• Title/Summary/Keyword: HS 모델

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Identification of First-order Plus Dead Time Model from Step Response Using HS Algorithm (HS 알고리즘을 이용한 계단응답으로부터 FOPDT 모델 인식)

  • Lee, Tae-Bong
    • Journal of Advanced Navigation Technology
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    • v.19 no.6
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    • pp.636-642
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    • 2015
  • This paper presents an application of heuristic harmony search (HS) optimization algorithm for the identification of linear continuous time-delay system from step response. Identification model is first-order plus dead time (FOPDT), which describes a linear monotonic process quite well in most chemical processes and HAVC process and is often sufficient for PID controller tuning. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the identification method has been demonstrated through a number of simulation examples.

CNN-based Recommendation Model for Classifying HS Code (HS 코드 분류를 위한 CNN 기반의 추천 모델 개발)

  • Lee, Dongju;Kim, Gunwoo;Choi, Keunho
    • Management & Information Systems Review
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    • v.39 no.3
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    • pp.1-16
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    • 2020
  • The current tariff return system requires tax officials to calculate tax amount by themselves and pay the tax amount on their own responsibility. In other words, in principle, the duty and responsibility of reporting payment system are imposed only on the taxee who is required to calculate and pay the tax accurately. In case the tax payment system fails to fulfill the duty and responsibility, the additional tax is imposed on the taxee by collecting the tax shortfall and imposing the tax deduction on For this reason, item classifications, together with tariff assessments, are the most difficult and could pose a significant risk to entities if they are misclassified. For this reason, import reports are consigned to customs officials, who are customs experts, while paying a substantial fee. The purpose of this study is to classify HS items to be reported upon import declaration and to indicate HS codes to be recorded on import declaration. HS items were classified using the attached image in the case of item classification based on the case of the classification of items by the Korea Customs Service for classification of HS items. For image classification, CNN was used as a deep learning algorithm commonly used for image recognition and Vgg16, Vgg19, ResNet50 and Inception-V3 models were used among CNN models. To improve classification accuracy, two datasets were created. Dataset1 selected five types with the most HS code images, and Dataset2 was tested by dividing them into five types with 87 Chapter, the most among HS code 2 units. The classification accuracy was highest when HS item classification was performed by learning with dual database2, the corresponding model was Inception-V3, and the ResNet50 had the lowest classification accuracy. The study identified the possibility of HS item classification based on the first item image registered in the item classification determination case, and the second point of this study is that HS item classification, which has not been attempted before, was attempted through the CNN model.

A Study on Seismic Performance Evaluation of Tunnel to Considering Material Nonlinearity (재료의 비선형성을 고려한 터널의 내진성능평가에 관한 연구)

  • Choi, Byoungil;Ha, Myungho;Noh, Euncheol;Park, Sihyun;Kang, Gichun
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.26 no.3
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    • pp.92-102
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    • 2022
  • Various numerical analysis models can be used to evaluate the behavior characteristics of tunnel facilities which are representative underground structures. In general, the Mohr-Coulomb model, which is most often used for numerical analysis, is an elastic-perfect plastic behavior model. And the deformation characteristics are the same during the load increase-load reduction phase. So there is a problem that the displacement may appear different from the field situation in the case of excavation analysis. In contrast, the HS-small strain stability model has a wide range of applications for each ground. And it is known that soil deformation characteristics can be analyzed according to field conditions by enabling input of initial elastic modulus and nonlinear curve parameter and so on. However, civil engineers are having difficulty using nonlinear models that can apply material nonlinear properties due to difficulties in estimating ground property coefficients. In this study, the necessity of rational model selection was reviewed by comparing the results of seismic performance evaluation using the Mohr-Coulomb model, which civil engineers generally apply for numerical analysis of tunnels, and the HS Small strain Stiffness model, which can consider ground nonlinearity.

Analysis of Diagnosability for Hyper-Star Network Under the PMC and the Comparison Diagnosis Model (PMC 모델과 비교진단 모델을 이용한 하이퍼-스타 연결망의 진단도 분석)

  • Kim Jong-Seok;Lee Hyeong-Ok
    • The KIPS Transactions:PartA
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    • v.13A no.1 s.98
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    • pp.19-26
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    • 2006
  • Diagnosability is an important factor in measuring the reliability of an interconnection network. Typical models of fault diagnosis are the PMC and the comparison diagnosis model. In this paper, we prove that the regular network Hyper-Star HS(2n,n) under two models is n-diagnosable.

