• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.37-44
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• 2020

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.68-84
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• 2020

This study analyzed the relationship between spatial characteristics and heat waves in the distribution area of the elderly population in Changwon, Gyeongsangnam-do. For analysis, the Statistics Census data, the Ministry of Environment land cover, Landsat 8 surface temperature, and the Meteorological Agency's heat wave days data were used. The spatial characteristics of the distribution of the elderly population was classified into 5 types through K-mean cluster analysis considering the land use types. The characteristics of the elderly population by spatial type were higher in the urbanized type(cluster-3), but the proportion of the elderly population was higher in the agricultural and forest area types(cluster-1, cluster-2). In the characteristics of the surface temperature and the heat wave days, the surface temperature was the highest in the urban area, but heat wave days were the highest in the rural area. As a result of analyzing the heat wave characteristics according to the spatial type of the distribution area of elderly population, cluster-2 with the largest area in agricultural areas was highest at 15.95 days, and cluster-3 with a large area in urbanized types was the lowest at 9.41 days and 9.18 days. In other words, the elderly population living in rural areas is more exposed to heat waves than the elderly population living in urban areas, and the damage is expected to increase. The results of this study could be used as basic data to prepare various policy measures for effective management and prevention of vulnerable areas in summer.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.142-142
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• 2017

본 연구에서는 파프리카를 자동 수확하기 위한 시스템 개발의 일환으로 파프리카 재배환경에서 획득한 영상 내에 존재하는 파프리카 영역과 비 파프리카 영역의 RGB 정보를 입력으로 하는 인공신경망을 설계하고 학습을 수행하고자 하였다. 학습된 신경망을 이용하여 영상 내 파프리카 영역과 비 파프리카 영역의 구분이 가능 할 것으로 사료된다. 심층 신경망을 설계하기 위하여 MS Visual studio 2015의 C++, MFC와 Python 및 TensorFlow를 사용하였다. 먼저, 심층 신경망은 입력층과 출력층, 그리고 은닉층 8개를 가지는 형태로 입력 뉴런 3개, 출력 뉴런 4개, 각 은닉층의 뉴런은 5개로 설계하였다. 일반적으로 심층 신경망에서는 은닉층이 깊을수록 적은 입력으로 좋은 학습 결과를 기대 할 수 있지만 소요되는 시간이 길고 오버 피팅이 일어날 가능성이 높아진다. 따라서 본 연구에서는 소요시간을 줄이기 위하여 Xavier 초기화를 사용하였으며, 오버 피팅을 줄이기 위하여 ReLU 함수를 활성화 함수로 사용하였다. 파프리카 재배환경에서 획득한 영상에서 파프리카 영역과 비 파프리카 영역의 RGB 정보를 추출하여 학습의 입력으로 하고 기대 출력으로 붉은색 파프리카의 경우 [0 0 1], 노란색 파프리카의 경우 [0 1 0], 비 파프리카 영역의 경우 [1 0 0]으로 하는 형태로 3538개의 학습 셋을 만들었다. 학습 후 학습 결과를 평가하기 위하여 30개의 테스트 셋을 사용하였다. 학습 셋을 이용하여 학습을 수행하기 위해 학습률을 변경하면서 학습 결과를 확인하였다. 학습률을 0.01 이상으로 설정한 경우 학습이 이루어지지 않았다. 이는 학습률에 의해 결정되는 가중치의 변화량이 너무 커서 비용 함수의 결과가 0에 수렴하지 않고 발산하는 경향에 의한 것으로 사료된다. 학습률을 0.005, 0.001로 설정 한 경우 학습에 성공하였다. 학습률 0.005의 경우 학습 횟수 3146회, 소요시간 20.48초, 학습 정확도 99.77%, 테스트 정확도 100%였으며, 학습률 0.001의 경우 학습 횟수 38931회, 소요시간 181.39초, 학습 정확도 99.95%, 테스트 정확도 100%였다. 학습률이 작을수록 더욱 정확한 학습이 가능하지만 소요되는 시간이 크고 국부 최소점에 빠질 확률이 높았다. 학습률이 큰 경우 학습 소요 시간이 줄어드는 반면 학습 과정에서 비용이 발산하여 학습이 이루어지지 않는 경우가 많음을 확인 하였다.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.148-148
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• 2017

