• Transactions on Semiconductor Engineering
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• v.2 no.1
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• pp.17-20
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• 2024

This paper presents a novel lightweight object detection model tailored for low-powered edge devices, addressing the limitations of traditional resource-intensive computer vision models. Our proposed detector, inspired by the Single Shot Detector (SSD), employs a compact yet robust network design. Crucially, it integrates an 'enhancer block' that significantly boosts its efficiency in detecting smaller objects. The model comprises two primary components: the Light_Block for efficient feature extraction using Depth-wise and Pointwise Convolution layers, and the Enhancer_Block for enhanced detection of tiny objects. Trained from scratch on the Udacity Annotated Dataset with image dimensions of 300x480, our model eschews the need for pre-trained classification weights. Weighing only 5.5MB with approximately 0.43M parameters, our detector achieved a mean average precision (mAP) of 27.7% and processed at 140 FPS, outperforming conventional models in both precision and efficiency. This research underscores the potential of lightweight designs in advancing object detection for edge devices without compromising accuracy.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.21-30
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• 2023

This paper tried to determine whether emotional dysregulation would function as a mediating variable when social exclusion affects SNS addiction tendencies. For this purpose, a survey was conducted targeting students enrolled in a university located in the central region. Responses from 298 people were analyzed. The analysis results show that social exclusion completely mediates emotional dysregulation and influences SNS addiction tendencies. Meanwhile, we set the two sub-dimensions of social exclusion as independent variables to determine whether it influences SNS addiction tendency through emotion regulation. It was found that the experience of being ignored influenced the tendency to addict to SNS by partially mediating the experience of emotion regulation, while the experience of rejection was found to affect the tendency to addict to SNS by fully mediating the experience of emotion regulation. These analysis results show that when establishing social exclusion as an influential factor in SNS addiction tendency, it is meaningful not only to set it as an overall factor but also to approach it by dividing it into individual factors.

• Journal of The Korean Association For Science Education
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• v.28 no.6
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• pp.618-632
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• 2008

Despite the rapidly growing body of research on science teachers' pedagogical content knowledge (PCK), most of the research has mainly focused on the definition of PCK and its components. The main purpose of this research was to explore the operational definition of PCK and to suggest another form by re-conceptualizing PCK, with the newly defined and conceptualized PCK capable of being used as standards and/or criteria in selecting the curricular content of and deciding the subject area of science teacher education. In this research, the science teachers' PCK was defined as the "practical knowledge and skills that are acquired through the curriculum of science education and in the course of teaching experiences, and which are used in their teaching of secondary school science." The science teachers' PCK was further defined as consisting of two integrated and/or combined dimensions: subject matter and pedagogy, each of which comprises several components. In this paper, the PCK is called science education literacy. The paper also presents the ways to apply the operational definition of PCK and re-conceptualized PCK and a few suggestions for the research on science teachers' PCK.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.77-84
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• 2024

In this paper, we propose a method for accurately and rapidly detecting defects in wire harnesses by utilizing computer vision to calculate six crucial measurement values: the length of crimped terminals, the dimensions (width) of terminal ends, and the width of crimped sections (wire and core portions). We employ Harris corner detection to locate object positions from two types of data. Additionally, we generate reference points for extracting measurement values by utilizing features specific to each measurement area and exploiting the contrast in shading between the background and objects, thus reflecting the slope of each sample. Subsequently, we introduce a method using the Euclidean distance and correction coefficients to predict values, allowing for the prediction of measurements regardless of changes in the wire's position. We achieve high accuracy for each measurement type, 99.1%, 98.7%, 92.6%, 92.5%, 99.9%, and 99.7%, achieving outstanding overall average accuracy of 97% across all measurements. This inspection method not only addresses the limitations of conventional visual inspections but also yields excellent results with a small amount of data. Moreover, relying solely on image processing, it is expected to be more cost-effective and applicable with less data compared to deep learning methods.

• Information Systems Review
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• v.18 no.3
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• pp.31-50
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• 2016

This study investigates the impact of social-face sensitivity of smartphones on the adoption of iPhone in China. Social-face sensitivity is divided into three dimensions, namely, other-directed sensitivity, self-directed sensitivity, and formality-directed sensitivity. We surveyed 218 university students in China through an online survey site. The results showed that formality-directed and other-directed sensitivity have significant impacts on iPhone preferences. Self-directed sensitivity was not significant. We investigated two moderate variables, namely, financial ability and brand sensitivity. Both variables showed significantly moderate impacts on the intention to purchase iPhone. The impact of social-face sensitivity on iPhone preferences implies that the iPhone has dual characteristics to the Chinese, namely, as utility and luxury goods. This finding offers managerial implications for Apple and other mobile service companies in terms of production and marketing strategies.

• Safety and Health at Work
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• v.15 no.1
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• pp.42-52
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• 2024

Background: The lack of headforms that accurately reflect the head characteristics of Koreans and the demographic composition of the Korean population can lead to inadequate FFR testing and reduced effectiveness of FFRs. Method: Direct measurements of 5,110 individuals and 3D measurements of 2,044 individuals, aged between 9 and 69 years, were sampled from the data pool of Size Korea surveys based on the age and gender ratios of the Korean resident demographics. Seven head dimensions were selected based on the ISO 16976-2, availability of Size Korea measurements, and their relevance to the fit performance of FFRs. A principal component analysis (PCA) was performed using the direct measurements to extract the main factors explaining the head characteristics and then the main factors were standardized and remapped to 3D measurements, creating five size categories representing Korean head shapes. Lastly, representative 3D headforms were constructed by averaging five head shapes for each size category. Results: The study identified two main factors explaining Korean head characteristics by the PCA procedure specified in ISO 16976-2 and developed five representative headforms reflecting the anthropometric features of Korean heads: medium, small, large, short & wide, and long & narrow. Conclusion: This study developed representative headforms tailored to the Korean population for conducting total inward leakage (TIL) tests on filtering facepiece respirators (FFRs). The representative headforms can be used for TIL testing by employing robotic headforms to enhance the performance of FFRs for the Korean target population.

