• Title/Summary/Keyword: mining test

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The effect of micro parameters of PFC software on the model calibration

  • Ajamzadeh, M.R.;Sarfarazi, Vahab;Haeri, Hadi;Dehghani, H.
    • Smart Structures and Systems
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    • v.22 no.6
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    • pp.643-662
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    • 2018
  • One of the methods for investigation of mechanical behavior of materials is numerical simulation. For simulation, its need to model behavior is close to real condition. PFC is one of the rock mechanics software that needs calibration for models simulation. The calibration was performed based on simulation of unconfined compression test and Brazilian test. Indeed the micro parameter of models change so that the UCS and Brazilian test results in numerical simulation be close to experimental one. In this paper, the effect of four micro parameters has been investigated on the uniaxial compression test and Brazilian test. These micro parameters are friction angle, Accumulation factor, expansion coefficient and disc distance. The results show that these micro parameters affect the failure pattern in UCS and Brazilian test. Also compressive strength and tensile strength are controlled by failure pattern.

DISCOVERY TEMPORAL FREQUENT PATTERNS USING TFP-TREE

  • Jin Long;Lee Yongmi;Seo Sungbo;Ryu Keun Ho
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.454-457
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    • 2005
  • Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. And calendar based on temporal association rules proposes the discovery of association rules along with their temporal patterns in terms of calendar schemas, but this approach is also adopt an Apriori-like candidate set generation. In this paper, we propose an efficient temporal frequent pattern mining using TFP-tree (Temporal Frequent Pattern tree). This approach has three advantages: (1) this method separates many partitions by according to maximum size domain and only scans the transaction once for reducing the I/O cost. (2) This method maintains all of transactions using FP-trees. (3) We only have the FP-trees of I-star pattern and other star pattern nodes only link them step by step for efficient mining and the saving memory. Our performance study shows that the TFP-tree is efficient and scalable for mining, and is about an order of magnitude faster than the Apriori algorithm and also faster than calendar based on temporal frequent pattern mining methods.

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Hydrogen Depth Profiling by Nuclear Resonance Reaction (공명 핵반응을 이용한 수소적층 분석)

  • Kim, Y. S.;Kim, J. M.;Hong, W.;Kim, D. K.;Cho, S. Y.;Woo, H. J.;Kim, N. B.
    • Journal of the Korean Vacuum Society
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    • v.2 no.4
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    • pp.416-423
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    • 1993
  • Hydrogen depth profiling was performed by H(19F, $\alpha$${\gamma}$) nuclear resonance reactin . A cesium sputtering ion sorce and 1.7MV Tandem Van de Graaff accelerator was used for the production of 6.5MeV 19F ion. The ${\gamma}$ rays produced by the reaction were measure dby 3" $\times$3" and 6" $\times$8" Nal detectors . A test measurement was done for hydrogen contaminatin layer of a bare silicon wafer, Si3N4(H) and Zr(O)a-Si/Si for the purpose of verifying the applicability , detection limit and the reliability of the method.ility of the method.

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Design of Manufacturing Data Analysis System using Data Mining Techniques (데이터마이닝 기법을 이용한 생산데이터 분석시스템 설계)

  • Lee H.W.;Lee G.A.;Choi S.;Park H.K.;Bae S.M.
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2006.05a
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    • pp.611-612
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    • 2006
  • Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

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A study of creative human judgment through the application of machine learning algorithms and feature selection algorithms

  • Kim, Yong Jun;Park, Jung Min
    • International journal of advanced smart convergence
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    • v.11 no.2
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    • pp.38-43
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    • 2022
  • In this study, there are many difficulties in defining and judging creative people because there is no systematic analysis method using accurate standards or numerical values. Analyze and judge whether In the previous study, A study on the application of rule success cases through machine learning algorithm extraction, a case study was conducted to help verify or confirm the psychological personality test and aptitude test. We proposed a solution to a research problem in psychology using machine learning algorithms, Data Mining's Cross Industry Standard Process for Data Mining, and CRISP-DM, which were used in previous studies. After that, this study proposes a solution that helps to judge creative people by applying the feature selection algorithm. In this study, the accuracy was found by using seven feature selection algorithms, and by selecting the feature group classified by the feature selection algorithms, and the result of deriving the classification result with the highest feature obtained through the support vector machine algorithm was obtained.

Energy analysis-based core drilling method for the prediction of rock uniaxial compressive strength

  • Qi, Wang;Shuo, Xu;Ke, Gao Hong;Peng, Zhang;Bei, Jiang;Hong, Liu Bo
    • Geomechanics and Engineering
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    • v.23 no.1
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    • pp.61-69
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    • 2020
  • The uniaxial compressive strength (UCS) of rock is a basic parameter in underground engineering design. The disadvantages of this commonly employed laboratory testing method are untimely testing, difficulty in performing core testing of broken rock mass and long and complicated onsite testing processes. Therefore, the development of a fast and simple in situ rock UCS testing method for field use is urgent. In this study, a multi-function digital rock drilling and testing system and a digital core bit dedicated to the system are independently developed and employed in digital drilling tests on rock specimens with different strengths. The energy analysis is performed during rock cutting to estimate the energy consumed by the drill bit to remove a unit volume of rock. Two quantitative relationship models of energy analysis-based core drilling parameters (ECD) and rock UCS (ECD-UCS models) are established in this manuscript by the methods of regression analysis and support vector machine (SVM). The predictive abilities of the two models are comparatively analysed. The results show that the mean value of relative difference between the predicted rock UCS values and the UCS values measured by the laboratory uniaxial compression test in the prediction set are 3.76 MPa and 4.30 MPa, respectively, and the standard deviations are 2.08 MPa and 4.14 MPa, respectively. The regression analysis-based ECD-UCS model has a more stable predictive ability. The energy analysis-based rock drilling method for the prediction of UCS is proposed. This method realized the quick and convenient in situ test of rock UCS.

