• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.33 no.4
• /
• pp.122-129
• /
• 2010

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance (MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction performance.

• Speech Sciences
• /
• v.13 no.4
• /
• pp.177-186
• /
• 2006

In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate of the speaker-independent speech recognition, we employ the Mahalanobis distance in the calculation of the similarity measure between feature vectors. It is assumed that the metric matrix of the Mahalanobis distance be diagonal for the sake of cost reduction in memory and time of calculation. We propose that the diagonal elements be given in terms of the variations of the feature vector components. Geometrically, this prescription tends to redistribute the set of data in the shape of a hypersphere in the feature vector space. The idea is applied to the speech recognition by hidden Markov model with fuzzy vector quantization. The result shows that the recognition is improved by an appropriate choice of the relevant adjustable parameter. The Viterbi score difference of the two winners in the recognition test shows that the general behavior is in accord with that of the recognition error rate.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.34 no.12
• /
• pp.1871-1876
• /
• 2010

The reduction of vehicle interior noise has been the main interest of NVH engineers. The driver's perception of the vehicle noise is strongly affected by the psychoacoustic characteristics of the noise and the SPL. The existing methods to evaluate the SQ for vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of subjective SQ values by neural network. However, these methods strongly depend on jury tests, this leads to difficulties. To reduce the important of the jury tests, we suggest a new method using the Mahalanobis distance for SQ evaluation. And, the optimal characteristic values that influenced the results of sound quality evaluation on the basis by main effect. Finally, we developed a new method based on the MD method to evaluate sound quality. The result of noise evaluation revealed that the sound quality could be well improved by changing the structural characteristics of the vehicle.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.3
• /
• pp.747-754
• /
• 2014

In spite of its usefulness for health monitoring of structures on steady external load, the statistical pattern recognition technology (SPRT), based on Mahalanobis distance theory (MDT), is not good enough for the health monitoring of structures on large variability external load like earthquake. Damage is usually determined by the difference between the average measured value of undamaged structure and the measure value of damaged one. So when external variability gets larger, the difference gets bigger along, which is thus easily mistaken for a damage. This paper aims to overcome the problem and develop an improved Mahalanobis distance theory (IMDT), that is, a SPRT with revised MDT in order to decrease external variability so that we will be able to continue to monitor the structure on uncertain external variability. This method is experimentally tested to see if it precisely evaluates the health of a cable-stayed bridge on each general random load and earthquake load. As a result, the IMDT is found to be valid in locating structural damage made by damaged cables by means of data from undamaged cables. So it is proved to be effectively applicable to the health monitoring of bridges on external load of variability.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.417-419
• /
• 2023

Hypertension is one of the leading causes of mortality worldwide. In recent years, the incidence of hypertension has increased dramatically, not only among the elderly but also among young people. In this regard, the use of machine-learning methods to diagnose the causes of hypertension has increased in recent years. In this study, we improved the prediction of hypertension detection using Mahalanobis distance-based multivariate outlier removal using the KNHANES database from the Korean national health data and the COVID-19 dataset from Kaggle. This study was divided into two modules. Initially, the data preprocessing step used merged datasets and decision-tree classifier-based feature selection. The next module applies a predictive analysis step to remove multivariate outliers using the Mahalanobis distance from the experimental dataset and makes a prediction of hypertension. In this study, we compared the accuracy of each classification model. The best results showed that the proposed MAH_RF algorithm had an accuracy of 82.66%. The proposed method can be used not only for hypertension but also for the detection of various diseases such as stroke and cardiovascular disease.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.27 no.6
• /
• pp.172-181
• /
• 2023

In this study, real-time damage evaluation of cable-stayed bridges was conducted for cable damage. ICP type acceleration sensors were used for real-time damage assessment of cable-stayed bridges, and Kinetic Energy Optimization Techniques (KEOT) were used to select the optimal conditions for the location and quantity of the sensors. When a structure vibrates by an external force, KEOT measures the value of the maximum deformation energy to determine the optimal measurement position and the quantity of sensors. The damage conditions in this study were limited to cable breakage, and cable damage was caused by dividing the cable-stayed bridge into four sections. Through FE structural analysis, a virtual model similar to the actual model was created in the real-time damage evaluation method of cable. After applying random oscillation waves to the generated virtual model and model structure, cable damage to the model structure was caused. The two data were compared by defining the response output from the virtual model as a corruption-free response and the response measured from the real model as a corruption-free data. The degree of damage was evaluated by applying the data of the damaged cable-stayed bridge to the Improved Mahalanobis Distance (IMD) theory from the data of the intact cable-stayed bridge. As a result of evaluating damage with IMD theory, it was identified as a useful damage evaluation technology that can properly find damage by section in real time and apply it to real-time monitoring.

• Journal of Crop Science and Biotechnology
• /
• v.10 no.2
• /
• pp.86-91
• /
• 2007

A representative group of 38 improved basmati lines including maintainers of sterile lines were studied for genetic diversity utilizing Mahalanobis $D^2$ statistics. A wide diversity was observed having ten clusters with high intra- and inter-cluster distance. Heterosis was estimated utilizing the cytoplasmic male sterile lines from the clusters having high intra- and inter-cluster distance. Highly heterotic hybrids were obtained from the hybridization programme. Cross combinations IR68281A/Pusa 1235-95-73-1-1, IR68281A/RP 3644-41-9-5, Pusa 3A/UPR 2268-4-1, IR 68281A/Pusa Basmati-1, IR68281A/BTCE 10-98, and IR58025A/HKR 97-401 were found to be highly heterotic for grain yield/plant with other agronomic and quality traits. Additionally, a positive association of intra-cluster distance with heterosis was observed, which could be utilized as a guideline for predicting heterosis in basmati hybrid rice breeding program. Also, a positive association between inter-cluster distance and heterosis was observed.

• Journal of Korea Multimedia Society
• /
• v.15 no.1
• /
• pp.63-70
• /
• 2012

In this paper an extension to an original active shape model(ASM) for facial feature extraction is presented. The original ASM suffers from poor shape alignment by aligning the shape model to a new instant of the object in a given image using a simple similarity transformation. It exploits only informations such as scale, rotation and shift in horizontal and vertical directions, which does not cope effectively with the complex pose variation. To solve the problem, new shape alignment with 6 degrees of freedom is derived, which corresponds to an affine transformation. Another extension is to speed up the calculation of the Mahalanobis distance for 2-D profiles by trimming the profile covariance matrices. Extensive experiment is conducted with several images of varying poses to check the performance of the proposed method to segment the human faces.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.11
• /
• pp.1551-1558
• /
• 2014

We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.4
• /
• pp.397-404
• /
• 2012

Jeju where concentrates on agriculture and tourism, conversion of outdoor culture into cultivation under structure happens actively for the purpose of increasing profit so continuous examination on house cultivation area is very important for this region. This paper is to suggest the effective image classification method using high resolution satellite image to detect the greenhouse. We carried out classification of greenhouse using the supervised classification and rule-based classification method about Formosat-2 images. Connecting result of two classification try to find accuracy improvement for greenhouse detection. Results about each classification method were calculated the accuracy by comparing with the result of visual detection. As a result, mahalanobis distance among the supervised methods was resulted in the highest detection. Also, it could be checked that detection accuracy was improved by tying with result of supervised method and result of rule-based classification. Therefore, it was expected that effective detection of greenhouse would be feasible if henceforward further study is performed in the process of connecting supervised classification and rule-based classification.