• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.89-97
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• 2010

This study researches the effects of common features on a no-choice option with respect to regulatory focus theory. The primary interest is in three factors and their interrelationship: common features, no-choice option, and regulatory focus. Prior studies have compiled vast body of research in these areas. First, the "common features effect" has been observed bymany noted marketing researchers. Tversky (1972) proposed the seminal theory, the EBA model: elimination by aspect. According to this theory, consumers are prone to focus only on unique features during comparison processing, thereby dismissing any common features as redundant information. Recently, however, more provocative ideas have attacked the EBA model by asserting that common features really do affect consumer judgment. Chernev (1997) first reported that adding common features mitigates the choice gap because of the increasing perception of similarity among alternatives. Later, however, Chernev (2001) published a critically developed study against his prior perspective with the proposition that common features may be a cognitive load to consumers, and thus consumers are possible that they are prone to prefer the heuristic processing to the systematic processing. This tends to bring one question to the forefront: Do "common features" affect consumer choice? If so, what are the concrete effects? This study tries to answer the question with respect to the "no-choice" option and regulatory focus. Second, some researchers hold that the no-choice option is another best alternative of consumers, who are likely to avoid having to choose in the context of knotty trade-off settings or mental conflicts. Hope for the future also may increase the no-choice option in the context of optimism or the expectancy of a more satisfactory alternative appearing later. Other issues reported in this domain are time pressure, consumer confidence, and alternative numbers (Dhar and Nowlis 1999; Lin and Wu 2005; Zakay and Tsal 1993). This study casts the no-choice option in yet another perspective: the interactive effects between common features and regulatory focus. Third, "regulatory focus theory" is a very popular theme in recent marketing research. It suggests that consumers have two focal goals facing each other: promotion vs. prevention. A promotion focus deals with the concepts of hope, inspiration, achievement, or gain, whereas prevention focus involves duty, responsibility, safety, or loss-aversion. Thus, while consumers with a promotion focus tend to take risks for gain, the same does not hold true for a prevention focus. Regulatory focus theory predicts consumers' emotions, creativity, attitudes, memory, performance, and judgment, as documented in a vast field of marketing and psychology articles. The perspective of the current study in exploring consumer choice and common features is a somewhat creative viewpoint in the area of regulatory focus. These reviews inspire this study of the interaction possibility between regulatory focus and common features with a no-choice option. Specifically, adding common features rather than omitting them may increase the no-choice option ratio in the choice setting only to prevention-focused consumers, but vice versa to promotion-focused consumers. The reasoning is that when prevention-focused consumers come in contact with common features, they may perceive higher similarity among the alternatives. This conflict among similar options would increase the no-choice ratio. Promotion-focused consumers, however, are possible that they perceive common features as a cue of confirmation bias. And thus their confirmation processing would make their prior preference more robust, then the no-choice ratio may shrink. This logic is verified in two experiments. The first is a $2{\times}2$ between-subject design (whether common features or not X regulatory focus) using a digital cameras as the relevant stimulus-a product very familiar to young subjects. Specifically, the regulatory focus variable is median split through a measure of eleven items. Common features included zoom, weight, memory, and battery, whereas the other two attributes (pixel and price) were unique features. Results supported our hypothesis that adding common features enhanced the no-choice ratio only to prevention-focus consumers, not to those with a promotion focus. These results confirm our hypothesis - the interactive effects between a regulatory focus and the common features. Prior research had suggested that including common features had a effect on consumer choice, but this study shows that common features affect choice by consumer segmentation. The second experiment was used to replicate the results of the first experiment. This experimental study is equal to the prior except only two - priming manipulation and another stimulus. For the promotion focus condition, subjects had to write an essay using words such as profit, inspiration, pleasure, achievement, development, hedonic, change, pursuit, etc. For prevention, however, they had to use the words persistence, safety, protection, aversion, loss, responsibility, stability etc. The room for rent had common features (sunshine, facility, ventilation) and unique features (distance time and building state). These attributes implied various levels and valence for replication of the prior experiment. Our hypothesis was supported repeatedly in the results, and the interaction effects were significant between regulatory focus and common features. Thus, these studies showed the dual effects of common features on consumer choice for a no-choice option. Adding common features may enhance or mitigate no-choice, contradictory as it may sound. Under a prevention focus, adding common features is likely to enhance the no-choice ratio because of increasing mental conflict; under the promotion focus, it is prone to shrink the ratio perhaps because of a "confirmation bias." The research has practical and theoretical implications for marketers, who may need to consider common features carefully in a practical display context according to consumer segmentation (i.e., promotion vs. prevention focus.) Theoretically, the results suggest some meaningful moderator variable between common features and no-choice in that the effect on no-choice option is partly dependent on a regulatory focus. This variable corresponds not only to a chronic perspective but also a situational perspective in our hypothesis domain. Finally, in light of some shortcomings in the research, such as overlooked attribute importance, low ratio of no-choice, or the external validity issue, we hope it influences future studies to explore the little-known world of the "no-choice option."

