed images are undergone. © 2023 IEEE.

关键词： Classification Diabetic retinopathy Retino images Squeezenet

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