• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5C
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• pp.464-471
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• 2010

The demand for high quality of multimedia applications, which far surpasses the rapid evolution of transmission and storage technologies, makes better compression coding capabilities ever increasingly more important. In order to provide enhanced video coding performance, this paper proposes an efficient competition-based motion vector coding scheme. The proposed algorithm adaptively forms the motion vector predictors based on the contexts of scene characteristics such as camera motion and nearby motion vectors, providing more efficient candidate predictors than the previous competition-based motion vector coding schemes which resort to the fixed candidates optimized by extensive simulations. Up to 200% of compression gain was observed in the experimental results for the proposed scheme applied to the motion vector selection for skip mode processing.

• Journal of Navigation and Port Research
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• v.30 no.3 s.109
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• pp.227-233
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• 2006

As North-east Chinese parts including Shanghai port grow rapidly, competition among the Far-East parts to be a hub port is getting higher, somebody has even raised a crisis of Busan port. However there are same constraints an the North-east parts such as weather aggravation and long distance from main truck routes. When we consider the competitiveness of port, weather aggravation should be considered as one of the significant factors. But previous studies have rarely examined these kinds of external factors of port operation Therefore, this study analyze constraints an the North-east Chinese parts through a survey of same national flag shipping companies and agencies of foreign shipping companies. The result shows that the mast significant constraint in the North-east ports is weather aggravation which causes problems for regular schedules of shipping, operation cast and customer service qualities etc.

• Smart Structures and Systems
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• v.31 no.4
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.