• Title/Summary/Keyword: Accuracy of behavior

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Deformation Analysis of Soft Foundation with Vertical Drain Wells using the Interface Element Method -With Emphasis on Model Foundation and Actual Sand Drain Well Foundation- (접합요소에 의한 Vertical Drain Well 지반의 변형해석 - 모델지반과 실제 Sand Drain Well 지반을 중심으로 -)

  • Lee, Jean Soo
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.13 no.4
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    • pp.227-237
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    • 1993
  • This paper dealt with numerical analysis of sand drain considering the smear effect around drain wells and discontinuous deformation behavior due to difference in rigidity between drain materials and adjacent clayey soils. Biot's equation was selected as governing equation coupled with MODCAM (Modified Cam-clay) model or EVP(Elasto-Viscoplastic) model as constitutive equation. The validity as well as the accuracy of the method developed by author was checked by comparing the proposed method with those by Siriwardane and Ghaboussi using joint element. The FEM analysis developed in this study was applied to both 2-dimensional model foundation and actual foundation. the result of which proved to be satisfactory.

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Evaluation on Flexural Performance of Precast Decks with Ribbed Joint by FEM (유한요소해석에 의한 요철형 이음단면을 갖는 프리캐스트 바닥판의 휨성능 평가)

  • Oh, Hyun-Chul;Chung, Chul-Hun;Kang, Myoung-Gu;Park, Se-Jin;Shin, Dong-Ho
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.20 no.1
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    • pp.85-94
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    • 2016
  • In this study, a non-linear FEM model is presented to predict the static flexural performance of precast bridge decks with ribbed joint and is verified with previous experiment results through comparison. The several theory of material properties were applied to each mechanical properties in FEM model and FEM model's input variables were determined through experiment result and parametric study. The FEM results showed good accuracy in predicting the structural performance of the specimens and FEM model's average error rate was 5%. Also, each specimen's cracking aspect and failure mode can be predicted through FEM's plastic strain distribution. Thus, this FEM model can be used effectively for predicting the ultimate behavior and parametric study to development of design formula for joint.

Multi-Modal Based Malware Similarity Estimation Method (멀티모달 기반 악성코드 유사도 계산 기법)

  • Yoo, Jeong Do;Kim, Taekyu;Kim, In-sung;Kim, Huy Kang
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.29 no.2
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    • pp.347-363
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    • 2019
  • Malware has its own unique behavior characteristics, like DNA for living things. To respond APT (Advanced Persistent Threat) attacks in advance, it needs to extract behavioral characteristics from malware. To this end, it needs to do classification for each malware based on its behavioral similarity. In this paper, various similarity of Windows malware is estimated; and based on these similarity values, malware's family is predicted. The similarity measures used in this paper are as follows: 'TF-IDF cosine similarity', 'Nilsimsa similarity', 'malware function cosine similarity' and 'Jaccard similarity'. As a result, we find the prediction rate for each similarity measure is widely different. Although, there is no similarity measure which can be applied to malware classification with high accuracy, this result can be helpful to select a similarity measure to classify specific malware family.

Classification Performance Improvement of UNSW-NB15 Dataset Based on Feature Selection (특징선택 기법에 기반한 UNSW-NB15 데이터셋의 분류 성능 개선)

  • Lee, Dae-Bum;Seo, Jae-Hyun
    • Journal of the Korea Convergence Society
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    • v.10 no.5
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    • pp.35-42
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    • 2019
  • Recently, as the Internet and various wearable devices have appeared, Internet technology has contributed to obtaining more convenient information and doing business. However, as the internet is used in various parts, the attack surface points that are exposed to attacks are increasing, Attempts to invade networks aimed at taking unfair advantage, such as cyber terrorism, are also increasing. In this paper, we propose a feature selection method to improve the classification performance of the class to classify the abnormal behavior in the network traffic. The UNSW-NB15 dataset has a rare class imbalance problem with relatively few instances compared to other classes, and an undersampling method is used to eliminate it. We use the SVM, k-NN, and decision tree algorithms and extract a subset of combinations with superior detection accuracy and RMSE through training and verification. The subset has recall values of more than 98% through the wrapper based experiments and the DT_PSO showed the best performance.

