• Title/Summary/Keyword: 인공신경망

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Design and Implementation of Trip Generation Model Using the Bayesian Networks (베이지안 망을 이용한 통행발생 모형의 설계 및 구축)

  • Kim, Hyun-Gi;Lee, Sang-Min;Kim, Kang-Soo
    • Journal of Korean Society of Transportation
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    • v.22 no.7 s.78
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    • pp.79-90
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    • 2004
  • In this study, we applied the Bayesian Networks for the case of the trip generation models using the Seoul metropolitan area's house trip survey Data. The household income was used for the independent variable for the explanation of household size and the number of cars in a household, and the relationships between the trip generation and the households' social characteristics were identified by the Bayesian Networks. Furthermore, trip generation's characteristics such as the household income, household size and the number of cars in a household were also used for explanatory variables and the trip generation model was developed. It was found that the Bayesian Networks were useful tool to overcome the problems which were in the traditional trip generation models. In particular the various transport policies could be evaluated in the very short time by the established relationships. It is expected that the Bayesian Networks will be utilized as the important tools for the analysis of trip patterns.

유리화 비정형 탄소(vitreous carbon)를 이용하여 제작한 전계방출 소자의 균일성 증진방법

  • 안상혁;이광렬
    • Proceedings of the Korean Vacuum Society Conference
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    • 1999.07a
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    • pp.53-53
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    • 1999
  • 전계방출을 이용한 평판 표시장치는 CRT가 가진 장점을 모두 갖는 동시에 얇고 가벼우며 낮은 전력소모로 완벽한 색을 구현할 수 있는 차세대 표시장치로서 이에 대한 여국가 활발히 이루어지고 있다. 여기에 사용되는 음극물질로서 실리콘이나 몰리 등을 팁모양으로 제작하여 사용해 왔다. 하지만 잔류가스에 의한 역스퍼터링이나 화학적 반응에 의해서 전계방출 성능이 점차 저하되는 등의 해결해야할 많은 문제가 있다. 이러한 문제들을 해결하기 위하여 탄소계 재료로서 다이아몬드, 다이아몬드상 카본 등을 이용하려는 노력이 진행되어 왔다. 이중 유리화 비정형 탄소는 다량의 결함을 가지고 있는 유리질의 고상 탄소 재로로서, 전기전도도가 우수하면서 outgassing이 적고 기계적 강도가 뛰어나며 고온에서도 화학적으로 안정하여 전계방출 소자의 음극재료로서 알맞은 것으로 생각된다. 유리화 비정형 탄소가루를 전기영동법으로 기판에 코팅하여 전계방출 소자를 제작하였다. 전기영동 용액으로 이소프로필알코올에 질산마그네슘과 소량의 증류수, 유리화 비정형 탄소분말을 섞어주었고 기판으로는 몰리(Mo)가 증착된 유리를 사용하였다. 균일한 증착을 위해서 증착후 역전압을 걸어 주는 방법과 증착 후 플라즈마 처리를 하는 등의 여러 가지 방법을 사용했다. 전계방출 전류는 1$\times$10-7Torr이사에서 측정하였다. 1회 제작된 용액으로 반복해서 증착한 횟수에 따라 표면의 거치기, 입자의 분포, 전계방출 측정 결과 등의 차이가 관찰되었다. 발광이미지는 전압에 따라 변화하였고, 균일한 발광을 관찰하기 위해서 오랜 시간동안 aging 과정을 거쳐야 했다. 그리고 구 모양의 양극을 사용해서 위치를 변화시키며 시동 전기장을 관찰하여 위치에 따른 전계방출의 차이를 조사하여 발광의 균일성을 알 수 있었다.on microscopy로 분석하였으며 구조 분석은 X-선 회절분석, X-ray photoelectron spectroscopy 그리고Auger electron spectroscope로 하였다. 증착된 산화바나듐 박막의 전기화학적 특성을 분석하기 위하여 리튬 메탈을 anode로 하고 EC:DMC=1:1, 1M LiPF6 액체 전해질을 사용한 Half-Cell를 구성하여 200회 이상의 정전류 충 방전 시험을 행하였다. Half-Cell test 결과 박막의 결정성과 표면상태에 따라 매우 다른 전지 특성을 나타내었다.도상승율을 갖는 경우가 다른 베이킹 시나리오 모델에 비해 효과적이라 생각되며 초대 필요 공급열량은 200kW 정도로 산출되었다. 실질적인 수치를 얻기 위해 보다 고차원 모델로의 해석이 필요하리라 생각된다. 끝으로 장기적인 관점에서 KSTAR 장치의 베이킹 계획도 살펴본다.습파라미터와 더불어, 본 연구에서 새롭게 제시된 주기분할층의 파라미터들이 모형의 학습성과를 높이기 위해 함께 고려된다. 한편, 이러한 학습과정에서 추가적으로 고려해야 할 파라미터 갯수가 증가함에 따라서, 본 모델의 학습성과가 local minimum에 빠지는 문제점이 발생될 수 있다. 즉, 웨이블릿분석과 인공신경망모형을 모두 전역적으로 최적화시켜야 하는 문제가 발생한다. 본 연구에서는 이 문제를 해결하기 위해서, 최근 local minimum의 가능성을 최소화하여 전역적인 학습성과를 높여 주는 인공지능기법으로서 유전자알고리즘기법을 본 연구이 통합모델에 반영하였다. 이에 대한 실증사례 분석결과는 일일 환율예측문제를 적용하였을 경우, 기존의 방법론보다 더 나운 예측성과를 타나내었다.pective" to workflow architectural discussions. The vocabulary suggested

