Earticle

우리나라의 IT 기반 공공서비스는 UN 전자정부 세계 순위 평가에서 3회 연속 세계 1위로 선 정될 정도로 세계의 선도적인 수준이다. 이러한 환경을 기반으로 정부 부처나 기업이 해외 공공서비 스 시장에 진출하려는 움직임이 활발해지고 있으나 대부분의 실적이 전략적 접근보다 단기 프로젝트 형태를 취하고 있어서 IT 서비스의 잠재적 효과를 기대하기 어려운 실정이다. 특히 중소기업의 경우 대기업과 달리 수출 국가 발굴 및 신 시장 개척에 대해 체계적인 노하우가 부족하여 수출 역량과 시 장 교섭력에서 큰 한계를 가질 수밖에 없어 우리나라의 공공서비스 수출은 구조적 개선이 필요하다. 공공서비스 수출 지원 플랫폼은 이러한 한계와 구조적 문제를 개선하기위한 시스템적 대안 중에 하나 이며, 플랫폼 구조의 핵심 엔진은 본 논문에서 제안하는 수출 대상 국가/서비스 선정 및 컨소시엄 구 성 의사결정 지원 시뮬레이터 모형이다. 시뮬레이터의 매칭 모듈에서는 온톨로지(ontology)와 메타테 이터(metadata)를 사용하여 수출 비즈니스 과정의 컨텍스트(context)를 파악하고 최적화 알고리즘을 통하여 특정 서비스에 적합한 수출 국가 또는 특정 국가에 적합한 수출 서비스의 우선순위를 산출한 다. 조합 모듈에서는 수출 대상 서비스/국가의 우선순위 리스트가 생성되면 갭(gap) 분석과 참여 기업 의 역량 평가 등을 통하여 조합 가능한 컨소시엄 후보 기업 리스트를 산출하여 협업 의사결정을 지원 한다. 본 논문에서 제안하는 시뮬레이터 기반의 공공서비스 수출지원 플랫폼은 이해 당사자 모두에게 수출 노하우, 타겟 서비스/국가 결정, 협업 등에서 맞춤 컨설팅 서비스를 제공할 것으로 기대하며, 특 히 정보 인프라 및 수출 경험과 노하우가 부족한 중소기업에게 상대적으로 더 큰 도움이 될 것이다.

Over the past three consecutive worldwide assessments of the UN e-Government forum, Korea's IT-based public services were recognized as the top level in the world. To link this recognition with the export of public services, relevant government agencies have supported private companies with various policy instruments. However, the most recent cases are the short-term projects without strategic objectives. Moreover, small and medium-sized enterprises are very limited to access the foreign markets and contact points, and accordingly are limited on the bargain-ing power at the foreign markets. A decision support platform is proposed as one of the systematic alternatives to support and promote export business of Korean public services, where a simulator is the core engine to evaluate and determine the priorities of target countries, services, or enterprises for export consortium. With the proposed platform, all and every stakeholder could have benefits to receive one-stop customized consulting service.

In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine these two algorithms and propose one novel noise reduction method, showing a strong performance on par or even better than state of the art methods. We first use the least mean square algorithm to reduce the average intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise ratio of original speech data and improves the final noise reduction performance.

본 논문에서는 충격 잡음에 강인한 ℓ1-norm 기반의 볼록 조합 구조의 알고리즘을 제안한다. 구체적으로, 기존 볼록 조합 구조의 알고리즘은 ℓ1-norm 기반 적응 필터 알고리즘을 사용하여 조합 계수를 업데이트하기 때문에 전체 필터의 수렴 성능이 충격 잡음에 민감한 단점이 있다. 이러한 문제를 해결하기 위해, ℓ1-norm 기반 적응 필터 알고리즘을 활용하여 조합 계수를 업데이트하는 방법을 제안한다. 그 결과, 제안된 알고리즘은 충격 잡음에 강인한 수렴 성능을 보이며, 이를 컴퓨터 시뮬레이션을 통해 검증한다.

