• Title/Summary/Keyword: $A^*$ algorithm

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Assessment of changes on water quality and aquatic ecosystem health in Han river basin by additional dam release of stream maintenance flow (하천유지유량 추가 댐방류에 따른 한강유역의 수질 및 수생태계 건강성 변화 평가)

  • Woo, So Young;Kim, Seong Joon;Hwang, Sun Jin;Jung, Chung Gil
    • Journal of Korea Water Resources Association
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    • v.52 no.spc2
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    • pp.777-789
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    • 2019
  • The purpose of this study is to evaluate changes in water quality and aquatic ecosystem health by additional dam release of stream maintenance flow from multipurpose dams in Han river basin ($34,148km^2$) using SWAT (Soil and Water Assessment Tool). The period of additional release was spring (April to June) and autumn (August to October) to evaluate the changes with the data of aquatic ecosystem health survey. The amount of additional release was set proportional to the present dam release, and the maximum release amount was controlled not to exceed the officially notified stream maintenance flow from dam. The 10 percent to 50 percent additional releases showed that the stream water quality (T-N, $NH_4$, T-P, and $PO_4-P$) concentrations except $NO_3-N$ decreased in spring while increased in autumn period. Using the stream water quality results and applying with Random Forest algorithm, the grade of aquatic ecosystem health index (FAI, TDI, and BMI) was improved for both periods especially in the downstream of basin. This study showed that the additional release of stream maintenance flow was more effective in spring than autumn period for the improvement of water quality and aquatic ecosystem.

An Investigation of Emission of Particulate Matters and Ammonia in Comparison with Animal Activity in Swine Barns (양돈사 내 동물 활동도에 따른 암모니아 및 미세먼지 배출농도 특성 분석)

  • Park, Jinseon;Jeong, Hanna;Lee, Se Yeon;Choi, Lak Yeong;Hong, Se-woon
    • Journal of The Korean Society of Agricultural Engineers
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    • v.63 no.6
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    • pp.117-129
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    • 2021
  • The movement of animals is one of the primary factors that influence the variation of livestock emissions. This study evaluated the relationship between animal activity and three major emissions, PM10, PM2.5, and ammonia gas, in weaning, growing, and fattening pig houses through continuous monitoring of the animal activity. The movement score of animals was quantified by the developed image analysis algorithm using 10-second video clips taken in the pig houses. The calculated movement scores were validated by comparison with six activity levels graded by an expert group. A comparison between PMs measurement and the movement scores demonstrated that an increase of the PMs concentrations was obviously followed by increased movement scores, for example, when feeding started. The PM10 concentrations were more affected by the animal activity compared to the PM2.5 concentrations, which were related to the inflow of external PM2.5 due to ventilation. The PM10 concentrations in the fattening house were 1.3 times higher than those in the weaning house because of the size of pigs while weaning pigs were more active and moved frequently compared to fattening pigs showing 2.45 times higher movement scores. The results also indicated that indoor ammonia concentration was not significantly influenced by animal activity. This study is significant in the sense that it could provide realistic emission factors of pig farms considering animal's daily activity levels if further monitoring is carried out continuously.

A Study on the P~q~t Charts Applicability for Quality Improvement of Water-Sealing&Reinforcement Grouting in Tunneling Work Underneath the City (도심지 지하 터널시공 중 차수·보강 그라우팅 공사의 품질향상을 위한 P~q~t charts 적용성 연구)

  • Kim, Jin-Chun;Kim, Seok-Hyun;Yoo, Byung-Sun;Kang, Hee-Jin
    • Journal of Korean Society of Disaster and Security
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    • v.14 no.3
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    • pp.51-63
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    • 2021
  • This study is for the quality improvement of water-sealing & reinforcement grouting in tunnel of the construction of the underground transportation network underneath the city. Existing tunnel grouting process did not technically utilize P~q~t charts fully. It is due to the absence of technical methods to decide how P~q~t charts change in the presence of trouble and what the change represents in grouting. There were no standards to decide which chart pattern represents which ground characteristics, how to categorize ground types, and how to take measures according to the standards. This paper studies on the grouting type, ground characteristics, ground type categorizing method, and countermeasures for both general and algorithm-processed grouting in soil and rock layer to address the aforementioned problems. Newly improved P~q~t charts from grouting in soil was categorized into six different types. Different characteristics and categorization method was developed for each type. Countermeasures for each type of grouting process were developed so that on-site application can be readily available. Improved P~q~t charts for rock layer also have six different types of grouting. Each type was given the countermeasures for rock layer grouting process for easier applications. Therefore, it is expected to be used through out the entire process of grouting from preparation to the last report of the water-sealing & reinforcement grouting in tunnel of the construction of the underground transportation network underneath the city.

