• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.105-112
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• 2015

The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.130-138
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• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.88-97
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• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• Bulletin of the Korean Mathematical Society
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• v.44 no.3
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• pp.547-567
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• 2007

We obtain spatial-temporal decay rates of weak solutions of incompressible flows in exterior domains. When a domain has a boundary, the pressure term yields difficulties since we do not have enough information on the pressure term near the boundary. For our calculations we provide an idea which does not require any pressure information. We also estimated the spatial and temporal asymptotic behavior for strong solutions.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.473-475
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• 2003

To overcome the limitation that traditional GISs lose much information for the real world, 4-dimensional GIS has the additional reference systems including object's height and temporal dimension. This paper describes the 4-dimensional geometric object model and components. The prototype for 4-dimensional GIS consists of the data provider, manager, and renderer components. We show the virtual city that its database contains topographic maps, buildings, roads and temporal history data. This provides spatial, temporal operations and analysis functions.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.372-380
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• 2003

Temporal databases support time-varying events so that conventional aggregate functions are extended to be processed with time for temporal aggregate functions. In the previous approach, it is done repeatedly to find time intervals and is calculated the result of each interval whenever target events are different. This paper proposes a method which processes temporal aggregate function queries using time point sequence. We can make time point sequence storing the start time and the end time of events in temporal databases in advance. It is also needed to update time point sequence due to insertion or deletion of events in temporal databases. Because time point sequence maintains the information of time intervals, it is more efficient than the previous approach when temporal aggregate function queries are continuously requested, which have different target events.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.105-111
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• 2015

Domestic 2D maps includes only most up-to-date information at the time of production without historical information. Therefore, it is hard to identify the change history of real world objects. In this research, Spatio-temporal model for 2D map were developed and it's compatibility was verified through the pilot project conducted on the Gwanggyo area of Gyeonggi province. Also, the procedure to generate 2D spatio-temporal database using maps made periodically on the same target area was introduced for showing the possibility of realizing nation wide spatio-temporal 2D map using the national base map updated periodically.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.871-874
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• 1998

This paper presents a new morphological spatio-temporal segmentation algorithm. The algorithm incorporates intensity and motion information simultaneously, and uses morphological tools such as morphological filters and watershed algorithm. The procedure toward complete segmetnation consists of three steps: joint marker extraction, boundary decision, and motion-based region fusion. By incorporating spatial and temporal information simultaneously, we can obtain visually meaningful segmentation results. Simulation results demonstrates the efficiency of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.168-170
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• 2003

In this paper, we discuss the concept of Spatio-Temporal Data Warehouse and analyze the organization model of spatio-temporal data. Based on the above, we found the framework of Spatio-Temporal Data Warehouse composed of data source, processing tools and application, which covers the whole process from building warehouse to supplying services.