• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.499-499
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• 2016

본 연구는 오염된 농업용 저수지에 대하여 수층에 따른 수질 변화 특성을 조사하고, 저수지 저부의 퇴적물에 대한 용출 실험을 통해 수층별 수질 특성이 퇴적물의 용출에 미치는 영향을 분석하고자 수행되었다. 연구 대상 저수지의 선정은 설치년도 1960년대를 기준으로 이전과 이후로 분류하였으며, 주요 오염원을 축산계와 생활계 오염원으로 분류하여 각 다른 특성을 가진 저수지를 선정하여 조사하였다. 내부 오염 부하량이 수체에 미치는 영향을 평가하기 위해 실시한 용출량 실험 결과, 호기 조건에서 축산계오염원 저수지의 T-P가 미미하게 용출이 일어난 것을 제외하고는 생활계, 축산계오염원 저수지에서 용출이 일어나지 않았다. 반면에 혐기 조건의 경우에는 생활계오염원 보다는 축산계오염원 저수지에서, 1960년대 이후 설치된 저수지에서 보다는 1960년대 이전에 설치된 저수지에서 용출이 크게 일어나는 것으로 조사 되었다. 혐기 조건에서 T-N의 경우 생활계오염원과 1960년대 이후 설치된 저수지에서는 용출이 일어나지 않았으나, 그 외의 항목에서는 모두 용출이 일어나는 것으로 분석되었다. 특히 혐기조건에서는 모든 연구대상 저수지에서 T-P의 용출이 크게 일어나는 것으로 조사되었는데, 이는 부영양화의 주요 영향인자로 작용할 수 있으므로 저수지 저부의 혐기조건이 형성되는 것을 제어?관리해야 할 필요성이 있다고 하겠다. 연구 저수지에 대한 현장 조사 결과, 가뭄의 영향으로 수위가 평년에 비해 상당히 낮았으며, 저수량의 부족으로 저수지 주변의 바닥이 드러나거나 유출이 없는 등의 특징을 보였다. 수심이 낮은 5~7월의 조사 시기에는 표층의 DO 농도가 높음에도 저층부의 DO 농도는 1.2~2.2 mg/L를 나타내 약혐기조건이 형성됨을 확인하였다. 또한 현장측정기(HYDROLAB_Quanta)를 이용한 DO측정시, 저층부 퇴적물의 재부유를 막기 위해 저층경계면에서 30~50cm 윗부분을 측정했다는 점에서 저층부 바닥면의 DO 농도가 더 낮을 수 있다고 추정할 수 있다. 이는 이 시기에 퇴적층에 존재하는 오염물질의 용출이 충분히 일어날 수 있으며, 이후 8~11월에 수계 내 환란 및 순환에 의해 오염물질이 수중으로 이동하게 되고, 수질을 악화시키는 원인으로 작용할 수 있음을 추론할 수 있다. 이와 같이 퇴적되어 있던 오염물질이 계속적으로 용출 및 순환을 해마다 반복한다면 해당 저수지는 장기간 수질 오염 저수지가 될 수밖에 없을 것이다. 따라서 이러한 내부오염부하가 크다고 판단되는 저수지에 대하여 오염퇴적물의 관리가 필요하다고 하겠다.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.436-440
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• 2016

본 연구에서는 농업용 저수지의 수질을 악화시키는 원인으로 판단되는 퇴적물의 현황을 파악하기 위하여 저수지의 퇴적물에 영향을 미칠 수 있는 물리성 및 오염원 특성에 따라 유형 분류를 실시하여 각 유형별 대상 저수지를 선정하여 현장 모니터링을 통해 기초자료를 확보하였고, 이를 국내외 자료 및 수질자료와 비교 분석을 통해 총체적인 오염도를 평가하였다. 연구 대상 저수지의 분류 기준은 저수지 퇴적물의 성상 및 특성에 영향을 미칠 수 있는 주요 요소인 저수지 오염원과 설치년도로 하였다. 이에 따라, 현장 답사 및 사전조사 결과를 토대로 가장 적합한 조사대상 저수지를 선별하였다. 최종적으로 선정된 연구 대상 저수지는 생활계의 인평(1960년대 이전), 업성 저수지(1960년대 이후)와 축산계의 이담(1960년대 이전), 공리 저수지(1960년대 이후)이다. 내부 오염 부하량이 수체에 미치는 영향을 평가하기 위해 실시한 용출량 실험 결과, 호기 조건의 경우에는 축산계의 T-P를 제외하고 생활계, 축산계 모두 용출이 일어나지 않는 것으로 조사되었다. 반면에 혐기 조건의 경우에는 생활계 보다는 축산계가, 1960년대 이후보다는 1960년대 이전에 축조된 저수지의 용출이 보다 활발한 것으로 나타났다. 조건별 용출량이 수체에 미치는 영향은 호기 조건에서 생활계 및 1960년대 이후의 경우 음의 값을 보여 수질이 개선되는 것으로 나타났다. 반면, 혐기 조건에서는 T-N의 경우 생활계와 1960년대 이후 저수지에서는 용출이 일어나지 않았으나 그 외의 항목에서는 모두 용출이 일어나는 것으로 나타났다. 특히 혐기조건은 T-P의 용출에 영향을 크게 미치는 것으로 나타났는데, 수질에 미치는 영향을 기여율로 환산할 경우 생활계에서 11%, 축산계에서 13.7%, 1960년대 이전에서 18.3%, 1960년대 이후에서 6.39% 영향을 미치는 것으로 나타났다.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.1-8
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• 2019