방역 차량의 약액탱크, 차량의 연료, 워셔액 등의 탱크 내부에는 잔존량을 측정하기 위해 기둥과 floating box로 이루어진 부력식 수위레벨센서가 사용되고 있으나 액체레벨에 따라 float이 상하로 움직이는 측정원리상 차량 주행 중 정확성이 매우 떨어진다(Park et al. 2016). 방역차량이 주행 중 분사할 때, 슬로싱 현상과 방역소독기의 노즐과 펌프에서 발생하는 진동으로 인해 기존의 부력식 센서를 이용한 약제 살포량 측정방법은 정확성이 매우 떨어지는 경향이 있다. 본 연구의 목적은 방역차량이 주행하면서 분사할 때, 수위레벨 센서를 이용한 약제살포량 측정의 정확성을 개선하는 것으로 디지털 칼만필터, Low pass filter와 댐퍼를 제작하여 이용했다. 본 연구에서는 압력식 레벨센서를 이용해 약액탱크의 높이당 단면적과 수위를 측정하여 약제살포량을 계산했다. Python 2.7을 이용해 디지털 칼만필터와 Low pass filter(LPF)를 구현하였으며 3D프린터를 이용해 댐퍼를 제작했다. 실내에서 슬로싱 현상을 인공적으로 만들어 필터와 댐퍼의 수위 측정 정확성 개선효과를 확인 후 실제 방역차량에 부착하여 비포장도로에서 주행하면서 분사할 때 필터와 댐퍼의 효과를 확인하였다. 댐퍼의 공극률(p)을 바꿔가며 수위 측정 정확성 개선효과를 확인하였다. 실내, 현장 실험 결과, 칼만필터가 LPF보다 개선효과가 더 크지만 데이터 50개 처리에 1.71초의 시간지연이 발생했다. 댐퍼는 수위센서를 고정시키고 유체의 운동을 방해하여 이상치와 큰 오차제거에 효과적이었다. 칼만필터와 댐퍼를 동시에 이용할 경우, 수위 측정정확성 $R^2$는 0.9985, 0.9981로 ${\pm}4.3cm$의 범위내에서 수위를 측정할 수 있었다. 필터의 시간지연과 수위 측정정확성을 고려하여 데이터 기록간격을 3초로 설정하면 ${\pm}3cm$이내에서 약탱크 내 수위를 측정할 수 있었다. 공극률(p)가 0.294, 0.291, 0.17에서 측정정확성 $R^2$는 각각 0.9897, 0.9858, 0.9872 로 p가 0.294에서 개선효과가 가장 좋았으나 개선효과의 차이는 크지 않았다.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.859-864
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• 2017

IoT is a new emerging technology to lead the $4^{th}$ industry renovation and has been widely used in industry and home to increase the quality of human being. In this paper, IoT based face detection and recognition system for a smart elevator is developed. Haar cascade classifier is used in a face detection system and a proposed PCA algorithm written in Python in the face recognition system is implemented to reduce the execution time and calculates the eigenfaces. SVM or Euclidean metric is used to recognize the faces detected in the face detection system. The proposed system runs on RaspberryPi 3. 200 sample images in ORL face database are used for training and 200 samples for testing. The simulation results show that the recognition rate is over 93% for PP+EU and over 96% for PP+SVM. The execution times of the proposed PCA and the conventional PCA are 0.11sec and 1.1sec respectively, so the proposed PCA is much faster than the conventional one. The proposed system can be suitable for an elevator monitoring system, real time home security system, etc.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.19-33
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• 2020

This study aims to analyze on the changes of opinion in terms of Sejong City construction using big data. The research data are newspaper articles related to the argument of construction in Sejong City. The newspaper article data was reported by Hankyoreh, Dong-A Ilbo and Hankook Ilbo. The arguments related to the construction of Sejong City was included the new administrative capita, multifunctional administrative city and amendments of Sejong City. The analysis method used in this study is frequency analysis, sentiment analysis and social network analysis using python and gephi 0.9.2 programs. The results of the analysis are as follows. First, as a result of frequency analysis, the keywords of Hankyoreh showed the characteristics of consent - consent - dissent according to the construction period of Sejong City. The Dong-A Ilbo showed positions of dissent - dissent - consent. In addition, the Hankook Ilbo was analyzed to have the characteristics of dissent - consent - dissent tendency. Secondly, results of sentiment analysis, The Hankyoreh showed positive - positive - negative tone. The characteristic of Dong-A Ilbo is that the focus has changed from negative to negative to positive. The Hankook Ilbo showed that changed from negative to positive to negative. Finally, the results of social network analysis are as follows. At the time of the construction of Sejong City, each opinion of media was showed a changes in issues according to political and ideological characteristics such as conservative, progressive and moderation. In detail, Hankyoreh focused on balanced regional development. The Dong-A Ilbo represented the opinion of the Conservative Party. The Hankook Ilbo was highlighting the issues confronting the conservative party and progressive party during the construction of Sejong City.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.1-8
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• 2020

