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Selection and Resource Allocation Strategies for Chatbot Technologies in Higher Education: An optimization Model Approach

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IEEE ACCESS 2025年 13卷 13156-13174页

作者： Wu, Suhan Luo, Min Nanjing Polytech Inst Sch Econ & Management Nanjing 210048 Peoples R China Shenzhen Inst Informat Technol Sch Management Shenzhen 518172 Peoples R China

Chatbot technologies are increasingly integral to higher education, offering significant potential to enhance teaching effectiveness and student engagement. This study explores the strategic integration of chatbot technologies, focusing on optimal selection and resource allocation to maximize educational efficacy. We developed a mathematical optimization model and introduced the "Relative Net Utility Differential" (RNUD) as a pivotal metric to facilitate the decision-making process for chatbot technology selection and resource distribution. Our findings reveal that when RNUD values for all chatbots fall within a specified range, the model efficiently determines the optimal resource allocations, providing clear analytical solutions for each technology. Additionally, we devised an algorithm for scenarios where RNUD values vary significantly, effectively isolating optimal solutions by excluding suboptimal choices. Numerical analysis confirms that this algorithm maintains robust performance and high computational efficiency, particularly when the chatbot count does not exceed 20. This efficiency persists across various distributions of chatbots' technical attributes, and increasing the number of candidate chatbots generally enhances overall educational utility. This suggests that a diverse technological environment can lead to more tailored and effective educational interventions. Moreover, our study highlights a competitive interaction among chatbots: those with superior technical attributes tend to receive more resources. However, when the technical attributes of one chatbot improve, the resources allocated to others decrease, and vice versa. These insights underscore the importance of not only providing more sophisticated chatbots but also carefully managing their interactions to maximize overall educational utility.

关键词： Chatbots Resource management Education Artificial intelligence Electronic learning Ethics Solid modeling optimization models Generative AI Decision making Chatbot technology technology selection resource allocation mathematical modeling higher education

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optimization of New Energy Vehicle Industry Structure Based on optimization Link Prediction Similarity Algorithm and CGE

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IEEE ACCESS 2025年 13卷 47722-47733页

作者： Xie, Zhenjun Chongqing Business Vocat Coll Sch E Commerce & Logist Chongqing 401331 Peoples R China

In light of the growing global concerns over energy and climate change, new energy vehicles are confronted with both opportunities and challenges posed by industrial structural transformation. To foster the optimization of the new energy vehicle industry structure while considering environmental impact, this paper first uses an improved link prediction algorithm to construct an industrial structure optimization model. Then, a computable general equilibrium model is used to explore the effect of a carbon tax on the new energy vehicle industry. The proposed model had the best area under the curve on the Karate network and FWFW network, which were 90.64% and 65.05%. In the Jazz network, the prediction accuracy was 92.65%. The prediction accuracy of the proposed model for the "Yangtze River Delta" and "Beijing Tianjin Hebei" regions was 86.37% and 85.62%. With the gradual rise of carbon tax rates and emission reductions, the demand for electricity, coal, and oil in the new energy vehicle manufacturing industry was gradually decreasing. The gross domestic product and total output of the manufacturing industry were gradually decreasing, and the total investment was gradually increasing. This result demonstrates the application effect of the proposed industrial structure optimization model and indicates that carbon tax collection will have an impact on carbon emissions and energy demand. This research will help promote the optimization of the new energy vehicle industry structure and promote the sustainable growth of the national economy.

关键词： Climate change Carbon tax Vehicles Energy management optimization models Manufacturing industries Computational modeling Accuracy Predictive models Carbon emissions Supply and demand Renewable energy sources Battery powered vehicles Modeling Market opportunities New energy vehicles industrial structure link prediction computable general equilibrium model carbon tax

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Joint Power, Bandwidth, and Subchannel Allocation in a UAV-Assisted DFRC Network

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IEEE INTERNET OF THINGS JOURNAL 2025年第9期12卷 11633-11651页

作者： Zhang, Haowei Liu, Weijian Zhang, Qun Zhang, Lin Liu, Baobao Xu, He-Xiu Air Force Engn Univ Air Def & Missile Def Coll Xian 710051 Shaanxi Peoples R China Wuhan Elect Informat Inst Wuhan 430019 Hubei Peoples R China Air Force Engn Univ Informat & Nav Inst Xian 710077 Shaanxi Peoples R China Xian Polytech Univ Sch Comp Sci & Technol Xian 710048 Shaanxi Peoples R China

