• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.149-160
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• 2007

Automobile black boxes store and provide accident and driving information. The accident and driving information can be utilized to build scientific traffic-event database and can be applied in various industries. The objective of this study is to develop a recognition system of dangerous driving through analyzing the driving characteristic patterns. In this paper, possible dangerous driving models are classified into four models on the basis of vehicle behaviors(acceleration, deceleration, rotation) and accident types from existing statistical data. Dangerous driving data have been acquired through vehicle tests using automobile black boxes. Characteristics of driving patterns have been analyzed in order to classify dangerous driving models. For the recognition of dangerous driving, this study selected critical value of each dangerous driving model and developed the recognition algorithm of dangerous driving. The study has been verified by the application of recognition algorithm of dangerous driving and vehicle tests using automobile black boxes. The presented recognition methods of dangerous driving can be used for on-line/off-line management of drivers and vehicles.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.72-78
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• 2003

A new self-organizing neuro-fuzzy network based rate control algorithm for MPEG-4 video encoder is proposed in this paper. Contrary to the traditional methods that construct the rate-distorion (RD) model based on experimental equations, the proposed method effectively exploits the non-stationary property of the video date with neuro-fuzzy network that self-organizes the RD model online and adaptively updates the structure. The method needs not require off-line pre-training; hence it is geared toward real-time coding. The comparative results through the experiments suggest that our proposed rate control scheme encodes the video sequences with less frame skip, providing good temporal quality and higher PSNR, compared to VM18.0.

• The Journal of the Convergence on Culture Technology
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• v.2 no.1
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• pp.93-98
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• 2016

As information technologies are rapidly developed recently, social networking services through a variety of mobile devices and smart screen is becoming popular. SNS is a social networking based services which is online forms from existed offline. SNS can also be used differently which is confused with the online community. A modelling algorithm is a variety of techniques, which are assocoation, clustering, neural networks, and decision trees, etc. By utilizing this technique, it is necessary to study to effectively using the large number of materials. In this paper, we evaluate in particular the performance of the algorithm based on the results of the clustering using Facebook Insights data for the EM algorithm to be evaluated as a good performance in clustering. Through this analysis it was based on the results of the application of the experimental data of the change and the South Australian state library according to the performance of the EM algorithm.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.121-122
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• 2008

본 논문에서는 2차 경로의 추정을 통하여 안정성이 강화된 능동소음 제어 시스템을 제안한다. LMS 알고리즘은 구조가 단순하고 계산량이 적은 장전이 있지만, 광대역 소음원에 적용할 경우 수렴 성능이 좋지 않은 단점이 있으며, 2차 경로 및 소음원 입력의 파워가 시변 할 경우 적응 알고리즘의 안정성이 약화되는 문제점이 발생한다. 본 논문에서는 지속적으로 누적 오차를 추적하여 일정 값 이상으로 증가하면 자동적으로 2차 경로 전달함수를 새로 추정하는 자동오프라인 추정 기법을 제안하였다. 공조용 덕트에 광대역 소음을 적용하여 제안된 능동소음제어 시스템을 실험을 수행한 결과 우수한 특성을 얻었다.

• The KIPS Transactions:PartA
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• v.12A no.1 s.91
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• pp.7-12
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• 2005

Scattered look-ahead(SLA) pipelining method can be efficiently used for high-speed or low-power applications of digital II R filters. Although the pipelined filters are guaranteed to be stable by this method, these filters suffer from large roundoff noise when the poles are crowded within some critical regions. An angle and radius constrained II R fille. design approach using modified Remez exchange algorithm and least squares algorithm is proposed to avoid tight pole-crowding in pipelined filters, resulting in improved frequency responses and reduced coefficient sensitivities. Experimental results demonstrate that our proposed method leads to chip area reduction by $33{\%}$ and low power by $45{\%}$ against the conventional method.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.238-252
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• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.

• Journal of Aerospace System Engineering
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• v.12 no.4
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• pp.64-74
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• 2018

Small high-resolution Earth observation satellites require precise optical alignment at the submicron level. However, misalignments can occur due to the influence of external factors during the launch and operation despite the sufficient alignment processes that take place before the launch. Thus, satellites need to realign their optical elements in orbit in what is known as a refocusing process to compensate for any misalignments. Refocusing algorithms developed for satellites have only considered de-space, which is the most sensitive factor with respect to image quality. However, the existing algorithms can cause correction error when inner and external forces generate tilt amount in an optical system. The present work suggests an improved online refocusing algorithm by considering the tilting effect for application in the case of a de-spaced and tilted optical system. In addition, the algorithm is considered to be efficient in terms of time and cost because it is designed to be used as an online method that does not require ground communication.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.979-991
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• 2016

Measuring nation images was a challenging task when employing offline surveys was the only option. It was not only prohibitively expensive, but too much time-consuming and therefore unfitted to this rapidly changing world. Although demands for monitoring real-time nation images were ever-increasing, an affordable and reliable solution to measure nation images has not been available up to this date. The researcher in this study developed a semi-automatic ontology construction algorithm, named "double-crossing double keyword collection (or DCDKC)" to measure nation images from Wikipedia in real-time. The ontology, WikiOnto, can be used to reflect dynamic image changes. In this study, an instance of WikiOnto was constructed by applying the algorithm to the big-three exporting countries in East Asia, Korea, Japan, and China. Then, the numbers of page views for words in the instance of WikiOnto were counted. A collection of the counting for each country was compared to each other to inspect the possibility to use for dynamic nation images. As for the conclusion, the result shows how the images of the three countries have changed for the period the study was performed. It confirms that DCDKC can very well be used for a real-time nation-image monitoring system.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.141-149
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• 2005

The paper's suggestion is Web based Tele-Medicine System will be open to public, low-budget featuring high quality of extensive medical services. It will keep a log of personal medical history to allow doctors to share information on patients and their families. This will result in the reduction of erroneous diagnosis and ensure successful e-business. On top of this, the new system will provide a solution for membership (client) management. It will combine online and offline medical services and be available 24 hours a day to anywhere users have Internet access, setting itself apart from the existing distance medical services system. Specially, The paper's suggestion is sharing medical treatment information between family members is suggested. This approach makes possible understanding physical constitution and environment between family members, and can result in bringing a faster treatment effect if some family member suffers from a similar disease.