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The Development of Prediction Models for the Number of People for Meal at University Cafeteria (대학교 교내식당을 위한 식사 인원 예측 모델 개발)

  • Kwangwon Jung;Taegeun Jo;Keewon Kim
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2023.07a
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    • pp.535-536
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    • 2023
  • 본 논문에서는 대학교 교내 식당의 실제 데이터를 사용해 식사 인원 예측 모델을 개발하여 교내식당에서 발생하는 적자, 음식 품절, 대량 잔반 발생을 경감 시키고자 한다. 모델 개발에 사용되는 데이터는 2018년도, 2019년도 학기 중 식당 데이터와 기상청 날씨 데이터를 사용하였다. 2018년도, 2019년도 데이터를 이용해 EDA 분석 및 전처리를 통해 필요한 변수를 추출하였다. 전체 데이터의 70%를 기반으로 GridSearch와 XGBoostRegressor를 사용해 평일과 주말에 대한 식사 인원 예측 모델을 생성하였다. 그리고 나머지 데이터의 30%를 사용해 생성한 두 모델의 성능을 평가한다. 평일 식사 인원 예측 모델에 대한 MAE값이 조식 16명, 중식 23명, 석식 25명으로 준수한 결과를 보였고 주말 식사 인원 예측 모델에 대한 MAE값은 조식 16명, 중식 23명, 석식 25명으로 좋은 성능을 보였다.

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System of Agricultural Land Monitoring Using UAV (무인항공기를 이용한 농경지 모니터링 시스템)

  • Kang, Byung-Jun;Cho, Hyun-Chan
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.6
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    • pp.372-378
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    • 2016
  • The purpose of this study is to develop a system configuration for gathering data and building a database for agriculture. Some foreign agriculture-related companies have already constructed such a database for scientific agriculture. The hardware of this system is composed of automatic capturing equipment based on aerial photography using a UAV. The software is composed of parts for stitching images, matching GPS data with captured images, and building a database of collected weather information, farm operation data, and aerial images. We suggest a method for building the database, which can include information about the amount of agricultural products, weather, farm operation, and agricultural land images. The images of this system are about 5 times better than satellite images. Factors such as farm working and environmental factors can be basic data for analyzing the full impact of agriculture land. This system is expected to contribute to the scientific analysis of Korea's agriculture.

A Study on a Two-Axis Solar Tracking System Based on Fuzzy Logic Control (퍼지 논리 제어를 기반으로 한 2축 태양광 추적시스템에 관한 연구)

  • Ahn, Byeongwon;Lee, Hui-Bae;Bae, Cherl-O
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.21 no.5
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    • pp.531-537
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    • 2015
  • In order to maximize power output from the solar panels, one needs to keep the panels aligned with the sun. So solar tracker having high reliability must be designed. This paper cares about the design and evaluation of a two-axis solar tracker system based on fuzzy logic control with LabVIEW. The research focus on planning mechanical parts, making an intelligent controller which controls and monitors all parameters via user interface implemented of a fuzzy decision support system for control of photovoltaic panel movement. We also develop a real solar tracker system and analyze the influence indexes such as environment, weather, season, and light condition. The solar tracker is tested in real condition and all parameters related to the system operation are recorded and analyzed. The developed solar tracking system got a much higher efficiency about 38 % compare to fixed solar panel although the weather condition is affected a lot to the solar panel. So we confirmed the our auto tracking system is more effective and can allow more energy to be produced.

The Study of Scattering Dust and Radiation Dose in Pedestrian Tunnels in Metropolitan Area (수도권 보행터널 내부에 존재하는 비산 먼지와 방사선량의 연구)

  • Jung, Hongmoon
    • Journal of the Korean Society of Radiology
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    • v.14 no.4
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    • pp.385-390
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    • 2020
  • In the present, external environmental factors affect human health. In particular, the most important issue is fine dust in these days. Because fine dust is inhaled through the human respiratory system is known to be harmful to health. Tunnels for cars and people can also be easily seen around us. This study, the amount of scattering radiation was measured for walkable tunnels about dust. For the measurement method, dust and radiation dose in the tunnel were measured on good weather (fine dust level: 0 ~ 30 ㎍/㎥) and normal day (fine dust level: 0 ~ 80 ㎍/㎥). The measurement resulted in an increase of 10~20 % of dust in the center of the tunnel on a good weather day and an increase of 20~30 % of dust in the center of the tunnel on normal weather. On the other hand, the results of tunnel measurement of radiation dose increased by 10~20 % at the center of the tunnel non-depending on the weather. As a result, pedestrians should pay attention to scattering dust and scattered radiation while moving through the tunnel. Therefore, it is recommended to wear a filter mask of PM2.5 or less during frequent tunnel walking.

