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A Technology-Driven Approach to Fostering Social and Emotional Skills in First-Year Students at the University of Danang - University of Technology and Education

A Technology-Driven Approach to Fostering Social and Emotion...

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Green Buildings (ATiGB), Applying New Technology in

作者： Mi Do Hoang Ngan the Tan Hoa Nguyen thi-thanh-Van Phan Ly Hoang Rong Do Van Phuc Tran thanh Son Dang University of Danang - University of Technology and Education Vietnam

ISBN: (数字)9798331505042

ISBN: (纸本)9798331505059

Social-Emotional Learning (SEL) is increasingly recognized as essential for education across all levels, including higher education. Social and Emotional Competence (SEC) forms the foundation for mental health and academic success, particularly crucial for first-year students due to their need for social connections and the complex emotions characteristic of this age group. this paper presents a study on the SEC status of first-year students at UD-UTE using the Social-Emotional Competence Questionnaire (SECQ). the findings show that while the overall SEC of these students is above average, there are disparities among its five components, providing a basis for developing technology projects to enhance SEC.

关键词： Surveys Green buildings Scalability Education Mental health Gain measurement Robustness programming profession

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An assessment of a ROS class using an educational mobile robot

An assessment of a ROS class using an educational mobile rob...

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international Engineering, Sciences and Technology conference (IESTEC)

作者： José Varela-Aldás Christian Junta Jorge Buele Guillermo Palacios-Navarro Centro de Investigaciones de Ciencias Humanas y de la Educación - CICHE Universidad Indoamérica Ambato Ecuador SISAu Research Group Facultad de Ingeniería Industria y Producción Universidad Indoamérica Ambato Ecuador Department of Electronic Engineering and Communications University of Zaragoza Teruel España

ISBN: (数字)9798350377156

ISBN: (纸本)9798350377163

the Robot Operating System (ROS) is a middleware that standardizes robot programming, both in simulation and with real equipment. Despite this open-source tool being available for several years, there’s still a need to enhance its utilization in robotics education across all educational levels. In this study, a ROS class is assessed among university students using a commercial educational robot. the primary objective is to measure academic emotions in learning and student performance to determine the impact of the class using the open-access tool from a Github repository (https://***/joseVarelaAldas/ROS-Crowbot). this tool is based on the rosserial package, compatible with the ESP32 board. For class, CrowBot robots connected to the local wireless network via WiFi are used. Tthe participants in this study were eight students from an electronics degree program at a higher education institution, who had no prior experience with ROS and received practical training using the educational mobile robot. For data collection on class performance, three parameters are assessed: execution time, functionality, and motivation, and to measure academic emotions, a validated self-report instrument is used. the results show an overall performance of 82.1%, and in the self-report on academic emotions, a high score in enjoyment (95%) and the lowest score in boredom (24.1 %) were obtained. In conclusion, the repository provides an interesting, practical, and accessible tool for an introduction into the world of robotics using ROS.

关键词： Training Educational robots Wireless networks Operating systems Particle measurements Time measurement Mobile robots Wireless fidelity Software development management Robot programming

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Pitfalls and safeguards in real-time digital systems with emphasis on programming 87

Pitfalls and safeguards in real-time digital systems with em...

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Proceedings of the 9th international conference on Software Engineering

作者： W. A. Hosier Systems Engrg. and Management Operation Sylvania Electronic Systems Div. Needham Mass.

ISBN: (纸本)9780897912167

Real-time digital systems are largely a technical innovation of the past decade, but they appear destined to become more wide spread in the future. they monitor or control a real physical environment, such as an air-traffic situation, as distinguished from simulating that environment on an arbitrary time scale. the complexity and rapid variation of such an environment necessitates use of a fast and versatile central-control device, a role well suited to digital computers. the usual system will include some combination of sensors, communication, control, display, and effectors. Although many parts of such a system pose no novel management problems, their distinguishing feature, the central digital device, frequently presents unusually strict requirements for speed, capacity, reliability and compatibility, together with the need for a carefully designed stored program. these features, particularly the last, have implications that are not always foreseen by management. An attempt is made to point out specific hazards common to most real-time digital systems and to show a few ways of minimizing the risks associated with them.