This paper carried out a study to reduce damage from jellyfish whose population has increased due to global warming. The emergence of jellyfish on the beach could result in casualties from jellyfish stings and economic losses from closures. This paper confirmed from the preceding studies that the pattern of jellyfish's appearance is predictable through machine learning. This paper is an extension of The prediction model of emergence of Busan coastal jellyfish using SVM. In this paper, we used deep neural network to expand from the existing methods of predicting the existence of jellyfish to the classification by index. Due to the limitations of the small amount of data collected, the 84.57% prediction accuracy limit was sought to be resolved through data expansion using bootstraping. The expanded data showed about 7% higher performance than the original data, and about 6% better performance compared to the transfer learning. Finally, we used the test data to confirm the prediction performance of jellyfish appearance. As a result, although it has been confirmed that jellyfish emergence binary classification can be predicted with high accuracy, predictions through indexation have not produced meaningful results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1595-1603
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• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.641-648
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• 2022

This paper is a study to find out the factors affecting the defects that occur during the use of Electron Beam Melting (EBM), one of the 3D printer output methods, through data analysis. By referring to factors identified as major causes of defects in previous studies, log files occurring between processes were analyzed and related variables were extracted. In addition, focusing on the fact that the data is time series data, the concept of a window was introduced to compose variables including data from all three layers. The dependent variable is a binary classification problem with the presence or absence of defects, and due to the problem that the proportion of defect layers is low (about 4%), balanced training data were created through the SMOTE technique. For the analysis, I use XGBoost using Gridsearch CV, and evaluate the classification performance based on the confusion matrix. I conclude results of the stuy by analyzing the importance of variables through SHAP values.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.940-943
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• 2022

This study aims to analyze the Korean dataset by performing Korean sentence Emotion Analysis in the radio listeners' text messages collected personally. Currently, in Korea, research on the Emotion Analysis of Korean sentences is variously continuing. However, it is difficult to expect high accuracy of Emotion Analysis due to the linguistic characteristics of Korean. In addition, a lot of research has been done on Binary Sentiment Analysis that allows positive/negative classification only, but Multi-class Emotion Analysis that is classified into three or more emotions requires more research. In this regard, it is necessary to consider and analyze the Korean dataset to increase the accuracy of Multi-class Emotion Analysis for Korean. In this paper, we analyzed why Korean Emotion Analysis is difficult in the process of conducting Emotion Analysis through surveys and experiments, proposed a method for creating a dataset that can improve accuracy and can be used as a basis for Emotion Analysis of Korean sentences.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.433-443
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• 2022

Purpose: In this paper, we propose a study of deep learning algorithms that estimate and predict sarcopenia by exploiting the high penetration rate of smart devices. Method: To utilize deep learning techniques, experimental data were collected by using the inertial sensor embedded in the smart device. We implemented a smart device application for data collection. The data are collected by labeling normal and abnormal gait and five states of running, falling and squat posture. Result: The accuracy was analyzed by comparative analysis of LSTM, CNN, and RNN models, and binary classification accuracy of 99.87% and multiple classification accuracy of 92.30% were obtained using the CNN-LSTM fusion algorithm. Conclusion: A study was conducted using a smart sensoring device, focusing on the fact that gait abnormalities occur for people with sarcopenia. It is expected that this study can contribute to strengthening the safety issues caused by sarcopenia.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.81-87
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• 2023

Intrusion detection systems that learn metadata of network packets have been proposed recently. However these approaches require time to analyze packets to generate metadata for model learning, and time to pre-process metadata before learning. In addition, models that have learned specific metadata cannot detect intrusion by using original packets flowing into the network as they are. To address the problem, this paper propose a natural language processing-based intrusion detection system that detects intrusions by learning the packet payload as a single sentence without an additional conversion process. To verify the performance of our approach, we utilized the UNSW-NB15 and Transformer models. First, the PCAP files of the dataset were labeled, and then two Transformer (BERT, DistilBERT) models were trained directly in the form of sentences to analyze the detection performance. The experimental results showed that the binary classification accuracy was 99.03% and 99.05%, respectively, which is similar or superior to the detection performance of the techniques proposed in previous studies. Multi-class classification showed better performance with 86.63% and 86.36%, respectively.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.397-402
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• 2007

In this paper, we apply a support vector machine(SVM) that incorporates an optimized nonlinear decision rule over different sets of feature vectors to improve the performance of statistical model-based voice activity detection(VAD). Conventional method performs VAD through setting up statistical models for each case of speech absence and presence assumption and comparing the geometric mean of the likelihood ratio (LR) for the individual frequency band extracted from input signal with the given threshold. We propose a novel VAD technique based on SVM by treating the LRs computed in each frequency bin as the elements of feature vector to minimize classification error probability instead of the conventional decision rule using geometric mean. As a result of experiments, the performance of SVM-based VAD using the proposed feature has shown better results compared with those of reported VADs in various noise environments.

• The Journal of the Acoustical Society of Korea
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• v.26 no.2
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• pp.75-79
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• 2007

We propose an effective voice-based gender identification method using a support vector machine(SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model(GMM) using the mel frequency cepstral coefficients(MFCC). A novel means of incorporating a features fusion scheme based on a combination of the MFCC and pitch is proposed with the aim of improving the performance of gender identification using the SVM. Experiment results indicate that the gender identification performance using the SVM is significantly better than that of the GMM. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.