• Journal of Biomedical and Translational Research
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• v.19 no.4
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• pp.110-115
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• 2018

Transient ischemic attack (TIA) indicates high risk for major stroke and is considered a medical emergency. Diffusion-weighted imaging (DWI) enables detection of acute ischemic lesions. The clinical significance of DWI positive lesions in TIA is obscure and its prevalence, clinical features are not established. Therefore, we performed a clinical, etiological and prognostic analysis through a cross-sectional analysis of 235 TIA patients, grouped according to presence of DWI lesion. Clinical features, underlying risk factors for stroke, outcome and rate of recurrence were analyzed. 3 months follow-up of modified Rankin Scales (mRS) were done with telephone survey. DWI positive lesions were present in 14.0% of patients. Etiological factors significantly associated with DWI lesions in TIA patients were male sex (p = 0.038), stroke history (p = 0.012) and atrial fibrillation (p < 0.001). Presence of at least one medium or high risk of cardioembolism from TOAST classification were not associated with lesions when excluding association to atrial fibrillation (p = 0.108). Clinical features showed no significant difference. Whether the patients had lesion-positive DWI was not related to an increase in mRS score during the hospital stay or at the 3-month follow-up after discharge. Future studies should include multi-center samples with large numbers, considering each unique medical environment. Routine acquisition of follow-up DWI for proper evaluation of the tissue-based definition of TIA should also be considered.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.5
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• pp.57-67
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• 2019

Water-Birds are the birds that occupy the highest proportion in Korea, inland wetlands and reservoirs provide them with a good environment as habitat, but their habitats have been losing because of thoughtless development. Therefore, artificial plant islands in reservoirs are important for improving habitat environment and providing food resources. However, there are no research and standards on the built and management of artificial plant islands. So this study is to find out the density of bird using artificial plant island as habitat through monitoring using UAV focus on the Cheonho-reservoirs located in Seobuk-gu, Cheonan-si(Middle Chungcheong Province). Further, the correlation analysis with environmental factors was conducted to determine the effect of artificial plant islands as habitats for water-birds. The supervised classification of the three-time images taken by the drone identified 244 white-billed ducks and 46 mandarin ducks. The utilization rate was different for each photographed date, and more individuals were identified in wet artificial plant islands than dry ones. As a result of analyzing the utilization follow environmental factors, the distance from the trail showed a significant correlation, and the other factors did not have a statistically significant effect. This study is the first case of the UAV monitoring method of the water-birds using artificial plant islands in the reservoir, and can be used as the basic data for the built and management.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.33-41
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• 2022

In this article, we proposed to predict natural gas (NG) leakage levels through feature selection based on a factor analysis (FA) of the integrating the Korean Meteorological Agency data and natural gas leakage data for considering complex factors. The paper has been divided into three modules. First, we filled missing data based on the linear interpolation method on the integrated data set, and selected essential features using FA with OrdinalEncoder (OE)-based normalization. The dataset is labeled by K-means clustering. The final module uses four algorithms, K-nearest neighbors (KNN), decision tree (DT), random forest (RF), Naive Bayes (NB), to predict gas leakage levels. The proposed method is evaluated by the accuracy, area under the ROC curve (AUC), and mean standard error (MSE). The test results indicate that the OrdinalEncoder-Factor analysis (OE-F)-based classification method has improved successfully. Moreover, OE-F-based KNN (OE-F-KNN) showed the best performance by giving 95.20% accuracy, an AUC of 96.13%, and an MSE of 0.031.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.77-84
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• 2022

In this paper, we propose a Label Reassignment Strategy to improve the performance of an object detection algorithm. Our approach involves two stages: an inference stage and an assignment stage. In the inference stage, we perform multi-scale inference with predefined scale sizes on a trained model and re-infer masked images to obtain robust classification results. In the assignment stage, we calculate the IoU between bounding boxes to remove duplicates. We also check box and class occurrence between the detection result and annotation label to re-assign the dominant class type. We trained the YOLOX-L model with the re-annotated dataset to validate our strategy. The model achieved a 3.9% improvement in mAP and 3x better performance on AP_S compared to the model trained with the original dataset. Our results demonstrate that the proposed Label Reassignment Strategy can effectively improve the performance of an object detection model.