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Multi-attention DeepEMD for Few-Shot Learning in Remote Sensing

Multi-attention DeepEMD for Few-Shot Learning in Remote Sens...

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IEEE Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Zhengwu Yuan Wendong Huang College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Currently in the field of remote sensing (RS), scene classification is a crucial research subject. the usual method is to gather plenty of scene classification data manually annotated by experts in the field, and then use the dataset to train a scene classification model. Typically, the better the deep learning model requires more labeled data. therefore, how to obtain a model with good classification performance when there is only a small amount of labeled data, this becomes a well-known few-shot classification problem. In this study, a new few-shot learning scheme is developed for RS scene classification. Our method is based on deepEMD with spatial and channel attention modules(CBAM) to capture subtle but distinguishing features. To get the significant weights of pixels in the EMD formulation, an attention-reference mechanism is designed, which can sufficiently leverage subtle local features and minimize the influence posed by the cluttered background. To avoid overfitting, label smoothing that can improve the generalization performance of the model is introduced. Comprehensive experiments are conducted to validate the method and achieve new state-of-the-art performance on three popular RS scene datasets, namely UC Merced, AID and OPTIMAL-31. Furthermore, two ablative studies also indicate that the components are conducive to fast convergence and high accuracy.

关键词： Image analysis Smoothing methods Learning (artificial intelligence) Linear programming Data models Task analysis Remote sensing

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Double Deep RL-Based Strategy for UAV-Assisted Energy Harvesting Optimization in Disaster-Resilient IoT Networks

Double Deep RL-Based Strategy for UAV-Assisted Energy Harves...

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international conference on Mechatronics (ICOM)

作者： Nahla Nur Elmadina Rashid A. Saeed Elsadig Saeid Elmustafa Sayed Ali Ibtehal Nafea Mayada A. Ahmed Rania A. Mokhtar Othman O. Khalifa Department of Electrical Engineering Alzaiem Alazhari University Khartoum Sudan Electronics Department Sudan University of Science and Technology Khartoum Sudan College of Computer Science and Engineering Taibah University Medina Saudi Arabia Faculty of Eng. School of ElecsEng. Sudan Uni. of Sci. and Tech. Khartoum Sudan Dept of Elec and Comp Eng Inter. Islamic Uni. Malaysia KL Malaysia Libyan Center for Engineering Research and Information Technology Bani Walid Libya

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Unmanned Aerial Vehicles (UAVs) are increasingly crucial for emergency-response scenarios, including tasks like wireless power transfer (WPT) and data collection in disaster zones. this paper proposes a Double Deep Reinforcement Learning (DDRL) framework for energy harvesting (EH) in such scenarios. Our framework involves a UAV swarm navigating an area to provide WPT. the primary goal is to enhance service quality in critical areas while enabling dynamic swarm management for tasks like recharging. We formulate this as a nonlinear programming (NLP) optimization problem, maximizing EH from IoT devices and optimizing UAV trajectories under constraints like mission duration and energy limits. Due to the problem's complexity, we propose a lightweight DDRL solution capable of efficiently learning system dynamics. Extensive simulations and comparisons with Deep RL and DDPG algorithms demonstrate the superior performance of DDRL in enhancing EH, covering strategic locations effectively, and achieving high satisfaction and accuracy rates.

关键词： Wireless power transfer programming Autonomous aerial vehicles Nonlinear dynamical systems Trajectory Internet of things Energy harvesting

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T-SNE with High Order Truncation Fractional Gradient Descent Method

T-SNE with High Order Truncation Fractional Gradient Descent...

