• Tribology and Lubricants
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• v.39 no.4
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• pp.133-138
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• 2023

This paper presents a discussion on lubricating oil filters. The maintenance of lubricating oil filters can improve the performance of mechanical systems and extend the service life of the lubricating oil. Therefore, the effective management of the lubricating oil can extend the service life of the machine and reduce maintenance costs. A representative method for managing lubricating oil is filtering the lubricating oil using a lubricant filter. However, effectively managing a lubricating oil using a lubricant filter requires an understanding of the related knowledge. In this paper, we present the definition, classification, characteristics, specifications, performance, and self-cleaning function of lubricating oil filters. The lubricant filters are classified based on the filter material, filtering method, filtering location, and amount of filtered fluid. Cellulose and glass fiber materials are conventionally used as materials for lubricant filters, and recently, metal materials, which show excellent durability, are being increasingly adopted. The filtering methods can be classified into physical, chemical, magnetic, and electric field methods, and the lubricant filters can be classified according to their location in the lubrication system. The beta ratio and efficiency of the lubricant filter can be determined based on the performance of the filter. Finally, there are many products or technologies that add a self-cleaning function to the filter to remove foreign substances or contaminants for efficient management.

• Proceedings of the ESK Conference
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• 1997.04a
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• pp.56-60
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• 1997

The study of human erroneous actions has traditionally taken place along two different lines of approach. One has been concerned with finding and explaining the causes of erroneous actions, such as studies in the psychology of "error". The other has been concerned with the qualitative and quantitative prediction of possible erroneous actions, exemplified by the field of human reliability analysis (HRA). Another distinction is also that the former approach has been dominated by an academic point of view, hence emphasising theories, models, and experiments, while the latter has been of a more pragmatic nature, hence putting greater emphasis on data and methods. We have been developing a method to make predictions about error modes. The input to the method is a detailed task description of a set of scenarios for an experiment. This description is then analysed to characterise thd nature of the individual task steps, as well as the conditions under which they must be carried out. The task steps are expressed in terms of a predefined set of cognitive activity types. Following that each task step is examined in terms of a systematic classification of possible error modes and the likely error modes are identified. This effectively constitutes a qualitative analysis of the possibilities for erroneous action in a given task. In order to evaluate the accuracy of the predictions, the data from a large scale experiment were analysed. The experiment used the full-scale nuclear power plant simulator in the Halden Man-Machine Systems Laboratory (HAMMLAB) and used six crews of systematic performance observations by experts using a pre-defined task description, as well as audio and video recordings. The purpose of the analysis was to determine how well the predictions matiched the actually observed performance failures. The results indicated a very acceptable rate of accuracy. The emphasis in this experiment has been to develop a practical method for qualitative performance prediction, i.e., a method that did not require too many resources or specialised human factors knowledge. If such methods are to become practical tools, it is important that they are valid, reliable, and robust.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.147-152
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• 2023

Mostly in motor fault detection the instantaneous values 3 axis vibration and 3phase current in time domain are acquired and converted to frequency domain. Vibrations are more useful in diagnosing the mechanical faults and motor current has remained more useful in electrical fault diagnosis. With having some experience and knowledge on the behavior of acquired data the electrical and mechanical faults are diagnosed through signal processing techniques or combine machine learning and signal processing techniques. In this paper, a single-layer LSTM based condition monitoring system is proposed in which the instantaneous values of three phased motor current are firstly acquired in simulated motor in in health and supply imbalance conditions in each of three stator currents. The acquired three phase current in time domain is then used to train a LSTM network, which can identify the type of fault in electrical supply of motor and phase in which the fault has occurred. Experimental results shows that the proposed single layer LSTM algorithm can identify the electrical supply faults and phase of fault with an average accuracy of 88% based on the three phase stator current as raw data without any processing or feature extraction.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.528-535
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• 2022

Although the domestic aviation industry has made rapid progress with the development of aircraft manufacturing and transportation technologies, aviation safety accidents continue to occur. The supervisory agency classifies hazards and risks based on risk-based aviation safety data, identifies safety trends for each air transportation operator, and conducts pre-inspections to prevent event and accidents. However, the human classification of data described in natural language format results in different results depending on knowledge, experience, and propensity, and it takes a considerable amount of time to understand and classify the meaning of the content. Therefore, in this journal, the fine-tuned KoBERT model was machine-learned over 5,000 data to predict the classification value of new data, showing 79.2% accuracy. In addition, some of the same result prediction and failed data for similar events were errors caused by human.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.135-146
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• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• Journal of Environmental Health Sciences
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• v.40 no.6
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• pp.454-468
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• 2014