Deep Learning-based Happiness Index Model Considering Social Variables and Individual Emotional Index (사회적 변수와 개개인의 감정지수를 함께 고려한 딥러닝 기반 행복 지수 모델 설계)

  • Sumin Oh;Minseo Park
    • The Journal of the Convergence on Culture Technology
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    • v.10 no.1
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    • pp.489-493
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    • 2024
  • Happiness index is a measurement system for understanding collective happiness. As values change, studies have been proposed to add the value of behavior to the happiness index. However, there is a lack of studies analyze the relationship using individual emotions. Using a deep learning model, we predicted happiness index using social variables and individual emotional index. First, we collected social and emotional variables from January 2005 to December 2020. Second, we preprocessed the data and identified significant variables. Finally, we trained deep learning-based regression model. Our proposed model was evaluated using 5-fold cross validation. The proposed model showed 90.86% accuracy on test sets. Our model will be expected to analyze the significant factors of country-specific happiness index.

The Effects of Price Salience on Consumer Perception and Purchase Intentions (개격현저대소비자감지화구매의도적영향(价格显著对消费者感知和购买意图的影响))

  • Martin-Consuegea, David;Millan, Angel;Diaz, Estrella;Ko, Eun-Ju
    • Journal of Global Scholars of Marketing Science
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    • v.20 no.2
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    • pp.149-163
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    • 2010
  • Previous studies have shown that retail price promotion change consumers' purchase behavior and that retailers use price promotion more frequently. Keeping constant the benefits received by consumers, there are several ways for retailers to communicate a price promotion. For example, retailers can present a price reduction in absolute terms ($, ${\euro}$), percentage terms (%), or some combinations of these two methods (Della Bitta et al. 1981). Communicating a price promotion in different ways is similar to the framing of purchase decisions (Monroe 1990). Framing effects refers to the finding that subjects respond differently to different descriptions of the same decision question (Frisch 1993). Thus, the presentation of the promotion has an impact on consumer deal evaluation and hence retail sales. In fact, much research in marketing attests to the effects of price presentation on deal perception (Lichtenstein and Bearden 1989; Urbany et al. 1988; Yadav and Monroe 1993). In this sense, a number of marketing researches have argued that deal perceptions are also determined by the degree to which consumers are able to calculate the discounts and final purchase prices accurately (Estelami 2003a; Morwitz et al. 1998), which suggests that marketers may be able to enhance responses to discounts by improving calculation accuracy. Consequently, since calculation inaccuracies in the aggregate lead to the underestimation of discounts (Kim and Kramer 2006), consumers are more likely to appreciate a discounted offer following deeper processing of price information that enables them to evaluate a price discount more accurately. The purpose of this research is to examine the effect of different presentations of discount prices on consumer price perceptions. To be more precise, the purpose of this study is to investigate how different implementations of the same price promotion (semantic and visual salience) affect consumers' perceptions of the promotion and their purchase decisions. Specifically, the analysis will focus on the effect of price presentation on evaluation, purchase intentions and perception of savings. In order to verify the hypotheses proposed in the research, this paper will present an experimental analysis dealing with several discount presentations. In this sense, a2 (Numerical salience presentation: absolute and relative) x2 (Worded salience presentation: novel and traditional) x2 (Visual salience: red and blue) design was employed to investigate the effects of discount presentation on three dependent variables: evaluation, purchase intentions and perception of savings. Respondents were exposed to a hypothetical advertisement that they had to evaluate and were informed of the offer conditions. Once the sample finished evaluating the advertisement, they answered a questionnaire related to price salience and dependent dimensions. Then, manipulation checks were conducted to ensure that respondents remembered their treatment conditions. Next, a $2{\times}2{\times}2$ MANOVA and follow-up univariate tests were conducted to verify the research hypotheses suggested and to examine the effects of the individual factors (price salience) on evaluation, purchase intentions and perceived savings. The results of this research show that semantic and visual salience presentations have significant main effects and interactions on evaluation, purchase intentions and perception of savings. Significant numerical salience interactions affected evaluation and purchase intentions. Additionally, a significant worded salience main effect on perception of savings and interactions on evaluation and purchase intentions were found. Finally, visual salience interactions have significant effects on evaluation. The main findings of this research suggest practical implications that firms should consider when planning promotion-based discounts to attract consumer attention. Consequently, because price presentation has important effects on consumer perception, retailers should consider which effect is wanted in order to design an effective discount presentaion. Specifically, retailers should present discounts with a traditional style that facilitates final price calculation. It is thus important to investigate ways in which marketers can enhance the accuracy of consumers' mental arithmetic to improve responses to price discounts. This preliminary study on the effect of price presentation on consumer perception and purchase intentions opens the line of research for further research. The results obtained in this research may have been determined by a number of limiting conceptual and methodological factors. In this sense, the research deals with a variety of discount presentations as well as with their effects; however, the analysis could include additional salience dimensions and effects on consumers. Furthermore, a similar study could be carried out including a larger, more inclusive and heterogeneous sample of consumers. In addition, the experiment did not require sample individuals to actually buy the product, so it is advisable to compare the effects obtained in the research with real consumer behavior and perception.

