Nianxue LUO - Academia.edu (original) (raw)
Papers by Nianxue LUO
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The local climate zone (LCZ) classification scheme is effective for climatic studies, and thus, t... more The local climate zone (LCZ) classification scheme is effective for climatic studies, and thus, timely and accurate LCZ mapping becomes critical for scientific climate research. Remote sensing images can efficiently capture the information of large-scale landscapes overhead, while street-level images can supplement the ground-level information, thus helping improve the LCZ mapping. Previous study has proven the usefulness of street-level images in enhancing LCZ mapping results; however, how they help to improve the results still remains unexplored. To unveil the underlying mechanism and fill the gap, in this study, the feature importance analysis is performed on classification experiments using different data sources to reveal the contributions of different components, while feature correlation analysis is adopted to find the relationship between street view images and key LCZ indicators. The results show that fusing street view images can help improve the classification performance considerably, especially for compact urban types such as compact highrise and compact midrise. In addition, the results further show that the building and sky information embedded in the street view images contribute the most. The feature correlation analysis further demonstrates their strong correlations with key LCZ indicators, which define the LCZ scheme. The findings of the study can help us better understand how street-level images can contribute to LCZ mapping and facilitate future urban climate studies.
ISPRS International Journal of Geo-Information
Analysis of the spatiotemporal distribution of online public opinion topics can help understand t... more Analysis of the spatiotemporal distribution of online public opinion topics can help understand the hotspots of public concern. The topic model is employed widely in public opinion topic clustering for social media data. In order to handle topic-clustering of low-quality geospatial social media data, such as microblog data, with short text and timeliness characteristics, this study proposed a Dirichlet multinomial mixture over time (DMMOT) model to cluster microblog topic for public opinion analysis. The DMMOT model assumes that a single document belongs to a single topic, in line with the characteristics of a short text, and it introduces the probability distribution of “topic-time” in the process of topic generation. The model parameter inference process was presented in detail by exploring the Gibbs sampling method. Results generated using the DMMOT model in case study show that the “topic-word” distribution is semantically aggregated within various topics, and “topic-time” distr...
International Journal of Applied Earth Observation and Geoinformation
2011 International Conference on Communications, Computing and Control Applications (CCCA), 2011
The multi-source information holds a great importance in processing complex and imprecise data. U... more The multi-source information holds a great importance in processing complex and imprecise data. Unfortunately, it requires an adequate formalism capable to modelize and to fuse several information. The evidence theory distinguishes from all formalism by its capacity to modelize and treat imprecise and imperfect data. In this context, the high resolution images represent a huge amount of data and needs multi-source information to perform pattern recognition. In this paper, we present an adaption of the distance operator introduced by Denoeux for estimating belief functions. This proposed approach will be used to classify forest image remote sensing by identifying the tree crown classes.
Sensors, 2019
The fingerprint method has been widely adopted in Wi-Fi indoor positioning because of its advanta... more The fingerprint method has been widely adopted in Wi-Fi indoor positioning because of its advantage in non-line-of-sight channels between access points (APs) and mobile users. However, the received signal strength (RSS) during the fingerprint positioning process generally varies due to the dissimilar hardware configurations of heterogeneous smartphones. This difference may degrade the accuracy of fingerprint matching between fingerprint and test data. Thus, this paper puts forward a fingerprint method based on grey relational analysis (GRA) to approach the challenge of heterogeneous smartphones and to improve positioning accuracy. Initially, the grey relational coefficient (GRC) between the RSS comparability sequence of each reference point (RP) and the RSS reference sequence of the test point (TP) is calculated. Subsequently, the grey relational degree (GRD) between each RP and TP is determined on the basis of GRC, and the K most relational RPs are selected in accordance with the v...
Remote Sensing, 2019
The fingerprint method has been widely adopted for Wi-Fi indoor positioning. In the fingerprint m... more The fingerprint method has been widely adopted for Wi-Fi indoor positioning. In the fingerprint matching process, user poses and user body shadowing have serious impact on the received signal strength (RSS) data and degrade matching accuracy; however, this impact has not attracted large attention. In this study, we systematically investigate the impact of user poses and user body shadowing on the collected RSS data and propose a new method called the pose recognition-assisted support vector machine algorithm (PRASVM). It fully exploits the characteristics of different user poses and improves the support vector machine (SVM) positioning performance by introducing a pose recognition procedure. This proposed method firstly establishes a fingerprint database with RSS and sensor data corresponding to different poses in the offline phase, and fingerprints of different poses in the database are extracted to train reference point (RP) classifiers of different poses and a pose classifier usi...
IEEE Access, 2020
Decision making are critical for disaster prevention and emergency response. To fully improve the... more Decision making are critical for disaster prevention and emergency response. To fully improve the effectiveness of emergency decisions by taking spatiotemporal information into consideration, this paper proposes a new method STGA-CBR for spatiotemporal case matching by using an integrated approach comprising a newly proposed spatiotemporal trajectory similarity measurement algorithm (Position-Frequency algorithm), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) similar spatiotemporal trajectory retrieval; (2) weight determination; and (3) attribute similarity calculation. The proposed approach was employed in typhoon disaster, which contains a variety of spatiotemporal information. The results of matching were validated by comparing STGA-CBR with ST-CBR, GA-CBR and traditional simple CBR. The experimental results proved that the proposed STGA-CBR effectively screens out similar spatiotemporal trajectories and demonstrates higher matching performance than there other methods, indicating the high efficiency of the proposed similar case retrieval approach. The case pair selected are then used for prediction of the post-disaster social and economic loss and the average accuracy of prediction results are calculated, among which the integrated model rank the highest, thus rendering our approach superior in comparison to other traditional methods. INDEX TERMS CBR, data analysis, decision making, risk analysis, similarity assessment.
