• Journal of Information Technology Applications and Management
• /
• v.20 no.4
• /
• pp.315-338
• /
• 2013

As a result of New media that affects the marketing system, it is appeared a new marketing system called Market Platform in terms of market governance of trading. Market Platform is a new marketing system for ideal market system through the community structure from a hierarchy structure. It is determined that Market Platform as the role of adjustment of each side of market, buyer and seller, developed an existing marketing system. In this study, to target the Market Platform has recently emerged as the center of the market management in the new media environment. This study investigates what characteristics and impact of infrastructure of Market Platform will affect the cognitive impact on both buyers and sellers to clarify the impact on the self efficacy of the shopping process. Through this investigation, modeling for the impact that new media environment will attract Market Platform will be developed from the investigation of attractiveness of mobile market platform environment. Changes in the technical media environment gave the characteristics of the customer in the market platform, so consumers could involve not only consumed, but also manufactured. Thus, it is possible to increase the attractiveness if market must be able to not only for the convenience of shopping, but also make people to enjoy the experience of value co-creation. In other words, the new media, as a result of affecting the marketing system, mobile market platform is organized around the market of communication base. Network centrality, visible openness, ubiquitous multiplexity, 3 properties of new media increase the market self-efficacy of their customers. Therefore, the structure of market platform that enhance self-efficacy has higher attractiveness.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.7
• /
• pp.3176-3183
• /
• 2011

Traffic and congestion control in the wireless sensor network is an important parameter that decides the throughput and QoS (Quality of Service). This paper proposes a transmission rate priority-based traffic control scheme to serve digital contents streaming in wireless sensor networks. In this paper, priority for transmission rate decides on the real-time traffic and non-real-time with burst time and length. This transmission rate-based priority creates low latency and high reliability so that traffic can be efficiently controlled when needed. Traffic control in this paper performs the service differentiation via traffic detection process, traffic notification process and traffic adjustment. The simulation results show that the proposed scheme achieves improved performance in delay rate, packet loss rate and throughput compared with those of other existing CCF and WCA.

• The Journal of Korean Institute of Information Technology
• /
• v.16 no.12
• /
• pp.31-40
• /
• 2018

We designed and implemented the attachable smart adapter for the general management of each company's agricultural machine regardless of whether it is equipped with a CAN (Controller Area Network) module. The smart adapter consists of a main board (Raspberry Pi3B), which operates agricultural machine's management software in Linux environment, and a self-developed interface board for power adjustment and status sensing. For the status monitoring, a sensing interface using a serial input was defined between the smart adapter and the sensors of the agricultural machine, and the state diagram of the agricultural machine was defined for diagnosis. We made a panel to simulate the sensors of the agricultural machine using the switch's on/off contact point, and confirmed the status monitoring and diagnostic functions by inputting each state of the farm machinery from the simulator panel.

• Industry Promotion Research
• /
• v.6 no.1
• /
• pp.1-7
• /
• 2021

We investigated multi-layer perceptrons and supervised learning algorithms, and also examined how to model functional relationships between covariates and response variables using a package called neuralnet. The algorithm applied in this paper is characterized by continuous adjustment of the weights, which are parameters to minimize the error function based on the comparison between the actual and predicted values of the response variable. In the neuralnet package, the activation and error functions can be appropriately selected according to the given situation, and the remaining parameters can be set as default values. As a result of using the neuralnet package for the infertility data, we found that age has little influence on infertility among the four independent variables. In addition, the weight of the neural network takes various values from -751.6 to 7.25, and the intercepts of the first hidden layer are -92.6 and 7.25, and the weights for the covariates age, parity, induced, and spontaneous to the first hidden neuron are identified as 3.17, -5.20, -36.82, and -751.6.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.11-20
• /
• 2024

Among the generative models in Artificial Intelligence (AI), especially Generative Adversarial Network (GAN) has been successful in various applications such as image processing, density estimation, and style transfer. While the GAN models including Conditional GAN (CGAN), CycleGAN, BigGAN, have been extended and improved, researchers face challenges in real-world applications in specific domains such as disaster simulation, healthcare, and urban planning due to data scarcity and unstable learning causing Image distortion. This paper proposes a new progressive learning methodology called Progressive Step Training (PST) based on the Auxiliary Classifier GAN (ACGAN) that discriminates class labels, leveraging the progressive learning approach of the Progressive Growing of GAN (PGGAN). The PST model achieves 70.82% faster stabilization, 51.3% lower standard deviation, stable convergence of loss values in the later high resolution stages, and a 94.6% faster loss reduction compared to conventional methods.

