Eun-Chan Kim

Localization schemes using a mobile beacon have similar effects as the use of many static beacons in terms of improving localization accuracy. Specifically, the localization scheme with mobile beacons proposed by Ssu et al. has finegrained accuracy, scalability, and power efficiency without requiring measured distance or angle information. However, this scheme often has large location errors in ill-conditioned cases. To improve the localization accuracy in Ssu's scheme, this letter proposes a lo

Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server wh

This study empirically analyzes factors affecting the adoption and recommendation intentions for next-generation electronic authentication services based on a convergent model of the value-based adoption model (VAM), protection motivation theory (PMT), and theory of planned behavior (TPB). As a result of the analyses, perceived benefits and perceived sacrifices (based on the VAM), perceived threats (based on the PMT), and adoption motives (based on the TPB) are found to significantly impact the

Localization is essential in wireless sensor networks to handle the reporting of events from sensor nodes. For 3-D applications, we propose a mobile beacon-based localization using classical multidimensional scaling (MBL-MDS) by taking full advantage of MDS with connectivity and measurements. To further improve location performance, MBL-MDS adopts a selection rule to choose useful reference points, and a decision rule to prevent a failure case due to reference points placed on the same plane. Si

Research on the advanced detection of harmful objects in airport cargo for passenger safety against terrorism has increased recently. However, because associated studies are primarily focused on the detection of relatively large objects, research on the detection of small objects is lacking, and the detection performance for small objects has remained considerably low. Here, we verified the limitations of existing research on object detection and developed a new model called the Small Hazardous

This study investigates the adoption and application of digital transformation in the financial sector and analyzes the process and outcomes of digitization and digitalization in the field of the finance industry of South Korea and overseas, in order to seek both managerial and strategic implications for successful implementation of digital transformation in the future. The findings show that, for successful digital transformation, it is necessary to maximize active and systematic use of advance

Recently, pre-trained language representation models such as bidirectional encoder representations from transformers (BERT) have been performing well in commonsense question answering (CSQA). However, there is a problem that the models do not directly use explicit information of knowledge sources existing outside. To augment this, additional methods such as knowledge-aware graph network (KagNet) and multi-hop graph relation network (MHGRN) have been proposed. In this study, we propose to use the

Machine learning is widely used in various academic fields, and recently it has been actively applied in the medical research.In the medical field, machine learning is used in a variety of ways, such as speeding up diagnosis, discovering new biomarkers, or discovering latent traits of a disease.In the respiratory field, a relative regional air volume change (RRAVC) map based on quantitative inspiratory and expiratory computed tomography (CT) imaging can be used as a useful functional imaging bio

In underwater sensor networks (UWSNs), localization is an important issue and a challenging task due to harsh environments for people to access. In this paper, we propose a distributed algorithm to locate nodes deployed in 3-D space using long-range beacons floating on the sea surface. Long-range beacons allow underwater nodes directly to obtain reference positions of beacons and to measure distances to beacons. Because all beacons are placed on the same plane, i.e. the sea surface, the proposed

In this paper, we propose a floating beacon-assisted 3-D localization for variable sound speed in underwater sensor networks (FBL-VSS). Most underwater localization schemes have assumed that the sound speed is constant under water for simplicity; however, it is actually variable depending on depth and seasonal variation. Taking into account various sound speed, FBL-VSS utilizes novel beacons moored to the sea floor. Each beacon has a transmitter floating on the sea surface and a receiver placed

This study empirically analyzed the variables affecting the adoption intention of a financial data retrieval service (i.e. My Data) through the convergence of the stimulus-organism-response (S-O-R) model, the value-based adoption model (VAM), and the unified theory of acceptance and use of technology (UTAUT1). This approach examined the causal relationships where the characteristics of My Data services (stimulus) influence users' perceived benefit and sacrifice (proposed by VAM), as well as perf

Background: Predicting a stroke in advance or through early detection of subtle prodromal symptoms is crucial for determining the prognosis of the remaining life. Electromyography (EMG) has the advantage of easy and quick collection of biological data in clinical settings; however, its application in data processing and utilization is somewhat limited. Thus, this study aims to verify how simple signal processing and feature extraction utilize EMG in machine learning (ML)-based prediction models.