Development of Daily Peak Demand Forecasting Algorithm Using Recurrent Neural Network

Fauzan Hanif Jufri,Wonjun Lee,Youngil Kim,Jaesung Jung 대한전기학회 2017 대한전기학회 학술대회 논문집 Vol.2017 No.11

Development of Photovoltaic Failure Detection Model Using k-Nearest Neighbors Algorithm

Fauzan Hanif Jufri(파우잔 한니프 주프리),Jaesung Jung(정재성) 대한전기학회 2018 대한전기학회 학술대회 논문집 Vol.2018 No.-

Day-Ahead System Marginal Price Forecasting Using Artificial Neural Network and Similar-Days Information

Fauzan Hanif Jufri,오성문,정재성 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.2

Day-ahead system marginal price (SMP) forecasting constitutes essential information in the competitive energy market. Hence, this paper presents the development of a day-ahead SMP forecasting model via implementing an artifi cial neural network (ANN) algorithm. The accuracy of the ANN-based model is improved by including long-term historical data in addition to short-term historical data and by applying the k -fold cross-validation optimization algorithm. The selection of the short-term type input variable applies the Pearson correlation coeffi cient. Whereas the long-term type input variable is selected by applying the discrete Fréchet distance in conjunction with the information related to the season and type of the day to fi nd the Similar-Days Index. In order to verify the model, the forecasted load and actual SMP for 15 years of historical data are used. The results indicate that the proposed model can forecast SMP with higher accuracy than the conventional forecasting model.

Comparison study on yield and ash content of solvent-extracted coal and torrefied biomass by 1-methylnaphthalene

Fauzan Ryan Fitrian Sofwan,Chun Donghyuk(전동혁),Yoo Jiho(유지호),Kim Sangdo(김상도),Lim Jeonghwan(임정환),Rhim Youngjoon(임영준),Lee Sihyun(이시훈),Choi Hokyung(최호경) 한국에너지기후변화학회 2016 한국에너지기후변화학회 학술대회 Vol.2016 No.11

Drug induced liver injury (DILI) causes: Literatur review

Rahmat FAUZAN 한국간담췌외과학회 2022 Annals of hepato-biliary-pancreatic surgery Vol.26 No.-

The Proposal for the Model of Users' Addictions in Social Gaming

Tengku Fauzan,송승근 한국만화애니메이션학회 2015 만화애니메이션연구 Vol.- No.40

The objective of this study proposes the new user's addiction model in 'Social Network Games' (SNGs). Research model is derived from the separation of two characteristics. First one is logical characteristics that includes 'Functional' (F), 'Keystroke' (K), and 'Goal' (G). Second one is feeling characteristics that consists a few factors such as 'Emotion' (E), 'Social' (S), and 'Affection' (A). For the pre-test, a total of 30 participants responded to survey in order to inspect the fitness of research questionnaire, roughly validity of the proposed model, and the direction of this reseach. After that for the main test, a total 300 users participated in this research. The final number of effective participants were 261 because 39 were insincere respondents and without playing SNGs who were excluded. Then we examined the measurement model by performing 'Partial Least Squares - Structural Equation Modeling' (PLS-SEM) analysis to test the research hypothesis empirically. The results of the measurement and structural model test lend support to the proposed research model by providing a good fit to the construct data. Interestingly, the model showed the significant effects of the interaction between eleven hypothesis(H1,H2,H3,H4,H5,H6,H7,H8,H9,H10, H12). Only one hypothesis decision t-value not supported that is involved the relationship between SNGs Addiction and Keystroke, H11(1.193). This research expect to contributes to an exploratory SNGs research to clarify the base of addition and will aids understanding of users' behavior associated with SNGs development.

