The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time...The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.展开更多
A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filte...A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.展开更多
Objective: The mortality and morbidity rates associated with pancreatic cancer (PaCa) are extremely high. Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 2030, raisi...Objective: The mortality and morbidity rates associated with pancreatic cancer (PaCa) are extremely high. Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 2030, raising more concern for scholars to find effective methods to prevent and treat in order to improve the pancreatic cancer outcome. Using bioinformatic analysis, this study aims to pinpoint key genes that could impact PaCa patients’ prognosis and could be used as therapeutic targets. Methods: The TCGA and GEO datasets were integratively analyzed to identify prognosis-related differentially expressed genes. Next, the STRING database was used to develop PPI networks, and the MCODE and CytoNCA Cytoscape in Cytoscape were used to screen for critical genes. Through CytoNCA, three kinds of topology analysis were considered (degree, betweenness, and eigenvector). Essential genes were confirmed as potential target treatment through Go function and pathways enrichment analysis, a developed predictive risk model based on multivariate analysis, and the establishment of nomograms using the clinical information. Results: Overall, the GSE183795 and TCGA datasets associated 1311 and 2244 genes with pancreatic cancer prognosis, respectively. We identified 132 genes that were present in both datasets. The PPI network analysis using, the centrality analysis approach with the CytoNCA plug-in, showed that CDK2, PLK1, CCNB1, and TOP2A ranked in the top 5% across all three metrics. The independent analysis of a risk model revealed that the four key genes had a Hazard Ratio (HR) > 1. The monogram showed the predictive risk model and individual patient survival predictions were accurate. The results indicate that the effect of the selected vital genes was significant and that they could be used as biomarkers to predict a patient’s outcome and as possible target therapy in patients with pancreatic cancer. GO function and pathway analysis demonstrated that crucial genes might affect the P53 signaling pathway and FoxO signaling pathway, through which Meiotic nuclear division and cell cycle may have a significant function in essential genes affecting the outcome of patients who have pancreatic cancer. Conclusions: This study suggests that CDK2, CCNB1, PLK1 and TOP2A are four key genes that have a significant influence on PaCa migration and proliferation. CDK2, CCNB1, PLK1, and TOP2A can be used as potential PaCa prognostic biomarkers and therapeutic targets. However, experimental validation is necessary to confirm these predictions. Our study comes into contributions to the development of personalized target therapy for pancreatic cancer patients.展开更多
Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The m...Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.展开更多
OBJECTIVE Numerous references made clear that triphala is revered as a multiuse therapeutic and perhaps even panacea historically.Nevertheless,the protective mechanism of triphala on cardio-cerebral vascular diseases(...OBJECTIVE Numerous references made clear that triphala is revered as a multiuse therapeutic and perhaps even panacea historically.Nevertheless,the protective mechanism of triphala on cardio-cerebral vascular diseases(CCVDs)remains not comprehensive understanding.Hence,a network pharmacology-based method was suggested in this study to address this problem.METHODS This study was based on network pharmacology and bioinformatics analysis.Information on compounds in herbal medicines of triphala formula was acquired from public databases.Oral bioavailability as well as drug-likeness were screened by using absorption,distribution,metabolism,and excretion(ADME)criteria.Then,components of triphala,candidate targets of each component and known therapeutic targets of CCVDs were collected.Compound-target gene and compounds-CCVDs target networks were created through network pharmacology data sources.In addition,key targets and pathway enrichment were analyzed by STRING database and DAVID database.Moreover,we verified three of the key targets(PTGS2,MMP9 and IL-6)predicted by using Western blotting analysis.RESULTS Network analysis determined 132 compounds in three herbal medicines that were subjected to ADME screening,and 23 compounds as well as 65 genes formed the principal pathways linked to CCVDs.And 10 compounds,which actually linked to more than three genes,are determined as crucial chemicals.Core genes in this network were IL-6,TNF,VEGFA,PTGS2,CXCL8,TP53,CCL2,IL-10,MMP9 and SERPINE1.And pathways in cancer,TNF signaling path⁃way,neuroactive ligand-receptor interaction,etc.related to CCVDs were identified.In vitro experiments,the results indi⁃cated that compared with the control group(no treatment),PTGS2,MMP9 and IL-6 were up-regulated by treatment of 10μg·L^-1 TNF-α,while pretreatment with 20-80 mg·L^-1 triphala could significantly inhibit the expression of PTGS2,MMP9 and IL-6.With increasing Triphala concentration,the expression of PTGS2,MMP9 and IL-6 decreased.CON⁃CLUSION Complex components and pharmacological mechanism of triphala,and obtained some potential therapeutic targets of CCVDs,which could provide theoretical basis for the research and development of new drugs for treating CCVDs.展开更多
