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Course Preparation Time optimization system for Improved Didactic Outcome

Course Preparation Time Optimization System for Improved Did...

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International conference on Information Technology Based Higher Education and Training, IthET

作者： thomas Fuhrmann Faculty of Electrical Engineering and Information Technology OTH Regensburg Regensburg Germany

ISBN: (纸本)9781665489096

Due to the various demands for lecturers, there is only a limited time to prepare lectures and lab courses. therefore, it is necessary to invest the time target-oriented for optimal student learning success. A theoretic model is developed to structure course preparation work regarding scientific content, didactic preparation, and course presentation. Model parameters have to be chosen for each course depending on topic complexity, the lecturer’s prior knowledge, and the already available preparation from the prior semesters. With these parameters, a course preparation model for a complete semester is developed. Analytic models for different optimization strategies are introduced according to the overall goal of the lecturer. Numerical optimization is done to find the appropriate course preparation times to reach an optimal course preparation for high student learning success. It is seen that due to the different optimization strategies, the preparation time results vary and no single truth is given. But this optimization system gives hints on how to invest preparation time target-oriented for high student learning success.

关键词： Training Analytical models Numerical models Complexity theory Information technology optimization

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Compact modeling of the pin-fin heat sink with variable fin density cooled by natural convection 20

Compact modeling of the pin-fin heat sink with variable fin ...

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20th InterSociety conference on thermal and thermomechanical Phenomena in Electronic systems (Itherm)

作者： Lee, Young Jin Kim, Sung Jin Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 305701 South Korea

ISBN: (纸本)9781728185392

thermal performance of the pin-fin heat sink cooled by natural convection is numerically investigated when the fin density is allowed to vary along the flow direction. the distribution of the fin density is thermally optimized by using a compact modeling method, in which the pin-fin heat sink is modeled as a fluid-saturated porous medium. In the porous region, the volume-averaged governing equations are used;the Brinkman-Forchheimer-extended Darcy equation for fluid flow and the one-equation model for heat transfer. Based on the analogy between convective heat transfer between the fin and air and that carried by the fluid flowing through the porous medium, a new model for the effective thermal conductivity is proposed. the proposed model is validated by the experimental data available in literature and the numerical results obtained from a direct numerical simulation. For minimal thermal resistance, optimization is conducted using the Kriging method under the constraint of fixed base plate and fin height dimensions. As a result, the optimal distribution of the fin density is obtained as a function of the porosity (phi). along the flow direction. the optimal distribution of the fin density is found to be monotonically increasing from phi = 0.898 to phi = 0.946, which indicates the densely-populated fins in the lower part and the coarsely-populated fins in the upper part. Consequently, the optimized pin-fin heat sink with variable fin density has 11% lower thermal resistance and 30% less weight than those of the uniformly-populated pin-fin heat sink with phi = 0.891 optimized using an existing correlation. Owing to its improved thermal performance and reduced weight, the pin-fin heat sink with variable fin density is expected to be a promising solution for more efficient thermal management of electronics cooled by natural convection.

关键词： Pin-fin Variable fin density Heat sink optimization Natural convection Compact modeling

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Industrial Insurance Groups Cost Management. Mathematical modeling and Applied Software 20th

Industrial Insurance Groups Cost Management. Mathematical Mo...

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20th IFAC conference on Technology, Culture, and International Stability (TECIS)

作者： Zalozhnev, Alexey Yu Peremezhko, Denis, V Russian Acad Sci Inst Control Sci Moscow Russia JSC SELLART Moscow Russia

this paper covers acquisition cost control optimization in the insurance sector through managerial tools that include optimization models applied method, and analysis within the insurance companies to achieve higher efficiency. the model development with presented costs volume and supposed market volume in each product branch is needed to optimize acquisition costs in insurance companies. the model has been simplified due to the presence of big data in each product branch. Our study is based on classic works and textbooks on optimization and non-linear programming the results of digital computer calculations for two types of insurance activity are present here. the applied software tool for the acquisition costs allocation is also considered. Copyright (C) 2021 the Authors.

