• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.2
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• pp.62-76
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• 1995

The safety analysis becomes very essential in the crash environment with the growth of automobile industry. Recently, an airbag system is required to protect the occupant. The effects of an airbag can be evaluated exactly from the barrier or sled test which is quite expensive. The airbag system in a passenger car is analyzed with the occupant analysis program. The modeling of the passenger car including an airbag is established and the results are verified by comparisons with real crash tests. However, the solution of an airbag design can not be obtained easily with the conventional method such as an optimization due to the nonlinearity and complexity of the problem. An iterative design algorithm using the orthogonal array is proposed to overcome the difficulties. The design trend of an airbag is recommended to minimize the injury of an occupant with the proposed design algorithm and the results are discussed.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.2
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.4
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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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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• v.55 no.12
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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.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.551-558
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• 2017

The growth of blogs has two aspect of providing various information and marketing. This study collected the rankings of blog posts of large portal using OpenAPI and investigated the features of blogs ranked through the exploratory data analysis technique. As a result of the analysis, it was found that the influence of the blogger and the recent creation date of the post were highly influential factors in the top rank. Due to the weakness of these evaluation algorithms, there was a problem of showing the search results which is concentrated to the power blogger's post. In this study, we propose an algorithm that improves the reliability of content by adding the reliability DB information which is verified by the experts and reflects the fairness of the application of the ranking score through the trend index indicating various public interests. Improved algorithms have made it possible to provide more reliable information in the search results of the relevant field and have an effect of making it difficult to manipulate ranking by illegal applications that increase the number of visitors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1234-1239
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• 2019

This paper proposes a method for measuring fine dust concentration through digital processing of images captured by only existing CCTVs without additional equipment. This image processing algorithm consists of noise reduction, edge sharpening, ROI setting, edge strength calculation, and correction through HSV conversion. This algorithm is implemented using the C ++ OpenCV library. The algorithm was applied to CCTV images captured over a month. The edge strength values calculated for the ROI region are found to be closely related to the fine dust concentration data. To infer the correlation between the two types fo data, a trend line in the form of a power equation is established using MATLAB. The number of data points deviating from the trend line accounts for around 12.5%. Therefore, the overall accuracy is about 87.5%.

• Journal of Forest and Environmental Science
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• v.28 no.3
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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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• v.22 no.1
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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.

• Electronics and Telecommunications Trends
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• v.35 no.5
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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.