By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution a...By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.展开更多
Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algor...Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.展开更多
Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing th...Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.展开更多
Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal...Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.展开更多
Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused signif...Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.展开更多
As the days go by, there are technologies that are being introduced everyday, whether it is a tiny music player iPod nano or a robot “Asimo” that runs 6 kilometers per hour. These technologies entertain, facilitate ...As the days go by, there are technologies that are being introduced everyday, whether it is a tiny music player iPod nano or a robot “Asimo” that runs 6 kilometers per hour. These technologies entertain, facilitate and make the day easier for the human being. It is not arguable anymore that the people need these technologies with the smart systems to lead their regular life smoothly. The smarter the system is;the more people like to use it. One major part of this smartness of the system depends on how well the system can interact with the person or the user. It is not a dream anymore that a system will be able to interact with a human just the way that one human interacts with another. To make that happen, it is obvious that the system must be intelligent enough to understand a human being. For example, if we need a Robot that can have a random conversation with a human, the system must recognize and understand the spoken word to reply the human. And the reply will be based on the current mood and behavior of the human. In this scenario, a human uses his senses to receive the inputs such as voice through the hearing senses, behavior and movement of the body parts, and facial expression through seeing sense from the speaking human. And it is now apparently possible to take such inputs for a system which can be stored as data;later it is possible to analyze the data using various algorithms and also to teach the system through Machine Learning algorithms. We will briefly discuss issues related to the relevance and the possible impact of research in the field of Artificial Intelligence, with special attention to the Computer Vision and Pattern Recognition, Natural Language Processing, Human Computer Interaction, Data Warehouse and Data Mining that is used to identify and analyze data like psychological signals, voice, conversation, geo location, and geo weather, etc. In our research, we have used heart rate that is a successful physiological signal to detect human mood and used smartphone usage data to train the system and detect mood more accurately than other methods.展开更多
As the complexity of flight missions continues to increase,sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents.Monitoring pilots’physi...As the complexity of flight missions continues to increase,sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents.Monitoring pilots’physiological data,real-time evaluation of mission load is a feasible technical way to achieve this.In this paper,a set of flight tasks including aircraft control,humancomputer interaction and mental arithmetic tests are designed to simulate five mission loads at different flight difficulty levels.A sensitivity analysis method based on a comprehensive test is proposed to select a set of sensitive physiological factors.Then,based on the SVM hierarchical combination classification method,the pilot mission load real-time evaluation model is established.The test results show significant differences in EMG,respiration rate(abdomen),heart rate,blood oxygen saturation,pupil area,fixation duration,number of fixations,and saccades.The high accuracy obtained from experiments proved that the proposed real-time evaluation model is applicable to meet the requirements of real working environments.The findings can provide methodological references for mission load evaluation research in other fields.展开更多
The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they ofte...The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they often face challenges such as lengthy computation times and limited accuracy.To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape,this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network(CNN).First,a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space.Next,the internal ballistic time-series data is encoded into three-channel images,establishing a potential relationship between the ballistic curves and their image representations.A CNN is then constructed and trained using these encoded images.Once trained,the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve.This paper conducts comparative experiments across various neural network models,validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images,as well as its generalization capability across different CNN architectures.Ignition tests were performed based on the predicted propellant grain.The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%,confirming the validity and feasibility of the proposed reverse design methodology.展开更多