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IEEE Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Xiangyu Ma Dan Li Jiayi Zhang Ting Sheng Zan Yang Wei Nai Yuan Sun Tongji Zhejiang College Jiaxing China Faculty of Science Tongji Zhejiang College Jiaxing China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

T-distributed stochastic neighbor embedding (T-SNE) algorithm with high order truncation is a kind of dimensionality reduction method, which belongs to nonlinear dimensionality reduction and is a popular learning method. In the T-SNE algorithm with high-order truncation, the T-distribution is used to replace the Gaussian distribution when calculating the probability between sample points in the low-dimensional space, and the objective function is solved by Kullback-Leibler divergence and high-order truncation. T-SNE is developed from SNE, and its long tail effect effectively solves the crowding problem of SNE. However, there is no unique optimal solution for T-SNE, and there is no estimated part. the number of iterations and the convergence of the objective function are the reasons that affect the visualization effect of T-SNE. In terms of the convergence of the objective function, it is found that the extreme value of fractional order is not equal to the true extreme value of the objective function, which will make the fractional step method impractical. In order to enhance the global convergence, the new fractional gradient descent method of high-order truncation is used to iterate to obtain the optimal solution of the objective function and optimize the visualization effect of T-SNE.

关键词： Dimensionality reduction Visualization Search methods Probability Linear programming Optimization Convergence

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Methodologies of Loop Offset voltage Trimming for Inverting-Buck-Boost for AMOLED Display Application

Methodologies of Loop Offset voltage Trimming for Inverting-...

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international conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Venkatesh Kadlimatti Sumit Bhat Sankalp Semiconductor Pvt Ltd Hubli

ISBN: (数字)9781728166872

ISBN: (纸本)9781728166889

Methodologies of loop offset voltage trimming are presented in this paper for an inverting-Buck-Boost (IBB) having a wide output voltage (VNEG) range (-0.9V to -5.3V) used in an AMOLED display driver. Furthermore, the circuit is proposed and analyzed to meet the brightness control requirement of the AMOLED display. the error contribution from each sub-blocks of the brightness control circuit to VNEG is noted, and efficient ways of trimming the output voltage are analyzed. Achieving 0.3% of trimmed accuracy becomes challenging with the optimum number of trim bits as the magnitude of VNEG voltage reduces. three different ways of trimming the VNEG, namely; three-step trimming, two-point trimming, and three-point trimming, are proposed and compared in terms of complexity and accuracy. the proposed methodologies of trimming are implemented for Inverting-Buck-Boost in BCD-180nm technology. three-step trimming uses 17 bits (5-6-6 bits) resistor trimming and has the worst accuracy of ±0.17% across VNEG. Two-point trimming uses 11 bits (6-5 bits) and has the worst accuracy of ±0.3%, whereas the three-point trimming uses only 14bits (6-4-4 bits) and has the worst accuracy of ±0.21%.

关键词： Resistors Active matrix organic light emitting diodes Rails Voltage control Brightness DC-DC power converters programming

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A Learning Mechanism for BRBES Using Enhanced Belief Rule-Based Adaptive Differential Evolution

A Learning Mechanism for BRBES Using Enhanced Belief Rule-Ba...

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international conference on Informatics, Electronics & Vision (ICIEV)

作者： Raihan Ul Islam Mohammad Shahadat Hossain Karl Andersson Pervasive and Mobile Computing Laboratory Luleå University of Technology Sweden University of Chittagong Bangladesh

ISBN: (数字)9781728193311

ISBN: (纸本)9781728193328

Nowadays, belief rule-based expert systems (BRBESs) are widely used in various domains which provides a framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. Learning plays an important role in BRBES to upgrade its knowledge base and parameters values, necessary for the improvement of the prediction accuracy. Different optimal training procedures such as Particle Swarm Optimisation (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) have been used as learning mechanisms. Among these procedures, DE performs comparatively better than others. However, DE's performance depends significantly in assigning near optimal values to its control parameters including cross over and mutation factors. therefore, the objective of this article is to present a novel optimal training procedure by integrating DE with BRBES. this is named as enhanced belief rule-based adaptive differential evolution (eBRBaDE) algorithm because it has the ability to determine the near-optimal values of both the control parameters while ensuring the balanced exploitation and exploration in the search space. In addition, a new joint optimization learning mechanism by using eBRBaDE is presented where both parameter and structure of BRBES are considered. the reliability of the eBRBaDE has been compared with evolutionary optimization algorithms such as GA, PSO, BAT, DE and L-SHADE. this comparison has been carried out by taking account of both conjunctive and disjunctive BRBESs while predicting the Power Usage Effectiveness (PUE) of a datacentre. the comparison demonstrates that the eBRBaDE provides higher prediction accuracy of PUE than from other evolutionary optimization algorithms. Contribution-An enhanced differential evolution algorithm has been proposed in this paper, which is later used as a novel optimal training procedure for BRBES.