关键词： Cost oriented automation intelligent systems sustainable design and control industrial insurance cost management acquisition cost control optimization applied software tools design

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Downlink Design and Operating Mode optimization of Remote Sensing Satellite Data Transmission system 21

Downlink Design and Operating Mode Optimization of Remote Se...

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IEEE/ACIS 21st International Fall Virtual conference on Computer and Information Science (ICIS Fall)

作者： Zhang, Yi He, Yifeng Dong, Gang Xian Inst Space Radio Technol Xian Peoples R China

ISBN: (纸本)9781728176796

With the rapid development of space technology, the design of satellite-ground links in the field of satellite communication system research has become one of the hot spots in the field of satellite communication research. A good link design provides a low BER transmission channel for satellite-ground information interaction and gives full play to the observation and communication capabilities of satellites. By designing the RF transmitting and receiving systems in the satellite-ground transmission link, the performance of the satellite-ground link can be predicted after the satellite is in orbit by considering the losses in the transmission path. the article illustrates the compression coding algorithm, modulation mode coding selection, link design and implementation from the subsystem level, which is a strong practical guidance for satellite data transmission subsystem link design and working mode selection.

关键词： Satellite data transmission system Downlink design and implementation Operating mode optimization

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A Deep Q-Learning Framework for Enhanced QoE and Energy optimization in Fog Computing

A Deep Q-Learning Framework for Enhanced QoE and Energy Opti...

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International conference on Distributed Computing in Sensor systems (DCOSS)

作者： Sabiha Tasnim Sumona Syed Sabbir Hasan Abu Yousuf Tamzid Palash Roy Md. Abdur Razzaque Redowan Mahmud Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green Networking Research Group University of Dhaka Bangladesh School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Fog computing, an evolution of cloud computing, has become increasingly popular for its ability to lessen the burden of such a centralized computing paradigm by distributing tasks generated by IoT across fog layers. Effectively managing real-time, delay-sensitive, and diverse IoT applications to enhance the Quality-of-Experience (QoE) presents significant challenges due to the dispersed nature and limited resources of fog nodes. Previous studies in fog computing task offloading have typically focused on either energy consumption or service delay. this paper introduces an optimization framework for task offloading within fog computing environments that aims to balance improved user QoE with reduced energy consumption, employing Mixed-Integer Linear Programming (MILP). Given the NP-hard nature of this framework, we have devised a Deep Q-Learning (DQL) based model for task offloading, termed ELTO-DQL, which aims for near-optimal solutions in polynomial time. Experimental results indicate that the ELTO-DQL model enhances energy efficiency and QoE by up to 19% and 15% respectively, outperforming contemporary benchmarks.

关键词： Energy consumption Q-learning Computational modeling Mixed integer linear programming Quality of experience Internet of things Resource management

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Query Processing And optimization Method of Ethnic Music Data Based on Deep Belief Network

Query Processing And Optimization Method of Ethnic Music Dat...

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Data Analytics, Computing and Artificial Intelligence (ICDACAI), International conference on

作者： Sixian He Siyuan He Xin Gao College of Music and Dance North Minzu University School Yinchuan China College of Music Ningxia University Yinchuan China Faculty of Music and Performing Arts Universiti Pendidikan Sultan Idris Tanjung Malim Perak Malaysia School of Materials Science & Engineering North Minzu University Yinchuan China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the development of the Internet and the large-scale digital music industry, the acquisition and listening of music are presented to users in a more convenient way. How to find the music that users like to hear from the massive digital music data on the Internet has become a key issue in the field of ethnic music information retrieval. Key issues and main goals addressed. there is a complex relationship between ethnic music data, which is suitable for modeling and query processing with data structure. However, the existing query processing algorithms only focus on structural features, and are not optimized for folk music metadata and music content data, and the query efficiency is not high. Currently, the amount of data in various media is exploding, and digital music is everywhere and increasing day by day. At the same time, various music applications have also greatly contributed to the advancement of information technology, such as music sharing, which made the ancient peer-to-peer technology mainstream at the end of the 20th century. the method used in this paper is a deep belief network. Compared with other classification algorithms, its error convergence speed is faster, and the final recognition error for the data set is relatively smaller.

