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TechRxiv 2022年

作者： Lin, Hao Wang, Chun-Dong Gao, Hao-Yu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing Novel Software Technology Ministry of Education Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China College of Data Science and Application Inner Mongolia University of Technology Hohhot China

Cybersecurity researchers always ignore the "people" behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— "How to solve cyberspace security issues via social engineering?" We briefly review existing frameworks for social engineering attacks and propose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four "guidelines" for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field. © 2022, CC BY.

关键词： Cybersecurity

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Novel Semi-Supervised Sparse Stacked Autoencoder Integrated with Local Linear Embedding for Industrial Soft Sensing

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ISA Transactions 2025年

作者： Yan-Lin He Yu Jiang Hui-Hui Gao Yuan Xu Qun-Xiong Zhu College of Information Science & Technology Beijing University of Chemical Technology Beijing 100029 China School of Information Science and Technology Engineering Research Center of Digital Community Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing 100124 China

Data-driven industrial soft sensor modeling techniques have been widely applied in predicting key variables in complex industrial processes. However, with industrial processes becoming increasingly intricate, the data they produce exhibit characteristics such as strong temporal dependencies, high dimensionality, and local structures, posing significant challenges for soft *** address these issues, this paper proposes novel Semi-Supervised Sparse Stacked Autoencoder integrated with the Local Linear Embedding algorithm (SS-SAE-LLE). Unlike traditional autoencoders (AE), which capture hierarchical data features by minimizing global fitting error, SS-SAE-LLE simultaneously accounts for the spatio-temporal characteristics of the data through the local linear embedding algorithm. Moreover, it incorporates supervised tuning by leveraging labeled data and training with a semi-supervised learning framework, further improving prediction accuracy. To evaluate the feasibility of the proposed method, experiments are conducted on PTA solvent and SRU system datasets. The simulation results demonstrate that SS-SAE-LLE achieves higher prediction accuracy than other models, highlighting its applicability in the field of industrial soft sensor modeling.

关键词： Data-driven modelling Industrial processes Industrial soft sensors Local features

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Cosmology with fast radio bursts in the era of SKA

arXiv

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arXiv 2023年

作者： Zhang, Ji-Guo Zhao, Ze-Wei Li, Yichao Zhang, Jing-Fei Li, Di Zhang, Xin Department of Physics College of Sciences Northeastern University Shenyang110819 China Northeastern University Shenyang110819 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

We present a forecast of the cosmological parameter estimation using fast radio bursts (FRBs) from the upcoming Square Kilometre Array (SKA), focusing on the issues of dark energy, the Hubble constant, and baryon density. We simulate 105 and 106 localized FRBs from a 10-year SKA observation, and find that: (i) using 106 FRB data alone can tightly constrain dark-energy equation of state parameters better than CMB+BAO+SNe, providing an independent cosmological probe to explore dark energy;(ii) combining the FRB data with gravitational-wave standard siren data from 10-year observation with the Einstein Telescope, the Hubble constant can be constrained to a sub-percent level, serving as a powerful low-redshift probe;(iii) using 106 FRB data can constrain the baryon density Ωbh to a precision of ∼ 0.1%. Our results indicate that SKA-era FRBs will provide precise cosmological measurements to shed light on both dark energy and the missing baryon problem, and help resolve the Hubble tension. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

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Impolite Pedestrian Detection by Using Enhanced YOLOv3-Tiny

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Journal on Artificial intelligence 2020年第3期2卷 113-124页

作者： Yanming Wang Kebin Jia Pengyu Liu Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Laboratory of Advanced Information Networks Beijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124China

