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检索条件"任意字段=19th International Conference on Software Engineering and Knowledge Engineering"

共 37411 条记录，以下是741-750 订阅

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Interdisciplinary Requirements engineering for Deploying Social Robots in Public Library's Results of an Interdisciplinary Expert Workshop 24

Interdisciplinary Requirements Engineering for Deploying Soc...

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19th Annual ACM/IEEE international conference on Human-Robot Interaction (HRI)

作者： Lisetschko, Artur Jansen, Nadine Doganguen, Ayseguel Mader, Aiden Danny Ruhr West Univ Appl Sci Inst Posit Comp Bottrop Germany

ISBN: (纸本)9798400703232

Social robots can enhance customer service of municipalities, but lacks a comprehensive ecosystem. Addressing this, the following paper explores an interdisciplinary approach to requirements engineering for deploying social robots in the real-world by combining user research with expert assessment of economic factors. We interviewed library regulars and developed personas focusing on user experience (UX). Afterwards, a collaborative workshop with experts from the fields of human-computer interaction (HCI) and business administration (BA) was conducted to determine factors of economical validity based on the use case of social robots for customer service in public libraries. First, experts evaluated user personas from a customer experience (CX) perspective. then, experts were asked to put themselves in the role of one of two administrators to assess what requirements respective stakeholders would impose in order to agree on the use of a social robot. Results yield valuable insights to support and guide the design and deployment of social robots in public libraries with implications extending in relevance to other municipal institutions.

关键词： social robots library persona workshop economics ux user experience cx customer experience ecosystem

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CAVDL:A Comparative Analysis Of Diverse DL Models For Enhancing COVID-19 Images 6

CAVDL:A Comparative Analysis Of Diverse DL Models For Enhanc...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Jain, Arti Kaliyar, Rohit Kumar Sharma, Shallu School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India

ISBN: (纸本)9798350369748

the global health crisis has brought to light the critical role that medical imaging, particularly X-rays, play in the diagnosis and surveillance of COVID-19 infections. the capacity to increase X-ray picture quality and diagnostic accuracy has greatly improved with the development of sophisticated image enhancing methods. the objective of this research is to assess and compare the efficacy of several image enhancement models in improving the visual quality, feature preservation, and diagnostic usefulness of COVID-19 X-ray pictures. To give a comprehensive understanding of each model’s limitations and robustness, we use a strict evaluation process that includes both quantitative indicators and qualitative assessments. We employed a variety of evaluation criteria, such as PSNR (peak signal-to-noise ratio) and structural similarity index metrics (SSIM), to do the comparison. In this study, using a carefully chosen COVID-19 X-ray dataset, we thoroughly compared different state-of-the-art models with existing classification models. © 2024 IEEE.

关键词： COVID-19

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Control Strategy of Lithium Battery Charging System Based on Improved Particle Swarm Optimized PID Algorithm 19

Control Strategy of Lithium Battery Charging System Based on...

引用

19th international conference on Natural Computation, Fuzzy Systems and knowledge Discovery, ICNC-FSKD 2023

作者： Wang, XingWei Jiao, JiYe Xi'an University of Posts and Telecommunications School of Electronic Engineering Xi'an China

ISBN: (纸本)9798350304398

the study of Li-ion battery charging system control strategy can improve Li-ion battery charging efficiency and avoid safety accidents. To address the shortcomings of the traditional particle swarm optimization (PSO) algorithm, which is slow to converge and easily falls into local optimal solutions, the particle swarm algorithm is improved to iteratively find the optimal proportion integration differentiation (PID) parameters of the controller. Initially, a mathematical model is developed for the charging system, followed by the design of an objective function for the improved particle swarm algorithm that focuses on controlling the charging current and voltage. Secondly, the particle swarm algorithm optimization using linearly updated inertia factor and learning factor is used to rectify the PID parameters and to build a system controller model for validation using simulation experiments. According to the findings, the control strategy analyzed in this study notable control performance and adaptability, which surpasses that of traditional PID and particle swarm optimized (PSO-PID) control strategies, and can accurately output the target charging current and voltage to achieve precise control. therefore, the control strategy of lithium battery charging system based on improved particle swarm algorithm PID control has good application value and promotion value. © 2023 IEEE.

