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 use of meteorological information is essential in the industrial society. More specialized weather services are required to perform better industrial activities including forestry. A topoclimatological technique, in this study, which makes use of empirical relationships between the topography and the weather in Cheju Island was applied to produce reasonable estimates of monthly air temperatures over remote land area where routine observations are rare. Altitude values of the 250m grid points were first read from a 1 : 25000 topographic map. The mean altitude and other valuable topographical variables were then determined for each $1km^2$ land area. Daily minimum, maximum and mean air temperature data were collected from 19 points in Cheju Island from June 1987 to September 1988. The data were analyzed and grouped into 36 sets by type of air temperature and by month. Each of data set was regressed to the topographical variables to delineate empirical relationships between the local air temperature and the site topography. The total of 36 regression equations were finally selected and the equations were used to calculate the monthly air temperature for each $1km^2$ land area. The outputs were presented in a fine-mesh grid map with a 6-level contour capability.
Purpose - It is widely accepted that the process of developing marketing strategy is composed of three steps: market segmentation, target market selection and positioning. However, mass marketing strategy based on cost reduction through economies of scale and standardized products, can be also an effective strategic option. Many marketing scholars including Theodore Levitt emphasize the importance of applying the mass production concept to various industries including service industries. Especially, in times of economic downturn, the capability of providing consumers with low-priced, value products can be an important source of competitive advantage, as well as the ability of providing high-priced premium products. Marketers should decide whether they will implement mass marketing strategy or target marketing strategy. The present study theoretically shows that firms should understand the target customers' price elasticity as well as the firm's cost structure in order to make such a strategic decision. Research design, data, and methodology - Instead of implementing an empirical study, this study provides a theoretical(mathematical) investigation on the effect of consumers' price elasticity on a firm's optimal price level, profit, sales volume, revenue, and cost. The results are mostly deduced from derivative calculations and several graphs are utilized to represent the results on the relationships between the variables under study. Results - The analytical results suggest that it is more profitable for a firm to adopt the segment/target marketing strategy (more specifically the differentiation strategy) when the degree of consumers' heterogeneity is high and the proportion of the fixed cost in the total cost is low. On the other hand, if the degree of consumers' heterogeneity is low and the fixed cost is high, it is better to adopt the mass marketing strategy or the cost leadership strategy. The strategy of concentrating on a single target market will be effective when consumers' needs are highly heterogeneous but the fixed cost is high. Any of the three types of generic strategies proposed my Porter(1980, 1985) can be applied when both the consumers' heterogeneity and the fixed cost are low. This study also proposes the contribution-margin-based method for developing the optimal pricing strategy. Conclusions - One of the primary roles of marketers is to find a proper compromise between the two conflicting goals of maximizing customer satisfaction and minimizing cost. In order to do so, he or she should understand the characteristics of the target customers as well as the cost structure of the firm. In addition to the theoretical analyses, this study discusses several business cases and explains how superior companies find the optimal compromise position between these two goals and dominate the market. One of the radical changes recently taking place in business arena is the reduction of production and distribution costs of both physical goods and information due to the advancement and the wide diffusion of information technology. The cost reduction combined with lowered priced elasticity incurred by customized products and services, will enable many firms to adopt the mass customization strategy.
As of entering the 21st century, a trend in the field of a private security industry among the advanced countries have been increased a qualification system and train session to meet the needs of professionalism. Intensifying the professionalism in Korea, education and train system has been initiated to change but the oligopoly market already formulated due to impractical selection standard and management of education system. Issuing certification and offering basic training through a designated institution for the purpose of improving quality of the private security industry worker, its practical effectiveness were lower than expectation. Rather certification-holder or security agency, institution or truster's rent-seeking behavior have been increased by occupational licensing system. The founded results, which were associated to problems in selecting and educating to the private security guard, in this study were that any verification has been initiated towards dual-system in official approval and structural problems in education system, and non-existence of verification for professionalism and management capability to security agency owner and its upper managerial level. Current a dual system in an officially authorized verification system and completion of security guard credential requested change to an unified official qualification verification system to solve those problems. Ranges of an applicant to the unified official qualification verification system should be extend to the whole population in the private security industry. Moreover, minimization of the dead-weigh loss, which is caused by oligopoly phenomenon while using its market-dominant status, increasement number of designated institution, which allows self-regulating competition, and endowment of autonomy, which is in selecting education and agency, were requested to solve the problems in selecting and educating to the private security guard. In order to minimize stated problems while maintaining objectiveness, a new manage and supervise institution, which is called a 'private security industry committee', should be establish. The private security industry committee is a formation of governance network which are participated from professional group to civil organization.
