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Method and Practice of Trusted Embedded Computing and Data Transmission Protection Architecture Based on Android

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Chinese Journal of Electronics 2024年第3期33卷 623-634页

作者： Yichuan WANG Wen GAO xinhong hei Yanning DU School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

In recent years,the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile *** with it comes the security problems brought by the mobile *** for Android system,due to its open source nature,malicious applications continue to emerge,which greatly threatens the data security of ***,this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and *** conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption,storage overhead and *** experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process *** with the native system,the additional performance overhead is small.

关键词： Embedded computing Protocols Data security Computer architecture Encryption Internet Data communication

Motion trajectory prediction based on a CNN-LSTM sequential model

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Science China(Information Sciences) 2020年第11期63卷 248-268页

作者： Guo XIE Anqi SHANGGUAN Rong FEI Wenjiang JI Weigang MA xinhong hei School of Automation and Information Engineering School of Computer Science and EngineeringXi'an University of Technology

Accurate monitoring the surrounding environment is an important research direction in the field of unmanned systems such as bio-robotics, and has attracted much research attention in recent years. The trajectories of surrounding vehicles should be predicted accurately in space and time to realize active defense and running safety of an unmanned system. However, there is uncertainty and uncontrollability in the process of trajectory prediction of surrounding obstacles. In this study, we propose a trajectory prediction method based on a sequential model, that fuses two neural networks of a convolutional neural network(CNN) and a long short-term memory network(LSTM). First, a box plot is used to detect and eliminate abnormal values of vehicle trajectories, and valid trajectory data are obtained. Second, the trajectories of surrounding vehicles are predicted by merging the characteristics of CNN space expansion and LSTM time expansion;the hyper-parameters of the model are optimized according to a grid search algorithm, which satisfies the double-precision prediction requirement in space and time. Finally, data from next generation simulation(NGSIM) and Creteil roundabout in France are taken as test cases; the correctness and rationality of the method are verified by prediction error indicators. Experimental results demonstrate that the proposed CNNLSTM method is more accurate and features a shorter time cost, which meets the prediction requirements and provides an effective method for the safe operation of unmanned systems.

关键词： bio-robots unmanned system outlier detection hyper-parameters trajectory prediction

A car-following model based on the optimized velocity and its security analysis

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Transportation Safety and Environment 2023年第4期5卷 127-134页

作者： Rong Fei Lu Yang xinhong hei Bo Hu Aimin Li School of Computer Science and Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an 710048ShaanxiP.R.China Software Design Department Hangzhou Holly Sys Automation Co.Ltd.No.29Jinye 1st RoadXi’an 710065ShaanxiP.R.China

An enhanced optimal velocity model(EOVM)that considers driving safety is established to alleviate traffic congestion and ensure driving *** headway is introduced as a criterion for determining whether the car is *** the time headway is less discussed to ensure the model's safety and maintain the following state.A stability analysis of the model was carried out to determine than the minimum time headway(TH_(min))or more than the most comfortable time headway(TH_(com)),the acceleration constraints are the stability conditions of the *** EOVM is compared with the optimal velocity model(OVM)and fuzzy car-following model using the real *** show that the EOVM model has the smallest error in average,maximum and median with the real *** confirm the model's safety,design fleet simulation experiments were conducted for three actual scenarios of starting,stopping and uniform process.

关键词： optimized velocity constraint optimization security analysis

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An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

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Computer Modeling in Engineering & Sciences 2023年第1期134卷 637-655页

作者： Zhaoxi Ma Qin Zhao Tianyou Cang Zongjian Li Yiyun Zhu xinhong hei Department of Civil Engineering and Architecture Xi’an University of TechnologyXi’an710048China State Key Laboratory of Rail Transit Engineering Informatization(FSDI) Xi’an710043China Department of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China

To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable *** one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction *** cutting waste increases construction costs and generates a considerable amount of CO_(2)*** study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable *** the proposed algorithm,the integer linear programming algorithm was applied to solve the *** was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data *** proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction *** the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation *** the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.

关键词： Building construction rebar work cutting stock problem optimization algorithm integer linear programming

A Risk Prediction Model Based on Crash History Data for Railway Trams

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Chinese Journal of Electronics 2023年第5期32卷 963-971页

作者： JI Wenjiang YANG Jiangcheng WANG Yichuan ZHU Lei QIU Yuan hei xinhong School of Computer Science and Engineering Xi'an University of Technology

Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means clustering algorithm and the least squares deep convolution generative adversarial network(LS-DCGAN). The data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the extreme gradient boosting recursive feature elimination based feature selection algorithm to retain the key features. The LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%,which can greatly enhances the tram driving safety.

关键词： Solid modeling Clustering algorithms Feature extraction Prediction algorithms Rail transportation Safety History

An Research for Formal Verification of Safety-Critical Software

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An Research for Formal Verification of Safety-Critical Softw...

Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021:A survey

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作者： Weigang Ma xinhong hei Key Laboratory of Computer Networks and Information Security (Ministry of Education) Xidian University School of Computer Science and Engineering Xi'an University of Technology

A verification methodology is presented for railway interlocking system which is regarded as a safety-critical system. The methodology utilizes UML to model the function requirement and LTL to verify the safety requirements of the specification. The device specifications of railway interlocking system are modeled with UML, then translate into FSM. The safety specification is translated LTL and analyzed with NuSMV. We try to show the feasibility of improving the reliability and reducing revalidation efforts when designing and developing a decentralized railway signaling system.

关键词： SafetyCritical Software FSM LTL UML

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Brain Science Advances 2022年第2期8卷 127-141页

作者： Jing Luo Qi Mao Yaojie Wang Zhenghao Shi xinhong hei Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and EngineeringXi’an University of TechnologyXi’an 710054ShaanxiChina

Objective:From September 10 to 13,2021,the finals of the BCI Controlled Robot Contest in World Robot Contest 2021 were held in Beijing,*** teams participated in the Algorithm Contest of Calibration-free Motor Imagery *** participants employed both traditional electroencephalograph(EEG)analysis methods and deep learning-based methods in the *** this paper,we reviewed the algorithms utilized by the participants,extracted the trends and highlighted interesting approaches from these methods to inform future contests and research ***:First,we analyzed the algorithms in separate steps,including EEG channel and signal segment setup,prepossessing technology,and classification ***,we emphasized the highlights of each ***,we compared the competition algorithm with the SOTA ***:The algorithm employed in the finals performed better than that of the SOTA *** the final stage of the contest,four of the top five teams used convolutional neural network models,suggesting that with the rapid development of deep learning,convolutional neural network-based models have been the most popular methods in the field of motor imagery BCI.

关键词： brain-computer interface motor imagery con-volutional neural network World Robot Contest

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Game strategies for distributed denial of service defense in the Cloud of Things

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Journal of Communications and Information Networks 2016年第4期1卷 143-155页

作者： WANG Yichuan ZHANG Yefei hei xinhong JI Wenjiang MA Weigang Faculty of Computer Science and Engineering Xi’an University of TechnologyXi’an 710048China

Integration of the IoT(Internet of Things)with Cloud Computing,termed as the CoT(Cloud of Things)can help achieve the goals of the envisioned IoT and future *** a typical CoT infrastructure,the data collected from wireless sensor networks and IoTs is transmitted through a SG(Smart Gateway)to the *** bandwidth between an IoT access point and SG becomes a bottleneck for information transmission between the IoT and the *** propose a novel game theory model to describe the CoT attacker,who expects to use minimum set and energy consumption of IoT attack devices to occupy as many bandwidth resources as possible in a given time period;and the defender,who expects to minimize false *** analyzing this model,we have found that the game theory model is a non-cooperative and repeated incomplete information game,and Nash equilibrium is existent,perfected by the *** best strategy for each stage of the attack is to adjust the attack link number dynamically based on the comparison results of value E and turning point E_(0)for each time *** the same time,the defender adjusts the threshold valueβdynamically,based on the comparison results of the Load value and expected value ofαfor each time *** simulation result shows that our strategy can significantly mitigate the harm of a distributed denial of service attack.

关键词： Cloud of Things network security DDoS attack smart gateway energy consumption

Deep Reinforcement Learning Object Tracking Based on Actor-Double Critic Network

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CAAI Artificial Intelligence Research 2023年第1期2卷 32-44页

作者： Jing Xin Jianglei Zhou xinhong hei Pengyu Yue Jia Zhao School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China School of Computer Science and Engineering Xi’an University of TechnologyXi’an 710048China

Aiming at the problem of poor tracking robustness caused by severe occlusion,deformation,and object rotation of deep learning object tracking algorithm in complex scenes,an improved deep reinforcement learning object tracking algorithm based on actor-double critic network is *** offline training phase,the actor network moves the rectangular box representing the object location according to the input sequence image to obtain the action value,that is,the horizontal,vertical,and scale transformation of the ***,the designed double critic network is used to evaluate the action value,and the output double Q value is averaged to guide the actor network to optimize the tracking *** design of double critic network effectively improves the stability and convergence,especially in challenging scenes such as object occlusion,and the tracking performance is significantly *** online tracking phase,the well-trained actor network is used to infer the changing action of the bounding box,directly causing the tracker to move the box to the object position in the current *** comparative tracking experiments were conducted on the OTB100 visual tracker benchmark and the experimental results show that more intensive reward settings significantly increase the actor network’s output probability of positive *** makes the tracking algorithm proposed in this paper outperforms the mainstream deep reinforcement learning tracking algorithms and deep learning tracking algorithms under the challenging attributes such as occlusion,deformation,and rotation.

关键词： object tracking deep reinforcement learning actor-double critic network