• International Journal of Naval Architecture and Ocean Engineering
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• 제11권2호
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• pp.809-818
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• 2019

The current trend in shipyard industry is to reduce the weight of ships to support the reduction of CO₂ emissions. In this study, the stiffened plate was optimized that is used for building most of the ship-structure. Further, this study proposed the hybrid Genetic Algorithm (GA) technique, which combines a genetic algorithm and subsequent optimization methods. The design variables included the number and type of stiffeners, stiffener spacing, and plate thickness. The number and type of stiffeners are discrete design variables that were optimized using the genetic algorithm. The stiffener spacing and plate thickness are continuous design variables that were determined by subsequent optimization. The plate deformation was classified into global and local displacement, resulting in accurate estimations of the maximum displacement. The optimization result showed that the proposed hybrid GA is effective for obtaining optimal solutions, for all the design variables.

• 대한기계학회논문집A
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• 제26권4호
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• pp.587-595
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• 2002

Recent trend changes a criterion for evaluation of humors that environmental problems are raised as a global issue. Burners with higher thermal efficiency and lower oxygen in the exhaust gas, evaluated better. To comply with environmental regulations, burners must satisfy the NO/sub x/ and CO regulation. Consequently, 'good burner'means one whose thermal efficiency is high under the constraint of NO/sub x/ and CO consistency. To make existing burner satisfy recent criterion, it is highly recommended to develop a feedback control scheme whose output is the consistency of NO/sub x/ and CO. This paper describes the development of a real time flame diagnosis technique that evaluate and diagnose the combustion states, such as consistency of components in exhaust gas, stability of flame in the quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using Neuro-Fuzzy algorithm. This study focuses on the relation of the color of the flame and the state of combustion. Neuro-Fuzzy loaming algorithm is used in obtaining the fuzzy membership function and rules. Using the constructed inference algorithm, the amount of NO/sub x/ and CO of the combustion gas was successfully inferred.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.156-157
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• 2011

In this work, we study the checkpoint/restart problem for message-passing parallel applications running on cloud computing environment. This is a new direction which arises from the trend of enabling the applications to run on the cloud computing environment. The main objective is to propose an efficient checkpoint algorithm for message-passing parallel applications considering communications with external systems. We further implement the novel algorithm by modifying gSOAP and OpenMPI (the open source libraries) which support service calls and checkpoint message-passing parallel programs, especially. The simulation showed that additional costs to the executing and checkpointing application of the algorithm are negligible. Ultimately, the algorithm supports efficiently the checkpoint/restart service for message-passing parallel applications, that send requests to external services.

• Nuclear Engineering and Technology
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• 제55권12호
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• pp.4591-4596
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• 2023

Single-photon emission computed tomography SPECT image reconstruction methods have a significant influence on image quality, with filtered back projection (FBP) and ordered subset expectation maximization (OSEM) being the most commonly used methods. In this study, we proposed newly-designed adaptive non-blind deconvolution with a structural similarity (SSIM) index that can take advantage of the FBP and OSEM image reconstruction methods. After acquiring brain SPECT images, the proposed image was obtained using an algorithm that applied the SSIM metric, defined by predicting the distribution and amount of blurring. As a result of the contrast to noise ratio (CNR) and coefficient of variation evaluation (COV), the resulting image of the proposed algorithm showed a similar trend in spatial resolution to that of FBP, while obtaining values similar to those of OSEM. In addition, we confirmed that the CNR and COV values of the proposed algorithm improved by approximately 1.69 and 1.59 times, respectively, compared with those of the algorithm involving an inappropriate deblurring process. To summarize, we proposed a new type of algorithm that combines the advantages of SPECT image reconstruction techniques and is expected to be applicable in various fields.

• 디지털콘텐츠학회 논문지
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• 제18권3호
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• pp.551-558
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• 2017