In recent years,the problem of“Impolite Pedestrian”in front of the zebra crossing has aroused widespread concern from all walks of *** traffic sector’s governance measures have become more *** traditional way of governance is on-site law enforcement,which requires a lot of manpower and material resources and is low *** enhanced YOLOv3-tiny model is proposed for pedestrians and vehicle detection in traffic *** modifying the backbone network structure of YOLOv3-tiny model,introducing deep detachable convolution operation,and designing the basic residual block unit of the network,the feature extraction ability of the backbone network is *** improved model is trained on the VOC2007+VOC2012 training set,and the trained model is tested for performance on the test data *** experimental results show that:the mean Average Precision(mAP)increased from 0.672 to 0.732,increasing the measurement accuracy by 9%.The Intersection over Union(IoU)increased from 0.783 to 0.855,increasing the coverage accuracy by 7.2%.The enhanced YOLOv3-tiny model has higher measurement accuracy than the original *** this model to the 1080P traffic video on the NVIDIA RTX 2080,the detection speed is 150 FPS,which can fully achieve real-time *** the analysis of pedestrians and vehicle coordinates,it is judged whether or not illegal acts *** illegal vehicles,save three pictures as the basis for law enforcement,which forms an important supplement to off-site law enforcement.

关键词： Impolite pedestrian YOLOv3-tiny deep detachable convolution residual block off-site law enforcement

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Food Safety Crime Risk Assessment Based on Set Pair Analysis

Food Safety Crime Risk Assessment Based on Set Pair Analysis

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第33届中国控制与决策会议

作者： Yupeng Zhai Xiaoli Li Kang Wang Yang Li Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Engineering Research Center of Digital Community Ministry of EducationBeijing University of Technology School of International Studies Communication University of China (CUC)

With the development of economy and society,major activities such as conferences and sports events held in China are becoming more *** is of great significance to ensure food safety for major *** paper focuses on the risk of man-made food safety crime,analyzes the possibility of food crime of suspicious person from four aspects:social text sentiment classification,personnel profile,trajectory analysis,and food crime incident related element analysis,and evaluates the risk of food safety crime by means of Set Pair *** result shows that Set Pair Analysis method is easy to calculate and the evaluation result is reliable.

关键词： Food Safety Risk Assessment Set Pair Analysis

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Rotation Matrix Oriented Visual Inertial Online Initialization

Rotation Matrix Oriented Visual Inertial Online Initializati...

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IEEE International Conference on Real-time computing and Robotics (RCAR)

作者： Tao Zheng Guoyu Zuo Zichen Xu Daoxiong Gong Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

In the monocular vision inertial system, an accurate real-time initialization state estimator is the prerequisite for the system to maintain steady operation. However, in the initialization of most current monocular visual inertial systems, the impact of rotation matrix on the other external parameters, such as gyroscope bias, accelerometer bias and velocity, is often ignored. Moreover, in global optimization, the keyframe constraints in the sliding window are not fully utilized, resulting in the continuous accumulation of estimated path errors. In order to solve the above problems, we propose our monocular visual inertial system based on the initialization state estimator with online external calibration and the relative pose based marginalization. In this system, the external parameters of the camera and IMU are calibrated during the initialization phase, the estimations of speed, gyroscope bias, accelerometer bias, scale factor and gravity are performed, and the relative pose optimization method is adopted to retain the constraint relation of keyframes to be marginalized in the optimization window and then input into the global optimization. Experiments show that our system can give real-time and accurate estimation in initialization, and can realize more stable and accurate pose estimations, even with continuous error accumulation.

关键词： Optical wavelength conversion Gyroscopes Calibration Cameras Visualization Optimization Acceleration

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Low-Quality Rendering-Driven 6D Object Pose Estimation from Single RGB Image

Low-Quality Rendering-Driven 6D Object Pose Estimation from ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Guoyu Zuo Chengwei Zhang Hongxing Liu Daoxiong Gong Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The 6D object pose obtained from single RGB image has broad applications such as robotic manipulation and virtual reality. Among many existing methods, the deep learning-based approaches for object pose estimation from single RGB image are widely used. However, they often require a large amount of training data, which has great challenges in high cost of data collection and lack of 3D information. In this paper, we introduce an object pose estimation architecture that takes a single RGB image as input and directly outputs rotation angles and translation vectors. A data generation pipeline that applies the idea of domain randomization is used to generate millions of low-quality rendering images. Then the pose estimation is realized by fusing the architecture and the domain randomization approach to utilize the generated information and low the data collection cost. We synthesized a big dataset called Pose6DDR whose images are similar to those in the LineMod dataset. Experiments demonstrated the effectiveness of the proposed 6D object pose estimation architecture as compared to the relevant competing technologies.