关键词： Query processing Refining Signal processing algorithms optimization methods Music Prototypes Signal processing Internet Peer-to-peer computing Pattern matching

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A Techno-Economic Modelling and Component Sizing in Renewable Energy Communities: the Perspective of Technical Facilitators

A Techno-Economic Modelling and Component Sizing in Renewabl...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Vishal Kachhad Amit Joshi Valerio Mariani Vincenzo Raffa Luigi Glielmo Department of Engineering University of Sannio Benevento Italy Italian National Agency for New Technologies Energy and Sustainable Economic Development (ENEA) Portici NA Italy Friendly Power Srl Italy Department of Electrical Engineering and Information Technologies University of Naples Federico II Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

this article presents a mathematical model to find the optimal size of solar Photovoltaics (PVs) and Battery Energy Storage systems (BESSs) within the distinctive framework of a Renewable Energy Community (REC) with energy exchanges among PV+BESS system and participants. Our optimization considers dynamic factors, such as electricity pricing and financial aspects including capital investment and operational costs of PV-BESS systems. the replacement costs and efficiencies associated with charging and discharging BESS units are also considered. Mixed Integer Non-Linear Programming (MINLP) is employed to maximize the Net Profit (NP) of the REC. Additionally, we introduce an individual marginal contribution method to allocate incentives among REC participants equitably. this article provides practical insights for Technical Facilitators (TFs) to guide them in selecting the most suitable system size for their energy requirements in the REC. To show real-world applicability, we present the case study of a REC in Roseto Valfortore, a small town in the Province of Foggia, Italy.

关键词： Photovoltaic systems Renewable energy sources Costs Electricity Pricing Programming Mathematical models Planning optimization Investment

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FertilizeSmart: Exploiting IoT and Differential Evolution for Optimizing Crop Fertilization

FertilizeSmart: Exploiting IoT and Differential Evolution fo...

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Annual conference on Wireless On Demand Network systems and Services (WONS)

作者： Xu Tao Christian Cumini Alessio Sacco Simone Silvestri Salmeron Cortasa Montserrat Guido Marchetto Computer Science University of Kentucky USA Automatica e Informatica Politecnico di Torino Italy Plant and Soil Sciences University of Kentucky

ISBN: (数字)9783903176713

ISBN: (纸本)9798331522025

Efficient crop fertilization (e.g., nitrogen) is crucial for maximizing agricultural productivity, ensuring food security, and promoting sustainable farming practices. Traditional methods, such as fixed-rate fertilizer applications or soil sampling, often result in inefficiencies, over-fertilization, and environmental harm, as they fail to account for dynamic in-season weather conditions and varying nutrient needs at different crop growth stages. In this work, we introduce FertilizeSmart, an innovative framework that optimizes crop fertilization by leveraging Internet of things (IoT) technologies. the goal is to determine the optimal fertilization strategy throughout the season. To this purpose, at the core of FertilizeSmart, is an optimization problem that maximizes crop yield while minimizing the amount of fertilizer used. the crop yield in response to different timings and rates of applied fertilizer is estimated using a process-based crop simulation model, namely the Decision Support system for Agrotechnology Transfer (DSSAT). the optimization problem is then solved periodically, by an improved Differential Evolution (DE) algorithm that trades off exploration and exploitation of available solutions, throughout the crop growth cycle, following a Model Predictive Control (MPC) approach. this adaptive approach allows FertilizeSmart to respond to dynamic weather conditions and adjust fertilizer application to meet varying nutrient demands across growth stages. Moreover, we perform extensive simulation experiments and results show that FertilizeSmart significantly outperforms existing fertilizer recommendations, achieving yields approximately 20% higher while reducing fertilizer usage by up to 32% compared to the fixed application rate.