Enhancing Classification Model Performance through Noise Data Refinement (노이즈 데이터 정제를 통한 분류모델 성능 향상)

  • Unkuk Jeong;Seungshik Kang
    • Proceedings of the Korea Information Processing Society Conference
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    • 2024.05a
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    • pp.559-562
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    • 2024
  • 자연어 기반의 분류모델을 개발할 때 높은 성능을 획득하기 위해서는 데이터의 품질이 중요한 요소이다. 특히 무역상품 국제 분류체계 HS-CODE에서 상품명을 기반으로 HS코드를 분류할 때, 라벨링 된 데이터의 품질에 의해서 분류모델의 성능이 좌우된다. 하지만 현실적으로 확보 가능한 데이터셋에는 데이터 라벨링 오류나 데이터로 활용하기에 특징점이 부족한 데이터들이 다수 존재하기도 한다. 본 연구에서는 분류모델 학습 데이터의 정제 방법론으로, 딥러닝 기반 노이즈 검출 알고리즘을 제안한다. 분류 대상의 특징점이 분류 경계값 주변에 존재한다면 분류하기 모호한 노이즈 데이터일 가능성이 높다고 가정하고, 해당 노이즈 데이터를 검출하는 방법으로 딥러닝 기술을 활용한다. 해당 경계값 노이즈 검출 알고리즘으로 데이터를 정제한 뒤 학습모델의 성능비교 결과, 기존 대비 우수한 분류 정확도를 기록하였다.

Multi-modal Representation Learning for Classification of Imported Goods (수입물품의 품목 분류를 위한 멀티모달 표현 학습)

  • Apgil Lee;Keunho Choi;Gunwoo Kim
    • Journal of Intelligence and Information Systems
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    • v.29 no.1
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    • pp.203-214
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    • 2023
  • The Korea Customs Service is efficiently handling business with an electronic customs system that can effectively handle one-stop business. This is the case and a more effective method is needed. Import and export require HS Code (Harmonized System Code) for classification and tax rate application for all goods, and item classification that classifies the HS Code is a highly difficult task that requires specialized knowledge and experience and is an important part of customs clearance procedures. Therefore, this study uses various types of data information such as product name, product description, and product image in the item classification request form to learn and develop a deep learning model to reflect information well based on Multimodal representation learning. It is expected to reduce the burden of customs duties by classifying and recommending HS Codes and help with customs procedures by promptly classifying items.

A Study on Signal Integrity of High Speed Interface for Ultra High Definition Video Pattern Control Signal Generator (초고해상도 영상패턴 제어 신호발생기의 고속 인터페이스 신호 무결성 실험에 관한 연구)

  • Son, Hui-Bae;Jun, June-Su;Kwon, Sai-Hoan
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2014.06a
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    • pp.150-152
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    • 2014
  • 디지털 평판 LCD TV의 영상신호 전송에 LVDS가 사용되어 왔으나 케이블간의 타이밍 문제가 대두되고 초고해상도의 컬러 Depth 확장으로 인해 보다 빠른 전송속도가 요구되어진다. V-by-One HS는 초고해상도 영상처리 IC 및 TCON 간의 새로운 인터페이스 기술로서 최대 3840*2160@240Hz의 해상도 영상구현이 가능하다. 동작 주파수 대역의 공진모드 전압 분포와 V-by-One HS IBIS(Input/Output Buffer Information Specification) 모델 시뮬레이션을 통하여 PCB 설계 방법을 제안한다. 본 논문에서는 V-by-One HS 인터페이스 기술을 사용하여 초고해상도 영상패턴 제어 신호발생기의 시스템 구성을 제안하고 고속영상 신호에 대한 신호 무결성을 검증하고자 한다.