Uncrewed aerial vehicle (UAV)-assisted joint radar and communication (JRC) systems are widely adopted in Internet of Things applications. This is due to their convenience, affordability, and space and resource-saving. A joint power, bandwidth, and subchannel allocation (JPBSA) strategy is proposed for a UAV-assisted dual-function radar and communication (DFRC) network. The predicted posterior Cram & eacute;r-Rao lower bound (PCRLB) is utilized to measure the target tracking accuracy. The optimization model is established as minimizing the sum of weighted predicted PCRLBs while meeting the communication data ratio (CDR) requirements, total power and bandwidth budget. It is shown that the JPBSA problem involves a mixed-integer programming (MIP) problem. Even worse, the bandwidth and subchannel are coherent. A three-stage alternating optimization method (TSAOM) is constructed for the solution. By integrating the radar and communication power allocation as a whole, the alternating ascent-descent method (AADM) is employed to solve the power allocation. Then, two propositions are proposed to provide the upper bounds for bandwidth allocation. Finally, the subchannel allocation is solved using a greedy search-based method. Simulation results confirm the effectiveness and efficiency of the proposed method, compared with the state-of-the-art methods. It also shows that using the PCRLB as the optimization metric is better than the signal-to-interference-plus-noise ratio (SINR) and mutual information (MI).

关键词： Radar Resource management Interference Target tracking Radar tracking Bandwidth optimization models Autonomous aerial vehicles Signal to noise ratio Channel allocation Alternating ascent-descent method (AADM) dual-function radar and communication (DFRC) system joint resource allocation strategy uncrewed aerial vehicle (UAV)-assisted Internet of Things (IoT)

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A General Simplification and Acceleration Method for Distribution System optimization Problems

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PROTECTION AND CONTROL OF MODERN POWER SYSTEMS 2025年第1期10卷 148-167页

作者： Xiao, Jun Zhou, Yupeng She, Buxin Bao, Zhenyu Tianjin Univ Key Lab Minist Educ Smart Power Grids Tianjin 300072 Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA State Grid Zhejiang Elect Power Co Ltd Ningbo Power Supply Co Ningbo 315000 Peoples R China

Solving optimization problems plays a vital role in ensuring the secure and economic operation of distribution systems. To enhance computational efficiency, this paper proposes a general simplification and acceleration method for distribution system optimization problems. Firstly, the capacity boundary and voltage boundary model of distribution systems are established. The relative position between the two boundaries reflects the strength of capacity and voltage constraints, leading to the definition of two critical feeder lengths (CFLs) to quantify these strengths. Secondly, simplification criteria and an acceleration method are proposed. Given a distribution system, if the distance from the end load/DG node to the slack bus is less than the corresponding CFL, we can conclude that the capacity constraints are stricter than the voltage constraints. Then, the distribution system can be simplified by adopting DC power flow model or disregarding the voltage constraints. After that, the reference value tables of CFL are presented. Finally, the effectiveness of the proposed method is verified by exemplifying the method in network reconfiguration and reactive power optimization problems. By implementing the proposed acceleration method, a significant reduction in computation time is achieved while ensuring accuracy. This method applies to most urban distribution systems in optimization problems involving power flow equations or voltage constraints.

关键词： Economics Reactive power Computational modeling optimization models Voltage Relaxation methods Protection optimization Load flow Load modeling Distribution system optimization problem power flow equation security region simplification criteria acceleration method network reconfiguration reactive power optimization

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Optimizing Data Transmission in Large-Scale Sensor Networks Using UAVs: A Segmentation-Clustering and Sequential Approach

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IEEE SENSORS JOURNAL 2024年第16期24卷 26770-26787页

作者： Liu, Hao Chen, Renwen Zhang, Junyi Jiang, Zihao Lu, Guoqiang Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Nanjing 210016 Peoples R China

In outdoor environments, the demand for real-time data transmission from large-scale sensor networks necessitates innovative solutions. This study introduces a system architecture aimed at optimizing real-time data transmission performance using fixed-wing unmanned aerial vehicles (UAVs). By deriving energy consumption and network throughput models for UAV flight, we investigate the relationships between key parameters such as sensor node (SN) distribution, UAV flight center position, and flight radius with throughput and UAV flight energy consumption. Subsequently, we establish an optimization model for UAV flight parameters to enhance both data transmission efficiency of SNs and energy efficiency of UAVs. To address the computational challenges posed by large-scale sensor network applications, we propose the segmentation-iteration clustering (SEG-C) algorithm to reduce computational complexity. Additionally, we develop the sequential optimization algorithm (SOA) by decomposing and reconstructing the complex nonconvex multiobjective optimization model and its constraints. Simulation results demonstrate that the SOA achieves optimal UAV flight parameters while maintaining the throughput threshold. Furthermore, the SEG-C algorithm significantly reduces computational scale and enhances solution efficiency, particularly in managing large-scale sensor applications.

