• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.620-623
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• 2016

열차의 진로를 제어하는 선로전환기는 열차의 안정적인 주행에 있어서 매우 중요한 시설이다. 본 논문에서는 선로전환기의 작동 시 발생하는 소리 정보를 이용하여 선로전환기의 스트레스를 탐지하는 시스템을 제안한다. 먼저 제안하는 시스템은 선로전환기에 스트레스가 쌓인 상태의 소리 정보와 스트레스가 제거된 소리 정보를 수집한 후, 다양한 소리특징들을 추출한다. 추출된 특징들로 부터 t-test를 이용하여 유의성이 확보된 소리 특징 파마미터만을 최종 특징벡터로 선택한다. 마지막으로, 소리 특징 벡터를 입력으로 하는 이진 분류기인 SVM(Support Vector Machine)을 이용하여, 선로전환기의 스트레스 상태 여부를 실시간으로 탐지한다. 실제 테스트용 선로전환기에서 취득한 소리 정보 데이터 셋을 이용하여 본 논문에서 제안하는 시스템의 성능을 실험적으로 검증한다.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.979-987
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• 2020

This study used the Deep Learning models used in previous studies, we selected the basic model. The selected model was selected as ZFNet among ZFNet, Googlenet and ResNet, and the object was detected using a ZFNet based FRCNN. In order to reduce the detection error rate of FRCNN, location of four types of objects detected inside the image was designed by SVM classifier and location-based filtering was applied. As simulation results, it showed similar performance to the lane marking classification method with conventional 경계 detection, with an average accuracy of about 88.8%. In addition, studies using the Linear-parabolic Model showed a processing speed of 165.65ms with a minimum resolution of 600 × 800, but in this study, the resolution was treated at about 33ms with an input resolution image of 1280 × 960, so it was possible to classify lane marking at a faster rate than the previous study by CNN-based End to End method.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.1-10
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• 2021

Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.874-890
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• 2021

With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.

• Current Optics and Photonics
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• v.6 no.1
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• pp.92-103
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• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.113-117
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• 2023

As sputter equipment becomes more complex, it becomes increasingly difficult to understand the parameters that affect the thickness uniformity of thin metal film deposited by sputter. To address this issue, we verified a deep learning model that can predict complex relationships. Specifically, we trained the model to predict the height of 36 magnets based on the thickness of the material, using Support Vector Machine (SVM), Multilayer Perceptron (MLP), 1D-Convolutional Neural Network (1D-CNN), and 2D-Convolutional Neural Network (2D-CNN) algorithms. After evaluating each model, we found that the MLP model exhibited the best performance, especially when the dataset was constructed regardless of the thin film material. In conclusion, our study suggests that it is possible to predict the sputter equipment source using film thickness data through a deep learning model, which makes it easier to understand the relationship between film thickness and sputter equipment.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.499-499
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• 2023

최근 컴퓨터 성능 향상과 새로운 머신러닝 알고리즘 개발됨에 따라, 각 분야별 연구자들이 이를 활용한 연구를 다양하게 수행하고 있으며, 하수처리시설의 경우에는 막대한 양의 운영자료가 축척됨에 따라 머신러닝을 활용한 다양한 연구가 가속화 되고 있다. 기존 하수처리장의 물리학적 모델은 적용된 영향 인자에 여러 가지 가정이 고려되어 모델 정확도가 부정확해지는 경향이 있었으며, 이러한 문제점을 보완하기 위해 하수처리장의 수집된 운영자료 및 머신러닝 기반의 예측 모델을 활용하여 예측 모델 정확도를 향상하는 선행 연구들이 진행되고 있다. A 하수처리장의 부지 내에 설치된 센서를 통하여 운영자료가 중앙제어실 서버에 실시간으로 저장되는 자료를 활용하여 NN (Neural Network), SVM (Support Vector Machine), RF (Random Forest) 등과 같은 다양한 머신러닝 모델을 적용하였고, 하수처리장 운영자료를 적용할 경우 어느 모델이 가장 높은 성능이 나타나는지 인사이트를 도출하고자 하였다. 금회 연구는 A 하수처리장을 대상으로 여러 머신러닝 기반 예측 모델을 개발하고, 각 모델의 예측정확도를 서로 평가함으로써, 머신러닝 모델 최적화를 수행할 수 있었다. 이번 연구에서 도출된 결과를 활용하여 하수처리장 예측 모델 최적화를 진행할 경우, 향후 비교적 짧은 시간에 하수처리장 머신러닝 기반 예측 모델 개발이 가능하다는 점에 의의가 있다.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.47-56
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• 2015

Automated detection of interesting web pages could be used in many different application domains. Determining a user's interesting web pages can be performed implicitly by observing the user's behavior. The task of distinguishing interesting web pages belongs to a classification problem, and we choose white box learning methods (fixed effect logit regression and support vector machine) to test empirically. The result indicated that (1) fixed effect logit regression, fixed effect SVMs with both polynomial and radial basis kernels showed higher performance than the linear kernel model, (2) a personalization is a critical issue for improving the performance of a model, (3) when asking a user explicit grading of web pages, the scale could be as simple as yes/no answer, (4) every second the duration in a web page increases, the ratio of the probability to be interesting increased 1.004 times, but the number of scrollbar clicks (p=0.56) and the number of mouse clicks (p=0.36) did not have statistically significant relations with the interest.

• Korean Journal of Cognitive Science
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• v.21 no.4
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• pp.459-480
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• 2010

A dialogue system includes various morphological analyses for recognizing a user's intention from the user's utterances. However, a user can represent various intentions via emotional states in addition to morphological expressions. Thus, a user's emotion recognition can analyze a user's intention in various manners. This paper presents a new method to automatically recognize a user's emotion for a dialogue system. For general emotions, we define nine categories using a psychological approach. For an optimal feature set, we organize a combination of sentential, a priori, and context features. Then, we employ a support vector machine (SVM) that has been widely used in various learning tasks to automatically classify a user's emotions. The experiment results show that our method has a 62.8% F-measure, 15% higher than the reference system.

• Journal of Biomedical Engineering Research
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• v.40 no.2
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.