Journal of Marine Science and Engineering, 2022
China is one of the countries most affected by typhoon disasters. It is of great significance to ... more China is one of the countries most affected by typhoon disasters. It is of great significance to study the mechanism of typhoon disasters and construct a typhoon disaster chain for emergency management and disaster reduction. The evolution process of typhoon disaster based on expert knowledge and historical disaster data has been summarized in previous studies, which relied too much on artificial experience while less in-depth consideration was given to the disaster exposure, the social environment, as well as the spatio-temporal factors. Hence, problems, such as incomplete content and inconsistent expression of typhoon disaster knowledge, have arisen. With the development of computer technology, massive Web corpus with numerous Web news and various improvised content on the social media platform, and ontology that enables consistent expression new light has been shed on the knowledge discovery of typhoon disaster. With the Chinese Web corpus as its source, this research proposes a ...
As the mature integrated scheme for various online monitoring systems of the electrical devices h... more As the mature integrated scheme for various online monitoring systems of the electrical devices has not been formed currently, and mature integrated analysis software has also not been formed, it is very necessary to build an integrated system, which can be used to monitor running status of power transmission and transformation equipment, for developing the smart grid based on IEC61850 and Web Service techniques. This paper introduces the design idea, design framework, network topology structure, integrated model and implement function of the integrated platform based on the design and development methods of software engineering. And we specially focus on the modeling information of substation configuration based on IEC61850. The system can integrate multi-online monitoring systems belonged to the status monitoring of electrical devices of different manufactures, different communication protocol into the unified control platform. And its feasibility and effectiveness have been verif...
Sensors, 2020
Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicator... more Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicators at the national and regional scales to study regional economic development. However, most previous studies only chose a single measurement indicator (such as GDP) and adopted simple regression methods to investigate the economic development of a certain area based on DMSP-OLS or NPP-VIIRS stable NTL data. The status quo shows the problems of using a single evaluation index—it has a low evaluation precision. The LJ1-01 satellite is the first dedicated NTL remote sensing satellite in the world, launched in July 2018. The data provided by LJ1-01 have a higher spatial resolution and fewer blooming phenomena. In this paper, we compared the accuracy of the LJ1-01 data and NPP-VIIRS data in detecting county-level multidimensional economic development. In three provinces in China, namely, Hubei, Hunan and Jiangxi, 20 socioeconomic parameters were selected from the following five perspectives: ...
International Journal of Environmental Research and Public Health, 2020
Constructed emergency response scenarios provide a basis for decision makers to make management d... more Constructed emergency response scenarios provide a basis for decision makers to make management decisions, and the development of such scenarios considers earlier historical cases. Over the decades, the development of emergency response scenarios has mainly implemented the elements of historic cases to describe the grade and influence of an accident. This paper focuses on scenario construction and proposes a corresponding framework based on natural language processing (NLP) using text reports of marine oil spill accidents. For each accident, the original textual reports are first divided into sentence sets corresponding to the temporal evolution. Each sentence set is regarded as a textual description of a marine oil spill scenario. A method is proposed in this paper, based on parsing, named entity recognition (NER) and open information extraction (OpenIE) to process the relation triples that are extracted from the sentence sets. Finally, the relation triples are semantically cluster...
Applied Sciences, 2020
Case-based reasoning (CBR) systems often provide a basis for decision makers to make management d... more Case-based reasoning (CBR) systems often provide a basis for decision makers to make management decisions in disaster prevention and emergency response. For decades, many CBR systems have been implemented by using expert knowledge schemes to build indexes for case identification from a case library of situations and to explore the relations among cases. However, a knowledge elicitation bottleneck occurs for many knowledge-based CBR applications because expert reasoning is difficult to precisely explain. To solve these problems, this paper proposes a method using only knowledge to recognize marine oil spill cases. The proposed method combines deep reinforcement learning (DRL) with strategy selection to determine emergency responses for marine oil spill accidents by quantification of the marine oil spill scenario as the reward for the DRL agent. These accidents are described by scenarios and are considered the state inputs in the hybrid DRL/CBR framework. The challenges and opportunit...