The seed industry is a core strategic sector for national food security.Due to high R&D investment,long operating cycles,and dual impacts from natural and market factors,listed seed companies exhibit distinct fina...The seed industry is a core strategic sector for national food security.Due to high R&D investment,long operating cycles,and dual impacts from natural and market factors,listed seed companies exhibit distinct financial risk characteristics with temporal dynamics.This study takes 6 leading A-share listed seed companies as research samples,using time-series financial data from authoritative databases such as CSMAR and Wind covering Q12016 to Q32024.Integrating enterprise risk management(ERM)theory and anomaly detection theory,a financial risk evaluation index system is constructed,encompassing 6 dimensions:solvency,profitability,operational capacity,growth potential,cash flow capacity,and seed industry-specific indicators.After dimension reduction via factor analysis,three predictive models,logistic regression(LR),XGBoost,and LSTM time-series model,are established for empirical research on financial risk prediction,with their performance compared.The results show that the LSTM model achieves the optimal fit for time-series financial data of listed seed companies,with a test set AUC value of 0.889,significantly outperforming the traditional LR model(0.758)and XGBoost model(0.821).Incorporating industry-specific indicators such as R&D investment ratio and seed production cost rate improves the model’s prediction accuracy by 11.8%,verifying the importance of industry-specific indicators for risk prediction.Based on empirical findings,optimization strategies for financial risk control of listed seed companies are proposed from enterprise,industry,and regulatory perspectives,providing empirical reference and practical pathways for constructing intelligent financial risk early warning systems in the seed industry.展开更多
All previous methods identify cycles in COVID-19 daily and weekly data based on a subjective interpretation of the results.This poses difficulties for researchers interested in conducting comprehensive studies to inve...All previous methods identify cycles in COVID-19 daily and weekly data based on a subjective interpretation of the results.This poses difficulties for researchers interested in conducting comprehensive studies to investigate the presence of cycles in country/territory/area(CTA).Hence,we propose an algorithm that automatically detects the fundamental period T_(0)and its harmonics.Based on previous literature,we used T_(0)=7 days for daily data and T_(0)=52 weeks for weekly data.The new algorithm was applied to the time series from 236 CTAs collected by the WHO.The detection results are reported by considering the WHO region to which the CTA belongs or the latitudinal position of the CTA capital.Our results confirm the findings of other researchers in WHO and latitudebased groups.Concurrently,the results provide new information about CTAs for which COVID-19 time-series data have not been carefully examined.展开更多
Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role...Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties.展开更多
In recent years, sedimentation conditions in Dongting Lake have varied greatly because of signifi cant changes in runoff and sediment load in the Changjiang(Yangtze) River following the construction of Three Gorges Da...In recent years, sedimentation conditions in Dongting Lake have varied greatly because of signifi cant changes in runoff and sediment load in the Changjiang(Yangtze) River following the construction of Three Gorges Dam. The topography of the lake bottom has changed rapidly because of the intense exchange of water and sediment between the lake and the Changjiang River. However, time series information on lake-bottom topographic change is lacking. In this study, we introduced a method that combines remote sensing data and in situ water level data to extract a record of Dongting Lake bottom topography from 2003 to 2011. Multi-temporal lake land/water boundaries were extracted from MODIS images using the linear spectral mixture model method. The elevation of water/land boundary points were calculated using water level data and spatial interpolation techniques. Digital elevation models of Dongting Lake bottom topography in different periods were then constructed with the multiple heighted waterlines. The mean root-mean-square error of the linear spectral mixture model was 0.036, and the mean predicted error for elevation interpolation was-0.19 m. Compared with fi eld measurement data and sediment load data, the method has proven to be most applicable. The results show that the topography of the bottom of Dongting Lake has exhibited uneven erosion and deposition in terms of time and space over the last nine years. Moreover, lake-bottom topography has undergone a slight erosion trend within this period, with 58.2% and 41.8% of the lake-bottom area being eroded and deposited, respectively.展开更多
Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to r...Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.展开更多
In this work,we design a multisensory IoT-based online vitals monitor(hereinafter referred to as the VITALS)to sense four bedside physiological parameters including pulse(heart)rate,body temperature,blood pressure,and...In this work,we design a multisensory IoT-based online vitals monitor(hereinafter referred to as the VITALS)to sense four bedside physiological parameters including pulse(heart)rate,body temperature,blood pressure,and periph-eral oxygen saturation.Then,the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery.The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment,a powerful microcontroller,a reliable wireless communication module,and a big data analytics system.It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis.We use Apache Kafka(to gather live data streams from connected sen-sors),Apache Spark(to categorize the patient vitals and notify the medical pro-fessionals while identifying abnormalities in physiological parameters),Hadoop Distributed File System(HDFS)(to archive data streams for further analysis and long-term storage),Spark SQL,Hive and Matplotlib(to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals).In addition,we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely.Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing,data processing,and data transmission mechanisms.To validate the system accuracy,we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor,the Welch Allyn®Spot Check.Our pro-posed system provides improved care solutions,especially for those whose access to care services is limited.展开更多
