• Genomics & Informatics
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• v.12 no.4
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• pp.254-260
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• 2014

Best linear unbiased prediction (BLUP) has been used to estimate the fixed effects and random effects of complex traits. Traditionally, genomic relationship matrix-based (GRM) and random marker-based BLUP analyses are prevalent to estimate the genetic values of complex traits. We used three methods: GRM-based prediction (G-BLUP), random marker-based prediction using an identity matrix (so-called single-nucleotide polymorphism [SNP]-BLUP), and SNP-SNP variance-covariance matrix (so-called SNP-GBLUP). We used 35,675 SNPs and R package "rrBLUP" for the BLUP analysis. The SNP-SNP relationship matrix was calculated using the GRM and Sherman-Morrison-Woodbury lemma. The SNP-GBLUP result was very similar to G-BLUP in the prediction of genetic values. However, there were many discrepancies between SNP-BLUP and the other two BLUPs. SNP-GBLUP has the merit to be able to predict genetic values through SNP effects.

• Journal of the Korean GEO-environmental Society
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• v.20 no.10
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• pp.29-38
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• 2019

In stability and settlement management of soft ground, the settlement prediction technology has been continuously developed and used to reduce construction cost and confirm the exact land use time. However, the preexistence prediction methods such as hyperbolic method, Asaoka method and Hoshino method are difficult to predict the settlement accurately at the beginning of consolidation because the accurate settlement prediction is possible only after many measurement periods have passed. It is judged as the reason for estimating the future settlement through the proportionality assumption of the slope which the preexistence prediction method computes from the settlement curve. In this study, ARIMA technique is introduced among time series analysis techniques and compared with preexistence prediction methods. ARIMA method was predictable without any distinction of ground conditions, and the results similar to the existing method are predicted early (final settlement).

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• Architectural research
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• v.24 no.1
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• pp.1-11
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• 2022

As the occurrence of a crime is dependent on different factors, their correlations are beyond the ordinary cognitive range. Owing to this limitation, systems face difficulty in correlating various factors, thereby requiring the assistance of artificial intelligence (AI) to overcome such limitations. Therefore, AI has become indispensable for crime prediction. Crimes can cause severe and irrevocable damage to a society. Recently, big data has been introduced for developing highly accurate models for crime prediction. Prediction of night crimes should be given significant consideration, because crimes primarily occur during nights, when the spatiotemporal characteristics become vulnerable to crimes. Many environmental factors that influence crime rate are applied for crime prediction, and their influence on crime rate may differ based on temporal characteristics and the nature of crime. This study aims to identify the environmental factors that influence sex and theft crimes occurring at night and proposes an artificial neural network (ANN) model to predict sex and theft crimes at night in random areas. The crime data of A district in Seoul for 12 years (2004-2015) was used, and environmental factors that influence sex and theft crimes were derived through multiple regression analysis. Two types of crime prediction models were developed: Type A using all environmental factors as input data; Type B with only the significant factors (obtained from regression analysis) as input data. The Type B model exhibited a greater accuracy than Type A, by 3.26 and 9.47 % higher for theft and sex crimes, respectively.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.167-176
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• 2023

This study presents the performance analysis of real-time orbit determination and prediction for navigation message generation of Regional Navigation Satellite System (RNSS). Since the accuracy of ephemeris and clock correction in navigation message affects the positioning accuracy of the user, it is essential to construct a ground segment that can generate this information precisely when designing a new navigation satellite system. Based on a real-time architecture by an extended Kalman filter, we simulated orbit determination and prediction of RNSS satellites in order to assess the accuracy of orbit and clock prediction and signal-in-space ranging errors (SISRE). As a result of the simulation, the orbit and clock accuracy was at 0.5 m and 2 m levels for 24 hour determination and six hour prediction after the determination, respectively. From the prediction result, we verified that the SISRE of RNSS for six hour prediction was at a 1 m level.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.589-594
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• 2015

In reflection of road expansion and increasing use rates, interest has blossomed in predicting driving environment. In addition, a gigantic scale of big data is applied to almost every area around the world. Recently, technology development is being promoted in the area of road traffic particularly for traffic information service and analysis system in utilization of big data. This study examines actual cases of road management systems and road information analysis technologies, home and abroad. Based on the result, the limitations of existing technologies and road management systems are analyzed. In this study, a development direction and expected effort of the prediction of road information are presented. This study also examines regression analysis about relationship between guide name and traffic volume. According to the development of driving environment prediction platform, it will be possible to serve more reliable road information and also it will make safe and smart road infrastructures.

