• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.53-59
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• 2023

Detection of children with autism spectrum disorder (ASD) based on speech has relied on predefined feature sets due to their ease of use and the capabilities of speech analysis. However, clinical impressions may not be adequately captured due to the broad range and the large number of features included. This paper demonstrates that the knowledge-driven speech features (KDSFs) specifically tailored to the speech traits of ASD are more effective and efficient for detecting speech of ASD children from that of children with typical development (TD) than a predefined feature set, extended Geneva Minimalistic Acoustic Standard Parameter Set (eGeMAPS). The KDSFs encompass various speech characteristics related to frequency, voice quality, speech rate, and spectral features, that have been identified as corresponding to certain of their distinctive attributes of them. The speech dataset used for the experiments consists of 63 ASD children and 9 TD children. To alleviate the imbalance in the number of training utterances, a data augmentation technique was applied to TD children's utterances. The support vector machine (SVM) classifier trained with the KDSFs achieved an accuracy of 91.25%, surpassing the 88.08% obtained using the predefined set. This result underscores the importance of incorporating domain knowledge in the development of speech technologies for individuals with disorders.

• Annual Conference of KIPS
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• 2023.05a
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• pp.417-419
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• 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 Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1117-1125
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• 2023

The DC part of the DC microgrid power conversion system uses capacitors for buffers of charge and discharge energy for smoothing voltage and plays important roles such as high frequency component absorption, power balancing, and voltage ripple reduction. The capacitor uses an aluminum electrolytic capacitor, which has advantages of capacity, low price, and relatively fast charging/discharging characteristics. Aluminum electrolytic capacitors(AEC) have previous advantages, but over time, the capacity of the capacitors decreases due to deterioration and an increase in internal temperature, resulting in a decrease in use efficiency or an accident such as steam extraction due to electrolyte evaporation. It is necessary to take measures to prevent accidents because the failure diagnosis and detection of such capacitors are a very important part of the long-term operation, safety of use, and reliability of the power conversion system because the failure of the capacitor leads to not only a single problem but also a short circuit accident of the power conversion system.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.11 no.2
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• pp.92-96
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• 2006

NLPCA(Nonlinear Principal Component Analysis) is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(Auto Associative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from risk management system is executed.

• Convergence Security Journal
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• v.23 no.5
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• pp.73-79
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• 2023

Cyber threats are evolving and becoming more sophisticated with the development of new technologies, and consequently the number of service failures caused by DDoS attacks are continually increasing. Recently, DDoS attacks have numerous types of service failures by applying a large amount of traffic to the domain address of a specific service or server. In this paper, after generating the data of the Syn Flooding attack, which is the representative attack type of bandwidth exhaustion attack, the data were compared and analyzed using Random Forest, Decision Tree, Multi-Layer Perceptron, and KNN algorithms for the effective detection of attacks, and the optimal algorithm was derived. Based on this result, it will be useful to use as a technique for the detection policy of Syn Flooding attacks.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.539-554
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• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

• Journal of Auto-vehicle Safety Association
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• v.16 no.2
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• pp.20-26
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• 2024

The delivery using drones has been attracting attention because it can innovatively reduce the delivery time from the time of order to completion of delivery compared to the current delivery system, and there have been pilot projects conducted for safe drone delivery. However, the current drone delivery system has the disadvantage of limiting the operational efficiency offered by fully autonomous delivery drones in that drones mainly deliver goods to pre-set landing sites or delivery bases, and the final delivery is still made by humans. In this paper, to overcome these limitations, we propose obstacle detection and landing site selection algorithm based on a vision sensor that enables safe drone landing at the delivery location of the product orderer, and experimentally prove the possibility of station-to-door delivery. The proposed algorithm forms a 3D map of point cloud based on simultaneous localization and mapping (SLAM) technology and presents a grid segmentation technique, allowing drones to stably find a landing site even in places without prior information. We aims to verify the performance of the proposed algorithm through streaming data received from the drone.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.177-182
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• 2004

This study presents the application of aerial photographs and KOMPSAT-1 Electro-Optical Camera(EOC) imagery in detecting the change of an urban area that has been rapidly growing. For the study, we used multi-time images which were acquired by two different sensors. For all of the images, the coordinate reference system and scale were first made identical through the 1st and 2nd geometric corrections and then image resampling were carried out to spatial resolution of 7m to detect changes under the same conditions. The Image Differencing was employed as a change detection technique. It was confirmed to be able to detect the changes of terrestrial surface like building, structure and road features from aerial photos and KOMPSAT EOC images with single band. The changes could be detected to some extent with the images acquired from different kinds of sensors as well as the same kinds of sensors.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.212-218
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• 2008

Collision avoidance is a fundamental and important task of an autonomous mobile robot for safe navigation in real environments with high uncertainty. Obstacles are classified into static and dynamic obstacles. It is difficult to avoid dynamic obstacles because the positions of dynamic obstacles are likely to change at any time. This paper proposes a scheme for vision-based avoidance of dynamic obstacles. This approach extracts object candidates that can be considered moving objects based on the labeling algorithm using depth information. Then it detects moving objects among object candidates using motion vectors. In case the motion vectors are not extracted, it can still detect the moving objects stably through their color information. A robot avoids the dynamic obstacle using the dynamic window approach (DWA) with the object path estimated from the information of the detected obstacles. The DWA is a well known technique for reactive collision avoidance. This paper also proposes an algorithm which autonomously registers the obstacle color. Therefore, a robot can navigate more safely and efficiently with the proposed scheme.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.548-553
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• 2011

The lock-in thermography was employed to evaluate the defects in railway bogies. Prior to the actual application on railway bogies, in order to assess the detectability of known flaws, the calibration reference panel was prepared with various dimensions of artificial flaws. The panel was composed of polymer matrix composites, which were the same material with actual bogies. Through lock-in thermography evaluation, the optimal frequency of heat source was determined for the best flaw detection. Based on the defects information, the actual defect assessments on railway bogie were conducted with different types of railway bogies, which were used for the current operation. In summary, it was found that the novel infrared thermography technique could be an effective way for the inspection and the detection of surface defects on bogies since the infrared thermography method provided rapid and non-contact investigation of railway bogies.