To protect the environment,the discharged sewage’s quality must meet the state’s discharge standards.There are many water quality indicators,and the pH(Potential of Hydrogen)value is one of them.The natural water’s...To protect the environment,the discharged sewage’s quality must meet the state’s discharge standards.There are many water quality indicators,and the pH(Potential of Hydrogen)value is one of them.The natural water’s pH value is 6.0–8.5.The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard.This paper aims to study the deep learning prediction model of wastewater’s pH.Firstly,the research uses the random forest method to select the data features and then,based on the sliding window,convert the data set into a time series which is the input of the deep learning training model.Secondly,by analyzing and comparing relevant references,this paper believes that the CNN(Convolutional Neural Network)model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers.After alternating the combination of the convolutional layer and pooling layer,all features are integrated into a full-connected neural network.Thirdly,the number of input samples of the CNN model directly affects the prediction effect of the model.Therefore,this paper adopts the sliding window method to study the optimal size.Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers.The last full-connection layer contains two layers and 64 neurons per layer.The sliding window size selects as 12.Finally,the research has carried out data prediction based on the optimal CNN deep learning model.The predicted pH of the sewage is between 7.2 and 8.6 in this paper.The result is applied in the monitoring system platform of the“Intelligent operation and maintenance platform of the reclaimed water plant.”展开更多
Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the ta...Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.展开更多
Differential privacy has recently become a widely recognized strict privacy protection model of data release.Differential privacy histogram publishing can directly show the statistical data distribution under the prem...Differential privacy has recently become a widely recognized strict privacy protection model of data release.Differential privacy histogram publishing can directly show the statistical data distribution under the premise of ensuring user privacy for data query,sharing,and analysis.The dynamic data release is a study with a wide range of current industry needs.However,the amount of data varies considerably over different periods.Unreasonable data processing will result in the risk of users’information leakage and unavailability of the data.Therefore,we designed a differential privacy histogram publishing method based on the dynamic sliding window of LSTM(DPHP-DL),which can improve data availability on the premise of guaranteeing data privacy.DPHP-DL is integrated by DSW-LSTM and DPHK+.DSW-LSTM updates the size of sliding windows based on data value prediction via long shortterm memory(LSTM)networks,which evenly divides the data stream into several windows.DPHK+heuristically publishes non-isometric histograms based on k-mean++clustering of automatically obtaining the optimal K,so as to achieve differential privacy histogram publishing of dynamic data.Extensive experiments on real-world dynamic datasets demonstrate the superior performance of the DPHP-DL.展开更多
Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid h...Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements.展开更多
Through improving the redundant data filtering of unreliable data filter for radio frequency identification(RFID) with sliding-window,a data filter which integrates self-adaptive sliding-window and Euclidean distance ...Through improving the redundant data filtering of unreliable data filter for radio frequency identification(RFID) with sliding-window,a data filter which integrates self-adaptive sliding-window and Euclidean distance is proposed.The input data required being filtered have been shunt by considering a large number of redundant data existing in the unreliable data for RFID and the redundant data in RFID are the main filtering object with utilizing the filter based on Euclidean distance.The comparison between the results from the method proposed in this paper and previous research shows that it can improve the accuracy of the RFID for unreliable data filtering and largely reduce the redundant reading rate.展开更多
Data archiving is one of the most critical issues for modern astronomical observations.With the development of a new generation of radio telescopes,the transfer and archiving of massive remote data have become urgent ...Data archiving is one of the most critical issues for modern astronomical observations.With the development of a new generation of radio telescopes,the transfer and archiving of massive remote data have become urgent problems to be solved.Herein,we present a practical and robust file-level flow-control approach,called the Unlimited Sliding-Window(USW),by referring to the classic flow-control method in the TCP protocol.Based on the USW and the Next Generation Archive System(NGAS)developed for the Murchison Widefield Array telescope,we further implemented an enhanced archive system(ENGAS)using ZeroMQ middleware.The ENGAS substantially improves the transfer performance and ensures the integrity of transferred files.In the tests,the ENGAS is approximately three to twelve times faster than the NGAS and can fully utilize the bandwidth of network links.Thus,for archiving radio observation data,the ENGAS reduces the communication time,improves the bandwidth utilization,and solves the remote synchronous archiving of data from observatories such as Mingantu spectral radioheliograph.It also provides a better reference for the future construction of the Square Kilometer Array(SKA)Science Regional Center.展开更多
