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Prediction of Isothermal and Reacting Flows in Widely-Spaced Coaxial Jet, Diffusion-Flame Combustor (큰 지름비를 가지는 동축제트 확산화염 연소기내의 등온 및 연소 유동장의 예측)

  • O, Gun-Seop;An, Guk-Yeong;Kim, Yong-Mo;Lee, Chang-Sik
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.20 no.7
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    • pp.2386-2396
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    • 1996
  • A numerical simulation has been performed for isothermal and reacting flows in an exisymmetric, bluff-body research combustor. The present formulation is based on the density-weighted averaged Navier-Stokes equations together with a k-epsilon. turbulence model and a modified eddy-breakup combustion model. The PISO algorithm is employed for solution of thel Navier-Stokes system. Comparison between measurements and predictions are made for a centerline axial velocities, location of stagnation points, strength of recirculation zone, and temperature profile. Even though the numerical simulation gives acceptable agreement with experimental data in many respects, the present model is defictient in predicting the recoveryt rate of a central near-wake region, the non-isotropic turbulence effects, and variation of turbulent Schmidt number. Several possible explanations for these discrepancies have been discussed.

Improvement of 2-pass DInSAR-based DEM Generation Method from TanDEM-X bistatic SAR Images (TanDEM-X bistatic SAR 영상의 2-pass 위성영상레이더 차분간섭기법 기반 수치표고모델 생성 방법 개선)

  • Chae, Sung-Ho
    • Korean Journal of Remote Sensing
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    • v.36 no.5_1
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    • pp.847-860
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    • 2020
  • The 2-pass DInSAR (Differential Interferometric SAR) processing steps for DEM generation consist of the co-registration of SAR image pair, interferogram generation, phase unwrapping, calculation of DEM errors, and geocoding, etc. It requires complicated steps, and the accuracy of data processing at each step affects the performance of the finally generated DEM. In this study, we developed an improved method for enhancing the performance of the DEM generation method based on the 2-pass DInSAR technique of TanDEM-X bistatic SAR images was developed. The developed DEM generation method is a method that can significantly reduce both the DEM error in the unwrapped phase image and that may occur during geocoding step. The performance analysis of the developed algorithm was performed by comparing the vertical accuracy (Root Mean Square Error, RMSE) between the existing method and the newly proposed method using the ground control point (GCP) generated from GPS survey. The vertical accuracy of the DInSAR-based DEM generated without correction for the unwrapped phase error and geocoding error is 39.617 m. However, the vertical accuracy of the DEM generated through the proposed method is 2.346 m. It was confirmed that the DEM accuracy was improved through the proposed correction method. Through the proposed 2-pass DInSAR-based DEM generation method, the SRTM DEM error observed by DInSAR was compensated for the SRTM 30 m DEM (vertical accuracy 5.567 m) used as a reference. Through this, it was possible to finally create a DEM with improved spatial resolution of about 5 times and vertical accuracy of about 2.4 times. In addition, the spatial resolution of the DEM generated through the proposed method was matched with the SRTM 30 m DEM and the TanDEM-X 90m DEM, and the vertical accuracy was compared. As a result, it was confirmed that the vertical accuracy was improved by about 1.7 and 1.6 times, respectively, and more accurate DEM generation was possible with the proposed method. If the method derived in this study is used to continuously update the DEM for regions with frequent morphological changes, it will be possible to update the DEM effectively in a short time at low cost.

