下一代个性化学习:生成式人工智能增强智能辅导系统

长期以来,教育技术领域的研究者致力于利用计算机开展个性化自适应教学。智能辅导系统作为这一领域的核心,其目标是通过计算机技术提供个性化学习体验和支持。近年来,生成式人工智能,尤其是大语言模型的发展,为智能辅导系统带来了突破性机遇,为个性化学习开辟了新的路径。本文回顾了研究者为实现个性化学习所作的努力及其成果和局限,探讨了大语言模型在个性化学习中的潜力和影响,介绍了“苏格拉底游乐园”——一个基于对话的智能辅导系统,如何利用大语言模型实施苏格拉底式教学,揭示了生成式人工智能如何革新个性化学习方式。针对大语言模型的潜在误用,文章提出了相应的建议,并构建了框架,旨在引导研发者在生成式人工智能和大语言模型范式下开发高效的智能教育应用。

特约主编寄语

持续提高能源利用效率、削减能源使用成本和降低排放,已成为解决人类社会发展过程中所面临的能源、经济和环境“三难困境”的必然路径和选择。当前,我国将综合能源系统建设提升到了前所未有的高度,并提出了“二氧化碳排放力争于2030年前达到峰值,努力争取2060年前实现碳中和”的“双碳”目标。综合能源系统以其庞大的能源需求规模、高度集中的用能需求、集冷/热/电等多种负荷与储能设施于一体的特点而备受关注.

特约主编寄语

在面向碳达峰、碳中和的新型电力系统建设路径中,电力系统源、网、荷、储各环节都将经历多种深刻的创新与变革。而源网荷储的协同规划运行,则是高效且经济地实现电力系统清洁化、低碳化转型的关键技术。一方面,源网荷储的协同能充分挖掘电力系统各环节的互补潜力,以满足新型电力系统接纳高比例新能源的灵活性需求以及应对极端灾害的弹性需求;另一方面,园区级的多能耦合和源网荷储一体化项目能有效整合局部资源,助力新型电力系统安全稳定运行,促进新型电力系统的可持续发展。

极端天气条件下新型电力系统风险评估与弹性提升

近年来,随着温室效应加剧,世界范围内极端天气频发,对电力安全可靠供应造成了严重影响。为达成国家“碳达峰、碳中和”的重大战略目标,构建新型电力系统已成为一种必然趋势。目前,电力系统发展呈现风、光等强不确定性新能源占比不断提高,电力负荷复杂化、多样化的特点,当极端天气条件出现时,将进一步放大电力供应、负荷需求以及设备故障多环节不确定性的影响。因此,开展极端天气条件下新型电力系统风险评估与弹性提升研究,具有十分重要的意义。

“语义通信的数学理论”序言

客观世界由物质、能量和信息组成。探索和利用信息是信息时代的主要驱动力。回顾信息与通信的历史,如180年的经典通信和半个世纪的移动通信,我们看到了诸多通信技术的杰出成就,如超高速光通信、卫星通信、互联网、3G/4G/5G移动通信等。

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

Distributed Optimal Formation Control for Unmanned Surface Vessels by a Regularized Game-Based Approach

Dear Editor,This letter explores optimal formation control for a network of unmanned surface vessels(USVs).By designing an individual objective function for each USV,the optimal formation problem is transformed into a noncooperative game.Under this game theoretic framework,the optimal formation is achieved by seeking the Nash equilibrium of the regularized game.A modular structure consisting of a distributed Nash equilibrium seeker and a regulator is proposed.

Dendritic Learning-Incorporated Vision Transformer for Image Recognition

Dear Editor,This letter proposes to integrate dendritic learnable network architecture with Vision Transformer to improve the accuracy of image recognition.In this study,based on the theory of dendritic neurons in neuroscience,we design a network that is more practical for engineering to classify visual features.Based on this,we propose a dendritic learning-incorporated vision Transformer(DVT),which out-performs other state-of-the-art methods on three image recognition benchmarks.

Dendritic Deep Learning for Medical Segmentation

Dear Editor,This letter presents a novel segmentation approach that leverages dendritic neurons to tackle the challenges of medical imaging segmentation.In this study,we enhance the segmentation accuracy based on a SegNet variant including an encoder-decoder structure,an upsampling index,and a deep supervision method.Furthermore,we introduce a dendritic neuron-based convolutional block to enable nonlinear feature mapping,thereby further improving the effectiveness of our approach.

