• Knowledge Management Research
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• v.20 no.3
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• pp.155-171
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• 2019

In the entertainment industry which has great uncertainty, it is essential to predict public preference first. Thanks to various mass media channels such as cable TV and internet-based streaming services, the reality audition program has been getting big attention every day and it is being used as a new window to new entertainers' debut. This phenomenon means that it is changing from a closed selection process to an open selection process, which delegates selection rights to the public. This is characterized by the popularity of the public being reflected in the selection process. Therefore, this study aims to implement a machine learning model which predicts the winner of , which has recently been popular in South Korea. By doing so, this study is to extend the research method in the cultural industry and to suggest practical implications. We collected the data of winners from the 1st, 2nd, and 3rd seasons of the Produce 101 and implemented the predictive model through the machine learning method with the accumulated data. We tried to develop the best predictive model that can predict winners of by using four machine learning methods such as Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network. This study found that the audience voting and the amount of internet news articles on each participant were the main variables for predicting the winner and extended the discussion by analyzing the precision of prediction.

• Journal of the Korean Society of Safety
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• v.30 no.5
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• pp.108-113
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• 2015

Plastic injection moulding machine is widely used for many industrial field. It is classified into mandatory safety certification machinery in Industrial Safety and Health Act because of its high hazard. In order to prevent industrial accidents by plastic injection moulding machine, it is necessary for designer to identify hazardous factors and assess the failure modes to mitigate them. This study tabulates the failure modes of main parts of plastic injection moulding machine and how their failure has affect on the machine being considered. Failure Mode & Effect Analysis(FMEA) method has been used to assess the hazard on plastic injection moulding machine. Risk and risk priority number(RPN) has been calculated in order to estimate the hazard of failures using severity, probability and detection. Accidents caused by plastic injection moulding machine is compared with the RPN which was estimated by main regions such as injection unit, clamping unit, hydraulic and system units to find out the most dangerous region. As the results, the order of RPN is injection unit, clamping unit, hydraulic unit and system units. Barrel is the most dangerous part in the plastic injection moulding machine.

• Journal of the Korea Safety Management & Science
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• v.23 no.3
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• pp.27-33
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• 2021

The purpose of this study is to present a novel indicator for analyzing machine failure based on its idle time and productivity. Existing machine repair plan was limited to machine experts from its manufacturing industries. This study evaluates the repair status of machines and extracts machines that need improvement. In this study, F-RPN was calculated using the etching process data provided by the 2018 PHM Data Challenge. Each S(S: Severity), O(O: Occurence), D(D: Detection) is divided into the idle time of the machine, the number of fault data, and the failure rate, respectively. The repair status of machine is quantified through the F-RPN calculated by multiplying S, O, and D. This study conducts a case study of machine in a semiconductor etching process. The process capability index has the disadvantage of not being able to divide the values outside the range. The performance of this index declines when the manufacturing process is under control, hereby introducing F-RPN to evaluate machine status that are difficult to distinguish by process capability index.

• Journal of Korean Society for Quality Management
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• v.28 no.3
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• pp.91-103
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• 2000

It is very important to rationally allocate manufacturing processes because customer's needs are being various, and product life cycle in the market Is being shortened. The process allocation is needed before making process control plan and is mostly being done by the field experience or intuition of machine operator, process engineer and/or maintenance technician. Therefore, a modified allocation method using the process variation is proposed. This paper presents the result of comparative studies between current method and the proposed one.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.309-314
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• 2001

The balance of high speed spindle system with high precision rotation like grinding machine is very important. Traditionally, we use trial and error method to balance the spindle. It takes much time. So we are developing the automatic balancing equipment being used in the grinding machine. The balancing head we develop is wireless. It will be used high-speed grinding machine. We use influence coefficient method to control the automatic balancer. Experiments are based on automatic and manual balancing. We perform test of the vibration filter. It helps to remove noise. The filter and experiments with automatic balancing controller show that automatic balancing control can be successfully achieved with the quick response and good stability characteristics.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.585-588
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• 2003

As the advance in technology requires micro mechanical systems, the production methods for micro parts are of a great interest of many researchers. Although MEMS is one of the most popular methods. it can only produce 2D microstructures. The micro manufacturing with micro-mill and micro-lathe has a great potential for producing arbitrary 3D shapes and are being researched. In this paper, a PC based 5-axis milling machine with high precision was developed. To evaluate the machine performance, micro ribs and micro columns were machined. The machining experiments of micro impeller and micro turbine blade confirmed the possibility of micro system manufacturing by using the developed milling machine.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.121-124
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• 2003

The conventional CNC(Computerized Numerical Controller) of machine tool, which is dependent to CNC maker, was a closed architecture type. Therefore, it is impossible to implement a special user-define function to CNC. But recently, CNC is changed to OAC(Open Architecture Controller) type increasingly and the general function of CNC can be upgrade efficiently. This paper describes web service application for remote monitoring regarding the faults or machine tool with open architecture CNC. The major faults of CNC machine tool can be defined to the operational faults to be charged over 70%. Those faults are unpredictable because of being occurred without any warning. To generalize the fault diagnosis efficiently. two diagnosis models such as SF(Switching Function) and SSF(Step Switching Function) are proposed and the function of fault diagnosis is implemented to internal function of OAC. Also, to service remotely the faults of CNC machine tool. the suitable web environment is proposed and practical function is programmed to evaluate its operation on web.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.651-654
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• 1988

This paper proposes a microcode generating system which automatically generates the microcode of various target machine by inputing the intermediate language (MDIL) from the machine independent HLML-C (High Level Microprograming Language C) language. The MOP's (Microoperations) which is modeled 7-tuples generate to extend MDIL by table driven method with the information of translation table for each target machine. As compaction being considered and the hardware resource of target machine used, the conflicts of hardware elements are removed possibly. This proposed system is implemented with C language and yacc on VAX-11/750 (UNIX 4.3 BSD).

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.313-318
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• 2005

The present study deals design and manufacture of automatic polishing machine that can cut diamond to have 58 facets in a brilliant cutting which has been hardly achieved by a conventional manually operating polishing machine. Upon the 3-dimensional Sarin M/C test and analysis on the diamond processed by the automatic polishing machine developed in this study its proportion and finishing turned out to be better than the diamond processed by the conventional method, by being rated as 'very good'.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.