Parkinson’s disease（PD）is the second most common neurodegenerative disease affecting 1%of the population over 60 years of age.The progressive degeneration of dopaminergic neurons at the substantia nigra pars compa...Parkinson’s disease（PD）is the second most common neurodegenerative disease affecting 1%of the population over 60 years of age.The progressive degeneration of dopaminergic neurons at the substantia nigra pars compacta（SNpc）results in a severe and gradual depletion of dopamine content in the striatum,a phenomena that is responsible for the characteristic motor symptoms of this disease.展开更多
Sensor scheduling is essential to collaborative target tracking in wireless sensor networks （WSNs）. In the existing works for target tracking in WSNs, such as the information-driven sensor query （IDSQ）, the taskin...Sensor scheduling is essential to collaborative target tracking in wireless sensor networks （WSNs）. In the existing works for target tracking in WSNs, such as the information-driven sensor query （IDSQ）, the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energy-efficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter （EKF）-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.展开更多
Remote tracking for mobile targets is one of the most important applications in wireless sensor networks （WSNs）. A target tracking protoco–exponential distributed predictive tracking （EDPT） is proposed. To reduce...Remote tracking for mobile targets is one of the most important applications in wireless sensor networks （WSNs）. A target tracking protoco–exponential distributed predictive tracking （EDPT） is proposed. To reduce energy waste and response time, an improved predictive algorithm–exponential smoothing predictive algorithm （ESPA） is presented. With the aid of an additive proportion and differential （PD） controller, ESPA decreases the system predictive delay effectively. As a recovery mechanism, an optimal searching radius （OSR） algorithm is applied to calculate the optimal radius of the recovery zone. The simulation results validate that the proposed EDPT protocol performes better in terms of track failed ratio, energy waste ratio and enlarged sensing nodes ratio, respectively.展开更多
Wireless sensor network (WSN) of active sensors suffers from serious inter-sensor interference (ISI) and imposes new design and implementation challenges. In this paper, based on the ultrasonic sensor network, two tim...Wireless sensor network (WSN) of active sensors suffers from serious inter-sensor interference (ISI) and imposes new design and implementation challenges. In this paper, based on the ultrasonic sensor network, two time-division based distributed sensor scheduling schemes are proposed to deal with ISI by scheduling sensors periodically and adaptively respectively. Extended Kalman filter (EKF) is used as the tracking algorithm in distributed manner. Simulation results show that the adaptive sensor scheduling scheme can achieve superior tracking accuracy with faster tracking convergence speed.展开更多
Target tracking is considered as one of the cardinal applications of a wireless sensor network. Tracking multiple targets is more challenging than tracking a single target in a wireless sensor network due to targets’...Target tracking is considered as one of the cardinal applications of a wireless sensor network. Tracking multiple targets is more challenging than tracking a single target in a wireless sensor network due to targets’ movement in different directions, targets’ speed variations and frequent connectivity failures of low powered sensor nodes. If all the low-powered sensor nodes are kept active in tracking multiple targets coming from different directions of the network, there is high probability of network failure due to wastage of power. It would be more realistic if the tracking area can be reduced so that less number of sensor nodes will be active and therefore, the network will consume less energy. Tracking area can be reduced by using the target’s kinematics. There is almost no method to track multiple targets based on targets’ kinematics. In our paper, we propose a distributed tracking method for tracking multiple targets considering targets’ kinematics. We simulate our method by a sensor network simulator OMNeT++ and empirical results state that our proposed methodology outperforms traditional tracking algorithms.展开更多
Target tracking is a well studied topic in wireless sensor networks. It is a procedure that nodes in the network collaborate in detecting targets and transmitting their information to the base-station continuously, wh...Target tracking is a well studied topic in wireless sensor networks. It is a procedure that nodes in the network collaborate in detecting targets and transmitting their information to the base-station continuously, which leads to data implosion and redundancy. To reduce traffic load of the network, a data compressing based target tracking protocol is proposed in this work. It first incorporates a clustering based data gather method to group sensor nodes into clusters. Then a novel threshold technique with bounded error is proposed to exploit the spatial correlation of sensed data and compress the data in the same cluster. Finally, the compact data presentations are transmitted to the base-station for targets localization. We evaluate our approach with a comprehensive set of simulations. It can be concluded that the proposed method yields excellent performance in energy savings and tracking quality.展开更多