Rain and snow seriously degrade outdoor video quality.In this work,a primary-secondary background model for removal of rain and snow is built.First,we analyze video noise and use a sliding window sequence principal co...Rain and snow seriously degrade outdoor video quality.In this work,a primary-secondary background model for removal of rain and snow is built.First,we analyze video noise and use a sliding window sequence principal component analysis de-nosing algorithm to reduce white noise in the video.Next,we apply the Gaussian mixture model(GMM)to model the video and segment all foreground objects primarily.After that,we calculate von Mises distribution of the velocity vectors and ratio of the overlapped region with referring to the result of the primary segmentation and extract the interesting object.Finally,rain and snow streaks are inpainted using the background to improve the quality of the video data.Experiments show that the proposed method can effectively suppress noise and extract interesting targets.展开更多
A lane-level intersection map is a cornerstone in high-definition(HD) traffic network maps for autonomous driving and high-precision intelligent transportation systems applications such as traffic management and contr...A lane-level intersection map is a cornerstone in high-definition(HD) traffic network maps for autonomous driving and high-precision intelligent transportation systems applications such as traffic management and control, and traffic accident evaluation and prevention. Mapping an HD intersection is time-consuming, labor-intensive, and expensive with conventional methods. In this paper, we used a low-channel roadside light detection and range sensor(LiDAR) to automatically and dynamically generate a lane-level intersection, including the signal phases, geometry, layout, and lane directions. First, a mathematical model was proposed to describe the topology and detail of a lane-level intersection. Second, continuous and discontinuous traffic object trajectories were extracted to identify the signal phases and times. Third, the layout, geometry, and lane direction were identified using the convex hull detection algorithm for trajectories. Fourth, a sliding window algorithm was presented to detect the lane marking and extract the lane, and the virtual lane connecting the inbound and outbound of the intersection were generated using the vehicle trajectories within the intersection and considering the traffic rules. In the field experiment, the mean absolute estimation error is 2 s for signal phase and time identification. The lane marking identification Precision and Recall are96% and 94.12%, respectively. Compared with the satellite-based,MMS-based, and crowdsourcing-based lane mapping methods,the average lane location deviation is 0.2 m and the update period is less than one hour by the proposed method with low-channel roadside LiDAR.展开更多
An efficient observability analysis method is proposed to enable online detection of performance degradation of an optimization-based sliding window visual-inertial state estimation framework.The proposed methodology ...An efficient observability analysis method is proposed to enable online detection of performance degradation of an optimization-based sliding window visual-inertial state estimation framework.The proposed methodology leverages numerical techniques in nonlinear observability analysis to enable online evaluation of the system observability and indication of the state estimation performance.Specifically,an empirical observability Gramian based approach is introduced to efficiently measure the observability condition of the windowed nonlinear system,and a scalar index is proposed to quantify the average system observability.The proposed approach is specialized to a challenging optimizationbased sliding window monocular visual-inertial state estimation formulation and evaluated through simulation and experiments to assess the efficacy of the methodology.The analysis result shows that the proposed approach can correctly indicate degradation of the state estimation accuracy with real-time performance.展开更多
On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is e...On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is essential for on-site programming big data.Duplicate data detection is an important step in data cleaning,which can save storage resources and enhance data consistency.Due to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed.The efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding window.Experimental results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.展开更多
Sports matches are very popular all over the world.The prediction of a sports match is helpful to grasp the team's state in time and adjust the strategy in the process of the match.It's a challenging effort to...Sports matches are very popular all over the world.The prediction of a sports match is helpful to grasp the team's state in time and adjust the strategy in the process of the match.It's a challenging effort to predict a sports match.Therefore,a method is proposed to predict the result of the next match by using teams'historical match data.We combined the Long Short-Term Memory(LSTM)model with the attention mechanism and put forward an ASLSTM model for predicting match results.Furthermore,to ensure the timeliness of the prediction,we add the time sliding window to make the prediction have better timeliness.Taking the football match as an example,we carried out a case study and proposed the feasibility of this method.展开更多
