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An empirical analysis on the cultivation of English innovative application ability of English majors in application-oriented universities based on dynamic optimization algorithm in blended teaching environment

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SOFT COMPUTING 2023年第15期27卷 10839-10850页

作者： Guo, Zongjuan Nanchang Jiaotong Inst Sch Humanities & Law Nanchang 330100 Jiangxi Peoples R China

With the development of the information age, digital education runs through education. Whether it is primary education or higher education, we need continuous self-innovation. With the development of today's society, dynamically optimized data algorithm permeates into higher education, which has a certain impact on English majors' innovative application ability. Project-based teaching in a blended teaching environment combines the advantages of traditional classroom teaching with the convenience of online learning, which can stimulate English majors' interest in English learning, and through the selection and optimization of teaching content, stimulate students' love for English, and enhance their innovative application ability from the inside. In this thesis, the English learning of English majors in application-oriented universities is dynamically optimized under the blended teaching environment to promote the improvement of students' innovative English application ability.

关键词： Blended teaching dynamic optimization algorithm English major Innovative application ability

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Efficient Rollout of a dynamic optimization algorithm 5

Efficient Rollout of a Dynamic Optimization Algorithm

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5th International Conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Neuhauser, Philipp Cihal, Rudolf Wagner, Stefan Floessholzer, Harald Univ Appl Sci Upper Austria Josef Ressel Ctr Adapt Optimizat Dynam Environm Softwarepk 11 A-4232 Hagenberg Austria Voestalpine Stahl GmbH Business Unit Slab Voestalpine Str 3 A-4020 Linz Austria

A successful deployment process of a complex software solution, such as optimization algorithms in a dynamic production environment is an extremely complicated and time-consuming task. Over this, the acceptance by those employees where the software solution is to be used later should not be underestimated;it is essential to involve them in the entire development process, as they can be game changers. This paper summarizes some best practices and lessons learned about the full integration of an optimization algorithm in the dynamic steelmaking environment of voestalpine Stahl GmbH. The focus is on the development of a practical test strategy and ultimately a generally valid rollout concept for an arbitrary complex software system for every conceivable industry. Over that, emerging gaps between theory and practice are highlighted, an attempt is made to justify their cause, and, as a result, possible solutions to tackle those challenges are presented. (C) 2024 The Authors. Published by Elsevier B.V.

关键词： software rollout strategy efficient software deployment dynamic optimization algorithm innovation gap

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Efficient Rollout of a dynamic optimization algorithm

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Procedia Computer Science 2024年 232卷 77-86页

作者： Philipp Neuhauser Rudolf Cihal Stefan Wagner Harald Flößholzer Josef Ressel Center for Adaptive Optimization in Dynamic Environments Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 11 4232 Hagenberg Austria Business Unit Slab voestalpine Stahl GmbH voestalpine Straße 3 4020 Linz Austria

A successful deployment process of a complex software solution, such as optimization algorithms in a dynamic production environment is an extremely complicated and time-consuming task. Over this, the acceptance by those employees where the software solution is to be used later should not be underestimated; it is essential to involve them in the entire development process, as they can be game changers. This paper summarizes some best practices and lessons learned about the full integration of an optimization algorithm in the dynamic steelmaking environment of voestalpine Stahl GmbH. The focus is on the development of a practical test strategy and ultimately a generally valid rollout concept for an arbitrary complex software system for every conceivable industry. Over that, emerging gaps between theory and practice are highlighted, an attempt is made to justify their cause, and, as a result, possible solutions to tackle those challenges are presented.

关键词： software rollout strategy efficient software deployment dynamic optimization algorithm innovation gap

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dynamic Charging optimization algorithm for Electric Vehicles to Mitigate Grid Power Peaks

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WORLD ELECTRIC VEHICLE JOURNAL 2024年第7期15卷 324页

作者： Aoun, Alain Adda, Mehdi Ilinca, Adrian Ghandour, Mazen Ibrahim, Hussein Univ Quebec Rimouski UQAR Dept Math Informat & Genie Rimouski PQ G5L 3A1 Canada Ecole Technol Super ETS Dept Genie Mecan Montreal PQ H3C 1K3 Canada Lebanese Univ Fac Engn Beirut 1003 Lebanon Univ Quebec Trois Rivieres UQTR Ctr Natl Integre Manufacturier Intelligent CNIMI Drummondville PQ J2C 0R5 Canada