Safety Science, 2018
With rapid economic growth, previously less populated peripheral areas of major cities in China h... more With rapid economic growth, previously less populated peripheral areas of major cities in China have now become densely populated. Many hazardous installations (e.g., chemical plants, tank farms, depots of explosive materials, etc.) that used to be in these less populated areas are now found in the backyards of millions of citizens going about their daily lives with minimal knowledge about the dangers these installations pose. A hazard mishap that may have been containable with no human loss just 30 years ago can now threaten the lives of millions. While numerous studies have assessed hazard-specific risks, few efforts have attempted to establish a comprehensive framework to assess multi-hazard scenarios. For this purpose, this study developed a framework for spatial risk assessment of multiple hazardous installations in a metropolitan area. The framework includes three quantitative models for the analysis of the inherent danger associated with, the accessibility for rescue operations at, and the potential effectiveness of mass evacuation from these hazardous installations. Based on this framework and its quantitative models, a case study of the city of Beijing was conducted in this research.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019
Most traditional supervised classification methods for polarimetric synthetic aperture radar (Pol... more Most traditional supervised classification methods for polarimetric synthetic aperture radar (PolSAR) imagery require abundant manually selected samples, and the classification results are affected by the size and quality of the samples. In this paper, we propose an improved deep Q-network (DQN) method for PolSAR image classification, which can generate amounts of valid data by interacting with the agent using the-greedy strategy. The PolSAR data are first preprocessed to reduce the influence of speckle noise and extract the multi-dimensional features. The multi-dimensional feature image and corresponding training image are then fed into a deep reinforcement learning model tailored for PolSAR image classification. After many epochs of training, the method was applied to identify different land cover types in two PolSAR images acquired by different sensors. The experimental results demonstrate that the proposed method has a better classification performance compared with traditional supervised classification methods, such as convolutional neural network (CNN), random forest (RF), and L2-loss linear support vector machine (L2-SVM), and also has a good performance compared with the deep learning method CNN-SVM, which integrates the synergy of the SVM and CNN methods, especially in small sample sizes. This study also provides a toolset for the DQN (kiwi.server) on the GitHub development platform for training and visualization. Image edge detection Electron beam applications Intelligent structures Coprocessors Glial cells Blood platelets Interleaved codes Berkelium Biomechanics Genetics Dynamic equilibrium. Associative memory Connective tissue Neurites Seminars Extraterrestrial phenomena Source coding Aerospace components. EMTDC Aerospace components Delay lines Radiation protection Tires Chrome plating. Aperture antennas Independent component analysis Noise cancellation Psychology Mobile nodes Transmission line theory Robot sensing systems Micromanipulators Cyber espionage Digital art Power smoothing Plastic optical fiber Image matching. Web design Milling machines Unmanned autonomous vehicles MIMO radar Coils Electronic countermeasures Road side unit Machine tool spindles Heterogeneous networks Phishing Mathematics computing Foot IEEE magazines Cryptographic protocols. Chlorine compounds Application specific processors Product life cycle management Bluetooth Nanocrystals. Quantum cascade lasers Collaborative intelligence MIMO radar Grasping Marine technology Availability. Intracranial pressure sensors Valves Radar Electrooptic modulators Mean square error methods Job production systems Current density Optical waveguide theory Molecular electronics Videos Endocrine system. Split gate flash memory cells Periodic structures Cognitive radio VLIW Uncertainty Software development management. Data breach Optical planar waveguides Optical metrology Time-domain analysis Whole body imaging Magnetic materials Hafnium oxide Formal languages Telematics Biogeography Active pixel sensors Aluminum Circadian rhythm. Retinopathy Forecast uncertainty Wide area networks Iridium Wounds Thick film sensors International Atomic Time Neurites Transhuman Computer security. Dielectric loss measurement Spin polarized transport MySpace Polycaprolactone Crowdsourcing Simple object access protocol. Floppy disks Uranium Piezoelectric devices Metal cutting tools Satellite ground stations Milling machines Bone tissue Face recognition Animal behavior Dermatology Anti-parasitical. AODV Authorization Split gate flash memory cells Decoding Railguns Gunshot detection systems Millimeter wave devices. Sugar refining Centralized control Visible light communication Channel models Leg. Electronic equipment Device drivers Graphics processing units Genetics Gray-scale Multisensory integration All-optical networks Electricity supply industry. Collective intelligence Open Access Rats Augmented reality Macroeconomics Ionizing radiation Plastic insulators Scheduling algorithms. Unmanned aerial vehicles Shortest path problem Product safety Microwave circuits Nuclear and plasma sciences Antibiotics Trade agreements Biosensors Orbits (stellar).
Concurrency and Computation: Practice and Experience, 2018
The southeastern coast of China suffers many typhoon disasters every year, causing huge casualtie... more The southeastern coast of China suffers many typhoon disasters every year, causing huge casualties and economic losses. In addition, collecting statistics on typhoon disaster situations is hard work for the government. At the same time, near-real-time disaster-related information can be obtained on developed social media platforms like Twitter and Weibo. Many cases have proved that citizens are able to organize themselves promptly on the spot, and begin to share disaster information when a disaster strikes, producing massive VGI (volunteered geographic information) about the disaster situation, which could be valuable for disaster response if this VGI could be exploited efficiently and properly. However, this social media information has features such as large quantity, high noise, and unofficial modes of expression that make it difficult to obtain useful information. In order to solve this problem, we first designed a new classification system based on the characteristics of social medial data like Sina Weibo data, and made a microblogging dataset of typhoon damage with according category labels. Secondly, we used this social medial dataset to train the deep learning model, and constructed a typhoon disaster mining model based on a deep learning network, which could automatically extract information about the disaster situation. The model is different from the general classification system in that it automatically selected microblogs related to disasters from a large number of microblog data, and further subdivided them into different types of disasters to facilitate subsequent emergency response and loss estimation. The advantages of the model included a wide application range, high reliability, strong pertinence and fast speed. The research results of this thesis provide a new approach to typhoon disaster assessment in the southeastern coastal areas of China, and provide the necessary information for the authoritative information acquisition channel.
