• Journal of the Korea Management Engineers Society
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• v.23 no.4
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• pp.87-103
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• 2018

This paper introduces the method to develop two main types of the fuzzy OEE (Overall Equipment Effectiveness) models via triangular membership function for measuring uncertainty. The fuzzy OEE includes model type 1 and model type 2. The model type 1 is used when the theoretical machine speed only reflects the time loss whereas model type 2 is used when the actual machine speed reflects both time and speed loss. Model type 2 has shown to perform a lower availability rate and a higher performance rate compared to model type 1. In addition, the fuzzy UPH (Unit Per Hour) which is derived from using the fuzzy OEE is presented to satisfy demand uncertainty. The fuzzy UPH can easily measure the fuzzy tact time and cycle time by reciprocating itself. Finally, this study demonstrates the fuzzy OEE models using IVIFS (Interval-Valued Intuitionistic Fuzzy Set) based on the characterization via membership function, non-membership function and hesitant function. For the purpose of analyzing the fuzzy system OEE, the OEE for each machine of plant structure is considered triangular interval-valued intuitionistic fuzzy number. Regardless of plant structure, the validity degree of fuzzy membership function of system OEE decreases when the number of machine with worst value of the validity degree increases. Corresponding examples are presented in this paper for practitioner to understand the applicability and practicability of the proposed fuzzy OEE methods.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.229-237
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• 2021

Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1021-1031
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• 2023

State-sponsored cyber attacks are highly impactful because they are carried out to achieve pre-planned goals. As a defender, it is difficult to respond to them because of the large scale of the attack and the possibility that unknown vulnerabilities may be exploited. In addition, overreacting can reduce the availability of users and cause business disruption. Therefore, there is a need for a response policy that can effectively defend against attacks while ensuring user availability. To solve this problem, this paper proposes a method to collect the number of processes and sessions of defense assets in real time and use them for learning. Using this method to learn reinforcement learning-based policies on a cyber attack simulator, the attack duration based on 100 time-steps was reduced by 27.9 time-steps and 3.1 time-steps for two attacker models, respectively, and the number of "restore" actions that impede user availability during the defense process was also reduced, resulting in an overall better policy.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.1
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• pp.1-5
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• 2005

The vector magnetic property is defined as the relationship between the magnetic field strength vector H and the magnetic flux density vector B. It is very important for the development of high efficiency and the high-density electric machines. The electrical steel sheet for the machine core shows the remarkable vector behavior by the high magnetic flux density level. In this paper, the magnetic characteristic analysis using E&S2 model is introduced as the useful technology for the design and development.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.144-149
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• 1996

The automation of mesh generation in BEM is bery important in numerical analysys field for the time and efficiency. To be solve this problem Probram and Algorithm, to achive purpose of making input data and automation of mesh generation based on Expert system is developed in this study. And function of this program can be rotating and zooming, To prove efficiency and availability of program in result the stress intensity factor which is criteria of fracture mechanics is caculated and compared with other results.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.39-44
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• 1999

Hydroforming is a forming process enabling circular metal tubes to be produced in complex cross sections along curved axial paths With the availability of advanced machine design and control They offer advantages over stamped sheet metal in lower tooling cost and structural mass The technology is relatively new so that there is no large knowledge base to assist the fundamentals of tube hydroforming technology. The purpose of this paper is found that adaptive bending condition and contact condition for bended part has uniform thickness distribution.

• Journal of Korean Society for Quality Management
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• v.10 no.1
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• pp.13-15
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• 1982

A device is said to have three states if it has one good state and two mutually exclusive failure modes ; e. g., in one failure mode, it operates when it should not, in the other it doesn't operate when it Should. Some examples of such device include a fluid flow valve, an automatic machine, and an explosive. A Markov model is developed to obtain the availability Function of a 2-unit standby redundant system of such devices.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.11a
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• pp.380-381
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• 2008

This paper consider the condition monitoring technology of electrical machine in metro substation need for efficient and effective management and diagnosis. Developing management system using condition monitoring system is very competitive field, and still to a great extent seen as an unnecessary cost. There are several approaches to maintenance management in urban transit such as reliability-centered maintenance, availability-target maintenance and preventive maintenance to advanced approaches involving condition monitoring techniques. In this paper we give a brief introduction to condition-based management and diagnosis in metro substation and which management system satisfying various demanding in railway.

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.929-940
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• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.