韓国電力業の歩み

早大学位記番号:新6948早稲田大

Acupuncture sensation during ultrasound guided acupuncture needling

Although acupuncture sensation (also known as de qi) is a cornerstone of traditional acupuncture therapy, most research has accepted the traditional method of defining acupuncture sensation only through subjective patient reports rather than on any quantifiable physiological basis

Orthorectification of KOMPSAT Optical Images Using Various Ground Reference Data and Accuracy Assessment

Along with the appearance of high resolution satellite images, image correction using Rational Polynomial Coefficients (RPCs) has become common. Location accuracy of Korea Multipurpose Satellite (KOMPSAT) standard images is still not adequate, so, in order to leverage the KOMPSAT images for applications such as mapping and change detection, it is necessary to orthorectify the images. In this study, using updated RPCs, we performed orthorectification of KOMPSAT-2, KOMPSAT-3, and KOMPSAT-3A images using various data. Through this study, we discovered that the orthorectification result using precise Ground Control Points (GCPs) and Digital Elevation Model (DEM) is the best, but it was found that the correction results through image matching are also excellent. In particular, it was confirmed that orthoimages with a planimetric accuracy around 3 m (Root Mean Square Error (RMSE)) can be generated by using well-known matching algorithms with open data such as OpenStreetMap (OSM) and Shuttle Radar Topography Mission (SRTM) DEM, which can be acquired by anyone. Although the accuracy was low in some mountainous terrain, it was confirmed that it could be used for generating KOMPSAT orthoimages using open data. This paper describes the results for orthorectifying high resolution KOMPSAT optical images using various reference data

An Android Malware Detection System using a Knowledge-based Permission Counting Method

As the number of cases of damage caused by malicious apps increases, accurate detection is required through various detection conditions, not just detection using simple techniques. In this paper, we propose a knowledge-based machine learning method using authority information and adding its usage counting features. This method is classifying training apps and malicious apps through machine learning using permission features in manifest.xml of Android apps. As a result of the experiment, accuracy, recall, precision, F1 score are 99.01%, 97.70%, 100.0%, 99.01%, respectively. Since Recall is higher than other indicators, it accurately predicts malicious apps as malicious. In other words, the proposed system is effective in preventing the distribution of malicious apps

Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter

The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user’s motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which localize the user leveraging both the unscented transform of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by fusing positional data obtained from both DR and fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and RSS fingerprinting. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical experiments in real environments show that the use of the SKPF algorithm and iBeacon in our indoor localization scheme can achieve very satisfactory performance in terms of localization accuracy, computational cost, and energy efficiency

Water Resource Vulnerability Characteristics by District’s Population Size in a Changing Climate Using Subjective and Objective Weights

The goal of this study is to derive water resource vulnerability characteristics for South Korea according to individual district populations in a changing climate. The definition of water resource vulnerability in this study consists of potential flood damage and potential water scarcity. To quantify these vulnerabilities, key factors, or indicators affecting vulnerability, are integrated with a technique for order of preference by similarity to ideal solution (TOPSIS), which is a multi-criteria decision-making approach to determine the optimal alternative by considering both the best and worst solutions. The weight for each indicator is determined based on both the Delphi technique and Shannon’s entropy, which are employed to reduce the uncertainty in the process of determining the weights. The Delphi technique reflects expert opinions, and Shannon’s entropy reflects the uncertainty of the performance data. Under A1B climate change scenarios, medium-sized districts (200,000–300,000 inhabitants) are the most vulnerable regarding potential flood damage; the largest districts (exceeding 500,000 inhabitants) are found to be the most vulnerable with respect to potential water scarcity. This result indicates that the local governments of cities or districts with more than 200,000 inhabitants should implement better preventative measures for water resources. In addition, the Delphi and entropy methods show the same rankings for flood vulnerability; however, these approaches produce slightly different rankings regarding water scarcity vulnerability. Therefore, it is suggested that rankings from not only subjective but also objective weights should be considered in making a final decision to implement specific adaptive measures to climate change

Rare-Earth-Doped Barium Molybdate Up-Conversion Phosphor with Potential Application in Optical Temperature Sensing

A BaMoO4:[Er3+]/[Yb3+] up-conversion (UC) phosphor was synthesized by co-precipitation and calcination of the precursor at 800 °C. The main peak (112) for the synthesized phosphor was strongly detected in the XRD pattern and had a tetragonal structure. The doping of rare-earth ions affected the crystal lattice by shifting the main peak, decreasing the lattice constant, and shifting the position of the Raman signal. The synthesized upconverted phosphor exhibited strong green signals at 530 and 553 nm and weak red signals at 657 nm when excited at 980 nm. The green light emission intensity of the UC phosphor increased as the pump power of the laser increased due to the two-photon effect. The synthesized upconverted phosphor was prepared as a pellet and flexible composite. Thermal quenching led to a decrease in luminescence intensity as the temperature increased, which means that the phosphor can be applied to optical temperature sensing