• Journal of the Korean Institute of Landscape Architecture
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• v.25 no.3
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• pp.89-100
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• 1997

The purpose of this paper was to analyze the content of impact prediction in EISS, in order to find the degree of the acuracy of impact prediction . 30 EISS were selected as analysis objects through variance miximization strategy. Content analysis of the selected EISS was performed by 5 analysis items, such as quantification of measurement, range of impact area, time frame of impact, likelihood of impact, and explict characterization of impact significance. The results showed that the accuracy investigated by the 5 items was very low. In conclusion, 5 suggestions were proposed in order to improve the credibility of EIS as a scientific report. The 5 suggestions were : 1) impact prediction should be described by quantitative measurement; 2) In establishing the time frame of the impact and the referent populatioin influenced by the impact, the characteristics of the proposed action should be carefully considerd; 3) the significance of the predicted impact should be quantitatively described; 4) specific description should also be used in the likelihood or the probability of the predicted impact in a real world; 5) equal emphasis should be put on the three environment, including natural and social as well as living environment.

• Transactions of Materials Processing
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• v.16 no.6
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• pp.479-487
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• 2007

Die life is generally estimated taking failure life and wear amount into consideration. In this study, the forging die life was investigated considering both of these two factors. The fatigue life prediction for the die was performed using the stress-life method, i.e. Goodman's and Gerber's equations. The Archard's wear model was used in the wear life simulation. These die life prediction techniques were applied to the die used in the forging process of the socket ball joint of a transportation system. A rigid-plastic finite element analysis for the die forging process of the socket ball was carried out and also the elastic stress analysis for the die set was performed in order to get basic data for the die fatigue life prediction. The wear volume of the die was measured using a 3-dimensional measurement apparatus. The simulation results were relatively in good agreement with the experimental measurements.

• Structural Engineering and Mechanics
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• v.9 no.6
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• pp.557-568
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• 2000

As time-variant reliability approaches become increasingly used for service life prediction of the aging infrastructure, the demand for computer solution methods continues to increase. Effcient computer techniques have become well established for the reliability analysis of structural systems. Thus far, however, this is largely limited to time-invariant reliability problems. Therefore, the requirements for time-variant reliability prediction of deteriorating structural systems under time-variant loads have remained incomplete. This study presents a computer program for $\underline{REL}$iability of $\underline{T}$ime-Variant $\underline{SYS}$tems, RELTSYS. This program uses a combined technique of adaptive importance sampling, numerical integration, and fault tree analysis to compute time-variant reliabilities of individual components and systems. Time-invariant quantities are generated using Monte Carlo simulation, whereas time-variant quantities are evaluated using numerical integration. Load distribution and post-failure redistribution are considered using fault tree analysis. The strengths and limitations of RELTSYS are presented via a numerical example.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.738-741
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• 2003

In general, the essential requirement for cruisers or car ferries is the reduction in noise to ensure high quality and comfort. Recently, the Ro-Ro Passengers Ferry (ROPAX) was built in Hyundai Heavy Industries. In order to minimize the noise levels, careful attention have to De paid by the special committee of experts from the initial design stage to the sea trial. Proper countermeasures, considering the characteristics of sources and receiver spaces, were applied from the noise prediction and various experiment results. Finally, this ship was successfully delivered with excellent noise properties. This paper describes the procedure of noise analysis, the countermeasures of noise control, and the measurement results of the sea trial. Onboard noise analysis had been carried out by statistical energy analysis program and outdoor noise prediction program based on ISO9614. The prediction results are in good agreements with the measurement results. The technology to minimize the noise levels for cruisers or car ferries has been established throughout the construction of this ship.