The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new challenges for grid operators and EV owners equally. The uncoordinated nature of electric vehicle charging may lead to the emergence of new peak loads. Grid operators typically plan for peak demand periods and deploy resources accordingly to ensure grid stability. Uncoordinated EV charging can introduce unpredictability and variability into peak load patterns, making it more challenging for operators to manage peak loads effectively. This paper examines the implications of uncoordinated EV charging on the electric grid to address this challenge and proposes a novel dynamic optimization algorithm tailored to manage EV charging schedules efficiently, mitigating grid power peaks while ensuring user satisfaction and vehicle charging requirements. The proposed "Proof of Need" (PoN) charging algorithm aims to schedule the charging of EVs based on collected data such as the state of charge (SoC) of the EV's battery, the charger power, the number of connected vehicles per household, the end-user's preferences, and the local distribution substation's capacity. The PoN algorithm calculates a priority index for each EV and coordinates the charging of all connected EVs at all times in a way that does not exceed the maximum allocated power capacity. The algorithm was tested under different scenarios, and the results offer a comparison of the charging power demand between an uncoordinated EV charging baseline scenario and the proposed coordinated charging model, proving the efficiency of our proposed algorithm, thus reducing the charging demand by 40.8% with no impact on the overall total charging time.

关键词： energy management electric vehicle charging optimization smart charging dynamic optimization algorithm state of charge efficiency peak load management grid modernization

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A DE and PSO based hybrid algorithm for dynamic optimization problems

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SOFT COMPUTING 2014年第7期18卷 1405-1424页

作者： Zuo, Xingquan Xiao, Li Beijing Univ Posts & Telecommun Comp Sch Beijing 100088 Peoples R China Beijing Univ Posts & Telecommun Automat Sch Beijing 100088 Peoples R China

Many real world optimization problems are dynamic in which the fitness landscape is time dependent and the optima change over time. Such problems challenge traditional optimization algorithms. For such problems, optimization algorithms not only have to find the global optimum but also need to closely track its trajectory. In this paper, a new hybrid algorithm integrating a differential evolution (DE) and a particle swarm optimization (PSO) is proposed for dynamic optimization problems. Multi-population strategy is adopted to enhance the diversity and try to keep each subpopulation on a different peak in the fitness landscape. A hybrid operator combining DE and PSO is designed, in which each individual is sequentially carried out DE and PSO operations. An exclusion scheme is proposed that integrates the distance based exclusion scheme with the hill-valley function to track the adjacent peaks. The algorithm is applied to the set of benchmark functions used in CEC 2009 competition for dynamic environment. Experimental results show that it is more effective in terms of overall performance than other comparative algorithms.

关键词： dynamic optimization algorithm Differential evolution Particle swarm optimization Exclusion scheme

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Infrared small target detection based on the dynamic particle swarm optimization

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INFRARED PHYSICS & TECHNOLOGY 2021年 117卷 103837-103837页

作者： Shahraki, Hadi Aalaei, Shokoufeh Moradi, Saed Univ Sistan & Baluchestan Fac Ind & Min Dept Comp Engn Khash Iran Mashhad Univ Med Sci Sch Med Dept Med Informat Mashhad Razavi Khorasan Iran Univ Isfahan Fac Engn Dept Elect Engn Esfahan Iran

In this paper, a new method is introduced for detecting small targets in infrared images. The proposed method is based on the particle swarm optimization algorithm (PSO). Considering the nature of the small target detection problem, a dynamic optimization algorithm is developed to detect targets. The proposed algorithm is called the dynamic particle swarm detector (DPSD). Unlike common small target detection algorithms, the computational complexity of the proposed algorithm is not linear to number of pixels in the input image. Therefore, it is capable of operating on a large-scale image in a relatively small timeslot. Many experiments are carried out to evaluate the effectiveness of the proposed method, where the DPSD is compared to seven well-known methods in terms of quantitative detection metrics. The results show that the proposed detector outperforms the baseline algorithms.

关键词： Artificial intelligence Infrared search and track dynamic optimization algorithm Particle swarm optimization algorithm Real-time small target detection

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A new cooperative control solution of subway BAS: an improved fuzzy PID control algorithm

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PEERJ COMPUTER SCIENCE 2024年 10卷 e1765-e1765页

作者： Fang, Hui Yang, Shusong Shi, Ying Wang, Yang Jiang, Yue Song, Chaochao Zhang, Wei Ningbo Rail Transit Grp Co Ltd Ningbo Zhejiang Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China SUPCON Technol Co Ltd New Business Incubat Dept Hangzhou Zhejiang Peoples R China

The building automation system (BAS) of a subway is the core component of monitoring and managing urban rail transit systems. For the current problems such as low control efficiency, insufficient accuracy, and poor stability of metro BAS, this article proposes a cooperative control framework based on an improved fuzzy proportional -integral -derivative (PID) algorithm. Firstly, the concept of an integrated supervisory control system (ISCS) for subways is introduced by summarizing the previously implemented engineering construction and combining it with advanced automation technology. The system's overall design under the ISCS framework is also improved by integrating it with the fire alarm system (FAS) with the BAS as the core unit of the reliance. Then, an improved seeker optimization algorithm (ISOA) is employed to optimize the parameters of the fuzzy PID control algorithm to achieve a coordinated control of the system based on considering the time lag problem. Finally, the accuracy, efficiency, and stability of the coordinated control response of the BAS under the ISCS framework are tested experimentally. The results suggest that the proposed cooperative control solution of BAS employing the improved fuzzy PID algorithm has good control accuracy and response efficiency and can also ensure the BAS's higher stability in the coordinated control process, which thus greatly improves the automation level of the subway and provides a safer and more reliable high-performance for the ISCS of the subway in the urban rail transportation industry.

