• Computers and Concrete
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• v.33 no.4
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• pp.425-443
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• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.374-386
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• 2004

The mammogram provides the way to observe detailed internal organization of breasts to radiologists for the early detection. This paper is mainly focused on efficiently detecting the Microcalcification's Region Of Interest(ROI)s. Breast cancers can be caused from either microcalcifications or masses. Microcalcifications are appeared in a digital mammogram as tiny dots that have a little higher gray levels than their surrounding pixels. We can roughly determine the area which possibly contain microcalifications. In general, it is very challenging to find all the microcalcifications in a digital mammogram, because they are similar to some tissue parts of a breast. To efficiently detect microcalcifications ROI, we used four sequential processes; preprocessing for breast area detection, modified multilevel thresholding, ROI selection using simple thresholding filters and final ROI selection with two stages of neural networks. The filtering process with boundary conditions removes easily-distinguishable tissues while keeping all microcalcifications so that it cleans the thresholded mammogram images and speeds up the later processing by the average of 86％. The first neural network shows the average of 96.66％ recognition rate. The second neural network performs better by showing the average recognition rate 98.26％. By removing all tissues while keeping microcalcifications as much as possible, the next parts of a CAD system for detecting breast cancers can become much simpler.

• Journal of Internet Computing and Services
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• v.4 no.6
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• pp.57-67
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• 2003

The recent rapid development of multimedia and optical technologies brings great attention to application systems to process facial Image features. The previous research efforts in facial image processing have been mainly focused on the recognition of human face and facial expression analysis, using front face images. Not much research has been carried out Into image-based detection of face direction. Moreover, the existing approaches to detect face direction, which normally use the sequential Images captured by a single camera, have limitations that the frontal image must be given first before any other images. In this paper, we propose a method to detect face direction by using facial features such as facial trapezoid which is defined by two eyes and the lower lip. Specifically, the proposed method forms a facial direction formula, which is defined with statistical data about the ratio of the right and left area in the facial trapezoid, to identify whether the face is directed toward the right or the left. The proposed method can be effectively used for automatic photo arrangement systems that will often need to set the different left or right margin of a photo according to the face direction of a person in the photo.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.165-171
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• 2003

Visual search in an unstructured search field is a fruitful research area for computational modeling. Search models that describe relationship between search time and probability of target detection have been used for prediction of human search performance and provision of ideal goals for search training. Until recently, however, most of models were focused on detecting a single target in a search field, although, in practice, a search field includes multiple targets and search models for multiple targets may differ from search models for a single target. This study proposed a random search model for multiple targets, generalizing a random search model for a single target which is the most typical search model. To test this model, human search data were collected and compared with the model. This model well predicted human performance in visual search for multiple targets. This paper also proposed how to determine optimal stopping time in multiple-target search.

• Atmosphere
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• v.25 no.2
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• pp.367-374
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• 2015

Korean Peninsula has high potential for occurrence of aviation turbulence. A Korean aviation Turbulence Guidance (KTG) system focused on the Korean Peninsula, named Korean-Peninsula KTG (KP-KTG) system, is developed using the high resolution (horizontal grid spacing of 1.5 km) Local Data Assimilation and Prediction System (LDAPS) of the Korea Meteorological Administration (KMA). The KP-KTG system is constructed first by selection of 15 best diagnostics of aviation turbulence using the method of probability of detection (POD) with pilot reports (PIREPs) and the LDAPS analysis data. The 15 best diagnostics are combined into an ensemble KTG predictor, named KP-KTG, with their weighting scores computed by the values of area under curve (AUC) of each diagnostics. The performance of the KP-KTG, represented by AUC, is larger than 0.84 in the recent two years (June 2012~May 2014), which is very good considering relatively small number of PIREPs. The KP-KTG can provide localized turbulence forecasting in Korean Peninsula, and its skill score is as good as that of the operational-KTG conducting in East Asia.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.369-372
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• 2008

