• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.59-64
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• 2024

As the aging population grows, the incidence of cancer is increasing. Skin cancer appears externally, but people often don't notice it or simply overlook it. As a result, if the early detection period is missed, the survival rate in the case of late stage cancer is only 7.5-11%. However, the disadvantage of diagnosing, serious skin cancer is that it requires a lot of time and money, such as a detailed examination and cell tests, rather than simple visual diagnosis. To overcome these challenges, we propose an Attention-based CNN model skin cancer classification system. If skin cancer can be detected early, it can be treated quickly, and the proposed system can greatly help the work of a specialist. To mitigate the problem of image data imbalance according to skin cancer type, this skin cancer classification model applies the Over Sampling, technique to data with a high distribution ratio, and adds a pre-learning model without an Attention layer. This model is then compared to the model without the Attention layer. We also plan to solve the data imbalance problem by strengthening data augmentation techniques for specific classes.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.95-126
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• 2009

The present contribution addresses the parallelization of advanced simulation methods for structural reliability analysis, which have recently been developed for large-scale structures with a high number of uncertain parameters. In particular, the Line Sampling method and the Subset Simulation method are considered. The proposed parallel algorithms exploit the parallelism associated with the possibility to simultaneously perform independent FE analyses. For the Line Sampling method a parallelization scheme is proposed both for the actual sampling process, and for the statistical gradient estimation method used to identify the so-called important direction of the Line Sampling scheme. Two parallelization strategies are investigated for the Subset Simulation method: the first one consists in the embarrassingly parallel advancement of distinct Markov chains; in this case the speedup is bounded by the number of chains advanced simultaneously. The second parallel Subset Simulation algorithm utilizes the concept of speculative computing. Speedup measurements in context with the FE model of a multistory building (24,000 DOFs) show the reduction of the wall-clock time to a very viable amount (<10 minutes for Line Sampling and ${\approx}$ 1 hour for Subset Simulation). The measurements, conducted on clusters of multi-core nodes, also indicate a strong sensitivity of the parallel performance to the load level of the nodes, in terms of the number of simultaneously used cores. This performance degradation is related to memory bottlenecks during the modal analysis required during each FE analysis.

• Survey Research
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• v.5 no.2
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• pp.49-70
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• 2004

This paper is a broad review about general regression estimators, which are very useful when auxiliary variables are available in survey sampling. We investigate the process of development of general regression estimators from birth to suggestion of variance estimation method and examine some properties of general regression estimators by comparing with calibration and QR estimators. We also present some forms of general regression estimators available under complex sampling designs such as stratified sampling and cluster sampling. Finally, we comment some advantages as well as disadvantages of general regression estimators and theoretical and practical development in the future.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.62-72
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• 1993

The phased array antenna has the ability to perform adaptive sampling by directing the radar beam without inertia in any direction. The adaptive sampling capability of the phased array antenna allows each sampling time interval to be varied for each target, depending on the acceleration of each target at any time. In this paper we design a three dimensional adaptive target tracking algorithm for the phased array radar system with a given set of measurement parameters. The tracking algorithm avoids taking unnecessarily frequent samples, while keeping the angular prediction error within a fraction of antenna beamwidth so that the probability of detection will not be degraded during a track updata illuminations. In our algorithm, the target model and the sampling rate are selected depending on the target range and the target maneuver status which is determined by a maneuver level detector. A detailed simulation is conducted to test the validity of our tracking algorithm for target trajectories under various conditions of maneuver.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.164-171
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• 2023

To control the spread of COVID-19, it is important to identify the infection in its incipient stages so that the infected persons can be dealt with accordingly. The currently used face to face sampling method may increase the risk of infection for medical professionals as it exposes them to the asymptomatic yet infectious patients. This can result in further increases in the load on the medical system and workload of the medical staff. As a solution to this problem, in this paper, we present a robotic system for rapid non-face-to-face automatic nasopharyngeal swab sample collection. The system consists of a custom designed 7-DoF manipulator equipped with a specially developed safety mechanism for restricting the maximum force applied by the tip of the swab. During the swab sampling process, the force applied by the tip of the swab is continuously monitored in real-time by a 3-axis force sensor in order to detect contact with the nasopharynx. The possibility of using this system for automaticnasopharyngeal swab sample collection is proven through experimentation with a phantom model.

