• KIEE International Transactions on Power Engineering
• /
• 제5A권3호
• /
• pp.228-234
• /
• 2005

This paper presents the evaluation of reactive power pricing through the control of generator voltages under the assumption that the reactive power market has been transformed into the real power market. By applying the concept of economic dispatch, which minimizes the total cost of real power generation to reactive power generation, the algorithm for implementing reactive power pricing is proposed to determine the optimum voltage profiles of generators. It consists of reactive power voltage equation, the objective function that minimizes the total cost of reactive power generation, and linear analysis of inequality constraints in relation to the load voltages. From this algorithm, the total cost of the reactive power generation can be yielded to the minimum value within network constraints as the range of load voltages. This may provide the fair and reasonable price information for reactive power generation in the deregulated electricity market. The proposed algorithm has been tested on the IEEE 14-bus system using MATLAB.

• 통합자연과학논문집
• /
• 제11권4호
• /
• pp.167-183
• /
• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권3호
• /
• pp.1238-1259
• /
• 2019

Saving energy is a big challenge for Wireless Sensor Networks (WSNs), which becomes even more critical in large-scale WSNs. Most energy waste is communication related, such as collision, overhearing and idle listening, so the schedule-based access which can avoid these wastes is preferred for WSNs. On the other hand, clustering technique is considered as the most promising solution for topology management in WSNs. Hence, providing interference-free clustering is vital for WSNs, especially for large-scale WSNs. However, schedule management in cluster-based networks is never a trivial work, since it requires inter-cluster cooperation. In this paper, we propose a clustering method, called Interference-Free Clustering Protocol (IFCP), to partition a WSN into interference-free clusters, making timeslot management much easier to achieve. Moreover, we model the clustering problem as a multi-objective optimization issue and use non-dominated sorting genetic algorithm II to solve it. Our proposal is finally compared with two adaptive clustering methods, HEED-CSMA and HEED-BMA, demonstrating that it achieves the good performance in terms of delay, packet delivery ratio, and energy consumption.

• International Journal of Advanced Culture Technology
• /
• 제10권1호
• /
• pp.253-264
• /
• 2022

In the era of the pandemic, the importance of a wireless LAN environment has become increasingly important, especially as the era of smart working and non-face-to-face education has become a universal and daily routine. Smartphones, tablets, laptops, PCs, smart watches, and wearable devices, collective referred to as wireless terminals, can be accessed anytime and anywhere through the internet, allowing for consistent and constant access to offices, factories, warehouses, shopping malls, railways, hotels, hospitals, schools, logistics centers, airports, exhibition halls, etc. This sort of access is currently being used in roads, parks, traditional markets, and ports. Since the release of the IEEE 802.11 Legacy Standard in 1997, Wi-Fi technology has been continuously supplemented and revised, and the standard has been continuously developed. In the era of smart working, the importance of efficient wireless deployment and scientific design has become more important. The importance of wireless in the smart factory, in the metaverse era, in the era of pursuing work and life that transcend time and space by using AR, VR, MR, and XR, it is more urgent to solve the shadow area of Wi-Fi. Through this study, we intend to verify the wireless failure problem of the xr training center and suggest improvement measures.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권11호
• /
• pp.3780-3797
• /
• 2022

The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권8호
• /
• pp.2587-2605
• /
• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• Korean Journal of Radiology
• /
• 제24권7호
• /
• pp.698-714
• /
• 2023

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

• 정보과학회 논문지
• /
• 제43권12호
• /
• pp.1356-1364
• /
• 2016

본 논문에서는 딥러닝 기법 중 하나인 deep convolutional neural network (DCNN)을 이용하여 영상의 구도를 개선하는 방법을 제시한다. 기존의 구도 개선 방법들은 영상의 주요 물체의 위치를 바탕으로 한 구도 평가 점수를 정의한 뒤 최적화를 통해 평가 점수를 향상시키는 방향으로 영상을 개선한다. 이는 계산량이 많고 기존 주요 물체 검출 알고리즘의 성능에 종속적이기 때문에 영상에 따라 구도 개선이 제대로 수행되지 않는 경우가 존재한다. 본 논문에서는 영상의 특징 추출에 뛰어난 성능을 보이는 DCNN을 이용해 영상을 반복적으로 크롭하여 미학적으로 구도가 개선된 영상을 얻는 방법을 제안한다. 실험 결과 및 사용자 평가를 통해 본 논문에서 제안한 알고리즘이 주어진 영상을 특정 구도 가이드라인(삼분할법, 주요 물체의 크기 등)을 따르도록 자동으로 크롭한다는 것을 보인다.

• 한국공간구조학회논문집
• /
• 제20권2호
• /
• pp.51-58
• /
• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• 한국전자통신학회논문지
• /
• 제12권1호
• /
• pp.75-84
• /
• 2017

초고속 환경에서 대용량 데이터에 대한 안정적 기록 및 효율적인 데이터 접근의 필요성은 갈수록 높아지고 있다. 이와 관련된 기초과학의 한 분야로 방대한 천체 관측 데이터를 생산하는 VLBI(: Very Long Baseline Interferometer)가 있는데 고분해능, 고감도 관측 연구를 수행하기 위해서는 고성능의 데이터 저장 시스템이 요구된다. 하지만 시장에 출시된 대다수 클라우드 기반 스토리지는 일반 IT, 금융, 행정 서비스 지원을 위한 저용량, 복수 스트림의 비정형 데이터에 최적화되어 있기 때문에 빅 스트림 데이터 기록을 위한 최적의 대안이 될 수 없다. 본 논문에서는 이를 극복하기 위한 방안으로 데이터 입출력 처리에 있어 고성능, 동시성에 최적화된 데이터 저장 시스템을 설계하고자 한다. 이를 위해 멀티 코어 CPU 환경에서 libpcap, pf_ring 등의 API 호출을 통해 패킷 입출력 모듈을 구현하였고 외부로부터 유입되는 데이터를 효율적으로 처리할 수 있도록 소프트웨어 RAID(: Redundant Array of Inexpensive Disks) 기반의 확장성 있는 스토리지를 구축하였다.