• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.364-370
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• 2002

A banknote counter is a machine that automates counting the money in some agencies to treat much banknotes as well as general banking agencies. The banknote counter materialized in this paper is the machine that adds the function of banknote sorting, detecting plural banknote and detecting counterfeit banknote to an existing banknote counter. The technique of sensor signal processing are used for banknote sorting. The technique of sensor application and data processing are used for detecting counterfeit banknote. The technique of precision equipment design and microprocessor application are used for high speed count. Software improved in debugging and difficulties to link with additional hardware. It was materialized through effective control algorithm and real-time signal processing with C-language on the basis of RTOS(real-time operating system) Photodiode, its applications and a magnetic resistance sensor are used as a sensor device with regard to hardware cost -cutting and process velocity. PCF80C552-24 of Philips using Intel I8051 core is used as a control microprocessor. As the results so far achieved, counterfeit banknotes made by the use of a color duplicator and a color Printer, are distinguished from real banknotes through mixing an optical with a magnetic sensor. and, in case that there are some different banknotes while counting, it is prevented for them to be counted without discriminating from the same kind of banknotes in addition to the fu notion of banknote sorting.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2364-2369
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• 2015

In this paper, the optimum RPM was suggested comparing rotor filling rate of RPM through the analysis of rotor's filling rate as studying and developing centrifugal-casting machine's method for high precision rotor in order to increase the related types of business's productivity. The result was similar to other result in industrial site, showing 99.47% of filling rate when rotational speeds are 600 rpm, so it is considered that if this result is conducted with additional research, it will be possible to plan a better process design. Besides, the optimum temperature of compact ladle was examined to produce high quality casting product through the analysis of compact ladle's conductivity. In the case of the heating device's absence using nicrome wire, Al solution solidifies falling drastically into $427^{\circ}C$. However, it is feasible to work over $427^{\circ}C$ which is the melting temperature of aluminium solution when the heating device of nicrome wire is included. It reveals that there is little temperature change.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.1-7
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• 2019

Recently, various approaches to detect network attacks using machine learning have been studied and are being applied to detect new attacks and to increase precision. However, the machine learning method is dependent on feature extraction and takes a long time and complexity. It also has limitation of performace due to learning data imbalance. In this study, we propose a method to solve the degradation of classification performance due to imbalance of learning data among the limit points of detection system. To do this, we generate data using Generative Adversarial Networks (GANs) and propose a classification method using Convolutional Neural Networks (CNNs). Through this approach, we can confirm that the accuracy is improved when applied to the NSL-KDD and UNSW-NB15 datasets.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.445-452
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• 2022

Class distribution of unbalanced data is an important part of the digital world and is a significant part of cybersecurity. Abnormal activity of unbalanced data should be found and problems solved. Although a system capable of tracking patterns in all transactions is needed, machine learning with disproportionate data, which typically has abnormal patterns, can ignore and degrade performance for minority layers, and predictive models can be inaccurately biased. In this paper, we predict target variables and improve accuracy by combining estimates using Synthetic Minority Oversampling Technique (SMOTE) and Light GBM algorithms as an approach to address unbalanced datasets. Experimental results were compared with logistic regression, decision tree, KNN, Random Forest, and XGBoost algorithms. The performance was similar in accuracy and reproduction rate, but in precision, two algorithms performed at Random Forest 80.76% and Light GBM 97.16%, and in F1-score, Random Forest 84.67% and Light GBM 91.96%. As a result of this experiment, it was confirmed that Light GBM's performance was similar without deviation or improved by up to 16% compared to five algorithms.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• Journal of the Semiconductor & Display Technology
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• v.6 no.4
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• pp.23-27
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• 2007

Rotary stages in semiconductor, display industry and many other fields require challenging accuracy to perform their functions properly. Especially, Axis of rotation error on rotary system is significant; such as the spindle error motion of the aligner, wire bonder and inspector machine which result in the poor quality products. To evaluate and improve the performance of such precision rotary stage, undesired movements on the other 5 degrees of freedom of the rotary stage must be measured and analyzed. In this paper, we have measured the three translations and two tilt motions of the worm gear type spindle with high precision capacitive sensors. To obtain the radial error motion, we have used Donaldson's reversal technique. And the axial components of the spindle tilt error motion can be obtained accurately from the axial direction outputs of sensors by Estler face motion reversal technique. Further more we have designed and developed the sensor mounting jig with standard cylinder for reversal method.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.93-100
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• 2002