심혈관질환의 예방 및 영양관리

  • KOREA ASSOCIATION OF HEALTH PROMOTION
    • 건강소식
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    • v.26 no.12 s.289
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    • pp.6-13
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    • 2002
  • 뇌졸중, 심근경색 등의 심혈관질환은 암과 더불어 우리나라의 주요 사망원인이다. 2002년 통계청이 발표한 2001년 사망원인통계를 보면 인구 10만명 당 암이 123.5명으로 1위, 뇌졸중 등 뇌혈관 질환이 73.8명으로 2위, 심장질환(허혈성 심장질환과 기타 심장질환 포함)이 34.2명으로 3위였다. 이러한 심혈관질환의 예방을 위해서는 평소 뇌혈관질환을 일으킬 수 있는 위험인자들을 잘 관리할 필요가 있다. 사망률이 높은 심혈관질환의 주요 위험요인은 성별, 연령 등 선천적으로 타고나는 고정요인과 흡연, 비만 등 본인의 노력에 따라 생활습관을 바꿔 위험을 낮출 수 있는 변동요인으로 나눌 수 있다. 성별, 연령은 바꿀수 없지만 생활습관은 바꿀 수 있다. 심혈관 질환 예방에 있어서 가장 중요한 것은 기본적인 건강관리 수칙인 금연, 과음하지 않기, 균형있는 올바른 식생활, 규칙적인 운동, 표준체중 유지하기, 정기검진 등이다.특히 심혈관 질환은 채소류와 식물성 단백질, 식물성 지방 등을 위주로 한 식이요법이 도움을 줄 수 있고 튼튼한 혈관을 가지기 위해서 질 좋은 단백질과 비타민ㆍ무기질을 충분히 섭취해야 하며 콜레스테롤의 배설을 돕기 위해 섬유소가 충분한 식사를 한다. 갑자기 추워진 날씨로 생명을 다투는 뇌졸중이나 협심증, 심근경색 등 심혈관 관련 질병이 발생하기 쉬운 겨울철이다. 행사가 많은 연말에 과음을 삼가며 금연하고, 평소 담백한 한식 위주의 식사, 꾸준한 운동 등으로 건강관리에 힘써서 치명적인 심혈관 질환에 미리미리 대비하도록 한다.

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Forage Preservation (조사료 조제 및 저장)

  • 신정남
    • Journal of The Korean Society of Grassland and Forage Science
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    • v.12 no.3
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    • pp.134-140
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    • 1992
  • 사일리지의 사양가치에 가장 크게 영향을 미치는 요인은 사초의 종류에 기인되는 화학조성분이며 사일리지를 적기에 제조하므로 단위면적당 최고의 양분수량을 거둘 수 있다. 유기산의 생성량이 많고 낙산이나 암모니아태질소의 함량이 많으면 사일리지의 품질이 떨어지고 섭취량이 감소되므로 사일리지 품질 증진을 위한 연구방향은 발효를 감소시키고 단백질의 분해를 막는 것이다. 사일리지의 발효에 영향을 미치는 중요한 요인으로는 사초의 화학조성분인 수분, 수용성탄수화물 등과 제조기술에 크게 좌우되는 공기(산소)혼입량이다. 재료의 수분함량은 사일리지 발효품질에 큰 영향을 미치며 수분이 많은 재료는 적당히 예건되면 젖산발효는 덜 제한되는 반면 불필요한 발효가 줄어들고 낙산발효를 억제할 수 있어 품질이 향상되고 기호성이 증진된다. 또한 사초를 알맞은 길이로 절단하므로 답압이 잘되어 공기 배출이 양호해 혐기상태의 유지가 가능하게 되고 사일리지의 취급또한 편리해 진다. 사일리지 첨가제는 재료를 적절히 예건하지 못할 때 필요하고 또한 특별한 조건하에서는 권장되고 있다. 이와같이 사일리지 발효에 도움을 준다는 가능성에도 불구하고 첨가제의 잇점은 양질 사일리지 제조를 위한 제반 처리를 대신할 수 없다. 양건 건초제조에 소요되는 기간은 기후에 크게 영향을 받게 된다. 건초제조 과정 중 포장에서 생기는 건물손실은 외기에 오랫동안 노출되면 잎이나 연한 줄기 부분이 부서지고 카로틴이 파괴된다. 또한 말리는 과정에 비를 맞힐 경우 양분이 용해되어 소화되기 쉬운 영양소와 건물 손실이 증가되며 섬유소함량이 증가되어 소화율과 섭취량이 감소된다. 그러므로 일기 예보에 따라 좋은 날씨가 $3\sim4$일 계속되는 시기를 택하여 적기 수확하고, 줄기의 압쇄(condition), 건조시 풀의 두께를 얇게, 뒤집기, 적절한 수분함량일 때 거둬들이는 조치가 필요하다.

