• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.33-49
• /
• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Journal of Pharmaceutical Investigation
• /
• v.40 no.spc
• /
• pp.1-7
• /
• 2010

Since the introduction of the biopharmaceutics classification system (BCS) in 1995, it has viewed as an effective tool to categorize drugs in terms of prediction for bioavailability (BA) and bioequivalence (BE). The BCS consist of four drug categories: class I (highly soluble and highly permeable), class II (low soluble and highly permeable), class III (highly soluble and low permeable) and class IV (low soluble and low permeable), and almost all drugs belong to one of these categories. Likewise, classifying drugs into four categories according to their solubility and permeability is simple and relatively not controversial, and thus the FDA adopted the BCS as a science-based approach in establishing a series of regulatory guidance for the industry. Actually, many pharmaceutical companies have gained a lot of benefits, which directly connect to cost loss and failure decrease in the early stage of drug development. Recently, instead of solubility, using dissolution characteristics (e.g. intrinsic dissolution rate) have provided an improvement in the classification in correlating more closely with in vivo drug dissolution rather than solubility by itself. Furthermore, a newly modified-version of BCS, biopharmaceutics drug disposition classification system (BDDCS), which classify drugs into four categories according to solubility and metabolism, has been introduced and gained much attention as a new insight in respect with the drug classification. This report gives a brief overview of the BCS and its implication, and also introduces the recent new trend of drug classification.

• Tribology and Lubricants
• /
• v.34 no.2
• /
• pp.49-54
• /
• 2018

Hydraulic reciprocating seal has been widely used to prevent fluid leakage in hydraulic systems. Also, hydraulic reciprocating seal plays a significant role to provide lubricant film at contacting interface to minimize tribological problems due to sliding with counter material. To predict lifetime of hydraulic reciprocating seal, quantitative understanding of wear characteristics with respect to operating conditions such as normal force and sliding speed is needed. In this work, effect of sliding speed on wear of polyurethane (PU) hydraulic reciprocating seal were experimentally investigated using a pin-on-disk tribo-tester. The wear characteristics of PU specimens were quantitatively determined by comparing the confocal microscope data before and after test. It was found that the wear rate of PU specimens decreased from $4.9{\times}10^{-11}mm^3$ to $1.1{\times}10^{-11}mm^3/Nm$ as sliding speed increased from 120 mm/s to 940 mm/s. Also, it was observed that the friction decreased slightly as the sliding speed increased. Improvement of lubrication state with increasing sliding speed was likely to be responsible for this enhanced friction and wear characteristics. This result also suggests that decrease in sliding distance between PU elastomer and counter materials at lower sliding speed is preferred. Furthermore, the quantitative assessment of wear characteristics of PU specimen may be useful in prediction of lifetime of PU hydraulic reciprocating seal if the allowed degree of wear for failure of the seal is provided.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.6
• /
• pp.703-709
• /
• 2017

Korea Utility Helicopter(KUH) is a project to replace the aging helicopter presently being operated by the military with a domestic helicopter. The auxiliary power unit is a device that supplies emergency power to the system by an alternator installed when the main generator fails and the start motor converts the electrical energy of the battery into mechanical rotational energy to rotate the auxiliary power unit engine. With continuing power and operation, defects of the starting motor were found and improvements were carried out to solve it. In the failure mode analysis, the causes of possible defects were classified into 5 categories. Analysis of the 5 factors revealed that the main cause of defects is mechanical wear, which was found to be related to spring pressure. 250 tests were conducted through the theoretically determined pressure and regression analysis was performed with 4 sampling.The results showed that pressure was related to wear rate. In conclusion, early wear and breakage due to wear can be controlled through spring force and test showed the prediction of wear and the validity of the result were confirmed.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.11
• /
• pp.144-150
• /
• 2019

In this study, resistance spot welding was performed using a high tensile steel plate SGAFC 780. The shear tensile strength, fracture profile, nugget diameter, and simulation were compared according to the conditions. After the nugget diameter calibration, the minimum diameter of welding was more than 4.3mm when the welding current was 8kVA or more. At 9kVA and above 10kVA, the minimum nugget diameter of 4.3mm was satisfied. On the other hand, due to the high current and time, the fly phenomenon occurred and the deep indentation remained. An evaluation of the weldability confirmed that there was an interval that was evaluated as weld failure due to the creep phenomenon, which satisfied the tensile shear strength and minimum nugget diameter. On the other hand, areas that have sufficient load bearing capacity even when drift has occurred were also identified. The simulation results show that the error rate was less than 4.2% when comparing the nugget diameter in the simulation and the experimental results in the appropriate weld zone, and confirmed the reliability of the simulation.

