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检索条件"任意字段=7th International Conference on the Art, Science, and Engineering of Programming, Programming 2023"

共 1136 条记录，以下是391-400 订阅

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Comfort-Oriented Control for Half-Car Semi-active Suspension Using a Model Predictive Approach

Comfort-Oriented Control for Half-Car Semi-active Suspension...

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conference on Vehicle Control and Intelligence (CVCI)

作者： Huichao Zhao Qing Yuan Weiwei Miao Hongliang Zhou Songlin Chen Zhen Yu Zhenqian Cao New Energy Development Institude China FAW Group Co. Ltd ChangChun China Control Science and Engineering Harbin Institute of Technology Harbin China Chassis Development Department Institude China FAW Group Co. Ltd ChangChun China

this paper presents a controller design for magnetorheological (MR) semi-active vehicle suspensions, with the main objective of optimizing suspension performance while ensuring that the desired damping coefficients of the front and rear suspension dampers generated by the control law satisfies the physical constraints of the MR damper. To tackle these control objectives, a constrained quadratic programming problem is formulated within the framework of model predictive control (MPC). Integrated Analytic Hierarchy Process (IAHP) and Particle Swarm Optimization (PSO) algorithm are then utilized to optimize the performance of the controller. the optimal control law is determined using the GRAMPC toolbox. Simulation results based on CarSim-MATLAB cosimulations are presented to validate the effectiveness and superiority of the proposed controller compared to conventional control methods.

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Writer Identification: Deep Learning with ResNet50 for Offline Urdu Handwritten Documents 7

Writer Identification: Deep Learning with ResNet50 for Offli...

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7th international conference on Image Information Processing, ICIIP 2023

作者： Nabi, Syed Tufael Singh, Paramjeet Kumar, Munish Maharaja Ranjit Singh Punjab Technical University College of Engineering And Technology Department of Computer Science And Engineering GZS Campus Punjab Bathinda151001 India Maharaja Ranjit Singh Punjab Technical University Department of Computational Sciences Punjab Bathinda151001 India

ISBN: (纸本)9798350371406

the field of writer identification, a crucial aspect of document analysis, has undergone a significant evolution through the incorporation of deep learning models. this paper presents an innovative writer identification system, meticulously designed around the ResNet50 deep learning architecture, with a specific emphasis on its adaptability for processing offline Urdu handwritten documents. the system underwent rigorous training and thorough testing using an Urdu handwritten dataset that encompasses contributions from 418 Urdu writers. Our ResNet50-based model demonstrates an exceptional level of accuracy, achieving an impressive identification rate of 99.26%. Within this paper, we delve into a comprehensive examination of the model's architecture, intricate feature extraction methods, and the subtle intricacies of its training methodologies, all of which collectively contribute to this outstanding performance. Beyond pushing the boundaries of current writer identification capabilities, the application of deep learning, particularly the ResNet 50 model, to Urdu handwritten documents holds significant promise across various domains, including historical document preservation, forensic analysis, and robust authentication. this paper not only introduces a state-of-the-art writer identification system but also underscores the broader impact of deep learning in the field of document analysis. the remarkable accuracy demonstrated by our ResNet50-based model highlights the transformative potential of deep learning, enhancing the precision and effectiveness of writer identification tasks. Furthermore, it illuminates new avenues for research and practical applications, spanning diverse linguistic and cultural contexts. © 2023 IEEE.

关键词： Feature extraction

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A Novel Deep Learning Approach for Capturing Time Series Dependencies and Improving Short-term Weather Forecasting 7

A Novel Deep Learning Approach for Capturing Time Series Dep...