Due to the particularity of its location algorithm,there are some unique difficulties and features regarding the test of target motion states of multilateration(MLAT)system for airport surface surveillance.This paper ...Due to the particularity of its location algorithm,there are some unique difficulties and features regarding the test of target motion states of multilateration(MLAT)system for airport surface surveillance.This paper proposed a test method applicable for the airport surface surveillance MLAT system,which can effectively determine whether the target is static or moving at a certain speed.Via a normalized test statistic designed in the sliding data window,the proposed method not only eliminates the impact of geometry Dilution of precision(GDOP)effectively,but also transforms the test of different motion states into the test of different probability density functions.Meanwhile,by adjusting the size of the sliding window,it can fulfill different test performance requirements.The method was developed through strict theoretical extrapolation and performance analysis,and simulations results verified its correctness and effectiveness.展开更多
With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify hea...With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy.In this paper,we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters.This method is called EBF sketches.Our sketch structure allows for effective summarization of streams over time-based sliding windows with guaranteed probabilistic accuracy.It can be employed to address problems such as maintaining frequency statistics and finding heavy hitters.Our experimental results validate our theoretical claims and verifies the effectiveness of our techniques.展开更多
In this paper,battery aging diversity among independent cells was studied in terms of available capacity degradation.During the aging process of LiFePO_(4)batteries,the phenomenon of aging diversity can be observed.Wh...In this paper,battery aging diversity among independent cells was studied in terms of available capacity degradation.During the aging process of LiFePO_(4)batteries,the phenomenon of aging diversity can be observed.When batteries with same specification were charged and discharged repeatedly under the same working conditions,the available capacity of different cell decreased at different rates along the cycle number.In this study,accelerated aging tests were carried out on multiple new LiFePO_(4)battery samples of different brands.Experimental results show that under the same working conditions,the actual available capacity of all cells decreased as the number of aging cycle increased,but an obvious aging diversity was observed even among different cells of same brand with same specification.This aging diversity was described and analysed in detail,and the common aging features of different cells beneath this aging diversity was explored.Considering this aging diversity,a probability density concept was adopted to estimate battery’s state of health(SOH).With this method,a relationship between battery SOH and its aging feature parameter was established,and a dynamic sliding window optimization technique was designed to ensure the optimal quality of aging feature extraction.Finally,the accuracy of this SOH estimation method was verified by random test.展开更多
This paper proposes an efficient object localization method based on a vanishing line. The proposed method can be much improved in time efficiency since it requires scanning only vanishing line area. It requires the t...This paper proposes an efficient object localization method based on a vanishing line. The proposed method can be much improved in time efficiency since it requires scanning only vanishing line area. It requires the time complexity of O(n) while the existing sliding window method requires the time complexity O(n<sup>2</sup>) for detecting all objects in the entire image. In addition, the range of detection area can be also remarkably reduced when compared with the sliding window method. As a result, the total range and times for searching in the proposed method can be significantly reduced by considering together the distance and position of the object. The experiment on the proposed method is performed with the virtual road data set known as SYNTHIA, and the competitive results are obtained.展开更多
Visual simultaneous localization and mapping(VSLAM) are essential technologies to realize the autonomous movement of vehicles. Visual-inertial odometry(VIO) is often used as the front-end of VSLAM because of its rich ...Visual simultaneous localization and mapping(VSLAM) are essential technologies to realize the autonomous movement of vehicles. Visual-inertial odometry(VIO) is often used as the front-end of VSLAM because of its rich information, lightweight, and robustness. This article proposes the FPL-VIO, an optimization-based fast vision-inertial odometer with points and lines. Traditional VIO mostly uses points as landmarks;meanwhile, most of the geometrical structure information is ignored. Therefore, the accuracy will be jeopardized under motion blur and texture-less area. Some researchers improve accuracy by adding lines as landmarks in the system.However, almost all of them use line segment detector(LSD) and line band descriptor(LBD) in line processing, which is very time-consuming. This article first proposes a fast line feature description and matching method based on the midpoint and compares the three line detection algorithms of LSD, fast line detector(FLD), and edge drawing lines(EDLines). Then, the measurement model of the line is introduced in detail. Finally, FPL-VIO is proposed by adding the above method to monocular visual-inertial state estimator(VINSMono), an optimization-based fast vision-inertial odometer with lines described by midpoint and points. Compared with VIO using points and lines(PL-VIO), the line processing efficiency of FPL-VIO is increased by 3-4 times while ensuring the same accuracy.展开更多
