• Title/Summary/Keyword: location detection

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Development of Safety Sensor for Vehicle-Type Forest Machine in Forest Road

  • Ki-Duck Kim;Hyun-Seung Lee;Gyun-Hyung Kim;Boem-Soo Shin
    • Journal of Forest and Environmental Science
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    • v.39 no.4
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    • pp.254-260
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    • 2023
  • A sensor system has been developed that uses an ultrasonic sensor to detect the downhill slope on the side of a forest road and prevents a vehicle-type forest machine from rolling down a mountainside. A specular reflection of ultrasonic wave might cause severe issues in measuring distances to targets. By investigating the installation angle of the sensor to minimize the negative effects of specular reflection, the installation angle of lateral monitoring ultrasonic sensor could be determined based on the width of road shoulder. Obstacles such as small rocks or piece of log in a forest road may cause the forest machine to be overturned while the machine riding over due to excessive its posture change. It was determined that the laser sensor could be a part of a sensor system capable of specifying the location and size of small obstacles. Not only this sensor system including ultrasonic and laser sensors can issue a warning of dangerous sections to drivers in forest forwarders currently in use, but also it can be used as a driving safety sensor in autonomous forest machine or remote-control forest machine in the future.

Research on High-resolution Seafloor Topography Generation using Feature Extraction Algorithm Based on Deep Learning (딥러닝 기반의 특징점 추출 알고리즘을 활용한 고해상도 해저지형 생성기법 연구)

  • Hyun Seung Kim;Jae Deok Jang;Chul Hyun;Sung Kyun Lee
    • Journal of the Korean Society of Systems Engineering
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    • v.20 no.spc1
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    • pp.90-96
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    • 2024
  • In this paper, we propose a technique to model high resolution seafloor topography with 1m intervals using actual water depth data near the east coast of the Korea with 1.6km distance intervals. Using a feature point extraction algorithm that harris corner based on deep learning, the location of the center of seafloor mountain was calculated and the surrounding topology was modeled. The modeled high-resolution seafloor topography based on deep learning was verified within 1.1m mean error between the actual warder dept data. And average error that result of calculating based on deep learning was reduced by 54.4% compared to the case that deep learning was not applied. The proposed algorithm is expected to generate high resolution underwater topology for the entire Korean peninsula and be used to establish a path plan for autonomous navigation of underwater vehicle.

Optimization of Ceramide Analysis Method Using LC-MS in Cosmetics

  • Su-Jin Park;Hee-Jin Yoo;Duck-Hyun Kim;Ji-Won Park;Eunji Jeon;Abhik Mojumdar;Kun Cho
    • Mass Spectrometry Letters
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    • v.15 no.1
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    • pp.49-53
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    • 2024
  • Ceramide is a lipid in which sphingoid bases and fatty acids are linked by amide bonds. As a marker of skin disease in the human stratum corneum, its disease-causing and therapeutic effects have been partially confirmed, and it is therefore an important element in commercially available cosmetic formulations. However, structural diversity caused by differences in the chain length, number, and location of hydroxyl groups makes quality control difficult. In this study, a method was established to separate different ceramide species using reversed-phase LC-MS/MS and thus enable qualitative evaluation. Separation of four standards was achieved within a short retention time, and the accuracy and sensitivity of the method were demonstrated by the low limit of detection (LOD) calculated based on the calibration curve showing linearity, with R2 > 0.994. After verification of reproducibility and reliability through intra- and inter-day analyses, the efficiency of the method was confirmed through analysis of commercial cosmetic raw materials.

A Study on the Installation of the Optimized Collapse Risk Detection Monitoring System for Small-Scale Private Buildings (소규모 민간 건축물을 위한 최적의 붕괴 위험 감지 모니터링 시스템 설치 방안 연구)

  • Heejae Kim;Geunyoung Kim
    • Journal of the Society of Disaster Information
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    • v.20 no.1
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    • pp.147-155
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    • 2024
  • Purpose: The purpose of this study is to analyze the danger signs of buildings and present a plan to install a building monitoring system to develop measurement technology for small private buildings in the blind spot of disaster safety. Method: The cause of building risk behavior, components of monitoring measuring equipment, location of measuring equipment installation, management plan, etc. are presented. Result: Measuring instruments essentially include acceleration sensors, tilt sensors, gyro sensors, GPS, etc. The measuring instrument should take into account the height and cross-sectional area of the pillar. Conclusion: The results of this study can strengthen disaster safety capabilities in preparation for disasters arising from building collapses that may occur in small private buildings.