Objectives: This study was conducted to investigate the actual condition of the farm work environment and personal protective equipment as part of the effort to improve livestock work for the safety and health of poultry farmers and provide basic data for establishing plans to improve and develop personal protective equipment. Methods: For this purpose, a questionnaire survey on general information about stables, the poultry work environment, accidents, the wearing of work clothes and personal protective equipment, and the level of awareness related to personal protective equipment was conducted among 148 poultry farmers. Results: As a result, it was found that poultry workplace environment was exposed to such risks as fine dusts; organic dusts; poisonous gases; odorous substances; chicken excrement; contact with chickens, bacteria or viruses; and accidents related to machine operation. Thirteen percent of respondents suffered severe respiratory diseases, and the most frequently injured sites due to accidents were the hands (25.7%), knees (23.8%), arms (17.3%), and head (10.9%). The most frequent type of accident was collisions between the body and obstacles or machinery during movement (36.4%), followed by erroneous machine operation such as feeders and electric shocks (8.5%). Regarding the wearing of work clothes and personal protective equipment, 51.7% of the respondents wore worn-out clothing or everyday clothes, whereas only 32.0% wore work clothes. The percentage of farmers who wore proper protective equipment for the work environment during poultry work was 48.4%. The most frequently used type of protective equipment was boots (38.9%), followed by mask (36.7%), gloves (36.3%), appropriate work clothes (22.6%), quarantine clothes (17.6%), helmets (13.4%), and goggles (12.6%). The rate of wearing goggles was low because they were considered inconvenient and lowered work efficiency. Furthermore, they purchased everyday products available on the market for their personal protective equipment which were not appropriate for maintaining safety in an actual harmful environment and its consequent risks. As a result of the survey of the awareness level related to personal protective equipment, their levels of awareness of accidents and attitude proved to be average or higher, but the practice of wearing protective equipment and the level of knowledge and management of personal protective equipment were lower. Conclusion: This survey found that the wearing status of personal protective equipment among poultry farmers was insufficient even though they were exposed to risks. Most respondents were aware of the necessity of wearing personal protective equipment and of the potential for accidents, but they did not wear proper protective equipment. Their wearing rate was low due to a lack of knowledge about protective equipment, as well as the inconvenience of wearing it. Therefore there is a need to improve and develop specialized personal protective equipment for respiration, hands, and eyes, as well as work clothes that can protect farmers from major harmful matter that is generated in the poultry workplace. Based on the results of this investigation, we will conduct further studies on the required performance and design directions of personal protective equipment while collecting more objective data through field-oriented assessments.

• Clinical and Experimental Pediatrics
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• v.51 no.10
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• pp.1058-1064
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• 2008

Purpose : This study aimed to assess current knowledge of and training experiences with the intraosseous (IO) line among emergency medicine (EM) and pediatric residents who care for critically ill children and to evaluate the educational effectiveness of the IO line workshop. Methods : During May and June 2008, a workshops on IO line insertion was held for EM and pediatric residents. The workshop comprised a 45-min lecture and a 15-min hands-on session. A semi-drill type EZ-IO machine was used for education. Self-assessment questionnaires gauged residents knowledge of and experiences with IO line insertion or bone marrow (BM) examination and their confidence with IO line insertion before and after the workshop. Performance tests were completed for skill evaluation. Results : Forty-five pediatric residents and 22 EM residents participated in the workshop. The pre-educational questionnaire revealed that EM residents had more educational experience in IO line insertion than pediatric residents (P<0.001), while pediatric residents reported more experience in BM examination (P<0.001). The post-educational questionnaire showed a statistically significant higher percentage of correct answers (P<0.001). Although the pediatric residents inserted an IO line more quickly (P=0.001), most residents (88.7%) succeeded in IO line insertion on their first attempt; there was no difference in the groups success rates. Both groups showed higher confidence in performing IO line insertion after training (P<0.001). Conclusion : Observed educational effectiveness in both knowledge and confidence of IO line insertion skill suggest educational opportunities for pediatric and EM residents should be increased.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.9
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• pp.61-67
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• 2017

Research into the selection of suitable materials and the development of fast processing methods for press die manufacturing is absolutely necessary to reduce the production time and cost. In particular, knowledge of its heat properties must be considered whendeveloping a long press die. Generally, as the main component materials of press dies, Cr, W low alloy tool steel, high carbon-high chrome steel, high speed steel, etc., are used as thetooling steel for the cold die. Machine tools and wire-cut electric discharge machining are mainly used for processing the press die parts. There are many differences in the machining time and life cycle of die parts depending on the machining process. The parts produced by milling and grinding have a high manufacturing time and cost with a long life cycle, while thosemade by milling and wire-cut discharge machining have areduced manufacturing time and cost,whereastheir die life cycle is reduced. Therefore, in this study, we will discuss amethod of improving the life cycle of the die parts by using heat treatment as a processing method that reduces the manufacturing time and cost. SEM, EDS analysis and the surface roughness analysis of the surface and center of the workpiece are used for analyzing the specimens produced by three machining methods, viz. milling - grinding, milling - wire cut discharge, and milling - wire cut discharge - heat treatment. A method of making die parts having the same life cycle as those produced by milling - grinding is developed with the milling - wire cut discharge - high temperature tempering method.