关键词： Autonomous aerial vehicles Data communication Sensors optimization optimization models Throughput Wireless sensor networks Real-time data transmission throughput optimization UAV energy consumption optimization unmanned aerial vehicle (UAV)-assisted communication

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optimization of a wideband discrete Raman amplifier in a P2O5-doped optical fiber using multi-objective grey wolf algorithm

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OPTICAL FIBER TECHNOLOGY 2024年 88卷

作者： Silva, Luis C. B. Rocha, Helder R. O. Segatto, Marcelo E. V. Castellani, Carlos E. S. Univ Fed Espirito Santo Dept Elect Engn Fernando Ferrari Ave BR-29075910 Vitoria Brazil

This paper demonstrates the use of the multi-objective grey wolf algorithm to optimize a discrete Raman amplifier (DRA) in a P2O5-doped optical fiber. Specifically, the multi-objective grey wolf algorithm is combined with the DRA to carry out a process that seeks to maximize the gain and minimize the ripple. The P2O5-doped optical fiber employed in this study has a Raman gain coefficient spectrum with multiple peaks with different frequency shifts. This allows them to be combined in more complex ways than optical fibers with a single peak in the Raman gain spectrum. Consequently, the gain curve produced with this fiber has the potential to be more adjustable even when fewer pumps are used. Thus, this paper explores this fact to perform, to the best of our knowledge, the first specialized optimization process reported in the scientific literature of a wideband discrete Raman amplifier in a P2O5-doped optical fiber. With a different gain profile of this fiber compared to those of traditional standard optical fibers, it was possible to design a wideband DRA, going from 1530 nm to 1675 nm, covering C+L+U bands, maintaining a ripple of up to 8 dB with a net gain of 14 dB using only 3 pumps. Moreover, this work demonstrates for the first time, through a comparative analysis, that the multi-objective grey wolf algorithm performs better than the standard and well-known non-dominated sorting genetic optimization algorithm to optimize a DRA in a P2O5-doped optical fiber. The proposed DRA is a feasible, low-cost, and simple alternative for building fiber amplifiers for future high-bandwidth and wideband wavelength division multiplexing (WDM) communication systems, network infrastructures such as data centers, undersea cables, 5G and beyond, and cutting-edge research applications.

关键词： Discrete Raman amplifier optimization models Multi-objective grey wolf algorithm Fiber optic devices

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Co-Design of USV Control System Based on Fuzzy Satisfactory optimization for Automatic Target Arriving and Berthing

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IEEE ACCESS 2024年 12卷 102449-102460页

作者： Chen, Taoyi Peng, Huixiang Chang, Xiaoyu Zhang, Xiaonan Wang, Didi 54th Res Inst China Elect Technol Grp Corp Shijiazhuang 050081 Peoples R China CETC Key Lab Aerosp Informat Applicat Shijiazhuang 050081 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Most of the previous research about unmanned surface vehicle (USV) control system just focuses on reaching the target but not considers berthing. Moreover, computation cost is rarely concerned. For automatic target arriving and berthing of USV in presence of multi-shape convex obstacles and disturbances, this paper designs a composite control system including trajectory optimization and trajectory tracking. In order to generate a smooth and optimal trajectory, two objectives including minimum running time and energy consumption are proposed, and their priority requirement is presented. Fuzzy satisfactory optimization is introduced to reformulate the objective functions, and the membership degree difference is used to model priority between two objectives. The obstacle avoidance constraint is developed based on the improved P-criterion. The comprehensive optimization model is established to obtain the maximum membership degree. Further, the priority optimization model is proposed to expand the feasible domain by relaxing the comprehensive optimization result. Two models are discretized by Gaussian pseudospectral method to find the optimal solution. To achieve good trajectory tracking under disturbances, two first-order active disturbance rejection control (ADRC) controllers are designed to follow the desired longitudinal velocity and course angular velocity. All the velocities of USV can converge. The simulation proves the effectiveness of the proposed co-designed system by comparing with other methods.

关键词： Underactuated surface vessels Trajectory optimization Trajectory tracking Control systems optimization models Energy consumption Kinematics Gaussian processes USV trajectory optimization trajectory tracking Gaussian pseudospectral method ADRC

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An Electric Power Regulator for Impact Power Suppression and Line Loss Reduction in Shore Power System

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Liu, Qianyi Li, Yuan Liu, Fang Li, Shanglong Li, Yong Cent South Univ Sch Automat Changsha 410083 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China

The widespread utilization of marine equipment, especially the floating crane, poses complex challenges for power regulation in shore power system. An electric power regulator, along with a power compensation strategy based on the scenario categorization, is presented in this article to address the issues of impact power and line loss in shore power system. The regulator's topology is first introduced, and subsequently the operational scenarios are divided according to the load power and state of charge of the battery. Then, the adjustable line loss optimization model is established, and the interval of the power compensation for loss reduction is obtained through solving the model. Furthermore, considering the demand of impact power suppression and limitations of the interval and regulator capacity, the power compensation reference of the regulator under corresponding scenarios is determined. Finally, a low-power experimental prototype is set up in the laboratory. The experimental results show that the rate of power impact suppression reaches 90%, the power factor increases by 18.07%, and the line loss decreases by 17.17%.