In this paper, we propose a robust ℓ1-norm based convex combination of two adaptive filters against impulsive interferences. Specifically, since the conventional convex combination schemes employ the ℓ1-norm based adaptive filtering in updating the mixing parameter for the two adaptive filters, the performance of the overall filter may be sensitive to impulsive interferences. To overcome the problem, a novel ℓ1-norm based adaptive algorithm is proposed to update the mixing parameter for the two adaptive filters. As a result, the proposed algorithm provides robust convergence performance in impulsive interference environments, which is demonstrated by computer simulations.

메타학습 알고리즘인 Adaboost는 단순한 분류기(classifier)인 Haar-like feature 필터와 결 합하여 빠른 속도와 높은 정확도를 나타내기 때문에 많은 객체 검출에 분야에 사용되고 있으며 그 중 특히 얼굴 인식 분야에서 뛰어난 성 능을 나타내고 있다. 그러나 Adaboost 알고리즘은 얼굴인식 시 정면 얼굴과 같은 단일 모델 검출에는 매우 효율적 이지만 측면 얼굴과 정면 얼굴이 혼합된 모델에서의 검출 시 성능이 저하되는 문제점을 가 지고 있다. 이 문제를 해 결하기 위해 본 논문에서는 혼합모델의 검출 성능을 높이기 위해 인스턴스 정보를 저장하여 학습하는 알고리즘인 IBL(Instance Based Learning)를 Haar-like feature 필터와 결합한 알고리즘을 제안한다.

Adaboost is a meta learning algorithm and it is used many area of object detection. In the field of face detection it is used in conjunction with Haar-like feature filtering because of its fast speed and high accuracy. Adaboost shows very effective performance for face detection in single distribution model such as front face but it shows reduced performance on multiple distribution models such as mixture of front face and side face. As a result of reduced performance this paper proposed to improve performance of multiple distribution models using IBL (Instance Based Learning) algorithm combined with the Haar-like feature.

천수방정식 기반 흐름해석모형은 하천 및 수로에서의 흐름을 수평 2차원적으로 수치모의하는데 활용되며 매개변수인 와점성계수와 조도계수에 큰 영향을 받는다. 따라서 적절한 매개변수의 선택은 흐름특성을 정확하게 모사하기 위해 매우 중요하다. 본 연구에서는 2차원 흐름해석에 적용되는 매개변수의 최적조합을 찾기 위한 자동화 알고리즘을 개발하고 적용하였다. 기존연구에서 2차원 흐름해석모형을 적용하는 경우 매개변수의 선정에 있어 경험적인 방법을 사용하여 최적의 매개변수를 찾는데 어려움이 있었다. 따라서 본 연구에서는 실험결과를 활용하여 다양한 매개변수의 조합에 따른 오차를 추적하고, 매개변수의 최적조합을 결정할 수 있는 알고리즘을 Python 언어를 이용하여 개발하고 적용하였다. 자동화 알고리즘은 121(11×11)개의 매개변수 조합에 따른 2차원 흐름해석결과 중 유속모의결과의 오차값을 비교하여 가장 정확한 조합을 손쉽게 결정하도록 구성되었다. 오차가 가장 작은 매개변수의 최적조합을 자동화 알고리즘의 적용을 통해 신속하고 간단하게 결정할 수 있다는 측면에서 높은 활용가치가 있을 것으로 기대된다.

Two-dimensional flow analysis, a fundamental component of hydrodynamics, plays a pivotal role in numerically simulating fluid behavior in rivers and waterways. This modeling approach heavily relies on parameters such as eddy viscosity and roughness coefficient to accurately represent flow characteristics. Therefore, combination of appropriate parameters is very important to accurately simulate flow characteristics. In this study, an automation algorithm was developed and applied to find the optimal combination of parameters. Previously, when applying a two-dimensional flow analysis model, former researchers usually depend on the empirical approach, which causes many difficulties in finding optimal variable values. Using the experimental data, we tracked errors according to the combination of various parameters and applied the algorithm that can determine the optimal combination of parameters with the Python language. The automation algorithm can easily determine the most accurate combination by comparing the flow velocity error values among the two-dimensional flow analysis results among the combinations of 121 (11×11) parameters. In the perspective of utilizing automation algorithm, there is an expected high utility in promptly and straightforwardly determining the optimal combination of parameters with the smallest error.

