• Journal of IKEEE
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• v.16 no.3
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• pp.182-188
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• 2012

Here we propose an image quality assessment (IQA) based on histogram of oriented gradients (HOG). This method makes use of the characteristic that the histogram of gradient image describes the state of input image. In the proposed method, the image quality is derived by the slope of the HOG obtained from the target image. The line representing the HOG is measured by a random sample consensus (RANSAC) on the HOG. Simulation results based on the LIVE image quality assessment database suggest that the proposed method aligns better with how the human visual system perceives image quality than several state-of-the-art IQAs.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.6
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• pp.546-552
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• 2012

This paper presents an active anti-islanding detection method using negative sequence voltage injection to the grid through a three-phase photovoltaic inverters. Because islanding operation mode can cause a variety of problems, the islanding detection of grid-connected photovoltaic inverter is the mandatory feature. The islanding mode is detected by measuring the magnitude of negative sequence impedance calculated by the negative sequence voltage and current at the point of common coupling. Simulation and experimental test are performed to verify the effectiveness of the proposed method which can detect the islanding mode in the specified time. The test has been done in accordance with the condition on IEEE Std 929-2000.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2423-2428
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• 2015

We have implemented a personal prescription management system which enables resource-limited mobile device to utilize the optical character recognition technique. The system enables us to automatically detect and recognize the text in the personal prescription by using a optical character recognition technique. We improved the recognition rate over a pre-processing in order to improve the character recognition rate of the original method. The examples such as a personal prescription management service, alarm service, and drug information service with mobile devices have been demonstrated by using the our system.

• Korean Journal of Artificial Intelligence
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• v.9 no.2
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• pp.35-40
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• 2021

In this paper, many game companies seek better optimization and easy-to-apply logic to prolong the game's lifespan and provide a better game environment for users. Therefore, research will be showing the game's key input response method called RoS (Response of Speed). The purpose of the method is to simultaneously perform various motions with the character showing natural motion without errors even if the character's control key is duplicated. This method is for the developers so they can reduce bugs and development time in future game development. To be used with quickly generating game environments, the new method compares with the popular motion method, so which method is faster and can adapt to diverse games. The paper suggested that the Response of Speed method is a better method for optimizing frames and reducing the number of reacting seconds by showing a faster response and speed). With the method popularity of scrollers, many 2D cross-scroll games follow the formula of Dash, Shoot, Walk, Stay, and Crouch. With the development of game engines, it is becoming easier to implement them. Therefore, although the method presented in the above paper differs from the popular method, it is expected that there will be no great difficulty in applying it to the game because transplantation is easy. In the future, we plan to study to minimize the delay of each connection of the character motion so that the game can be optimized to best.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.29-35
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• 2021

In this paper, we analyzed the factors of adoption and implemented a predictive model to activate the adoption of animals. Recently, animal shelters are saturated due to the abandonment and loss of companion animals. To address this, we need to find a way to encourage adoption. In this paper, a study was conducted using two data from an open data portal provided by Austin, Texas. First, a correlation analysis was conducted to identify the attributes that affect the result value, and it was found that Animal Type Intake, Intake Type, and Age upon Outcome influence the Outcome Type with correlation coefficients of 0.4, 0.26, and -0.2, respectively. For these attributes, the analysis was conducted using Multiclass Logistic Regression. As a result, dogs had a higher probability of Adoption than cats, and animals subjected to euthanasia were more likely to adopt. In the case of Public Assist and Stray, it was found that the Missing rate was high. Also, the length of stay for cats increased to 12.5 years of age, while dogs generally adopted smoothly at all ages. These results showed an overall accuracy of 62.7% and an average accuracy of 91.7%, showing a fairly reliable result. Therefore, it seems that it can be used to develop a plan to promote the adoption of animals according to various factors. Also, it can be expanded to various services by interlocking with the webserver.

• Korean Journal of Artificial Intelligence
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• v.10 no.2
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• pp.1-6
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• 2022

In this paper, the research and development aim to strengthen the digital accessibility of the elderly by developing a kiosk incorporating AI voice recognition technology that can replace the promotional signage currently being installed and spread in the elderly and social welfare centers most frequently used by the digital underprivileged. It was intended to develop a converged system for the use of bulletin board functions, educational functions, and welfare center facilities, and to seek ways to increase the user's digital device experience through direct experience and education. Through interviews and surveys of senior citizens and social welfare centers, it was intended to collect problems and pain Points that the elderly currently experience in the process of using kiosks and apply them to the development process, and improve problems through pilot services. Through this study, it was confirmed that voice recognition technology is 2 to 6 times faster than keyboard input, so it is helpful for the elderly who are not familiar with device operation. However, it is necessary to improve the problem that there is a difference in the accuracy of the recognition rate according to the surrounding environment with noise. Through small efforts such as this study, we hope that the elderly will be a little free from digital alienation.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.21-25
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• 2022

Korea has relatively less crime than other countries. However, the crime rate is steadily increasing. Many people think the crime rate is decreasing, but the crime arrest rate has increased. The goal is to check the relationship between CCTV and the crime rate as a way to lower the crime rate, and to identify the correlation between areas without CCTV and areas without CCTV. If you see a crime that can happen at any time, I think you should use a random forest algorithm. We also plan to use machine learning random forest algorithms to reduce the risk of overfitting, reduce the required training time, and verify high-level accuracy. The goal is to identify the relationship between CCTV and crime occurrence by creating a crime prevention algorithm using machine learning random forest techniques. Assuming that no crime occurs without CCTV, it compares the crime rate between the areas where the most crimes occur and the areas where there are no crimes, and predicts areas where there are many crimes. The impact of CCTV on crime prevention and arrest can be interpreted as a comprehensive effect in part, and the purpose isto identify areas and frequency of frequent crimes by comparing the time and time without CCTV.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.15-20
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• 2021

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.141-143
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• 2022

IT 자산관리(ITAM, IT Asset Management)는 조직이 소유한 IT 자산과 관련된 모든 이력 변경 정보, 비용, 계약 및 구매/리스 데이터를 관리하는 것이다. 자산 취득 및 처분에 관한 기술, 재무 및 계약 정보를 취득하고 통합한다. 그리고 유지하는 과정. 비즈니스의 효율성과 효율성을 높이기 위해 IT 인프라의 서비스 및 리소스에 대한 자산 관리의 4가지 요소(4C, 특성: 자산 수명 주기 정보, 구성: H/W와 S/W 정보, 계약: 서비스 및 보증, 종합관리: 보증 정보, 비용 및 재무 정보)는 회사 인프라 자원의 TCO 및 ROI를 개선하고 생산성의 범위를 확장하는 것을 목표로 한다. IT 자산 관리의 필요성은 사업에 필요한 IT 자산 정보 제공, 비용 관리, 구매 결정에 필요한 정보 제공, 소프트웨어 라이선스 및 하드웨어 자원의 재활용 촉진, 장치 노후화로 인한 추가 비용 방지를 위한 것이다. 비용 예측 및 관리, 내부 통제 및 외부 감사 대응, 사업부별 IT 비용 및 활용도 분석을 유지 및 지원한다. 사업부의 IT 자원 사용으로 인한 문제는 업무 효율성을 저하시키고 IT 부서의 업무를 증가시키며 결과적으로 기업의 비용을 증가시키며 이것이, IT 자산 관리의 현실이다. 본 논문에서는 적용 사례를 통해 더 나은 관리 방안을 마련하기 위한 방법을 제안한다.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.