Remote Sensing, 2019
Land cover classification of urban areas is critical for understanding the urban environment. Hig... more Land cover classification of urban areas is critical for understanding the urban environment. High-resolution remotely sensed imagery provides abundant, detailed spatial information for urban classification. In the meantime, OpenStreetMap (OSM) data, as typical crowd-sourced geographical information, have been an emerging data source for obtaining urban information. In this context, a land cover classification method that fuses high-resolution remotely sensed imagery and OSM data is proposed. Training samples were generated by integrating the OSM data and multiple information indexes. OSM data, which contain class attributes and location information of urban objects, served as the labels of initial training samples. Multiple information indexes that reflect spectral and spatial characteristics of different classes were utilized to improve the training set. Morphological attribute profiles were used because the structural and contextual information of images was effective in distingu...
International Journal of Environmental Research and Public Health, 2018
Healthcare accessibility has become an issue of social equity. An accurate estimation of existing... more Healthcare accessibility has become an issue of social equity. An accurate estimation of existing healthcare accessibility is vital to plan and allocate health resources. Healthcare capacity, population demand, and geographic impedance are three essential factors to measure spatial accessibility. Additionally, geographic impedance is usually represented with a function of travel time. In this paper, the three-step floating catchment area (3SFCA) method is improved from the perspectives of the temporal dimension and population demand. Specifically, the travel time from the population location to the service site is precisely calculated by introducing real-time traffic conditions instead of utilizing empirical speed in previous studies. Additionally, with the utilization of real-time traffic, a dynamic result of healthcare accessibility is derived during different time periods. In addition, since the medical needs of the elderly are higher than that of the young, a demand weight index...
SPIE Proceedings, 2015
As the development of Web 2.0, the social media like microblog, blogs and social network have sup... more As the development of Web 2.0, the social media like microblog, blogs and social network have supplied a bunch of information with locations (Volunteered Geographical Information, VGI).Recent years many cases have shown that, if disaster happened, the cyber citizens will get together very quickly and share the disaster information, this results a bunch of volunteered geographical information about disaster situation which is very valuable for disaster response if this VGIs are used efficiently and properly. This project will take typhoon disaster as case study. In this paper, we study the relations between weibo messages and the real typhoon situation, we proposed an analysis framework for mine the relations between weibo messages distribution and physical space. We found that the number of the weibo messages, key words frequency and spatial temporary distribution of the messages have strong relations with the disaster spread in the real world, and this research results can improve our disaster situation awareness in the future. The achievement of the study will give a method for typhoon disaster situation awareness based on VGI from the bottom up, and will locate the disaster spot and evolution quickly which is very important for disaster response and recover.
Journal of Intelligent & Fuzzy Systems, 2015
Spatio-temporal trajectory clustering can extract behavior and moving pattern of object with the ... more Spatio-temporal trajectory clustering can extract behavior and moving pattern of object with the change of time and space by exploring similar trajectories. Most of trajectory clustering method can be achieved by expanding the traditional clustering algorithms. Considering the limitations of fitness and optimization of most clustering algorithms, especially for spatio-temporal trajectory data sets, this paper proposes a trajectory fuzzy clustering method based on multi-objective mixed function, which can simultaneously optimize multiple objective function such as FCM and XB when perform particle swarm optimization method. And we also propose a new coarse-grained DTW based on interpolation point for generalization trajectory data and improvement the performance of measure the similarity between trajectories. The experimental results, which implement on the synthetized trajectory data and real vehicle history data by employing the new clustering algorithm, and clustering validity evaluation and hot spots analysis show that the proposed method, which combines different objective functions with different optimization criteria and particle swarm algorithm, can effectively solve the clustering problem and produce better clustering results than the traditional clustering method.