关键词： Charging (batteries)

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An improved machine learning approach for streamline the real time analysis in Big-data applications 15

An improved machine learning approach for streamline the rea...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Anu Singh, Digvijay Nayyar, Priti Ramani Bhakar, Ruchika Kumar, Viranshu Mishra, Saket Invertis University Department of Law Uttar Pradesh Bareilly243123 India Dev Bhoomi Uttarakhand University School of Engineering and Computer Uttarakhand 248007 India G.D Goenka University School of Law Gurugram Haryana Sohna122103 India K. R. Mangalam University School of Engineering & Technology Department of Computer Science & Engineering Haryana Gurugram122103 India Arka Jain University Department of Engineering & It Jharkhand Jamshedpur India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

the contemporary digital landscape has been modified notably because of the 1992 enactment of the Copyright regulation within the United States. PC engineering has been at the leading edge of many of those adjustments, supplying software and hardware answers that enable content material creators to safeguard their works. However, the digital landscape has additionally posed a chance for unmanned laptop engineers who might need to recognize the results of the Copyright regulation and the opportunities such a gadget of laws offers. knowledge of the virtual environment inside which copyrighted paintings are created, exploited, and shared calls for a familiarity with the Copyright law. this article will discuss the relationship between Copyright law and PC engineering by focusing on the three central rights created through the law: the right to breed, distribute, and develop spinoff works. It will offer laptop engineers a fundamental review of these concepts, describing how they're relevant to computer engineering tasks, including software, hardware, and other digital services. © 2024 IEEE.

关键词： Contemporary Developing Hardware Opportunities Supplying

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BiKD-Yolo: A Small Object Detection Algorithm from UAV Perspective Based on BiFormer Attention and knowledge Distillation 20

BiKD-Yolo: A Small Object Detection Algorithm from UAV Persp...

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20th international conference on Natural Computation, Fuzzy Systems and knowledge Discovery, ICNC-FSKD 2024

作者： Hua, Xiang Cai, Zeju Zhu, Xiangyuan Aijia, Ouyang School of Software Engineering Sun Yat-sen University Guangdong China School of Computer Science and Software Zhaoqing University Guangdong China School of Information Engineering Zunyi Normal University Guizhou China

ISBN: (纸本)9798350356328

Object detection is crucial for the real-time operations of Unmanned Aerial Vehicles (UAVs), particularly in identifying small objects within UAV imagery. While existing computer vision techniques have shown success in UAV applications, detecting small objects remains challenging due to limited information, small size, and low resolution. this study presents the BiKD-YOLO approach, which combines BiFormer attention and knowledge distillation to enhance both the precision and efficiency of small object detection. We employ SAHI (Slice-Assisted Hyper-Inference) cropping to enlarge small objects in images, followed by knowledge distillation for model compression, facilitating knowledge transfer from larger to smaller models for streamlined operation. through the incorporation of the BiFormer attention mechanism, the detection process is further refined by focusing on critical regions of interest, thereby improving overall accuracy. Experimental Results from tests on the VisDrone dataset show that BiKD-Yolo greatly surpasses the baseline, delivering superior performance in detection accuracy, mAP, and AP metrics. In summary, the BiKD-Yolo method presents a novel and effective solution for improving small object detection in UAV images, offering enhanced accuracy and speed. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Lightweight, Computation-Efficient CNN Framework for an Optimization-Driven Detection of Maize Crop Disease

A Lightweight, Computation-Efficient CNN Framework for an Op...

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19th international conference on software Technologies, ICSOFT 2024

作者： Manzoor, Shahinza Mughal, Muhammad Rizwan Irtaza, Syed Ali Islam, Saif Ul Boudjadar, Jalil Department of Computer Sciences Institute of Space Technology Pakistan Department of Electrical and Computer Engineering Sultan Qaboos University Oman WMG The University of Warwick United Kingdom Department of Electrical and Computer Engineering Aarhus University Denmark

ISBN: (纸本)9789897587061

Detecting and mitigating crop diseases can prevent significant yield losses and economic damage. However, most state-of-the-art solutions can be expensive computation-wise. this paper presents an efficient Lightweight multi-layer convolutional neural network (ML-CNN) to identify maize crop diseases. the proposed model aims to improve disease identification accuracy and reduce computational costs. the model was optimized by adjusting parameters, setting convolutional layers, changing the combinations of the pooling layer, and adding dropout layers. By optimizing the model architecture, we create a software tool that can be deployed in resource-limited environments, an ideal choice for deployment on embedded platforms. the PlantVillage dataset was used to train and test the model implementation, including images of healthy and two disease-affected leaves. the performance of the proposed model was compared with pre-trained models such as InceptionV3, VGG16, VGG19, and ResNet50. the analysis results show that the proposed model improved identification accuracy by 16.32%, 1.48%, 1.28%, and 2.26%, respectively. Additionally, the proposed model achieved identification accuracy of 99.60% on the training data and 98.16% on the testing data and also reduced iteration convergences and computational costs. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Convolutional neural networks

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Leveraging Transfer Learning for Robust Medical Image Segmentation 4

Leveraging Transfer Learning for Robust Medical Image Segmen...

