• The Journal of Korean Institute of Next Generation Computing
• /
• v.14 no.6
• /
• pp.87-97
• /
• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we proposed to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collected two new video datasets, and proposed a new deep learning architecture named Temporal and Spatial Segment Networks (TSSN) for rainfall depth recognition. Under TSSN, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. Also, the proposed TSSN architecture outperforms other architectures implemented in this paper.

• Journal of Korean Society for Atmospheric Environment
• /
• v.34 no.5
• /
• pp.735-747
• /
• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• Journal of KIISE:Databases
• /
• v.35 no.2
• /
• pp.169-181
• /
• 2008

Because moving objects usually move on spatial networks, efficient trajectory index structures are required to achieve good retrieval performance on their trajectories. However, there has been little research on trajectory index structures for spatial networks such as FNR-tree and MON-tree. But, because FNR-tree and MON-tree are stored by the unit of the moving object's segment, they can't support the whole moving objects' trajectory. In this paper, we propose an efficient trajectory index structure, named Trajectory of Moving objects on Network Tree(TMN Tree), for moving objects. For this, we divide moving object data into spatial and temporal attribute, and preserve moving objects' trajectory. Then, we design index structure which supports not only range query but trajectory query. In addition, we divide user queries into spatio-temporal area based trajectory query, similar-trajectory query, and k-nearest neighbor query. We propose query processing algorithms to support them. Finally, we show that our trajectory index structure outperforms existing tree structures like FNR-Tree and MON-Tree.

• KIISE Transactions on Computing Practices
• /
• v.23 no.5
• /
• pp.316-321
• /
• 2017

We analyze the online Hangul handwriting recognition problem (HHR) and present solutions based on recurrent neural networks. The solutions are organized according to the three kinds of sequence labeling problem - sequence classifications, segment classification, and temporal classification, with additional consideration of the structural constitution of Hangul characters. We present a stacked gated recurrent unit (GRU) based model as the natural HHR solution in the sequence classification level. The proposed model shows 86.2% accuracy for recognizing 2350 Hangul characters and 98.2% accuracy for recognizing the six types of Hangul characters. We show that the type recognizing model successfully follows the type change as strokes are sequentially written. These results show the potential for RNN models to learn high-level structural information from sequential data.

• Journal of Korea Spatial Information System Society
• /
• v.10 no.3
• /
• pp.1-18
• /
• 2008

Because we can usually get many information through analyzing trajectories of moving objects on spatial networks, efficient trajectory index structures are required to achieve good retrieval performance on their trajectories. However, there has been little research on trajectory index structures for spatial networks such as FNR-tree and MON-tree. Also, because FNR-tree and MON-tree store the segment unit of moving objects, they can't support the trajectory of whole moving objects. In this paper, we propose an efficient trajectory index structures based on signatures on a spatial network, named SigMO-Tree. For this, we divide moving object data into spatial and temporal attributes, and design an index structure which supports not only range query but trajectory query by preserving the whole trajectory of moving objects. In addition, we divide user queries into trajectory query based on spatio-temporal area and similar-tralectory query, and propose query processing algorithms to support them. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently Finally, we show from our performance analysis that our trajectory index structure outperforms the existing index structures like FNR-Tree and MON-Tree.

• Korean Journal of Clinical Laboratory Science
• /
• v.47 no.1
• /
• pp.46-50
• /
• 2015

The previous studies showed that the visual imagery activated the occipital and posterior inferior temporal area of the brain, and the damage to the occipital cortex impaired the visual mental imagery. We studied current-source distribution of electroencephalography (EEG) to observe neuronal activity during imagery tennis playing. Eleven healthy volunteers were enrolled. All volunteers were right-handed males and novices for tennis playing. The mean age of them was 24.9 years. The EEGs were recorded on the scalp electrodes located according to the International 10~20 System. The number of electrodes was 25 channels including subtemporal electrodes. The EEG recording session was 13 min including 5 segments: resting-I, scenery-slide show, resting-II, watching tennis-game video, and imagery-tennis playing. The recoding durations were 3, 2, 3, 2, and 3 min respectively. Five 'artifact free 3-sec segments' were selected in each segment of 'imagery-tennis playing' and 'resting-II'. We did the frequency domain analysis with the EEG segments using a distributed model of current-source analysis. The statistical-nonparametric maps (SnPMs) were obtained between the segments of 'imagery-tennis playing' and the segments of 'resting-II' (p<0.01). The significant change of current-source density was observed only in alpha-2 frequency band (10~12 Hz). The current-sourcedensity was increased in the hippocampus, parahippocampus, and occipital fusiform gyrus in the right cerebral hemisphere (p<0.01). Imaginary-tennis playing may activate the hippocampal-occipital alpha networks of nondominant hemisphere.