2015 11th International Conference on Natural Computation (ICNC), 2015
Generally, computing efficiency of many spatial data analysis algorithm will sharply decline as d... more Generally, computing efficiency of many spatial data analysis algorithm will sharply decline as data size increase. It is very meaningful for extending the analysis method of spatial data and enhancing computational efficiency by introducing the distributed parallel computing model. Considering the features of spatio-temporal trajectory data, which is massive, related to time and dynamic, we proposed the fast calculation method of the trajectory similarity based on coarse-grained Dynamic Time Warping. The algorithm will reduce the consuming time greatly when the length of trajectory sequences are very long. We also proposed the parallel trajectory clustering strategy of big data under the Hadoop MapReduce model in this paper. The big data of trajectory are sliced, and the trajectory similarity and the iteration computation of cluster center are dealt with by multiwork nodes simultaneously. The experimental results of the parallel trajectory clustering, which based on the open source project Mahout, implemented on the vehicle trajectory data show that the clustering results are valid. The computing performance of parallel clustering are obviously improved as the trajectory data size increases. And the new parallel clustering method outperforms the traditional algorithm like k-means algorithm.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The local climate zone (LCZ) classification scheme is effective for climatic studies, and thus, t... more The local climate zone (LCZ) classification scheme is effective for climatic studies, and thus, timely and accurate LCZ mapping becomes critical for scientific climate research. Remote sensing images can efficiently capture the information of large-scale landscapes overhead, while street-level images can supplement the ground-level information, thus helping improve the LCZ mapping. Previous study has proven the usefulness of street-level images in enhancing LCZ mapping results; however, how they help to improve the results still remains unexplored. To unveil the underlying mechanism and fill the gap, in this study, the feature importance analysis is performed on classification experiments using different data sources to reveal the contributions of different components, while feature correlation analysis is adopted to find the relationship between street view images and key LCZ indicators. The results show that fusing street view images can help improve the classification performance considerably, especially for compact urban types such as compact highrise and compact midrise. In addition, the results further show that the building and sky information embedded in the street view images contribute the most. The feature correlation analysis further demonstrates their strong correlations with key LCZ indicators, which define the LCZ scheme. The findings of the study can help us better understand how street-level images can contribute to LCZ mapping and facilitate future urban climate studies.
ISPRS International Journal of Geo-Information
Analysis of the spatiotemporal distribution of online public opinion topics can help understand t... more Analysis of the spatiotemporal distribution of online public opinion topics can help understand the hotspots of public concern. The topic model is employed widely in public opinion topic clustering for social media data. In order to handle topic-clustering of low-quality geospatial social media data, such as microblog data, with short text and timeliness characteristics, this study proposed a Dirichlet multinomial mixture over time (DMMOT) model to cluster microblog topic for public opinion analysis. The DMMOT model assumes that a single document belongs to a single topic, in line with the characteristics of a short text, and it introduces the probability distribution of “topic-time” in the process of topic generation. The model parameter inference process was presented in detail by exploring the Gibbs sampling method. Results generated using the DMMOT model in case study show that the “topic-word” distribution is semantically aggregated within various topics, and “topic-time” distr...
International Journal of Applied Earth Observation and Geoinformation
2011 International Conference on Communications, Computing and Control Applications (CCCA), 2011
The multi-source information holds a great importance in processing complex and imprecise data. U... more The multi-source information holds a great importance in processing complex and imprecise data. Unfortunately, it requires an adequate formalism capable to modelize and to fuse several information. The evidence theory distinguishes from all formalism by its capacity to modelize and treat imprecise and imperfect data. In this context, the high resolution images represent a huge amount of data and needs multi-source information to perform pattern recognition. In this paper, we present an adaption of the distance operator introduced by Denoeux for estimating belief functions. This proposed approach will be used to classify forest image remote sensing by identifying the tree crown classes.
Sensors, 2019
The fingerprint method has been widely adopted in Wi-Fi indoor positioning because of its advanta... more The fingerprint method has been widely adopted in Wi-Fi indoor positioning because of its advantage in non-line-of-sight channels between access points (APs) and mobile users. However, the received signal strength (RSS) during the fingerprint positioning process generally varies due to the dissimilar hardware configurations of heterogeneous smartphones. This difference may degrade the accuracy of fingerprint matching between fingerprint and test data. Thus, this paper puts forward a fingerprint method based on grey relational analysis (GRA) to approach the challenge of heterogeneous smartphones and to improve positioning accuracy. Initially, the grey relational coefficient (GRC) between the RSS comparability sequence of each reference point (RP) and the RSS reference sequence of the test point (TP) is calculated. Subsequently, the grey relational degree (GRD) between each RP and TP is determined on the basis of GRC, and the K most relational RPs are selected in accordance with the v...
Remote Sensing, 2019
The fingerprint method has been widely adopted for Wi-Fi indoor positioning. In the fingerprint m... more The fingerprint method has been widely adopted for Wi-Fi indoor positioning. In the fingerprint matching process, user poses and user body shadowing have serious impact on the received signal strength (RSS) data and degrade matching accuracy; however, this impact has not attracted large attention. In this study, we systematically investigate the impact of user poses and user body shadowing on the collected RSS data and propose a new method called the pose recognition-assisted support vector machine algorithm (PRASVM). It fully exploits the characteristics of different user poses and improves the support vector machine (SVM) positioning performance by introducing a pose recognition procedure. This proposed method firstly establishes a fingerprint database with RSS and sensor data corresponding to different poses in the offline phase, and fingerprints of different poses in the database are extracted to train reference point (RP) classifiers of different poses and a pose classifier usi...
IEEE Access, 2020
Decision making are critical for disaster prevention and emergency response. To fully improve the... more Decision making are critical for disaster prevention and emergency response. To fully improve the effectiveness of emergency decisions by taking spatiotemporal information into consideration, this paper proposes a new method STGA-CBR for spatiotemporal case matching by using an integrated approach comprising a newly proposed spatiotemporal trajectory similarity measurement algorithm (Position-Frequency algorithm), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) similar spatiotemporal trajectory retrieval; (2) weight determination; and (3) attribute similarity calculation. The proposed approach was employed in typhoon disaster, which contains a variety of spatiotemporal information. The results of matching were validated by comparing STGA-CBR with ST-CBR, GA-CBR and traditional simple CBR. The experimental results proved that the proposed STGA-CBR effectively screens out similar spatiotemporal trajectories and demonstrates higher matching performance than there other methods, indicating the high efficiency of the proposed similar case retrieval approach. The case pair selected are then used for prediction of the post-disaster social and economic loss and the average accuracy of prediction results are calculated, among which the integrated model rank the highest, thus rendering our approach superior in comparison to other traditional methods. INDEX TERMS CBR, data analysis, decision making, risk analysis, similarity assessment.