Micro-energy grids have shown superiorities over traditional electricity and heating management systems.This paper presents a hybrid optimization strategy for micro-energy grid dispatch with three salient features.Fir...Micro-energy grids have shown superiorities over traditional electricity and heating management systems.This paper presents a hybrid optimization strategy for micro-energy grid dispatch with three salient features.First,to enhance the ability to support new storage equipment,an energy hub model is proposed using the non-supplementary fired compressed air energy storage(NSF-CAES).This provides flexible dispatch for cooling,heating and electricity.Second,considering the unique characteristics of the NSF-CAES,a sliding time window(STW)method is designed for simple but effective energy dispatch.Third,for the optimization of energy dispatch,we blend the differential evolution(DE)with the hyper-spherical search(HSS)to formulate a hybrid DE-HSS algorithm,which enhances the global search ability and accuracy.Comparative case studies are performed using real data of scenarios to demonstrate the superiorities of the proposed scheme.展开更多
基金This research was funded by the National Key R&D Program of China(No.2018YFB2100603)the Key R&D Program of Hubei Province(No.2022BAA048)+2 种基金the National Natural Science Foundation of China program(No.41890822)the Open Fund of National Engineering Research Centre for Geographic Information System,China University of Geosciences,Wuhan 430074,China(No.2022KFJJ07)The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Centre of Wuhan University.
文摘To protect the environment,the discharged sewage’s quality must meet the state’s discharge standards.There are many water quality indicators,and the pH(Potential of Hydrogen)value is one of them.The natural water’s pH value is 6.0–8.5.The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard.This paper aims to study the deep learning prediction model of wastewater’s pH.Firstly,the research uses the random forest method to select the data features and then,based on the sliding window,convert the data set into a time series which is the input of the deep learning training model.Secondly,by analyzing and comparing relevant references,this paper believes that the CNN(Convolutional Neural Network)model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers.After alternating the combination of the convolutional layer and pooling layer,all features are integrated into a full-connected neural network.Thirdly,the number of input samples of the CNN model directly affects the prediction effect of the model.Therefore,this paper adopts the sliding window method to study the optimal size.Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers.The last full-connection layer contains two layers and 64 neurons per layer.The sliding window size selects as 12.Finally,the research has carried out data prediction based on the optimal CNN deep learning model.The predicted pH of the sewage is between 7.2 and 8.6 in this paper.The result is applied in the monitoring system platform of the“Intelligent operation and maintenance platform of the reclaimed water plant.”
基金Fundamental Research Funds for the Central Universities of Ministry of Education of China。
文摘Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.
基金supported by the National Nature Science Foundation of China(Grant Nos.91546108,and 71490725)the AnhuiProvincial Scienceand Technology Major Projects(201903a05020020)+2 种基金the Anhui Provincial Natural Science Foundation(1908085QG298)the Fundamental Research Funds for the Central Universities(JZ2019HGTA0053,JZ2019 HGBZ0128)the Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,China.
文摘Differential privacy has recently become a widely recognized strict privacy protection model of data release.Differential privacy histogram publishing can directly show the statistical data distribution under the premise of ensuring user privacy for data query,sharing,and analysis.The dynamic data release is a study with a wide range of current industry needs.However,the amount of data varies considerably over different periods.Unreasonable data processing will result in the risk of users’information leakage and unavailability of the data.Therefore,we designed a differential privacy histogram publishing method based on the dynamic sliding window of LSTM(DPHP-DL),which can improve data availability on the premise of guaranteeing data privacy.DPHP-DL is integrated by DSW-LSTM and DPHK+.DSW-LSTM updates the size of sliding windows based on data value prediction via long shortterm memory(LSTM)networks,which evenly divides the data stream into several windows.DPHK+heuristically publishes non-isometric histograms based on k-mean++clustering of automatically obtaining the optimal K,so as to achieve differential privacy histogram publishing of dynamic data.Extensive experiments on real-world dynamic datasets demonstrate the superior performance of the DPHP-DL.