A Date Mining Approach to Intelligent College Road Map Advice Service (데이터 마이닝을 이용한 지능형 전공지도시스템 연구)

  • Choe, Deok-Won;Jo, Gyeong-Pil;Sin, Jin-Gyu
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2005.05a
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    • pp.266-273
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    • 2005
  • Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilize Holland career search test results, TOEIC score, course work list, and GPA score as the input for data mining and generation the student advisory information. Factor analysis, AHP(Analytic Hierarchy Process), artificial neural net, and CART(Classification And Regression Tree) techniques are deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained with the human student advice experts.

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Finding associations between genes by time-series microarray sequential patterns analysis

  • Nam, Ho-Jung;Lee, Do-Heon
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2005.09a
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    • pp.161-164
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    • 2005
  • Data mining techniques can be applied to identify patterns of interest in the gene expression data. One goal in mining gene expression data is to determine how the expression of any particular gene might affect the expression of other genes. To find relationships between different genes, association rules have been applied to gene expression data set [1]. A notable limitation of association rule mining method is that only the association in a single profile experiment can be detected. It cannot be used to find rules across different condition profiles or different time point profile experiments. However, with the appearance of time-series microarray data, it became possible to analyze the temporal relationship between genes. In this paper, we analyze the time-series microarray gene expression data to extract the sequential patterns which are similar to the association rules between genes among different time points in the yeast cell cycle. The sequential patterns found in our work can catch the associations between different genes which express or repress at diverse time points. We have applied sequential pattern mining method to time-series microarray gene expression data and discovered a number of sequential patterns from two groups of genes (test, control) and more sequential patterns have been discovered from test group (same CO term group) than from the control group (different GO term group). This result can be a support for the potential of sequential patterns which is capable of catching the biologically meaningful association between genes.

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The Effect of Foreign Direct Investment Inflow on Exports: Evidence from Vietnam

  • DO, Duc Anh;SONG, Yinghua;DO, Huu Tung;TRAN, Thi Thu Hien;NGUYEN, Thanh Thuy
    • The Journal of Asian Finance, Economics and Business
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    • v.9 no.2
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    • pp.325-333
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    • 2022
  • Foreign direct investment (FDI) and export are now often regarded as two of the most important drivers of economic growth on a worldwide scale. The impact of foreign direct investment on Vietnam's exports is investigated in this study. The data for the time period 1985-2020 was obtained from the World Bank and the Vietnam General Statistics Office. The years 1985 to 2020 were chosen to evaluate the evolution of macroeconomic parameters since 1986. The impact of the Covid-19 epidemic on renovation reform. The Johansen co-integration test proved that FDI and domestic investment (DI) had a long-term positive impact on Vietnam's export growth. The Granger causality test revealed that there is a one-way relationship between FDI and export in the near term, but no such relationship exists between DI and export. The result of the variance decomposition study demonstrates that the FDI sector has a bigger impact on Vietnam's export growth than the DI sector. Furthermore, export activities are vulnerable to FDI sector shocks. As a result, in recent years, FDI has been regarded as the most important factor of export growth in Vietnam.

Behavior of F shape non-persistent joint under experimental and numerical uniaxial compression test

  • Sarfarazi, Vahab;Asgari, Kaveh;Zarei, Meisam;Ghalam, Erfan Zarrin
    • Advances in concrete construction
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    • v.13 no.2
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    • pp.199-213
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    • 2022
  • Experimental and discrete element approaches were used to examine the effects of F shape non-persistent joints on the failure behaviour of concrete under uniaxial compressive test. concrete specimens with dimensions of 200 cm×200 cm×50 cm were provided. Within the specimen, F shape non-persistent joint consisting three joints were provided. The large joint length was 6 cm, and the length of two small joints were 2 cm. Vertical distance between two small joints change from 1.5 cm to 4.5 cm with increment of 1.5 cm. In constant joint lengths, the angle of large joint change from 0° to 90° with increments of 30°. Totally 12 different models were tested under compression test. The axial load rate on the model was 0.05 mm/min. Concurrent with experimental tests, numerical simulation (Particle flow code in two dimension) were performed on the models containing F shape non-persistent joint. Distance between small joints and joint angles were similar to experimental one. the results indicated that the failure process was mostly governed by both of the Distance between small joints and joint angles. The axial loading rate on the model was 0.05 mm/min. The compressive strengths of the samples were related to the fracture pattern and failure mechanism of the discontinuities. Furthermore, it was shown that the compressive behaviour of discontinuities is related to the number of the induced tensile cracks which are increased by increasing the joint angle. In the first, there were only a few acoustic emission (AE) hits in the initial stage of loading, and then AE hits rapidly grow before the applied stress reached its peak. Furthermore, a large number of AE hits accompanied every stress drop. Finally, the failure pattern and failure strength are similar in both approaches i.e., the experimental testing and the numerical simulation approaches.