Design of Indoor Space Guidance System Using LiDAR and Camera on iPhone (iPhone의 LiDAR와 Camera를 이용한 실내 공간 안내를 위한 시스템 설계)

  • Junseok Jang;Kwangjae Sung
    • Journal of the Semiconductor & Display Technology
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    • v.23 no.1
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    • pp.71-78
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    • 2024
  • In indoor environments, since global positioning system (GPS) signals can be blocked by obstacles, such as building structure. the performance of GPS-based positioning methods can be degraded because of the loss of GPS signals. To solve this problem, various localization schemes using inertial measurement unit (IMU) sensors, such as gyroscope, accelerometer, and magnetometer, have been proposed to enhance the positioning accuracy in indoor environments. IMU-based positioning methods can estimate the location of the user by calculating the velocity and heading angle of the user without the help of GPS. However, low-cost MEMS IMUs may lead to drift error and large bias. In addition, positioning errors in IMU-based positioning approaches can be caused by the irrelevant motion of the pedestrian. In this study, we propose an enhanced indoor positioning method that provides more reliable localization results by using the camera, light detection and right (LiDAR), and ARKit framework on the iPhone. Through reliable positioning results and augmented reality (AR) experiences, our indoor positioning system can provide indoor space guidance services.

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Quantitative analysis of capsaicinoids in Capsicum annuum using HPLC/UV

  • Gia Han Tran;Hyejin Cho;Chohee Kim;Ohyeol Kweon;Jun Yeon Park;Sullim Lee;Sanghyun Lee
    • Journal of Applied Biological Chemistry
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    • v.66
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    • pp.320-327
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    • 2023
  • Capsicum annuum belongs to the Solanaceae family, crops of which are extensively cultivated worldwide. It is a food source containing various nutrients and vitamins and also serves as a medicine for treating ailments. The burning feeling experienced while consuming Capsicum fruits is due to the presence of capsaicinoids, particularly capsaicin and dihydrocapsaicin. This study aimed to assess the content of these two compounds in 34 varieties of capsicum and paprika. High-performance liquid chromatography with a gradient elution system and a reverse-phase YMC Pack-Pro column with UV detection at 280 nm was employed. The results revealed that, among the 34 samples, only six samples (samples 1, 15, 20, 29, 32, and 34) contained capsaicin and dihydrocapsaicin, and their highest contents were found in sample 1 - variety name: Sungil-c (capsaicin: 3.42 mg/g extract, dihydrocapsaicin: 1.20 mg/g extract). These findings suggest that the content of these two compounds is attributed to the variety and is influenced by geographical location and environmental factors. Additionally, this study provides a basis for establishing a C. annuum variety with high capsaicin and dihydrocapsaicin contents.

Data Preprocessing and ML Analysis Method for Abnormal Situation Detection during Approach using Domestic Aircraft Safety Data (국내 항공기 위치 데이터를 활용한 이착륙 접근 단계에서의 항공 위험상황 탐지를 위한 데이터 전처리 및 머신 러닝 분석 기법)

  • Sang Ho Lee;Ilrak Son;Kyuho Jeong;Nohsam Park
    • Journal of Platform Technology
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    • v.11 no.5
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    • pp.110-125
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    • 2023
  • In this paper, we utilize time-series aircraft location data measured based on 2019 domestic airports to analyze Go-Around and UOC_D situations during the approach phase of domestic airports. Various clustering-based machine learning techniques are applied to determine the most appropriate analysis method for domestic aviation data through experimentation. The ADS-B sensor is solely employed to measure aircraft positions. We designed a model using clustering algorithms such as K-Means, GMM, and DBSCAN to classify abnormal situations. Among them, the RF model showed the best performance overseas, but through experiments, it was confirmed that the GMM showed the highest classification performance for domestic aviation data by reflecting the aspects specialized in domestic terrain.

