• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.4
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• pp.333-351
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• 2023

This study investigates user behavior in library spaces through the lens of AI camera analytics. By leveraging the face recognition and tracking capabilities of AI cameras, we accurately identified the gender and age of visitors and meticulously collected video data to track their movements. Our findings revealed that female users slightly outnumbered male users and the dominant age group was individuals in their 30s. User visits peaked between Tuesday to Friday, with the highest footfall recorded between 14:00 and 15:00 pm, while visits decreased over the weekend. Most visitors utilized one or two specific spaces, frequently consulting the information desk for inquiries, checking out/returning items, or using the rest area for relaxation. The library stacks were used approximately twice as much as they were avoided. The most frequented subject areas were Philosophy(100), Religion(200), Social Sciences(300), Science(400), Technology(500), and Literature(800), with Literature(800) and Religion(200) displaying the most intersections with other areas. By categorizing users into five clusters based on space utilization patterns, we discerned varying objectives and subject interests, providing insights for future library service enhancements. Moreover, the study underscores the need to address the associated costs and privacy concerns when considering the broader application of AI camera analytics in library settings.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.895-897
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• 2005

인터넷의 발달로 디지털콘텐츠의 생성과 활용이 급격히 증가하면서 디지털콘텐츠에 대한 서비스 방식이 URL 방식에서 URN 방식으로 변화하고 있다. 한국과학기술정보연구원의 KOI나 한국전산원의 UCI가 그 예라고 할 수 있다. 그러나 식별체계 간의 등록$\cdot$변환서비스가 호환되지 않기 때문에 식별체계를 활용하여 디지털콘텐츠를 서비스하기 위해서는 커널메타데이터를 각각 등록하여야 하며, 디지털콘텐츠 변환서비스도 서로 연계가 이루어 지지 않기 때문에 개별적으로 서비스 되고 있다. 디지털콘텐츠를 보다 효율적으로 생성$\cdot$활용하기 위하여 식별체계간의 등록$\cdot$변환서비스 연계가 가능한 식별체계 간 연계시스템 구축 방안을 제시하고자 한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.229-235
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• 2013

In this paper, we investigate several important issues on the implementation of a totally implantable microsystem for brain-machine interface that has been attracting a lot of attention recently. So far most of the scientific research has been focused on the high performance, low power electronics or systems such as neural signal amplifiers and wireless signal transmitters, but the real application of the implantable microsystem is affected significantly by a number of factors, ranging from design of the encapsulation structure to physiological and anatomical characteristics of the brain. In this work, we discuss on the thermal effect of the system, the detecting volume of the neural probes, wireless data transmission and power delivery, and physiological and anatomical factors that are critically important for the actual implementation of a totally brain implantable neural interface microsystem.

• Journal of KIISE
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• v.44 no.9
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• pp.908-917
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• 2017

As web service technologies are widely used, there have been many efforts to develop approaches for recommending appropriate web services to users in complex and dynamic service environments. In addition, for the effective development of service mashups, service recommender systems that are specialized for service composition have been developed. However, existing service recommender systems for service mashups are not effective at recommending services in a personalized manner that reflect developers' preferences. To deal with this issue, we propose an approach that recommends services based on the similarities between mashup developers who have developed similar service mashups. The proposed approach is then evaluated by using the mashup data retrieved from ProgrammableWeb. The evaluation results clearly show that the proposed approach is an effective way of improving service recommendations compared to the traditional user-based collaborative filtering algorithm.

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.53-57
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• 2018

A poisoning accident is often caused by a situation in which people eat poisonous mushrooms because they cannot distinguish between edible mushrooms and poisonous mushrooms. In this paper, we propose an automatic mushroom recognition system by using the convolutional neural network. We collected 1478 mushroom images of 38 species using image crawling, and used the dataset for learning the convolutional neural network. A comparison experiment using AlexNet, VGGNet, and GoogLeNet was performed using the collected datasets, and a comparison experiment using a class number expansion and a fine-tuning technique for transfer learning were performed. As a result of our experiment, we achieve 82.63% top-1 accuracy and 96.84% top-5 accuracy on test set of our dataset.

• Journal of Platform Technology
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• v.6 no.3
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• pp.47-54
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• 2018

Predictive analysis in the hardware industry can be performed at an appropriate point in time to prevent failure of production facilities and reduce management costs. This helps to perform more efficient and scientific maintenance through automation of failure analysis. Among them, predictive management aims to prevent the occurrence of anomalous state by identifying and improving the abnormal state based on the gathering, analysis, and scientific data management of facilities using information technology and constructing prediction model do. In this study, we made a fault tree through the Relex tool and analyzed the error code of the hardware to study the safety.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.7
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• pp.717-722
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• 2007

Inverse synthetic aperture radar(ISAR) images represent two-dimensional(2-D) spatial distribution of electromagnetic scattering phenomenology against a target. Hence, they are usually used in the areas of automatic target recognition (ATR) or non-cooperative target recognition(NCTR), identifying a target using radar in a long distance. This paper makes use of Korea Miniature Synthetic Aperture Radar(KOMSAR) to generate ISAR images of a real and maneuvering aircraft. The data obtained from KOMSAR are processed to eliminate phase errors due to motion of a target, with the use of entropy-based ISAR autofocusing technique. Results show that we can successfully obtain ISAR images of a real aircraft, and the success of experiments implies that a significant step toward ATR using radar has been established.

• Proceedings of the Korean Society for Language and Information Conference
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• 2008.06a
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• pp.166-171
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• 2008

본 연구는 기존에 잘 알려지고 널리 사용되고 있는 어휘 의미망이나 시소러스를 활용하기 어려운 과학 기술 분야, 특히 IT 분야에서 대용량 용어간 관계를 빠른 시간 내에 구축하여 검색 브라우징, 내비게이션 용도로 활용하는 것을 목표로 한다. 시소러스 구축 절차를 따르는 경우에 분야 전문가에 의한 정교한 작업과 고비용을 필요로 하여 충분한 구축 크기를 확보하는 것에 현실적인 어려움이 있다. 시소러스 자동 구축 방법론을 사용하는 경우에도 해당 용어들이 출현하는 방대한 말뭉치를 확보해야 하며 관계 구축 결과에 대한 직관적 이해가 쉽지 않다는 단점이 있다. 본 연구는 해외 학술 논문 말뭉치와 메타데이터에서 획득한 37만 여 주제어들을 이용하여 상 하위 관계, 관련어, 형제 관계를 추출하기 위해 조어적 기준에 근거한 규칙들을 이용한다. 이들 규칙을 이용하여 추출한 관계 수는 상 하위 관계 60여 만 개, 관련어 640여 만 개, 형제 관계 2,000여 만 개 등이다. 또한, 추출 결과 중 일부를 수작업으로 분석하여 단순한 추출 규칙에서 발생하는 오류 유형을 찾아내고 향후 과제에서 해결할 수 있는 방안에 대해 논하자고 한다.