• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.271-278
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• 2014

In spite of that stop line and crosswalk should be aware of the most basic objects in transportation system, its features extracted are very limited. In addition to image-based recognition technology, laser and RF, GPS/INS recognition technology, it is difficult to recognize. For this reason, the limited research in this area has been done. In this paper, the algorithm to recognize the stop line and crosswalk is designed and implemented using image-based recognition technology with the images input through a vision sensor. This algorithm consists of three functions.; One is to select the area, in advance, needed for feature extraction in order to speed up the data processing, 'Region of Interest', another is to process the images only that white color is detected more than a certain proportion in order to remove the unnecessary operation, 'Color Pattern Inspection', the other is 'Feature Extraction and Recognition', which is to extract the edge features and compare this to the previously-modeled one to identify the stop line and crosswalk. For this, especially by using case based feature comparison algorithm, it can identify either both stop line and crosswalk exist or just one exists. Also the proposed algorithm is to develop existing researches by comparing and analysing effect of in-vehicle camera installation and changes in recognition rate of distance estimation and various constraints such as backlight and shadow.

• Explosives and Blasting
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• v.22 no.3
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• pp.57-64
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• 2004

There are two major types of underwater blasting at Korea, bridges and harbor construction work. Pier blasting for lay the foundation bridges construction is used dry excavation working (drilling and charging) after pump out water and then fire pump in water that is same as bench blasting. In contrast, underwater blasting for harbor construction and increase of harbor load depth is used to barge with digging equipment that is in oder to drilling on the surface and blasting work(charge, hook-up) under water. Thus, there are need to special concern such as charge method and hook-up method different from tunnel blasting work and bench blasting work. If do not use special concern breaks out dead pressure and mis fire because of there are so many difficult condition such as water pressure, obstruct field of vision. In this study underwater blasting at Busan Harbor Construction have consider with special concern that is plastic pipe charge method used to MegaMITE I and specialized buoy hook- up method make far initial system detonate on the surface used to TLD. The results is designed blast pattern charge per delay effect an inspection of verify between predict velocity and measure velocity. minimized break out mis fire consideration charge method, hook up method. According to result best underwater blasting design is 105mm drilling dia, MeGAMITE II, HiNLL Plus(non electric detonator).