• Journal of the Korean housing association
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• v.24 no.1
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• pp.61-68
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• 2013

This study tries to provide fundamental policy information for one-room housing by analyzing current living conditions of one-room housing near a university. For this purpose, this study conducts on-site survey as well as residents' survey. The main findings are follows. First, some one-room houses have been converted from single-family houses. Pilotis are often used as parking lots in newly built one-room houses. There are illegal equipments and illegal parkings around one-room village. Second, residents satisfy with proximity to workplace. However, they show strong dissatisfaction with physical environment such as noise, air flow, waste disposals. In particular, they feel very uncomfortable with gloomy lights and fear about potential crime. Third, residents like their independent lives, but complain about narrow living space and unprotected privacy. In addition, many of them feel lonely due to lack of public space in which residents can communicate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3171-3191
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• 2016

Due to growing demand on wireless data traffic, a large number of different types of base stations (BSs) have been installed. However, space-time dependent wireless data traffic densities can result in a significant number of idle BSs, which implies the waste of power resources. To deal with this problem, we propose an active state control algorithm based on semi-Markov decision process (SMDP) for a heterogeneous network. A MDP in discrete time domain is formulated from continuous domain with some approximation. Suboptimal on-line learning algorithm with a random policy is proposed to solve the problem. We explicitly include coverage constraint so that active cells can provide the same signal to noise ratio (SNR) coverage with a targeted outage rate. Simulation results verify that the proposed algorithm properly controls the active state depending on traffic densities without increasing the number of handovers excessively while providing average user perceived rate (UPR) in a more power efficient way than a conventional algorithm.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.495-500
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• 2012

A standard paradigm for the allocation of wireless resources in communication demands symmetry, whereby all users are assumed to be on equal footing and hence get equal shares of communication capabilities. However, there are situations in which "prime users" should be given higher priority, as for example in the transmission of emergency messages. In this paper, we examine a prioritization policy that can be implemented at the physical layer. In particular, we evaluate the performance of a prioritized transmission scheme based on spectrum pooling and on the assignment of higher signal-to-noise ratio channels to higher-priority users. This performance is compared to that of unprioritized (or "symmetric") schemes, and the impact of prioritization on the unprioritized users is discussed.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.242-249
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• 2003

When special voice control application services (VCS) such as voice-controlled web browsing or voice-controlled stock transactions are introduced in cellular systems, a channel quality better than that for ordinary voice communications service (OVS) is necessary in order to keep a suitable grade of VCS. To avoid ai. congestion, calls are normally admitted if there exists a channel-processing resource not occupied by other calls in the base as well as the interference level at the receiver is not higher than a predefined threshold. The threshold is usually 10㏈ noise-rise over the background noise level for voice communications service. When the base admits VCS attempts in exactly the same manner as it handles OVS calls. the same fraction of those will be not successful in taking the channel and then blocked. If the same noise-rise threshold is used as 10 ㏈, however, the admitted VCS calls might suffer from bad channel qualify and finally be dropped. From the user's point of view, the forced termination of ongoing calls is significantly undesirable than blocking new call attempts. When using a lower noise-rise threshold for VCS. on the other hand, the blocking probability of VCS gets higher than that of OVS. In this paper, a call admission policy that gives a priority to VCS is considered in order to reduce the blocking probability and keep an adequate channel quality.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.426-426
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• 1999

;There are many sources of uncertainty in a typical production and inventory system. There is uncertainty as to how many items customers will demand during the next day, week, month, or year. There is uncertainty about delivery times of the product. Uncertainty exacts a toll from management in a variety of ways. A spurt in a demand or a delay in production may lead to stockouts, with the potential for lost revenue and customer dissatisfaction. Firms typically hold inventory to provide protection against uncertainty. A cushion of inventory on hand allows management to face unexpected demands or delays in delivery with a reduced chance of incurring a stockout. The proposed strategies are used for the design of a probabilistic inventory system. In the traditional approach to the design of an inventory system, the goal is to find the best setting of various inventory control policy parameters such as the re-order level, review period, order quantity, etc. which would minimize the total inventory cost. The goals of the analysis need to be defined, so that robustness becomes an important design criterion. Moreover, one has to conceptualize and identify appropriate noise variables. There are two main goals for the inventory policy design. One is to minimize the average inventory cost and the stockouts. The other is to the variability for the average inventory cost and the stockouts The total average inventory cost is the sum of three components: the ordering cost, the holding cost, and the shortage costs. The shortage costs include the cost of the lost sales, cost of loss of goodwill, cost of customer dissatisfaction, etc. The noise factors for this design problem are identified to be: the mean demand rate and the mean lead time. Both the demand and the lead time are assumed to be normal random variables. Thus robustness for this inventory system is interpreted as insensitivity of the average inventory cost and the stockout to uncontrollable fluctuations in the mean demand rate and mean lead time. To make this inventory system for robustness, the concept of utility theory will be used. Utility theory is an analytical method for making a decision concerning an action to take, given a set of multiple criteria upon which the decision is to be based. Utility theory is appropriate for design having different scale such as demand rate and lead time since utility theory represents different scale across decision making attributes with zero to one ranks, higher preference modeled with a higher rank. Using utility theory, three design strategies, such as distance strategy, response strategy, and priority-based strategy. for the robust inventory system will be developed.loped.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.345-352
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• 2013