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4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Tyagi, Priya Sharma, Avadhesh Kumar Masson, Palak Manwal, Manika Goel, Pawan Kumar Varshney, Neeraj Nitra Technical Campus Department of CSE-AIML UP Ghaziabad India Gl Bajaj Institute of Technology and Management Electronics and Communication Engineering Uttar Pradesh Greater Noida201306 India Computer Science & Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University R/S Graphic Era Deemed to Be University Uttarakhand Dehradun India Gla University Department of Computer Engineering and Applications UP Mathura India

ISBN: (纸本)9798350364729

Transfer mastering is a powerful approach that has been applied to various tasks in many fields, including pc imaginative and prescient and herbal language processing. this paper describes the software of Switch gaining knowledge of medical photo segmentation, a complex project of appropriately isolating character objects from a scientific picture. this approach involves taking the parameters learned from a pre-educated deep mastering community and adapting them to a new scientific picture segmentation undertaking. the proposed method is tested on three unique datasets offering an expansion of abdominal, brain, and cardiac images. Outcomes display that the proposed technique produces higher segmentation outcomes than conventional deep getting-to-know fashions educated from scratch and might extensively reduce the time required to train and optimize those fashions. Furthermore, the proposed method can even outperform end-to-stop networks in some instances. therefore, the proposed switch gaining knowledge of the method has the ability for used for strong medical picture segmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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A NOVEL CONCEPT TO MANUFACTURE NANO/MICROCELLULAR FOAM STRUCTURES 19

A NOVEL CONCEPT TO MANUFACTURE NANO/MICROCELLULAR FOAM STRUC...

引用

ASME 2024 19th international Manufacturing Science and engineering conference, MSEC 2024

作者： Farahani, Saeed Hahn, Marlon Dardaei Joghan, Hamed Tekkaya, A. Erman Department of Automotive Engineering Composites Center Clemson University GreenvilleSC United States Institute of Forming Technology and Lightweight Components TU Dortmund University Dortmund Germany

ISBN: (纸本)9780791888100

Multi-material and multi-structure design, particularly the combination of metal-polymer and solid-foam, stands as a promising strategy to reduce component weight while increasing and diversifying their functionalities. However, adopting such design approaches faces significant challenges, primarily arising from the complex multi-stage manufacturing processes, necessitating multiple sets of equipment, tooling, and precise control at each production stage. In response to these formidable manufacturing hurdles, this paper introduces an innovative approach that integrates injection molding, forming, and foaming processes into a single hybrid process called Electromagnetic Forming Injection Foaming (EFIF). In this integration concept, the challenges related to the shrinkage and viscosity of polymer melt are addressed by utilizing supercritical fluid (SCF) foaming technology. Moreover, the incorporation of electromagnetic forming offers precise control over the foaming process, potentially enabling the creation of micro- to nanocellular structures in the injected polymeric material. As the proposed integration is a manufacturing innovation, with no available information in the literature directly relating to this concept, this study explores the basic principles, feasibility and effectiveness of the proposed integration in two preliminary studies. In the first study, the effects of pressure drop and drop rate on the cell nucleation and the morphology of the final foam structure are discussed along with various methods of creating pressure drop and drop rate in both conventional and hybrid foam injection molding processes. In the following, the experimental setup and study conducted to determine the impact of pressure drop and drop rate on the cell size and cell density are described, and the results are discussed. the second study focuses on the potential effects of integrating electromagnetic forming with the injection, forming, and foaming processes. A set of experimen

关键词： Injection molding

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Modelling and Optimization of a Mixed-Signal Accelerator for Deep Neural Networks 19

Modelling and Optimization of a Mixed-Signal Accelerator for...

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19th international conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, SMACD 2023

作者： Caselli, Michele Boni, Andrea University of Parma Department of Engineering and Architecture Italy

ISBN: (纸本)9798350332650

this paper proposes an analytical model for the optimized circuit design in an SRAM-based mixed-signal accelerator for Deep Neural Networks. the model, includes fundamental non-idealities to maintain the information content of the MAC operation, and it exploits a statistical approach to generates specification for the memory accelerator. In a case of study, the model optimization carried out with MATLAB allows to avoid three bits of ADC over-design, with large area and energy savings. © 2023 IEEE.

关键词： Deep neural networks

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Fine-Tuning TMDDPG for Dynamic IIoT Bandwidth Allocation 6

Fine-Tuning TMDDPG for Dynamic IIoT Bandwidth Allocation

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6th international conference on Electrical, Control and Instrumentation engineering, ICECIE 2024

作者： Adiga, Sourav S Purushotham, U. PES University Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350380040

the advancement of Industrial IoT (IIoT) calls for devices that can provide fast communication, for real time operations. Ensuring Quality of Service (QoS) for devices in a network requires effective management of bandwidth. Traditional fixed allocation methods struggle to meet changing needs. this study examines how software Defined Networking (SDN) with network slicing can tackle this issue. Network slicing divides the network into segments each tailored to QoS needs. In this context we suggest a system that uses a Deep Deterministic Policy Gradient (DDPG) algorithm based on Fine-tuned transfer learning for allocating bandwidth in network slices, for IIoT devices requiring ultra reliable low latency communication. this strategy uses knowledge from existing ML model to optimize allocation and reduce training time and computational resources. © 2024 IEEE.

关键词： Quality of service

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