자유낙하식 구명정의 진수시의 고려할 운동 형식에 대한 연구

Ahmad Fauzan Zakki,배동명(Dong Myung Bae),조박(Cao Bo) 대한조선학회 2012 대한조선학회 학술대회자료집 Vol.2012 No.5

The freefall lifeboats have been designed to be fast and reliable evacuation system. Once the occupants have gone on board, the lifeboat is simply sliding from a skid before the free-fall. Some second after the water impact, the propulsion system can be started and the lifeboat can sail away from parent vessel. During the launching process, trajectories of free-fall lifeboats can be divided into such categories, depending on the headway and advance speed after water entry and surfacing of the lifeboats. The aim of the paper is investigating the influence of the launching parameters such as, sliding distance, angle of skid and the falling height on the motion pattern of the new type free-fall lifeboats.

The Proposal for the Model of Users" Addictions in Social Gaming

Tengku Fauzan Tengku Anuar,Song Seung Keun(송승근) 한국만화애니메이션학회 2015 만화애니메이션연구 Vol.- No.40

본 연구의 목적은 소셜네트웍게임에서 사용자 중독에 대한 새로운 모델을 제안 하고자 한다. 본 모델은 논리적 특성과 정서적 특성에서 유래한다. 논리적 특성은 기능(F), 키스트록(K), 목표(G)로 구성된다. 정서적 특성은 감정(E), 사교(S), 감성(A)으로 구성된다. 30명의 참가자를 통해서 예비조사를 실시하여 설문문항의 적합성, 대략적인 모형의 타당성 및 연구의 방향성 등을 점검하였다. 이후 본 연구에서 300명의 피험자를 대상으로 조사를 실시하였다. 그중 261명만 채택하였다. 왜냐하면 39명은 SNG 게임을 전혀 해보지 않았으며 설문 응답에 불성실하게 이행하여 본 연구에서 제외하였다. 본 연구는 부분최소자승-구조방정식모델링 기법을 활용하여 가설검증을 하였다. 그 결과 모형적합도가 높게 나타났으며 12가지 가설 가운데 11가지가 유의미한 효과가 발생하였다(H1, H2, H3, H4, H5, H6, H7, H8, H9, H10, H12). 그러나 유일한 가설 H11인 소셜네트워크중독과 키스트록 간에는 유의미한 효과가 발생하지 않았다. 본 연구는 소셜네트워크게임 개발을 위한 사용자 행동을 이해하며 중독의 기저를 밝히는데 기초가 되는 탐색적 연구가 될 것으로 기대된다. The objective of this study proposes the new user"s addiction model in "Social Network Games" (SNGs). Research model is derived from the separation of two characteristics. First one is logical characteristics that includes "Functional" (F), "Keystroke" (K), and "Goal" (G). Second one is feeling characteristics that consists a few factors such as "Emotion" (E), "Social" (S), and "Affection" (A). For the pre-test, a total of 30 participants responded to survey in order to inspect the fitness of research questionnaire, roughly validity of the proposed model, and the direction of this reseach. After that for the main test, a total 300 users participated in this research. The final number of effective participants were 261 because 39 were insincere respondents and without playing SNGs who were excluded. Then we examined the measurement model by performing "Partial Least Squares - Structural Equation Modeling" (PLS-SEM) analysis to test the research hypothesis empirically. The results of the measurement and structural model test lend support to the proposed research model by providing a good fit to the construct data. Interestingly, the model showed the significant effects of the interaction between eleven hypothesis(H1,H2,H3,H4,H5,H6,H7,H8,H9,H10, H12). Only one hypothesis decision t-value not supported that is involved the relationship between SNGs Addiction and Keystroke, H11(1.193). This research expect to contributes to an exploratory SNGs research to clarify the base of addition and will aids understanding of users" behavior associated with SNGs development.