Air ducts installed for ventilation inside buildings accumulate contaminants during their service life. Robots are installed to clean the air duct at low cost, but they are still not fully automated and depend on manpower. In this study, an intersection detection algorithm for autonomous driving was applied to an air duct cleaning robot. Autonomous driving of the robot was achieved by calculating the distance and angle between the extracted point and the center point through the intersection detection algorithm from the camera image mounted on the robot. The training data consisted of CAD images of the duct interior as well as the cross-point coordinates and angles between the two boundary lines. The deep learning-based CNN model was applied as a detection algorithm. For training, the cross-point coordinates were obtained from CAD images. The accuracy was determined based on the differences in the actual and predicted areas and distances. A cleaning robot prototype was designed, consisting of a frame, a Raspberry Pi computer, a control unit and a drive unit. The algorithm was validated by video imagery of the robot in operation. The algorithm can be applied to vehicles operating in similar environments.

• Journal of Korean Medicine Rehabilitation
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• v.27 no.4
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• pp.97-109
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• 2017

Objectives The aim of this study is to analyze the patients who have low back pain through registry. Methods We registered patients with low back pain who visited department of korean rehabilitation medicine in university hospitals on study. We collected data from 116 subjects consisted of 51 inpatients and 65 outpatients and ruled out 8 who didn't have pattern identification data at the point of inpatient or outpatient visit so we analyzed 108 in total. We used Pearson's product moment correlation to find correlationship among variables, and analyzed statistical data using Phyton scipy library stats package. Results We set general features, region of the pain, physical examination, ROM, questionnaire results, pattern identification as variables and draw a conclusion by analyzing these variables. Conclusions Registry aimed at low back pain patients was established in department of korean rehabilitation medicine of university hospitals and exploratory analysis based on data were made. Through the registry, we expect that more advanced studies will be performed; for example, executing research which verifies effectiveness and stability of korean medical treatment or developing tools to fill the gap between pattern identification and disease identification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.466-472
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• 2020

The purpose of this study is to explore how to use location data for peer relations of infants in free-choice play activities. For this study, location data was collected using wearable devices for 14 students in one class at an early childhood education institution in Chungnam. For the pre-processing of the collected location data, a smoothing technique was applied to recover missing values during the collection process, and the data was visualized using Python's Matplotlib. Subsequently, the movement distance, distance between infants, and interaction types of infants were extracted from the location data using the formula. As a result of the study, it was possible to derive 1) change in moving distance, cumulative value, average value, 2) change in distance and average distance value between infants, and 3) change and trend in interaction type according to the passage of time. These results can provide valuable information on the process of forming peer groups for infants in situations where it is difficult for a teacher to closely observe all members, and can be used as meaningful information for the design and operation of educational programs.

• Investigative Magnetic Resonance Imaging
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• v.23 no.4
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• pp.328-340
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• 2019

Purpose: To generate phase images with free of motion-induced artifact and susceptibility-induced distortion using 3D radial ultrashort TE (UTE) MRI. Materials and Methods: The field map was theoretically derived by solving Laplace's equation with appropriate boundary conditions, and used to simulate the image distortion in conventional spin-warp MRI. Manufacturer's 3D radial imaging sequence was modified to acquire maximum number of radial spokes in a given time, by removing the spoiler gradient and sampling during both rampup and rampdown gradient. Spoke direction randomly jumps so that a readout gradient acts as a spoiling gradient for the previous spoke. The custom raw data was reconstructed using a homemade image reconstruction software, which is programmed using Python language. The method was applied to a phantom and in-vivo human brain and abdomen. The performance of UTE was compared with 3D GRE for phase mapping. Local phase mapping was compared with T₂* mapping using UTE. Results: The phase map using UTE mimics true field-map, which was theoretically calculated, while that using 3D GRE revealed both motion-induced artifact and geometric distortion. Motion-free imaging is particularly crucial for application of phase mapping for abdomen MRI, which typically requires multiple breathold acquisitions. The air pockets, which are caught within the digestive pathway, induce spatially varying and large background field. T₂* map, that was calculated using UTE data, suffers from non-uniform T₂* value due to this background field, while does not appear in the local phase map of UTE data. Conclusion: Phase map generated using UTE mimicked the true field map even when non-zero susceptibility objects were present. Phase map generated by 3D GRE did not accurately mimic the true field map when non-zero susceptibility objects were present due to the significant field distortion as theoretically calculated. Nonetheless, UTE allows for phase maps to be free of susceptibility-induced distortion without the use of any post-processing protocols.