The era of targeted cancer therapies has arrived.However,due to the complexity of biological systems,the current progress is far from enough.From biological network modeling to structural/dynamic network analysis,netw...The era of targeted cancer therapies has arrived.However,due to the complexity of biological systems,the current progress is far from enough.From biological network modeling to structural/dynamic network analysis,network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells.It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.展开更多
We study the target inactivation and recovery in two-layer networks. Five kinds of strategies are chosen to attack the two-layer networks and to recover the activity of the networks by increasing the inter-layer coupl...We study the target inactivation and recovery in two-layer networks. Five kinds of strategies are chosen to attack the two-layer networks and to recover the activity of the networks by increasing the inter-layer coupling strength. The results show that we can easily control the dying state effectively by a randomly attacked situation. We then investigate the recovery activity of the networks by increasing the inter-layer coupled strength. The optimal values of the inter-layer coupled strengths are found, which could provide a more effective range to recovery activity of complex networks. As the multilayer systems composed of active and inactive elements raise important and interesting problems, our results on the target inactivation and recovery in two-layer networks would be extended to different studies.展开更多
Traditional tracking algorithms based on static sensors have several problems. First, the targets only occur in a part of the interested area; however, a large number of static sensors are distributed in the area to g...Traditional tracking algorithms based on static sensors have several problems. First, the targets only occur in a part of the interested area; however, a large number of static sensors are distributed in the area to guarantee entire coverage, which leads to wastage of sensor resources. Second, many static sensors have to remain in active mode to track the targets, which causes an increase of energy consumption. To solve these problems, a target group tracking algorithm based on a hybrid sensor network is proposed in this paper, which includes static sensors and mobile sensors. First, an estimation algorithm is proposed to estimate the objective region by static sensors, which work in low-power sensing mode. Second, a movement algorithm based on sliding windows is proposed for mobile sensors to obtain the destinations. Simulation results show that this algorithm can reduce the number of mobile sensors participating in the tracking task and prolong the network lifetime.展开更多
In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents tr...In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.展开更多
In this paper,a new radar target identification scheme is presented based on adaptivediscrimination waveform synthesis and a nearest neighbor neural network.It can directly use theimpulse response of the target to syn...In this paper,a new radar target identification scheme is presented based on adaptivediscrimination waveform synthesis and a nearest neighbor neural network.It can directly use theimpulse response of the target to synthesize discrimination waveform,so the poles extractionprocedure is not required.Particularly,it can successfully operate on the case that the poles ofthe target are weakly dependent on the aspect angle.展开更多
In this paper, we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network. Since a group of targets moves collectively and is restricted within a limited region, it i...In this paper, we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network. Since a group of targets moves collectively and is restricted within a limited region, it is not worth consuming scarce resources of sensors in computing the trajectory of each single target. Hence, in this paper, the problem is modeled as tracking a geographical continuous region covered by all targets. A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period. Based on the locations of sensors and the azimuthal angle of arrival （AOA） information, the estimated region covering all the group members is obtained. Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group. Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error, which is between 10% and 40% of the hull algorithm, with a similar density of sensors. And when the density of sensors increases, the localization accuracy of the proposed algorithm improves dramatically.展开更多
It is a challenge to track the maneuvering targets with noise disturbance and unknown dynamics. In this paper, an adaptive recurrent neural network tracking filter (ARNNF) for use in maneuvering target tracking was p...It is a challenge to track the maneuvering targets with noise disturbance and unknown dynamics. In this paper, an adaptive recurrent neural network tracking filter (ARNNF) for use in maneuvering target tracking was provided. The scheme is based on recurrent neural networks of which the recurrence provides a potentially unlimited memory depth adjusted by the network adaptively ( i.e. , it finds the best duration to represent the input signals past), and thus can actually capture the dynamics of the system that produced a temporal signal. On the other hand, recurrent neural network can approximate arbitrary nonlinear functions in L 2 space. The theoretical analysis indicates that the ARNNF can track the maneuvering targets with optimal filtering performance. Comparisons with IMM and AIMM algorithm show that ARNNF has better performance, and furthermore the ARNNF does not rely on the assumption with the known maneuvering target models, measurement noise and system noise.展开更多