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7th international conference on Image Information Processing, ICIIP 2023

作者： Johri, Shiva Divyajyothi, M.G. Anitha, S. Rani, M. Sandya Murari, thejovathi Shirisha, N. Oriental College of Management Oriental Group of Institutes Department of Management Madhya Pradesh Bhopal462022 India University of Technology And Applied Sciences - Al Mussanah Department of Information Technology Al Muladha Muscat Oman Vels Institute of Science Technology And Advanced Studies Department of Computer Science Engineering Pallavaram Krishnapuram Tamil Nadu Chennai India St. Martin's Engineering College Secunderabad Telangana Dhulapally500100 India Acharya Nagarjuna University Department of Computer Science And Engineering Andhra Pradesh Guntur India Mlr Institute of Technology Department of Cse Hyderabad Dundigal India

ISBN: (纸本)9798350371406

Weather forecasting is a critical and challenging task that requires accurate predictions based on historical data and intricate dependencies between time series. Traditional neural networks, such as Back Propagation through Time (BPTT) trained RNNs, struggle to effectively capture these dependencies, leading to suboptimal performance in weather forecasting. In this study, we suggest a novel Integrated-Stack based Bi-LSTM Model, which associations the strengths of LSTM and Bi-LSTM networks, to address these limitations and enhance the accuracy of short-term weather predictions. the primary objective of this research is to develop a versatile deep learning model capable of maintaining long-term dependances and reading any length of sequence, while simultaneously providing real-time short-term weather forecasts. To achieve this, the Integrated-Stack based Bi-LSTM Model is designed to exploit the versatility and non-linear adaptive processing ability of neural networks. the proposed model's architecture involves the integration of LSTM and Bi-LSTM layers. By incorporating both forward and reverse direction processing, the model gains a comprehensive understanding of complex patterns present in time-series data. this bidirectional approach enables the model to capture dependencies effectively, which is essential for accurate weather predictions. To estimate the presentation of the Integrated-Stack based Bi-LSTM Model, we conducted experiments using real-world weather data. We compared the model's predictive capabilities against traditional LSTM models and other state-of-the-art weather forecasting methods. the assessment metrics included accuracy, root mean square error (RMSE), and mean absolute error (MAE). © 2023 IEEE.

关键词： Weather forecasting

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Blockchain and API Development to Improve Relational Database Integrity and System Interoperability 7

Blockchain and API Development to Improve Relational Databas...

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7th international conference on Computer science and Computational Intelligence, CSCI 2022

作者： Ramadhan, Andhika Naafi Pane, Kharisma Naufal Wardhana, Kun Rifki Suharjito Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Industrial Engineering Department BINUS Graduate Program-Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend of cryptocurrencies, this development has also resulted in crimes that hit the digital world, such as data leakage and cyber espionage. this threat can be prevented by applying blockchain technology to the database that has been used. therefore, we need a system that can facilitate the use of blockchain technology and can process data from an existing relational database into a blockchain-based database. the system developed in this study was built with the FastAPI framework that uses the Python programming language and the React framework that uses the Javascript programming language. this system was tested using Katalon and Wireshark software to perform throughput testing and man-in-the-middle attacks. Evaluation of this system is assessed based on the average throughput time and also the results of Wireshark packet capture. the system designed in this research is expected to help overcome interoperability problems when using blockchain and improve relational database integrity. the results of the test show that the system is safe from man-in-the-middle attacks while sending data through API and has a faster throughput time than BigchainDB system by 4.151 seconds. © 2022 Elsevier B.V.. All rights reserved.

关键词： Blockchain

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Micro-Doppler Parameter Estimation of Ships Based on the Viterbi Algorithm 7

Micro-Doppler Parameter Estimation of Ships Based on the Vit...

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7th international conference on Signal and Image Processing, ICSIP 2022

作者： Li, Fan Zhang, Shuning Zhu, Lingzhi Zhao, Huichang Chen, Si Nanjing University of Science and Technology Electronic Technology and Optoelectronic Engineering Academy Jiangsu Nanjing China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

