• Title/Summary/Keyword: multiple classification analysis

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A New Object Region Detection and Classification Method using Multiple Sensors on the Driving Environment (다중 센서를 사용한 주행 환경에서의 객체 검출 및 분류 방법)

  • Kim, Jung-Un;Kang, Hang-Bong
    • Journal of Korea Multimedia Society
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    • v.20 no.8
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    • pp.1271-1281
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    • 2017
  • It is essential to collect and analyze target information around the vehicle for autonomous driving of the vehicle. Based on the analysis, environmental information such as location and direction should be analyzed in real time to control the vehicle. In particular, obstruction or cutting of objects in the image must be handled to provide accurate information about the vehicle environment and to facilitate safe operation. In this paper, we propose a method to simultaneously generate 2D and 3D bounding box proposals using LiDAR Edge generated by filtering LiDAR sensor information. We classify the classes of each proposal by connecting them with Region-based Fully-Covolutional Networks (R-FCN), which is an object classifier based on Deep Learning, which uses two-dimensional images as inputs. Each 3D box is rearranged by using the class label and the subcategory information of each class to finally complete the 3D bounding box corresponding to the object. Because 3D bounding boxes are created in 3D space, object information such as space coordinates and object size can be obtained at once, and 2D bounding boxes associated with 3D boxes do not have problems such as occlusion.

Probabilistic Generation Modeling in Electricity Markets Considering Generator Maintenance Outage (전력시장의 발전기 보수계획을 고려한 확률적 발전 모델링)

  • Kim Jin-Ho;Park Jong-Bae
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.54 no.8
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    • pp.418-428
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    • 2005
  • In this paper, a new probabilistic generation modeling method which can address the characteristics of changed electricity industry is proposed. The major contribution of this paper can be captured in the development of a probabilistic generation modeling considering generator maintenance outage and in the classification of market demand into multiple demand clusters for the applications to electricity markets. Conventional forced outage rates of generators are conceptually combined with maintenance outage of generators and, consequently, effective outage rates of generators are newly defined in order to properly address the probabilistic characteristic of generation in electricity markets. Then, original market demands are classified into several distinct demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the original demand. We have found that generators have different effective outage rates values at each classified demand cluster, depending on the market situation. From this, therefore, it can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be more efficiently analyzed in electricity markets perspectives, for this classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

A GA-based Rule Extraction for Bankruptcy Prediction Modeling (유전자 알고리즘을 활용한 부실예측모형의 구축)

  • Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.7 no.2
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    • pp.83-93
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    • 2001
  • Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

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Modeling Generators Maintenance Outage Based on the Probabilistic Method (발전기 보수정지를 고려한 확률적 발전모델링)

  • Kim, Jin-Ho;Park, Jong-Bae;Park, Jong-Keun
    • Proceedings of the KIEE Conference
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    • 2005.07a
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    • pp.804-806
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    • 2005
  • In this paper, a new probabilistic generation modeling method which can address the characteristics of changed electricity industry is proposed. The major contribution of this paper can be captured in the development of a probabilistic generation modeling considering generator maintenance outage and in the classification of market demand into multiple demand clusters for the applications to electricity markets. Conventional forced outage rates of generators are conceptually combined with maintenance outage of generators and, consequently, effective outage rates of generators are new iy defined in order to properly address the probabilistic characteristic of generation in electricity markets. Then, original market demands are classified into several distinct demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the original demand. We have found that generators have different effective outage rates values at each classified demand cluster, depending on the market situation. From this, therefore, it can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be more efficiently analyzed in electricity markets perspectives, for this classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

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Comparative Application of Various Machine Learning Techniques for Lithology Predictions (다양한 기계학습 기법의 암상예측 적용성 비교 분석)

  • Jeong, Jina;Park, Eungyu
    • Journal of Soil and Groundwater Environment
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    • v.21 no.3
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    • pp.21-34
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    • 2016
  • In the present study, we applied various machine learning techniques comparatively for prediction of subsurface structures based on multiple secondary information (i.e., well-logging data). The machine learning techniques employed in this study are Naive Bayes classification (NB), artificial neural network (ANN), support vector machine (SVM) and logistic regression classification (LR). As an alternative model, conventional hidden Markov model (HMM) and modified hidden Markov model (mHMM) are used where additional information of transition probability between primary properties is incorporated in the predictions. In the comparisons, 16 boreholes consisted with four different materials are synthesized, which show directional non-stationarity in upward and downward directions. Futhermore, two types of the secondary information that is statistically related to each material are generated. From the comparative analysis with various case studies, the accuracies of the techniques become degenerated with inclusion of additive errors and small amount of the training data. For HMM predictions, the conventional HMM shows the similar accuracies with the models that does not relies on transition probability. However, the mHMM consistently shows the highest prediction accuracy among the test cases, which can be attributed to the consideration of geological nature in the training of the model.