关键词： Optimization Statistics Sociology Training Uncertainty Learning systems Linear programming

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Turkish Sign Language Video Generation from Pose Sequences Using a Conditional GAN Model

Turkish Sign Language Video Generation from Pose Sequences U...

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international conference on Computer Science and Engineering (UBMK)

作者： Feyza Ozkan Hatice Kubra Tekin Hacer Yalim Keles Computer Engineering Department Hacettepe University Ankara Turkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

Sign language is a visual gestural language that uses hand shapes, facial expressions, and body movements to convey meaning, and it is essential for communication among individuals with hearing impairments. Sign language users often face challenges in environments where sign language is not readily available. that is primarily because producing sign language requires a real signer, which can be time-consuming and labor-intensive. One potential solution to make more available sign language to its users is the use of artificial intelligence to generate sign language. While there have been global studies exploring AI-generated sign language videos for various sign languages, the research in this field is still evolving and active. Specifically, for Turkish Sign Language (TSL), only a few studies have been conducted. Our study introduces a model that combines pose sequences with reference images of signers to generate authentic sign language videos. Utilizing a conditional generative adver-sarial network (cGAN), the model generates video sequences by particularly focusing on complex finger details, arm positions while preserving the appearance of the reference image. the AUTSL dataset is employed for training and testing our model. Our early results demonstrate our model's capability to produce realistic hand and arm movements while effectively capturing finger details; we got 45.5 with th e Frechet inception distance (FID), 35.8 with the peak-signal-to-noise ratio (PSNR), and 0.9 with the structural similarity index measure (SSIM).

关键词： Training Sign language Visualization Shape Video sequences Refining Focusing Training data Linear programming Videos

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Harmonizing decisions in wind farm management to provide a stable energy supply

Harmonizing decisions in wind farm management to provide a s...

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international conference on Mathematics and Computers in Sciences and in Industry (MCSI)

作者： Zixuan Zhang Marek Makowski Zbigniew Nahorski Janusz Granat East China University of Science and Technology Shanghai China Systems Research Institute of the Polish Academy of Sciences Warsaw Poland International Institute for Applied Systems Analysis Laxenburg Austria Warsaw University of Technology Warsaw Poland

ISBN: (数字)9798350377224

ISBN: (纸本)9798350377231

Variable renewable energy generation requires management to provide a stable energy supply. the management consists of making decisions at different time scales; harmonization of such decisions is the main challenge addressed in the paper. the paper presents a mathematical programming model supporting the harmonization of the wind farm decision-making at three different time scales: (1) one-time investment in the energy storage system, (2) annual commitment of constant electricity supply for an industrial partner, and (3) operational (hourly) decisions on energy flows within the plant. the model prototype was tested on the rational configuration of a hydrogen-based energy storage system composed of electrolyzers, hydrogen tanks, and fuel *** model optimization provides the set of decisions maximizing the annual revenue: (1) the number of devices (of different types of each considered kind of device), i.e., the optimal configuration of the storage system; (2) the annually committed constant hourly energy supply, and (3) the operational decisions on energy flows within the wind farm, as well as an optional use of the energy adjusting market for handling temporal energy surplus or deficit.

关键词： Renewable energy sources Hydrogen storage Electricity Hydrogen Prototypes Wind farms Wind power generation Mathematical models Batteries Investment

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