To effectively predict cigarette sales and improve the competitiveness of tobacco business enterprises, the characteristics of actual cigarette sales were detailed analyzed. Due to the long-term growth trends, seasonal fluctuations and the nonlinearity of monthly sales, we established three single forecasting models, which are Exponential Smoothing (ES), Seasonal Decomposition (SD) and Radial Basis Function (RBF) neural network. After obtaining the predicted value of three single models, the combination forecasting model was proposed. The weights of the three single models were computed using Mean Absolute Error and the mean relative error respectively, the result shows that relative error is more effective. A dynamic weight combination forecasting method based on RBF is proposed and compared with fixed weight method. Finally, the prediction accuracy of different models was compared based on the criteria of MAPE and RMSE, and the effectiveness of the combination method was proved, the proposed model can take advantage of the strengths of the three single models, the results indicate that the combination forecasting model suitable for cigarette sales has higher prediction accuracy. In some cases, the prediction accuracy of the fixed weight combination model is better than the dynamic weight combination model. The results can provide a certain reference to cigarette sales forecasting.

As to the embedded system of multi-tasks, this paper proposes a hardware/software partitioning algorithm based on Clustering algorithm using reference and density and Genetic algorithm. The algorithm uses reference task to reflect attribute of system task node, and firstly finish classification of task nodes to decrease the scale of the system, and then partitions hardware/software based on genetic algorithm. The experiments results have showed that this algorithm can accelerate converge to appropriate solution of complex system with more tasks.

The extension of ant colony algorithm was proposed by Dorigo, the founder of ant colony algorithm, which is the latest ant colony algorithm for solving a continuous space optimization problem. Considering the blindness of man-made choice of initial solution and initial parameters of the algorithm, and according to the algorithm converging slowly and easily falling into local optimum, this paper has provided improvement strategy for this optimization. It has introduced quantum computing and genetic algorithm, chaos optimization to carry out combination and comparison, and it has carried out improvement on the weight internally solved by memory in the algorithm. The effectiveness of various combined algorithms was determined through the optimization of numerous multi-dimensional continuous functions.

According to the inherent deficiency of traditional beamformer on anti-noise and lateral resolution, this paper proposes a method based on the combination of Chirp-coded and adaptive dual focusing beamformer (CARDFB). Firstly, Chirp-coded technology is introduced to improve the transmitting average power, and then the dual focusing beamformer and adaptive beamformer are combined to obtain a higher quality beamforming signal. Finally, the signal filtered with matched filter is used for imaging. Experimental results indicate that the proposed CARDFB algorithm can achieve higher lateral resolution, better main-to-side lobe ratio and are more robust to noise compared with traditional dynamic focusing combined with adaptive focusing beamformer.

3×3의 9개 셀을 가진 격자 보드 판에서 4개 셀을 하나의 블록으로 하는 4개 합 실마리와 C개의 숫자 실마리를 가진 수지코 퍼즐은 다항시간으로 퍼즐을 풀 수 있는 방법이 알려져 있지 않은 NP-완전 문제이다. 이 퍼즐을 풀기 위해서는 (9-C)!의 가능한 모든 경우수를 대입해 보는 전수탐색 법을 적용해야 한다. 본 논문은 빈 셀들에 들어갈 수 있는 후보 숫자들을 축소시키는 방법으로 유일 숫자 셀의 실마리를 확정하였다. 유일 숫자 셀이 더 이상 존재하지 않으면 합 실마리 블록들 간의 교집합 숫자들을 선택하는 방법을 제안하였다. 제안된 알고리즘을 52개 벤치마킹 실험 데이터에 적용한 결과 모든 문제에 대해 퍼즐을 다항시간으로 풀 수 있음을 보였다.

In a grid board with 9 cells of 3X3, the Sujiko puzzle with 4 sum clues and C number clues as one block is an NP-complete problem with no known way to solve the puzzle in polynomial time. To solve this puzzle (9-C)! in all possible cases, a brute-force method should be applied to substitute the number. This paper confirmed the clue of the unique number cell by reducing the number of candidates that can enter empty cells. When the unique number cell no longer exists, a method of selecting the intersection combination numbers between the sum clue blocks has been proposed. Applying the proposed algorithm to 52 benchmarking experimental data showed that puzzles can be solved in polynomial time for all problems.