Approximation of Multiple Trait Effective Daughter Contribution by Dairy Proven Bulls for MACE (젖소 국제유전능력 평가를 위한 종모우별 다형질 Effective Daughter Contribution 추정)

  • Cho, Kwang-Hyun;Choi, Tae-Jeong;Cho, Chung-Il;Park, Kyung-Do;Do, Kyoung-Tag;Oh, Jae-Don;Lee, Hak-Kyo;Kong, Hong-Sik;Lee, Joon-Ho
    • Journal of Animal Science and Technology
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    • v.55 no.5
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    • pp.399-403
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    • 2013
  • This study was conducted to investigate the basic concept of multiple trait effective daughter contribution (MTEDC) for dairy cattle sires and calculate effective daughter contribution (EDC) by applying a five lactation multiple trait model using milk yield test records of daughters for the Multiple-trait Across Country Evaluation (MACE). Milk yield data and pedigree information of 301,551 cows that were the progeny of 2,046 Korean and imported dairy bulls were collected from the National Agricultural Cooperative Federation and used in this study. For MTEDC approximation, the reliability of the breeding value was separated based on parents average, own yield deviation and mate adjusted progeny contribution. EDC was then calculated by lactation using these reliabilities. The average number of recorded daughters per sire by lactations were 140.57, 94.24, 55.14, 29.20 and 14.06 from the first to fifth lactation, respectively. However, the average EDC per sire by lactation using the five lactation multiple trait model was 113.49, 89.28, 73.56, 54.02 and 35.08 from the first to fifth lactation, respectively, while the decrease of EDC in late lactations was comparably lower than the average number of recorded daughters per sire. These findings indicate that the availability of daughters without late lactation records is increased by genetic correlation using the multiple trait model. Owing to the relatedness between the EDC and reliability of the estimated breeding value for sire, understanding the MTEDC algorithm and continuous monitoring of EDC is required for correct MACE application of the five lactation multiple trait model.

Speech Recognition Using Linear Discriminant Analysis and Common Vector Extraction (선형 판별분석과 공통벡터 추출방법을 이용한 음성인식)

  • 남명우;노승용
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.4
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    • pp.35-41
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    • 2001
  • This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100% recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2% than conventional method.

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Design and Implementation of the SSL Component based on CBD (CBD에 기반한 SSL 컴포넌트의 설계 및 구현)

  • Cho Eun-Ae;Moon Chang-Joo;Baik Doo-Kwon
    • Journal of KIISE:Computing Practices and Letters
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    • v.12 no.3
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    • pp.192-207
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    • 2006
  • Today, the SSL protocol has been used as core part in various computing environments or security systems. But, the SSL protocol has several problems, because of the rigidity on operating. First, SSL protocol brings considerable burden to the CPU utilization so that performance of the security service in encryption transaction is lowered because it encrypts all data which is transferred between a server and a client. Second, SSL protocol can be vulnerable for cryptanalysis due to the key in fixed algorithm being used. Third, it is difficult to add and use another new cryptography algorithms. Finally. it is difficult for developers to learn use cryptography API(Application Program Interface) for the SSL protocol. Hence, we need to cover these problems, and, at the same time, we need the secure and comfortable method to operate the SSL protocol and to handle the efficient data. In this paper, we propose the SSL component which is designed and implemented using CBD(Component Based Development) concept to satisfy these requirements. The SSL component provides not only data encryption services like the SSL protocol but also convenient APIs for the developer unfamiliar with security. Further, the SSL component can improve the productivity and give reduce development cost. Because the SSL component can be reused. Also, in case of that new algorithms are added or algorithms are changed, it Is compatible and easy to interlock. SSL Component works the SSL protocol service in application layer. First of all, we take out the requirements, and then, we design and implement the SSL Component, confidentiality and integrity component, which support the SSL component, dependently. These all mentioned components are implemented by EJB, it can provide the efficient data handling when data is encrypted/decrypted by choosing the data. Also, it improves the usability by choosing data and mechanism as user intend. In conclusion, as we test and evaluate these component, SSL component is more usable and efficient than existing SSL protocol, because the increase rate of processing time for SSL component is lower that SSL protocol's.

