• Journal of the Semiconductor & Display Technology
• /
• v.13 no.3
• /
• pp.1-6
• /
• 2014

Visual defect inspection for electronics parts manufacturing processes is comprised of 2 steps - automatic visual inspection by machine and inspection by human inspectors. It is necessary that spatial points which were detected by the machine should be adequately clustered for subsequent human inspection. This research deals with the spatial clustering algorithm for the purpose of process productivity improvement. Distribution based clustering is newly developed and experimentally confirmed to show better clustering efficiency than existing algorithm - area based clustering.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2002.10a
• /
• pp.399-404
• /
• 2002

An efficient method of machining impeller is presented. In the roughing process, the cutting area is divided into two regions to reduce cutting time and select cutting tools. The regions are determined by characteristic point on the geometry of impeller blade. Then, the tool of the maximum radius is selected in each area. Tool interference in cutting areas is avoided by checking the intersection between cooing tool axis and ruling line on blade surface.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.477-478
• /
• 2007

This paper proposes a new concept of support vector machine (SVM) based voltage stability classifier using time-series phasor data. The classifier, based on a linear SVM, can provide very effective signals for identification of long-term voltage stability. In addition, the SVM output is applicable as an voltage stability indicator when an amount of corrective controls are performed just to make the system reach around at the maximum deliverable point.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.3
• /
• pp.535-541
• /
• 2009

In this paper, we present the improvement on the performance of driving part for single-phase MJ81 switch point machine which has been developed for localization. The single-phase motor's specification and reliability for speed and safety improvement of conventional line was investigated in "Development project for Speed-up on Conventional Line" We systemized the test procedure fur single-phase motor by investigating the feasibility for localization and the specification of function and performance. Also, we developed appropriate technology and proved the durability of the single-phase driving motor by executing synthesis test over 200,000 times.

• Journal of Digital Convergence
• /
• v.17 no.10
• /
• pp.251-258
• /
• 2019

In the future, various products are created in various fields using artificial intelligence. In this age, it is a very important problem to know the operation principle of artificial intelligence learning method and to use it correctly. This paper introduces artificial intelligence learning methods that have been known so far. Learning of artificial intelligence is based on the fixed point iteration method of mathematics. The GD(Gradient Descent) method, which adjusts the convergence speed based on the fixed point iteration method, the Momentum method to summate the amount of gradient, and finally, the Adam method that mixed these methods. This paper describes the advantages and disadvantages of each method. In particularly, the Adam method having adaptivity controls learning ability of machine learning. And we analyze how these methods affect digital signals. The changes in the learning process of digital signals are the basis of accurate application and accurate judgment in the future work and research using artificial intelligence.

• KNOM Review
• /
• v.23 no.1
• /
• pp.34-42
• /
• 2020

Network Function Virtualization (NFV) environment can deal with dynamic changes in traffic status with appropriate deployment and scaling of Virtualized Network Function (VNF). However, determining and applying the optimal VNF deployment is a complicated and difficult task. In particular, it is necessary to predict the situation at a future point because it takes for the process to be applied and the deployment decision to the actual NFV environment. In this paper, we randomly generate service requests in Multiaccess Edge Computing (MEC) topology, then obtain training data for machine learning model from an Integer Linear Programming (ILP) solution. We use the simulation data to train the machine learning model which predicts the optimal VNF deployment in a predefined future point. The prediction model shows the accuracy over 90% compared to the ILP solution in a 5-minute future time point.

• Science of Emotion and Sensibility
• /
• v.11 no.2
• /
• pp.207-214
• /
• 2008

The inspection processing is for reducing loss which occurs fault because of fabric appearance. Up to now inspection machine which is used from inspection process is classified with the macrography inspection machine and the full automatic inspection machine. The macrography inspection machine is low price and efficient equipment but does not record information of fault. On the other side, the automatic inspection machine is high price, also the detection rate of one changes with effect of environment variable but able to record information of fault. It developed semi-automatic cloth inspection machine with the weak point of the macrography inspection machine and the automatic inspection machine was complemented. And when it uses information which was collected by semi-automatic cloth inspection machine, the loss rate of original fabric is able to calculate. So sewing factories will be able to predict fabric consuming quantity.

• Journal of the Korean Society for Precision Engineering
• /
• v.31 no.10
• /
• pp.889-895
• /
• 2014

Machine parts and structures of a change in the displacement and strain can be evaluated safety is one of the important factors. Typically the strain gauge has been employed to measure the displacement and strain. However, this contact-type measurement method has disadvantages that are not measured under condition of specific object shape, surface roughness and temperature. In particularly, 3 point bending and 4 point bending test not use strain gauge. So its test used cross head displacement and deflect meter. Digital Image Correlation measurement methods have many advantages. It is non contact-type measurement method to measure the object displacements and strain. In addition, it is possible to measure the Map of full field displacements and strain. In this paper, measured the 3 point bending deflection using the Digital Image Correlation methods. In order to secure the reliability, Digital Image Correlation method and universal test machine were compared.

• Korean Chemical Engineering Research
• /
• v.48 no.6
• /
• pp.717-724
• /
• 2010

The flash point is one of the most important physical properties used to determine the potential for fire and explosion hazards of flammable liquids. Despite the needs of the experimental flash point data for the design and construction of chemical plants, there is often a significant gap between the demands for the data and their availability. This study have built and compared two models of partial least squares(PLS) and support vector machine(SVM) to predict the experimental flash points of 893 organic compounds out of DIPPR 801. As the independent variables of the models, 65 functional groups were chosen based on the group contribution method that was oriented from the assumption that each fragment of a molecule contributes a certain amount to the value of its physical property, and the logarithm of molecular weight was added. The prediction errors calculated from cross-validation were employed to determine the optimal parameters of two models. And, an optimization technique should be used to get three parameters of SVM model. This work adopted particle swarm optimization that is one of heuristic optimization methods. As the selection of training data can affect the prediction performance, 100 data sets of randomly selected data were generated and tested. The PLS and SVM results of the average absolute errors for the whole data range from 13.86 K to 14.55 K and 7.44 K to 10.26 K, respectively, indicating that the predictive ability of the SVM is much superior than PLS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.6
• /
• pp.405-416
• /
• 2019

Due to the development of information and communication technology, the production and processing speed of data is getting faster. To classify objects using machine learning, which is a field of artificial intelligence, data required for training can be easily collected due to the development of internet and geospatial information technology. In the field of geospatial information, machine learning is also being applied to classify or recognize objects using images and point clouds. In this study, the problem of manually constructing training data using existing digital map version 1.0 was improved, and the technique of classifying roads, buildings and vegetation using image and point clouds were proposed. Through experiments, it was possible to classify roads, buildings, and vegetation that could clearly distinguish colors when using true ortho-image with only RGB (Red, Green, Blue) bands. However, if the colors of the objects to be classified are similar, it was possible to identify the limitations of poor classification of the objects. To improve the limitations, random forest and support vector machine techniques were applied after band fusion of true ortho-image and normalized digital surface model, and roads, buildings, and vegetation were classified with more than 85% accuracy.