• KDI Journal of Economic Policy
• /
• v.44 no.3
• /
• pp.1-26
• /
• 2022

This paper analyzes the effects of the cut in the legal maximum interest rate (from 27.4% to 24%) that occurred in February of 2018 on loan interest rates, the default rates, and the loan approval rate of borrowers in the non-banking sector. We use the difference-in-difference identification strategy to estimate the effect of the cut in the legal maximum interest rate using micro-level data from a major credit-rating company. The legal maximum rate cut significantly lowers the loan interest rate and default rate of low-credit borrowers (i.e., high-credit-risk borrowers) in the non-banking sector. However, this effect is limited to borrowers who have not been excluded from the market despite the legal maximum interest rate cut. The loan approval rate of low-credit borrowers decreased significantly after the legal maximum interest rate cut. Meanwhile, the loan approval rate of high-credit and medium-credit (i.e., low credit risk and medium credit risk) borrowers increased. This implies that financial institutions in the non-banking sector should reduce the loan supply to low-credit borrowers who are no longer profitable while increasing the loan supply to high- and medium-credit borrowers.

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 2006.07a
• /
• pp.556-556
• /
• 2006

• Proceedings of the Korean Nuclear Society Conference
• /
• 2005.10a
• /
• pp.217-218
• /
• 2005

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.4
• /
• pp.407-413
• /
• 2011

This paper presents an electrical power monitoring system for home energy management and an automatic appliance-identification algorithm based on the electricity-usage patterns collected during the monitoring tests. This paper also discusses the results of the field tests of which the proposed system was voluntarily deployed at 13 homes. The proposed monitoring system periodically measures the amount of power consumption of each appliance with a pre-specified time interval and effectively displays the essential information provided by the monitored data which is required users to know in order to save power consumption. Regarding the field tests of the monitoring system, the households responded that the system was useful in saving electricity and especially the electricity-usage patterns per appliances. They also considered that the predicted amount of the monthly power consumption was effective. The proposed appliance-identification algorithm uses 4 patterns: Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO) and Duty Cycle(DC), which are applied over the 2 hour interval with 25% of it on state, and it yielded 82.1% of success rate in identifying 5 kinds of appliances: refrigerator, TV, electric rice-cooker, kimchi-refrigerator and washing machine.

• The KIPS Transactions:PartC
• /
• v.11C no.6 s.95
• /
• pp.851-862
• /
• 2004

RFID(Radio frequency IDentification) is a technology that automatically identifies objects containing the electronic tags by using radio wave. The multi-tag identification problem is the core issue in the RFID and could be resolved by the anti-collision algorithm. However, most of the existing anti-collision algorithms have a problem of heavy implementation cost and low performance. In this paper. we propose a new tree based memoryless anti-collision algorithm called a collision tracking tree algorithm and presents its performance evaluation results obtained by simulation. The Collision Tracking Tree algorithm proves itself the capability of an identification rate of 749 tags per second and the performance evaluation results also show that the proposed algorithm outperforms the other two existing tree-based memoryless algorithms, i.e., the tree-walking algorithm and the query tree algorithm about 49 and 2.4 times respectively.

• The Journal of Society for e-Business Studies
• /
• v.22 no.2
• /
• pp.1-18
• /
• 2017

Recently, identification of the extremely stressed condition of children is an essential skill for real-time recognition of a dangerous situation because incidents of children have been dramatically increased. In this paper, therefore, we present a model based on machine learning techniques for stress status identification of a child by using bio-signals such as voice and heart rate that are major factors for presenting a child's emotion. In addition, a smart band for collecting such bio-signals and a mobile application for monitoring child's stress status are also suggested. Specifically, the proposed method utilizes stress patterns of children that are obtained in advance for the purpose of training stress status identification model. Then, the model is used to predict the current stress status for a child and is designed based on conventional machine learning algorithms. The experiment results conducted by using a real-world dataset showed that the possibility of automated detection of a child's stress status with a satisfactory level of accuracy. Furthermore, the research results are expected to be used for preventing child's dangerous situations.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.39 no.1
• /
• pp.70-76
• /
• 2002

In this paper, we proposed a personal identification algorithm using the iris region which has discriminant features. First, we acquired the eye image with the black and white CCD camera and extracted the iris region by using a circular edge detector which minimizes the search space for real center and radius of the iris. And then, we localized the iris region into several circles and extracted the features by filtering signals on the perimeters of circles with one dimensional Gabor filter We identified a person by comparing ,correlation values of input signals with the registered signals. We also decided threshold value minimizing average error rate for FRR(Type I)error rate and FAR(Type II)error rate. Experimental results show that proposed algorithm has average error rate less than 5.2%.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.5
• /
• pp.1-8
• /
• 2017

Recently, with the development of ICT technology such as cloud and mobile, image utilization through social networks is increasing rapidly. These images contain personal information, and personal information leakage accidents may occur. As a result, studies are underway to recognize and mask personal information in images. However, optical character recognition, which recognizes personal information in images, varies greatly depending on brightness, contrast, and distortion, and Korean recognition is insufficient. Therefore, in this paper, we design and implement a personal information identification and masking system based on image recognition through deep learning application using CNN algorithm based on optical character recognition method. Also, the proposed system and optical character recognition compares and evaluates the recognition rate of personal information on the same image and measures the face recognition rate of the proposed system. Test results show that the recognition rate of personal information in the proposed system is 32.7% higher than that of optical character recognition and the face recognition rate is 86.6%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.3
• /
• pp.441-447
• /
• 2020

In a situation in which around 50 million metric tons of electrical and electronic products is generated globally per year, the total installed base of Internet of Things (IoT) devices is projected to amount to around 75 billion worldwide by 2025. However, there is very little research on identification schemes for end-of-life treatment (EoLT) of IoT devices. To address this issue, this paper proposes new identifiers including EoLT information such as recyclability rate (Rcyc) and recoverability rate (Rcov) of an IoT device, recycling rate (RCR) and recovery rate (RVR) of each part in the IoT device, etc. and implements them by using object identifier (OID), mobile radio frequency identification (RFID) and quick response (QR) code. The implemented OID and mobile RFID can be used with cooperation of a remote server via communication networks and the implemented QR code can be used simply with a smartphone app.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.5
• /
• pp.123-127
• /
• 2009

In this paper, we suggested new additional data transmission technology for T-DMB system using MOK-SS(Mary Orthogonal Keying- Spread Spectrum)mapping method. We applied revised watermarking method of TxID(Transmitter Identification) technology mainly had been used for conventional ATSC-DTV(Advanced Television Systems Committee - Digital TV). And proposed schemes enhanced additional data rate using MOK-SS mapping method. We certified availability of our proposed technology by using various simulation and error rate performance analysis.