• Korean Journal of Environment and Ecology
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• v.31 no.2
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• pp.152-165
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• 2017

The greater long-tail hamster, Tscherskia triton, is widely distributed in Northern China, Korea and adjacent areas of Russia. Except for its distribution, biological characteristics related to life history, behavior, and ecological influences for this species are rarely studied in Korea. This study was conducted to obtain biological information on breeding, growth and development that are basic to species-specific studies. The study adopted laboratory management of a breeding programme for T. triton collected in Jeju Island from March, 2015 to December, 2016. According to the study results, the conception rate was 31.67% and the mice in the large cages had a higher rate of conception than those in the small cages (56.7 vs. 6.7%). The gestation period was $22{\pm}1.6days$ (ranges from 21 to27 days), and litter size ranged from 2 to 7, with a mean of $4.26{\pm}1.37$ in the species. The minimum age for weaning was between $19.2{\pm}1.4days$ (range of 18-21 days). There were no significant differences by sex between mean body weight and external body measurements at birth. However, a significant sexual difference was found from the period of weaning (21 days old) in head and body length, as well as tail length (HBL-weaning, $106.50{\pm}6.02$ vs. $113.34{\pm}4.72mm$, p<0.05; HBL-4 months, $163.93{\pm}5.42$ vs. $182.83{\pm}4.32mm$, p<0.05; TL-4 months, $107.23{\pm}3.25$ vs. $93.95{\pm}2.15mm$, p<0.05). Gompertz and Logistic growth curves were fitted to data for body weight and lengths of head and body, tail, ear, and hind foot. In two types of growth curves, males exhibited greater asymptotic values ($164.840{\pm}7.453$ vs. $182.830{\pm}4.319mm$, p<0.0001; $163.936{\pm}5.415$ vs. $182.840{\pm}4.333mm$, p<0.0001), faster maximum growth rates ($1.351{\pm}0.065$ vs. $1.435{\pm}0.085$, p<0.05; $2.870{\pm}0.253$ vs. $3.211{\pm}0.635$, p<0.05), and a later age of maximum growth than females in head and body length ($5.121{\pm}0.318$ vs. $5.520{\pm}0.333$, p<0.05; $6.884{\pm}0.336$ vs. $7.503{\pm}0.453$, p<0.05). However, females exhibited greater asymptotic values ($105.695{\pm}5.938$ vs. $94.150{\pm}2.507mm$, p<0.001; $111.609{\pm}14.881$ vs. $93.960{\pm}2.150mm$, p<0.05) and longer length of inflection ($60.306{\pm}1.992$ vs. $67.859{\pm}1.330mm$, p<0.0001; $55.714{\pm}7.458$ vs. $46.975{\pm}1.074mm$, p<0.05) than males in tail length. These growth rate constants, viz. the morphological characters and weights of the males and females, were similar to each other in two types of growth curves. These results will be used as necessary data to study species specificity of T. triton with biological foundations.

• The Journal of Korean Society for Radiation Therapy
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• v.16 no.2
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• pp.9-17
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• 2004

Purpose : Although Improve of CT, MRI Radio-diagnosis and Radiation Therapy Planing, but we still use ICRU38 Planning system(2D film-based) broadly. 3-Dimensional ICR plan(CT image based) is not only offer tumor and normal tissue dose but also support DVH information. On this study, we plan irradiation-goal dose on CTV(CTV plan) and irradiation-goal dose on ICRU 38 point(ICRU38 plan) by use CT image. And compare with tumor-dose, rectal-dose, bladder-dose on both planning, and analysis DVH Method and Material : Sample 11 patients who treated by Ir-192 HDR. After 40Gy external radiation therapy, ICR plan established. All the patients carry out CT-image scanned by CT-simulator. And we use PLATO(Nucletron) v.14.2 planing system. We draw CTV, rectum, bladder on the CT image. And establish plan irradiation-$100\%$ dose on CTV(CTV plan) and irradiation-$100\%$ dose on A-point(ICRU38 plan) Result : CTV volume($average{\pm}SD$) is $21.8{\pm}26.6cm^3$, rectum volume($average{\pm}SD$) is $60.9{\pm}25.0cm^3$, bladder volume($average{\pm}SD$) is $116.1{\pm}40.1cm^3$ sampled 11 patients. The volume including $100\%$ dose is $126.7{\pm}18.9cm^3$ on ICRU plan and $98.2{\pm}74.5cm^3$ on CTV plan. On ICRU planning, the other one's $22.0cm^3$ CTV volume who residual tumor size excess 4cm is not including $100\%$ isodose. 8 patient's $12.9{\pm}5.9cm^3$ tumor volume who residual tumor size belows 4cm irradiated $100\%$ dose. Bladder dose(recommended by ICRU 38) is $90.1{\pm}21.3\%$ on ICRU plan, $68.7{\pm}26.6\%$ on CTV plan, and rectal dose is $86.4{\pm}18.3\%,\;76.9{\pm}15.6\%$. Bladder and Rectum maximum dose is $137.2{\pm}50.1\%,\;101.1{\pm}41.8\%$ on ICRU plan, $107.6{\pm}47.9\%,\;86.9{\pm}30.8\%$ on CTV plan. Therefore CTV plan more less normal issue-irradiated dose than ICRU plan. But one patient case who residual tumor size excess 4cm, Normal tissue dose more higher than critical dose remarkably on CTV plan. $80\%$over-Irradiated rectal dose(V80rec) is $1.8{\pm}2.4cm^3$ on ICRU plan, $0.7{\pm}1.0cm^3$ on CTV plan. $80\%$over-Irradiated bladder dose(V80bla) is $12.2{\pm}8.9cm^3$ on ICRU plan, $3.5{\pm}4.1cm^3$ on CTV plan. Likewise, CTV plan more less irradiated normal tissue than ICRU38 plan. Conclusion : Although, prove effect and stability about previous ICRU plan, if we use CTV plan by CT image, we will reduce normal tissue dose and irradiated goal-dose at residual tumor on small residual tumor case. But bigger residual tumor case, we need more research about effective 3D-planning.

