• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.643-651
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• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.867-878
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• 2021

In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multi-channel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multi-channel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3378-3393
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• 2014

We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today's data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.

• Annual Conference of KIPS
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• 2005.11a
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• pp.603-606
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• 2005

하나의 유전자는 또 다른 유전자의 단백질과 프로모터 영역에서 Binding 함으로써 그 유전자의 발현에 영향을 미칠 수 있다. 이러한 두 유전자간의 조절 상호 작용을 유전자 조절망이라 하며 유전체의 핵심적인 기능을 보다 간결하게 표현하는 조절망을 설계할 수 있다. 대표적인 설계 방법으로는 Time-Series Data 를 이용한 방법과 Steady-State Data 를 이용하는 방법이 있으며 이 논문에서는 Steady-State Data 즉, Knock-out Data 를 이용하여 유전자 조절망을 재구성함으로써 기존의 방법을 개선하여 보다 정확한 결과 예측을 목표로 한다.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.521-536
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• 2010

Spatio-temporal patterns extracted from historical trajectories of moving objects reveal important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of location symbols and derive frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations may be lost due to the inappropriate approximations of spatial and temporal properties. In this paper, we address the problem of mining spatio-temporal patterns from trajectory data. The inefficient description of temporal information decreases the mining efficiency and the interpretability of the patterns. We provide a formal statement of efficient representation of spatio-temporal movements and propose a new approach to discover spatio-temporal patterns in trajectory data. The proposed method first finds meaningful spatio-temporal regions and extracts frequent spatio-temporal patterns based on a prefix-projection approach from the sequences of these regions. We experimentally analyze that the proposed method improves mining performance and derives more intuitive patterns.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.213-221
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• 2022

Intelligent Character Recognition System for Account Payable (ICRS AP) Automation represents the process of capturing text from scanned invoices and extracting the key fields from invoices and storing the captured fields into properly structured document format. ICRS plays a very critical role in invoice data streamlining, we are interested in data like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. As companies attempt to cut costs and upgrade their processes, accounts payable (A/P) is an example of a paper-intensive procedure. Invoice processing is a possible candidate for digitization. Most of the companies dealing with an enormous number of invoices, these manual invoice matching procedures start to show their limitations. Receiving a paper invoice and matching it to a purchase order (PO) and general ledger (GL) code can be difficult for businesses. Lack of automation leads to more serious company issues such as accruals for financial close, excessive labor costs, and a lack of insight into corporate expenditures. The proposed system offers tighter control on their invoice processing to make a better and more appropriate decision. AP automation solutions provide tighter controls, quicker clearances, smart payments, and real-time access to transactional data, allowing financial managers to make better and wiser decisions for the bottom line of their organizations. An Intelligent Character Recognition System for AP Automation is a process of extricating fields like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. based on their x-axis and y-axis position coordinates.

• Annual Conference of KIPS
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• 2017.11a
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• pp.652-654
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• 2017

본 프로젝트의 목적은 소상공인들을 위한 상권 분석, 트렌드 분석, 창업 지원 정책 소개, 커뮤니티 등을 제공하는 빅 데이터 기반의 웹 서비스를 구축하는 것이다. 일반적인 창업 관련 사이트는 정형데이터를 DB(Data Base)에 저장 후 관리되는 시스템으로, 이는 사용자 개개인에 맞는 맞춤형 정보를 제공하기 힘들다. 따라서 본 논문에서는 실시간 검색어 수집 및 분석을 통해 소상공인들이 창업을 희망할 때, 사용자에 맞는 정보를 제공해주는 맞춤형 서비스 연구에 대한 내용이다.

• Journal of The Korean Association For Science Education
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• v.7 no.2
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• pp.37-43
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• 1987

Computer in one of the most tremendous achievements of the modern scientific technique. Not only in government, business, research and education but in our daily life. computers are widely utilized to assist in solving various problems. With increasing frequency, it is recognized that a right understanding of the computer is necessary: naturally, this recognition places a great emphasis on the computer education. In Korea computer is chosen either as an optional subject or as a kind of group activity in many schools. It is the purpose of this study to compare and analyze the internal sorting algorithms which are used frequently in data processing. and to present the results of program analysis. which will make it possible to choose the appropriate sorting algorithm for each data processing. Generally the algorithms are coded in a language appropriate for structured programming. like PASCAL: however, here the algorithms are expressed in BASIC which is widely used with the personal computers so that the students and the teachers may understand them easily.

• Annual Conference of KIPS
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• 2014.11a
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• pp.84-87
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• 2014

상황인지는 유비쿼터스 컴퓨팅 환경에서 사용자의 주변 상황을 인지하여 사용자가 원하는 서비스를 제공하기 위해 필요한 핵심 기술이다. 이러한 상황인지를 위해 여러 센서로부터 발생하는 저수준의 컨텍스트 정보를 처리하는 다양한 방법들이 존재한다. 그러나 현재 상황인지 처리에 관련된 표준 방법이 없어 서비스 도메인에 제한되고 복잡한 구현방법을 따라야 하며, 상황정보를 처리하는 시스템에 정형화된 상황정보를 제공하는데 어려움이 있다. 이에 본 논문에서는 정형화된 상황정보를 제공하기 위한 센터 데이터 변환 방법을 제안한다. 제안하는 센서 데이터의 변환 방법은 센서로부터 발생하는 저수준의 컨텍스트를 RDF 기반의 고수준의 상황정보로 변환하며, 변환된 정보는 상황인지 시스템에 제공된다.

• Annual Conference of KIPS
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• 2002.04b
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• pp.1281-1284
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• 2002

OAI는 간단한 프로토콜을 정의함으로써 디지털도서관 사이의 상호이용의 문제점을 해결하기 위해 제시된 프로토콜이다. OAI를 통해 디지털도서관사이의 상호이용을 가능하게 하기 위해, 디지털도서관이 가지고 있는 컨텐츠에 대한 메타데이터를 제공하기 위한 data provider와 이를 수집하여 유용한 서비스를 제공하기 위한 service provider라는 두개의 프레임웍이 필요하다. 본 논문에서는 OAI protocol을 따르는 많은 data provider들이 가지고 있는 정보들을 수집하고 수집된 정보를 통해 새로운 서비스를 제공하는 service provider의 기능을 설계 및 구현하였다.