• Journal of Distribution Science
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• v.13 no.4
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• pp.55-64
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• 2015

Purpose - Multi-dimensional prices comprise multiple components such as monthly payments and a number of payments rather than a single lump-sum amount. According to previous studies, an increase in the number of price dimensions leads to a massive amount of cognitive stress resulting in incorrect calculation, and deterioration in the consistency of the price judgment. However, an increase only in the level of complexity of calculating multi-dimensional prices does not always result in a corresponding decrease in the accuracy of price evaluation. Since diverse variables could affect consumers' purchase-decision-making process, the results of price evaluation would be different. In this study, an empirical analysis was performed to determine how the accuracy of price evaluation varies depending on the extent of the complexity of price dimensions using product involvement and brand preference as moderating variables. Research design, data, and methodology - A survey was conducted on 260 students, and 252 effective responses were used for analysis. The data was analyzed using t-test, one-way ANOVA, and two-way ANOVA. In this study, six hypotheses were developed to examine the effect of product involvement and brand preference on consumers' evaluation effort of multi-dimensional prices. Results - As the number of price dimensions increased, accuracy of price evaluation appeared to be low in high involvement, as expected. However, it showed no differences in price evaluation effort when the level of complexity of calculating multi-dimensional prices is low. When a small number of price dimensions are presented in both cases of high and low involvement, accuracy of price evaluation is much higher in a weak brand preference. On the contrary, a strong brand preference enhances an accuracy of price evaluation only in case of low involvement when the number of price dimensions is increased. An interaction effect of product involvement and brand preference on consumers' evaluation of multi-dimensional prices did not exist irrespective of the level of complexity of calculating prices being high or low. Conclusions - When the number of price dimensions is small, consumers' effort for price evaluation shows almost no difference without the moderating effect of involvement, and a weak brand preference leads to a higher accuracy of price evaluation in an effort to make the best selection. No interaction effect of product involvement and brand preference was found except for a main effect of brand preference. When a price is composed of multiple dimensions rendering it more difficult to calculate the final price, the effort for price evaluation was expected to decrease only slightly in case of combination of high involvement and strong brand preference. This is because people have a higher purchase intentions and trust for that particular brand. However, the accuracy of price evaluation was much lower in cases of high involvement, and there was no interaction effect between product involvement and brand preference except for a main effect of involvement and brand preference, respectively.

• Journal of Distribution Science
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• v.16 no.7
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• pp.67-75
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• 2018

Purpose - In the 21st century, as information technology advances alongside the emergence of the 4th generation, industrial age, industrial environment has become individualized and customized. It is important to hire good quality employees for good service in the industry. The e-learning market is growing every year. Although e-learning companies are finding better quality employees in e-learning, it is not easy to find it. Companies also spend a lot of time and cost to find employee. On the employees side, they want to get a job freely when they want, but they cannot find their job easily. Furthermore, the labor market environment is changing fast. In the 4th generation, industrial age, employers require to find manpower whenever they need and want at little cost. So of their own accord, we have considered the necessity of management of human resources for employees and employers in e-learning. The purpose of this study is to propose a human cloud platform framework for enabling an efficient management of human resources in e-learning industry. Research design, data, and methodology - To pinpoint the items of a human cloud platform framework, the study was initiated according to the following process. First, items of competency relating to e-learning instructional designer was analyzed. Second, based on the items of information from this analysis, selection and validity verification took place with 5 e-learning specialists group. Third, the opinion of experts who were in charge of hiring in e-learning companies were collated with the questionnaire. Lastly, the human cloud platform framework was proposed based on opinion results. Results - The framework was comprised of 7 domains and 27 items in order to develop the human cloud platform for e-learning instructional designer. The analysis results showed that the most highly considered item were 'skill (4.60)' that employee already have the capability. Following this (in order) were 'project type (4.56)', 'work competency (4.56)', and 'strength area of instructional design (4.52)'. Conclusions - The 27 items in the human cloud platform framework were suggested in this study. Following this, we can consider to develop the human cloud platform for finding a job and hiring e-learning instructional designer easily. For successful platform operation, we need to consider reliability between employer and employee. In addition, we need quality assurance system based on operation has public confidence.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.902-912
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• 2013