The scheme for tracking maneuvering target based on neural fuzzy network with incremental neural learning is proposed. When tracked target maneuver occurs, the scheme can detect maneuver immediately and estimate the m...The scheme for tracking maneuvering target based on neural fuzzy network with incremental neural learning is proposed. When tracked target maneuver occurs, the scheme can detect maneuver immediately and estimate the maneuver value accurately , then the tracking filter can be compensated correctly and duly by the estimated maneuver value. When environment changes, neural fuzzy network with incremental neural learning （INL-SONFIN） can find its optimal structure and parameters automatically to adopt to changed environment. So, it always produce estimated output very close to the true maneuver value that leads to good tracking performance and avoids misstracking. Simulation results show that the performance is superior to the traditional schemes and the scheme can fit changed dynamic environment to track maneuvering target accurately and duly.展开更多
The three-dimensional sensor networks are supposed to be deployed for many applications. So it is signifi-cant to do research on the problems of coverage and target detection in three-dimensional sensor networks. In t...The three-dimensional sensor networks are supposed to be deployed for many applications. So it is signifi-cant to do research on the problems of coverage and target detection in three-dimensional sensor networks. In this paper, we introduced Clifford algebra in 3D Euclidean space, developed the coverage model of 3D sensor networks based on Clifford algebra, and proposed a method for detecting target moving. With Clif-ford Spinor, calculating the target moving formulation is easier than traditional methods in sensor node’s coverage area.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.62276274)Shaanxi Natural Science Foundation(Grant No.2023-JC-YB-528)Chinese aeronautical establishment(Grant No.201851U8012)。
文摘The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.
基金supported by National Natural Science Foundation of China (Nos.62265010,62061024)Gansu Province Science and Technology Plan (No.23YFGA0062)Gansu Province Innovation Fund (No.2022A-215)。
文摘A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.
文摘Objective: The mortality and morbidity rates associated with pancreatic cancer (PaCa) are extremely high. Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 2030, raising more concern for scholars to find effective methods to prevent and treat in order to improve the pancreatic cancer outcome. Using bioinformatic analysis, this study aims to pinpoint key genes that could impact PaCa patients’ prognosis and could be used as therapeutic targets. Methods: The TCGA and GEO datasets were integratively analyzed to identify prognosis-related differentially expressed genes. Next, the STRING database was used to develop PPI networks, and the MCODE and CytoNCA Cytoscape in Cytoscape were used to screen for critical genes. Through CytoNCA, three kinds of topology analysis were considered (degree, betweenness, and eigenvector). Essential genes were confirmed as potential target treatment through Go function and pathways enrichment analysis, a developed predictive risk model based on multivariate analysis, and the establishment of nomograms using the clinical information. Results: Overall, the GSE183795 and TCGA datasets associated 1311 and 2244 genes with pancreatic cancer prognosis, respectively. We identified 132 genes that were present in both datasets. The PPI network analysis using, the centrality analysis approach with the CytoNCA plug-in, showed that CDK2, PLK1, CCNB1, and TOP2A ranked in the top 5% across all three metrics. The independent analysis of a risk model revealed that the four key genes had a Hazard Ratio (HR) > 1. The monogram showed the predictive risk model and individual patient survival predictions were accurate. The results indicate that the effect of the selected vital genes was significant and that they could be used as biomarkers to predict a patient’s outcome and as possible target therapy in patients with pancreatic cancer. GO function and pathway analysis demonstrated that crucial genes might affect the P53 signaling pathway and FoxO signaling pathway, through which Meiotic nuclear division and cell cycle may have a significant function in essential genes affecting the outcome of patients who have pancreatic cancer. Conclusions: This study suggests that CDK2, CCNB1, PLK1 and TOP2A are four key genes that have a significant influence on PaCa migration and proliferation. CDK2, CCNB1, PLK1, and TOP2A can be used as potential PaCa prognostic biomarkers and therapeutic targets. However, experimental validation is necessary to confirm these predictions. Our study comes into contributions to the development of personalized target therapy for pancreatic cancer patients.