• SUVANNABHUMI
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• v.16 no.1
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• pp.303-343
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• 2024

Globalization and the flow of foreign direct investment (FDI) in the post-pandemic context continue to play a critical role in shaping the workforce of emerging countries. In Vietnam, evidence obtained during the pandemic revealed that the well-being of employees, especially migrant workers, was extremely poor due to both work and non-work factors. This paper examines the most significant factors that impact the well-being of workers employed by various FDI companies in two Vietnamese industrial parks. The survey evidence (n=200) shows that worker well-being is influenced by seven key factors categorized in three dimensions, namely material stressors, social stressors, and human stressors. A further qualitative analysis of 60 participants provides an understanding of the ways in which each factor affects workers' well-being and how elements of well-being in the Vietnamese context are different compared with other countries. Low salaries, lack of social support, work-life imbalance due to job demands, and the interplay between these three determinants significantly affect the overall well-being of workers. In the current business climate, it is important to have well-targeted policies that encourage high-tech investments as well as persuade domestic firms to address low salaries and economic migration. To manage valuable human resources and keep competitive advantages, foreign firms need to authentically implement corporate social responsibility (CSR) initiatives focusing on workers' benefits, especially providing workforce housing. This will bring about win-win outcomes of improved employee well-being and business sustainability.

• Steel and Composite Structures
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• v.51 no.4
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• pp.441-456
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• 2024

This paper aims to develop Machine Learning (ML) algorithms to predict the buckling resistance of cold-formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckling behaviour of CFS channels subjected to pure axial compressive load or bending moment. Feedforward multi-layer Artificial Neural Networks (ANNs) were then trained on different datasets comprising CFS channels with various dimensions and properties, plate thicknesses, and restraining conditions on one or two flanges, while the elastic distortional buckling resistance of the elements were determined according to the Finite Strip Method (FSM). To develop less biased networks and ensure that every observation from the original dataset has the chance of appearing in the training and test set, a K-fold cross-validation technique was implemented. In addition, the hyperparameters of the ANNs were tuned using a grid search technique to provide ANNs with optimum performances. The results demonstrated that the trained ANNs were able to predict the elastic distortional buckling resistance of CFS flange-restrained elements with an average accuracy of 99% in terms of coefficient of determination. The developed models were then used to propose a simple ANN-based design formula for the prediction of the elastic distortional buckling stress of CFS flange-restrained elements. Finally, the proposed formula was further evaluated on a separate set of unseen data to ensure its accuracy for practical applications.

• Monthly Notices of the Royal Astronomical Society
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• v.496 no.4
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• pp.4787-4821
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• 2020

The merging rate of cosmic structures is computed, relying on the ansatz that they can be predicted in the initial linear density field from the coalescence of critical points with increasing smoothing scale, used here as a proxy for cosmic time. Beyond the mergers of peaks with saddle points (a proxy for halo mergers), we consider the coalescence and nucleation of all sets of critical points, including wall-saddle to filament-saddle and wall-saddle to minima (a proxy for filament and void mergers, respectively), as they impact the geometry of galactic infall, and in particular filament disconnection. Analytical predictions of the one-point statistics are validated against multiscale measurements in 2D and 3D realizations of Gaussian random fields (the corresponding code being available upon request) and compared qualitatively to cosmological N-body simulations at early times (z ≥ 10) and large scales (≥5 Mpc h^-1). The rate of filament coalescence is compared to the merger rate of haloes and the two-point clustering of these events is computed, along with their cross-correlations with critical points. These correlations are qualitatively consistent with the preservation of the connectivity of dark matter haloes, and the impact of the large-scale structures on assembly bias. The destruction rate of haloes and voids as a function of mass and redshift is quantified down to z = 0 for a Lambda cold dark matter cosmology. The one-point statistics in higher dimensions are also presented, together with consistency relations between critical point and critical event counts.

• Smart Structures and Systems
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• v.33 no.6
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• pp.399-414
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• 2024

This study discusses the long-term deformation monitoring and shape sensing of bridge girder surfaces with an automated extraction scheme for point clouds in the Region Of Interest (ROI), invariant to the position of a Light Detection And Ranging system (LiDAR). Advanced smart construction necessitates continuous monitoring of the deformation and shape of bridge girders during the construction phase. An automated scheme is proposed for reconstructing geometric model of ROI in the presence of noisy non-stationary background. The proposed scheme involves (i) denoising irrelevant background point clouds using dimensions from the design model, (ii) extracting the outer boundaries of the bridge girder by transforming and processing the point cloud data in a two-dimensional image space, (iii) extracting topology of pre-defined targets using the modified Otsu method, (iv) registering the point clouds to a common reference frame or design coordinate using extracted predefined targets placed outside ROI, and (v) defining the bounding box in the point clouds using corresponding dimensional information of the bridge girder and abutments from the design model. The surface-fitted reconstructed geometric model in the ROI is superposed consistently over a long period to monitor bridge shape and derive deflection during the construction phase, which is highly correlated. The proposed scheme of combining 2D-3D with the design model overcomes the sensitivity of 3D point cloud registration to initial match, which often leads to a local extremum.