Development of Multimedia Annotation and Retrieval System using MPEG-7 based Semantic Metadata Model (MPEG-7 기반 의미적 메타데이터 모델을 이용한 멀티미디어 주석 및 검색 시스템의 개발)

  • An, Hyoung-Geun;Koh, Jae-Jin
    • The KIPS Transactions:PartD
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    • v.14D no.6
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    • pp.573-584
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    • 2007
  • As multimedia information recently increases fast, various types of retrieval of multimedia data are becoming issues of great importance. For the efficient multimedia data processing, semantics based retrieval techniques are required that can extract the meaning contents of multimedia data. Existing retrieval methods of multimedia data are annotation-based retrieval, feature-based retrieval and annotation and feature integration based retrieval. These systems take annotator a lot of efforts and time and we should perform complicated calculation for feature extraction. In addition. created data have shortcomings that we should go through static search that do not change. Also, user-friendly and semantic searching techniques are not supported. This paper proposes to develop S-MARS(Semantic Metadata-based Multimedia Annotation and Retrieval System) which can represent and extract multimedia data efficiently using MPEG-7. The system provides a graphical user interface for annotating, searching, and browsing multimedia data. It is implemented on the basis of the semantic metadata model to represent multimedia information. The semantic metadata about multimedia data is organized on the basis of multimedia description schema using XML schema that basically comply with the MPEG-7 standard. In conclusion. the proposed scheme can be easily implemented on any multimedia platforms supporting XML technology. It can be utilized to enable efficient semantic metadata sharing between systems, and it will contribute to improving the retrieval correctness and the user's satisfaction on embedding based multimedia retrieval algorithm method.

A Destructive Method in the Connection of the Algorithm and Design in the Digital media - Centered on the Rapid Prototyping Systems of Product Design - (디지털미디어 환경(環境)에서 디자인 특성(特性)에 관한 연구(硏究) - 실내제품(室內製品) 디자인을 중심으로 -)

  • Kim Seok-Hwa
    • Journal of Science of Art and Design
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    • v.5
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    • pp.87-129
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    • 2003
  • The purpose of this thesis is to propose a new concept of design of the 21st century, on the basis of the study on the general signification of the structures and the signs of industrial product design, by examining the difference between modern and post-modern design, which is expected to lead the users to different design practice and interpretation of it. The starting point of this study is the different styles and patterns of 'Gestalt' in the post-modern design of the late 20th century from modern design - the factor of determination in industrial product design. That is to say, unlike functional and rational styles of modern product design, the late 20th century is based upon the pluralism characterized by complexity, synthetic and decorativeness. So far, most of the previous studies on design seem to have excluded visual aspects and usability, focused only on effective communication of design phenomena. These partial studies on design, blinded by phenomenal aspects, have resulted in failure to discover a principle of fundamental system. However, design varies according to the times; and the transformation of design is reflected in Design Pragnanz to constitute a new text of design. Therefore, it can be argued that Design Pragnanz serves as an essential factor under influence of the significance of text. In this thesis, therefore, I delve into analysis of the 20th century product design, in the light of Gestalt theory and Design Pragnanz, which have been functioning as the principle of the past design. For this study, I attempted to discover the fundamental elements in modern and post-modern designs, and to examine the formal structure of product design, the users' aesthetic preference and its semantics, from the integrative viewpoint. Also, with reference to history and theory of design my emphasis is more on fundamental visual phenomena than on structural analysis or process of visualization in product design, in order to examine the formal properties of modern and post-modern designs. Firstly, In Chapter 1, 'Issues and Background of the Study', I investigated the Gestalt theory and Design Pragnanz, on the premise of formal distinction between modern and post-modern designs. These theories are founded upon the discussion on visual perception of Gestalt in Germany in 1910's, in pursuit of the principle of perception centered around visual perception of human beings. In Chapter 2, I dealt with functionalism of modern design, as an advance preparation for the further study on the product design of the late 20th century. First of all, in Chapter 2-1, I examined the tendency of modern design focused on functionalism, which can be exemplified by the famous statement 'Form follows function'. Excluding all unessential elements in design - for example, decoration, this tendency has attained the position of the international style based on the spirit of Bauhause - universality and regularity - in search of geometric order, standardization and rationalization. In Chapter 2-2, I investigated the anthropological viewpoint that modern design started representing culture in a symbolic way including overall aspects of the society - politics, economics and ethics, and its criticism on functionalist design that aesthetic value is missing in exchange of excessive simplicity in style. Moreover, I examined the pluralist phenomena in post-modern design such as kitsch, eclecticism, reactionism, hi-tech and digital design, breaking away from functionalist purism of modern design. In Chapter 3, I analyzed Gestalt Pragnanz in design in a practical way, against the background of design trends. To begin with, I selected mass product design among those for the 20th century products as a target of analysis, highlighting representative styles in each category of the products. For this analysis, I adopted the theory of J. M Lehnhardt, who gradated in percentage the aesthetic and semantic levels of Pragnantz in design expression, and that of J. K. Grutter, who expressed it in a formula of M = O : C. I also employed eight units of dichotomies, according to the G. D. Birkhoff's aesthetic criteria, for the purpose of scientific classification of the degree of order and complexity in design; and I analyzed phenomenal aspects of design form represented in each unit. For Chapter 4, I executed a questionnaire about semiological phenomena of Design Pragnanz with 28 units of antonymous adjectives, based upon the research in the previous chapter. Then, I analyzed the process of signification of Design Pragnanz, founded on this research. Furthermore, the interpretation of the analysis served as an explanation to preference, through systematic analysis of Gestalt and Design Pragnanz in product design of the late 20th century. In Chapter 5, I determined the position of Design Pragnanz by integrating the analyses of Gestalt and Pragnanz in modern and post-modern designs In this process, 1 revealed the difference of each Design Pragnanz in formal respect, in order to suggest a vision of the future as a result, which will provide systemic and structural stimulation to current design.