the Viterbi algorithm is a dynamic programming algorithm used to find the Viterbi path-hidden state sequence that is most likely to generate the observed event sequence. the algorithm principle only needs iteration and backtracking, and it has a wide range of applications. Using the Viterbi algorithm-based micro-Doppler parameter estimation to separate multi-target multi-echo composite signals has a better effect. It only needs to be executed at the points of the image. the result of the processing is the signal curve. Moreover, according to the different characteristics of the signal image, it can ensure that the signal does not lose information to a certain extent. One or more targets often have multiple echo signals superimposed and coupled. However, because the micro-Doppler echo signals affected by the sea state are all coupled with the sine wave signal of the sea wave, different amplitudes determine paths of different lengths. Find the Viterbi path to extract the micro-Doppler echo signal. the target echo signal parameters of the micro-Doppler feature are of high precision, and the marine target has a micro-Doppler echo signal that is naturally affected by the ocean waves. the ship's stability determines this, and it is also doomed. According to the difference in the characteristic parameters of the echo signal, the extraction of the characteristic parameters can further identify the ship's target. © 2022 IEEE.

关键词： Viterbi algorithm

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COViT: Convolutions and ViT based Deep Learning Model for Covid19 and Viral Pneumonia Classification using X-ray Datasets 7

COViT: Convolutions and ViT based Deep Learning Model for Co...

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7th international conference on Computational Biology and Bioinformatics, ICCBB 2023

作者： Fazal, Athar Shahzad Khan, Somaiya Khan, Ali School of Science and Technology Nottingham Trent University NottinghamNG14FQ United Kingdom School of Electronics Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

ISBN: (纸本)9798400716331

artificial Intelligence based Covid19 through X-ray scans has revolutionized early diagnosis and treatment since the outbreak. there have been remarkable achievements in the research of Covid19 from Normal or other Pneumonia X-ray image classification using a convolutional neural network (CNN). CNN alone face problems in describing low-level features and can miss important information. Moreover, accurate diagnosis is important in the medical field with minimum false alarms. To answer the issue, the researchers of this paper have turned to self-attention mechanism inspired by the ViT, which has displayed state-of-the-art performance in the classification task. the proposed COViT method uses convolutions of 3 × 3 instead of patch embedding as in ViT, then alternate self-attention and MLP with hardswish function are added, and finally, MLP head is proposed which has average pooling, fully connected (FC) layer with ReLU function and kernel L2 as a classifier which improves the accuracy. Exhaustive experiments are carried out on three datasets. We have only considered Covid19 and Viral Pneumonia classes for our problem. the proposed model has achieved 98.98% classification accuracy on dataset1, 99.50% on dataset2 and 99.18% on dataset3, which validates the efficiency of COViT the proposed COViT which uses self-attention and CNN shows superiority over other SOTA models and has better accuracy than the methods in the literature. © 2023 ACM.

关键词： Classification (of information)

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Success Rate Analysis of Students in MOOC Credit Courses through Supervised Learning Algorithms under Professional Discipline

Success Rate Analysis of Students in MOOC Credit Courses thr...

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international conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Pampa Debnath Arpan Deyasi Anirban Mukherjee Department of Electronics and Communication Engineering RCC Institute of Information Technology Kolkata India Department of Information Technology RCC Institute of Information Technology Kolkata India

the present work explicitly deals with the success rate prediction of students while opting MOOC Credit courses for various professional domains following AICTE guideline. As the four-year professional courses are subcategorized in basic science, humanities, core engineering, professional elective, and free elective; therefore, mapping of courses for success rate evaluation are performed for all the sectors for a consecutive eight batches where choice of candidates are monitored using different supervised learning algorithms. Result reflects a distinct pattern of success in nontechnical areas as well as in free and professional electives, whereas core engineering suffers a lot, owing to the major consideration of programming related courses, as evident from the results obtained through various supervised learning techniques. Comparative study is carried out based on F1 score, TP, TN, FP, FN; and regression analysis is found the best adopted technique for the same. Only SWAYAM NPTEL courses are considered for the evaluation following the guideline published in India Gazette. artificial intelligence powered customized learning enhances the course enrollment rate as it augments re-designing and evaluation methodologies based on previous performances. Statistical findings provide improved direction about the mode of future learning, which is partially reflected in NEP 2020.

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Efficient Kinematic Calibration for Industrial Manipulators based on a Simplified POE model

Efficient Kinematic Calibration for Industrial Manipulators ...