Recently, landslides have frequently occurred on natural slopes during periods of intense rainfall. With a rapidly increasing population on or near steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is introduced. The system is focused to debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of sensor nodes, gateway, and server system. Sensor nodes comprising a sensing part and a communication part are developed to detect ground movement. Sensing part is designed to measure inclination angle and acceleration accurately, and communication part is deployed with Bluetooth (IEEE 802.15.1) module to transmit the data to the gateway. To verify the feasibility of this landslide prediction system, a series of experimental studies was performed at a small-scale earth slope equipped with an artificial rainfall dropping device. It is found that sensing nodes installed at slope can detect the ground motion when the slope starts to move. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.328-330
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• 2003

The land cover of burned area has changed dramatically since Daxinganling forest fire in Northeastern China during May 6 ? June 4, 1987. This research focused on determining the burn severity and assessment of forest recovery. Burned severity was classified into three levels from June 1987 Landsat TM data acquired just after the fire. A regression model was established between the forest canopy closure from 1999 forest stand map and the NDVI values from June 2000 Landsat ETM+ data. The map of canopy closure was got according to the regression model. And vegetation cover was classified into four types according to forest closure density. The change matrix was built using the classified map of burn severity and vegetation recovery. Then the change conversions of every forest type were analyzed. Results from this research indicate: forest recovery status is well in most of burned scars; and vegetation change detection can be accomplished using postclassification comparison method.

• Korean Journal of Remote Sensing
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• v.11 no.2
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• pp.59-73
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• 1995

The study is focused on the analysis of geomorphological environment changes of alluvial bar in lower Kum river using satellite-based multitemporal/multisensor data. Landsat datas for environment changes analysis consists of Landset MSS(2 scenes) and Landset TM(7 scenes) acquired from 1979 to 1994. This study is to develop the analysis techniques for the environment change detection of using ratio, classification, false color composite etc, of Landsat data especially useful to the geomorphological study of tidal flats and river channels. The results of this study can be summarized as follows : 1. The lower Kum River alluvial bar have had rapid geomorphological changes after the construction of the temporary dam to block the river flowing in 1983. The most alluvial bar located in the river has both bankway growth, especially the allurival bar in the Lower Kum River had grown between 1983 to 1990. 2. After construction of the estuarine barrage, no remarkable geomorphological changes have been found in Kum River area but the growth and formation of new underwater bar has continued. The enormous materials was needed for the growth and formations of new underwater barrier oslands and bar would be supplied from the sea bottom and river sediment to diminish of stream velocity after construction of the estuarine barrage.

• Journal of Plant Biotechnology
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• v.46 no.2
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• pp.61-70
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• 2019

There is vast genetic diversity of Myanmar Mangoes. This study mainly focused on indigenous thirteen different mango landraces cultivated in central area of Myanmar, Kyauk-se District and their fruit characteristics by 18 descriptors together with genetic relationship among them by 12 SSR markers. Based on the morpho-physical characters, a wide variation among accessions was found. Genetic characterization of thirteen mango genotypes resulted in the detection of 302 scorable polymorphic bands with an average of 4.33 alleles per locus and an average polymorphism information content (PIC) of 0.7. All the genotypes were grouped into two major clusters by UPGMA cluster analysis and a genetic similarity was observed in a range of 61 ~ 85%. This study may somehow contribute insights into the identification of regional mango diversity in Myanmar and would be useful for future mango breeding program.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.62-74
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• 2023

In response to diverse external stimuli, sensory receptors generate spiking nerve signals. These generated signals are transmitted to the brain along the neural pathway to advance to the stage of recognition or perception, and then they reach the area of discrimination or judgment for remembering, assessing, and processing incoming information. We review research trends in neuromorphic sensory perception technology inspired by biological sensory perception functions. Among the various senses, we consider sensory nerve decoding technology based on sensory nerve pathways focusing on touch and smell, neuromorphic synapse elements that mimic biological neurons and synapses, and neuromorphic processors. Neuromorphic sensory devices, neuromorphic synapses, and artificial sensory memory devices that integrate storage components are being actively studied. However, various problems remain to be solved, such as learning methods to implement cognitive functions beyond simple detection. Considering applications such as virtual reality, medical welfare, neuroscience, and cranial nerve interfaces, neuromorphic sensory recognition technology is expected to be actively developed based on new technologies, including combinatorial neurocognitive cell technology.