Performance Analysis of Object Detection Neural Network According to Compression Ratio of RGB and IR Images (RGB와 IR 영상의 압축률에 따른 객체 탐지 신경망 성능 분석)

  • Lee, Yegi;Kim, Shin;Lim, Hanshin;Lee, Hee Kyung;Choo, Hyon-Gon;Seo, Jeongil;Yoon, Kyoungro
    • Journal of Broadcast Engineering
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    • v.26 no.2
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    • pp.155-166
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    • 2021
  • Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.

Integrated Assessment for Commercialization of Road Hazardous Information Colleted by Commercial Vehicles (사업용 차량 기반 도로위험정보 제공의 상용화를 위한 통합 평가)

  • Yoo, Kyung-su;Chung, Kyungmin;Chae, Chandle
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.20 no.2
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    • pp.30-42
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    • 2021
  • The amount of compensation and the number of cases owing to car damage from pot holes on highways across the country increased by about 4.2 times and 3.5 times, respectively, in 2019 compared to 2015. Due to the increase in damage caused by these road hazards, the Ministry of Land, Infrastructure and Transport is developing technologies and services that can collect road hazard information by using devices on commercial vehicles (DTGs, black boxes, ADASs). In preparation for the development of these technologies, this study conducted an integrated assessment of algorithms developed for interrupted-flow and uninterrupted-flow traffic under three scenarios in order to provide road hazard information to drivers and road managers. As a result, the overall accuracy of the integrated assessment was derived at 81.88%. Errors generated in this integrated assessment reflect only missing data in less than 1 minute, GPS coordinate location and algorithm related errors, taking into account the purpose and assumptions of the assessment. Among them, we derive an accuracy of 90.15%overall by calibrating GPS error data. The results of this study can be used as basic data for improving the accuracy of location-based information collected by commercial vehicles and for policy development.

Evaluation of the future agricultural drought severity of South Korea by using reservoir drought index (RDI) and climate change scenarios (저수지 가뭄지수와 기후변화 시나리오를 이용한 우리나라 미래 농업가뭄 평가)

  • Kim, Jin Uk;Lee, Ji Wan;Kim, Seong Joon
    • Journal of Korea Water Resources Association
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    • v.52 no.6
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    • pp.381-395
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    • 2019
  • The purpose of this study is to predict agricultural reservoir storage rate (RSR) in a month. This algorithm was developed by multiple linear regression model (MLRM) which included the past 3 months RSRs data and the future climate change scenarios. In order to improve use of predicted RSR, this study need the severe criteria in terms of drought. So, the predicted RSR was indexed as the 3 months reservoir drought index (RDI3) and then it was disaggregated into drought duration, severity, and intensity. For the future RSR estimation by climate change scenarios, the 6 RCP 8.5 scenarios of HadGEM2-ES, CESM1-BGC, MPI-ESM-MR, INM-CM4, FGOALS-s2, and HadGEM3-RA were used in three future evaluation periods (S1: 2011~2040, S2: 2041~2070, S3: 2071~2099). The future S3 period of HadGEM2-ES scenario which has the biggest increase in precipitation and temperature showed the largest decrease to 60.2% among the 6 scenarios compared to the historical RSR (1976~2005) 77.3%. In contrast, INM-CM4 scenario which has smallest changes in precipitation and temperature in S3 period showed the smallest decrease to 72.8%. For the CESM1-BGC and MPI-ESM-MR, FGOALS-s2, and HadGEM3-RA, the S3 period RSR showed 72.6%, 72.6%, 67.4%, and 64.5% decrease respectively. The future severe drought condition of RDI3 below -0.25 showed the increase trend for the number and severity up to -2.0 during S3 period.