文摘Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.
基金National Natural Science Foundation of China(81603385)China Postdoctoral Science Foundation(2018M643843)+1 种基金Natural Science Foundation of Shaanxi Province(2017JM8056)Key Research and Development Foundation of Shaanxi province(2018SF-241)
文摘OBJECTIVE Numerous references made clear that triphala is revered as a multiuse therapeutic and perhaps even panacea historically.Nevertheless,the protective mechanism of triphala on cardio-cerebral vascular diseases(CCVDs)remains not comprehensive understanding.Hence,a network pharmacology-based method was suggested in this study to address this problem.METHODS This study was based on network pharmacology and bioinformatics analysis.Information on compounds in herbal medicines of triphala formula was acquired from public databases.Oral bioavailability as well as drug-likeness were screened by using absorption,distribution,metabolism,and excretion(ADME)criteria.Then,components of triphala,candidate targets of each component and known therapeutic targets of CCVDs were collected.Compound-target gene and compounds-CCVDs target networks were created through network pharmacology data sources.In addition,key targets and pathway enrichment were analyzed by STRING database and DAVID database.Moreover,we verified three of the key targets(PTGS2,MMP9 and IL-6)predicted by using Western blotting analysis.RESULTS Network analysis determined 132 compounds in three herbal medicines that were subjected to ADME screening,and 23 compounds as well as 65 genes formed the principal pathways linked to CCVDs.And 10 compounds,which actually linked to more than three genes,are determined as crucial chemicals.Core genes in this network were IL-6,TNF,VEGFA,PTGS2,CXCL8,TP53,CCL2,IL-10,MMP9 and SERPINE1.And pathways in cancer,TNF signaling path⁃way,neuroactive ligand-receptor interaction,etc.related to CCVDs were identified.In vitro experiments,the results indi⁃cated that compared with the control group(no treatment),PTGS2,MMP9 and IL-6 were up-regulated by treatment of 10μg·L^-1 TNF-α,while pretreatment with 20-80 mg·L^-1 triphala could significantly inhibit the expression of PTGS2,MMP9 and IL-6.With increasing Triphala concentration,the expression of PTGS2,MMP9 and IL-6 decreased.CON⁃CLUSION Complex components and pharmacological mechanism of triphala,and obtained some potential therapeutic targets of CCVDs,which could provide theoretical basis for the research and development of new drugs for treating CCVDs.
基金supported by FONDECYT-11140738 (G.M.).Michael J. Fox Foundation for Parkinson Research, Ring Initiative ACT1109+1 种基金FONDEF D11I1007 (C.H.). We also thank, FONDECYT-1140549Millennium Institute P09-015-F, COPEC-UC, and Frick Foundation (C.H.). V.C. is supported by CONICYT fellowship
文摘Parkinson’s disease（PD）is the second most common neurodegenerative disease affecting 1%of the population over 60 years of age.The progressive degeneration of dopaminergic neurons at the substantia nigra pars compacta（SNpc）results in a severe and gradual depletion of dopamine content in the striatum,a phenomena that is responsible for the characteristic motor symptoms of this disease.
基金partly supported by the Agency for Science,Technology and Research(A*Star)SERC(No.0521010037,0521210082)
文摘Sensor scheduling is essential to collaborative target tracking in wireless sensor networks （WSNs）. In the existing works for target tracking in WSNs, such as the information-driven sensor query （IDSQ）, the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energy-efficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter （EKF）-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.