基金supported by the National Natural Science Foundation of China(61701270)the Young Doctor Cooperation Foundation of Qilu University of Technology(Shandong Academy of Sciences)(2017BSHZ008)。
文摘Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements.
基金supported by the foundation of Science and Technology Commission of Shanghai Municipality (Grant No.13521103902)
文摘Through improving the redundant data filtering of unreliable data filter for radio frequency identification(RFID) with sliding-window,a data filter which integrates self-adaptive sliding-window and Euclidean distance is proposed.The input data required being filtered have been shunt by considering a large number of redundant data existing in the unreliable data for RFID and the redundant data in RFID are the main filtering object with utilizing the filter based on Euclidean distance.The comparison between the results from the method proposed in this paper and previous research shows that it can improve the accuracy of the RFID for unreliable data filtering and largely reduce the redundant reading rate.
基金Supported in part by the National Natural Science Foundation of China(61301091)the Natural Science Basic Research Plan in Shaanxi Province of China(2015JQ6262)+1 种基金the Open Foundation of State Key Laboratory of Information Security(2015-MS-14)the New Star Team of Xi’an University of Posts&Telecommunications
基金supported by the National Key Research and Development Program of China(2020SKA0110300)the Joint Research Fund in Astronomy(U1831204 and U1931141)under cooperative agreement between the National Natural Science Foundation of China(NSFC)+7 种基金the Chinese Academy of Sciences(CAS)(NSFC,No.11903009)the Funds for International Cooperation and Exchange of the NSFC(11961141001)Yunnan Key Research and Development Program(2018IA054)The Key Science and Technology Program of Henan Province(Nos.202102210152,212102210611 and 202102210125)the Research and Cultivation Fund Project of Anyang Normal University(AYNUKPY-2019-24 and AYNUKPY-2020-25)supported by Astronomical Big Data Joint Research Centerco-founded by the National Astronomical ObservatoriesChinese Academy of Sciences and Alibaba Cloud。
文摘Data archiving is one of the most critical issues for modern astronomical observations.With the development of a new generation of radio telescopes,the transfer and archiving of massive remote data have become urgent problems to be solved.Herein,we present a practical and robust file-level flow-control approach,called the Unlimited Sliding-Window(USW),by referring to the classic flow-control method in the TCP protocol.Based on the USW and the Next Generation Archive System(NGAS)developed for the Murchison Widefield Array telescope,we further implemented an enhanced archive system(ENGAS)using ZeroMQ middleware.The ENGAS substantially improves the transfer performance and ensures the integrity of transferred files.In the tests,the ENGAS is approximately three to twelve times faster than the NGAS and can fully utilize the bandwidth of network links.Thus,for archiving radio observation data,the ENGAS reduces the communication time,improves the bandwidth utilization,and solves the remote synchronous archiving of data from observatories such as Mingantu spectral radioheliograph.It also provides a better reference for the future construction of the Square Kilometer Array(SKA)Science Regional Center.
基金supported by the National Natural Science Foundation of China(Grant No.60702032)the Natural Science Foundation of Heilongjiang Province(No.F201021)the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology(No.HIT.NSRIF.2008.63).
文摘Rain and snow seriously degrade outdoor video quality.In this work,a primary-secondary background model for removal of rain and snow is built.First,we analyze video noise and use a sliding window sequence principal component analysis de-nosing algorithm to reduce white noise in the video.Next,we apply the Gaussian mixture model(GMM)to model the video and segment all foreground objects primarily.After that,we calculate von Mises distribution of the velocity vectors and ratio of the overlapped region with referring to the result of the primary segmentation and extract the interesting object.Finally,rain and snow streaks are inpainted using the background to improve the quality of the video data.Experiments show that the proposed method can effectively suppress noise and extract interesting targets.