• Journal of agricultural medicine and community health
• /
• v.23 no.2
• /
• pp.229-242
• /
• 1998

Face-to-face interviews were carried out to investigate the relationship between social networks and hypertension in 958 rural residents(males=440, females=518) aged 60-64 of a community-dwelling sample of Dalsung County from April to September in 1996. Eight elements of social network were measured : marital status, regular religious attendance, membership in groups, number of friends, relatives, siblings, children, grandchildren. Hypertensives were defined as meeting at least one of following criteria : hypertension history, systolic blood pressure more than 160 mmHg, diastolic blood pressure more than 95 mmHg. In univariate logistic regression for males, having 1-4 friends vs. none showed odds ratio 0.43 (95% Confidence interval CI 0.19-0.96) and having 2-3, 4 and more than 5 children had reduced prevalence of hypertension with odds ratios 0.21 (95% CI 0.06-0.72), 0.14 (95% CI 0.04-0.49), 0.24 (95% CI 0.07-0.82), respectively when compared with persons without children. In females, there was no elements of social network statistically significant. Having 5-9 grandchildren vs. none showed a marginally significant odds ratio 0.42. In multivariate logistic regression models for males with adjustment for age, education, body mass index, smoking and drinking, number of friends and children showed increased odds ratios and number of close relatives gained a statistically significant odds ratios (0.44-0.50). In females, the adjustment yielded little changes of odds ratios except number of grandchildren which gained a statistically significance. These results suggest that only a certain elements of social network may be associated with reduced risk of hypertension and they may be different between genders in rural resident aged 60-64.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2283-2286
• /
• 2003

This paper presents conservation of electrical energy in building with harmonics analysis and compensation which occur in electrical system. We use load controlling and management system in order to adjust load factor of system.The maximum demand limiting and controlling are used ,then the system can acquire the prediction and compare it to the maximum demand set point.The electrical signal analysis based on FFT technique. The harmonics are compensated by using harmonic filters.This system consists computer which works as controller, processor , analysis and database unit together with digital power meter in form of multidrop network through serial communication via RS-485.The load control system uses PLC to control load via serial communication RS-485. The A/D converter is used for sampling the electrical signals via parallel port of computer.The harmonic filters are controlled by a computer.The data of measurement such as voltage, current, power, power factor, total harmonic distortion, energy, etc., can be saved as database and analysis. The load factor is adjusted by limiting and controlling maximum demand. The load factor adjustment can reduce the cost of electric consumption and energy generation together with harmonics compensation in order to increase high efficiency of electrical system.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.11 no.4
• /
• pp.143-150
• /
• 1991

This paper presents an overview of a system of computer programs for the solution of a large geodetic network of about 2,000 stations. The system arranges the matrices in systematic sparse form which is applied to observation equations of RR(C)U (Row-wise Representation Complete Unordered) type and to normal equations of RR(U)U (Row-wise Representation Upper Unordered) type. The solution is done by a Modified Cholesky's algorithm in view of large networks. The implementation program are tested in PC-386 by korean new secondary networks, the results show that the sparse techniques are highly useful to geodetic networks in core-storage management and processing time.

• International Journal of Quality Innovation
• /
• v.6 no.1
• /
• pp.109-118
• /
• 2005

The parameter design is the most emphasized measure by researchers for a new products development. It is critical for makers to achieve simultaneously in both the time-to-market production and the quality enhancement. However, there are difficulties in practical application, such as (1) complexity and nonlinear relationships co-existed among the system's inputs, outputs and control parameters, (2) interactions occurred among parameters, (3) where the adjustment factors of Taguchi's two-phase optimization procedure cannot be sure to exist in practice, and (4) for some reasons, the data became lost or were never available. For these incomplete data, the Taguchi methods cannot treat them well. Neural networks have a learning capability of fault tolerance and model free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful fields include diagnostics, robotics, scheduling, decision-making, prediction, etc. This research is a case study of spherical annealing model. In the beginning, an original model is used to pre-fix a model of parameter design. Then neural networks are introduced to achieve another model. Study results showed both of them could perform the highest spherical level of quality.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.6
• /
• pp.96-103
• /
• 2002

The objective of this study is to adjust the roll gap fur the dimension accuracy of bar in hot bar rolling process considering roll wear. In this study hot bar rolling processes fur round and oval passes have been investigated. In order to predict the roll wear, the wear model is reformulated as an incremental from and then wear depth of roll is calculated at each deformation step on contact area using the results of finite element analysis, such as relative sliding velocity and normal pressure at contact area. Archard's wear model was applied to predict the roll wear. To know the effects of thermal softening of DCI (Ductile Cast Iron) roll material according to operating conditions, high temperature micro hardness test is executed and a new wear model has been proposed by considering the thermal softening of DCI roll expressed in terms of the main tempering curve. The new technique developed in this study for adjusting roll gap can give more systematically and economically feasible means to improve the dimension accuracy of bar with full usefulness and generality.