• Journal of Radiation Protection and Research
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• v.32 no.3
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• pp.105-110
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• 2007

Purpose : In spite of recent remarkable improvement of diagnostic imaging modalities such as CT, MRI, and PET and radiation therapy planing systems, ICR plan of uterine cervix cancer, based on recommendation of ICRU38(2D film-based) such as Point A, is still used widely. A 3-dimensional ICR plan based on CT image provides dose-volume histogram(DVH) information of the tumor and normal tissue. In this study, we compared tumor-dose, rectal-dose and bladder-dose through an analysis of DVH between CTV plan and ICRU38 plan based on CT image. Method and Material : We analyzed 11 patients with a cervix cancer who received the ICR of Ir-192 HDR. After 40Gy of external beam radiation therapy, ICR plan was established using PLATO(Nucletron) v.14.2 planing system. CT scan was done to all the patients using CT-simulator(Ultra Z, Philips). We contoured CTV, rectum and bladder on the CT image and established CTV plan which delivers the 100% dose to CTV and ICRU plan which delivers the 100% dose to the point A. Result : The volume$(average{\pm}SD)$ of CTV, rectum and bladder in all of 11 patients is $21.8{\pm}6.6cm^3,\;60.9{\pm}25.0cm^3,\;111.6{\pm}40.1cm^3$ respectively. The volume covered by 100% isodose curve is $126.7{\pm}18.9cm^3$ in ICRU plan and $98.2{\pm}74.5cm^3$ in CTV plan(p=0.0001), respectively. In (On) ICRU planning, $22.0cm^3$ of CTV volume was not covered by 100% isodose curve in one patient whose residual tumor size is greater than 4cm, while more than 100% dose was irradiated unnecessarily to the normal organ of $62.2{\pm}4.8cm^3$ other than the tumor in the remaining 10 patients with a residual tumor less than 4cm in size. Bladder dose recommended by ICRU 38 was $90.1{\pm}21.3%$ and $68.7{\pm}26.6%$ in ICRU plan and in CTV plan respectively(p=0.001) while rectal dose recommended by ICRU 38 was $86.4{\pm}18.3%$ and $76.9{\pm}15.6%$ in ICRU plan and in CTV plan, respectively(p=0.08). Bladder and rectum maximum dose was $137.2{\pm}50.1%,\;101.1{\pm}41.8%$ in ICRU plan and $107.6{\pm}47.9%,\;86.9{\pm}30.8%$ in CTV plan, respectively. Therefore, the radiation dose to normal organ was lower in CTV plan than in ICRU plan. But the normal tissue dose was remarkably higher than a recommended dose in CTV plan in one patient whose residual tumor size was greater than 4cm. The volume of rectum receiving more than 80% isodose (V80rec) was $1.8{\pm}2.4cm^3$ in ICRU plan and $0.7{\pm}1.0cm^3$ in CTV plan(p=0.02). The volume of bladder receiving more than 80% isodose(V80bla) was $12.2{\pm}8.9cm^3$ in ICRU plan and $3.5{\pm}4.1cm^3$ in CTV plan(p=0.005). According to these parameters, CTV plan could also save more normal tissue compared to ICRU38 plan. Conclusion : An unnecessary excessive radiation dose is irradiated to normal tissues within 100% isodose area in the traditional ICRU plan in case of a small size of cervix cancer, but if we use CTV plan based on CT image, the normal tissue dose could be reduced remarkably without a compromise of tumor dose. However, in a large tumor case, we need more research on an effective 3D-planing to reduce the normal tissue dose.