Journal of Marine Science and Engineering, 2022
China is one of the countries most affected by typhoon disasters. It is of great significance to ... more China is one of the countries most affected by typhoon disasters. It is of great significance to study the mechanism of typhoon disasters and construct a typhoon disaster chain for emergency management and disaster reduction. The evolution process of typhoon disaster based on expert knowledge and historical disaster data has been summarized in previous studies, which relied too much on artificial experience while less in-depth consideration was given to the disaster exposure, the social environment, as well as the spatio-temporal factors. Hence, problems, such as incomplete content and inconsistent expression of typhoon disaster knowledge, have arisen. With the development of computer technology, massive Web corpus with numerous Web news and various improvised content on the social media platform, and ontology that enables consistent expression new light has been shed on the knowledge discovery of typhoon disaster. With the Chinese Web corpus as its source, this research proposes a ...
As the mature integrated scheme for various online monitoring systems of the electrical devices h... more As the mature integrated scheme for various online monitoring systems of the electrical devices has not been formed currently, and mature integrated analysis software has also not been formed, it is very necessary to build an integrated system, which can be used to monitor running status of power transmission and transformation equipment, for developing the smart grid based on IEC61850 and Web Service techniques. This paper introduces the design idea, design framework, network topology structure, integrated model and implement function of the integrated platform based on the design and development methods of software engineering. And we specially focus on the modeling information of substation configuration based on IEC61850. The system can integrate multi-online monitoring systems belonged to the status monitoring of electrical devices of different manufactures, different communication protocol into the unified control platform. And its feasibility and effectiveness have been verif...
Sensors, 2020
Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicator... more Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicators at the national and regional scales to study regional economic development. However, most previous studies only chose a single measurement indicator (such as GDP) and adopted simple regression methods to investigate the economic development of a certain area based on DMSP-OLS or NPP-VIIRS stable NTL data. The status quo shows the problems of using a single evaluation index—it has a low evaluation precision. The LJ1-01 satellite is the first dedicated NTL remote sensing satellite in the world, launched in July 2018. The data provided by LJ1-01 have a higher spatial resolution and fewer blooming phenomena. In this paper, we compared the accuracy of the LJ1-01 data and NPP-VIIRS data in detecting county-level multidimensional economic development. In three provinces in China, namely, Hubei, Hunan and Jiangxi, 20 socioeconomic parameters were selected from the following five perspectives: ...
International Journal of Environmental Research and Public Health, 2020
Constructed emergency response scenarios provide a basis for decision makers to make management d... more Constructed emergency response scenarios provide a basis for decision makers to make management decisions, and the development of such scenarios considers earlier historical cases. Over the decades, the development of emergency response scenarios has mainly implemented the elements of historic cases to describe the grade and influence of an accident. This paper focuses on scenario construction and proposes a corresponding framework based on natural language processing (NLP) using text reports of marine oil spill accidents. For each accident, the original textual reports are first divided into sentence sets corresponding to the temporal evolution. Each sentence set is regarded as a textual description of a marine oil spill scenario. A method is proposed in this paper, based on parsing, named entity recognition (NER) and open information extraction (OpenIE) to process the relation triples that are extracted from the sentence sets. Finally, the relation triples are semantically cluster...
Applied Sciences, 2020
Case-based reasoning (CBR) systems often provide a basis for decision makers to make management d... more Case-based reasoning (CBR) systems often provide a basis for decision makers to make management decisions in disaster prevention and emergency response. For decades, many CBR systems have been implemented by using expert knowledge schemes to build indexes for case identification from a case library of situations and to explore the relations among cases. However, a knowledge elicitation bottleneck occurs for many knowledge-based CBR applications because expert reasoning is difficult to precisely explain. To solve these problems, this paper proposes a method using only knowledge to recognize marine oil spill cases. The proposed method combines deep reinforcement learning (DRL) with strategy selection to determine emergency responses for marine oil spill accidents by quantification of the marine oil spill scenario as the reward for the DRL agent. These accidents are described by scenarios and are considered the state inputs in the hybrid DRL/CBR framework. The challenges and opportunit...