Improved Performance of Image Semantic Segmentation using NASNet (NASNet을 이용한 이미지 시맨틱 분할 성능 개선)

  • Kim, Hyoung Seok;Yoo, Kee-Youn;Kim, Lae Hyun
    • Korean Chemical Engineering Research
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    • v.57 no.2
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    • pp.274-282
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    • 2019
  • In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

Correction of Lunar Irradiation Effect and Change Detection Using Suomi-NPP Data (VIIRS DNB 영상의 달빛 영향 보정 및 변화 탐지)

  • Lee, Boram;Lee, Yoon-Kyung;Kim, Donghan;Kim, Sang-Wan
    • Korean Journal of Remote Sensing
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    • v.35 no.2
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    • pp.265-278
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    • 2019
  • Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data help to enable rapid emergency responses through detection of the artificial and natural disasters occurring at night. The DNB data without correction of lunar irradiance effect distributed by Korea Ocean Science Center (KOSC) has advantage for rapid change detection because of direct receiving. In this study, radiance differences according to the phase of the moon was analyzed for urban and mountain areas in Korean Peninsula using the DNB data directly receiving to KOSC. Lunar irradiance correction algorithm was proposed for the change detection. Relative correction was performed by regression analysis between the selected pixels considering the land cover classification in the reference DNB image during the new moon and the input DNB image. As a result of daily difference image analysis, the brightness value change in urban area and mountain area was ${\pm}30$ radiance and below ${\pm}1$ radiance respectively. The object based change detection was performed after the extraction of the main object of interest based on the average image of time series data in order to reduce the matching and geometric error between DNB images. The changes in brightness occurring in mountainous areas were effectively detected after the calibration of lunar irradiance effect, and it showed that the developed technology could be used for real time change detection.

Auto-Positioning of Patient in X-ray Diagnostic Imaging (진단 엑스선 영상에서 환자 위치잡이의 자동화)

  • Yang, Won Seok;Son, Jung Min;Kwon, Su Chon
    • Journal of the Korean Society of Radiology
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    • v.12 no.6
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    • pp.793-799
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    • 2018
  • As interest in artificial intelligence has increased, artificial intelligence has been actively studied in the medical field. In Korea, artificial intelligence has been applied to medical imaging devices such as X-ray imaging, Computer Tomography and Magnetic Resonance Imaging and artificial intelligence capable of acquiring radiation images of patients without radiologists in the future Medical devices are expected to be invented. This study was an initial study on the automation of patient positioning in X - ray imaging. We used x-ray equipment and human phantoms to evaluate the positioning. The program used Visual Studio 2010 MFC and the image was in the size $1450{\times}1814$. The pixel values were converted to contrasts with values of 0 to 255 that can be visually recognized and output to the monitor. We developed a procedure algorithm program that predicts the angle of the output image through three pixel coordinate values and induces the patient to perform correct positioning according to the voice guidance according to the angle. In the next study, we will study the artificial intelligence to grasp the structure itself and calculate the angle, rather than conveying the reference of coordinates to artificial intelligence. In the future, it is expected that it will be helpful in the study of artificial intelligence from shooting to positioning through the automation of positioning.

Characteristic Polynomials of 90/150 CA <10 ⋯ 0> (90/150 CA <10 ⋯ 0>의 특성다항식)

  • Kim, Jin-Gyoung;Cho, Sung-Jin;Choi, Un-Sook;Kim, Han-Doo;Kang, Sung-Won
    • The Journal of the Korea institute of electronic communication sciences
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    • v.13 no.6
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    • pp.1301-1308
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    • 2018
  • 90/150 CA which are used as key generators of the cipher system have more randomness than LFSRs, but synthesis methods of 90/150 CA are difficult. Therefore, 90/150 CA synthesis methods have been studied by many researchers. In order to synthesize a suitable CA, the analysis of the characteristic polynomial of 90/150 CA should be preceded. In general, the characteristic of polynomial ${\Delta}_n$ of n cell 90/150 CA is obtained by using ${\Delta}_{n-1}$ and ${\Delta}_{n-2}$. Choi et al. analyzed $H_{2^n}(x)$ and $H_{2^n-1}(x)$, where $H_k(x)$ is the characteristic polynomial of k cell 90/150 CA with state transition rule <$10{\cdots}0$>. In this paper, we propose an efficient method to obtain $H_n(x)$ from $H_{n-1}(x)$ and an efficient algorithm to obtain $H_{2^n+i}(x)$ and $H_{2^n-i}(x)$ ($1{\leq}i{\leq}2^{n-1}$) from $H_{2^n}(x)$ by using this method.