• Phonetics and Speech Sciences
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• v.14 no.4
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• pp.35-43
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• 2022

This paper studies how to improve the identification rate of laryngeal disability speech data by convolutional neural network (CNN) and machine learning ensemble learning methods. In general, the number of laryngeal dysfunction speech data is small, so even if identifiers are constructed by statistical methods, the phenomenon caused by overfitting depending on the training method can lead to a decrease the identification rate when exposed to external data. In this work, we try to combine results derived from CNN models and machine learning models with various accuracy in a multi-voting manner to ensure improved classification efficiency compared to the original trained models. The Pusan National University Hospital (PNUH) dataset was used to train and validate algorithms. The dataset contains normal voice and voice data of benign and malignant tumors. In the experiment, an attempt was made to distinguish between normal and benign tumors and malignant tumors. As a result of the experiment, the random forest method was found to be the best ensemble method and showed an identification rate of 85%.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.23-30
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• 2022

The failure of the reefer container causes a great loss of cost, but the current reefer container alarm system is inefficient. Existing studies using simulation data of refrigeration systems exist, but studies using actual operation data of refrigeration containers are lacking. Therefore, this study classified the causes of failure using actual refrigerated container operation data. Data imbalance occurred in the actual data, and the data imbalance problem was solved by comparing the logistic regression analysis with ENN-SMOTE and class weight with the 2-stage algorithm developed in this study. The 2-stage algorithm uses XGboost, LGBoost, and DNN to classify faults and normalities in the first step, and to classify the causes of faults in the second step. The model using LGBoost in the 2-stage algorithm was the best with 99.16% accuracy. This study proposes a final model using a two-stage algorithm to solve data imbalance, which is thought to be applicable to other industries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• The Plant Pathology Journal
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• v.39 no.4
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• pp.409-416
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• 2023

Bacterial leaf blight of carrots caused by Xanthomonas hortorum pv. carotae (Xhc) is an important worldwide seed-borne disease. In 2012 and 2013, symptoms similar to bacterial leaf blight were found in carrot farms in Jeju Island, Korea. The phenotypic characteristics of the Korean isolation strains were similar to the type strain of Xhc. Pathogenicity showed symptoms on the 14th day after inoculation on carrot plants. Identification by genetic method was multi-position sequencing of the isolated strain JJ2001 was performed using four genes (danK, gyrB, fyuA, and rpoD). The isolated strain was confirmed to be most similar to Xhc M081. Furthermore, in order to analyze the genetic characteristics of the isolated strain, whole genome analysis was performed through the next-generation sequencing method. The draft genome size of JJ2001 is 5,443,372 bp, which contains 63.57% of G + C and has 4,547 open reading frames. Specifically, the classification of pathovar can be confirmed to be similar to that of the host lineage. Plant pathogenic factors and determinants of the majority of the secretion system are conserved in strain JJ2001. This genetic information enables detailed comparative analysis in the pathovar stage of pathogenic bacteria. Furthermore, these findings provide basic data for the distribution and diagnosis of Xanthomonas hortorum pv. carotae, a major plant pathogen that infects carrots in Korea.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.448-461
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• 2024

Purpose: The purpose of this study is to establish a life zone class suitable for Korean characteristics in the post-COVID-19 era and to classify the types for the installation and operation of living SOC facilities. Method: The concept of the life zone was established through policies and previous studies related to the life zone, and data in various fields such as population, employment, transportation, economy, and education were classified using the z-score technique. Result: Korea's life zones can be classified into metropolitan life zones, regional life zones, urban life zones, village life zones, and neighborhood life zones, and depending on their roles, they can be classified into central life zones, workplace-residential balanced life zones, residential life zones, industrial life zones, and low-density life zones. Conclusion: The results of this study show that proper life zone establishment and proper living SOC supply can prevent the decline of underdeveloped areas and contribute to balanced regional development