Nowaday, market size of digital image in world around is looks to rapidly growth. For this, Texture mapping has traditionally been used to add realism to computer graphics images. Therefore to make our image realistic, we need to give the various kind of objects material parameter and environment lighting. To present the completed marble we use passing back algorithm and combination with channel of a material. In experimental result of this paper that application by passing back algorithm and varying the parameter such as scale, period, distortion, octaves of noise make showing the superiority of optimized rendering of spheres and perfect another marble effects.

All of us has an experience of using the word 'stress'. During the life, we are influenced with various physical and spiritual pressure, complication, discouragement and shortage. That much, stress exists everywhere and everytime around us. It is not easy to examine how much stress you are getting. You can examine only through the health institutions. The examining method is constituted with the psychological method and physiological method, but these methods have the low accuracy about stress index because of disproportion of subjectivity, objectivity and scientific. Consequently, this thesis suggests the algorithms of processing index to help easing stress which is able to examine personally and indexing with the mixing of results of psychological and physiological methods.

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

컴퓨터 상에서 모든 한글 음절을 구현하고자 하면 현대한글 11,172음절의 완성형 코드나 조합형 코드를 사용해야 하는데 조합형의 경우 글자의 미려도가 떨어지는 문제성이 발생되므로 자소 벌수를 늘려 그 문제점을 보완하려는 연구가 진행되어 왔다. 이는 메모리 및 코드처리상 비효율적인 요소가 많으므로 본 논문에서는 자소는 초 중 종성 각각 6벌씩만 제작한 후 자소의 어울림에 따라 자소의 이동과 변형으로 그 미려도를 추구할 수 있는 방안을 제시하고자 한다.

활자용 한글디자인에는 지적디자인 분야와 미적디자인 분야가 있다. 지적디자인에는 한글의 고유 특성인 자소조합 알고리즘이 포함된다. 본 논문에서는 경제성을 강조한 조합알고리즘의 실용화에 관하여 연구한다. 사무자동화용급 품질의 인쇄 활자 구현용으로 발표된 한글 자소 조합 방식으로 728 자소 조합 방식이 있다. ‘릭스 OA 조합형 728자소’ 방식의 초성은 중성을 7 계열로 나누고, 이를 다시 받침 없음, 받침 있음의 두벌로 나누어 모두 14벌의 초성이 필요하다(받침 없음 7벌 + 받침 있음 7벌). 중성은 초성을 단자음과 복자음으로 구분하고 다시 받침 없음과 받침 있음의 2벌로 구분하여 모두 4벌의 중성으로 구분된다. 받침은 단자음 초성과 복자음 초성으로 나누고, 다시 중성을 7 계열로 나누어 모두 14벌이 된다. 자소 추출용 대표 음절을 추출하기 위하여 728개 자소를 FOX 데이터베이스 파일로 저장하고 대표 음절 중에서 중복되는 한글 음절을 찾아내서 하나씩만 남기고보니 686개의 음절이 나타났다. 686개의 음절로 728개 자소를 추출할 수 있다는 것을 알아낸 것이다. 이 686개 한글 음절을 가나다라 순서로 순차적으로 정렬시켜 음절 명세표를 작성하였다. 본 연구에서 한글 728자소를 추출하려면 한글 686개 음절을 그리면 된다는 결과를 도출한 것은 큰 성과이다. 기존에는 728개의 자소를 각기 그려서 조합하는 방식을 사용하였으나, 본 연구 결과로 약 5.7%인 42개 음절을 줄여서 686개의 음절로 728개를 모두 만들어낸다는 것을 발견한 최초의 논문이다.