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Frequency Domain Pattern Recognition Method for Damage Detection of a Steel Bridge (강교량의 손상감지를 위한 주파수 영역 패턴인식 기법)

  • Lee, Jung Whee;Kim, Sung Kon;Chang, Sung Pil
    • Journal of Korean Society of Steel Construction
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    • v.17 no.1 s.74
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    • pp.1-11
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    • 2005
  • A bi-level damage detection algorithm that utilizes the dynamic responses of the structure as input and neural network (NN) as pattern classifier is presented. Signal anomaly index (SAI) is proposed to express the amount of changes in the shape of frequency response functions (FRF) or strain frequency response function (SFRF). SAI is calculated using the acceleration and dynamic strain responses acquired from intact and damaged states of the structure. In a bi-level damage identification algorithm, the presence of damage is first identified from the magnitude of the SAI value, then the location of the damage is identified using the pattern recognition capability of NN. The proposed algorithm is applied to an experimental model bridge to demonstrate the feasibility of the algorithm. Numerically simulated signals are used for training the NN, and experimentally-acquired signals are used to test the NN. The results of this example application suggest that the SAI-based pattern recognition approach may be applied to the structural health monitoring system for a real bridge.

Film Line Scratch Detection using a Neural Network based Texture Classifier (신경망 기반의 텍스처 분류기를 이용한 스크래치 검출)

  • Kim, Kyung-Tai;Kim, Eun-Yi
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.43 no.6 s.312
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    • pp.26-33
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    • 2006
  • Film restoration is to detect the location and extent of defected regions from a given movie film, and if present, to reconstruct the lost information of each region. It has gained increasing attention by many researchers, to support multimedia service of high quality. In general, an old film is degraded by dust, scratch, flick, and so on. Among these, the most frequent degradation is the scratch. So far techniques for the scratch restoration have been developed, but they have limited applicability when dealing with all kinds of scratches. To fully support the automatic scratch restoration, the system should be developed that can detect all kinds of scratches from a given frame of old films. This paper presents a neurual network (NN)-based texture classifier that automatically detect all kinds of scratches from frames in old films. To facilitate the detection of various scratch sizes, we use a pyramid of images generated from original frames by having the resolution at three levels. The image at each level is scanned by the NN-based classifier, which divides the input image into scratch regions and non-scratch regions. Then, to reduce the computational cost, the NN-based classifier is only applied to the edge pixels. To assess the validity of the proposed method, the experiments have been performed on old films and animations with all kinds of scratches, then the results show the effectiveness of the proposed method.

Quantitative Analysis of Thallium-201 Myocardial Tomograms (Thallium-201 심근 단층영상의 정량적 분석)

  • Kim, Sang-Eun;Nam, Gi-Byoung;Choi, Chang-Woon;Choi, Kee-Joon;Lee, Dong-Soo;Sohn, Dae-Won;Ahn, Cu-Rie;Chung, June-Key;Lee, Myoung-Mook;Lee, Myung-Chul;Park, Young-Bae;Choi, Yun-Shik;Seo, Jung-Don
    • The Korean Journal of Nuclear Medicine
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    • v.25 no.2
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    • pp.165-176
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    • 1991
  • The purpose of this study was to assess the ability of quantitative Tl-201 tomography to identify and localize coronary artery disease (CAD). The study population consisted of 41 patients (31 males, 10 females; mean age $55{\pm}7$ yr) including 14 with prior myocardial infarction who underwent both exercise Tl-201 myocardium SPECT and coronary angiography for the evaluation of chest pain. From the short axis and vertical long axis tomograms, stress extent polar maps were generated by Cedars-Sinai Medical Center program, and the % stress defect extent (SDE) was quantified for each coronary artery territory. For the purpose of this study, the coronary circulation was divided into 6 arterial segments, and the "myocardial ischemic score" (MIS) was calculated from the coronary angiogram. Sensitivity for the detection of CAD ($\geq50%$ coronary stenosis by angiography) by angiography) by stress extent polar map was 95% in single vessel disease, and 100% in double and triple vessel deseases. Overall sensitivity was 97%. Sensitivity and specificity for the detection of individual diseased vessels were, respectively, 87% and 90% for the left anterior descending artery (LAD), 36% and 93% for the left circumflex artery (LCX), and 71% and 70% for the right coronary artery (RCA). Concordance for the detection of individual diseased vessels between the coronary angiography and stress polar map was fair for the LAD (kappa=0.70), and RCA (kappa=0.41) lesions, whereas it was poor for the LCX lesions (kappa : 0.32). There were siginificant correlations between the MIS and SDE in LAD (rs=0.56, p=0.0027), and RCA territory (rs=0.60, p=0.0094). No significant correlation was found in LCX territory. When total vascular territories were combined, there was a significant correlation between the MIS and SDE (rs=0.42, p=0.0116). In conclusion, the quantitative analysis of Tl-201 tomograms appears to be accurate for determining the presence and location of CAD.

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