检索结果-内蒙古大学图书馆

13th IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2024

作者： Klopp, Marco University of Applied Sciences Aschaffenburg Faculty of Engineering and Computer Science Aschaffenburg Germany

ISBN: (纸本)9798350376234

This paper presents a study which examines the pedagogical-interactional dimensions in computer science education at universities of applied sciences, focusing on the expectations and perceptions of students and professors. Data were collected using a modified version of the Learning Culture Inventory (LCI) questionnaire and analyzed both descriptively and *** descriptive statistics reveal differences in the mean values between the ideal and real states of educational conditions as perceived by students and professors. Inferential statistical analyses using t-tests identified significant differences between the perceptions of the two groups. The effect size Cohen's d was also calculated to determine the strength of the existing difference. The analyses show that there are significant differences in many dimensions of the LCI between the perspectives of students and professors. These differences are often associated with high effect sizes. The results therefore indicate that the students' expectations and perceptions deviate (considerably) from the professors' perspectives. © 2024 IEEE.

关键词： computer science university teaching Learning culture Professors' perspectives Students' perspectives

来源：评论

学校读者我要写书评

暂无评论

Centralized smart energy monitoring system for legacy home appliances

引用

Energy Informatics 2024年第1期7卷 29页

作者： Ahmad, Shahed S. Almasalha, Fadi Qutqut, Mahmoud H. Hijjawi, Mohammad Faculty of Information Technology Applied Science Private University Amman11937 Jordan Faculty of Computer Science University of New Brunswick FrederictonNBE3B 5A3 Canada

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants. © The Author(s) 2024.

关键词： Electric power utilization

来源：评论

学校读者我要写书评

暂无评论

Fog Computing - Desktop Review 4

Fog Computing - Desktop Review

引用

4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024

作者： Sithole, Sicelo Prince Sonhera, Naume Zuva, Tranos Vaal University of Technology Faculty of Applied and Computer Sciences Department of Computer Science Vanderbijlpark South Africa

ISBN: (纸本)9798350387988

Fog computing is an innovative approach that brings cloud computing capabilities to the edge of the network, and it has great potential for enterprises looking to improve real-time data processing and lower latency. However, the adoption of fog computing in organizations remains relatively unexplored. This review paper aims to investigate the factors influencing fog computing adoption in organizations. Through a systematic review of existing literature, case studies, and expert opinions, this paper identifies and analyses the unique adoption factors. The review examines challenges, characteristics, benefits, and Open Fog Computing Issues with the Internet of Things (IoT), shaping fog computing adoption decisions in organizations across various industries. This analysis highlights fog computing advantages over traditional cloud computing approaches, such as low latency, improved data privacy, and enhanced scalability. Furthermore, this review investigates the challenges hindering the adoption of fog computing, including security concerns, implementation complexity, connectivity, and lack of awareness, as well as open fog computing issues with IoT. Key findings indicate that while fog computing can offer low-latency solutions, organizations face considerable challenges, including security risks, implementation complexity, and a lack of skilled personnel. By synthesizing and analysing the findings, this paper provides valuable insights for decision-makers, researchers, and policymakers seeking to promote and facilitate fog computing adoption. © 2024 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

TRLLD:Load Level Detection Algorithm Based on Threshold Recognition for Load Time Series

引用

computers, Materials & Continua 2025年第5期83卷 2619-2642页

作者： Qingqing Song Shaoliang Xia Zhen Wu Faculty of Applied Mathematics and Computer Science Belarusian State UniversityMinsk220030Belarus Higher School of Management and Business Belarus State Economic UniversityMinsk220070Belarus

Load time series analysis is critical for resource management and optimization decisions,especially automated analysis *** research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a *** paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample *** utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load *** the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong *** to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis *** results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable *** correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high ***,it exhibits significant advantages in general and low sample scenarios.

关键词： Load time series load level detection threshold recognition density uniformity index outlier detection management systems engineering

来源：评论

学校读者我要写书评

暂无评论

Efficient Video Object Segmentation Using JIT K-Means Clustering 16

Efficient Video Object Segmentation Using JIT K-Means Cluste...

引用

16th International Conference on Knowledge and Smart Technology, KST 2024

作者： Khamprapai, Wanida Pensiri, Fuangfar Visutsak, Porawat Kasetsart University Faculty of Science at Sriracha Department of Computer Science and Information Sriracha Campus Chonburi Thailand KMUTNB Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798350370737

Video segmentation dissects video sequences into distinct regions based on visual cues like object borders, movement patterns, color variations, and textures. This partitioning aims to isolate individual objects and temporal events from the background, ultimately revealing a more structured and interpretable representation of the video content. In this work, we propose K-means clustering and MATLAB Just-In-Time (JIT) accelerator within a video segmentation framework to boost both accuracy and efficiency, especially in resource-limited environments. This work aims to evaluate K-means for video object segmentation, assess JIT's impact on its speed and resource usage, and optimize their integration considering feature representation and clustering parameters. Our experimental results show that the MATLAB JIT is able to achieve a significant speedup compared to the video segmentation in a normal mode with the average acceleration = 89.88%. © 2024 IEEE.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

Robust Line-Convex Polygon Intersection Computation in E2 using Projective Space Representation

引用

Machine Graphics and Vision 2023年第3-4期32卷 3-16页

作者： Skala, Vaclav Dept. of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic

This paper describes modified robust algorithms for a line clipping by a convex polygon in E2 and a convex polyhedron in E3. The proposed algorithm is based on the Cyrus-Beck algorithm and uses homogeneous coordinates to increase the robustness of computation. The algorithm enables computation fully in the projective space using the homogeneous coordinates and the line can be given in the projective space, in general. If the result can remain in projective space, no division operation is needed. It supports the use of vector-vector operations, SSE/AVX instructions, and GPU. © 2023 Institute of Information Technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： computer graphics

来源：评论

学校读者我要写书评

暂无评论

Reinforcement learning and digital twin-driven optimization of production scheduling with the digital model playground

引用

Discover Internet of Things 2024年第1期4卷 1-19页

作者： Seipolt, Arne Buschermöhle, Ralf Haag, Vladislav Hasselbring, Wilhelm Höfinghoff, Maximilian Schumacher, Marcel Wilbers, Henrik Faculty of Management Culture and Technology Osnabrück University of Applied Sciences Lingen Germany Department of Computer Science Kiel University Kiel Germany

The significance of digital technologies in the context of digitizing production processes, such as Artificial Intelligence (AI) and Digital Twins, is on the rise. A promising avenue of research is the optimization of digital twins through Reinforcement Learning (RL). This necessitates a simulation environment that can be integrated with RL. One is introduced in this paper as the Digital Model Playground (DMPG). The paper outlines the implementation of the DMPG, followed by demonstrating its application in optimizing production scheduling through RL within a sample process. Although there is potential for further development, the DMPG already enables the modeling and optimization of production processes using RL and is comparable to commercial discrete event simulation software regarding the simulation-speed. Furthermore, it is highly flexible and adaptable, as shown by two projects, which distribute the DMPG to a high-performance cluster or generate 2D/3D-Visualization of the simulation model with Unreal. This establishes the DMPG as a valuable tool for advancing the digital transformation of manufacturing systems, affirming its potential impact on the future of production optimization. Currently, planned extensions include the integration of more optimization algorithms and Process Mining techniques, to further enhance the usability of the framework. © The Author(s) 2024.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

applied Deep Reinforcement Learning for Solving the Vehicle Routing Problem with Time Windows 8

Applied Deep Reinforcement Learning for Solving the Vehicle ...

引用

8th International Conference on Computational Intelligence and Applications, ICCIA 2023

作者： Kosolsombat, Somkiat Ratanavilisagul, Chiabwoot Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798350339741

The Vehicle Routing Problem with Time Windows (VRPTW) is a challenging combinatorial optimization problem with many real-world applications. In this paper, we propose a Deep Reinforcement Learning (DRL) approach for solving the VRPTW using the Deep Q-Network (DQN) algorithm. Specifically, we use a variant of the DQN algorithm that incorporates the target network and experience replay techniques to improve the learning process. We also introduce a hybrid approach that combines a Greedy algorithm with the DQN algorithm (GDQN) to further enhance the exploration-exploitation trade-off in the search process. We evaluate the performance of the proposed approach on the Solomon benchmarks, which are widely used in the literature to benchmark the performance of various algorithms for the VRPTW. Our experimental results show that the proposed GDQN outperforms the DQN approach in terms of the quality of solutions generated, for type 25, achieving an average total used vehicle of 41.37% compared to 58.63% and average total distances of 52.34% compared to 47.66% for the DQN approach. For type 50, achieving an average total used vehicle of 42.22% compared to 57.78% and average total distances of 47.56% compared to 52.44% for the DQN approach. The proposed GDQN method provides an effective solution for the VRPTW problem in total used vehicle and total distances and use the same average total waiting time. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Modified Feature Extraction to apply PSO for solving Handwritten Digits 20

Modified Feature Extraction to apply PSO for solving Handwri...

引用

20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Samleepant, Jiraphat Ratanavilisagul, Chiabwoot Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798350300505

Handwritten Digit Recognition (HDR) is a widely used technology that has been demonstrated in various research topics. However, there are many limitations because everyone's handwriting is different, including important handwriting characteristics. The experimental results of the previously proposed methods were unsatisfactory when using limited amounts of the datasets for the training model and inefficient pre-processing. Therefore, we describe a new method for increasing the recognition rate through well-planned pre-processing and enhanced feature extraction for applying Particle Swarm Optimization (PSO). When compared to other techniques, the proposed method performed well when tested against an individual's handwritten digits and the original MNIST database. Our results are a high recognition rate and short runtime. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Modified Differential Evolution Algorithm for Solving Multi-Skill Resource-Constrained Project Scheduling Problem 15

Modified Differential Evolution Algorithm for Solving Multi-...

引用

15th International Conference on Information Technology and Electrical Engineering, ICITEE 2023

作者： Homwiseswongsa, Akarasate Ratanavilisagul, Chiabwoot Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798350304466

The multi-skills resource-constrained project scheduling problem (MS-RCPSP) is assigning tasks to employees while considering predecessor tasks and multi-skills constraints to create a feasible schedule with both the shortest completion duration. Recently, Differential Evolution with multidimensional real-valued functions (DEM) was introduced to address the MS-RCPSP. The experimental results demonstrated that DEM outperformed GreedyDO, HAntCO, and GA. However, DEM lacked a technique to handle local optimum problem, resulting in unsatisfactory search results. Hence, this research proposes a modification to DEM by enhancing the mutation technique, executing it twice per generation, to increase population diversity and obtain better outcomes. The proposed algorithm was tested on twenty-three test cases from the MS-RCPSP iMOPSE datasets and yielded more satisfactory results compared to the competing algorithms. © 2023 IEEE.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：