This study on the training of police officers job satisfaction and job performance is to identify the relationship. This study of 2012 Police Training Institute courses in related expenses 5 population in the process of initiation of a national police officer selection, and note that the sampling method used to extract a total of 300 samples, but the number of cases that were used in the final analysis, 268 people. Data processing by the SPSSWIN 18.0 factor analysis, reliability analysis, multiple regression, path analysis. Conclusions are as follows. First, the police officer's training affects job satisfaction. In other words, work-related, of course the more positive the evaluation of job training job satisfaction is high, education, the stronger the motivation and job satisfaction also higher education can be. Second, the education and training of police officers affects job performance. In other words, work-related, educational motivation, job training curriculum for the more positive job performance rating is also high. Third, the police officer's job satisfaction affects job performance. In other words, education can be a higher job performance and job satisfaction also high. Fourth, the training of police officers on the job satisfaction and job performance directly or indirectly affected. That is, the internal job satisfaction and job performance, job training parameters are the important variables.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.587-599
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• 2018

In building-integrated photovoltaic (BIPV) systems, power generation functions are integrated into building functions by installing solar modules in combination with building materials. While this integration appears to be attractive, a design method is needed to achieve maximum power generation. Previously, the influence of the design elements on power generation was analyzed by computer simulations and demonstration tools. On the other hand, problems remain due to the inaccuracy of power generation analysis and relationship analysis, and limited demonstration. To solve this problem, this paper proposed the use of an extended demonstration mock-up. The mock-up was designed and constructed by implementing the design elements of the module types, installation angles, and direction. The actual operation data for one year were analyzed to evaluate the effects of the design elements on power generation. These results can be used to determine the feasibility of future BIPV systems and the optimal selection of system design elements.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.503-510
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• 2006

Readout circuit is to convert signal occurred in a defector into suitable signal for image signal processing. In general, it has to possess functions of impedance matching with perception element, amplification, noise reduction and cell selection. It also should satisfies conditions of low-power, low-noise, linearity, uniformity, dynamic range, excellent frequency-response characteristic, and so on. The technical issues in developing image processing equipment for focal plane way (FPA) can be categorized as follow: First, ultraviolet (UV) my detector material and fine processing technology. Second, ReadOut IC (ROIC) design technology to process electric signal from detector. Last, package technology for hybrid bonding between detector and ROIC. ROIC enables intelligence and multi-function of image equipment. It is a core component for high value added commercialization ultimately. Especially, in development of high-resolution image equipment ROIC, it is necessary that high-integrated and low-power circuit design technology satisfied with design specifications such as detector characteristic, signal dynamic range, readout rate, noise characteristic, ceil pitch, power consumption and so on. In this paper, we implemented a $8\times8$ FPA prototype ROIC for reduction of period and cost. We tested unit block and overall functions of designed $8\times8$ FPA ROIC. Also, we manufactured ROIC control and image boards, and then were able to verify operation of ROIC by confirming detected image from PC's monitor through UART(Universal Asynchronous Receiver Transmitter) communication.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.448-461
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• 2011

H.264/AVC significantly outperforms the previous video coding standards with many new coding tools. Among these tools, several intra-block coding tools can particularly improve coding efficiency. For intra prediction, H.264/AVC supports most probable mode in the entropy coding process to reduce syntax elements indicating intra prediction modes and most probable mode selection ratio is very high. Also, in general, natural images and videos have many homogeneous regions whose high correlation with neighbouring blocks. In this paper, we propose intra prediction mode SKIP mode using decoder-side prediction to improve the coding efficiency. The proposed method is determined the optimal prediction mode using only neighbouring block's information and coded on the basis of the conventional prediction/transform coding. And the prediction modes are not send to decoder at all. Skipped intra prediction mode is determined by decoder. Experimental results show that the proposed method achieves coding gains of 1.40% for common intermediate format(CIF), 3.24% for 720p sequences against the H.264/AVC JM 17.0 reference software.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.324-325
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• 2014

