• 한국컴퓨터정보학회논문지
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• 제25권4호
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• pp.71-77
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• 2020

블록체인의 핵심기술인 합의알고리즘을 통하여 연결 노드 간 동일한 데이터를 정확하게 공유한다. 사용자 및 활용 환경을 고려한 적절한 합의 알고리즘 사용은 데이터 무결성 및 정확성 등을 효율적으로 유지하도록 한다. 본 논문에서는 프라이빗 블록체인 플랫폼의 특징을 고려하여 허가된 노드 간 합의 알고리즘 효율적 선정을 위한 성능평가방법을 제시하였으며 권위를 가진 연결노드의 수를 고려하여 해당 항목을 기존 공개된 수식에 변형하여 적용하였다. 이러한 과정을 통하여 노드 간 안정성을 고려한 합의과정의 단순화가 가능하였다. 제안한 연구내용을 통한 적절한 합의 알고리즘 선정을 통하여 합의 과정의 안정성을 높일 수 있다.

• Journal of Power Electronics
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• 제19권3호
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• pp.655-664
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• 2019

In order to analyze the nonlinear phenomena of the bifurcation and chaos caused by the switching of nonlinear switching devices in inductively coupled power transfer (ICPT) systems, a Jacobian matrix model, based on discrete mapping numerical modeling, is established to judge the system stability of the periodic closed orbit and to study the nonlinear behavior of Hopf bifurcation in a system under full resonance. The general flow of the parameter design, based on the stability principle for ICPT systems, is proposed to avoid the chaos and bifurcation phenomena caused by unreasonable parameter selection. Firstly, based on the state equation of SS-type compensation, a three-dimensional bifurcation diagram with the coupling coefficient as the bifurcation parameter is established with a numerical simulation to observe the nonlinear phenomena in the system. Then Filippov's method based on a Jacobian matrix model is adopted to deduce the boundary of stable operation and to judge the type of the bifurcation in the system. Then the general flow of the parameter design based on the stability principle for ICPT systems is proposed through the above analysis to realize stable operation under the conditions of weak coupling. Finally, an experimental platform is built to confirm the correctness of the numerical simulation and modeling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2069-2085
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• 2021

A Social Networking Service (SNS) platform provides digital footprints to discover users' interests and track the social diffusion of product adoptions. How to identify a small set of seed users in a SNS who is potential to adopt a new promoting product with high probability, is a key question in social networks. Existing works approached this as a social influence maximization problem. However, these approaches relied heavily on text information for topic modeling and neglected the impact of seed users' relation in the model. To this end, in this paper, we first develop a general product adoption function integrating both users' interest and social influence, where the user interest model relies on historical user behavior and the seed users' evaluations without any text information. Accordingly, we formulate a product adoption maximization problem and prove NP-hardness of this problem. We then design an efficient algorithm to solve this problem. We further devise a method to automatically learn the parameter in the proposed adoption function from users' past behaviors. Finally, experimental results show the soundness of our proposed adoption decision function and the effectiveness of the proposed seed selection method for product adoption maximization.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.286-293
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• 2021

Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.613-624
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• 2021

The use of social networking sites (SNS) for e-recruitment has shifted the focus away from traditional hiring and selection processes. They are commonly used in the search and acquisition of new employees and are projected to expand in the near future as an e-recruitment tool. However, there is a lack of material on SNS and their impact on an employers' intention to use these sites for e-recruitment, in the context of Malaysia. Hence, there is an acute necessity for research on the extent that the features of SNS can influence the employers' intention to use SNS for e-recruitment and to know how to keep utilizing the platform for future e-recruitment. This study aims to identify the key features of SNS that lead to employers' intention to use SNS for e-recruitment in Malaysia. In this cross-sectional study, random sampling was utilized to obtain data from 198 recruitment professionals using online survey. The findings show that data quality, reliability, networking spectrum and simplicity of navigation of SNS are the key predicting factors for intention to use SNS for e-recruitment. Therefore, employers should acknowledge these key features of SNS to achieve their e-recruitment goals.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• Smart Structures and Systems
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• 제31권1호
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• pp.69-78
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• 2023

The purpose of this paper is to develop a scalable grey predictive controller with unavoidable random delays. Grey prediction is proposed to solve problems caused by incorrect parameter selection and to eliminate the effects of dynamic coupling between degrees of freedom (DOFs) in nonlinear systems. To address the stability problem, this study develops an improved gray-predictive adaptive fuzzy controller, which can not only solve the implementation problem by determining the stability of the system, but also apply the Linear Matrix Inequality (LMI) law to calculate Fuzzy change parameters. Fuzzy logic controllers manipulate robotic systems to improve their control performance. The stability is proved using Lyapunov stability theorem. In this article, the authors compare different controllers and the proposed predictive controller can significantly reduce the vibration of offshore platforms while keeping the required control force within an ideal small range. This paper presents a robust fuzzy control design that uses a model-based approach to overcome the effects of modeling errors. To guarantee the asymptotic stability of large nonlinear systems with multiple lags, the stability criterion is derived from the direct Lyapunov method. Based on this criterion and a distributed control system, a set of model-based fuzzy controllers is synthesized to stabilize large-scale nonlinear systems with multiple delays.