The current from the feed motor of a machine tool contains substantial information about the machining state. There have been many researches that investigated the current as a measure for the cutting farces. However it has been reported that this indirect measurement of the cutting farces from the feed motor current is only feasible in low frequency. In this research, it was presented that the bandwidth of the current monitoring can be expanded to 130 Hz. And the unusual behavior of the current was examined in this bandwidth. The cross-feed directional cutting force influences the machined surface of the workpiece, which makes it necessary to estimate this force to control the roughness of the machined sulfate. The current exists in the stationary feed motor, and it can give the useful information on the quality of the machined surface. But the unpredictable behavior of the current prevents applying the current to prediction of the cutting state. Empirical approach was conducted to resolve the problem. As a result, the current was shown to be related to the accumulation of the accumulation of the infinitesimal rotation of the motor. rotation of the motor. Subsequently the relationship between the current and the cutting force was identified.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.2
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• pp.70-77
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• 2010

This study proposed an auto focusing method for a multi-focus image in assembling lens modules in digital camera phones. A camera module in a camera phone is composed of a lens barrel, an IR glass, a lens mount, a PCB board and aspheric lenses. Alignment among the components is one of the important factors in product quality. Auto-focus is essential to adjust image quality of an IR glass in a lens holder, but there are two focal points in the captured image due to thickness of IR glass. So, sharpness, probability and a scale factor are defined to find desired focus from a multi-focus image. The sharpness is defined as clarity of an image. Probability and a scale factors are calculated using pattern matching with a registered image. The presented algorithm was applied to a lens assembly machine which has 5 axes, two vacuum chucks and an inspection system. The desired focus can be determined on the local maximum of the sharpness, the probability and the scale factor in the experiment.

• Journal of Biomedical Engineering Research
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• v.45 no.2
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• pp.95-100
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• 2024

Vitiligo is a condition characterized by the destruction or dysfunction of melanin-producing cells in the skin, resulting in a loss of skin pigmentation. Facial vitiligo, specifically affecting the face, significantly impacts patients' appearance, thereby diminishing their quality of life. Evaluating the efficacy of facial vitiligo treatment typically relies on subjective assessments, such as the Facial Vitiligo Area Scoring Index (F-VASI), which can be time-consuming and subjective due to its reliance on clinical observations like lesion shape and distribution. Various machine learning and deep learning methods have been proposed for segmenting vitiligo areas in facial images, showing promising results. However, these methods often struggle to accurately segment vitiligo lesions irregularly distributed across the face. Therefore, our study introduces a framework aimed at improving the segmentation of vitiligo lesions on the face and providing an evaluation of vitiligo lesions. Our framework for facial vitiligo segmentation and lesion evaluation consists of three main steps. Firstly, we perform face detection to minimize background areas and identify the face area of interest using high-quality ultraviolet photographs. Secondly, we extract facial area masks and vitiligo lesion masks using a semantic segmentation network-based approach with the generated dataset. Thirdly, we automatically calculate the vitiligo area relative to the facial area. We evaluated the performance of facial and vitiligo lesion segmentation using an independent test dataset that was not included in the training and validation, showing excellent results. The framework proposed in this study can serve as a useful tool for evaluating the diagnosis and treatment efficacy of vitiligo.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1168-1188
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• 2018

Social Network Aggregators are used to maintain and manage manifold accounts over multiple online social networks. Displaying the Activity feed for each social network on a common dashboard has been the status quo of social aggregators for long, however retrieving the desired data from various social networks is a major concern. A user inputs the query desiring the specific outcome from the social networks. Since the intention of the query is solely known by user, therefore the output of the query may not be as per user's expectation unless the system considers 'user-centric' factors. Moreover, the quality of solution depends on these user-centric factors, the user inclination and the nature of the network as well. Thus, there is a need for a system that understands the user's intent serving structured objects. Further, choosing the best execution and optimal ranking functions is also a high priority concern. The current work finds motivation from the above requirements and thus proposes the design of a query processing system to retrieve information from social network that extracts user's intent from various social networks. For further improvements in the research the machine learning techniques are incorporated such as Latent Dirichlet Algorithm (LDA) and Ranking Algorithm to improve the query results and fetch the information using data mining techniques.The proposed framework uniquely contributes a user-centric query retrieval model based on natural language and it is worth mentioning that the proposed framework is efficient when compared on temporal metrics. The proposed Query Processing System to Retrieve Information from Social Network (QPSSN) will increase the discoverability of the user, helps the businesses to collaboratively execute promotions, determine new networks and people. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a significant breakthrough scoring up to precision and recall respectively.