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Robotics, Control and Automation engineering (RCAE), international conference on

作者： Jin Wang Ziyang Fang Chenchen Yu Haiyun Zhang Zhi Zheng Guodong Lu School of Mechanical Engineering Zhejiang University Hangzhou China Polytechnic Institute Zhejiang University Hangzhou China School of Robotics Ningbo University of Technology Ningbo China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

the Product-of-Exponential (POE) model simplifies kinematic modeling by establishing direct relationships between kinematic and end-effector errors using base coordinate and tool coordinates. However, It contains numerous parameters and high latitude, which may lead to reduced efficiency and precision in the case of limited test data. this paper introduces the Simplified Effective Kinematic Calibration (SEC) technique, combining the Simplified Product-of-Exponential (SPOE) model, Pre-processing Sequential Quadratic programming (PSQP), and Gaussian Process Regression (GPR) to enhance calibration accuracy and efficiency. the SEC technique reduces parameter identification dimension, identifies coordinate transformation, compensates for geometric errors, and uses GPR for non-geometric error compensation. Experiments show that SEC demonstrates better positioning accuracy and efficiency than traditional methods.

关键词： Support vector machines Accuracy Service robots Robot kinematics Computational modeling Error compensation Kinematics Calibration Quadratic programming Load modeling

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PAAN: Pyramid Attention Augmented Network for polyp segmentation 7

PAAN: Pyramid Attention Augmented Network for polyp segmenta...

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7th international conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Yi, Sida Xie, Jianhao Kan, Hao-Cheng Zhu, Yuesheng Luo, Guibo Peking University School of Electronic and Computer Engineering Shenzhen Graduate School China

Polyp segmentation is a task of segmenting polyp lesion regions from normal tissues in medical images, which is crucial for medical diagnosis and treatment planning. However, existing methods still suffer from low accuracy in polyp boundary delineation and insufficient suppression of irrelevant background due to the blurred boundaries and textures of polyps. To overcome these limitations, in this paper a Pyramid Attention Augmented Network (PAAN) is proposed, in which a pyramid feature diversion structure with spatial attention mechanism is developed so that good feature representation with low information loss can be achieved by conducting channel attention-based feature diversion and inter-layer fusion, while reducing computational complexity. Also, our framework includes an Enhanced Spatial Attention module (ESA), which can improve the quality of initial polyp segmentation predictions through spatial self-attention mechanism and multi-scale feature fusion. Our approach is evaluated on five challenging polyp datasets— Kvasir, CVC-ClinicDB, CVC-300, ETIS, and CVC-colonDB and achieves excellent results. In particular, we achieve 94.2% Dice and 89.7% IoU on Kvasir, outperforming other state-of-the-art methods. © 2024 CC-BY 4.0, S. Yi, J. Xie, H.-C. Kan, Y. Zhu & G. Luo.

关键词： Image segmentation

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Software Resurrection: Discovering programming Pearls by Showing Modernity to Historical Software

Software Resurrection: Discovering Programming Pearls by Sho...

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Software engineering: Software engineering Education and Training (ICSE-SEET), IEEE/ACM international conference on

作者： Abhishek Dutta Department of Engineering Science Visual Geometry Group University of Oxford

Reading computer program code and documentation written by others is, we are told, one of the best ways to learn the art of writing readable, intelligible and maintainable code and documentation. the software resurrection exercise, introduced in this paper, requires a motivated learner to compile and test a historical release (e.g. 20 years old) version of a well maintained and widely adopted open source software on a modern hardware and software platform. the learner develops fixes for the issues encountered during compilation and testing of the software on a platform that could not have been foreseen at the time of its release. the exercise concludes by writing a critique which provides an opportunity to critically reflect on the experience of maintaining the historical software. An illustrative example of the software resurrection exercise pursued on a version of the SQLite database engine released 20 years ago shows that software engineering principles (or, programming pearls) emerge during the reflective learning cycle of the software resurrection exercise. the concept of software resurrection has the potential to lay foundations for a lifelong willingness to explore and learn from existing code and documentation.

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