Acomputerized platform for multi-channel physiological signals is developed in our lab to highly improve the recording and review for the output of The Polygraph System. The platform mainly consists of a Pentium III P...Acomputerized platform for multi-channel physiological signals is developed in our lab to highly improve the recording and review for the output of The Polygraph System. The platform mainly consists of a Pentium III PC and a high speed A/D converter and is supported by Visual Basic 6.0 and Microsoft Access 2 000. The platform has powerful functions for data acquisition, real-time waveform display and review. It has proved its reliability and flexibility through practical animal experiments. Besides, its modulized program design provides interfaces for further data processing and analysis.展开更多
The COVID-19 pandemic continues to impact daily life worldwide.It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic.He...The COVID-19 pandemic continues to impact daily life worldwide.It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic.Here,we aim to demonstrate that it is feasible to analyze the patterns of the pandemic using a time-series clustering approach.In this work,we use dynamic time warping distance and hierarchical clustering to cluster time series of daily new cases and deaths from different countries into four patterns.It is found that geographic factors have a large but not decisive influence on the pattern of pandemic development.Moreover,the age structure of the population may also influence the formation of cluster patterns.Our proven valid method may provide a different but very useful perspective for other scholars and researchers.展开更多
基金supported by the open research fund of the Key Laboratory of Agri-informatics,Ministry of Agriculture and the fund of Outstanding Agricultural Researcher,Ministry of Agriculture,China
文摘By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.
文摘Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.
基金funded by the Ministry-level Scientific and Technological Key Programs of Ministry of Natural Resources and Environment of Viet Nam "Application of thermal infrared remote sensing and GIS for mapping underground coal fires in Quang Ninh coal basin" (Grant No. TNMT.2017.08.06)
文摘Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.
基金the Frontier Program of the Knowledge Innovation Program of Chinese Academy of Sciences
文摘Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.
基金The National Natural Science Foundation of China under contract No.42406188the Natural Science Foundation of Liaoning Province under contract No.2024-BS-022+1 种基金the Dalian High-Level Talent Innovation Program under contract No.2022RG02the Fundamental Research Funds for the Central Universities under contract No.3132025107.
文摘Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.
文摘As the days go by, there are technologies that are being introduced everyday, whether it is a tiny music player iPod nano or a robot “Asimo” that runs 6 kilometers per hour. These technologies entertain, facilitate and make the day easier for the human being. It is not arguable anymore that the people need these technologies with the smart systems to lead their regular life smoothly. The smarter the system is;the more people like to use it. One major part of this smartness of the system depends on how well the system can interact with the person or the user. It is not a dream anymore that a system will be able to interact with a human just the way that one human interacts with another. To make that happen, it is obvious that the system must be intelligent enough to understand a human being. For example, if we need a Robot that can have a random conversation with a human, the system must recognize and understand the spoken word to reply the human. And the reply will be based on the current mood and behavior of the human. In this scenario, a human uses his senses to receive the inputs such as voice through the hearing senses, behavior and movement of the body parts, and facial expression through seeing sense from the speaking human. And it is now apparently possible to take such inputs for a system which can be stored as data;later it is possible to analyze the data using various algorithms and also to teach the system through Machine Learning algorithms. We will briefly discuss issues related to the relevance and the possible impact of research in the field of Artificial Intelligence, with special attention to the Computer Vision and Pattern Recognition, Natural Language Processing, Human Computer Interaction, Data Warehouse and Data Mining that is used to identify and analyze data like psychological signals, voice, conversation, geo location, and geo weather, etc. In our research, we have used heart rate that is a successful physiological signal to detect human mood and used smartphone usage data to train the system and detect mood more accurately than other methods.
