• Title, Summary, Keyword: 인공신경망

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Seq2Seq model-based Prognostics and Health Management of Robot Arm (Seq2Seq 모델 기반의 로봇팔 고장예지 기술)

  • Lee, Yeong-Hyeon;Kim, Kyung-Jun;Lee, Seung-Ik;Kim, Dong-Ju
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.12 no.3
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    • pp.242-250
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    • 2019
  • In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.

Performance Enhancement Algorithm using Supervised Learning based on Background Object Detection for Road Surface Damage Detection (도로 노면 파손 탐지를 위한 배경 객체 인식 기반의 지도 학습을 활용한 성능 향상 알고리즘)

  • Shim, Seungbo;Chun, Chanjun;Ryu, Seung-Ki
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.18 no.3
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    • pp.95-105
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    • 2019
  • In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.

A ground condition prediction ahead of tunnel face utilizing time series analysis of shield TBM data in soil tunnel (토사터널의 쉴드 TBM 데이터 시계열 분석을 통한 막장 전방 예측 연구)

  • Jung, Jee-Hee;Kim, Byung-Kyu;Chung, Heeyoung;Kim, Hae-Mahn;Lee, In-Mo
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.21 no.2
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    • pp.227-242
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    • 2019
  • This paper presents a method to predict ground types ahead of a tunnel face utilizing operational data of the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) when running through soil ground. The time series analysis model which was applicable to predict the mixed ground composed of soils and rocks was modified to be applicable to soil tunnels. Using the modified model, the feasibility on the choice of the soil conditioning materials dependent upon soil types was studied. To do this, a self-organizing map (SOM) clustering was performed. Firstly, it was confirmed that the ground types should be classified based on the percentage of 35% passing through the #200 sieve. Then, the possibility of predicting the ground types by employing the modified model, in which the TBM operational data were analyzed, was studied. The efficacy of the modified model is demonstrated by its 98% accuracy in predicting ground types ten rings ahead of the tunnel face. Especially, the average prediction accuracy was approximately 93% in areas where ground type variations occur.

A Study on Mine Ventilation Network (광산 통기 네트워크 연구)

  • Kim, Soo Hong;Kim, Yun Kwang;Kim, Sun Myung;Jang, Yun Ho
    • Tunnel and Underground Space
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    • v.27 no.4
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    • pp.217-229
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    • 2017
  • This study focuses on the improvement of the working environment in domestic collieries where temperature is increasing due to heat of the earth that is caused by the long-term mining. In order to improve the working environment of the mine, a ventilation evaluation was carried out for Hwasoon Mining Industry. In order to increase the ventilation efficiency of the mine, numerical analysis of the effect on temperature was carried out by using climsim, a temperature prediction program. The analysis shows that A coal mine needs $6,152m^3/min$ for in-flow ventilation rate but the total input air flowrate is $4,710m^3/min$, $1,442m^3/min$ of in-flow ventilation rate shortage. The 93 m hypothetical ventilation shaft from -395 ML to -488 ML could result about $3^{\circ}C$ temperature drop in the coal mine of -488 ML far. As a result of predicting the $CO_2$ concentration at -523 ML development using artificial neural network, the emission of $CO_2$ increased as the amount of coal and coal bed thickness increased. The factors that have the greatest effect on the amount of $CO_2$ emissions were coal layer thickness and coal mining. And, as the air quantity increases, it has a great effect on the decrease of carbon dioxide concentration.

Reliable Assessment of Rainfall-Induced Slope Instability (강우로 인한 사면의 불안정성에 대한 신뢰성 있는 평가)