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Road Image Enhancement Method for Vision-based Intelligent Vehicle (비전기반 지능형 자동차를 위한 도로 주행 영상 개선 방법)

  • Kim, Seunggyu;Park, Daeyong;Choi, Yeongwoo
    • Korean Journal of Cognitive Science
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    • v.25 no.1
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    • pp.51-71
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    • 2014
  • This paper presents an image enhancement method in real road traffic scenes. The images captured by the camera on the car cannot keep the color constancy as illumination or weather changes. In the real environment, these problems are more worse at back light conditions and at night that make more difficult to the applications of the vision-based intelligent vehicles. Using the existing image enhancement methods without considering the position and intensity of the light source and their geometric relations the image quality can even be deteriorated. Thus, this paper presents a fast and effective method for image enhancement resembling human cognitive system which consists of 1) image preprocessing, 2) color-contrast evaluation, 3) alpha blending of over/under estimated image and preprocessed image. An input image is first preprocessed by gamma correction, and then enhanced by an Automatic Color Enhancement(ACE) method. Finally, the preprocessed image and the ACE image are blended to improve image visibility. The proposed method shows drastically enhanced results visually, and improves the performance in traffic sign detection of the vision based intelligent vehicle applications.

Development of Autonomous Vehicle Learning Data Generation System (자율주행 차량의 학습 데이터 자동 생성 시스템 개발)

  • Yoon, Seungje;Jung, Jiwon;Hong, June;Lim, Kyungil;Kim, Jaehwan;Kim, Hyungjoo
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.19 no.5
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    • pp.162-177
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    • 2020
  • The perception of traffic environment based on various sensors in autonomous driving system has a direct relationship with driving safety. Recently, as the perception model based on deep neural network is used due to the development of machine learning/in-depth neural network technology, a the perception model training and high quality of a training dataset are required. However, there are several realistic difficulties to collect data on all situations that may occur in self-driving. The performance of the perception model may be deteriorated due to the difference between the overseas and domestic traffic environments, and data on bad weather where the sensors can not operate normally can not guarantee the qualitative part. Therefore, it is necessary to build a virtual road environment in the simulator rather than the actual road to collect the traning data. In this paper, a training dataset collection process is suggested by diversifying the weather, illumination, sensor position, type and counts of vehicles in the simulator environment that simulates the domestic road situation according to the domestic situation. In order to achieve better performance, the authors changed the domain of image to be closer to due diligence and diversified. And the performance evaluation was conducted on the test data collected in the actual road environment, and the performance was similar to that of the model learned only by the actual environmental data.

Experimental Performance Analysis of BCJR-Based Turbo Equalizer in Underwater Acoustic Communication (수중음향통신에서 BCJR 기반의 터보 등화기 실험 성능 분석)

  • Ahn, Tae-Seok;Jung, Ji-Won
    • Journal of Navigation and Port Research
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    • v.39 no.4
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    • pp.293-297
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    • 2015
  • Underwater acoustic communications has been limited use for military purposes in the past. However, the fields of underwater applications expend to detection, submarine and communication in recent. The excessive multipath encountered in underwater acoustic communication channel is creating inter symbol interference, which is limiting factor to achieve a high data rate and bit error rate performance. To improve the performance of a received signal in underwater communication, many researchers have been studied for channel coding scheme with excellent performance at low SNR. In this paper, we applied BCJR decoder based ( 2,1,7 ) convolution codes and to compensate for the distorted data induced by the multipath, we applying the turbo equalization method. Through the underwater experiment on the Gyeungcheun lake located in Mungyeng city, we confirmed that turbo equalization structure of BCJR has better performance than hard decision and soft decision of Viterbi decoding. We also confirmed that the error rate of decoder input is less than error rate of $10^{-1}$, all the data is decoded. We achieved sucess rate of 83% through the experiment.

The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity (농산물 생산성 향상을 위한 딥러닝 기반 농업 의사결정시스템)

  • Park, Jinuk;Ahn, Heuihak;Lee, ByungKwan
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.11 no.5
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    • pp.521-530
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    • 2018
  • This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.