블로그의 성장은 다양한 정보제공이라는 긍정적 측면과 마케팅적 활용이라는 부정적 수단으로 사용되고 있는 문제를 가지고 있다. 본 연구는 대형 포털의 블로그 포스트의 랭킹 결과를 OpenAPI를 이용하여 수집하였고, 탐색적 데이터 분석기법을 통해서 상위 랭크된 블로그의 특징들을 조사하였다. 분석 결과를 보면 상위 랭크에 영향을 주는 요소로는 블로거의 영향력과 포스트의 최근 생성일에 관련성이 높은 것을 알 수 있었다. 이런 평가 알고리즘의 약점으로 인해 파워 블로거의 포스트 중심으로 검색 결과를 편중되게 보여주는 문제가 있었다. 본 연구에서는 다양한 대중의 관심사를 나타내는 트렌드 지수를 통해 랭킹 점수 적용의 공정성을 확보하고, 전문가에 의해 검증된 신뢰 DB정보를 추가하여 컨텐츠 신뢰성을 높이는 알고리즘을 제안하였다. 개선된 알고리즘을 맛집 검색 결과가 실제 지역 학생들의 추천 맛집정보와의 유사도가 높은 것을 확인하였다. 개선된 알고리즘으로 좀 더 신뢰할 수 있는 정보제공이 가능해 졌으며, 방문자수 증가시키는 불법 앱에 의한 순위 조작이 어려워지는 부가적 개선 효과가 기대된다.

• 한국정보통신학회논문지
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• 제23권10호
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• pp.1234-1239
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• 2019

본 논문에서는 추가 설비 없이 기존 CCTV 영상을 디지털 영상 처리를 통하여 미세먼지 농도를 측정하는 방법을 제안한다. 영상처리 알고리즘은 노이즈 제거, 샤프닝, ROI 지정, 엣지 강도 계산, HSV 변환을 통한 보정 순으로 구성되며 C++ OpenCV 라이브러리를 이용해 구현하였다. 한달동안 캡쳐한 CCTV 이미지들에 본 알고리즘을 적용한 결과 ROI 영역에 대해 계산된 엣지 강도는 미세먼지 농도와 밀접한 관계가 있는 것으로 나타났다. 두 데이터간 상관관계를 추론하고자 MATLAB을 이용하여 거듭제곱 방정식 형태의 추세선을 수립하였으며 그 추세선으로부터 이탈한 데이터 포인트들의 개수는 12.5% 내외로 나타나 전체적으로 약 87.5%의 정확도를 보였다.

• Journal of Forest and Environmental Science
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• 제28권3호
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• pp.144-151
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• 2012

Deforestation is a major and very critical problem faced by many tropical countries including Malaysia. Sabah is the second largest state in Malaysia and its deforestation rate has been accelerating. This study was conducted to monitor the deforestation in Sabah in the last two decades with Landsat images of 1990, 2000 and 2008. Supervised classification with maximum likelihood algorithm was conducted using the Landsat data for monitoring deforestation. In total, between 1990 and 2008, Sabah lost half of its intact forest, or more than 1.85 million ha in less than two decades. Overall, the deforestation rate for all forest types combined for the last two decades was 1.6% per year. Deforestation seemed to be accelerating because the deforestation rate between 1990 and 2000 was 0.9% per year and it had increased to 2.7% per year between 2000 and 2008. The deforestation trend seemed to follow a negative exponential from 1990 to 2008. In contrast, the agricultural areas increased rapidly with a total of increment more than 1 million ha. This confirmed that agriculture especially establishment of commercial plantation was the major factor of deforestation in Sabah for the last two decades.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• 전자통신동향분석
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• 제35권5호
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• pp.30-42
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• 2020

Unlike in previous generations of mobile technology, machine learning (ML)-based self-healing research trend are currently attracting attention to provide high-quality, effective, and low-cost 5G services that need to operate in the HetNets scenario where various wireless transmission technologies are added. Self-healing plays a vital role in detecting and mitigating the faults, and confirming that there is still room for improvement. We analyzed the research trend in self-healing framework and ML-based fault detection, fault diagnosis, and fault compensation. We propose that to ensure that self-healing is a proactive instead of being reactive, we have to design an ML-based self-healing framework and select a suitable ML algorithm for fault detection, diagnosis, and outage compensation.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1995년도 추계학술발표회논문집(1)
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• pp.293-299
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• 1995

A methodology is applied to identify tile learning trend related to the safety and availability of U.S. commercial nuclear power plants. The application is intended to aid in reducing likelihood of human errors. To assure that tile methodology ran be easily adapted to various types of classification schemes of operation data, a data bank classified by the Transient Analysis Classification and Evaluation(TRACE) scheme is selected for the methodology. The significance criteria for human-initiated events affecting tile systems and for events caused by human deficiencies were used. Clustering analysis was used to identify the learning trend in multi-dimensional histograms. A computer rode is developed based on tile K-Means algorithm and applied to find the learning period in which error rates are monotonously decreasing with plant age.