• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.631-644
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• 2002

In this paper efficient algorithms for content-based video retrieval using motion information are proposed, including temporal scale-invariant retrieval and temporal scale-absolute retrieval. In temporal scale-invariant video retrieval, the distance transformation is performed on each trail image in database. Then, from a given que교 trail the pixel values along the query trail are added in each distance image to compute the average distance between the trails of query image and database image, since the intensity of each pixel in distance image represents the distance from that pixel to the nearest edge pixel. For temporal scale-absolute retrieval, a new coding scheme referred to as Motion Retrieval Code is proposed. This code is designed to represent object motions in the human visual sense so that the retrieval performance can be improved. The proposed coding scheme can also achieve a fast matching, since the similarity between two motion vectors can be computed by simple bit operations. The efficiencies of the proposed methods are shown by experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2361-2376
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• 2023

The electrocardiogram (ECG) signal is commonly used to screen and diagnose cardiovascular diseases. In recent years, deep neural networks have been regarded as an effective way for automatic ECG disease diagnosis. The convolutional neural network is widely used for ECG signal extraction because it can obtain different levels of information. However, most previous studies adopt single scale convolution filters to extract ECG signal features, ignoring the complementarity between ECG signal features of different scales. In the paper, we propose a dual-scale network with convolution filters of different sizes for 12-lead ECG classification. Our model can extract and fuse ECG signal features of different scales. In addition, different spatial and time periods of the feature map obtained from the 12-lead ECG may have different contributions to ECG classification. Therefore, we add a spatial-temporal attention to each scale sub-network to emphasize the representative local spatial and temporal features. Our approach is evaluated on PTB-XL dataset and achieves 0.9307, 0.8152, and 89.11 on macro-averaged ROC-AUC score, a maximum F1 score, and mean accuracy, respectively. The experiment results have proven that our approach outperforms the baselines.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1931-1942
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• 2021

As the utility of an optical satellite image with a high spatial resolution (i.e., fine-scale) has been emphasized, recently, various studies of the land surface monitoring using those have been widely carried out. However, the usefulness of fine-scale satellite images is limited because those are acquired at a low temporal resolution. To compensate for this limitation, the spatiotemporal data fusion can be applied to generate a synthetic image with a high spatio-temporal resolution by fusing multiple satellite images with different spatial and temporal resolutions. Since the spatio-temporal data fusion models have been developed for mid or low spatial resolution satellite images in the previous studies, it is necessary to evaluate the applicability of the developed models to the satellite images with a high spatial resolution. For this, this study evaluated the applicability of the developed spatio-temporal fusion models for KOMPSAT-3A and Sentinel-2 images. Here, an Enhanced Spatial and Temporal Adaptive Fusion Model (ESTARFM) and Spatial Time-series Geostatistical Deconvolution/Fusion Model (STGDFM), which use the different information for prediction, were applied. As a result of this study, it was found that the prediction performance of STGDFM, which combines temporally continuous reflectance values, was better than that of ESTARFM. Particularly, the prediction performance of STGDFM was significantly improved when it is difficult to simultaneously acquire KOMPSAT and Sentinel-2 images at a same date due to the low temporal resolution of KOMPSAT images. From the results of this study, it was confirmed that STGDFM, which has relatively better prediction performance by combining continuous temporal information, can compensate for the limitation to the low revisit time of fine-scale satellite images.

• Journal of KIISE
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• v.43 no.9
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• pp.963-973
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• 2016

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

• Proceedings of the Korea Water Resources Association Conference
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• 2000.05a
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• pp.227-232
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• 2000

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3037-3054
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• 2019

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.231-242
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• 2016

In order to meet rapid changes of Web information, it is necessary to extend the current Web technologies to represent both the valid time and location of each fact and knowledge, and reason their relationships. Until recently, many researches on qualitative temporal reasoning have been conducted in laboratory-scale, dealing with small knowledge bases. However, in this paper, we propose the design and implementation of a parallel qualitative temporal reasoner, MRQUTER, which can make reasoning over Web-scale large knowledge bases. This parallel temporal reasoner was built on a Hadoop cluster system using the MapReduce parallel programming framework. It decomposes the entire qualitative temporal reasoning process into several MapReduce jobs such as the encoding and decoding job, the inverse and equal reasoning job, the transitive reasoning job, the refining job, and applies some optimization techniques into each component reasoning job implemented with a pair of Map and Reduce functions. Through experiments using large benchmarking temporal knowledge bases, MRQUTER shows high reasoning performance and scalability.

• Journal of Korea Water Resources Association
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• v.35 no.2
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• pp.125-136
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• 2002

The effects of the temporal scale of input hydrological data on runoff simulation have been studied using hydrological data with various time scales. TOPMODEL has been employed to explores these effects. The Genetic a1gorithm was used to calibrate model Parameters. The results of sensitivity analysis in various time scales provide the insight of parameter space for TOPMODEL operation of different time scale. The variation of temporal scale of input hydrological data appeared to have significant impacts on the model efficiency, average water table depth, the ratio of the surface runoff to the total runoff and the calibrated parameters. Generally, the longer the time scale, the more surface runoff and the less average water table death were calculated. It is found that the impact of lime scale to runoff simulation results from the structure of TOPMODEL and the hydrographic morphology.

• Journal of Soil and Groundwater Environment
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• v.16 no.3
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• pp.3-9
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• 2011

Partitioning tracers have been used with non-partitioning, inert tracer such Br, for detection, estimation, and monitoring of remediation performance of the subsurface contaminated with nonaqueous phase liquids (NAPLs). Various partitioning tracers with different partition coefficients between aqueous and nonaqueous phase liquids can be used to determine the hydraulic conductivity, dispersivity, and residual mass of NAPLs in the subsurface soil matrices. Temporal moment-generating equations were used to analyze the field pilot-scale test results. The pilot-scale tests included conservative tracer tests and partitioning tracer tests. Analyses of nonaqueous phase liquid distribution and characteristics of groundwater bearing soil media were performed.

• Archives of Craniofacial Surgery
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• v.19 no.2
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• pp.94-101
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• 2018

Background: Atrophy of muscle and fat often contributes to temporal hollowing after pterional craniotomy. However, the main cause is from the bony defect. Several methods to prevent temporal hollowing have been introduced, all with specific limitations. Autologous bone grafts are most ideal for cranial defect reconstruction. The authors investigated the effectiveness of bony defect coverage and temporal augmentation using pterional craniotomy bone flap. Methods: This study was conducted in 100 patients who underwent brain tumor excision through pterional approach from 2015 to 2016. Group 1 underwent pterional craniotomy with temporal augmentation and group 2 without temporal augmentation. In group 1, after splitting the calvarial bone at the diploic space, the inner table was used for covering the bone defect and as an onlay graft for temporal augmentation. The outcome is evaluated by computed tomography at 1-year follow-up. Results: The mean operative time for temporal augmentation was 45 minutes. The mean follow-up was 12 months. The ratio of temporal thickness of operated side to non-operated side was 0.99 in group 1 and 0.44 in group 2, which was statistically different. The mean visual analogue scale score was 1.77 in group 1 and 6.85 in group 2. Conclusion: This study demonstrated a surgical technique using autologous bone graft for successfully preventing the temporal hollowing and improved patient satisfaction.