关键词： Three-dimensional displays Pose estimation Two dimensional displays Computer architecture Training Robots Solid modeling

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A Neural Critic Learning Method for Nonlinear Decentralized Stabilization with Unknown Perturbation

A Neural Critic Learning Method for Nonlinear Decentralized ...

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第31届中国过程控制会议（CPCC 2020）

作者： HUO Yu WANG Ding QIAO Junfei Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology

In order to realize the robust decentralized stabilization of continuous-time nonlinear systems with general uncertainties, a neural-network-based online optimal control strategy is established, where the solution of the corresponding Hamilton-Jacobi-Bellman(HJB) equation is obtained. Specifically, as a necessary part of the control system, a neural network is involved with strong online learning ability. The novelty of this paper is that the well-known adaptive dynamic programming method is extended to deal with the nonlinear feedback control problem under uncertain and large-scale environment. First, the robust decentralized stabilization problem is transformed into the decentralized optimal control design problem. On this basis, a critic neural network is constructed to solve the modified HJB equation corresponding to the nominal system in an online form. Moreover, a novel updating rule compared to the traditional adaptive critic algorithms is proposed, which reduces the requirement of the initial stabilizing controller and brings in particular reinforcements to the adaptive critic control design. Finally, an example is included to show the feasibility and effectiveness of the obtained theoretical results.

关键词： Adaptive dynamic programming Dynamical uncertainty Neural networks Optimal control Robust decentralized stabilization

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Real-time Abnormal Behavior Recognition and Monitoring system Based on Panoramic Video

Real-time Abnormal Behavior Recognition and Monitoring Syste...

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第三十九届中国控制会议

作者： Jiangeng Li Haizheng Xie Zhibo Zang Guangsheng Wang College of Automation Faculty of Information TechnologyBeijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Traditional monitoring systems are mostly monocular cameras, the main function of which is to play and store video in the monitoring area and to achieve the collection and judgment of the information in the video through artificial observation. Usually, emergencies that are not found and processed in time will cause serious situations. Compared with the monocular camera, the panoramic camera has a wide field of view and no blind spots. This system uses a panoramic camera to achieve 360-degree video acquisition, and uses deep learning to process the video to obtain the posture of the human body, and then processes it through the human behavior recognition network to identify the abnormal human behavior in real time. Through the dual warnings of local and remote clients and the artificial re-judgment of the received abnormal behavior video, the manager can timely find and handle abnormal behavior. This system not only saves labor costs, but also improves the utilization efficiency of video. The 360-degree all-round perspective provides the area manager with an immersive environment reproduction, allowing him to grasp the details of the scene in real time. This system provides virtual on-site conditions for decision-making and handling of emergencies, enabling early warning and minimizing losses.

关键词： panoramic video deep learning abnormal behavior real-time recognition

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A curiosity-based mobile robot path planning method 4

A curiosity-based mobile robot path planning method

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4th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019

作者： Zhang, Jiahui Ruan, Xiaogang Huang, Jing Chai, Jie Wu, Yue Beijing University of Technology Faculty of Information Technology China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781728119076

Curiosity plays an important role in the development of biological cognition, which drives animals to explore unknown environments. Inspired by the research of psychology, this paper proposes a curiosity-based mobile robot path planning method with a mental energy function, which is suitable for robot state evaluation. In our proposed method, a novel mental energy function is designed to guide the learning direction of the robot in navigation task. The experimental results show that the novel method we proposed can effectively guide robot to learn environment information. By changing the simulation environment, robot can reach the end point through re-learning the environment, that reflects the autonomous learning ability and the environmental adaptability of robots. © 2019 IEEE.

关键词： Mobile robots

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