• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.202-204
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• 2013

요즘에는 하드웨어의 발달 덕분에, 많은 기업들이 과거에 사용했던 데이터보다 훨씬 많은 양의 데이터를 조작하고 관리해야만 한다. 이런 이유에서, 기업들은 폭발적으로 증가하는 데이터를 수집하고 저장하고 다루기 위해서, 체계화된 도구, 플랫폼, 분석 방법론을 끊임없이 긴급하게 필요로 하고 있다. 본 논문에서는 우선 빅 데이터의 주요 요소를 이해하고, 둘째로 이러한 요소들을 활용한 빅 데이터 애플리케이션을 위한 주요 요소를 정의한다. 셋째로, 빅 데이터 분석을 위한 다양한 분석 기법에 대해 연구하고, 마지막으로 빅 데이터 분석을 위한 인프라를 제안한다.

• 천문학회지
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• 제52권5호
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• pp.207-216
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• 2019

In this paper we introduce a software package, the Very long baseline interferometry Network SIMulator (VNSIM), which provides an integrated platform assisting radio astronomers to design Very Long Baseline Interferometry (VLBI) experiments and evaluate the network performance, with a user-friendly interface. Though VNSIM is primarily motivated by the East Asia VLBI Network, it can also be used for other VLBI networks and generic interferometers. The software package not only integrates the functionality of plotting (u, v) coverage, scheduling the observation, and displaying the dirty and CLEAN images, but also adds new features including sensitivity calculations for a given VLBI network. VNSIM provides flexible interactions on both command line and graphical user interface and offers friendly support for log reports and database management. Multi-processing acceleration is also supported, enabling users to handle large survey data. To facilitate future developments and updates, all simulation functions are encapsulated in separate Python modules, allowing independent invoking and testing. In order to verify the performance of VNSIM, we performed simulations and compared the results with other simulation tools, showing good agreement.

• The Journal of Asian Finance, Economics and Business
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• 제9권4호
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• pp.153-162
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• 2022

Nostalgia advertising is one of the key and effective tools for marketers to capture the attention of consumers. Prior studies have identified two types of nostalgia, personal and historical. The aim of this research is to learn more about historical nostalgia and its influence on consumer brand purchase intentions. A convenience sampling technique was used to obtain a sample of 182 respondents via Google forms. The study then used partial least square structural equation modeling. In PLS-SEM, we applied a measurement model to obtain the results related to reliability, validity, and model fitness. Once the desired results are achieved, the study proceeded to the structural model where results related to hypotheses were obtained. The study's findings corroborated the literature, revealing that historical nostalgia advertisements have a significant impact on consumers' cognition and emotions, leading to an effect on attitudes. The serial process has the effect of influencing consumer buying intentions. Hence, the importance of nostalgia advertising proposed in the study was established through empirical evidence. Policymakers, organizations, and advertising agencies in Pakistan are recommended to implement nostalgia advertising techniques based on the findings and are encouraged to do so for immediate benefits.

• Journal of Korean Dental Science
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• 제15권2호
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• pp.101-111
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• 2022

The purpose of laboratory tests in the field of oral medicine can be divided into two categories: (1) medical evaluation of patients with systemic diseases that are planning to receive dental care and (2) diagnosis of patients with certain oral diseases. First, laboratory tests are commonly used to evaluate patients with systemic diseases who need dental management. A combination of multiple tests is usually prescribed as a test panel to diagnose and assess a specific disease. Test panels closely related to oral medicine include those for rheumatoid arthritis, connective tissue disease/lupus, liver function, thyroid screening, anemia, and bleeding disorders. Second, laboratory tests are used as auxiliary diagnostic methods for certain oral diseases. They often provide crucial diagnostic information for infectious diseases caused by bacteria, fungi, and viruses that are associated with pathology in the oral and maxillofacial regions. Laboratory tests for infectious diseases are composed of growth-dependent methods, immunologic assays, and molecular biology. As the field develops, further application of laboratory tests, including synovial fluid analysis in temporomandibular joint disorders, salivary diagnostics, and hematologic biomarkers associated with temporomandibular disorders and orofacial pain conditions, is currently under scrutiny for their reliability as diagnostic tools.

• Fisheries and Aquatic Sciences
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• 제26권4호
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• pp.256-267
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• 2023

The suitability of any fish species for successful aquaculture requires basic information on its reproduction and growth. This study investigated some facets of the reproductive biology of Synodontis shall (Mochokidae) from River Siluko in Nigeria. Fish samples were collected forth-nightly for a duration of fourteen (14) months-March 2015 to April, 2016 with the assistance of artisanal fishermen. Fishes were identified using taxonomic guides and standard techniques were used for determination of sex ratio, gonad maturation and fecundity. Linear regression method was used to define the correlation between fecundity and fish length, body weight and ovary weight. Results showed that sex ratio did not indicate a significant divergence (p > 0.05) from the 1 male to 1 female distribution ratio (1:1.41). Gonad morphology revealed paired gonads. Testes and ovaries were classified into four maturity stages: immature, resting, ripening and ripe. Gonadosomatic index ranged from 0.04 to 5.68 (males) and 0.03 to 20.19 (females). Absolute fecundity ranged from 1,014 to 4,520 eggs (mean = 2,592 eggs) and did not correlate significantly (p > 0.05) to ovary weight. This study has contributed to existing data on the biology of freshwater fish species in Nigeria and provided valuable information for fishery management tools in the conservation and utilization of this valuable freshwater fish species.

