• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.36.1-36.1
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• 2020

The progenitor of Type Ia supernovae (SNe Ia) is mainly believed to be a carbon/oxygen white dwarf (WD) with non-degenerate (single degenerate) or another WD companion (double degenerate). However, there is little observational evidence of their progenitor system. Recent studies suggest that shock-breakout cooling emission after the explosion can constrain the size of the progenitor system. To do so, we obtained a optical/Near-IR light curve of SN 2019ein, a normal but slightly sub-luminous type Ia supernova, from the very early phase using our high-cadence observation of Intensive Monitoring Survey of Nearby Galaxies (IMSNG). Assuming the expanding fireball model, the simple power-law fitting of the early part of the light curve gives power indices of 1.91 (B) and 2.09 (R) implying radioactive decay of 56Ni is the dominant energy source. By comparison with the expected light curve of the cooling emission, the early observation provides us an upper limit of the companion size of R∗≤1R⊙. This result suggests that we can exclude a large companion such as red giants, which is consistent with the previous study.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.727-730
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• 2023

The operation of electric automatic windows is used in harsh environments, and the energy density decreases as charging and discharging are repeated, and as soundness deteriorates due to damage to the internal separator, the vehicle's mileage decreases and the charging speed slows down, so about 5 to 10 Batteries that have been used for about a year are classified as waste batteries, and for this reason, as the risk of battery fire and explosion increases, it is essential to diagnose batteries and estimate SOH. Estimation of current battery SOH is a very important content, and it evaluates the state of the battery by measuring the time, temperature, and voltage required while repeatedly charging and discharging the battery. There are disadvantages. In this paper, measurement of discharge capacity (C-rate) using a waste battery of a Tesla car in order to predict SOH estimation of a lithium-ion battery. A Support Vector Machine (SVM), one of the machine models, was applied using the data measured from the waste battery.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.87-101
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• 2012

In gas industries, the potential risks of serious accidents have been increased due to high technology application and process complexities. Especially, in case of gas-related accidents, the extent of demage is out of control since gas plants handle and produce combustible, flammable, explosive and toxic materials in large amounts. The characteristics of this kind of disaster is that accident frequency is low, while the impact of damage is high, extending to the neighboring residents, environment and related industries as well as employees involved. The hydrogen gases treated important things and it used the basic material of chemical plants and industries. Since 2000, this gas stood in the spotlight the substitution energy for reduction of the global warming in particular however it need to compress high pressure(more than 150 bar.g) and store by using the special cylinders due to their low molecular weight. And this gas led to many times the fire and explosion due to leak of it. To reduce these kinds of risks and accidents, it is necessary to improve the new safety management system through a risk management after technically evaluating potential hazards in this process. This study is to carry out the quantitative risk assesment for hydrogen filling plant which are very dangerous(fire and explosive) and using a basic materials of general industries. As a results of this risk assessment, identified the elements important for safety(EIS) and suggested the practical management tools and verified the reliability of this risk assessment model through case study of accident.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.97-105
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• 2017

An air-burst munition is limited in space, so there is a limit on the size of the fuze and the amount of ammunition. In order to increase a firepower to a target with limited ammunition, it is necessary to concentrate the firepower on the ground instead of the omnidirectional explosion after flying to the target. This paper explores the design and verification of a ground-detection sensor that detects the direction of the ground and determines the flight-distance of an air-burst munition using a single axis geomagnetic sensor. Prior to the design of the ground detection sensor, a geomagnetic sensor model mounted on the spinning air-burst munition is analyzed and a ground-detection algorithm by simplifying this model is designed. A high speed rotating device to simulate a rotation environment is designed and a geomagnetic sensor and a remote-recording system are fabricated to obtain geomagnetic data. The ground detection algorithm is verified by post-processing the acquired geomagnetic data. Taking miniaturization and low-power into consideration, the ground detection sensor is implemented with analog devices and the processor. The output signal of the ground detection sensor rotating at an arbitrary rotation speed of 200 Hz is connected to the LED (Light Emitting Diode) in the high speed rotating device and the ground detection sensor is verified using a high-speed camera.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.15-26
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• 2003

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an alternative approach which uses the least mean square (LMS) method and expectation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm In conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.1
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• pp.27-35
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• 2005

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature spare. This paper describes an alternative approach which uses the least mean square (LMS) method and exportation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximiBation (SAGE) algorithm ill conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor. Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.99-108
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• 2011