• Transactions of Materials Processing
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• v.29 no.2
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• pp.89-96
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• 2020

The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.

• Journal of the Earthquake Engineering Society of Korea
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• v.19 no.6
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• pp.293-302
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• 2015

ASCE 4 requires that a hard stop be built around the seismic isolation system in nuclear power plants. In order to maintain the function of the isolation system, this hard stop is required to have clearance-to-stop, which should be no less than the 90th-percentile displacements for 150% Design Basis Earthquake (DBE) shaking. Huang et al. calculated clearance-to-stop by using a Latin Hypercube Sampling technique, without considering the rocking behavior of the isolated structure. This paper investigates the effects on estimation of clearance-to-stop due to 1) rocking behavior of the isolated structure and 2) sampling technique for considering the uncertainties of isolation system. This paper explains the simplified analysis model to consider the rocking behavior of the isolated structure, and the input earthquakes recorded at Diablo Canyon in the western United States. In order to more accurately approximate the distribution tail of the horizontal displacement in the isolated structure, a modified Latin Hypercube Sampling technique is proposed, and then this technique was applied to consider the uncertainty of the isolation system. Through the use of this technique, it was found that rocking behavior has no significant effect on horizontal displacement (and thus clearance-to-stop) of the isolated structure, and the modified Latin Hypercube Sampling technique more accurately approximates the distribution tail of the horizontal displacement than the existing Latin Hypercube Sampling technique.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.560-570
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• 2006

This paper proposes a selectively cumulative sum (S-CUSUM) control chart with variable sampling intervals (VSI) for detecting shifts in the process mean. The basic idea of the VSI S-CUSUM chart is to adjust sampling intervals and to accumulate previous samples selectively in order to increase the sensitivity. The VSI S-CUSUM chart employs a threshold limit to determine whether to increase sampling rate as well as to accumulate previous samples or not. If a standardized control statistic falls outside the threshold limit, the next sample is taken with higher sampling rate and is accumulated to calculate the next control statistic. If the control statistic falls within the threshold limit, the next sample is taken with lower sampling rate and only the sample is used to get the control statistic. The VSI S-CUSUM chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L-consecutive control statistics fall outside the threshold limit. The number L is a decision variable and is called a 'control length'. A Markov chain model is employed to describe the VSI S-CUSUM sampling process. Some useful formulae related to the steady state average time-to signal (ATS) for an in-control state and out-of-control state are derived in closed forms. A statistical design procedure for the VSI S-CUSUM chart is proposed. Comparative studies show that the proposed VSI S-CUSUM chart is uniformly superior to the VSI CUSUM chart or to the Exponentially Weighted Moving Average (EWMA) chart with respect to the ATS performance.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.357-371
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• 2014

There are many studies related to imbalanced data in which the class distribution is highly skewed. To address the problem of imbalanced data, previous studies deal with resampling techniques which correct the skewness of the class distribution in each sampled subset by using under-sampling, over-sampling or hybrid-sampling such as SMOTE. Ensemble methods have also alleviated the problem of class imbalanced data. In this paper, we compare around a dozen algorithms that combine the ensemble methods and resampling techniques based on simulated data sets generated by the Backbone model, which can handle the imbalance rate. The results on various real imbalanced data sets are also presented to compare the effectiveness of algorithms. As a result, we highly recommend the resampling technique combining ensemble methods for imbalanced data in which the proportion of the minority class is less than 10%. We also find that each ensemble method has a well-matched sampling technique. The algorithms which combine bagging or random forest ensembles with random undersampling tend to perform well; however, the boosting ensemble appears to perform better with over-sampling. All ensemble methods combined with SMOTE outperform in most situations.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.337-347
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• 2023

Sampling designs often use stratified sampling, where elements or clusters of the study population are divided into strata and an independent sample is chosen from each stratum. The stratification strategy consists of stratification and sample allocation, which are important issues that are repeatedly considered in survey sampling. Although a stratified multistage sample design is often used in practice, the literature tends to discuss simple sampling in terms of stratum effects or stratum efficiency. This study examines an existing stratum efficiency measure for two-stage sampling and further proposes additional stratum efficiency measures using the design effect model. The proposed measures are used to evaluate the stratification strategy of the sample design for high school students of the 4^th Korean National Environmental Health Survey (KoNEHS).