基金supported by the National Basic Research Program of China (973 Program) (2010CB731800)the National Natural Science Foundation of China (60934003+2 种基金 60974123 60804010)the Hebei Provincial Educational Foundation of China (2008147)
文摘Remote tracking for mobile targets is one of the most important applications in wireless sensor networks （WSNs）. A target tracking protoco–exponential distributed predictive tracking （EDPT） is proposed. To reduce energy waste and response time, an improved predictive algorithm–exponential smoothing predictive algorithm （ESPA） is presented. With the aid of an additive proportion and differential （PD） controller, ESPA decreases the system predictive delay effectively. As a recovery mechanism, an optimal searching radius （OSR） algorithm is applied to calculate the optimal radius of the recovery zone. The simulation results validate that the proposed EDPT protocol performes better in terms of track failed ratio, energy waste ratio and enlarged sensing nodes ratio, respectively.
基金Supported by Science & Engineering Research Council of Singnpore (0521010037)
文摘Wireless sensor network (WSN) of active sensors suffers from serious inter-sensor interference (ISI) and imposes new design and implementation challenges. In this paper, based on the ultrasonic sensor network, two time-division based distributed sensor scheduling schemes are proposed to deal with ISI by scheduling sensors periodically and adaptively respectively. Extended Kalman filter (EKF) is used as the tracking algorithm in distributed manner. Simulation results show that the adaptive sensor scheduling scheme can achieve superior tracking accuracy with faster tracking convergence speed.
文摘Target tracking is considered as one of the cardinal applications of a wireless sensor network. Tracking multiple targets is more challenging than tracking a single target in a wireless sensor network due to targets’ movement in different directions, targets’ speed variations and frequent connectivity failures of low powered sensor nodes. If all the low-powered sensor nodes are kept active in tracking multiple targets coming from different directions of the network, there is high probability of network failure due to wastage of power. It would be more realistic if the tracking area can be reduced so that less number of sensor nodes will be active and therefore, the network will consume less energy. Tracking area can be reduced by using the target’s kinematics. There is almost no method to track multiple targets based on targets’ kinematics. In our paper, we propose a distributed tracking method for tracking multiple targets considering targets’ kinematics. We simulate our method by a sensor network simulator OMNeT++ and empirical results state that our proposed methodology outperforms traditional tracking algorithms.
文摘Target tracking is a well studied topic in wireless sensor networks. It is a procedure that nodes in the network collaborate in detecting targets and transmitting their information to the base-station continuously, which leads to data implosion and redundancy. To reduce traffic load of the network, a data compressing based target tracking protocol is proposed in this work. It first incorporates a clustering based data gather method to group sensor nodes into clusters. Then a novel threshold technique with bounded error is proposed to exploit the spatial correlation of sensed data and compress the data in the same cluster. Finally, the compact data presentations are transmitted to the base-station for targets localization. We evaluate our approach with a comprehensive set of simulations. It can be concluded that the proposed method yields excellent performance in energy savings and tracking quality.
基金the National Natural Science Foundation of China (31100961,81173082,and 30873083)
文摘The era of targeted cancer therapies has arrived.However,due to the complexity of biological systems,the current progress is far from enough.From biological network modeling to structural/dynamic network analysis,network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells.It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.
基金Supported by the National Basic Research Program of China under Grant Nos 2013CBA01502,2011CB921503 and 2013CB834100the National Natural Science Foundation of China under Grant Nos 11374040 and 11274051
文摘We study the target inactivation and recovery in two-layer networks. Five kinds of strategies are chosen to attack the two-layer networks and to recover the activity of the networks by increasing the inter-layer coupling strength. The results show that we can easily control the dying state effectively by a randomly attacked situation. We then investigate the recovery activity of the networks by increasing the inter-layer coupled strength. The optimal values of the inter-layer coupled strengths are found, which could provide a more effective range to recovery activity of complex networks. As the multilayer systems composed of active and inactive elements raise important and interesting problems, our results on the target inactivation and recovery in two-layer networks would be extended to different studies.