Safety Science, 2018
With rapid economic growth, previously less populated peripheral areas of major cities in China h... more With rapid economic growth, previously less populated peripheral areas of major cities in China have now become densely populated. Many hazardous installations (e.g., chemical plants, tank farms, depots of explosive materials, etc.) that used to be in these less populated areas are now found in the backyards of millions of citizens going about their daily lives with minimal knowledge about the dangers these installations pose. A hazard mishap that may have been containable with no human loss just 30 years ago can now threaten the lives of millions. While numerous studies have assessed hazard-specific risks, few efforts have attempted to establish a comprehensive framework to assess multi-hazard scenarios. For this purpose, this study developed a framework for spatial risk assessment of multiple hazardous installations in a metropolitan area. The framework includes three quantitative models for the analysis of the inherent danger associated with, the accessibility for rescue operations at, and the potential effectiveness of mass evacuation from these hazardous installations. Based on this framework and its quantitative models, a case study of the city of Beijing was conducted in this research.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019
Most traditional supervised classification methods for polarimetric synthetic aperture radar (Pol... more Most traditional supervised classification methods for polarimetric synthetic aperture radar (PolSAR) imagery require abundant manually selected samples, and the classification results are affected by the size and quality of the samples. In this paper, we propose an improved deep Q-network (DQN) method for PolSAR image classification, which can generate amounts of valid data by interacting with the agent using the-greedy strategy. The PolSAR data are first preprocessed to reduce the influence of speckle noise and extract the multi-dimensional features. The multi-dimensional feature image and corresponding training image are then fed into a deep reinforcement learning model tailored for PolSAR image classification. After many epochs of training, the method was applied to identify different land cover types in two PolSAR images acquired by different sensors. The experimental results demonstrate that the proposed method has a better classification performance compared with traditional supervised classification methods, such as convolutional neural network (CNN), random forest (RF), and L2-loss linear support vector machine (L2-SVM), and also has a good performance compared with the deep learning method CNN-SVM, which integrates the synergy of the SVM and CNN methods, especially in small sample sizes. This study also provides a toolset for the DQN (kiwi.server) on the GitHub development platform for training and visualization. Image edge detection Electron beam applications Intelligent structures Coprocessors Glial cells Blood platelets Interleaved codes Berkelium Biomechanics Genetics Dynamic equilibrium. Associative memory Connective tissue Neurites Seminars Extraterrestrial phenomena Source coding Aerospace components. EMTDC Aerospace components Delay lines Radiation protection Tires Chrome plating. Aperture antennas Independent component analysis Noise cancellation Psychology Mobile nodes Transmission line theory Robot sensing systems Micromanipulators Cyber espionage Digital art Power smoothing Plastic optical fiber Image matching. Web design Milling machines Unmanned autonomous vehicles MIMO radar Coils Electronic countermeasures Road side unit Machine tool spindles Heterogeneous networks Phishing Mathematics computing Foot IEEE magazines Cryptographic protocols. Chlorine compounds Application specific processors Product life cycle management Bluetooth Nanocrystals. Quantum cascade lasers Collaborative intelligence MIMO radar Grasping Marine technology Availability. Intracranial pressure sensors Valves Radar Electrooptic modulators Mean square error methods Job production systems Current density Optical waveguide theory Molecular electronics Videos Endocrine system. Split gate flash memory cells Periodic structures Cognitive radio VLIW Uncertainty Software development management. Data breach Optical planar waveguides Optical metrology Time-domain analysis Whole body imaging Magnetic materials Hafnium oxide Formal languages Telematics Biogeography Active pixel sensors Aluminum Circadian rhythm. Retinopathy Forecast uncertainty Wide area networks Iridium Wounds Thick film sensors International Atomic Time Neurites Transhuman Computer security. Dielectric loss measurement Spin polarized transport MySpace Polycaprolactone Crowdsourcing Simple object access protocol. Floppy disks Uranium Piezoelectric devices Metal cutting tools Satellite ground stations Milling machines Bone tissue Face recognition Animal behavior Dermatology Anti-parasitical. AODV Authorization Split gate flash memory cells Decoding Railguns Gunshot detection systems Millimeter wave devices. Sugar refining Centralized control Visible light communication Channel models Leg. Electronic equipment Device drivers Graphics processing units Genetics Gray-scale Multisensory integration All-optical networks Electricity supply industry. Collective intelligence Open Access Rats Augmented reality Macroeconomics Ionizing radiation Plastic insulators Scheduling algorithms. Unmanned aerial vehicles Shortest path problem Product safety Microwave circuits Nuclear and plasma sciences Antibiotics Trade agreements Biosensors Orbits (stellar).
Concurrency and Computation: Practice and Experience, 2018
The southeastern coast of China suffers many typhoon disasters every year, causing huge casualtie... more The southeastern coast of China suffers many typhoon disasters every year, causing huge casualties and economic losses. In addition, collecting statistics on typhoon disaster situations is hard work for the government. At the same time, near-real-time disaster-related information can be obtained on developed social media platforms like Twitter and Weibo. Many cases have proved that citizens are able to organize themselves promptly on the spot, and begin to share disaster information when a disaster strikes, producing massive VGI (volunteered geographic information) about the disaster situation, which could be valuable for disaster response if this VGI could be exploited efficiently and properly. However, this social media information has features such as large quantity, high noise, and unofficial modes of expression that make it difficult to obtain useful information. In order to solve this problem, we first designed a new classification system based on the characteristics of social medial data like Sina Weibo data, and made a microblogging dataset of typhoon damage with according category labels. Secondly, we used this social medial dataset to train the deep learning model, and constructed a typhoon disaster mining model based on a deep learning network, which could automatically extract information about the disaster situation. The model is different from the general classification system in that it automatically selected microblogs related to disasters from a large number of microblog data, and further subdivided them into different types of disasters to facilitate subsequent emergency response and loss estimation. The advantages of the model included a wide application range, high reliability, strong pertinence and fast speed. The research results of this thesis provide a new approach to typhoon disaster assessment in the southeastern coastal areas of China, and provide the necessary information for the authoritative information acquisition channel.