A Comparative Study on the 1-D and 3-D Load Follow Analysis Methods of Light Water Reactor (경수로의 부하추종 운전에 대한 1차원 및 3차원 해석방법의 비교 연구)

  • Kim, Chang-Hyo;Lee, Sang-Hoon;Chung, Chang-Hyun
    • Nuclear Engineering and Technology
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    • v.19 no.1
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    • pp.34-41
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    • 1987
  • This work concerns with a comparison of the 1-dimensional (or 1-D) load follow analysis method with reference to the detailed 3-dimensional (or 3-D) computations. For this purpose a 1-D two-group finite difference code, HLOFO, and a 3-D coarse-mesh code based on the modified Borresen's method, CMSNAC, are developed. The CMSNAC code is used to obtain the 3-D power peaks and reactivity parameters in response to power swing from 100 to 50 and back to 100% in the 12-3-6-3 load cycle for the BOL of the KORI Unit 1 PWR core. The 3-D result is then compared with the 1-D HLOFO computations, the cross section and buckling inputs of which are obtained by combining the flux-volume weighting scheme with the approximate flux from the auxiliary 3-D computations. It is shown that the 1-D computation has a limited accuracy, yet it is confirmed that it can describe the core axial average behavior which is fairly consistent with the detailed 3-D computation.

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A Static Analyzer for Detecting Memory Leaks based on Procedural Summary (함수 요약에 기반한 메모리 누수 정적 탐지기)

  • Jung, Yung-Bum;Yi, Kwang-Keun
    • Journal of KIISE:Software and Applications
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    • v.36 no.7
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    • pp.590-606
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    • 2009
  • We present a static analyzer that detects memory leaks in C programs. It achieves relatively high accuracy at a relatively low cost on SPEC2000 benchmarks and several open-source software packages, demonstrating its practicality and competitive edge against other reported analyzers: for a set of benchmarks totaling 1,777 KLOCs, it found 332 bugs with 47 additional false positives (a 12.4% false-positive ratio), and the average analysis speed was 720 LOC/sec. We separately analyze each procedure's memory behavior into a summary that is used in analyzing its call sites. Each procedural summary is parameterized by the procedure's call context so that it can be instantiated at different call sites. What information to capture in each procedural summary has been carefully tuned so that the summary should not lose any common memory-leak-related behaviors in real-world C program. Because each procedure is summarized by conventional fixpoint iteration over the abstract semantics ('a la abstract interpretation), the analyzer naturally handles arbitrary call cycles from direct or indirect recursive calls.

A Recommender System Model Combining Collaborative filtering and SOM Neural Networks (협동적 필터링과 SOM 신경망을 결합한 추천시스템 모델)

  • Lee, Mi-Hee;Woo, Young-Tae
    • Journal of Korea Multimedia Society
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    • v.11 no.9
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    • pp.1213-1226
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    • 2008
  • A recommender system supports people in making recommendations finding a set of people who are likely to provide good recommendations for a given person, or deriving recommendations from implicit behavior such as browsing activity, buying patterns, and time on task. We proposed new recommender system which combined SOM(Self-Organizing Map) neural networks with the Collaborative filtering which most recommender systems hat applied First, we segmented user groups according to demographic characteristics and then we trained the SOM with people's preferences as ito inputs. Finally we applied the classic collaborative filtering to the clustering with similarity in which an recommendation seeker belonged to, and therefore we didn't have to apply the collaborative filtering to the whose data set. Experiments were run for EachMovies data set. The results indicated that the predictive accuracy was increased in terms of MAE(Mean-Absolute-Error).

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Real Time Face detection Method Using TensorRT and SSD (TensorRT와 SSD를 이용한 실시간 얼굴 검출방법)

  • Yoo, Hye-Bin;Park, Myeong-Suk;Kim, Sang-Hoon
    • KIPS Transactions on Software and Data Engineering
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    • v.9 no.10
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    • pp.323-328
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    • 2020
  • Recently, new approaches that significantly improve performance in object detection and recognition using deep learning technology have been proposed quickly. Of the various techniques for object detection, especially facial object detection (Faster R-CNN, R-CNN, YOLO, SSD, etc), SSD is superior in accuracy and speed to other techniques. At the same time, multiple object detection networks are also readily available. In this paper, among object detection networks, Mobilenet v2 network is used, models combined with SSDs are trained, and methods for detecting objects at a rate of four times or more than conventional performance are proposed using TensorRT engine, and the performance is verified through experiments. Facial object detector was created as an application to verify the performance of the proposed method, and its behavior and performance were tested in various situations.