基金Project supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20140875)the Scientific Research Foundation of Nanjing University of Posts and Telecommunications,China(Grant No.NY213084)the National Natural Science Foundation of China(Grant No.61502243)
文摘Traditional tracking algorithms based on static sensors have several problems. First, the targets only occur in a part of the interested area; however, a large number of static sensors are distributed in the area to guarantee entire coverage, which leads to wastage of sensor resources. Second, many static sensors have to remain in active mode to track the targets, which causes an increase of energy consumption. To solve these problems, a target group tracking algorithm based on a hybrid sensor network is proposed in this paper, which includes static sensors and mobile sensors. First, an estimation algorithm is proposed to estimate the objective region by static sensors, which work in low-power sensing mode. Second, a movement algorithm based on sliding windows is proposed for mobile sensors to obtain the destinations. Simulation results show that this algorithm can reduce the number of mobile sensors participating in the tracking task and prolong the network lifetime.
基金supported by National Basic Research Program of China (973 Program) (No. 2010CB731800)Key Project of National Science Foundation of China (No. 60934003)+2 种基金National Nature Science Foundation of China (No. 61074065)Key Project for Natural Science Research of Hebei Education Department, PRC(No. ZD200908)Key Project for Shanghai Committee of Science and Technology (No. 08511501600)
文摘In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.
文摘In this paper,a new radar target identification scheme is presented based on adaptivediscrimination waveform synthesis and a nearest neighbor neural network.It can directly use theimpulse response of the target to synthesize discrimination waveform,so the poles extractionprocedure is not required.Particularly,it can successfully operate on the case that the poles ofthe target are weakly dependent on the aspect angle.
基金Project supported by the State Key Program of the National Natural Science Foundation of China(Grant No.60835001)the National Natural Science Foundation of China(Grant No.61104068)the Natural Science Foundation of Jiangsu Province China(Grant No.BK2010200)
文摘In this paper, we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network. Since a group of targets moves collectively and is restricted within a limited region, it is not worth consuming scarce resources of sensors in computing the trajectory of each single target. Hence, in this paper, the problem is modeled as tracking a geographical continuous region covered by all targets. A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period. Based on the locations of sensors and the azimuthal angle of arrival （AOA） information, the estimated region covering all the group members is obtained. Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group. Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error, which is between 10% and 40% of the hull algorithm, with a similar density of sensors. And when the density of sensors increases, the localization accuracy of the proposed algorithm improves dramatically.
文摘It is a challenge to track the maneuvering targets with noise disturbance and unknown dynamics. In this paper, an adaptive recurrent neural network tracking filter (ARNNF) for use in maneuvering target tracking was provided. The scheme is based on recurrent neural networks of which the recurrence provides a potentially unlimited memory depth adjusted by the network adaptively ( i.e. , it finds the best duration to represent the input signals past), and thus can actually capture the dynamics of the system that produced a temporal signal. On the other hand, recurrent neural network can approximate arbitrary nonlinear functions in L 2 space. The theoretical analysis indicates that the ARNNF can track the maneuvering targets with optimal filtering performance. Comparisons with IMM and AIMM algorithm show that ARNNF has better performance, and furthermore the ARNNF does not rely on the assumption with the known maneuvering target models, measurement noise and system noise.
基金This project was supported by Spaceflight Support Fund ( HIT01) and the Spaceflight Science Project Group
文摘The scheme for tracking maneuvering target based on neural fuzzy network with incremental neural learning is proposed. When tracked target maneuver occurs, the scheme can detect maneuver immediately and estimate the maneuver value accurately , then the tracking filter can be compensated correctly and duly by the estimated maneuver value. When environment changes, neural fuzzy network with incremental neural learning （INL-SONFIN） can find its optimal structure and parameters automatically to adopt to changed environment. So, it always produce estimated output very close to the true maneuver value that leads to good tracking performance and avoids misstracking. Simulation results show that the performance is superior to the traditional schemes and the scheme can fit changed dynamic environment to track maneuvering target accurately and duly.
文摘The three-dimensional sensor networks are supposed to be deployed for many applications. So it is signifi-cant to do research on the problems of coverage and target detection in three-dimensional sensor networks. In this paper, we introduced Clifford algebra in 3D Euclidean space, developed the coverage model of 3D sensor networks based on Clifford algebra, and proposed a method for detecting target moving. With Clif-ford Spinor, calculating the target moving formulation is easier than traditional methods in sensor node’s coverage area.
