• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.735-739
• /
• 2005

This paper presents a new system to estimate the head pose of human in interactive indoor environment that has dynamic illumination change and large working space. The main idea of this system is to suggest a new morphological feature for estimating head angle from stereo disparity map. When a disparity map is obtained from stereo camera, the matching confidence value can be derived by measurements of correlation of the stereo images. Applying a threshold to the confidence value, we also obtain the specific morphology of the disparity map. Therefore, we can obtain the morphological shape of disparity map. Through the analysis of this morphological property, the head pose can be estimated. It is simple and fast algorithm in comparison with other algorithm which apply facial template, 2D, 3D models and optical flow method. Our system can automatically segment and estimate head pose in a wide range of head motion without manual initialization like other optical flow system. As the result of experiments, we obtained the reliable head orientation data under the real-time performance.

• Bulletin of the Korean Chemical Society
• /
• v.35 no.7
• /
• pp.2097-2108
• /
• 2014

Chemical feature based quantitative pharmacophore models were generated using the HypoGen module implemented in DS2.5. The best hypothesis, Hypo1, which was characterized by the highest correlation coefficient (0.96), the highest cost difference (61.60) and the lowest RMSD (0.74), consisted of one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic and one ring aromatic. The reliability of Hypo1 was validated on the basis of cost analysis, test set, Fischer's randomization method and GH test method. The validated Hypo1 was used as a 3D search query to identify novel inhibitors. The screened molecules were further refined by employing ADMET, docking studies and visual inspection. Three compounds with novel scaffolds were selected as the most promising candidates for the designing of Mcl-1 antagonists. Finally, a 10 ns molecular dynamics simulation was carried out on the complex of receptor and the retrieved ligand to demonstrate that the binding mode was stable during the MD simulation.

• Proceedings of the KSR Conference
• /
• 2007.05a
• /
• pp.1882-1889
• /
• 2007

Surveying rocks for engineering aims for prediction of geological feature of the construction site. Conventionally, survey information at outcrops and bore holes are projected to the construction sites, such as tunnel and slopes, and rock properties of the sites are predicted by interpretations of specialists. This system, the "Fracjection", aims to assist the specialist for visualization of the projected fractures from borehole and outcrop survey. The Fracjection accepts the BIPS and outcrop survey data to its database and allows plotting them in AutoCad map. The software also reads elevation data from contours of the topographic map and constructs DEM of the construction sites. With user's guide, it generates 3D excavation sites such as slopes and tunnels at the topographic map. The s/w projects borehole and outcrop surveyed fractures onto the modeled excavation surface and allows analysis of failure criteria, such as plane, wedge, and toppling failures by built-in stereonet function. Projected fractures can further be analyzed for structural homogeneities and rock mass quality. Moving window style correlation comparison of stereonet plots are used for formal analyses, and RQD type counts of the projected fractures are adopted for the latter analyses.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.12 no.3
• /
• pp.173-180
• /
• 1992

A feature typical of natural rivers is the bend. The purpose of this study is to examine hydraulic and morphometric characteristics in channel bend reach by the deterministic approach. Cross section shape factor, "As" is suggested for a new cahracteristic factor of channel bend reach analysis. The variation of this new factor along the river reach showed the location of the concentration of the force due to the current all over the reach, that is curved or not. Some general meander factors are used for correlation with new factor suggested, and the applicability of "As" is verified. The range R/W values are concentrated 2~4, the meanning of this value can be regarded to the warning for bank erosion or breaking. And this paper dealt with prediction of cross section bed shape variation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.4176-4197
• /
• 2020

Botnet is a type of dangerous malware. Botnet attack with a collection of bots attacking a similar target and activity pattern is called bot group activities. The detection of bot group activities using intrusion detection models can only detect single bot activities but cannot detect bots' behavioral relation on bot group attack. Detection of bot group activities could help network administrators isolate an activity or access a bot group attacks and determine the relations between bots that can measure the correlation. This paper proposed a new model to measure the similarity between bot activities using the intersections-probability concept to define bot group activities called as B-Corr Model. The B-Corr model consisted of several stages, such as extraction feature from bot activity flows, measurement of intersections between bots, and similarity value production. B-Corr model categorizes similar bots with a similar target to specify bot group activities. To achieve a more comprehensive view, the B-Corr model visualizes the similarity values between bots in the form of a similar bot graph. Furthermore, extensive experiments have been conducted using real botnet datasets with high detection accuracy in various scenarios.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.4
• /
• pp.212-222
• /
• 2003

The ability to efficiently and robustly recover accurate 3D terrain models from sets of stereoscopic images is important to many civilian and military applications. A stereo matching has been an important tool for reconstructing three dimensional terrain. However, there exist many factors causing stereo matching error, such as occlusion, no feature or repetitive pattern in the correlation window, intensity variation, etc. Among them, occlusion can be only resolved by true multi-image stereo. In this paper, we present multi-image stereo method using DEM fusion as one of efficient and reliable true multi-image methods. Elevations generated by all pairs of images are combined by the fusion process which accepts an accurate elevation and rejects an outlier. We propose three fusion schemes: THD(Thresholding), BPS(Best Pair Selection) and MS(Median Selection). THD averages elevations after rejecting outliers by thresholding, while BPS selects the most reliable elevation. To determine the reliability of a elevation or detect the outlier, we employ the measure of self-consistency. The last scheme, MS, selects the median value of elevations. We test the effectiveness of the proposed methods with a quantitative analysis using simulated images. Experimental results indicate that all three fusion schemes showed much better improvement over the conventional binocular stereo in natural terrain of 29 Palms and urban site of Avenches.

• The Journal of Bigdata
• /
• v.1 no.1
• /
• pp.33-39
• /
• 2016

Recently, predicting personality with the help of smartphone usage becomes very interesting and attention grabbing topic in the field of research. At present there are some approaches towards detecting a user's personality which uses the smartphones usage data, such as call detail records (CDRs), the usage of short message services (SMSs) and the usage of social networking services application. In this paper, we focus on the assessing the correlation between MBTI based user personality and the smartphone usage data. We used $Na{\ddot{i}}ve$ Bayes and SVM classifier for classifying user personalities by extracting some features from smartphone usage data. From analysis it is observed that, among all extracted features facebook usage log working as the best feature for classification of introverts and extraverts; and SVM classifier works well as compared to $Na{\ddot{i}}ve$ Bayes.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.2
• /
• pp.33-38
• /
• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• Tunnel and Underground Space
• /
• v.15 no.2 s.55
• /
• pp.129-136
• /
• 2005

This paper describes the implementation of rock mass rating evaluation based on genetic algorithm(GA) and conditional simulation technique to estimate RMR in the area without sufficient borehole data RMR were estimated by GA and conditional simulation technique with reflecting distribution feature and spatial correlation. And RMR determined by GA were compared with the results from kriging. Through the analysis of the results from 30 simulations, the uncertainty of estimation could be quantified.

• Journal of Korean Association for Spatial Structures
• /
• v.21 no.2
• /
• pp.99-110
• /
• 2021

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.