소셜 게임 중독에 대한 메타분석

Tengku Fauzan,송승근(Song, Seung-Keun) 한국만화애니메이션학회 2015 만화애니메이션연구 Vol.- No.41

본 연구는 소셜게임중독에 대한 연구 경향과 특징에 대한 선행연구 고찰을 토대로 살펴보고 향후 연구를 위한 시사점을 제시하는 것을 목적으로 한다. 이를 위해 61개의 연구논문을 선택하여 메타분석을 실시하였다. 특히, 본 연구는 다음과 같은 연구문제를 갖고 있다. 1) 주요 연구 목적과 방법론은 무엇일까? 2) 어떤 연구가 가장 높게 인용이 되었을까? 그 결과, 1) 소셜게임 중독에서 가장 많이 연구한 주제는 소셜게임중독을 평가함에 의한 효과성을 규명하는 것이었다. 2) 조사 혹은 평가 형태의 연구임에도 불구하고 가장 선호하는 연구방법은 설문조사와 실험법이었다. 3) 소셜게임 중독 연구 대상은 주로 고등학생과 대학생이 피험자로 선정되어 연구하였다. 4) 소셜게임 중독 연구는 인문, 사회과학 분야의 연구자들이 빈번하게 연구하였다. 5) 가장 높게 인용한 논문은 영향을 분석하고 모형을 설계하여 소셜게임중독의 효과를 평가하는 효과성 연구에 치우쳐 있다. 그래서 소셜게임중독에서 주요 논쟁은 이전 연구를 보완하고 현재 연구분야에서 더 깊은 학술적 논쟁을 추구하는 중요한 기초를 제공한다. 본 연구는 서로 다른 중요한 관점에서 소셜게임 중독을 이해하는데 도움을 주었을 뿐만 아니라 향후 소셜게임 중독과 관련된 연구자와 교육자에게 유용한 시사점을 제공한다. Previous literature reviews have provided important insights into social game addictions, but the issue still needs to be examined from other directions such as the distribution of research purposes. For this purpose, 61 papers from selected journals were analyzed by a meta-analysis method. Specifically, this study poses the following three research questions: (1) What are the major of research purposes and methodologies? (2) What are the highly cited articles in studies of social game addictions? Results showed five new findings: (1) the research purpose of most social game addictions studies focuses on investigating the effect, followed by evaluating the influence of social game addictions. (2) Surveys and experimental methods were the preferred research methods, regardless of whether the research purpose focused on investigating or evaluation. (3) Social game addictions studies are most prevalent at the game users, higher education institutions, followed by schools. (4) Social game addictions studies most frequently supports researcher in the professions and applied sciences, followed by humanities, formal sciences and social sciences. (5) The most highly cited articles fall into the categories of investigating the effects and followed by evaluating the influence, designing a model and evaluating the effects of social game addictions. In this regard, this study of issues in social game addictions presents findings that can help supplement linkages with previous studies and forms an important reference base to pursue deeper academic discussions in the current research fields. These results and findings not only to supplement understanding of social game addictions based on different and important viewpoints, but also to provide useful insights for researchers and educators into issues related to social game addictions studies in future.

Development of Photovoltaic abnormal condition detection system using combined regression and Support Vector Machine

Jufri, Fauzan Hanif,Oh, Seongmun,Jung, Jaesung Elsevier 2019 ENERGY Vol.176 No.-

<P><B>Abstract</B></P> <P>It is essential to monitor and detect the abnormal conditions in Photovoltaic (PV) system as early as possible to maintain its productivity. This paper presents the development of a PV abnormal condition detection system by combining regression and Support Vector Machine (SVM) models. The regression model is used to estimate the expected power generation under the respective solar irradiance, which is used as the input for the SVM model. The SVM model is then used to identify the abnormal condition of a PV system. The proposed model does not require installing additional measurement devices and can be developed at low cost, because the data that is used as the input variable for the model is retrieved from the Power Conversion System (PCS). Furthermore, the accuracy of the detection system is improved by taking into consideration the daylight time and the interactions between the independent variables, as well as the implementation of the multi-stage k-fold cross-validation technique. The proposed detection system is validated by using actual data retrieved from a PV site, and the results show that it can successfully distinguish the normal condition, as well as identify the abnormal condition of a PV system by using the basic measurements.</P> <P><B>Highlights</B></P> <P> <UL> <LI> PV abnormal condition detection system is developed. </LI> <LI> The model does not require to install any additional measurement devices. </LI> <LI> Regression analysis is employed to estimate the ideal PV generation. </LI> <LI> Support Vector Machine (SVM) algorithm is used to identify PV abnormal condition. </LI> <LI> The proposed detection system is validated by using the actual data. </LI> </UL> </P>