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Image Super-Resolution for Improving Object Recognition Accuracy (객체 인식 정확도 개선을 위한 이미지 초해상도 기술)

  • Lee, Sung-Jin;Kim, Tae-Jun;Lee, Chung-Heon;Yoo, Seok Bong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.6
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    • pp.774-784
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    • 2021
  • The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.

Design of Network Attack Detection and Response Scheme based on Artificial Immune System in WDM Networks (WDM 망에서 인공면역체계 기반의 네트워크 공격 탐지 제어 모델 및 대응 기법 설계)

  • Yoo, Kyung-Min;Yang, Won-Hyuk;Kim, Young-Chon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.4B
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    • pp.566-575
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    • 2010
  • In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.

A Korean Community-based Question Answering System Using Multiple Machine Learning Methods (다중 기계학습 방법을 이용한 한국어 커뮤니티 기반 질의-응답 시스템)

  • Kwon, Sunjae;Kim, Juae;Kang, Sangwoo;Seo, Jungyun
    • Journal of KIISE
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    • v.43 no.10
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    • pp.1085-1093
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    • 2016
  • Community-based Question Answering system is a system which provides answers for each question from the documents uploaded on web communities. In order to enhance the capacity of question analysis, former methods have developed specific rules suitable for a target region or have applied machine learning to partial processes. However, these methods incur an excessive cost for expanding fields or lead to cases in which system is overfitted for a specific field. This paper proposes a multiple machine learning method which automates the overall process by adapting appropriate machine learning in each procedure for efficient processing of community-based Question Answering system. This system can be divided into question analysis part and answer selection part. The question analysis part consists of the question focus extractor, which analyzes the focused phrases in questions and uses conditional random fields, and the question type classifier, which classifies topics of questions and uses support vector machine. In the answer selection part, the we trains weights that are used by the similarity estimation models through an artificial neural network. Also these are a number of cases in which the results of morphological analysis are not reliable for the data uploaded on web communities. Therefore, we suggest a method that minimizes the impact of morphological analysis by using character features in the stage of question analysis. The proposed system outperforms the former system by showing a Mean Average Precision criteria of 0.765 and R-Precision criteria of 0.872.