The purpose of this study was to suggest implications for improving training performance by studying how the capacity of IPP workers and the characteristics of training programs affect the training performance through social support of employees. The study was conducted by distributing the online questionnaire to 270 IPP learning worker(of 9 university). As a result, it was found that the characteristics of the learning worker and the characteristics of training programs were positively related to the social support of the employees, and their social support was positively related to the training performance. The results of this study can contribute to the training performance when used as reference materials for selection of trainees and participating companies and development and operation of training courses. However, the limitation of this study is that the objectivity of the result is rather low by deriving the response centered on the recognition of the learning workers. In future studies, it is necessary to increase the objectivity of the results through three-dimensional cross-checks with training participants.
Journal of the Korean Institute of Telematics and Electronics S
/
v.36S
no.1
/
pp.29-37
/
1999
In general, the information processing capability of a neural network is determined by its architecture and efficient training patterns. However, there is no systematic method for designing neural network and selecting effective training patterns. Evolutionary Algorithms(EAs) are referred to as the methods of population-based optimization. Therefore, EAs are considered as very efficient methods of optimal system design because they can provide much opportunity for obtaining the global optimal solution. In this paper, we propose a new method for finding the optimal structure of neural networks based on competitive co-evolution, which has two different populations. Each population is called the primary population and the secondary population respectively. The former is composed of the architecture of neural network and the latter is composed of training patterns. These two populations co-evolve competitively each other, that is, the training patterns will evolve to become more difficult for learning of neural networks and the architecture of neural networks will evolve to learn this patterns. This method prevents the system from the limitation of the performance by random design of neural networks and inadequate selection of training patterns. In co-evolutionary method, it is difficult to monitor the progress of co-evolution because the fitness of individuals varies dynamically. So, we also introduce the measurement method. The validity and effectiveness of the proposed method are inspected by applying it to the visual servoing of robot manipulators.
Cities in Korea have rapidly urbanized and they are not well prepared for natural disasters which have been increased by climate change. In particular, they often struggle with urban flooding. Recently, green infrastructure has been emphasized as a critical strategy for flood mitigation in developed countries due to its capability to infiltrate water into the ground, provide the ability to absorb and store rainfall, and contribute to mitigating floods. However, in Korea, green infrastructure planning only focuses on esthetic functions or accessibility, and does not think how other functions such as flood mitigation, can be effectively realized. Based on this, we address this critical gap by suggesting the new green infrastructure planning framework for improving urban water cycle and maximizing flood mitigation capacity. This framework includes flood vulnerability assessment for identifying flood risk area and deciding suitable locations for green infrastructure. We propose the use of the combination of frequency ratio model and GIS for flood vulnerability assessment. The framework also includes the selection process of green infrastructure practices under local conditions such as geography, flood experience and finance. Finally, we applied this planning framework to the case study area, namely YeonJe-gu an Nam-gu in Busan. We expect this framework will be incorporated into green infrastructure spatial planning to provide effective decision making process regarding location and design of green infrastructure.
Korean Journal of Construction Engineering and Management
/
v.18
no.3
/
pp.84-94
/
2017
Recently, domestic construction companies have increasingly engaged in international PPP projects a result of both uncertainties in the domestic construction market and low competitiveness in international EPC project business. These international PPP projects usually require long-term preparations and substantial sales costs, which make it important for decision-makers to select winning-award potential project in early stage of the projects. However, most previous research has analyzed success factors in terms of project development across all stages. Thus, this study investigated 28 success factors of 4 categories in the early stage of 31 international PPP projects. First, results indicate that unsolicited PPP projects require better implementation capabilities and financial conditions compared to solicited PPP projects. Second, implementation capability is important because it is not easy to improve as the project proceeds. Third, commercial conditions are identified as important even if conditions are not fixed in the early stage of PPP projects. Fourth, non-commercial conditions, strategy, and public interest are not found to be meaningful in the early stage of PPP projects because they can vary as the project proceeds. This study helps to improve selection criteria aimed towards more winning-award potential project in the early stage of international PPP projects.
Korean Journal of Construction Engineering and Management
/
v.10
no.5
/
pp.135-147
/
2009
The world has witnessed the dramatic expansion of international construction markets during the last decades, particularly around the developing economies and energy resource-rich countries. However, despite the booming markets, the risks of emerging regions have also increased under the rapidly changing environments confronting the global contractors. Most of all, success in overseas business mainly depends on selecting the right market to enter. Accordingly, the right market selection requires global firms to carefully carry out the scientific market entry decision by evaluating country risks, market prospects, firm's capability, level of competition, and among others. This study aims at developing a market entry model by the use of real option analysis (ROA) and scenario planning, which addresses the corporate strategic flexibility against the uncertainties encompassing the overseas construction markets. Based on the suggested approach, global contractors are expected to make a better decision rather than a typically static approach in pursuing, postponing, or abandoning a prospective market to their capacity with a concurrent consideration of uncertainties as well as its option value.
The Transactions of The Korean Institute of Electrical Engineers
/
v.60
no.3
/
pp.639-647
/
2011
In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.
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