基金co-supported by the Aeronautical Science Foundation of China(No.2020Z023053002)the National Natural Science Foundation of China(No.61305133。
文摘As the complexity of flight missions continues to increase,sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents.Monitoring pilots’physiological data,real-time evaluation of mission load is a feasible technical way to achieve this.In this paper,a set of flight tasks including aircraft control,humancomputer interaction and mental arithmetic tests are designed to simulate five mission loads at different flight difficulty levels.A sensitivity analysis method based on a comprehensive test is proposed to select a set of sensitive physiological factors.Then,based on the SVM hierarchical combination classification method,the pilot mission load real-time evaluation model is established.The test results show significant differences in EMG,respiration rate(abdomen),heart rate,blood oxygen saturation,pupil area,fixation duration,number of fixations,and saccades.The high accuracy obtained from experiments proved that the proposed real-time evaluation model is applicable to meet the requirements of real working environments.The findings can provide methodological references for mission load evaluation research in other fields.
文摘The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they often face challenges such as lengthy computation times and limited accuracy.To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape,this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network(CNN).First,a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space.Next,the internal ballistic time-series data is encoded into three-channel images,establishing a potential relationship between the ballistic curves and their image representations.A CNN is then constructed and trained using these encoded images.Once trained,the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve.This paper conducts comparative experiments across various neural network models,validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images,as well as its generalization capability across different CNN architectures.Ignition tests were performed based on the predicted propellant grain.The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%,confirming the validity and feasibility of the proposed reverse design methodology.
文摘The seed industry is a core strategic sector for national food security.Due to high R&D investment,long operating cycles,and dual impacts from natural and market factors,listed seed companies exhibit distinct financial risk characteristics with temporal dynamics.This study takes 6 leading A-share listed seed companies as research samples,using time-series financial data from authoritative databases such as CSMAR and Wind covering Q12016 to Q32024.Integrating enterprise risk management(ERM)theory and anomaly detection theory,a financial risk evaluation index system is constructed,encompassing 6 dimensions:solvency,profitability,operational capacity,growth potential,cash flow capacity,and seed industry-specific indicators.After dimension reduction via factor analysis,three predictive models,logistic regression(LR),XGBoost,and LSTM time-series model,are established for empirical research on financial risk prediction,with their performance compared.The results show that the LSTM model achieves the optimal fit for time-series financial data of listed seed companies,with a test set AUC value of 0.889,significantly outperforming the traditional LR model(0.758)and XGBoost model(0.821).Incorporating industry-specific indicators such as R&D investment ratio and seed production cost rate improves the model’s prediction accuracy by 11.8%,verifying the importance of industry-specific indicators for risk prediction.Based on empirical findings,optimization strategies for financial risk control of listed seed companies are proposed from enterprise,industry,and regulatory perspectives,providing empirical reference and practical pathways for constructing intelligent financial risk early warning systems in the seed industry.
文摘All previous methods identify cycles in COVID-19 daily and weekly data based on a subjective interpretation of the results.This poses difficulties for researchers interested in conducting comprehensive studies to investigate the presence of cycles in country/territory/area(CTA).Hence,we propose an algorithm that automatically detects the fundamental period T_(0)and its harmonics.Based on previous literature,we used T_(0)=7 days for daily data and T_(0)=52 weeks for weekly data.The new algorithm was applied to the time series from 236 CTAs collected by the WHO.The detection results are reported by considering the WHO region to which the CTA belongs or the latitudinal position of the CTA capital.Our results confirm the findings of other researchers in WHO and latitudebased groups.Concurrently,the results provide new information about CTAs for which COVID-19 time-series data have not been carefully examined.