  • Kim, Yun-Ki;Choi, Jung-Chan;Lee, Seung-Rae;Seong, Joo-Hyun
    • Journal of the Korean Geotechnical Society
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    • v.25 no.5
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    • pp.53-64
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    • 2009
  • Many slope failures are induced by rainfall infiltration. A lot of recent researches are therefore focused on rainfall-induced slope instability and the rainfall infiltration is recognized as the important triggering factor. The rainfall infiltrates into the soil slope and makes the matric suction lost in the slope and even the positive pore water pressure develops near the surface of the slope. They decrease the resisting shear strength. In Korea, a few public institutions suggested conservative slope design guidelines that assume a fully saturated soil condition. However, this assumption is irrelevant and sometimes soil properties are misused in the slope design method to fulfill the requirement. In this study, a more relevant slope stability evaluation method is suggested to take into account the real rainfall infiltration phenomenon. Unsaturated soil properties such as shear strength, soil-water characteristic curve and permeability for Korean weathered soils were obtained by laboratory tests and also estimated by artificial neural network models. For real-time assessment of slope instability, failure warning criteria of slope based on deterministic and probabilistic analyses were introduced to complement uncertainties of field measurement data. The slope stability evaluation technique can be combined with field measurement data of important factors, such as matric suction and water content, to develop an early warning system for probably unstable slopes due to the rainfall.

Comparative Study of Machine learning Techniques for Spammer Detection in Social Bookmarking Systems (소셜 복마킹 시스템의 스패머 탐지를 위한 기계학습 기술의 성능 비교)

  • Kim, Chan-Ju;Hwang, Kyu-Baek
    • Journal of KIISE:Computing Practices and Letters
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    • v.15 no.5
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    • pp.345-349
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    • 2009
  • Social bookmarking systems are a typical web 2.0 service based on folksonomy, providing the platform for storing and sharing bookmarking information. Spammers in social bookmarking systems denote the users who abuse the system for their own interests in an improper way. They can make the entire resources in social bookmarking systems useless by posting lots of wrong information. Hence, it is important to detect spammers as early as possible and protect social bookmarking systems from their attack. In this paper, we applied a diverse set of machine learning approaches, i.e., decision tables, decision trees (ID3), $na{\ddot{i}}ve$ Bayes classifiers, TAN (tree-augment $na{\ddot{i}}ve$ Bayes) classifiers, and artificial neural networks to this task. In our experiments, $na{\ddot{i}}ve$ Bayes classifiers performed significantly better than other methods with respect to the AUC (area under the ROC curve) score as veil as the model building time. Plausible explanations for this result are as follows. First, $na{\ddot{i}}ve$> Bayes classifiers art known to usually perform better than decision trees in terms of the AUC score. Second, the spammer detection problem in our experiments is likely to be linearly separable.

Mobbing Value Algorithm for Improvement Victims Management - based on Social Network in Military - (집단 따돌림 희생자 관리 개선을 위한 모빙 지수 알고리즘 - 소셜 네트워크 기반 군 조직을 중심으로 -)

  • Kim, Guk-Jin;Park, Gun-Woo;Lee, Sang-Hoon
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.11
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    • pp.1-12
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    • 2009
  • Mobbing is going the rounds through a society rapidly and Military is not exception. Because mobbing of military is expressed not only psychology exclusion that is mobbing pattern of adult society but also sometimes psychologic and physical mobbing, is possible to join serious military discipline like a suicide and outrageous behavior. Specially military try to protect occurrence of victims that is public service through various rules and management plan but victims is going on happen. It means importance of grasp not only current mobbing victims but also potential mobbing victims better than preparation of various rules and management plans. Therefore this paper extracts seven factors and fifty attributes that are related to this matter mobbing. Next, by using Gunwoo's Social Network Service that is made for oneself and expressing extracting factors as '1' if they are related me or not '0'. And apply similarity function(Dice's coefficient) to attributes summation included in factors to calculate similarity between the users. Third, calculate optimizing weight choosing factors included attributes by applying neural network algorithm of SPSS Clementine and propose Mobbing Value(MV) Algorithm through this total summation. Finally through this algorithm which will contribute to efficient personnel management, we can grasp mobbing victims and tentative mobbing victims.