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Online news-based stock price forecasting considering homogeneity in the industrial sector (산업군 내 동질성을 고려한 온라인 뉴스 기반 주가예측)

  • Seong, Nohyoon;Nam, Kihwan
    • Journal of Intelligence and Information Systems
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    • v.24 no.2
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    • pp.1-19
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    • 2018
  • Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

Analysis of Genetics Problem-Solving Processes of High School Students with Different Learning Approaches (학습접근방식에 따른 고등학생들의 유전 문제 해결 과정 분석)

  • Lee, Shinyoung;Byun, Taejin
    • Journal of The Korean Association For Science Education
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    • v.40 no.4
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    • pp.385-398
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    • 2020
  • This study aims to examine genetics problem-solving processes of high school students with different learning approaches. Two second graders in high school participated in a task that required solving the complicated pedigree problem. The participants had similar academic achievements in life science but one had a deep learning approach while the other had a surface learning approach. In order to analyze in depth the students' problem-solving processes, each student's problem-solving process was video-recorded, and each student conducted a think-aloud interview after solving the problem. Although students showed similar errors at the first trial in solving the problem, they showed different problem-solving process at the last trial. Student A who had a deep learning approach voluntarily solved the problem three times and demonstrated correct conceptual framing to the three constraints using rule-based reasoning in the last trial. Student A monitored the consistency between the data and her own pedigree, and reflected the problem-solving process in the check phase of the last trial in solving the problem. Student A's problem-solving process in the third trial resembled a successful problem-solving algorithm. However, student B who had a surface learning approach, involuntarily repeated solving the problem twice, and focused and used only part of the data due to her goal-oriented attitude to solve the problem in seeking for answers. Student B showed incorrect conceptual framing by memory-bank or arbitrary reasoning, and maintained her incorrect conceptual framing to the constraints in two problem-solving processes. These findings can help in understanding the problem-solving processes of students who have different learning approaches, allowing teachers to better support students with difficulties in accessing genetics problems.

