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| 1 | 平行矿山:从数字孪生到矿山智能显示文摘针对新时代下我国矿区智能化发展诉求与矿山无人化进程中遇到的复现难、协同难的技术问题,本文融合智慧矿山理念、ACP(Artificial societies+computational experiments+parallel execution)平行智能理论和新一代智能技术,设计并实现了智慧矿山操作系统(Intelligent mine operation system,IMOS),为平行矿山智能管理与控制一体化提出了解决方案.本文首先分析露天煤矿产业发展趋势;国内外露天矿山智能化发展情况;面向露天矿山无人化与智能化需求,深度融合数字四胞胎理论,设计了虚实融合的IMOS架构;详细阐述了IMOS子系统架构与功能,包括:单车作业系统、多车协同系统、车路协同系统、无人驾驶智能系统、调度管理系统、平行系统、监管系统、远程接管系统和通信系统;并探讨了IMOS关键技术,即平行矿山仿真建模技术、无人驾驶技术、矿区通信技术和协同作业技术.该操作系统是国内首套露天矿山无人化与智能化的一体化解决方案,并能够迁移到不同矿区不同作业场景,推动矿区智能化无人化发展,减少人工干预从而降低安全风险,大幅度降低人工成本,提高生产作业效率,并可结合社会发展要素为实现绿色可持续发展矿区提供支撑. | 陈龙 王晓 杨健健 艾云峰 田滨 李宇宸 滕思宇 王健 曹东璞 葛世荣 王飞跃 | 2021 | 自动化学报2021，47，7： | 47 |
| 2 | A Survey on Smart Agriculture:Development Modes,Technologies,and Security and Privacy Challenges显示文摘With the deep combination of both modern information technology and traditional agriculture,the era of agriculture 4.0,which takes the form of smart agriculture,has come.Smart agriculture provides solutions for agricultural intelligence and automation.However,information security issues cannot be ignored with the development of agriculture brought by modern information technology.In this paper,three typical development modes of smart agriculture(precision agriculture,facility agriculture,and order agriculture)are presented.Then,7 key technologies and 11 key applications are derived from the above modes.Based on the above technologies and applications,6 security and privacy countermeasures(authentication and access control,privacy-preserving,blockchain-based solutions for data integrity,cryptography and key management,physical countermeasures,and intrusion detection systems)are summarized and discussed.Moreover,the security challenges of smart agriculture are analyzed and organized into two aspects:1)agricultural production,and 2)information technology.Most current research projects have not taken agricultural equipment as potential security threats.Therefore,we did some additional experiments based on solar insecticidal lamps Internet of Things,and the results indicate that agricultural equipment has an impact on agricultural security.Finally,more technologies(5 G communication,fog computing,Internet of Everything,renewable energy management system,software defined network,virtual reality,augmented reality,and cyber security datasets for smart agriculture)are described as the future research directions of smart agriculture. | Xing Yang Lei Shu Jianing Chen Mohamed Amine Ferrag Jun Wu Edmond Nurellari Kai Huang | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，2： | 8 |
| 3 | 基于信息物理融合和XGBoost-MPGA算法的燃煤电厂脱硫系统运行优化显示文摘针对脱硫系统运行优化过程中脱硫效率回归模型精度不够,及运行优化方案在实际运行中难以有效执行的问题。该文提出一种基于信息物理融合和XGBoost-MPGA的脱硫系统运行优化方法。通过构建XGBoost脱硫效率回归模型作为脱硫变动成本函数的变量,利用MPGA寻找脱硫变动成本最低时对应的脱硫效率,然后逆向求解出脱硫效率回归模型中液气比、吸收塔浆液pH值、吸收塔液位优化值。通过实例分析表明,XGBoost-MPGA相比BP神经网络、随机森林和GBRT回归模型具有更好的预测性能,且与XGBoost-SGA比较,在脱硫变动成本极值寻优过程具有更好的稳定性和收敛性。并通过信息物理融合方法消除了脱硫物理设备在运行优化操作后对脱硫变动成本信息的影响,提高了运行优化操作方案的可靠性和经济性。 | 肖祥武 李志金 舒畅 周宏贵 王鹏浩 | 2022 | 中国电机工程学报2022，42，14： | 3 |
| 4 | A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving:Deep Learning Approach显示文摘In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa. | Mohammad Al-Sharman David Murdoch Dongpu Cao Chen Lv Yahya Zweiri Derek Rayside William Melek | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，1： | 3 |
| 5 | Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs显示文摘Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs. | Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，11： | 3 |