Intelligent Small Sample Defect Detection of Concrete Surface Using Novel Deep Learning Integrating Improved YOLOv5

Dear Editor,This letter presents an intelligent small sample defect detection of concrete surface using novel deep learning integrating the improved YOLOv5 based on the Wasserstein GAN(WGAN)enhancement algorithm.The proposed method is capable of producing top-notch data sets to address the issues of insufficient samples and substandard quality.

特约主编寄语

随着物联网、边缘计算、5G等信息通信技术的高速发展,海量分布式发、储、用电设备资源接入,电力、冷热、天然气等综合能源系统自动化、智能化水平不断提升,能源物理系统与信息系统的融合愈发紧密。然而,随着系统网络规模及其感知与控制单元数量的增加,能源信息物理系统的架构与互动机理日益复杂,不同环节面临的不确定性风险逐渐增加。在此趋势下,传统控制模式的鲁棒性与公平性难以保证,传统决策算法的实时性与可靠性受到挑战。因此,如何高效利用海量数据与实体资源实现综合能源系统的优化控制,最大限度减少外部条件与人为扰动等因素下信息物理系统的安全风险与故障传播,是目前能源系统大规模发展亟待解决的重要问题之一。

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication

This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.

绿色氢能综合利用技术

氢气是一种清洁能源,燃烧时不产生温室气体和有害污染物。通过可再生能源电解水制取的氢气称为绿氢,绿氢制取过程中几乎没有碳排放,是未来节能减碳、能源转型的重要途径。电力系统与氢能产业相耦合是行业发展的关键环节,利用新能源风光互补发电制氢,不仅实现了能源的长期储存和利用,还提高了可再生能源消纳效率。为了深化绿氢综合利用成效,研究新能源制氢相关技术、电氢耦合技术、含氢能源的综合能源系统规划及运行调度技术等具有重要意义。

Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios

Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.

A Novel Multiple DBC-staked units Package to Parallel More Chips for SiC Power Module

Silicon carbide(SiC) power modules play an essential role in the electric vehicle drive system. To improve their performance, reduce their size, and increase production efficiency, this paper proposes a multiple staked direct bonded copper(DBC) unit based power module packaging method to parallel more chips. This method utilizes mutual inductance cancellation effect to reduce parasitic inductance. Because the conduction area in the new package is doubled, the overall area of power module can be reduced. Entire power module is divided into smaller units to enhance manufacture yield, and improve design freedom. This paper provides a detailed design, analysis and fabrication procedure for the proposed package structure. Additionally, this paper offers several feasible solutions for the connection between power terminals and DBC untis. With the structure, 18dies were paralleled for each phase-leg in a econodual size power module. Both simulation and double pulse test results demonstrate that, compared to conventional layouts, the proposed package method has 74.8% smaller parasitic inductance and 34.9% lower footprint.

Heterogeneous Image Knowledge Driven Visual Perception

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous images.Facing the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Vibration Suppression for Active Magnetic Bearings Using Adaptive Filter with Iterative Search Algorithm

Active Magnetic Bearing(AMB) is a kind of electromagnetic support that makes the rotor movement frictionless and can suppress rotor vibration by controlling the magnetic force. The most common approach to restrain the rotor vibration in AMBs is to adopt a notch filter or adaptive filter in the AMB controller. However, these methods cannot obtain the precise amplitude and phase of the compensation current. Thus, they are not so effective in terms of suppressing the vibrations of the fundamental and other harmonic orders over the whole speed range. To improve the vibration suppression performance of AMBs,an adaptive filter based on Least Mean Square(LMS) is applied to extract the vibration signals from the rotor displacement signal. An Iterative Search Algorithm(ISA) is proposed in this paper to obtain the corresponding relationship between the compensation current and vibration signals. The ISA is responsible for searching the compensating amplitude and shifting phase online for the LMS filter, enabling the AMB controller to generate the corresponding compensation force for vibration suppression. The results of ISA are recorded to suppress vibration using the Look-Up Table(LUT) in variable speed range. Comprehensive simulations and experimental validations are carried out in fixed and variable speed range, and the results demonstrate that by employing the ISA, vibrations of the fundamental and other harmonic orders are suppressed effectively.

A Novel Prescribed-Performance Path-Following Problem for Non-Holonomic Vehicles

The issue of achieving prescribed-performance path following in robotics is addressed in this paper,where the aim is to ensure that a desired path within a specified region is accu-rately converged to by the controlled vehicle.In this context,a novel form of the prescribed performance guiding vector field is introduced,accompanied by a prescribed-time sliding mode con-trol approach.Furthermore,the interdependence among the pre-scribed parameters is discussed.To validate the effectiveness of the proposed method,numerical simulations are presented to demonstrate the efficacy of the approach.