• Journal of Digital Convergence
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• v.14 no.11
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• pp.125-133
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• 2016

In recent years, various crimes such as "random killing' crime continue to rise. Despite the government's crime prevention efforts and crime related researches, crime increases and a different approach is needed. Therefore, this study proposes the alternative for crime prevention by analyzing big data. To achieve this objective, this study was to perform visualization utilizing the histogram, the bubble chart and the hit map and association analysis. To analyze the relationship between crime and some variables, this study analyzed data of Seongnam city, Korea National Police Agency and etc. The results of analysis showed that CCTV will be to reduce the crime rate and security light is not significantly relevant. And the result showed that other types of crime focused by time of the day and day of the week and showed that an increase of the foreigners and crime increase are associated. This study presents a scheme for reducing the crime rate on the basis of this analysis result.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.152-152
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• 2019

This study demonstrates a novel approach of remotely sensed estimates of stream flow at fifteen hydrological station in the Han River Basin, Korea. Multi-temporal data of the European Space Agency's Sentinel-1 SAR satellite from 19 January, 2015 to 25 August, 2018 is used to develop and validate the flow estimation model for each station. The flow estimation model is based on a power law relationship established between the remotely sensed surface area of water at a selected reach of the stream and the observed discharge. The satellite images were pre-processed for thermal noise, radiometric, speckle and terrain correction. The difference in SAR image brightness caused by the differences in SAR satellite look angle and atmospheric condition are corrected using the histogram matching technique. Selective area filtering is applied to identify the extent of the selected stream reach where the change in water surface area is highly sensitive to the change in stream discharge. Following this, an iterative procedure called the Optimum Threshold Classification Algorithm (OTC) is applied to the multi-temporal selective areas to extract a series of water surface areas. It is observed that the extracted water surface area and the stream discharge are related by the power law equation. A strong correlation coefficient ranging from 0.68 to 0.98 (mean=0.89) was observed for thirteen hydrological stations, while at two stations the relationship was highly affected by the hydraulic structures such as dam. It is further identified that the availability of remotely sensed data for a range of discharge conditions and the geometric properties of the selected stream reach such as the stream width and side slope influence the accuracy of the flow estimation model.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.789-798
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• 2021

In this study, for the purpose of collecting and analyzing target-specific RCS data of target signals simulator for verification/improvement of radar system performance, VHF band monostatic/bistatic RCS of civil aircraft(B-747, B-737) and fighter(F-16) models were analyzed by EM simulation tool. In order to reduce the RCS analysis time, the analysis time and RCS data were compared and cross-verified. Also, the analysis range was selected by examining the interpolation error according to the analysis angle resolution. The RCS data obtained for each model were analyzed separately by the incident/reflection elevation angle and frequency. The RCS characteristics according to the shape of the aircraft and the incident/reflection azimuth angle were described. Finally, the statistical RCS distribution value of each model is presented through RCS distribution histogram analysis. In the future, the RCS database obtained by this study will be used for the target signals simulator of the VHF band radar system.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.24-34
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• 2019

In this research, a practical deep learning framework to differentiate the lesions and nodules in breast acquired with ultrasound imaging has been proposed. 7408 ultrasound breast images of 5151 patient cases were collected. All cases were biopsy proven and lesions were semi-automatically segmented. To compensate for the shift caused in the segmentation, the boundaries of each lesion were drawn using Fully Convolutional Networks(FCN) segmentation method based on the radiologist's specified point. The data set consists of 4254 benign and 3154 malignant lesions. In 7408 ultrasound breast images, the number of training images is 6579, and the number of test images is 829. The margin between the boundary of each lesion and the boundary of the image itself varied for training image augmentation. The training images were augmented by varying the margin between the boundary of each lesion and the boundary of the image itself. The images were processed through histogram equalization, image cropping, and margin augmentation. The networks trained on the data with augmentation and the data without augmentation all had AUC over 0.95. The network exhibited about 90% accuracy, 0.86 sensitivity and 0.95 specificity. Although the proposed framework still requires to point to the location of the target ROI with the help of radiologists, the result of the suggested framework showed promising results. It supports human radiologist to give successful performance and helps to create a fluent diagnostic workflow that meets the fundamental purpose of CADx.