| 6 | Approximate Dynamic Programming for Stochastic Resource Allocation Problems显示文摘A stochastic resource allocation model, based on the principles of Markov decision processes(MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource requests for both instant and future needs. The considered framework can handle two types of reservations(i.e., specified and unspecified time interval reservation requests), and implement an overbooking business strategy to further increase business revenues. The resulting dynamic pricing problems can be regarded as sequential decision-making problems under uncertainty, which is solved by means of stochastic dynamic programming(DP) based algorithms. In this regard, Bellman’s backward principle of optimality is exploited in order to provide all the implementation mechanisms for the proposed reservation pricing algorithm. The curse of dimensionality, as the inevitable issue of the DP both for instant resource requests and future resource reservations,occurs. In particular, an approximate dynamic programming(ADP) technique based on linear function approximations is applied to solve such scalability issues. Several examples are provided to show the effectiveness of the proposed approach. | Ali Forootani Raffaele Iervolino Massimo Tipaldi Joshua Neilson | 2020 | IEEE/CAA Journal of Automatica Sinica2020，7，4： | 3 |
| 7 | 人机物CPSS智能融合的平行创作架构与关键技术研究显示文摘随着人工智能探索领域的不断拓展,艺术创作成为人工智能发展和应用的重要研究热点。基于平行系统理论与ACP方法构建风格多样、内容逼真、笔触灵活和描述精准的平行艺术创作元宇宙,为提升人工智能的创造能力提供了一种可行的实现途径,并提供了应用案例。通过AI算法创作、人类筛选和评估、机器人执行,构建了人机物CPSS智能融合的平行创作架构,阐述了基于计算实验的绘画风格迁移、内容组合、笔触生成和图像描述等关键技术,并对所构建的平行创作系统进行了实验验证。平行创作系统融合了人、AI创作算法、机器人的优势,提升了人工智能艺术创作系统在虚拟和物理空间中的创作水平,促进了人机物协同艺术创作的发展。 | 郭超 鲁越 王晓 易达 王虓 王飞跃 | 2022 | 智能科学与技术学报2022，4，3： | 2 |
| 8 | Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach显示文摘This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment. | Yanni Wan Jiahu Qin Xinghuo Yu Tao Yang Yu Kang | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，1： | 1 |
| 9 | Learning-Based Switched Reliable Control of Cyber-Physical Systems With Intermittent Communication Faults显示文摘This study deals with reliable control problems in data-driven cyber-physical systems(CPSs) with intermittent communication faults, where the faults may be caused by bad or broken communication devices and/or cyber attackers. To solve them, a watermark-based anomaly detector is proposed, where the faults are divided to be either detectable or undetectable.Secondly, the fault's intermittent characteristic is described by the average dwell-time(ADT)-like concept, and then the reliable control issues, under the undetectable faults to the detector, are converted into stabilization issues of switched systems. Furthermore,based on the identifier-critic-structure learning algorithm, a datadriven switched controller with a prescribed-performance-based switching law is proposed, and by the ADT approach, a tolerated fault set is given. Additionally, it is shown that the presented switching laws can improve the system performance degradation in asynchronous intervals, where the degradation is caused by the fault-maker-triggered switching rule, which is unknown for CPS operators. Finally, an illustrative example validates the proposed method. | Xin Huang Jiuxiang Dong | 2020 | IEEE/CAA Journal of Automatica Sinica2020，7，3： | 1 |