In this paper, a depth estimate method is proposed using GPU(Graphics Processing Unit) in monocular image. a monocular image is a 2D image with missing 3D depth information due to the camera projection and we used a monocular cue to recover the lost depth information by the projection present. The proposed algorithm uses an energy function which takes a variety of cues to create a more generalized and reliable depth map. But, a processing time is late because energy function is defined from the various monocular cues. Therefore, we propose a depth estimate method using GPGPU(General Purpose Graphics Processing Unit). The objective effectiveness of the algorithm is shown using PSNR(Peak Signal to Noise Ratio), a processing time is decrease by 61.22%.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.359-364
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• 2014

In this paper, we propose a method for extracting a road area by using the mean-shift method and connected-component method. Mean-shift method is very effective to divide the color image by the method of non-parametric statistics to find the center mode. Generally, the feature points of road are extracted by using the information located in the middle and bottom of the road image. And it is possible to extract a road region by using this feature-point and the partitioned color image. However, if a road region is extracted with only the color information and the position information of a road image, it is possible to detect not only noise but also off-road regions. This paper proposes the method to determine the road region by eliminating the noise with the closing / opening operation of the morphology, and by extracting only the portion of the largest area using a connected-components method. The proposed method is simulated and verified by applying the captured road images.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.401-409
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• 2020

The purpose of this study is to develop a convergence inpatient medical service patient experience management model(IMSPEMM) that can help in the management strategy of a medical institution to create a patient-centered medical culture. Using the original data from the 2018 Medical Service Experience Survey, 593 people with medical services inpatient(MSI) over the age of 15 were analyzed. By using the decision tree model, we developed a prediction model for overall satisfaction(OS) with the inpatient medical service experience(IMSE) and the intention to recommend patient experience(RI), and were classified into 4 and 7 types. The accuracy of the model was 68.9% and 78.3%. The OS level of IMSE was the nurse area and the hospital room noise management area, and the RI decision factor was the nurse area. It is significant that the IMSPEMM for MSI was presented and confirmed that the nurse area and the noise management area of the hospital room are important factors for the inpatient experience. It is considered that further research is needed to generalize the IMSPEMM.

• Journal of Korean society of Dental Hygiene
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• v.14 no.3
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• pp.381-390
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• 2014

Objectives : The purpose of the study is to investigate the relationship between job-stress and temporomandibular joint(TMJ) disorder in dental hygienists. This study will provide the basic data to improve the working condition and the quality of life. Methods : The subjects were 229 dental hygienists at general hospitals in Seoul, Korea. A self-reported questionnaire was filled out from May 20 to June 20, 2013. The questionnaire consisted of 4 questions of demographic features, 11 questions for TMJ symptoms and 5 questions for job stress. The data were analyzed by frequency analysis, chi-square test, Mann-Whitney U test and multiple job-stress logistic regression analysis using SPSS version 21.0. Results : During the last six months, 53.3%(122 persons) of the dental hygienists had TMJ disorder symptoms including joint noise(40.6%, 93 persons), TMJ pain(31.4%, 71 persons) and limitation of TMJ(21.8%, 50 persons). Job-stress is divided into two ranges including high stress group(4.3-5.0 points) and low stress group(0.0-3.6 points) in TMJ pain and joint noise(p<0.05). TMJ pain was closely related to low back pain, pelvis pain and tension headache arising from the uncomfortable working posture. Conclusions : It is necessary to prevent the job stress in the dental hygienists by the improvement of working condition, emotional stability, and frequent postural change.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.11
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• pp.76-88
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• 1998

While most of the research on reinforcement learning assumed a discrete control space, many of the real world control problems need to have continuous output. This can be achieved by using continuous mapping functions for the value and action functions of the reinforcement learning architecture. Two questions arise here however. One is what sort of function representation to use and the other is how to determine the amount of noise for search in action space. The ubiquitous neural network is used here to learn the value and policy functions. Next, the reinforcement predictor that is intended to predict the next reinforcement is introduced that also determines the amount of noise to add to the controller output. The proposed reinforcement learning architecture is found to have a sound on-line learning control performance especially at high-speed road following of high curvature road. Both computer simulation and actual experiments on a test vehicle have been performed and their efficiency and effectiveness has been verified.