Context Prediction Using Right and Wrong Patterns to Improve Sequential Matching Performance for More Accurate Dynamic Context-Aware Recommendation (보다 정확한 동적 상황인식 추천을 위해 정확 및 오류 패턴을 활용하여 순차적 매칭 성능이 개선된 상황 예측 방법)

  • Kwon, Oh-Byung
    • Asia pacific journal of information systems
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    • v.19 no.3
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    • pp.51-67
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    • 2009
  • Developing an agile recommender system for nomadic users has been regarded as a promising application in mobile and ubiquitous settings. To increase the quality of personalized recommendation in terms of accuracy and elapsed time, estimating future context of the user in a correct way is highly crucial. Traditionally, time series analysis and Makovian process have been adopted for such forecasting. However, these methods are not adequate in predicting context data, only because most of context data are represented as nominal scale. To resolve these limitations, the alignment-prediction algorithm has been suggested for context prediction, especially for future context from the low-level context. Recently, an ontological approach has been proposed for guided context prediction without context history. However, due to variety of context information, acquiring sufficient context prediction knowledge a priori is not easy in most of service domains. Hence, the purpose of this paper is to propose a novel context prediction methodology, which does not require a priori knowledge, and to increase accuracy and decrease elapsed time for service response. To do so, we have newly developed pattern-based context prediction approach. First of ail, a set of individual rules is derived from each context attribute using context history. Then a pattern consisted of results from reasoning individual rules, is developed for pattern learning. If at least one context property matches, say R, then regard the pattern as right. If the pattern is new, add right pattern, set the value of mismatched properties = 0, freq = 1 and w(R, 1). Otherwise, increase the frequency of the matched right pattern by 1 and then set w(R,freq). After finishing training, if the frequency is greater than a threshold value, then save the right pattern in knowledge base. On the other hand, if at least one context property matches, say W, then regard the pattern as wrong. If the pattern is new, modify the result into wrong answer, add right pattern, and set frequency to 1 and w(W, 1). Or, increase the matched wrong pattern's frequency by 1 and then set w(W, freq). After finishing training, if the frequency value is greater than a threshold level, then save the wrong pattern on the knowledge basis. Then, context prediction is performed with combinatorial rules as follows: first, identify current context. Second, find matched patterns from right patterns. If there is no pattern matched, then find a matching pattern from wrong patterns. If a matching pattern is not found, then choose one context property whose predictability is higher than that of any other properties. To show the feasibility of the methodology proposed in this paper, we collected actual context history from the travelers who had visited the largest amusement park in Korea. As a result, 400 context records were collected in 2009. Then we randomly selected 70% of the records as training data. The rest were selected as testing data. To examine the performance of the methodology, prediction accuracy and elapsed time were chosen as measures. We compared the performance with case-based reasoning and voting methods. Through a simulation test, we conclude that our methodology is clearly better than CBR and voting methods in terms of accuracy and elapsed time. This shows that the methodology is relatively valid and scalable. As a second round of the experiment, we compared a full model to a partial model. A full model indicates that right and wrong patterns are used for reasoning the future context. On the other hand, a partial model means that the reasoning is performed only with right patterns, which is generally adopted in the legacy alignment-prediction method. It turned out that a full model is better than a partial model in terms of the accuracy while partial model is better when considering elapsed time. As a last experiment, we took into our consideration potential privacy problems that might arise among the users. To mediate such concern, we excluded such context properties as date of tour and user profiles such as gender and age. The outcome shows that preserving privacy is endurable. Contributions of this paper are as follows: First, academically, we have improved sequential matching methods to predict accuracy and service time by considering individual rules of each context property and learning from wrong patterns. Second, the proposed method is found to be quite effective for privacy preserving applications, which are frequently required by B2C context-aware services; the privacy preserving system applying the proposed method successfully can also decrease elapsed time. Hence, the method is very practical in establishing privacy preserving context-aware services. Our future research issues taking into account some limitations in this paper can be summarized as follows. First, user acceptance or usability will be tested with actual users in order to prove the value of the prototype system. Second, we will apply the proposed method to more general application domains as this paper focused on tourism in amusement park.