关键词： BAS dynamic optimization algorithm Fuzzy PID control algorithm Cooperative control solution

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Cost efficient optimization based supervisory controller for supermarket subsystems with heat recovery

Cost efficient optimization based supervisory controller for...

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European Control Conference (ECC)

作者： Minko, Tomasz Wisniewski, Rafal Bendtsen, Jan Dimon Izadi-Zamanabadi, Roozbeh Aalborg Univ Automat & Control Aalborg Denmark Danfoss AS Elect Controllers & Serv Nordborg Denmark

ISBN: (纸本)9783952426937

In this paper, we present a simple modelling approach for a thermal system, which consists of heating, ventilation, air conditioning system (HVAC) and a vapour compression cycle (VCC) system, with one loop heat recovery. In addition a simple model for water tank is presented, in which the reclaimed heat is stored and/or which can be used for heating purposes of the building. We present a dynamic optimization algorithm that according to the received price signal, occupancy information and ambient temperature minimizes the operation cost of the whole system and distributes set points to local controllers of supermarkets subsystems. We find that when reliable information about the high price period is available, it is profitable to use the refrigeration system to generate heat during the low price period, store it and use it to substitute the conventional heater during the high price period.

关键词： cost reduction dynamic programming heat recovery refrigeration space heating tanks (containers) thermal energy storage HVAC VCC system building heating purpose cost efficient optimization based supervisory controller dynamic optimization algorithm heating ventilation air conditioning system operation cost minimization reclaimed heat storage refrigeration system supermarket subsystem local controller thermal system vapour compression cycle system water tank Buildings Heat recovery Heat transfer Refrigerants Water heating

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An Optimal Novel Approach for dynamic Energy-Efficient Task Offloading in Mobile Edge-Cloud Computing Networks

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SN Computer Science 2024年第5期5卷 655页

作者： Mondal, Avijit Chatterjee, Pinaki Sankar Ray, Niranjan K. School of Computer Engineering KIIT University Odisha Bhubaneswar India

The rapid evolution of mobile devices has greatly advanced secure medical image transmission, yet challenges persist due to resource limitations and security concerns inherent to these devices. In response, this paper introduces a dynamic Energy-Efficient Offloading algorithm (DEEO), seamlessly integrated into the Mobile Edge-Cloud Computing (MECC) environment. DEEO empowers mobile devices to efficiently offload computationally intensive secure image transmission tasks to the nearest edge server or fog access point. This integration optimizes resource utilization, minimizes energy consumption, and ensures the confidentiality and integrity of sensitive medical image data. Through rigorous evaluations and comparative analysis, our approach demonstrates clear superiority over existing solutions. This integrated framework is poised to significantly enhance healthcare applications, offering heightened efficiency, elevated security, and an overall improved user experience. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： dynamic decision-making dynamic optimization algorithm Energy efficient offloading Genetic algorithm Hill climbing Mobile cloud computing Mobile edge computing Mobile energy consumption PSO Scalability analysis Statistical analysis Task offloading Workload analysis

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Robust one-class classification with support vector data description and mixed exponential loss function

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期122卷

作者： Zheng, Yunfei Wang, Shiyuan Chen, Badong Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China

Support vector data description (SVDD) has received a lot of attention due to its outstanding performance to perform one-class classification or novelty detection tasks. However, the same weight is directly imposed on all slack variables in the process of modeling, which may result in degraded learning performance when training data are contaminated by some outliers or mislabeled observations. In this paper, an extended SVDD model is therefore proposed by reformulating the original optimization problem of SVDD with a mixed exponential loss function. Since this loss function can emphasize the importance of the samples that tend to be the target class, and weaken the influence of those tending to be outliers, it can be viewed as a weighted SVDD. However, the weights in the new model are automatically calculated rather than being calculated in advance using some specific manners. To solve the optimization problem of the proposed model effectively, the half-quadratic optimization technique has been adopted to perform the optimization, generating a dynamic optimization algorithm. Meanwhile, the convergence and computational complexity of this dynamic optimization algorithm are analyzed from a theoretical respective. Experimental results on a synthetic data set and some publicly available real world data sets are reported to demonstrate the performance superiority of the new method in comparison with SVDD and other competitive SVDD variants.

关键词： One-class classification Support vector data description Mixed exponential loss function Half-quadratic optimization technique dynamic optimization algorithm

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