• 유통과학연구
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• 제22권4호
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• pp.91-104
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• 2024

Purpose: The study aims to assess the current status of domestic public food delivery apps and analyze the process through which sellers choose between private delivery apps and public delivery apps. This involves exploring strategiesto achieve the original purpose of public food delivery apps, which is to enhance the small business owners income and promote consumer welfare by preventing the monopoly of private food delivery apps. Research design, data and methodology: the research methodology is based on a model that introduces adjustments for non-economic effects, considering the preferences of multi-homing consumers, to more realistically reflect the benefits of sellers' choices. For data analysis, real business performance data from 'Daeguro', 'Meokkaebi', and 'Somunnan Shop' were used. Results: The study revealed that if the market share of public delivery apps within a specific region increases beyond a certain level, the benefits for small-business sellers also increase. This leads to the strategic advantage of simultaneously using both delivery apps. Furthermore, the results exhibit a tendency similar to real social phenomena. Conclusions: This analysis confirmed the role of public food delivery apps in the domestic delivery app market and presents policy recommendations, including application integration and the implementation of exclusive public interest functions, to effectively fulfill this role.

• 한국정보전자통신기술학회논문지
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• 제11권6호
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• pp.638-644
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• 2018

최근 5G에 기술에서는 신호 감쇄와 신호 도달 거리 확장을 위해 사용될 수 있는 릴레이(Relay)를 통한 통신 기술이 주목 받고 있다. 릴레이는 소형 기지국으로 사용이 가능하며, 셀룰러 망으로 지원하기 어려운 환경하에서 통신 기기들이 협력하여 통신하는 자율 네트워크 기법에 사용이 가능하기 때문에, 저전력화와 무선 용량 증대에 활용이 가능할 것으로 기대되고 있다. 한정된 릴레이 자원을 활용하여 최적의 성능을 달성하기 위해서는 효과적인 릴레이 선택 기법이 필요하다. 특히, 두 개의 노드가 릴레이를 통해 메시지를 교환하는 경우, 릴레이 선택 방법에 따라서, 릴레이의 공간적 위치를 활용하여 간섭을 줄이고, 시스템 전송률을 최대화 할 수 있다. 이를 위해서는 릴레이 선택에 따른 평균 데이터 전송률에 대한 분석이 선행되어야 한다. 본 논문은 두 노드가 릴레이를 이용하여 동시 전송을 통해 메시지를 교환할 경우, 평균 데이터 전송률을 분석한다. 이를 위해 Nakagami-m 페이딩 채널 환경하에서 복호 후 전송(Decode and Forward) 방식으로 동작하는 이중홉(dual-hop) 릴레이의 동시 전송으로 인한 간섭을 고려하여 전체 데이터 전송률을 유도한다. 분석식은 m=1인 Rayleigh 페이딩 채널을 포함하여 다양한 Nakagami-m 페이딩 채널에 대한 전체 데이터 전송률을 보여준다. 유도된 분석은 몬테카를로 모의실험을 통해 정확성을 입증하였으며, 요구되는 데이터 전송률이 높을수록, 자원 효율적인 동시 전송 방식이 전체 시스템의 성능을 향상시킬 수 있음을 확인하였다.

• 한국응용과학기술학회지
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• 제39권5호
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• pp.701-709
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• 2022

본 연구의 목적은 국내 발레단 소속 여자 무용수를 대상으로 연속 회전점프 시 시각선택과 회전순서에 따른 차이를 도약과 착지구간으로 분류하여 비교하였다. 10명의 대상자(연령: 26.0±2.9 yrs, 신장: 163.4±3.3 cm, 체중: 46.8±3.6 kg, 발레경력: 12.3±5.9 yrs)가 연구에 참여하였다. 3차원 동작분석기와 지면반력기를 이용하여 신체중심의 높이와 도약과 착지 시 지면반력을 측정하였다. 시각선택(양눈 사용, 왼눈 사용, 오른눈 사용)과 회전순서(첫번째 회전점프, 두 번째 회전점프)에 따른 차이를 반복측정 이원변량 분석을 통하여 분석하였다. 신체중심의 높이는 첫 번째 회전점프가 높게 나타났다. 도약 시 지면반력의 좌우힘은 좌우발 모두 두 번째 회전점프에서 왼발은 외측힘, 오른발은 내측힘이 강하게 나타났고, 전후힘은 오른발에서 첫 번째 회전점프 시 전방힘이 강하게 나타났으며, 수직힘은 좌우발 모두 차이가 없었다. 착지 시 전후힘은 왼발에서 두 번째 착지에서 후방힘이 강하게 나타났고, 오른발은 왼쪽 시각 사용에서 후방힘이 강하게 나타났다. 수직힘은 왼발에서 두 번째 착지, 오른발은 첫 번째 착지에서 강하게 나타났다.