Heeyoung Kim

The mean absolute percentage error (MAPE) is one of the most widely used measures of forecast accuracy, due to its advantages of scale-independency and interpretability. However, MAPE has the significant disadvantage that it produces infinite or undefined values for zero or close-to-zero actual values. In order to address this issue in MAPE, we propose a new measure of forecast accuracy called the mean arctangent absolute percentage error (MAAPE). MAAPE has been developed through looking at MAPE

In semiconductor manufacturing, a wafer bin map (WBM) is a map that consists of assigned bin values for dies based on wafer test results (e.g., value 1 for good dies and value 0 for defective dies). The bin values of adjacent dies are often spatially correlated, forming some systematic defect patterns. These non-random defect patterns occur due to assignable causes; therefore, it is important to identify these systematic defect patterns in order to know the root causes of failure and to take act

The objective of this study is to express the four types of personality of a robot based on Myers-Briggs Type Indicator by controlling the size, speed, and frequency of the gestures of a robot and to examine userpsila impressions of the robot by controlling the gesture design factors. The independent variables were three gesture design factors (speed, velocity, and frequency) with two levels each, producing eight gesture types. The eight gesture types were presented in two robot positions, a spe

In ocean transportation, detecting vessel delays in advance or in real time is important for fourth-party logistics (4PL) in order to fulfill the expectations of customers and to help customers reduce delay costs. However, the early detection of vessel delays faces the challenges of numerous uncertainties, including weather conditions, port congestion, booking issues, and route selection. Recently, 4PLs have adopted advanced tracking technologies such as satellite-based automatic identification

Purpose: This study was conducted to design and implement a fundamental nursing practice based on flipped learning and to examine the effects. Methods: Participants were 57 students who were taking the fundamental nursing practice course at D university in N city. The study included processes of instructional design, action/effects and reflection. Data were analyzed using paired t-test with the SPSS/WIN 23.0. Results: In the instructional design stage, the class consisted of 3 parts: outside cla

In semiconductor manufacturing, the package test is a process that verifies whether the product specifications are satisfied before the semiconductor products are finally shipped to customers. The packaged chips are classified as good or defective according to the verification results. To ensure high-quality products and customer satisfaction, it is important to detect defective chips during the package test. In this article, we consider the problem of predicting potential defects in advance usi

Latent class models have been widely used in longitudinal studies to uncover unobserved heterogeneity in a population and find the characteristics of the latent classes simultaneously using the class allocation probabilities dependent on predictors. However, previous latent class models for longitudinal data suffer from uncertainty in the choice of the number of latent classes. In this study, we propose a Bayesian nonparametric latent class model for longitudinal data, which allows the number of

The spatially varying coefficient process model is a nonstationary approach to explaining spatial heterogen-eity by allowing coefficients to vary across space. In this article, we develop a methodology for generalizing this model to accommodate geographically hierarchical data. This article considers two-level hierarchical structures and allow for the coefficients of both low-level and high-level units to vary over space. We assume that the spatially varying low-level coefficients follow the mul

Deep generative models have demonstrated an excellent ability to generate data by learning their distribution. Despite their unsupervised nature, these models can be implemented in semi-supervised learning scenarios by treating the class labels as additional latent variables. In this article, we propose a deep generative model for semi-supervised learning that offsets label noise, which is a ubiquitous feature in large-scale datasets owing to the high cost of annotation. We assume that noisy lab

decomposition reaction in the liquid-solid system. We show that the Pt NWs had higher catalytic activity because of the surface defect sites on the Pt NW surface. We observed a higher chemicurrent yield on the Pt NW/Si nanodiode compared with the Pt film/Si nanodiode, which is associated with the shortened travel length for the hot electrons at the edge of the Pt nanowires and results in a higher transmission probability for hot electron transport through metal-oxide interfaces.

The Taguchi robust design method traditionally deals with single‐characteristic problems. Various methods have been developed for extending the Taguchi single‐characteristic robust design method to the case of multi‐characteristic robust design problems. However, most of those methods have shortcomings in that they do not properly consider the variance–covariance structures among performance characteristics and/or do not preserve the original properties of the Taguchi signal‐to‐noise ratio for s

Summary The growth of the global airline network has increased the importance of modelling origin–destination air passenger flows for better operational planning and scheduling. Origin–destination air passenger flows are correlated both spatially and temporally because of spatial and temporal relationships of human behaviours and environments. However, most existing studies for modelling air passenger flows have assumed that these relationships are independent; few studies have considered either

The problem of integrating multifidelity data has been studied extensively, due to integrated analyses being able to provide better results than separately analyzing various data types. One popular approach is to use linear autoregressive models with location- and scale-adjustment parameters. Such parameters are typically modeled using stationary Gaussian processes. However, the stationarity assumption may not be appropriate in real-world applications. To introduce nonstationarity for enhanced f