• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1995년도 추계학술대회 논문집
• /
• pp.672-675
• /
• 1995

Because of a sudden growth of the research of fatigue failure, recent machines or structures have been designed by damage tolerance design in many fields. Consequently, it is the most primary factor to clarity the specific character of fatique failure in the design of machines or structures considering reliability. A statistical analysis is required to analyze the outcome of an experiment or a life estimate by reason of that fatigue failure contains lots of random elements. Reliability analysis which has tukenn the place of the existing analyses in the consideration of the uncertainty of a material, is a very efficient way. Even reliability analysis, however, is not a perfect way to analyses the uncertainties of all the materials. This thesis would refer to a newly conceived data analysis that the coefficient of a system could cause the ambiguity of the relationship of an input and output.

• 한국군사과학기술학회지
• /
• 제12권2호
• /
• pp.139-145
• /
• 2009

This paper includes that there is the way to make the prediction of the remaining life of the barrel in small arms using analyzing dispersion. There are some ways to know the period to change the barrel such as the method of detecting the inner surface directly or inspecting the scratch using the optical sensor. However, it is a more easy way to check the dispersion for soldiers and the directors in a logistics command. Therefore, this study is conducted to focusing on the relation between firing round and dispersion. And the simple equation experimentally derives from pre-tests and analyses. Also, this equation is confirmed through the firing tests during the period of developing K11. In that sense, it can be easily applied to know the period of changing the barrel of small arms in the field army.

• 한국소음진동공학회논문집
• /
• 제16권3호
• /
• pp.270-282
• /
• 2006

In general, the vertical vibration problems for strength of members and serviceability of building structures are not considered in structural design process, but the prediction of the vertical vibration is very important and essential to structural design process. This study aims to investigate the characteristics of vertical vibration in terms of the transfer of horizontal directions to near-rooms on the shear wall building structures. In order to examine the characteristics of vertical vibration, the modal test and the impact (heel-drop and hammer) excitation experiments were conducted several times on two building structure. The results from the experiments are analyzed and compared with the results. The results of this study suggest that the characteristics of vertical vibration transfer in horizontal way are effected from the fundamental frequency of the slabs, and are effected the shear wall on the Path of the vibration transfer.

• 대한간호학회지
• /
• 제45권3호
• /
• pp.429-438
• /
• 2015

Purpose: This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). Methods: This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. Results: We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. Conclusion: KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

• Journal of Computing Science and Engineering
• /
• 제10권2호
• /
• pp.58-67
• /
• 2016

With the large amount of complex network data that is increasingly available on the Web, link prediction has become a popular data-mining research field. The focus of this paper is on a link-prediction task that can be formulated as a binary classification problem in complex networks. To solve this link-prediction problem, a sparse-classification algorithm called "Truncated Kernel Projection Machine" that is based on empirical-feature selection is proposed. The proposed algorithm is a novel way to achieve a realization of sparse empirical-feature-based learning that is different from those of the regularized kernel-projection machines. The algorithm is more appealing than those of the previous outstanding learning machines since it can be computed efficiently, and it is also implemented easily and stably during the link-prediction task. The algorithm is applied here for link-prediction tasks in different complex networks, and an investigation of several classification algorithms was performed for comparison. The experimental results show that the proposed algorithm outperformed the compared algorithms in several key indices with a smaller number of test errors and greater stability.

• International Journal of Computer Science & Network Security
• /
• 제22권10호
• /
• pp.171-176
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• 제29권2호
• /
• pp.251-261
• /
• 2022

As AI has a wide range of influence on human social life, issues of transparency and ethics of AI are emerging. In particular, it is widely known that due to the existence of historical bias in data against ethics or regulatory frameworks for fairness, trained AI models based on such biased data could also impose bias or unfairness against a certain sensitive group (e.g., non-white, women). Demographic disparities due to AI, which refer to socially unacceptable bias that an AI model favors certain groups (e.g., white, men) over other groups (e.g., black, women), have been observed frequently in many applications of AI and many studies have been done recently to develop AI algorithms which remove or alleviate such demographic disparities in trained AI models. In this paper, we consider a problem of using the information in the sensitive variable for fair prediction when using the sensitive variable as a part of input variables is prohibitive by laws or regulations to avoid unfairness. As a way of reflecting the information in the sensitive variable to prediction, we consider a two-stage procedure. First, the sensitive variable is fully included in the learning phase to have a prediction model depending on the sensitive variable, and then an imputed sensitive variable is used in the prediction phase. The aim of this paper is to evaluate this procedure by analyzing several benchmark datasets. We illustrate that using an imputed sensitive variable is helpful to improve prediction accuracies without hampering the degree of fairness much.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
• /
• 한국우주과학회 1992년도 한국우주과학회보 제2권1호
• /
• pp.20-20
• /
• 1993

An accurate prediction of the satellite ground track is essential to optimise the maneuver design. It requres a prediction model that considers all perturflations that cause significant variations in the satellite ground track. We developed a prediction model that includes the effects of the fifth-order zonal harmonics, atmospheric drag, and luni-solar gravitational perturbations. Luni-solar gravity perturbations have been obtained in an ether way which is different from the method we used in previous paper(Yi and Choi, 1992). In this case, we consrtuct our own disturbing fuction including inclination term by the Algebraic Manipulation. Luni-solar perturbations reduce the maneuver magnitude required to offset eastward ground track drift due to drag, the amount dependent on current luni-solar phasing geometry.

• 한국지구물리탐사학회:학술대회논문집
• /
• 한국지구물리탐사학회 2003년도 Proceedings of the international symposium on the fusion technology
• /
• pp.252-254
• /
• 2003

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. ill addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.

• 한국자동차공학회논문집
• /
• 제3권6호
• /
• pp.154-161
• /
• 1995

There are several kinds of driving conditions according to the characteristic of each vehicle diver. Automaker produces vehicle strong enough to satisfy this several driving conditions at the point of vehicle durability. In order to develop the car in a short period, Automaker engineer tests vehicle at serveral accelerated durability test roads. Before testing the vehicle durability, test engineer must know how much this test road severe than general field road which is composed of high way, city road, paved road and unpaved road. This paper suggests two types of road severity test method that is using relative fatigue life prediction method and using absolute fatigue life prediction method, and also present the merits and demerits of two test methods.