A novel efficient track initiation method is proposed for the harsh underwater target tracking environment(heavy clutter and large measurement errors): track splitting, evaluating, pruning and merging method(TSEPM). T...A novel efficient track initiation method is proposed for the harsh underwater target tracking environment(heavy clutter and large measurement errors): track splitting, evaluating, pruning and merging method(TSEPM). Track initiation demands that the method should determine the existence and initial state of a target quickly and correctly.Heavy clutter and large measurement errors certainly pose additional difficulties and challenges, which deteriorate and complicate the track initiation in the harsh underwater target tracking environment. There are three primary shortcomings for the current track initiation methods to initialize a target:(a) they cannot eliminate the turbulences of clutter effectively;(b) there may be a high false alarm probability and low detection probability of a track;(c) they cannot estimate the initial state for a new confirmed track correctly. Based on the multiple hypotheses tracking principle and modified logic-based track initiation method, in order to increase the detection probability of a track,track splitting creates a large number of tracks which include the true track originated from the target. And in order to decrease the false alarm probability, based on the evaluation mechanism, track pruning and track merging are proposed to reduce the false tracks. TSEPM method can deal with the track initiation problems derived from heavy clutter and large measurement errors, determine the target’s existence and estimate its initial state with the least squares method. What’s more, our method is fully automatic and does not require any kind manual input for initializing and tuning any parameter. Simulation results indicate that our new method improves significantly the performance of the track initiation in the harsh underwater target tracking environment.展开更多
Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin c...Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.展开更多
A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and...A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.展开更多
Based on the job demands-resources(JD-R)model,this study aims to investigate how algorithmic behavioral constraint and algorithmic tracking evaluation affect gig workers’turnover intention by increasing relative depr...Based on the job demands-resources(JD-R)model,this study aims to investigate how algorithmic behavioral constraint and algorithmic tracking evaluation affect gig workers’turnover intention by increasing relative deprivation and the extent to which this mediating role is moderated by algorithmic standardized guidance.Data from 242 gig workers were collected in two rounds and used to test the hypotheses.The results reveal that algorithmic behavioral constraints and algorithmic tracking evaluation are positively related to turnover intention,while algorithmic standardized guidance is negatively related to turnover intention.Moreover,algorithmic behavioral constraint and algorithmic tracking evaluation are positively related to turnover intention through relative deprivation,with algorithmic standardized guidance weakening this effect.展开更多
基金financially supported by the Key Research Program of the Chinese Academy of Sciences(Grant No.KGFZD-125-014)the National Natural Science Foundation of China(Grant No.61273334)State Key Laboratory of Robotics Foundation(Grant No.2017-Z05)
文摘A novel efficient track initiation method is proposed for the harsh underwater target tracking environment(heavy clutter and large measurement errors): track splitting, evaluating, pruning and merging method(TSEPM). Track initiation demands that the method should determine the existence and initial state of a target quickly and correctly.Heavy clutter and large measurement errors certainly pose additional difficulties and challenges, which deteriorate and complicate the track initiation in the harsh underwater target tracking environment. There are three primary shortcomings for the current track initiation methods to initialize a target:(a) they cannot eliminate the turbulences of clutter effectively;(b) there may be a high false alarm probability and low detection probability of a track;(c) they cannot estimate the initial state for a new confirmed track correctly. Based on the multiple hypotheses tracking principle and modified logic-based track initiation method, in order to increase the detection probability of a track,track splitting creates a large number of tracks which include the true track originated from the target. And in order to decrease the false alarm probability, based on the evaluation mechanism, track pruning and track merging are proposed to reduce the false tracks. TSEPM method can deal with the track initiation problems derived from heavy clutter and large measurement errors, determine the target’s existence and estimate its initial state with the least squares method. What’s more, our method is fully automatic and does not require any kind manual input for initializing and tuning any parameter. Simulation results indicate that our new method improves significantly the performance of the track initiation in the harsh underwater target tracking environment.
文摘Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.
基金Project(51175159)supported by the National Natural Science Foundation of ChinaProject(2013WK3024)supported by the Science andTechnology Planning Program of Hunan Province,ChinaProject(CX2013B146)supported by the Hunan Provincial InnovationFoundation for Postgraduate,China
文摘A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.
基金supported by the National Natural Science Foundation of China(#71832003,#72272054)Humanities and Social Science Foundation project of Ministry of Education(#22YJA630049)+1 种基金Hunan Provincial Natural Science Foundation Project(#2022JJ30208)Key Project of Changsha Natural Science Foundation(#kq2202302).
文摘Based on the job demands-resources(JD-R)model,this study aims to investigate how algorithmic behavioral constraint and algorithmic tracking evaluation affect gig workers’turnover intention by increasing relative deprivation and the extent to which this mediating role is moderated by algorithmic standardized guidance.Data from 242 gig workers were collected in two rounds and used to test the hypotheses.The results reveal that algorithmic behavioral constraints and algorithmic tracking evaluation are positively related to turnover intention,while algorithmic standardized guidance is negatively related to turnover intention.Moreover,algorithmic behavioral constraint and algorithmic tracking evaluation are positively related to turnover intention through relative deprivation,with algorithmic standardized guidance weakening this effect.
