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A hybrid monotone decision tree model for interval-valued attributes

Advances in Computational intelligence 2021年第1期2卷 1-11页

作者： Chen, Jiankai Li, Zhongyan Wang, Xin Zhai, Junhai School of Control and Computer Engineering North China Electric Power University Beijing China Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China School of Mathematics and Physics North China Electric Power University Beijing China

The existing monotonic decision tree algorithms are based on a linearly ordered constraint that certain attributes are monotonously consistent with the decision, which could be called monotonic attributes, whereas others, called non-monotonic attributes. In practice, monotonic and non-monotonic attributes coexist in most classification tasks, and some attribute values are even evaluated as interval numbers. In this paper, we proposed a fuzzy rank-inconsistent rate based on probability degree to judge the monotonicity of interval numbers. Furthermore, we devised a hybrid model composed of monotonic and non-monotonic attributes to construct a mixed monotone decision tree for interval-valued data. Experiments on artificial and real-world data sets show that the proposed hybrid model is effective.

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Dispatching and control Information Freshness-Aware Federated Learning for Simplified Power IoT

Dispatching and Control Information Freshness-Aware Federate...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Zehan Jia Ziqi Yu Haijun Liao Zhao Wang Zhenyu Zhou Xiaoyan Wang Guoqing He Shahid Mumtaz Mohsen Guizani Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Beijing Baoding Hebei China Graduate School of Science and Engineering Ibaraki University Ibaraki-ken Japan State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China Instituto de Telecomunicações Aveiro Portugal Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (纸本)9781665435413

Dispatching and control information freshness conducts an important impact on the training accuracy of distributed energy dispatching and control model. Poor information freshness will increase the loss function of the training model, and reduce the reliability and economy of dispatching and control. Simplified power internet of things can provide plug-and-play and multi- mode fusion communication support, but it still faces challenges of the coupling of model training and data transmission as well as the difficulty in guaranteeing dispatching and control information freshness. In this paper, a semi-distributed federated learning- based framework for dispatching and control model training decision-making is proposed, and a dispatChing and control informAtion fReshness-aware batch size Optimization aLgorithm (CAROL) is presented. CAROL leverages deep Q network and dispatching and control information freshness awareness to learn the batch size optimization strategy. CAROL can minimize model loss function while guaranteeing long-term dispatching and control information freshness constraints. Compared with existing feder- ated learning algorithms, CAROL achieves superior performance in global loss function and information freshness.

关键词： Training Computational modeling Simulation Decision making Minimization Dispatching Data models

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Progress of the TianQin project