According to the taxology, there are two fields in Hangeul Design; that is Intellectual design and Aesthetic design. Hangeul Combination algorithm is appertained to Intellectual design. In this thesis, study a combination algorithm emphasized on development costs to practical use. It is used for producing font for office automation quality that is 728 Korean Jaso(phoneme, alphabet) combination algorithm method. The Cho-seong(the initial of Korean syllable) of "Leeks's 728 Jaso OA combination method" is consist of 7 Jung-seong(the medial of Korean syllable) and divided again into 2 groups which has Bat-chim group and no Bat-chim group. That means 14 consonant sets of Cho-seong is needed(14 sets x 19 Korean consonants = 266 Jaso). And 4 sets of vowels is necessary for Jung-seong(4 sets x 21 Korean vowels = 84 Jaso). Bat-chim(the final of Korean syllable) has 14 sets of final consonants(14 sets x 27 Korean consonants = 378 Jaso). 266 Jaso plus 84 Jaso plus 378 Jaso are total 728 Jaso. To select the representative syllable for each 728 Jaso, create database file using Foxplus program. And extract the 686 Korean syllables after totalling the 728 Jaso database file from the Korean syllable ‘Ga’ to the Korean syllable ‘Hig’. Making list of 686 Korean representative syllables with sorting them afterward. This thesis is the first study of putting 728 Korean Jaso combination algorithm method to practical use.

디자이너가 생각하는 모델을 3차원 입력 장치를 사용하여 공간상에서 스케치를 할 경우 상대적으로 깊이 방향의 위치를 정확히 입력하기 어렵기 때문에 원하는 모델을 단시간에 그리기 어렵다. 본 논문에서는 2차원 스케치에서 습관적으로 사용하는 다중, 반복 스트로크를 공간 스케치 시스템에 적용하여 입력 위치의 에러를 보정함과 동시에 효율적으로 개념상의 모델을 실체화 할 수 있는 모델링 알고리즘을 제안한다. 디자이너는 곡면을 그리기 위하여 스트로크를 입력한 후 바로 그 결과를 확인할 수 있으며, 추가적인 스트로크를 입력할 때마다 곡면은 변형되고 자신이 원하는 형태로 변형되는 지 확인하면서 새로운 스트로크를 입력할 수 있다. 이를 위하여 드로잉 공간을 격자 형태의 공간으로 나누고, 각각의 격자 내부에는 격자안에서 이동이 자유로운 내부 정점을 정의하여 드로잉 공간에서의 데이타를 단순화하였다. 이정점을 인접격자와의 상호관계 패턴으로 정의하여 디자이너가 의도한 개념상의 모델을 효과적으로 3차원모델로 구체화하는 3차원 공간 스케치 시스템을 구현하였다.

In case of sketching a conceptual model in 3D space, it's not easy for designer to recognize the depth cue accurately and to draw a model correctly in short time. In this paper, multi-strokes based sketch is adopted not only to reduce the error of input point but to substantiate the shape o) the conceptual design effectively. The designer can see the drawing result immediately after stroking some curves. The shape can also be modified by stroking curves repeatedly and be confirmed the modified shape in real time. However, the multi-strokes based sketch needs to manage the great amount of input data. Therefore, the drawing space is divided into the limited spatial cubical grids and the movable infernal vertex in each spatial grid is implemented and used to define the surface by the multi-strokes. We implemented the spatial sketching system which allows the concept designer's intention to 3D model data efficiently.

We propose a adaptive combination of affine projection algorithm (APA) filters with different update interval. Two APA filters with different update interval are adapted independently in order to keep the advantages of both component filters. This novel scheme provides improvement of performance in term of the convergence rate and the steady-state error. Experimental results show good properties of the proposed algorithm.

Powell-Miller theory is a good method to express or treat incorrect information. But it has limitation that requires too much time to apply to actual situation because computational complexity increases in exponential and functional way. Accordingly, there have been several attempts to reduce computational complexity but side effect followed - certainty factor fell. This study suggested expanded Approximation Algorithm. Expanded Approximation Algorithm is a method to consider both smallest supersets and largest subsets to expand basic space into a space including inverse set and to reduce Approximation error. By using expanded Approximation Algorithm suggested in the study, basic probability assignment function value of subsets was alloted and added to basic probability assignment function value of sets related to the subsets. This made subsets newly created become Approximation more efficiently. As a result, it could be known that certain function value which is based on basic probability assignment function is closely near actual optimal result. And certainty in correctness can be obtained while computational complexity could be reduced. by using Algorithm suggested in the study, exact information necessary for a system can be obtained.