| 10 | Conflict-Aware Safe Reinforcement Learning:A Meta-Cognitive Learning Framework显示文摘In this paper,a data-driven conflict-aware safe reinforcement learning(CAS-RL)algorithm is presented for control of autonomous systems.Existing safe RL results with predefined performance functions and safe sets can only provide safety and performance guarantees for a single environment or circumstance.By contrast,the presented CAS-RL algorithm provides safety and performance guarantees across a variety of circumstances that the system might encounter.This is achieved by utilizing a bilevel learning control architecture:A higher metacognitive layer leverages a data-driven receding-horizon attentional controller(RHAC)to adapt relative attention to different system’s safety and performance requirements,and,a lower-layer RL controller designs control actuation signals for the system.The presented RHAC makes its meta decisions based on the reaction curve of the lower-layer RL controller using a metamodel or knowledge.More specifically,it leverages a prediction meta-model(PMM)which spans the space of all future meta trajectories using a given finite number of past meta trajectories.RHAC will adapt the system’s aspiration towards performance metrics(e.g.,performance weights)as well as safety boundaries to resolve conflicts that arise as mission scenarios develop.This will guarantee safety and feasibility(i.e.,performance boundness)of the lower-layer RL-based control solution.It is shown that the interplay between the RHAC and the lower-layer RL controller is a bilevel optimization problem for which the leader(RHAC)operates at a lower rate than the follower(RL-based controller)and its solution guarantees feasibility and safety of the control solution.The effectiveness of the proposed framework is verified through a simulation example. | Majid Mazouchi Subramanya Nageshrao Hamidreza Modares | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，3： | 1 |
| 11 | 基于平行测试的认知自动驾驶智能架构研究显示文摘在大数据、云计算和机器学习等新一代人工智能技术的推动下,自动驾驶的感知智能在近年来得到显著的提升与发展.然而,与人类驾驶过程中隐含的以自我目的实现为引导的自探索性和自主性相比,现阶段自动驾驶技术主要以辅助驾驶功能为主,还停留在以被动感知、规划与控制为主的初级智能自动驾驶阶段.为实现车辆智能从数据驱动的环境感知、辅助决策、被动规划到知识驱动的场景认知、推理决策、主动规划的提升,亟需增强车辆自身对复杂外界信息归纳提炼、推理决策、评价估计等类人能力.首先回顾自动驾驶关键技术演化及其应用发展历程;随后分析测试对车辆智能评估的效用;然后基于平行测试理论,提出自动驾驶车辆认知智能训练、测试与评估空间的构建方法,并设计基于平行测试的认知自动驾驶智能训练框架.该项研究工作预期能为推动自动驾驶从感知智能向认知智能的升级提供可行的技术支撑与实现路径. | 王晓 张翔宇 周锐 田永林 王建功 陈龙 孙长银 | 2024 | 自动化学报2024，50，2： | 0 |
| 12 | Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets,and Future Directions显示文摘In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0. | Mohamed Amine Ferrag Lei Shu Othmane Friha Xing Yang | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，3： | 0 |
| 13 | CLAB模型:一种乘客出租出行需求短时预测的深度学习模型显示文摘乘客出行需求预测是智能交通系统的组成部分,准确的出行需求预测,对于车辆调度具有重要的意义;然而现有的预测方法无法准确的挖掘其潜在的时空相关性,且大都忽略历史流入量对出行需求的影响。为了进一步挖掘时空大数据中的时空特性及提升模型预测乘客出行需求的精度,本文提出了一种乘客出租出行需求短时预测CLAB(Conv-LSTM Attention BiLSTM)模型。CLAB模型设置了3个模块分别为基于注意力机制的Conv-LSTM模块和2个BiLSTM模块,基于注意力机制的Conv-LSTM模块提取临近时刻乘客出行需求量中的空间特征和短时时间特征,其中注意力机制能自动分配不同的权重来判别不同时间的需求量序列重要性;为了探索长期时间特征,用2个BiLSTM模块来提取历史流入量序列时间特征和日乘客需求量序列的时间特征。采用厦门岛的网约车和巡游车的订单数据进行实验,结果表明:(1)CLAB模型更适用于使用30 min历史数据预测未来5 min短时乘客出行需求;(2)与基准预测模型相比,CLAB模型的整体的效果误差更低,具有更好的预测效果,CLAB模型比CNN-LSTM、LSTM、BiLSTM、CNN和Conv-LSTM的平均绝对误差(MAE)分别降低了33.179%、33.153%、33.204%、5.401%和5.914%,均方根误差(RMSE)分别降低了34.389%、34.423%、34.524%、6.772%和6.669%;(3)同时发现CLAB模型在规律性较高的工作日预测效果优于非工作日,且工作日早高峰预测效果最佳。 | 周榆欣 邬群勇 | 2023 | 地球信息科学学报2023，25，1： | 0 |
| 14 | 并行深度强化学习的柴油机动力系统VGT智能控制显示文摘针对智能网联(ICV)在动力总成控制领域缺乏相关研究,传统动力总成控制既不智能也不网联的情况,采用最新的深度强化学习算法控制可变截面涡轮(VGT),促进传统内燃动力向智能网联发展。以某台可变几何截面涡轮柴油机为列,分别采用深度强化学习控制方法和PID控制方法进行仿真。结果表明:并行深度强化学习明显优于传统控制方法,最终收敛奖励值超过PID控制,4线程和8线程控制的绝对误差分别提升了37.87%和42.71%。 | 赖晨光 伍朝兵 李家曦 孙友长 胡博 | 2022 | 重庆理工大学学报(自然科学)2022，36，6： | 0 |
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