arXiv

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arXiv 2025年

作者： Luo, Jun Bai, Shaojun Bai, Yan-Zheng Cai, Lin Dang, Hao Dong, Qijia Duan, Hui-Zong Du, Yuanbo Fan, Lei Fu, Xinju Gao, Yong Gou, Xingyu Gu, Defeng Guo, Changlei Hong, Wei Hu, Bin Hu, Heran Hu, Ming Hu, Yi-Ming Huang, Fa Peng Ji, Xin Jiang, Yuan-Ze Li, En-Kun Li, Hongyin Li, Ming Li, Ming Li, Yong Li, Zhu Li, Zizheng Lian, JunXiang Liang, Yu-Rong Lin, Xudong Liu, Jianping Liu, Lin-Xia Liu, Kui Liu, Li Liu, Minghe Liu, Qi Liu, Yan-Chong Liu, Yue Luo, Peng-Shun Luo, Yingxin Ma, Yi-Qiu Ma, Yun Meng, Yunhe Milyukov, Vadim Peng, Jian-Guo Postnov, Konstantin Qu, Shao-Bo Shan, Tilei Shao, Cheng-Gang Shi, Changfu Song, Pei-Yi Song, Yunfei Su, Wei Tan, Ding Yin Tan, Shuping Tan, Yu-Jie Tan, Wenhai Tu, Liangcheng Wang, Cheng-Rui Wang, Guoyong Wang, Lijiao Wang, Pan-Pan Wang, Shun Wang, Xiaoyong Wang, Xudong Wang, Yan Wei, Ran Wu, Shu-Chao Xu, Jie Xu, Zhi-Lin Xue, Chao Yan, Hao Yan, Yong Yang, Changpeng Yang, Shanqing Yeh, Hsien-Chi Yin, Hang Tong, Yelong Yu, Jian-Bo Yuan, Wen-Hao Zhang, Bu-Tian Zhang, Dexuan Zhang, Jian-Dong Zhang, Jie Zhang, Lihua Zhang, Xuefeng Zhao, Guoying Zhao, Liqian Zhao, Xin Zhou, An-Nan Zhou, Hao Zhou, Peng Zhou, Yupeng Zhou, Ze-Bing Zhu, Fan Zhu, Liang-Gui Zhu, Lin Zou, Kui Mei, Jianwei MOE Key Laboratory of TianQin Mission TianQin Research Center for Gravitational Physics School of Physics and Astronomy Frontiers Science Center for TianQin Gravitational Wave Research Center of CNSA Sun Yat-sen University Zhuhai Campus Zhuhai519082 China National Gravitation Laboratory MOE Key Laboratory of Fundamental Physical Quantities Measurement School of Physics Huazhong University of Science and Technology Wuhan430074 China DFH Satellite Co. Ltd. Beijing100094 China Beijing Institute of Space Mechanics & Electricity Beijing100076 China National Key Laboratory of Spacecraft Thermal Control of Institute of Spacecraft System Engineering Beijing100094 China Space Star Technology CO. LTD. Beijing100095 China Beijing Institute of Control Engineering Beijing100094 China School of Artificial Intelligence Sun Yat-sen University Zhuhai Campus Zhuhai519082 China Shanxi Xi’an710199 China Sternberg Astronomical Institute M.V. Lomonosov Moscow State University Moscow119234 Russia Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China

TianQin is a future space-based gravitational wave observatory targeting the frequency window of 10−4 Hz ∼ 1 Hz. A large variety of gravitational wave sources are expected in this frequency band, including the merger of massive black hole binaries, the inspiral of extreme/intermediate mass ratio systems, stellar-mass black hole binaries, Galactic compact binaries, and so on. TianQin will consist of three Earth orbiting satellites on nearly identical orbits with orbital radii of about 105 km. The satellites will form a normal triangle constellation whose plane is nearly perpendicular to the ecliptic plane. The TianQin project has been progressing smoothly following the "0123" technology roadmap. In step "0", the TianQin laser ranging station has been constructed and it has successfully ranged to all the five retro-reflectors on the Moon. In step "1", the drag-free control technology has been tested and demonstrated using the TianQin-1 satellite. In step "2", the inter-satellite laser interferometry technology will be tested using the pair of TianQin-2 satellites. The TianQin-2 mission has been officially approved and the satellites will be launched around 2026. In step "3", i.e., the TianQin-3 mission, three identical satellites will be launched around 2035 to form the space-based gravitational wave detector, TianQin, and to start gravitational wave detection in space. © 2025, CC BY.

关键词： Laser interferometry

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ZND-ZeaD Models and Theoretics Including Proofs for Takagi Factorization of Complex Time-Dependent Symmetric Matrix

ZND-ZeaD Models and Theoretics Including Proofs for Takagi F...