• Journal of Industrial Convergence
• /
• v.21 no.11
• /
• pp.13-27
• /
• 2023

This study aims to contribute to the reduction of the failure rate and social costs resulting from business failures by analyzing factors that affect the insolvency of social enterprises, as the role of social enterprises is increasing in our economy. The data used in this study were classified as normal and insolvent companies among social enterprises (including prospective social enterprises) that were established between 2009 and 2018 and received credit guarantees from credit guarantee institutions as of the end of June 2022. Among the collected data, 439 social enterprises with available financial information were targeted; 406 (92.5%) were normal enterprises, and 33 (7.5%) were insolvent enterprises. Through a literature review, eight non-financial factors commonly used for insolvency prediction were selected. The cross-analysis results showed that four of these factors were significant. Logistic regression analysis revealed that two variables, including corporate credit rating and the personal credit rating of the representative, were significant. Financial factors such as debt ratio, sales operating profit rate, and total asset turnover were used as control variables. The empirical analysis confirmed that the two independent variables maintained their influence even after controlling for financial factors. Given that government-led support and development policies have limitations, there is a need to shift policy direction so that various companies aspiring to create social value can enter the social enterprise sector through private and regional initiatives. This would enable the social economy to create an environment where local residents can collaborate to realize social value, and the government should actively support this.

• Radiation Oncology Journal
• /
• v.23 no.1
• /
• pp.9-16
• /
• 2005

Purpose : To prospectively evaluate the use of positron emission tomography with the glucose analog fluoro-deoxyglucose (FDG-PET) to deoxyglucose (FDG-PET) to predict disease-free survival (DFS) after concurrent chemo-radiotherapy (CCRT) in patients with non-disseminated nasopharyngeal carcinoma (NPC). Materials and Methods : We studied 41 patients with non-disseminated NPC scheduled to undergo platinum-based CCRT were eligible for this study. Patients were studied by FDG-PET prior to the CCRT. FDG uptake of tumors were measured with the maximal standardized uptake value (SUV). Results : Complete response rate was $100\%$. In ten patients who presented with any component of treatment failure, the median $SUV_{max}$ was 8.55 (range: $2.49\~14.81$) in any component of failure and the median $SUV_{max}$ was 5.48 (range: $2.31\~26.07$) In the remaining patients without any such failure. Patients having tumors with high FDG uptake had a significantly lower 3-year DFS ($51\%\;{\nu}91\%$, p=0.0070) compared with patients having low uptake tumors. Conclusion : FDG uptake, as measured by the SUV, has potential value in predicting DFS in NPC treated by CCRT, High FDG uptake may be a useful parameter for Identifying patients requiring more aggressive treatment approaches.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.11
• /
• pp.256-264
• /
• 2020

Industrial Wireless Sensor Networks (IWSNs) is exploited to achieve various objectives such as improving productivity and reducing cost in the diversity of industrial application, and it has requirements such as low-delay and high reliability packet transmission. To accomplish the requirement, the network manager performs graph construction and resource allocation about network topology, and determines the transmission cycle and path of each node in advance. However, this network management scheme cannot treat mobile devices that cause continuous topology changes because graph reconstruction and resource reallocation should be performed as network topology changes. That is, despite the growing need of mobile devices in many industries, existing scheme cannot adequately respond to path failure caused by movement of mobile device and packet loss in the process of path recovery. To solve this problem, a network management scheme is required to prevent packet loss caused by mobile devices. Thus, we analyse the location and movement cycle of mobile devices over time using machine learning for predicting the mobility pattern. In the proposed scheme, the network manager could prevent the problems caused by mobile devices through performing graph construction and resource allocation for the predicted network topology based on the movement pattern. Performance evaluation results show a prediction rate of about 86% compared with actual movement pattern, and a higher packet delivery ratio and a lower resource share compared to existing scheme.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.2
• /
• pp.67-76
• /
• 2017

In industrial site in various fields it will be generated in combination with large amounts of data have a correlation. It is able to collect a variety of data in types of industry process, but they are unable to integrate each other's association between each process. For the data of the existing industry, the set values of the molding condition table are input by the operator as an arbitrary value When a problem occurs in the work process. In this paper, design the fusion and analysis processing model of data collected for each industrial type, Prediction Case(Automobile Connect), a through for corporate earnings improvement and process manufacturing industries such as master data through standard molding condition table and the production history file comparison collected during the manufacturing process and reduced failure rate with a new molding condition table digitized by arbitrary value for worker, a new pattern analysis and reinterpreted for various malfunction factors and exceptions, increased productivity, process improvement, the cost savings. It can be designed in a variety of data analysis and model validation. In addition, to secure manufacturing process of objectivity, consistency and optimization by standard set values analyzed and verified and may be optimized to support the industry type, fits optimization(standard setting) techniques through various pattern types.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.2
• /
• pp.227-235
• /
• 2018

Along with the recent increase in traffic volume of vehicles, accidents involving rollover of vehicles have been rapidly increased, resulting in an increase casualties. And to prevent this, various technologies such as vehicle crash test equipment and analysis program development have been advanced. In this study, the applied vehicle model is FORD EXPLORER model, and PC-Crash program for vehicle collision analysis is used to predict the rollover accident behavior of SUV and the collision velocity. Compared with the actual rollover behavior of SUV through the FMVSS No 208 regulations, the analysis results showed similar results, the characteristics of the collision velocity and roll angle showed a tendency that the error rate slightly increased after 1000 msec. Then, as a result of considering using the database of NHTSA, it is shown that the rollover accident occur most frequently in the range of the collision velocity of 15~77 km/h and the collision angle of $22{\sim}74^{\circ}$. And it is possible to estimate the vehicle speed and collision time when the vehicle roof is broken by reconstructing the vehicle starting position, the roof failure position and the stop position by applying the actual accident case.