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| 1 | Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring显示文摘Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity monitoring.The scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ analysis.Recording and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination server.Benchmark tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions. | Zhengguo Sheng Saskia Pfersich Alice Eldridge Jianshan Zhou Daxin Tian Victor C.M.Leung | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，1： | 6 |
| 2 | A cohort study of adolescents with depression in China:tracking multidimensional outcomes and early biomarkers for intervention显示文摘Background Depression in adolescents is recognised as a global public health concern,but little is known about the trajectory of its clinical symptoms and pathogenesis.Understanding the nature of adolescents with depression and identifying earlybiomarkers can facilitatepersonalised intervention andreducediseaseburden.Aims To track multidimensional outcomes of adolescents with depression and develop objective biomarkers for diagnosis,as well as response to treatment,prognosis and guidance for early identification and intervention.Methods This is a multidimensional cohort study on the Symptomatic trajectory and Biomarkers of Early Adolescent Depression(sBEAD).We planned to recruit more than 1000 adolescents with depression and 300 healthy controls within 5 years.Multidimensional clinical presentations and objective indicators are collected at baseline,weeks 4,8,12 and 24,and years 1,2,3,4 and 5.Conclusions To the best of our knowledge,this is the first longitudinal cohort study that examines multidimensional clinical manifestations and multilevel objective markers in Chinese adolescents with depression.This study aims at providing early individualised interventions for young,depressed patients to reduce the burden of disease. | Xiaofei Zhang Yanling Zhou Jiaqi Sun Ruilan Yang Jianshan Chen Xiaofang Cheng Zezhi Li Xinlei Chen Chanjuan Yang Xinhong Zhu Liping Cao | 2022 | General Psychiatry2022，35，4： | 2 |
| 3 | Simultaneous determination of dopamine, ascorbic acid and uric acid on ordered mesoporous carbon/Nafion composite film显示文摘 | Dan Zheng Jianshan Ye Liang Zhou Yang Zhang Chengzhong Yu | 2008 | Journal of Electroanalytical Chemistry2008，，1： | 1 |
| 4 | V2I Based Environment Perception for Autonomous Vehicles at Intersections显示文摘In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges.V2I(Vehicle-to-Infrastructure)communication is a potential solution to enable cooperative intelligence of vehicles and roads.In this paper,the RGB-PVRCNN,an environment perception framework,is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology.This framework integrates vision feature based on PVRCNN.The normal distributions transform(NDT)point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection.The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy. | Xuting Duan Hang Jiang Daxin Tian Tianyuan Zou Jianshan Zhou Yue Cao | 2021 | China Communications2021，18，7： | 1 |
| 5 | Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving显示文摘Rule-based autonomous driving systems may suffer from increased complexity with large-scale intercoupled rules,so many researchers are exploring learning-based approaches.Reinforcement learning(RL)has been applied in designing autonomous driving systems because of its outstanding performance on a wide variety of sequential control problems.However,poor initial performance is a major challenge to the practical implementation of an RL-based autonomous driving system.RL training requires extensive training data before the model achieves reasonable performance,making an RL-based model inapplicable in a real-world setting,particularly when data are expensive.We propose an asynchronous supervised learning(ASL)method for the RL-based end-to-end autonomous driving model to address the problem of poor initial performance before training this RL-based model in real-world settings.Specifically,prior knowledge is introduced in the ASL pre-training stage by asynchronously executing multiple supervised learning processes in parallel,on multiple driving demonstration data sets.After pre-training,the model is deployed on a real vehicle to be further trained by RL to adapt to the real environment and continuously break the performance limit.The presented pre-training method is evaluated on the race car simulator,TORCS(The Open Racing Car Simulator),to verify that it can be sufficiently reliable in improving the initial performance and convergence speed of an end-to-end autonomous driving model in the RL training stage.In addition,a real-vehicle verification system is built to verify the feasibility of the proposed pre-training method in a real-vehicle deployment.Simulations results show that using some demonstrations during a supervised pre-training stage allows significant improvements in initial performance and convergence speed in the RL training stage. | Yunpeng WANG Kunxian ZHENG Daxin TIAN Xuting DUAN Jianshan ZHOU | 2021 | Frontiers of Information Technology & Electronic Engineering2021，22，5： | 1 |
| 6 | Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks显示文摘In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes significant.Motivated by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected vehicle.Aiming at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making mechanism.In addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access network.This paper proposes a novel framework where the bio-inspired mechanism is combined with TOPSIS.In a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global sense.The experimental results confirm that the proposed method performs better when compared with conventional schemes. | Xuting Duan Jingyi Wei Daxin Tian Jianshan Zhou Haiying Xia Xin Li Kunxian Zheng | 2019 | Computers, Materials & Continua2019，，9： | 1 |
| 7 | Combined fitness function based particle swarm optimization algorithm for system identification显示文摘 | Lu Jianshan Xie Weidong Zhou Hongbo | 2016 | Computers&Industrial Engineering2016，95，： | 1 |
| 8 | Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning显示文摘Dynamic channel assignment(DCA)is significant for extending vehicular ad hoc network(VANET)capacity and mitigating congestion.However,the un-known global state information and the lack of centralized control make channel assignment performances a challenging task in a distributed vehicular direct communication scenario.In our preliminary field test for communication under V2X scenario,we find that the existing DCA technology cannot fully meet the communication performance requirements of VANET.In order to improve the communication performance,we firstly demonstrate the feasibility and potential of reinforcement learning(RL)method in joint channel selection decision and access fallback adaptation design in this paper.Besides,a dual reinforcement learning(DRL)-based cooperative DCA(DRL-CDCA)mechanism is proposed.Specifically,DRL-CDCA jointly optimizes the decision-making behaviors of both the channel selection and back-off adaptation based on a multi-agent dual reinforcement learning framework.Besides,nodes locally share and incorporate their individual rewards after each communication to achieve regional consistency optimization.Simulation results show that the proposed DRL-CDCA can better reduce the one-hop packet delay,improve the packet delivery ratio on average when compared with two other existing mechanisms. | Xuting Duan Yuanhao Zhao Kunxian Zheng Daxin Tian Jianshan Zhou Jian Gao | 2021 | Computers, Materials & Continua2021，，2： | 0 |
| 9 | 3D Environmental Perception Modeling in the Simulated Autonomous-Driving Systems显示文摘Self-driving vehicles require a number of tests to prevent fatal accidents and ensure their appropriate operation in the physical world.However,conducting vehicle tests on the road is difficult because such tests are expensive and labor intensive.In this study,we used an autonomous-driving simulator,and investigated the three-dimensional environmental perception problem of the simulated system.Using the open-source CARLA simulator,we generated a CarlaSim from unreal traffic scenarios,comprising 15000 camera-LiDAR(Light Detection and Ranging)samples with annotations and calibration files.Then,we developed Multi-Sensor Fusion Perception(MSFP)model for consuming two-modal data and detecting objects in the scenes.Furthermore,we conducted experiments on the KITTI and CarlaSim datasets;the results demonstrated the effectiveness of our proposed methods in terms of perception accuracy,inference efficiency,and generalization performance.The results of this study will faciliate the future development of autonomous-driving simulated tests. | Chunmian Lin Daxin Tian Xuting Duan Jianshan Zhou | 2021 | Complex System Modeling and Simulation2021，1，1： | 0 |
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