• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.881-888
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• 1997

A new algorithm for detection of tool fracture in milling process was developed. The variation of the peak-to-valley value of cutting load was used in this algorithm. Various kinds of vectors representing the condition of tool, such as tool condition vector, reference tool condition vector, tool condition variation vector were defined. Using these vectors, tool fracture index which represents the magnitude of tool fracture and is independent of tool run-outs is developed. Small and large tool fracture and chipping under various cutting condition could be detected using proposed tool fracture index, which was proved with cutting force model and experiments.

• 한국정밀공학회지
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• 제15권5호
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• pp.100-109
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• 1998

In order to increase productivity through unmanned machining in CNC milling process, in-process tool fracture detection is required. In this paper, a new algorithm for tool fracture detection using cutting load variations was developed. For this purpose, developed were tool condition vector which is dimensionless indicator of cutting load and tool fracture index (TFI) which represents magnitude of tool fracture. Through cutting force simulation, tool fracture index was shown to be independent of tool run-outs and cutting condition variations. Using tool fracture index, the ratio of the tool fracture to feed per tooth could be indentified.

• Kyungpook Mathematical Journal
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• 제55권1호
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• pp.13-20
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• 2015

As a vector space provides a fundamental tool for the study of Euclidean geometry, a gyrovector space provides an algebraic tool for the study of hyperbolic geometry. In general, the gyrovector spaces do not satisfy the distributivity with scalar multiplication. In this article, we see under what condition the distributivity with scalar multiplication is satisfied.

• 한국정밀공학회지
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• 제16권2호통권95호
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• pp.234-243
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• 1999

Using the geometric and the vector methods, three dimensional surface texture and roughness models in flat end milling are developed. In these models, rear cutting effect on surface generation is considered along with tool run-out and tool setting error including tool tilting and eccentricity between tool center and spindle rotational center. Rear cutting is the secondary cutting of the already machined surface by the trailing cutting edge. The effects of tool geometry and tool deflection on surface roughness are also considered. For representing the surface texture more practically, three dimentional surface topography parameters such as RMS deviaiton, skewness and kurtosis are introduced and used in expressing the surface texture characteristics. Under various cutting conditions, it is confirmed that the developed models predict the real surface profile precisely. These models could contribute to the cutter design and cutting condition selection for the reduction of machining and manual finishing time.

• 한국건축친환경설비학회 논문집
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• 제12권6호
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• 한국기계가공학회지
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• 제15권4호
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• pp.23-29
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• 2016

Rotary machine is major equipment in industry. The rotary machine is applied for a machine tool, ship, vehicle, power plant, and so on. But a spindle fault increase product's expense and decrease quality of a workpiece in machine tool. A turbine in power plant is directly connected to human safety. National crisis could be happened by stopping of rotary machine in nuclear plant. Therefore, it is very important to know rotary machine condition in industry field. This study mentioned fault diagnosis algorithm with statistical parameter and empirical mode decomposition. Vibration locations can be found by analyze kurtosis of data from triaxial axis. Support vector of data determine threshold using hyperplane with fault location. Empirical mode decomposition is used to find fault caused by intrinsic mode. This paper suggested algorithm to find direction and causes from generated fault.

• Design & Manufacturing
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• 제17권1호
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• pp.27-32
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• 2023

In this paper, we intend to predict the mass of the spiral using CAE and machine learning. First, We generated 125 data for the experiment through a complete factor design of 3 factors and 5 levels. Next, the data were derived by performing a molding analysis through CAE, and the machine learning process was performed using a machine learning tool. To select the optimal model among the models learned using the learning data, accuracy was evaluated using RMSE. The evaluation results confirmed that the Support Vector Machine had a good predictive performance. To evaluate the predictive performance of the predictive model, We randomly generated 10 non-overlapping data within the existing injection molding condition level. We compared the CAE and support vector machine results by applying random data. As a result, good performance was confirmed with a MAPE value of 0.48%.

• 대한의생명과학회지
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• 제26권2호
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• pp.114-119
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• 2020

Of the various types of mice used for genome editing, conditional knock-out (cKO) mice serve as an important model for studying the function of genes. cKO mice can be produced using loxP knock-in (KI) mice in which loxP sequences (34 bp) are inserted on both sides of a specific region in the target gene. These mice can be used as KO mice that do not express a gene at a desired time or under a desired condition by cross-breeding with various Cre Tg mice. Genome editing has been recently made easy by the use of third-generation gene scissors, the CRISPR-Cas9 system. However, very few laboratories can produce mice for genome editing. Here we present a more efficient method for producing loxP KI mice. This method involves the use of an HDR vector as the target vector and ssODN as the donor DNA in order to induce homologous recombination for producing loxP KI mice. On injecting 20 ng/µL of ssODN, it was observed that the target exon was deleted or loxP was inserted on only one side. However, on injecting 10 ng/µL of the target HDR vector, the insertion of loxP was observed on both sides of the target region. In the first PCR, seven mice were identified to be loxP KI mice. The accuracy of their gene sequences was confirmed through Sanger sequencing. It is expected that the loxP KI mice produced in this study will serve as an important tool for identifying the function of the target gene.

• 품질경영학회지
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• 제47권4호
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• 한국미생물학회:학술대회논문집
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• 한국미생물학회 2002년도 추계학술대회
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• pp.118-120
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• 2002

Genetic materials including DNA plasmid are effective delivery vehicle to express interesting gene efficiently and safely not to generate replication competent virus. Moreover, it has advantages to design a better vector and to simplify manufacturing and storage condition. To understand a possible pathogenic mechanism by a flavivirus, West Nile virus (WNV), WNV genome sequence was aligned to other pathogenic viral genome. Interestingly, WNV capsid (Cp) amino acid sequence has some homology to HIV-l Vpr protein. These proteins induce apoptosis in human cell lines as well as in vivo and cell cycle arrest. Therefore, DNA plasmid carrying apoptosis-inducing and cell cycle arresting viral proteins including a HIV-1 Vpr and a WNV Cp protein can be useful for anti-cancer therapeutic applications. This WNV Cp protein is an early expressed protein which can be a reasonable target antigen (Ag) for vaccine design. Immunization of a DNA construct encoding WNV Cp protein induces a strong Ag-specific humoral and Th1-type immune responses in animal. Therefore, DNA plasmid encoding apoptotic viral proteins can be useful tool for therapeutic and prophylactic applications.