• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.69-78
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• 2023

With the global rise of digital data, the uncontrolled quantity of data is susceptible to cyber warfare or cyber attacks. Therefore, it is necessary to improve cyber security systems. This research studies the behavior of malicious acts and uses Higuchi Fractal Dimension (HFD), which is a non-linear mathematical method to examine the intricacy of the behavior of these malicious acts and anomalies within the cyber physical system. The HFD algorithm was tested successfully using synthetic time series network data and validated on real-time network data, producing accurate results. It was found that the highest fractal dimension value was computed from the DoS attack time series data. Furthermore, the difference in the HFD values between the DoS attack data and the normal traffic data was the highest. The malicious network data and the non-malicious network data were successfully classified using the Receiver Operating Characteristics (ROC) method in conjunction with a scaling stationary index that helps to boost the ROC technique in classifying normal and malicious traffic. Hence, the suggested methodology may be utilized to rapidly detect the existence of abnormalities in traffic with the aim of further using other methods of cyber-attack detection.

• 대한전자공학회논문지TE
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• 제37권3호
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• pp.66-71
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• 2000

다중 폴딩 스너버 망(folding snubber network)에 의하여 동작되는 새로운 형태의 영전압 영전류 스위칭 의사 공진형 컨버터를 제안한다. 이 새로운 컨버터는 기존의 의사 공진형 컨버터에 수동소자인 케패시턴스와 다이오드로 구성된 폴딩 스너버 망(folding snubber network)을 결합하여 구성된다. 컨버터의 주 정류 다이오드의 역 회복 손실(reverse recovery loss)은 다중공진 기법으로 억제된다. 이 제안된 컨버터는 매우 높은 효율을 구현하므로 고 출력에 매우 적합하다. 이 개념을 다른 컨버터에도 적용시켜 새로운 소프트 스위칭 의사 공진형 컨버터(soft switching quasi-resonance converter) 군을 할 수 있다.

• 지역연구
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• 제15권2호
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• pp.5-19
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• 1999

This paper identifies five factors that limited urban network formation in Northeast Asia over the past half millennium, questions the extent to which they are being overcome in the 1990s, and sketches a network of cities that could boost regionalism. It briefly traces the historical evolution of these factors, including comparisons with European integration, while focusing primarily on the policies of the 1990s that have affected their continuing role. First is the factor of closed national markets with weak regional integration. Second is the preeminence of administrative means of integration over commercial ones. Third is the character of localism, shackled by overcentralization and weak cross-border linkages. Fourth is the limited nature of internationalism, dominated by state catch-up policies with one-sided global involvement. Fifth is a lack of regional consciousness. Just as national urban integration was essential for regional networks to form, without regional integration it is difficult to contemplate Northeast Asian cities taking their rightful place in a global urban network. After noting the failures of the 1990s, the paper points to the potential role as dragon's heads for sub-regional urban networks of potential front-line cities: Tumen, Sapporo, Irkutsk, and what I call the Amur triangle. Also of interest are how the capitals of Beijing, Moscow, Seoul, and Tokyo will adjust to a transformed urban network. After all, their current skepticism must be overcome with a program that links the benefits on all sides in order to build trust in regionalism. This requires internationalism and symbols of a balanced approach to each country's needs.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.414-420
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• 2005

The ShortStack Micro Server enables any product that contains a microcontroller or microprocessor to quickly and inexpensively become a networked, Internet-accessible device. The ShortStack Micro Server provides a simple way to add LonWorks networking to new or existing smart devices. . It implements the LonTalk protocol and provides the physical interface with the LonWorks communication. The ShortStack host processor can be an 8, 16, or 32-bit microprocessor or microcontrollers. The ShortStack API and driver typically require about 4kbytes of program memory on the host processor and less than 200 bytes of RAM. The interface between host processor and the ShortStack Micro Server may be a Serial Communication Interface (SCI). The LonWorks control module with a high performance is developed, which is composed of the 8 bit PIC Microprocessor for host processor and the smart neuron chip for the ShortStack Micro Server. This intelligent control board is verified as proceeding the various function tests from experimental system with an boost pump and inverter driving systems. It is also confirmed that the developed control module provides stably 0-10VDC linear signal to the input signal of inverter driving system for varying the induction motor speed. Thus, the experimental results show that the fabricating intelligent board carried out very well the various functions in the wide operating ranges of boost pump system. This developed control module expect to apply to industrial fields to require the comparatively exact control and monitoring such as multi-motor driving system with inverter, variable air volume system and the boost pump water supply systems.