• 한국환경보건학회지
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• 제42권4호
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• pp.280-292
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• 2016

Objectives: The objective of this study was to build COD regression models for the Han River and evaluate water quality. Methods: Water quality data sets for the dry season (as of January) during a four-year period (2012-2015) were collected from the database of the Han River automatic water quality monitoring stations. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR) were used to build five-descriptor COD models. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) are useful tools for extracting meaningful information. Results: The $r^2$ of the best COD models provided significant high values (> 0.8) between 2012 and 2015. Total organic carbon (TOC) was a surrogate indicator for COD (as COD/TOC) with high reliability ($r^2=0.63$ in 2012, $r^2=0.75$ for 2013, $r^2=0.79$ for 2014 and $r^2=0.85$ for 2015). The ratios of COD/TOC were calculated as 2.08 in 2012, 1.79 in 2013, 1.52 and 1.45 in 2015, indicating that biodegradability in the water body of the Han River was being sustained, thereby further improving water quality. The BOD/COD ratio supported these findings. The cluster analysis revealed higher annual levels of microorganisms and phosphorous at stations along the Hangang-Seoul and Hantangang areas. Nevertheless, the overall water quality over the last four years showed an observable trend toward continuous improvement. These findings also suggest that non-point pollution control strategies should consider the influence of upstreams and downstreams to protect water quality in the Han River. Conclusion: This data analysis procedure provided an efficient and comprehensive tool to interpret complex water quality data matrices. Results from a trend analysis provided much important information about sources and parameters for Han River water quality management.

• International Journal of Aeronautical and Space Sciences
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• 제10권1호
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• pp.98-103
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• 2009

The forecasting, mitigation and preparedness of the natural disaster impacts require relevant information regarding the disaster desirable in real time. In the meantime it is requiring the rapid and continuous data and information generation or gathering for possible prediction and monitoring of the natural disaster. Since disasters that cause huge social and economic disruptions normally affect large areas or territories and are linked to global change. The use of traditional and conventional methods for management of the natural disaster impact can not be effectively implemented for intial data col1ection with the further processing. The space technology or remote sensing tools offer excellent possibilities of collecting vital data. The main reason is capability of this technology of collecting data at global and regional scales rapidly and repetitively. This is unchallenged advantage of the space methods and technology. The satellite or remote sensing techniques can be used to monitor the current situation, the situation before based on the data in sight. as well as after disaster occurred. They can be used to provide baseline data against which future changes can be compared while the GIS techniques provide a suitable framework for integrating and analyzing the many types of data sources required for disaster monitoring. Developed GIS is an excellent instrument for definition of the social impact status of the natural disaster which can be undertaken in the future database developments. This methodology is a good source for analysis and dynamic change studies of the natural disaster impacts.

• 한국토양비료학회지
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• 제47권6호
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• pp.418-424
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• 2014

The Agricultural Land Remodeling Project was launched for agricultural fields with potential risk of flooding which were placed in low-lying area as a part of agricultural sectors of the National 4 River Project. It induced of the reclaimed agricultural fields on a national scale. The arable lands reestablished by reclamation have caused several big problems such as destruction of soil physicochemical properties, and thus the techniques to solve disadvantages were urgently required. In this study, we collected experimental samples from top soils in three agricultural areas, one from conventional agricultural fields (Hwasun, Jeollanam-do) and the others from reclaimed (remodelled) agricultural fields (Naju, Jeollanam-do and Gumi, Gyeongsangbuk-do), The soil chemistry data were analyzed using statistic tools such as semi-variance and kriging, and differences between natural and reconstructed soils were examined. The score, R (Ao) which indicates a dependence distance between each chemical element, was as follows; 21.8~43.5 (Conventional, Hwasoon), 4.4~70.6 (Remodelled, Naju) and 5.3~43.6 (Remodelled, Gumi). These results suggested that chemical properties of the reclaimed agricultural fields had a huge variation. Moreover, the result of kiriging maps also represented a ununiform pattern in the reclaimed lands. As a result of this study, it is strongly required to build up the soil type-specific management techniques for the reclaimed agricultural lands.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.792-796
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• 2005

The methods of times series analysis have been recognized as important tools for assisting in solving problems related to the management of water resources. Especially, After more than 40 years the so-called Hurst effect remains an open problem in stochastic hydrology. Until now, its existence has been explained fly R/S analysis that roots in early work of the British hydrologist H.E. Hurst(1951). Today, the Hurst analysis is mostly used for the hydrological studies for memory and characteristics of time series and many methodologies have been developed for the analysis. So, there are many different techniques for the estimation of the Hurst exponent(H). However, the techniques can produce different characteristics for the persistence of a time series each other. We found that DFA is the most appropriate technique for the Hurst exponent estimation for both the shot term memory and long term memory. We analyze the SOI(Southern Oscillations Index) and 6 tree-ring series for USA sites by means of DFA and the BDS statistic is used for nonlinearity test of the series. From the results, we found that SOI series is nonlinear time series which has a long term memory of H=0.92. Contrary to earlier work of Rao(1999), all the tree- ring series are not random from our analysis. A certain tree ring series show a long term memory of H=0.97 and nonlinear property. Therefore, we can say that the SOI and tree-ring series may show long memory and nonlinearity.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.