• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제8권11호
• /
• pp.4016-4027
• /
• 2014

Understanding network latency is important for providing consistent and acceptable levels of services in network-based applications. However, due to the difficulty of estimating applications' network demands and the difficulty of network latency modeling the management of network resources has often been ignored. We expect that, since network latency repeats cycles of congested states, a systematic classification method for network status would be helpful to simplify issues in network resource managements. This paper presents a simple empirical method to classify network status with a real operational network. By observing oscillating behavior of end-to-end latency we determine networks' status in run time. Five typical network statuses are defined based on a long-term stability and a short-term burstiness. By investigating prediction accuracies of several simple numerical models we show the effectiveness of the network status classification. Experimental results show that around 80% reduction in prediction errors depending on network status.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
• /
• pp.473-477
• /
• 2001

Artificial neural network (ANN) is known to identify relationships even when some of the input data are very complex, ill-defined and ill-structured. One of the advantages in ANN is that it can discriminate the linearly inseparable data. This study presents an application of ANN to classify and predict the symptomatic status of HIV/AIDS patients. Even though ANN techniques have been applied to a variety of areas, this study has a substantial contribution to the HIV/AIDS care and prevention planning area. ANN model in classifying both the HIV and AIDS status of HIV/AIDS patients is developed and analyzed. The diagnostic accuracy of the ANN in classifying both the HIV status and AIDS status of HIV/AIDS status is evaluated. Several different ANN topologies are applied to AIDS Cost and Services Utilization Survey (ACSUS) datasets in order to demonstrate the model\`s capability. If ANN design models are different, it would be interesting to see what influence would have on classification of HIV/AIDS-related persons.

• International Journal of Computer Science & Network Security
• /
• 제21권2호
• /
• pp.198-204
• /
• 2021

Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and high-grade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.

• International Journal of Internet, Broadcasting and Communication
• /
• 제14권4호
• /
• pp.173-180
• /
• 2022

In the naked eye observation, the health of livestock can be controlled by the range of activity, temperature, pulse, cough, snot, eye excrement, ears and feces. In order to confirm the health of livestock, this paper uses calf face image data to classify the health status by image shape, color and texture. A series of images that have been processed in advance and can judge the health status of calves were used in the study, including 177 images of normal calves and 130 images of abnormal calves. We used GLCM calculation and Convolutional Neural Networks to extract 6 texture attributes of GLCM from the dataset containing the health status of calves by detecting the image of calves and learning the composite image of Convolutional Neural Networks. In the research, the classification ability of GLCM-CNN shows a classification rate of 91.3%, and the subsequent research will be further applied to the texture attributes of GLCM. It is hoped that this study can help us master the health status of livestock that cannot be observed by the naked eye.

• 한국정보통신학회논문지
• /
• 제24권1호
• /
• pp.37-43
• /
• 2020

최근 심전도 (ECG) 신호를 사용하여 심장병을 진단하는 많은 연구가 이루어지고 있다. 이러한 심전도 신호는 비정상적인 심장 상태를 나타내는 부정맥을 모니터링하고 진단하는 데 유용하게 쓰인다. 본 논문에서는 1차원 합성곱 신경망을 사용하여 ECG 신호에 대하여 부정맥을 분류하는 시스템을 제안한다. 제안하는 신경망 알고리즘은 부정맥 신호의 특징을 세밀하게 추출하도록 4개의 합성곱 계층으로 구성하고 매개변수를 최적화하도록 설계되었다. MIT-BIH 부정맥 데이터베이스에 대해 학습한 신경망은 시뮬레이션을 통해 99% 이상의 정확도의 분류 성능을 가진다는 것을 보여준다. 비교적 합성곱 커널의 개수가 많을수록 ECG 신호의 특성을 더 잘 나타내기 때문에 좋은 성능을 나타내는 것으로 분석되었다. 또한 제안된 신경망을 활용한 실제 시스템을 구현하여 실시간으로 부정맥을 분류하는 결과를 검증하였다.

• 한국멀티미디어학회논문지
• /
• 제23권2호
• /
• pp.146-154
• /
• 2020

Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.

• 풍력에너지저널
• /
• 제13권4호
• /
• pp.58-69
• /
• 2022

This study reviews previous studies on the scale of offshore wind power industry ecosystems to provide basic data for a revitalization strategy for the offshore wind power industry and proposes an analysis of transaction networks based on the special classification of the renewable energy industry. First, we examine the localization rate, technology level, and price level of the offshore wind industry. Second, this research compares the methodology and estimation results of previous studies estimating the scale of the wind power industry. Third, we examine the details related to the enactment of a special classification of the renewable energy industry statistics and review the Korea Energy Agency's renewable energy industry statistics (focusing on 2019 and 2020). Finally, this study suggests the necessity of analyzing an inter-company transaction network based on special classifications of the renewable energy industry to grasp the status of each region and value chain of the offshore wind industry.

• 산업경영시스템학회지
• /
• 제43권2호
• /
• pp.33-38
• /
• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권10호
• /
• pp.3608-3626
• /
• 2021

The detection of bolts is an important task in high-speed train inspection systems, and it is frequently performed to ensure the safety of trains. The difficulty of the vision-based bolt inspection system lies in small sample defect detection, which makes the end-to-end network ineffective. In this paper, the problem is resolved in two stages, which includes the detection network and cascaded classification networks. For small bolt detection, all bolts including defective bolts and normal bolts are put together for conducting annotation training, a new loss function and a new boundingbox selection based on the smallest axis-aligned convex set are proposed. These allow YOLOv3 network to obtain the accurate position and bounding box of the various bolts. The average precision has been greatly improved on PASCAL VOC, MS COCO and actual data set. After that, the Siamese network is employed for estimating the status of the bolts. Using the convolutional Siamese network, we are able to get strong results on few-shot classification. Extensive experiments and comparisons on actual data set show that the system outperforms state-of-the-art algorithms in bolt inspection.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권8호
• /
• pp.2801-2815
• /
• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.