关键词： Power systems Regulators Batteries Reactive power optimization models Fluctuations Topology Regulation Power generation Power quality Electric power regulator impact power suppression loss reduction shore power system

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Two-Stage Distributionally Robust optimization for Infrastructure Resilience Enhancement: A Case Study of 220 kV Power Substations Under Earthquake Disasters

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IEEE TRANSACTIONS ON RELIABILITY 2024年第2期74卷 2576-2590页

作者： Zou, Changjie Wang, Kai Xiahou, Tangfan Cao, Di Liu, Yu Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety Chengdu 611731 Peoples R China

Power substations are widely used as crucial components of power grids but also exposed to a high risk of earthquakes. Enhancing the resilience of power substations plays an important role for power grids to resist earthquakes. However, the uncertainty of equipment failure has posed a significant obstacle to enhancing resilience. In this article, considering the equipment hardening strategy for the power substation, a two-stage distributionally robust optimization model is put forth for earthquake resilience enhancement of the power substations. The optimal equipment hardening strategy is determined prior to an earthquake, and the recovery sequence of the damaged equipment is optimized after the earthquake to enhance the resilience of the power substation. A decision-dependent moment-based ambiguity set is constructed to model the impact of the hardening strategy on the uncertain failure probability of equipment. A customized nested column-and-constraint generation algorithm (NC&CG) is put forth to solve the optimization model. To improve the computational efficiency, we propose a novel heuristic algorithm to solve the main problem of the inner NC&CG according to the power substation structure. The proposed method is demonstrated in the case of a simplified 220 kV power substation. The results show that the distributionally robust approach can effectively enhance the earthquake resilience of power infrastructure under uncertainty of equipment failure. The value of implementing an effective algorithm to quickly obtain the optimal solution in large-scale problems is also highlighted.

关键词： Substations Earthquakes Resilience Power grids Uncertainty optimization optimization models Moment-based ambiguity set power substation resilience optimization two-stage distributionally robust optimization (DRO)

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Energy Efficiency Maximization in UAV-Assisted Intelligent Autonomous Transport System for 6G Networks With Energy Harvesting

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年

作者： Huang, Jie Yu, Tao Zhu, Xiaogang Yang, Fan Lai, Xianzhi Alfarraj, Osama Yu, Keping Chongqing Univ Technol Sch Elect & Elect Engn Chongqing 400054 Peoples R China Nanchang Univ Sch Publ Policy & Adm Nanchang 330031 Jiangxi Peoples R China Jiangxi Inst Ind Technol Internet Things Yingtan 335000 Jiangxi Peoples R China King Saud Univ Community Coll Dept Comp Sci Riyadh 11437 Saudi Arabia Hosei Univ Grad Sch Sci & Engn Tokyo 1848584 Japan

The unmanned aerial vehicle-assisted 6G supported intelligent transportation systems (UAV-assisted 6G-ITS) have great potential to make transportation systems efficient, smart, and sustainable. However, when connected and autonomous vehicles communicate with UAVs, it can lead to issues such as energy consumption and overlapping interference, which can affect system performance. Therefore, this paper proposes an interference tolerance-based energy harvesting (EH) resource allocation (IT-EHRA) strategy, aiming to improve the energy efficiency (EE) of UAV-assisted 6G-ITS and mitigate the overlapping interference. Firstly, we established an interference hypergraph model and analyzed the types and relationships of interference in the network. Based on this model, an interference tolerance method was designed to alleviate overlapping interference. Then, an EH optimization model with imperfect channel state information (CSI) is proposed based on the interference model, aiming to maximize the network EE and reduce energy consumption. Finally, we adopt the IT-EHRA algorithm to reduce the impact of imperfect CSI and use duality theory to obtain the optimal solution, thereby achieving maximum network EE. Simulation results show that the algorithm effectively ensures the EE requirements of the network, improves the throughput of the network, and promotes the sustainable development of the network.

关键词： Interference 6G mobile communication Resource management Device-to-device communication Autonomous aerial vehicles optimization models Energy efficiency Intelligent transportation systems unmanned aerial vehicles overlapping interference energy harvesting energy efficiency

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