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第33届中国控制与决策会议

作者： Zhuosong Fu Min Yang Jinjin Guo Jianrong Chen Yunong Zhang School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Electronics and Information Technology Sun Yat-sen University School of Public Health and Management Youjiang Medical University for Nationalities

Takagi factorization(TF) is a kind of matrix factorization(also termed,decomposition) *** this paper,the problem of TF for complex time-dependent symmetric matrix(simply termed,time-dependent Takagi factorization)is presented and theoretically *** simplify the tough problem,we first consider converting it into an equivalent equation ***,a continuous-time(CT) TF(CTTF) model is presented and derived theoretically,by adopting ZND(Zhang neural dynamics,or termed,Zhang neural network) method and a dimensional reduction *** order to facilitate the implementation on digital hardware platforms,the corresponding discrete-time(DT) ZND solution models(or termed,algorithms) with different discretization precisions of the CTTF model are presented and taken into theoretical ***,we present and investigate a discrete-time TF(DTTF) model,termed eight-instant DTTF(8IDTTF) model,by using a new eight-instant Zhang et al discretization(ZeaD,also termed,Zhang time discretization,ZTD) formula to discretize the CTTF ***,two other DTTF models,termed four-instant DTTF(4IDTTF) and six-instant DTTF(6IDTTF) models,are presented respectively,on the basis of two corresponding multiinstant ZeaD ***,the effective solution step-size intervals of the three DTTF models are further presented and proved.

关键词： Time-dependent Takagi factorization Dimensional reduction Zhang neural dynamics(ZND) Kronecker product Zhang et al discretization(ZeaD) formulas

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Highly Sensitive and Rapidly Responding Room-Temperature NH3 Gas Sensor that is Based on Exfoliated Black Phosphorus

SSRN

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SSRN 2022年

作者： Han, Dan Han, Xiaomei Zhang, Xiaqin Wang, Weili Li, Donghui Li, Hongwei Sang, Shengbo Shanxi Province Cancer Hospital Chinese Academy of Medical Sciences Cancer Hospital Shanxi Medical University Taiyuan030000 China Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education Taiyuan University of Technology Taiyuan030024 China

Black phosphorus (BP) has recently attracted significant interest in gas sensing because of its outstanding electronic and optical properties. In this paper, we demonstrated a series of gas sensors for detecting NH3 at room temperature that were based on BP nanosheets that were exfoliated for various durations. The BP nanosheet gas sensors showed excellent sensitivity for the detection of NH3 down to 100 ppb (~32%) under ambient conditions compared to previous reports. The BP nanosheets that were exfoliated for 60 min showed the highest response to 100 ppm NH3, namely, 121%, which was 2.5 times greater than the response of ground BP. The corresponding sensor also exhibited a stable response in the humidity range of 22% RH to 35% RH, which is the typical humidity range for non-rainy days. Moreover, the prepared NH3 gas sensor showed high selectivity, fast response and recovery, and excellent repeatability. According to various experimental and theoretical studies, the response and binding energy of BP towards NOx were higher than those towards NH3. However, very interestingly, the response of the exfoliated BP nanosheets to NH3 was higher than that to NOx in our work. The selectivity transformation from NOx to NH3 was mainly caused by the different layer spacing of the ground BP and the change in spacing between the BP nanosheet layers with different exfoliation times, increased oxygen incorporation, phosphate ions and defects that is induced by the liquid-phase exfoliation process, which led to the variations in its band gap and surface states. © 2022, The Authors. All rights reserved.

关键词： Nanosheets

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Three-Variable and Five-Variable Weng-Zhang Algorithms of Different Traversal Types Applied to UKHP Prediction

Three-Variable and Five-Variable Weng-Zhang Algorithms of Di...

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Chinese Automation Congress (CAC)

作者： Yunong Zhang Yining Zhang Lilian Li School of Compute Science and Engineering Sun Yat-sen University Guangzhou China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Key Laboratory of Modern Control Technology Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665426480

UK hung parliament (UKHP) prediction is a concern. It may not only cause political issues, but also lead to troubles in other fields such as economics. It is important to make predictions on UKHP. In this paper, we use three-variable and five-variable Weng-Zhang algorithms (also termed addition- subtraction and frequency [ASF] algorithms) of different traversal types to predict the risky time-intervals and years of UKHP to give an early alert for avoidance. Experiment results show that 2021-2023, 2047-2068, and 2098-2104 are more potentially risky, and 2023, 2053, 2055, 2061, 2068, and 2098 have a greater risk for the occurrence of UKHP.