基金supported by the Natural Science Foundation of Hubei Province, China (2017CFB434)the National Natural Science Foundation of China (41506208 and 61501200)the Basic Research Funds for Yellow River Institute of Hydraulic Research, China (HKYJBYW-2016-06)
文摘Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties.
基金Supported by the National Basic Research Program of China(973 Program)(No.2012CB417001)the National Natural Science Foundation of China(No.41271125)
文摘In recent years, sedimentation conditions in Dongting Lake have varied greatly because of signifi cant changes in runoff and sediment load in the Changjiang(Yangtze) River following the construction of Three Gorges Dam. The topography of the lake bottom has changed rapidly because of the intense exchange of water and sediment between the lake and the Changjiang River. However, time series information on lake-bottom topographic change is lacking. In this study, we introduced a method that combines remote sensing data and in situ water level data to extract a record of Dongting Lake bottom topography from 2003 to 2011. Multi-temporal lake land/water boundaries were extracted from MODIS images using the linear spectral mixture model method. The elevation of water/land boundary points were calculated using water level data and spatial interpolation techniques. Digital elevation models of Dongting Lake bottom topography in different periods were then constructed with the multiple heighted waterlines. The mean root-mean-square error of the linear spectral mixture model was 0.036, and the mean predicted error for elevation interpolation was-0.19 m. Compared with fi eld measurement data and sediment load data, the method has proven to be most applicable. The results show that the topography of the bottom of Dongting Lake has exhibited uneven erosion and deposition in terms of time and space over the last nine years. Moreover, lake-bottom topography has undergone a slight erosion trend within this period, with 58.2% and 41.8% of the lake-bottom area being eroded and deposited, respectively.
文摘Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.
文摘In this work,we design a multisensory IoT-based online vitals monitor(hereinafter referred to as the VITALS)to sense four bedside physiological parameters including pulse(heart)rate,body temperature,blood pressure,and periph-eral oxygen saturation.Then,the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery.The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment,a powerful microcontroller,a reliable wireless communication module,and a big data analytics system.It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis.We use Apache Kafka(to gather live data streams from connected sen-sors),Apache Spark(to categorize the patient vitals and notify the medical pro-fessionals while identifying abnormalities in physiological parameters),Hadoop Distributed File System(HDFS)(to archive data streams for further analysis and long-term storage),Spark SQL,Hive and Matplotlib(to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals).In addition,we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely.Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing,data processing,and data transmission mechanisms.To validate the system accuracy,we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor,the Welch Allyn®Spot Check.Our pro-posed system provides improved care solutions,especially for those whose access to care services is limited.
基金Sponsored by China Ministry of Science and Technology:Joint Chinese - Israel Research Grant (99M- 0 0 4 14 15 )
文摘Acomputerized platform for multi-channel physiological signals is developed in our lab to highly improve the recording and review for the output of The Polygraph System. The platform mainly consists of a Pentium III PC and a high speed A/D converter and is supported by Visual Basic 6.0 and Microsoft Access 2 000. The platform has powerful functions for data acquisition, real-time waveform display and review. It has proved its reliability and flexibility through practical animal experiments. Besides, its modulized program design provides interfaces for further data processing and analysis.
基金jointly supported by the National Natural Science Foundation of China(Grant No.:11971074.61671005).
文摘The COVID-19 pandemic continues to impact daily life worldwide.It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic.Here,we aim to demonstrate that it is feasible to analyze the patterns of the pandemic using a time-series clustering approach.In this work,we use dynamic time warping distance and hierarchical clustering to cluster time series of daily new cases and deaths from different countries into four patterns.It is found that geographic factors have a large but not decisive influence on the pattern of pandemic development.Moreover,the age structure of the population may also influence the formation of cluster patterns.Our proven valid method may provide a different but very useful perspective for other scholars and researchers.