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The Development and Application of Teaching Programs about Molecular Genetics Based on the HS-CPS Model for Gifted Students (영재 학생들을 위한 과학사-CPS 수업 모형을 활용한 분자생물 영역 수업 프로그램의 개발 및 적용)

  • Lee, Ju-Hyun;Lee, Mi-Sook;Ju, Hee-Young;Lee, Kil-Jae
    • Journal of Science Education
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    • v.35 no.2
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    • pp.262-273
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    • 2011
  • This research was aimed to develop and apply the molecular genetics teaching program based on the history of science and creative problem solving model (HS-CPS model) for gifted students. Based on the strategies of creative problem solving and scientific theory development, the HS-CPS teaching program were developed. This program was applied to 8 first and second graders of the special class for invention activity in a high school. Creative problem solving ability in science and the understanding of DNA and gene concept were tested in pre and post of 12 lessons. The results were as follows: First, creative problem solving ability in science was improved meaningfully. Second, HS-CPS teaching program was effective in the understanding of DNA and gene concepts. Third, the students responded positively to the program evaluation questionnaire.

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Relationship between HsCRP and Pulse Transit Time (HsCRP와 맥파전달시간에 대한 연구)

  • Kim, Yun-Jin;Min, Hong-Gi;Kim, Young-Joo;Jeon, Ah-Young;Jeon, Gye-Rok;Ye, Soo-Young
    • Journal of Life Science
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    • v.17 no.2 s.82
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    • pp.218-222
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    • 2007
  • The purpose of this study is to evaluate the relationship between high sensitive C-reactive protein (hsCRP) and pulse transit time (PPT). Apparently healthy 233 subjects had been enrolled in the health promotion center of the Pusan National University Hospital from Jan. 29 to Feb. 26, 2004. They had no previous history of diabetes, hypertension and hyperlipidemia. Subjects were categorized according to tertiles of hsCRP level [Group 1: first tertile $(0.01\;{\sim}\;0.02\;mg/dl)$, Group 2: second tertile $(0.03\;{\sim}\;0.05\;mg/dl)$, Group 3: third tertile $(0.06\;{\sim}\;0.12\;mg/dl)$, and Group 4: Fourth tertile $(0.13\;{\sim}\;16.8\;mg/dl)$]. PTT body mass index (BMI), total cholesterol (T-C), LDL-cholesterol(LDL-C), blood sugar (BS), systolic blood pressure (sBP) and diastolic blood pressure (dBP) were significantly different among hsCRP groups (p<0.05). HsCRP is positively related with BMI, tryglyceride (TG), LDL, sBP and dBP (p<0.05), and negatively related with PTT and HDL-cholesterol (HDL-C) (p<0.05). PTT is significantly negatively related with hsCRP, T-C, TG, LDL-C, BS, dBP and sBP (p<0.05). The hsCRP and PTT were related before controlling BMI, T-C, LDL-C, sBP, and dBP, but not related after conkolling. The relationship between hsCRP and PTT depends on cardiovascular disease risk factors.

Study on a Prediction Model of the Tensile Strain Related to the Fatigue Cracking Performance of Asphalt Concrete Pavements Through Design of Experiments and Harmony Search Algorithm (실험계획법 및 하모니 검색 알고리즘을 이용한 아스팔트 포장체의 피로균열 공용성 관련 인장변형률 추정모델 연구)

  • Lee, Chang-Joon;Kim, Do-Wan;Mun, Sung-Ho;Yoo, Pyeong-Jun
    • International Journal of Highway Engineering
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    • v.14 no.2
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    • pp.11-17
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    • 2012
  • This research describes how to predict a model of the tensile strain related to the fatigue cracking performance of several asphalt concrete structures through design of experiments(e.g., Response Surface Methodology) and harmony search(HS) algorithm. The axisymmetric analysis program of finite element method, which is the KICTPAVE, was used to determine the strain level at the interface layer between asphalt layer and lean concrete layer. Once the training database set of various strain levels was constructed under the several condition of layer stiffnesses and thicknesses in the asphalt concrete structures, the data set was trained through the HS algorithm in order to determine the regression coefficients defined based on a response surface methodology. Furthermore, the testing set, which was not used for the training procedure of HS algorithm, was also constructed in order to evaluate whether the regression coefficients of a prediction model can be appropriately applied for other cases in asphalt concrete structures.