Design and Implementation of a Real-Time Face Detection System (실시간 얼굴 검출 시스템 설계 및 구현)

  • Jung Sung-Tae;Lee Ho-Geun
    • Journal of Korea Multimedia Society
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    • v.8 no.8
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    • pp.1057-1068
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    • 2005
  • This paper proposes a real-time face detection system which detects multiple faces from low resolution video such as web-camera video. First, It finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next, it generates reduced feature vector for each face region candidate by using principle component analysis. Finally, it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine) based binary classification. According to experiment results, the proposed method achieves real-time face detection from low resolution video. Also, it reduces the false detection rate than existing methods by using PCA and SVM based face classification step.

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Tumor Detection Algorithm by using Mammogram Image Processing (맘모그램 영상처리를 이용한 종양검출 알고리즘)

  • Song, Kyohyuk;Chon, Minhee;Joo, Wonjong;Kim, Gibom
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.22 no.3_1spc
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    • pp.496-503
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    • 2013
  • Recently, the death rate owing to breast cancers has been increasing, and the occurrence age for breast cancers is lowering every year. Mammography is known to be a reliable detection method for breast cancers and works by detecting texture changes, calcifications, and other potential symptoms. In this research on breast cancer detection, candidate objects were detected by using image processing on mammograms, and feature analysis was used to classify candidate objects as benign tumors and malignant tumors. To find candidate objects, image pre-processing and binarization using multiple thresholds, and the grouping of micro-calcifications were used. More than 50 shape features and intensity features were used in the classification. The performance of the detection algorithm by using Euclidian distance method for benign tumors was 93%, and the classification error rate was approximately 2%.

A Study on Comparison of responses to the questionnaire based on Sasang institution's differences-Questionnaire of Sasang Constitution Classification II (QSCCII) (QSCC II 설문지(設問紙) 문항(問項)에서의 체질별(體質別) 응답(應答) 차이(差異) 비교(比較) 분석(分析) 연구(硏究))

  • Park, Seong-Sik;Park, Eun-Kyung
    • Journal of Sasang Constitutional Medicine
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    • v.12 no.2
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    • pp.78-93
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    • 2000
  • The problem lies in 'Sasang Constitution Medicine' is a subjectiveness of diagnosis that the result of diagnosis varies according to the doctors. In order to supplement to this demerit, multiple approach has done to diagnose objectively. This study has focused on evaluating the effectiveness and accuracy of the questionnaire which considered as a basic data to identify constitution By analysing the tendency of the respondents who has defined constitution by clinical diagnosis and comparing of their answers, the result of their constitution analysis by our questionnaire were re-examined. The answer of each question to each constitution were tested how it is relevant to a scale of a constitution. As a result, we have found that there is limitation to find out constitution by the questionnaire we used, one we hope that suitable questionnaire will be developed for the research in the future

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Using Deep Learning for automated classification of wall subtypes for semantic integrity checking of Building Information Models (딥러닝 기반 BIM(Building Information Modeling) 벽체 하위 유형 자동 분류 통한 정합성 검증에 관한 연구)

  • Jung, Rae-Kyu;Koo, Bon-Sang;Yu, Young-Su
    • Journal of KIBIM
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    • v.9 no.4
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    • pp.31-40
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    • 2019
  • With Building Information Modeling(BIM) becoming the de facto standard for data sharing in the AEC industry, additional needs have increased to ensure the data integrity of BIM models themselves. Although the Industry Foundation Classes provide an open and neutral data format, its generalized schema leaves it open to data loss and misclassifications This research applied deep learning to automatically classify BIM elements and thus check the integrity of BIM-to-IFC mappings. Multi-view CNN(MVCC) and PointNet, which are two deep learning models customized to learn and classify in 3 dimensional non-euclidean spaces, were used. The analysis was restricted to classifying subtypes of architectural walls. MVCNN resulted in the highest performance, with ACC and F1 score of 0.95 and 0.94. MVCNN unitizes images from multiple perspectives of an element, and was thus able to learn the nuanced differences of wall subtypes. PointNet, on the other hand, lost many of the detailed features as it uses a sample of the point clouds and perceived only the 'skeleton' of the given walls.

Forest Type Classification and Successional Trends in the Natural Forest of Mt. Deogyu (덕유산 일대 천연림의 산림형 분류와 천이경향)

  • Hwang, Kwang Mo;Chung, Sang Hoon;Kim, Ji Hong
    • Journal of Korean Society of Forest Science
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    • v.105 no.2
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    • pp.157-166
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    • 2016
  • This study was carried out to classify the current forest cover types and to propose the successional trends in the natural forest of Mt. Deogyu. The vegetation data were collected by the point-centered quarter method. The forest cover types were classified by various multivariate statistical analysis methods such as cluster analysis, indicator species analysis and multiple discriminant analysis. This forests were classified into five forest types by the species composition of upper layer and topographic positions: Quercus mongolica forest in the ridge, Fraxinus mandushurica-F. rhynchophylla-Cornus controversa forest and F. mandushurica forest in the valley, the Q. serrata - Pinus densiflora - Q. mongolica forest and P. densiflora forest in the low-slope. As a result of the forest successional trends depending on ecological and environmental characteristics in each forest type, the current forest types were expected that the forest succession would be proceeded toward Q. mongolica forest, F. mandshurica forest, mixed mesophytic forest, and oak-Carpinus laxiflora forest.