• Agricultural and Biosystems Engineering
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• v.7 no.1
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• pp.18-26
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• 2006

In-field site-specific nitrogen (N) management increases crop yield, reduces N application to minimize the risk of nitrate contamination of ground water, and thus reduces farming cost. Real-time N sensing and fertilization is required for efficient N management. An 'on-the-go' site-specific N management system was developed and evaluated for the supplemental N application to com (Zea mays L.). This real-time N sensing and fertilization system monitored and assessed N fertilization needs using a vision-based spectral sensor and controlled the appropriate variable N rate according to N deficiency level estimated from spectral signature of crop canopies. Sensor inputs included ambient illumination, camera parameters, and image histogram of three spectral regions (red, green, and near-infrared). The real-time sensor-based supplemental N treatment improved crop N status and increased yield over most plots. The largest yield increase was achieved in plots with low initial N treatment combined with supplemental variable-rate application. Yield data for plots where N was applied the latest in the season resulted in a reduced impact on supplemental N. For plots with no supplemental N application, yield increased gradually with initial N treatment, but any N application more than 101 kg/ha had minimal impact on yield.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.186-193
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• 2008

We propose a novel real-time obstacle avoidance method for rescue robots. This method, named the ELA(Emergency Level Around), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward safe position. In the ELA, we consider two sensor modules, PSD(Position Sensitive Detector) infrared sensors taking charge of obstacle detection in short distance and LMS(Laser Measurement System) in long distance respectively. Hence if a robot recognizes an obstacle ahead by PSD infrared sensors first, and judges impossibility to overcome the obstacle based on driving mode decision process, the order of priority is transferred to LMS which collects data of radial distance centered on the robot to avoid the confronted obstacle. After gathering radial information, the ELA algorithm estimates emergency level around a robot and generates a polar histogram based on the emergency level to judge where the optimal free space is. Finally, steering angle is determined to guarantee rotation to randomly direction as well as robot width for safe avoidance. Simulation results from wandering in closed local area which includes various obstacles and different conditions demonstrate the power of the ELA.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.739-743
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• 2008

In this paper, we introduce a method for automatically generating accompaniment music, according to user's input melody. The initial accompaniment chord is generated by analyzing user's input melody. Then next chords are generated continuously based on markov chain probability table in which transition probabilities of each chord are defined. The probability table is learned according to reinforcement learning mechanism using sample data of existing music. Also during playing accompaniment, the probability table is learned and refined using reward values obtained in each status to improve the behavior of playing the chord in real-time. The similarity between user's input melody and each chord is calculated using pitch class histogram. Using our method, accompaniment chords harmonized with user's melody can be generated automatically in real-time.

• ETRI Journal
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• v.36 no.3
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• pp.479-489
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• 2014

This paper presents a model-based image steganography method based on Watson's visual model. Model-based steganography assumes a model for cover image statistics. This approach, however, has some weaknesses, including perceptual detectability. We propose to use Watson's visual model to improve perceptual undetectability of model-based steganography. The proposed method prevents visually perceptible changes during embedding. First, the maximum acceptable change in each discrete cosine transform coefficient is extracted based on Watson's visual model. Then, a model is fitted to a low-precision histogram of such coefficients and the message bits are encoded to this model. Finally, the encoded message bits are embedded in those coefficients whose maximum possible changes are visually imperceptible. Experimental results show that changes resulting from the proposed method are perceptually undetectable, whereas model-based steganography retains perceptually detectable changes. This perceptual undetectability is achieved while the perceptual quality - based on the structural similarity measure - and the security - based on two steganalysis methods - do not show any significant changes.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.669-672
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• 2009

The techniques extract shape descriptors from 3D models and use these descriptors for indices for comparing shape similarities. Most similarity search techniques focus on comparisons of each individual 3D model from databases. However, our similarity search technique can compare not only each individual 3D model, but also partial shape similarities. The partial shape matching technique extends the user's query request by finding similar parts of 3D models and finding 3D models which contain similar parts. We have implemented an experimental partial shape-matching search system for 3D pagoda models, and preliminary experiments show that the system successfully retrieves similar 3D model parts efficiently.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.487-494
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• 2013

Medical image watermarking has received extensive attention as wide security services in the healthcare information system. This paper proposes a blind medical image watermarking approach on the segmented gray-matter (GM) images by utilizing discrete wavelet transform (DWT) and discrete cosine transform (DCT) along with enhanced suppressed fuzzy C-means (EnSFCM) for the optimal selection of sub-blocks position to insert a watermark. Experimental results show that the proposed approach outperforms other methods in terms of peak signal to noise ratio (PSNR) and M-SVD. In addition, the proposed approach shows better robustness than other methods in normalized correlation (NC) values against several attacks, such as noise addition, filtering, JPEG compression, blurring, histogram equalization, and cropping.