• 한국정보통신학회논문지
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• 제15권6호
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• pp.1213-1220
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• 2011

본 논문에서는 분류자들 속에 중요도 정보를 삽입하여 동적 중요도 결정이 가능한 앙상블 시스템을 제안하였다. 그동안 앙상블 시스템에서 중요도는 훈련이 끝나고 결정된 중요도를 사용하였다. 한 번 결정된 중요도는 테스트 데이터에 상관없이 정적으로 사용되었다. 이 문제를 푸는 방법으로 관문 네트워크에서 구조적으로 계층을 두는 프로세스를 추가하여 동적 중요도 결정이 가능하게 하는 방법이 있지만 프로세스가 추가된다는 단점이 있다. 본 논문에서는 이런 추가적인 프로세스 없이 간단하게 동적 중요도 결정이 가능한 방법을 보여주고 구조적 변경 없이 기존의 시스템에 쉽게 적용할 수 있으며 AdaBoost보다 나은 성능을 보여주는 알고리즘을 제안한다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2018년도 전력전자학술대회
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• pp.192-194
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• 2018

In this paper a new single-stage ac-dc converter with high frequency isolation and low components count is introduced. The proposed converter is constructed using two interleaved boost circuits in the grid side and non-regulating full bridge in the DC side. An optimized switching is implemented on the two interleaved boost circuits resulting in a ripple-free grid current without a ripple cancellation network; hence very small filter inductors are used. A simple and reliable closed-loop control system is easily implemented, since the phase-shift angle is the only independent variable. Moreover, current imbalance is avoided in the presented topology without current control loop in each phase. The proposed charger charges the battery with a sinusoidal-like current instead of a constant direct current. ZVS turn on of all switches is achieved throughout the operation in both directions of power flow without any additional components.

• Steel and Composite Structures
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• 제46권2호
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• pp.153-173
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• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.367-373
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• 2022

Femtocells have recently been recognized for their potential to boost network capacity, improve end-user QoS and throughput, and do so at a cheap cost and with ease of implementation. The use of femtocells in indoor environments, such as residential buildings with neighboring homes, is becoming more popular. Femtocells are subject to interference from other femtocells, and the unwanted effects of interference are amplified when femtocells are deployed in close proximity to one another. As a consequence, the network's overall performance is degraded to a significant degree. One of the strategies that is thought to be effective in reducing the impact of interference is altering the transmission power of the femtocells. In this paper, a dynamic downlink transmission power of femtocells is suggested. In accordance with the observed cost function unit, each femtocell automatically changes its transmission power. If a femtocell causes too much interference for its neighbors, its transmission power level will be limited by that interference's rate. A simulation experiment is conducted to validate the effectiveness of the suggested system when compared with other schemes. When compared to previous schemes, which are addressed in this study, the numerical results show that the proposed strategy could provide more capacity while also ideally mitigating the influence of interference among co-channel deployed femtocells.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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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.

• 산업진흥연구
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• 제7권1호
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• pp.1-8
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• 2022

본 논문은 중도중단 데이터가 포함된 생존데이터의 경우 적용할 수 있는 기계학습 방법에 대해 살펴보았다. 우선 탐색적인 자료분석으로 각 특성에 대한 분포, 여러 특성들 간의 관계 및 중요도 순위를 파악할 수 있었다. 다음으로 독립변수에 해당하는 여러 특성들과 종속변수에 해당하는 특성(사망여부) 간의 관계를 분류문제로 보고 logistic regression, K nearest neighbor 등의 기계학습 방법들을 적용해본 결과 적은 수의 데이터이지만 통상적인 기계학습 결과에서와 같이 logistic regression보다는 random forest가 성능이 더 좋게 나왔다. 하지만 근래에 성능이 좋다고 하는 artificial neural network나 gradient boost와 같은 기계학습 방법은 성능이 월등히 좋게 나오지 않았는데, 그 이유는 주어진 데이터가 빅데이터가 아니기 때문인 것으로 판명된다. 마지막으로 Kaplan-Meier나 Cox의 비례위험모델과 같은 통상적인 생존분석 방법을 적용하여 어떤 독립변수가 종속변수 (t_i, δ_i)에 결정적인 영향을 미치는지 살펴볼 수 있었으며, 기계학습 방법에 속하는 random forest를 중도중단 데이터가 포함된 생존데이터에도 적용하여 성능을 평가할 수 있었다.