• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.17 no.2
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• pp.93-103
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• 1981

It is well-known that nowadays heat resisting and anti-corrosive materials have been widely used as the components materials of gas turbines, nuclear power plants and engines etc. In the fields of machine production industry. And materials for engine components, like as the exhaust valve of internal combustion engine, have been required to operate under the high temperature range of $700^{\circ}C$-$800^{\circ}C$ and high pressured gas with repeated mechanical load for the high performance of engines. For these components, friction welding for bonding of dissimilar steels can be applied for in order to obtain process shortening, production cost reduction and excellent bonding quality. And age hardening recently has been noticed to the heat resisting materials for further strengthening of high temperature strength, especially high temperature fatigue strength. However, it is difficult to find out any report concerning the effects of age hardening for strengthening high temperature fatigue strength to the Friction welded heat resisting and anti-corrosive materials. In this study the experiment was carried out as the high temperature rotary bending fatigue testing under the condition of $700^{\circ}C$ high temperature to the friction welded domestic heat resisting steels, SUH3-SUS303, which were 10hr., 100hr. aging heat treated at $700^{\circ}C$ after solution treatment 1hr. at $1, 060^{\circ}C$ for the purpose of observing the effects of the high temperature fatigue strength and fatigue fracture behaviors as well as with various mechanical properties of welded joints. The results obtained are summarized as follows: 1) Through mechanical tests and micro-structural examinations, the determined optimum welding conditions, rotating speed 2420 rpm, heating pressure 8kg/mm super(2), upsetting pressure 22kg/mm super(2), the amount of total upset 7mm (heating time 3 sec and upsetting time 2 sec) were satisfied. 2) The solution treated material SUH 3, SUS 303, have the highest inclination gradient on S-N curve due to the high temperature fatigue testing for long time at $700^{\circ}C$. 3) The optimum aging time of friction welded SUH3-SUS 303, has been recognized near the 10hr. at $700^{\circ}C$ after the solution treatment of 1hr. at $1, 060^{\circ}C$. 4) The high temperature fatigue limits of aging treated materials were compared with those of raw material according to the extender of aging time, on 10hr. aging, fatigue limits were increased by SUH 3 75.4%, SUS 303 28.5%, friction welded joints SUH 3-SUS 303 44.2% and 100hr. aging the rates were 64.9%, 30.4% and 36.6% respectively. 5) The fatigue fractures occurred at the side of the base matal SUS303 of the friction welded joints SUH 3-SUS 303 and it is difficult to find out fractures at the friction welding interfaces. 6) The cracking mode of SUS 303, SUH 3-303 is intergranular in any case, but SUH 3 is fractured by transgranular cracking.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of the Korean Applied Science and Technology
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• v.34 no.1
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• pp.12-24
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• 2017

The main purpose of this research is for the investigation on the impact of engine oil aging on PM and DPF. It is widely known that lubricant specifications and consumption from an ICE have significantly influenced on the regulated and unregulated harmful emissions as the engine operating conditions. Considering DPF clogging phenomena with lubricant-derived soot/ash components, simulated aging mode for the DPF was newly designed for engine dynamometer testing. PM/ash accumulation cycle were developed in reflecting real-world engine operating conditions for the increment of engine oil consumption and natural DPF regeneration for the ash accumulation. The test duration for DPF aging reached around 100hrs with high- and low-SAPS engine oils, respectively. Using high SAPs engine oil made more PM/ash accumulation compared with low SAPs engine oils and it could accelerate fouling of EGR in engine. Fouling of EGR made effects on more harmful exhaust gases emissions. The test results on engine lubricant under engines operating conditions will deliver for the establishment of regulated and unregulated toxic emissions policy, lubricant quality standard.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.6
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• pp.626-632
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• 2015

The International Maritime Organization (IMO) has adopted several regulations for the prevention of air pollution from ships. In addition, there is a requirement for shipping liners to reduce greenhouse gas emissions. Accordingly, we need to take measurements to ensure that the steps taken are both efficient and environmentally friendly. It has been determined that the application of the Miller cycle in diesel engines has the effect of both reducing the amount of NOx and improving thermal efficiency. However, this method requires a considerably larger charge air pressure. Therefore, we consider a two-stage turbo-charging system, which not only results in a high charging pressure, but also improves the part load performance with an exhaust-gas bypass system or the application of the Miller cycle. Because of complications associated with the two-stage turbo-charging system, it is complex and difficult to realize a design that optimizes matching between diesel engine and turbo-chargers. Accordingly, it is necessary to perform a quantitative analysis to determine the effects and optimal conditions of these different systems in the early stage of system design. In this paper, we develop a simulation program to model these systems, and we verify that the results of this program are reliable. Further, we discuss methods that can be employed to improve its efficiency.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.829-833
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• 2012

SNG (synthetic natural gas or substitute natural gas) could contribute greatly toward energy security. In addition, HCNG (or $H_2CNG$) is expected to be used as a fuel gas for internal combustion engines and home appliances because it has extremely low emissions and high thermal efficiency. However, the hydrogen contained in SNG or HCNG can deteriorate the mechanical properties of the materials used in existing natural gas infrastructure. Therefore, it is necessary to investigate the effect of hydrogen on the mechanical properties of such materials so that SNG or HCNG can be transported and distributed safely and reliably. In this study, the effect of highly pressurized hydrogen gas on the tensile properties of a low-alloy steel used for manufacturing CNG storage vessels was investigated using the so-called hollow tensile specimen technique.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.964-970
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• 2023

As part of the International Maritime Organization ef orts to reduce greenhouse gas emissions, the maritime industry is exploring low-carbon fuels such as liquefied natural gas and methanol, as well as zero-carbon fuels such as hydrogen and ammonia, evaluating them as environmentally friendly alternatives. Particularly, ammonia has substantial operational experience as cargo on transport ships, and ammonia ship engines are expected to be available in the second half of 2024, making it relatively accessible for commercial use. However, overcoming the toxicity challenges associated with using ammonia as a fuel is imperative. Detection is possible at levels as low as 5 ppm through olfactory senses, and exposure to concentrations exceeding 300 ppm for more than 30 min can result in irreparable harm. Using the KORA program provided by the Chemical Safety Agency, an assessment of the potential risks arising from leaks during ammonia bunkering was conducted. A 1-min leak could lead to a 5 ppm impact within a radius of approximately 7.5 km, affecting key areas in Busan, a major city. Furthermore, the potentially lethal concentration of 300 ppm could have severe consequences in densely populated areas and schools near the bunkering site. Therefore, given the absence of regulations related to ammonia bunkering, the potential for widespread toxicity from even minor leaks highlights the requirement for the development of legislation. Establishing an integrated system involving local governments, fire departments, and environmental agencies is crucial for addressing the potential impacts and ensuring the safety of ammonia bunkering operations.