Remote Sensing, 2019
Land cover classification of urban areas is critical for understanding the urban environment. Hig... more Land cover classification of urban areas is critical for understanding the urban environment. High-resolution remotely sensed imagery provides abundant, detailed spatial information for urban classification. In the meantime, OpenStreetMap (OSM) data, as typical crowd-sourced geographical information, have been an emerging data source for obtaining urban information. In this context, a land cover classification method that fuses high-resolution remotely sensed imagery and OSM data is proposed. Training samples were generated by integrating the OSM data and multiple information indexes. OSM data, which contain class attributes and location information of urban objects, served as the labels of initial training samples. Multiple information indexes that reflect spectral and spatial characteristics of different classes were utilized to improve the training set. Morphological attribute profiles were used because the structural and contextual information of images was effective in distingu...
International Journal of Environmental Research and Public Health, 2018
Healthcare accessibility has become an issue of social equity. An accurate estimation of existing... more Healthcare accessibility has become an issue of social equity. An accurate estimation of existing healthcare accessibility is vital to plan and allocate health resources. Healthcare capacity, population demand, and geographic impedance are three essential factors to measure spatial accessibility. Additionally, geographic impedance is usually represented with a function of travel time. In this paper, the three-step floating catchment area (3SFCA) method is improved from the perspectives of the temporal dimension and population demand. Specifically, the travel time from the population location to the service site is precisely calculated by introducing real-time traffic conditions instead of utilizing empirical speed in previous studies. Additionally, with the utilization of real-time traffic, a dynamic result of healthcare accessibility is derived during different time periods. In addition, since the medical needs of the elderly are higher than that of the young, a demand weight index...
SPIE Proceedings, 2015
As the development of Web 2.0, the social media like microblog, blogs and social network have sup... more As the development of Web 2.0, the social media like microblog, blogs and social network have supplied a bunch of information with locations (Volunteered Geographical Information, VGI).Recent years many cases have shown that, if disaster happened, the cyber citizens will get together very quickly and share the disaster information, this results a bunch of volunteered geographical information about disaster situation which is very valuable for disaster response if this VGIs are used efficiently and properly. This project will take typhoon disaster as case study. In this paper, we study the relations between weibo messages and the real typhoon situation, we proposed an analysis framework for mine the relations between weibo messages distribution and physical space. We found that the number of the weibo messages, key words frequency and spatial temporary distribution of the messages have strong relations with the disaster spread in the real world, and this research results can improve our disaster situation awareness in the future. The achievement of the study will give a method for typhoon disaster situation awareness based on VGI from the bottom up, and will locate the disaster spot and evolution quickly which is very important for disaster response and recover.
Journal of Intelligent & Fuzzy Systems, 2015
Spatio-temporal trajectory clustering can extract behavior and moving pattern of object with the ... more Spatio-temporal trajectory clustering can extract behavior and moving pattern of object with the change of time and space by exploring similar trajectories. Most of trajectory clustering method can be achieved by expanding the traditional clustering algorithms. Considering the limitations of fitness and optimization of most clustering algorithms, especially for spatio-temporal trajectory data sets, this paper proposes a trajectory fuzzy clustering method based on multi-objective mixed function, which can simultaneously optimize multiple objective function such as FCM and XB when perform particle swarm optimization method. And we also propose a new coarse-grained DTW based on interpolation point for generalization trajectory data and improvement the performance of measure the similarity between trajectories. The experimental results, which implement on the synthetized trajectory data and real vehicle history data by employing the new clustering algorithm, and clustering validity evaluation and hot spots analysis show that the proposed method, which combines different objective functions with different optimization criteria and particle swarm algorithm, can effectively solve the clustering problem and produce better clustering results than the traditional clustering method.
2015 11th International Conference on Natural Computation (ICNC), 2015
Generally, computing efficiency of many spatial data analysis algorithm will sharply decline as d... more Generally, computing efficiency of many spatial data analysis algorithm will sharply decline as data size increase. It is very meaningful for extending the analysis method of spatial data and enhancing computational efficiency by introducing the distributed parallel computing model. Considering the features of spatio-temporal trajectory data, which is massive, related to time and dynamic, we proposed the fast calculation method of the trajectory similarity based on coarse-grained Dynamic Time Warping. The algorithm will reduce the consuming time greatly when the length of trajectory sequences are very long. We also proposed the parallel trajectory clustering strategy of big data under the Hadoop MapReduce model in this paper. The big data of trajectory are sliced, and the trajectory similarity and the iteration computation of cluster center are dealt with by multiwork nodes simultaneously. The experimental results of the parallel trajectory clustering, which based on the open source project Mahout, implemented on the vehicle trajectory data show that the clustering results are valid. The computing performance of parallel clustering are obviously improved as the trajectory data size increases. And the new parallel clustering method outperforms the traditional algorithm like k-means algorithm.