关键词： Economics Time-frequency analysis Automation Human factors Prediction algorithms

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AADL and Modelica model combination and model conversion based on CPS 4

AADL and Modelica model combination and model conversion bas...

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4th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2020

作者： Zhu, Yifeng Cao, Zining Wang, Fujun Lu, Weiwei College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China College of Computer Science and Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Miit Key Laboratory of Pattern Analysis and Machine Intelligence Luoyang China Science and Technology on Electro-optic Control Laboratory Luoyang China

ISBN: (纸本)9781450387811

Cyber-Physical system (CPS), which realizes the close integration of physical resources and information resources, is a distributed and asynchronous dynamic hybrid system running in different time and space. In this paper, we use AADL to model information system and use Modelica to model physical system. However, they are all semi-formal models such that we cannot do future work about formal verification. To overcome this defect, we convert them to formal models like automaton through conversion algorithms and rules. Due to the fact of probabilistic events and resource consumption in CPS, this paper adopts the weighted probabilistic hybrid automaton as formal model, and adds the properties of probability and weight to the behavior annex of AADL. Then we conduct the rule and algorithm of the model conversion. In order to ensure consistency of further work about model checking, the paper prove the equivalence of the two models by bisimulation. Finally, the capability of modeling of the extended modeling language of AADL and the significance of conversion to automaton is illustrated by the aircraft system. © 2020 ACM.

关键词： Cyber Physical system

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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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A Multi-Objective Optimization Algorithm: Power system Hybrid Static Economic Dispatch with Wind Energy

SSRN

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SSRN 2022年

作者： Qu, Linan Li, Lingling Tseng, Ming-Lang Chen, Chao Lim, Ming K. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Nanjing210003 China Institute of Innovation and Circular Economy Asia University Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Adam Smith Business School University of Glasgow United Kingdom

This study proposes a novel multi-objective optimization based on improved pathfinder algorithm (IPFA) algorithm and contributes to build a hybrid static dynamic economic dispatch with wind energy. The novel IPFA optimizes the built hybrid static economic dispatch model, while considering inequality and equality constraints during the model solving process. Especially, new energy system reduces the fuel consumption and operation cost of the system;however, the randomness of renewable energy output destroys the stability of the system. The upper limit of wind power grid connection is limited by the permeability constraint, and the constraints such as forbidden operation area constraint and slope rate constraint are considered. The solution speed and stability of the IPFA algorithm are improved through introducing Kent map initialization, nonlinear adaptation factor, and following correction strategy. The validity of the built model and the optimization performance of the proposed method is verified by two cases. The test results reveal that the operation cost obtained by the IPFA considering wind energy is 9.3% and 7.5% lower than that without considering wind energy. The novel method contributes in ensuring the economy and stability of the power system and improving the utilization rate of renewable energy. © 2022, The Authors. All rights reserved.

关键词： Electric load dispatching

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Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors 13

Cogging Force Identification Based on Self-Adaptive Hybrid S...

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13th International Symposium on Linear Drives for Industry Applications, LDIA 2021

作者： Fu, Xuewei Ding, Chenyang Zanchetta, Pericle Yang, Xiaofeng Tang, Mi Liu, Yang State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical University of Pavia Pavia Italy Power Electronics Machine and Control Group University of Nottingham Nottingham United Kingdom Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9781728172101

The cogging force arising due to the strong attraction forces between the iron core and the permanent magnets, is a common inherent property of the linear motors, which significantly affects the control performance. Therefore, significant research efforts have been devoted to the compensation of the cogging force. In this paper, an identification approach based on the radial basis function neural network (RBFNN) is proposed to obtain an accurate model of the cogging force. A self-adaptive hybrid self-learning teaching-learning-based optimization (SHSLTLBO) method is utilized to train the neural network. Finally, the experimental results confirm the effectiveness and the superiority of the proposed cogging force identification method. © 2021 IEEE

关键词： Radial basis function networks

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