An Algorithm of Fingerprint Image Restoration Based on an Artificial Neural Network (인공 신경망 기반의 지문 영상 복원 알고리즘)

  • Jang, Seok-Woo;Lee, Samuel;Kim, Gye-Young
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.8
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    • pp.530-536
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    • 2020
  • The use of minutiae by fingerprint readers is robust against presentation attacks, but one weakness is that the mismatch rate is high. Therefore, minutiae tend to be used with skeleton images. There have been many studies on security vulnerabilities in the characteristics of minutiae, but vulnerability studies on the skeleton are weak, so this study attempts to analyze the vulnerability of presentation attacks against the skeleton. To this end, we propose a method based on the skeleton to recover the original fingerprint using a learning algorithm. The proposed method includes a new learning model, Pix2Pix, which adds a latent vector to the existing Pix2Pix model, thereby generating a natural fingerprint. In the experimental results, the original fingerprint is restored using the proposed machine learning, and then, the restored fingerprint is the input for the fingerprint reader in order to achieve a good recognition rate. Thus, this study verifies that fingerprint readers using the skeleton are vulnerable to presentation attacks. The approach presented in this paper is expected to be useful in a variety of applications concerning fingerprint restoration, video security, and biometrics.

A Supervised Learning Framework for Physics-based Controllers Using Stochastic Model Predictive Control (확률적 모델예측제어를 이용한 물리기반 제어기 지도 학습 프레임워크)

  • Han, Daseong
    • Journal of the Korea Computer Graphics Society
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    • v.27 no.1
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    • pp.9-17
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    • 2021
  • In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

Prediction of Distillation Column Temperature Using Machine Learning and Data Preprocessing (머신 러닝과 데이터 전처리를 활용한 증류탑 온도 예측)

  • Lee, Yechan;Choi, Yeongryeol;Cho, Hyungtae;Kim, Junghwan
    • Korean Chemical Engineering Research
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    • v.59 no.2
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    • pp.191-199
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    • 2021
  • A distillation column, which is a main facility of the chemical process, separates the desired product from a mixture by using the difference of boiling points. The distillation process requires the optimization and the prediction of operation because it consumes much energy. The target process of this study is difficult to operate efficiently because the composition of feed flow is not steady according to the supplier. To deal with this problem, we could develop a data-driven model to predict operating conditions. However, data preprocessing is essential to improve the predictive performance of the model because the raw data contains outlier and noise. In this study, after optimizing the predictive model based long-short term memory (LSTM) and Random forest (RF), we used a low-pass filter and one-class support vector machine for data preprocessing and compared predictive performance according to the method and range of the preprocessing. The performance of the predictive model and the effect of the preprocessing is compared by using R2 and RMSE. In the case of LSTM, R2 increased from 0.791 to 0.977 by 23.5%, and RMSE decreased from 0.132 to 0.029 by 78.0%. In the case of RF, R2 increased from 0.767 to 0.938 by 22.3%, and RMSE decreased from 0.140 to 0.050 by 64.3%.

Study on predicting the commercial parts discontinuance using unstructured data and artificial neural network (상용 부품 비정형 데이터와 인공 신경망을 이용한 부품 단종 예측 방안 연구)

  • Park, Yun-kyung;Lee, Ik-Do;Lee, Kang-Taek;Kim, Du-Jeoung
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.10
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    • pp.277-283
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    • 2019
  • Advances in technology have allowed the development and commercialization of various parts; however this has shortened the discontinuation cycle of the components. This means that repair and logistic support of weapon system which is applied to thousands of part components and operated over the long-term is difficult, which is the one of main causes of the decrease in the availability of weapon system. To improve this problem, the United States has created a special organization for this problem, whereas in Korea, commercial tools are used to predict and manage DMSMS. However, there is rarely a method to predict life cycle of parts that are not presented DMSMS information at the commercial tools. In this study, the structured and unstructured data of parts of a commercial tool were gathered, preprocessed, and embedded using neural network algorithm. Then, a method is suggested to predict the life cycle risk (LC Risk) and year to end of life (YTEOL). In addition, to validate the prediction performance of LC Risk and YTEOL, the prediction value is compared with descriptive statistics.

Multimodal Sentiment Analysis Using Review Data and Product Information (리뷰 데이터와 제품 정보를 이용한 멀티모달 감성분석)

  • Hwang, Hohyun;Lee, Kyeongchan;Yu, Jinyi;Lee, Younghoon
    • The Journal of Society for e-Business Studies
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    • v.27 no.1
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    • pp.15-28
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    • 2022
  • Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.