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Smoothing Effect in X-ray Microtomogram and Its Influence on the Physical Property Estimation of Rocks (X선 토모그램의 Smoothing 효과가 암석의 물성 예측에 미치는 영향 분석)

  • Lee, Min-Hui;Keehm, Young-Seuk
    • Geophysics and Geophysical Exploration
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    • v.12 no.4
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    • pp.347-354
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    • 2009
  • Physical properties of rocks are strongly dependant on details of pore micro-structures, which can be used for quantifying relations between physical properties of rocks through pore-scale simulation techniques. Recently, high-resolution scan techniques, such as X-ray microtomography and high performance computers make it possible to calculate permeability from pore micro-structures of rocks. We try to extend this simulation methodology to velocity and electrical conductivity. However, the smoothing effect during tomographic inversion creates artifacts in pore micro-structures and causes inaccurate property estimation. To mitigate this artifact, we tried to use sharpening filter and neural network classification techniques. Both methods gave noticeable improvement in pore structure imaging and accurate estimation of permeability and electrical conductivity, which implies that our method effectively removes the smoothing effect in pore structures. However, the calculated velocities showed only incremental improvement. By comparison between thin section images and tomogram, we found that our resolution is not high enough, and it is mainly responsible for the inaccuracy in velocity despite the successful removal of the smoothing effect. In conclusion, our methods can be very useful for pore-scale modeling, since it can create accurate pore structure without the smoothing effect. For accurate velocity estimation, the resolution of pore structure should be at least three times higher than that for permeability simulation.

The Interpretation Of Chlorophyll a And Transparency In A Lake Using LANDSAT TM Imagery (LANDSAT TM 영상을 이용한 호소의 클로로필 a및 투명도 해석에 관한 연구)

  • 이건희;전형섭;김태근;조기성
    • Korean Journal of Remote Sensing
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    • v.13 no.1
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    • pp.47-56
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    • 1997
  • In this paper, remote sensing is used to estimate trophic state which is primary concern in a lake. In using remote sensing, this study estimated trophic state not with conventional method such as regression equations but with classification methods. As europhication is caused by the extraodinary proliferation of the algae, chlorophyll a and transparency are applied to remote sensing data.. Maximum Likelihood Classification and Minimum Distance Classification which are kinds of classification methods enabled trophic state to be confirmed in a lake. These are obtained as the result of applying remote sensing to classify trophic state in a lake. Firest, when we evaluate tropic state in a large area of water body, the application of remote sensing data can obtain more than 70% accuracies just in using basic classification methods. Second, in the aspect of classification, the accuracy of Minimum Distance Classification is usually better than that of Maximum Likelihood Classification. This result is caused that samples have normal distribution, but their numbers are a few to apply statistical method. Therefore, classification method is required such as artificial neural networks which are not influenced by statistical distribution. Third, this study enables the trophic state of water body to be analyzed and evaluated rapidly, periodically and visibly. Also, this study is good for forming proper countermeasure accompanying with trophic state progress extent in a lake and is useful for basic-data.

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Quantitative preliminary hazard level simulation for tunnel design based on the KICT tunnel collapse hazard index (KTH-index) (터널 붕괴 위험도 지수(KTH-index)에 기반한 터널 설계안의 정량적 사전 위험도 시뮬레이션)

  • Shin, Hyu-Soung;Kwon, Young-Cheul;Kim, Dong-Gyou;Bae, Gyu-Jin;Lee, Hong-Gyu;Shin, Young-Wan
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.11 no.4
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    • pp.373-385
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    • 2009
  • A new indexing methodology so called KTH-index was developed to quantitatively evaluate a potential level for tunnel collapse hazard, which has been successfully applied to tunnel construction sites to date. In this study, an attempt is made to apply this methodology for validating an outcome of tunnel design by checking the variation of KTH-index along longitudinal tunnel section. In this KTH-index simulation, it is the most important to determine the input factors reasonably. The design factor and construction condition are set up based on the designed outcome. Uncertain ground conditions are arranged based on borehole test and electro-resistivity survey data. Two scenarios for ground conditions, best and worst scenarios, are set up. From this simulation, it is shown that this methodology could be successfully applied for providing quantitative validity of a tunnel design and also potential hazard factors which should be carefully monitored in construction stage. The hazard factors would affect sensitively the hazard level of the tunnel site under consideration.

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