Quantum wells infrared photodetectors (QWIPs) have been used to detect infrared radiations through the principle based on the localized stated in quantum wells (QWs) [1]. The mature III-V compound semiconductor technology used to fabricate these devices results in much lower costs, larger array sizes, higher pixel operability, and better uniformity than those achievable with competing technologies such as HgCdTe. Especially, GaAs/AlGaAs QWIPs have been extensively used for large focal plane arrays (FPAs) of infrared imaging system. However, the research efforts for increasing sensitivity and operating temperature of the QWIPs still have pursued. The modification of heterostructures [2] and the various fabrications for preventing polarization selection rule [3] were suggested. In order to enhance optical performances of the QWIPs, double barrier quantum well (DBQW) structures will be introduced as the absorption layers for the suggested QWIPs. The DBWQ structure is an adequate solution for photodetectors working in the mid-wavelength infrared (MWIR) region and broadens the responsivity spectrum [4]. In this study, InGaAs/GaAs/AlGaAs double barrier quantum well infrared photodetectors (DB-QWIPs) are successfully fabricated and characterized. The heterostructures of the InGaAs/GaAs/AlGaAs DB-QWIPs are grown by molecular beam epitaxy (MBE) system. Photoluminescence (PL) spectroscopy is used to examine the heterostructures of the InGaAs/GaAs/AlGaAs DB-QWIP. The mesa-type DB-QWIPs (Area : $2mm{\times}2mm$) are fabricated by conventional optical lithography and wet etching process and Ni/Ge/Au ohmic contacts were evaporated onto the top and bottom layers. The dark current are measured at different temperatures and the temperature and applied bias dependence of the intersubband photocurrents are studied by using Fourier transform infrared spectrometer (FTIR) system equipped with cryostat. The photovoltaic behavior of the DB-QWIPs can be observed up to 120 K due to the generated built-in electric field caused from the asymmetric heterostructures of the DB-QWIPs. The fabricated DB-QWIPs exhibit spectral photoresponses at wavelengths range from 3 to $7{\mu}m$. Grating structure formed on the window surface of the DB-QWIP will induce the enhancement of optical responses.

• Korean Chemical Engineering Research
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• v.54 no.4
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• pp.548-554
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• 2016

In this study, semi-aqueous solutions containing carboxylic acids such as oxalic acid (OA), lactic acid (LA) and citric acid (CA) were formulated for the removal of copper etching residues produced at the interconnection process, and their characteristics were analyzed. Carboxylic acids in the solutions were apt to form various copper complexes according to the value of pH. Semi-aqueous solution containing 10 wt% CA showed the lowest etching rate of copper in the range from pH2 to pH7 and the highest selectivity in the range of pH 2 to pH 4. However, the cleaning solution containing 10 wt% LA revealed the superior selectivity at the range from pH 5 to pH 7. Appropriate selection of carboxylic acid should be required to improve the performance of cleaning solution. In the case of CA, the etching selectivity of copper oxide complex to copper was increased with the concentration of CA in the solution, when the solutions contain over 5 wt% CA, the copper interconnection layer has a metallic copper surface more than 88% in the area. The result shows that CA contained semi-aqueous solution has a relatively good cleaning ability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.414-419
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• 2020

An artificial intelligence-based cable tension estimation model was developed to expand the utilization of data obtained from cable accelerometers of cable-stayed bridges. The model was based on an algorithm for selecting the natural frequency in the tension estimation process based on the vibration method and an applied artificial neural network (ANN). The training data of the ANN was composed after converting the cable acceleration data into the frequency, and machine learning was carried out using the characteristics with a pattern on the natural frequency. When developing the training data, the frequencies with various amplitudes can be used to represent the frequencies of multiple shapes to improve the selection performance for natural frequencies. The performance of the model was estimated by comparing it with the control criteria of the tension estimated by an expert. As a result of the verification using 139 frequencies obtained from the cable accelerometer as the input, the natural frequency was determined to be similar to the real criteria and the estimated tension of the cable by the natural frequency was 96.4% of the criteria.

• Smart Media Journal
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• v.9 no.3
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• pp.31-40
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• 2020

Currently, black-box-based machine learning algorithms are used to analyze big data in manufacturing. This algorithm has the advantage of having high analytical consistency, but has the disadvantage that it is difficult to interpret the analysis results. However, in the manufacturing industry, it is important to verify the basis of the results and the validity of deriving the analysis algorithms through analysis based on the manufacturing process principle. To overcome the limitation of explanatory power as a result of this machine learning algorithm, we propose a manufacturing big data analysis method using genetic programming. This algorithm is one of well-known evolutionary algorithms, which repeats evolutionary operators such as selection, crossover, mutation that mimic biological evolution to find the optimal solution. Then, the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. Through this, input and output variable relations are derived to formulate the results, so it is possible to interpret the intuitive manufacturing mechanism, and it is also possible to derive manufacturing principles that cannot be interpreted based on the relationship between variables represented by formulas. The proposed technique showed equal or superior performance as a result of comparing and analyzing performance with a typical machine learning algorithm. In the future, the possibility of using various manufacturing fields was verified through the technique.