Almost all companies have paid much attention to the safety management ranging from maintenance to operation even at the stage of designing in order to prevent accidents, but fatal accidents continue to increase throughout the world. In particular, it is essential to systematically prevent such fatal accidents as fire, explosion or leakage of toxic gas at factories in order to not only protect the workers and neighbors but also prevent economic losses and environmental pollution. Though it is well known that accident probability is very low in NPP(Nuclear Power Plants), the reason why many researches are still being performed about the accidents is the results may be so severe. HRA is the main process to make preparation for possibility of human error in designing of the NPP. But those techniques have some problems and limitation as follows; the evaluation sensitivity of those techniques are out of date. And the evaluation of human error is not coupled with the design process. Additionally, the scope of the human error which has to be included in reliability assessment should be expanded. This work focuses on the coincidence of human error and mechanical failure for some important performance shaping factors to propose a method for improving safety effectively of the process industries. In order to apply in these purposes into the thesis, I found 63 critical Performance Shaping Factors of the eight dimensions throughout studies that I executed earlier. In this study, various analysis of opinion of specialists(Personal Factors, Training, Knowledge or Experience, Procedures and Documentation, Information, Communications, HMI, Workplace Design, Quality of Environment, Team Factors) and the guideline for construction of PSF were accomplished. The selected method was AHP which simplifies objective conclusions by maintaining consistency. This research focused on the implementation process of PSF to evaluate the process of PSF at each phase. As a result, we propose an evaluation model of PSF as a tool to find critical problem at each phase and improve on how to resolve the problems found at each phase. This evaluation model makes it possible to extraction of PSF succesfully by presenting the basis of assessment which will be used by enterprises to minimize the trial and error of construction process of PSF.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.79-93
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• 2015

With the rapid development of information technology, the ways of usage have changed drastically. The ways and efficiency of traditional service application to data processing already could not satisfy the requirements of modern users. Nowadays, users have already understood the importance of data. Therefore, the processing and saving of big data have become the main research of the Internet service company. In China, with the rise and explosion of 115 Cloud leads to other technology companies have began to join the battle of cloud services market. Although currently Chinese cloud services are still mainly dominated by cloud storage service, the series of service contents based on cloud storage service have been affirmed by users, and users willing to try these new ways of services. Thus, how to let users to keep using cloud services has become a topic that worth for exploring and researching. The academia often uses the TAM model with statistical analysis to analyze and check the attitude of users in using the system. However, the basic TAM model obviously already could not satisfy the increasing scale of system. Therefore, the appropriate expansion and adjustment to the TAM model (i. e. TAM2 or TAM3) are very necessary. This study has used the status of Chinese internet users and the related researches in other areas in order to expand and improve the TAM model by adding the brand influence, hardware environment and external environments to fulfill the purpose of this study. Based on the research model, the questionnaires were developed and online survey was conducted targeting the cloud services users of four Chinese main cities. Data were obtained from 210 respondents were used for analysis to validate the research model. The analysis results show that the external factors which are service contents, and brand influence have a positive influence to perceived usefulness and perceived ease of use. However, the external factor hardware environment only has a positive influence to the factor of perceived ease of use. Furthermore, the perceived security factor that is influenced by brand influence has a positive influence persistent intention to use. Persistent intention to use also was influenced by the perceived usefulness and persistent intention to use was influenced by the perceived ease of use. Finally, this research analyzed external variables' attributes using other perspective and tried to explain the attributes. It presents Chinese cloud service users are more interested in fundamental cloud services than extended services. In private cloud services, both of increased user size and cooperation among companies are important in the study. This study presents useful opinions for the purpose of strengthening attitude for private cloud service users can use this service persistently. Overall, it can be summarized by considering the all three external factors could make Chinese users keep using the personal could services. In addition, the results of this study can provide strong references to technology companies including cloud service provider, internet service provider, and smart phone service provider which are main clients are Chinese users.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.6
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• pp.525-538
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• 2014

Safety of reactive systems is one of the most important research areas in the field of weapon development. A NoGo response or at least a low-order explosion should be ensured to prevent unexpected accidents when the reactive system is impacted by high-velocity projectile. We investigated the shock-induced detonation of cased reactive systems subject to a normal projectile impact to the cylindrical surface based on two-dimensional hydrodynamic simulations using the I&G chemical rate law. Two types of energetic materials, namely LX-17 and AP-based solid propellant, were considered to compare the dynamic responses of the reactive system when subjected to the threshold impact velocity. It was found that shock-to-detonation transition phenomena occurred in the cased LX-17, whereas no full reaction occurred in the propellant.