基金supported in part by the Scientific Research Project of the Education Department of Jilin Province (JJKH20221020KJ)the National Natural Science Foundation of China (51408257)the Graduate Innovation Fund of Jilin University (101832020CX150)。
文摘A lane-level intersection map is a cornerstone in high-definition(HD) traffic network maps for autonomous driving and high-precision intelligent transportation systems applications such as traffic management and control, and traffic accident evaluation and prevention. Mapping an HD intersection is time-consuming, labor-intensive, and expensive with conventional methods. In this paper, we used a low-channel roadside light detection and range sensor(LiDAR) to automatically and dynamically generate a lane-level intersection, including the signal phases, geometry, layout, and lane directions. First, a mathematical model was proposed to describe the topology and detail of a lane-level intersection. Second, continuous and discontinuous traffic object trajectories were extracted to identify the signal phases and times. Third, the layout, geometry, and lane direction were identified using the convex hull detection algorithm for trajectories. Fourth, a sliding window algorithm was presented to detect the lane marking and extract the lane, and the virtual lane connecting the inbound and outbound of the intersection were generated using the vehicle trajectories within the intersection and considering the traffic rules. In the field experiment, the mean absolute estimation error is 2 s for signal phase and time identification. The lane marking identification Precision and Recall are96% and 94.12%, respectively. Compared with the satellite-based,MMS-based, and crowdsourcing-based lane mapping methods,the average lane location deviation is 0.2 m and the update period is less than one hour by the proposed method with low-channel roadside LiDAR.
文摘An efficient observability analysis method is proposed to enable online detection of performance degradation of an optimization-based sliding window visual-inertial state estimation framework.The proposed methodology leverages numerical techniques in nonlinear observability analysis to enable online evaluation of the system observability and indication of the state estimation performance.Specifically,an empirical observability Gramian based approach is introduced to efficiently measure the observability condition of the windowed nonlinear system,and a scalar index is proposed to quantify the average system observability.The proposed approach is specialized to a challenging optimizationbased sliding window monocular visual-inertial state estimation formulation and evaluated through simulation and experiments to assess the efficacy of the methodology.The analysis result shows that the proposed approach can correctly indicate degradation of the state estimation accuracy with real-time performance.
基金supported by the National Key R&D Program of China(Nos.2018YFB1003905)the National Natural Science Foundation of China under Grant No.61971032,Fundamental Research Funds for the Central Universities(No.FRF-TP-18-008A3).
文摘On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is essential for on-site programming big data.Duplicate data detection is an important step in data cleaning,which can save storage resources and enhance data consistency.Due to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed.The efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding window.Experimental results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.
文摘Sports matches are very popular all over the world.The prediction of a sports match is helpful to grasp the team's state in time and adjust the strategy in the process of the match.It's a challenging effort to predict a sports match.Therefore,a method is proposed to predict the result of the next match by using teams'historical match data.We combined the Long Short-Term Memory(LSTM)model with the attention mechanism and put forward an ASLSTM model for predicting match results.Furthermore,to ensure the timeliness of the prediction,we add the time sliding window to make the prediction have better timeliness.Taking the football match as an example,we carried out a case study and proposed the feasibility of this method.
基金supported by the National Science and Technology Pillar Program of China (No.2011BAH24B06)the National Nature Science Foundation of China+1 种基金Chinese Civil Aviation Jointly Funded Foundation Project (No.U1433129)the Sichuan Provincial Department of Education Foundation(No.13ZB0287)
文摘Due to the particularity of its location algorithm,there are some unique difficulties and features regarding the test of target motion states of multilateration(MLAT)system for airport surface surveillance.This paper proposed a test method applicable for the airport surface surveillance MLAT system,which can effectively determine whether the target is static or moving at a certain speed.Via a normalized test statistic designed in the sliding data window,the proposed method not only eliminates the impact of geometry Dilution of precision(GDOP)effectively,but also transforms the test of different motion states into the test of different probability density functions.Meanwhile,by adjusting the size of the sliding window,it can fulfill different test performance requirements.The method was developed through strict theoretical extrapolation and performance analysis,and simulations results verified its correctness and effectiveness.
基金This study is supported by National key research and development program(2016YFB0801200).
文摘With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy.In this paper,we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters.This method is called EBF sketches.Our sketch structure allows for effective summarization of streams over time-based sliding windows with guaranteed probabilistic accuracy.It can be employed to address problems such as maintaining frequency statistics and finding heavy hitters.Our experimental results validate our theoretical claims and verifies the effectiveness of our techniques.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51877187)the Key Program of University Technology Plan of Hebei Province(Grant No.ZD2017081).