关键词： Decision support systems Wireless communication Wireless sensor networks Nutrients Timing Internet of things optimization Crop yield Fertilizers Meteorology

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Metaheuristic optimization for Path Planning in UAV Networks for Long-Distance Inspection Tasks

Metaheuristic Optimization for Path Planning in UAV Networks...

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International conference on system modeling & Advancement in Research Trends (SMART)

作者： J. Akshya M. Manochitra R. Indu Poornima T. Jeevitha M. Saravanakumar M. Sundarrajan Mani Deepak Choudhry Department of Computational Intelligence SRM Institute of Science and Technology Chennai Computer Science and Engineering (Cyber Security) Dr. N.G.P. Institute of Technology Coimbatore Department of Information Technology KGiSL Institute of Technology Coimbatore Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Networking and Communications SRM Institute of Science and Technology Chennai Deportment of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

With broad applicability in surveillance, delivery, and environmental monitoring, multi-drone path planning has received a great deal of attention recently. So far, wide applicability has been explored by various applications of Genetic Algorithms, Particle Swarm optimization, and Ant Colony optimization, which have already established reliable solutions to navigation and coverage through drone path optimization. However, in dynamic, obstacle-dense environments, these algorithms fail to adapt in real-time with energy efficiency. Traditional GA and PSO approaches can be effective for generating an initial path but fail to respond to collision avoidance and efficient resource management. In complex and unpredictable conditions, ACO fails to converge rapidly, which leads to suboptimal paths and increased energy consumption. To overcome such barriers, we present here an HGARL for enhanced multi-drone path optimization. the proposed algorithm thus combines the population-based search capability of GA with RL's adaptive learning ability, therefore providing an algorithm that in addition to finding optimal paths adjusts the paths according to changing environmental situations. HGARL presents better performance compared to existing methods. In path convergence, it reaches up to 15%, and in energy consumption, it reduces up to 20% with safe collision-free navigation. the strong potential of HGARL for robust, efficient, and adaptive solutions of real-time multi-drone operation in complex and dynamic environments has set a new benchmark for UAV path planning.

关键词： Energy consumption Navigation Heuristic algorithms Reinforcement learning Path planning Real-time systems Reliability Genetic algorithms Drones Convergence

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Comparative Study for Implementation of Various Performance Indices on the Coupled Tank Level Control system

Comparative Study for Implementation of Various Performance ...

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IEEE India conference (INDICON)

作者： Hafiz Shaikh Neelima Kulkarni Mayuresh Bakshi Dept. of Electrical Engineering PES Modern College of Engineering Pune India Dept. of Engg. and Applied Sciences VIIT Pune India

this paper presents comparative analysis of the effect of implementing various performance indices for water level control in a coupled tank Multiple Input Multiple Output (MIMO) system. the control objective is to regulate the water levels in each tank to desired set points while maintaining stability and minimizing disturbances. the purpose of this paper is to investigate the impact of incorporating different performance indices in the control strategy and evaluate its effectiveness in achieving desired performance of interconnected coupled tanks. Traditional control techniques may struggle to handle the inherent complexity and interactions present in such a system. To address this challenge, various performance indices are introduced to quantify the system’s performance and guide the control strategy. Integral absolute error (IAE), Integral of Time multiplied Absolute Error (ITAE), Integral of Time multiplied square error (ISTE) and Integral square error (ISE) are incorporated as the performance indices in the proposed work. the performance index takes into account various factors such as overshoot, steady-state error and settling time. By optimizing the performance index, the control strategy can be tailored to achieve the desired water level control efficiently. the analysis begins with the development of a suitable mathematical model for the coupled tank MIMO system, considering the dynamics of the tanks, interconnections, and control inputs. In addition to the experimentation on hardware, simulations are also conducted to evaluate the responses of different performance indices using particle swarm optimization based PID as a control strategy. the results of the analysis demonstrate the advantages and limitations of incorporating a particular performance index in the control strategy for water level control in a coupled tank system. the findings provide insight into the benefits and challenges of using such an approach and suggest guidelines for designing effi

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