文摘In this paper,battery aging diversity among independent cells was studied in terms of available capacity degradation.During the aging process of LiFePO_(4)batteries,the phenomenon of aging diversity can be observed.When batteries with same specification were charged and discharged repeatedly under the same working conditions,the available capacity of different cell decreased at different rates along the cycle number.In this study,accelerated aging tests were carried out on multiple new LiFePO_(4)battery samples of different brands.Experimental results show that under the same working conditions,the actual available capacity of all cells decreased as the number of aging cycle increased,but an obvious aging diversity was observed even among different cells of same brand with same specification.This aging diversity was described and analysed in detail,and the common aging features of different cells beneath this aging diversity was explored.Considering this aging diversity,a probability density concept was adopted to estimate battery’s state of health(SOH).With this method,a relationship between battery SOH and its aging feature parameter was established,and a dynamic sliding window optimization technique was designed to ensure the optimal quality of aging feature extraction.Finally,the accuracy of this SOH estimation method was verified by random test.
文摘This paper proposes an efficient object localization method based on a vanishing line. The proposed method can be much improved in time efficiency since it requires scanning only vanishing line area. It requires the time complexity of O(n) while the existing sliding window method requires the time complexity O(n<sup>2</sup>) for detecting all objects in the entire image. In addition, the range of detection area can be also remarkably reduced when compared with the sliding window method. As a result, the total range and times for searching in the proposed method can be significantly reduced by considering together the distance and position of the object. The experiment on the proposed method is performed with the virtual road data set known as SYNTHIA, and the competitive results are obtained.
基金by National Natural Science Foundation of China(Nos.61774157 and 81771388)Beijing National Natural Science Foundation of China(No.4182075)National Key Research and Development.Plan(Nos.2020YFC2004501,2020YFC2004503 and 2017YFF0107704).
文摘Visual simultaneous localization and mapping(VSLAM) are essential technologies to realize the autonomous movement of vehicles. Visual-inertial odometry(VIO) is often used as the front-end of VSLAM because of its rich information, lightweight, and robustness. This article proposes the FPL-VIO, an optimization-based fast vision-inertial odometer with points and lines. Traditional VIO mostly uses points as landmarks;meanwhile, most of the geometrical structure information is ignored. Therefore, the accuracy will be jeopardized under motion blur and texture-less area. Some researchers improve accuracy by adding lines as landmarks in the system.However, almost all of them use line segment detector(LSD) and line band descriptor(LBD) in line processing, which is very time-consuming. This article first proposes a fast line feature description and matching method based on the midpoint and compares the three line detection algorithms of LSD, fast line detector(FLD), and edge drawing lines(EDLines). Then, the measurement model of the line is introduced in detail. Finally, FPL-VIO is proposed by adding the above method to monocular visual-inertial state estimator(VINSMono), an optimization-based fast vision-inertial odometer with lines described by midpoint and points. Compared with VIO using points and lines(PL-VIO), the line processing efficiency of FPL-VIO is increased by 3-4 times while ensuring the same accuracy.
基金This work was supported by the Fundamental Research Funds for the Central Universities(No.2019JBM004)the National Natural Science Foundation of China(No.51977004)the Beijing Natural Science Foundation(No.4212042).
文摘Micro-energy grids have shown superiorities over traditional electricity and heating management systems.This paper presents a hybrid optimization strategy for micro-energy grid dispatch with three salient features.First,to enhance the ability to support new storage equipment,an energy hub model is proposed using the non-supplementary fired compressed air energy storage(NSF-CAES).This provides flexible dispatch for cooling,heating and electricity.Second,considering the unique characteristics of the NSF-CAES,a sliding time window(STW)method is designed for simple but effective energy dispatch.Third,for the optimization of energy dispatch,we blend the differential evolution(DE)with the hyper-spherical search(HSS)to formulate a hybrid DE-HSS algorithm,which enhances the global search ability and accuracy.Comparative case studies are performed using real data of scenarios to demonstrate the superiorities of the proposed scheme.