• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.10 s.340
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• pp.39-50
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• 2005

In this paper, we propose a performance improving MAC algerian for the IEEE 802.11 DCF. WLAN based IEEE 802.11 uses two control methods called 'Distributed Coordination Function(UF)' and 'Point Coordination Function(PCF)'. The nF controls the Urnsmission based on carrier sense multiple access with collision detection(CSMA/CA), that decides a random backoff time with the range of contention window for each terminal. Normally, each terminal the CW double after collision, and reduces the CW to the minimum after successful transmission. This paper proposes an enhanced DCF algorithm that decreases the CW smoothly after successful transmission in order to reduce the collision Probability by utilizing the current status information of WLAN. We also analyze the throughput and delay performance for the unsaturated case mathematically. Simulation results show that our algorithm enhances the saturation throughput of WLAN. They also coincide well with the analytical results.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.435-444
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• 2021

The purpose of this study is a descriptive research to understand the effects of isolation, depression, and resilience of nursing students on career identity. Data were collected on an online basis using a structured questionnaire from 136 nursing students at a university. The collected data were analyzed by t-test, ANOVA, Kruskal-Wallis test, Pearson's correlation coefficients and multiple linear regression analysis using SPSS Window 25.0 program. The influence factors on the career identity was confirmed by the resilience (β=0.32, p=.001) total explanatory power of the variables was found to be 20.5%. Therefore, based on the results of this study, it is necessary to strategy establish a positive career identity and develop an effective career identity promotion program by exerting individual resilience against to negative emotions such as isolation and depression.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.847-855
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• 2022

The purpose of this study is descriptive research to understand the effects of somatic symptoms, aging anxiety, and social support of middle-aged women on depression. Data collection was conducted from March 2020 to May 2020 using a structured questionnaire from a sample of 110 middle-aged women. The collected data were analyzed using descriptive statistics, t-test, ANOVA, Kruskal-Wallis test, Pearson's correlation coefficients and multiple linear regression analysis using SPSS Window 25.0 program. As a result of the study, the influence factors on the depression were confirmed by the somatic symptoms(β=0.42, p<.001), aging anxiety(β=0.35, p<.001) and total explanatory power of the variables was found to be 48.0%. Therefore, based on the result of this study, it suggests that more research is needed a program that considers somatic symptoms and aging anxiety when developing interventions to reduce depression and to verify the effectiveness.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.563-571
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• 2023

The purpose of the study is to understand the impact of nursing college students' awareness and self-esteem on nursing professionals. The research design of this study is a descriptive investigative study using convenient samples. The data collection collected structured questionnaires and Google's online survey methods for first- to fourth-year nursing college students at three universities in North Chungcheong Province. The collected data were analyzed using the SPSS window 25.0 program as frequency, percentage, mean and standard deviation, t-test and one-way ANOVA, and post-test as Scheffétest, Pearson correlation coefficient, and multiple regression. The study found that 21.7% (==-.181, p<.001), 2.8% major satisfaction, and 24.5% (β=.420, p<.001), so it is recommended to use it as basic data to establish a curriculum and teaching learning strategy to improve major satisfaction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1657-1662
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• 2014

In this paper, the impact of substrate bias($V_{BS}$) on the zero capacitor RAM(Z-RAM) in n-channel junctionless multiple gate MOSFET(MuGFET) has been analyzed experimentally. Junctionless transistors with fin width of 50nm and 1 fin exhibits a memory window of 0.34V and a sensing margin of $1.8{\times}10^4$ at $V_{DS}=3.5V$ and $V_{BS}=0V$. As the positive $V_{BS}$ is applied, the memory window and sensing margin were improved due to an increase of impact ionization. When $V_{BS}$ is increased from 0V to 10V, not only the memory window is increased from 0.34V to 0.96V but also sensing margin is increased slightly. The sensitivity of memory window with different $V_{BS}$ in junctionless transistor was larger than that of inversion-mode transistor. A retention time of junctionless transistor is better than that of inversion-mode transistor due to low Gate Induced Drain Leakage(GIDL) current. To evaluate the device reliability of Z-RAM, the shifts in the Set/Reset voltages and current were measured.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4929-4947
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• 2017

In visual object tracking, Correlation Filter-based Tracking (CFT) systems have arouse recently to be the most accurate and efficient methods. The CFT's circularly shifts the larger search window to find most likely position of the target. The need of larger search window to cover both background and object make an algorithm sensitive to the background and the target occlusions. Further, the use of fixed-sized windows for training makes them incapable to handle scale variations during tracking. To address these problems, we propose two layer target representation in which both global and local appearances of the target is considered. Multiple local patches in the local layer provide robustness to the background changes and the target occlusion. The target representation is enhanced by employing additional reversed RGB channels to prevent the loss of black objects in background during tracking. The final target position is obtained by the adaptive weighted average of confidence maps from global and local layers. Furthermore, the target scale variation in tracking is handled by the statistical model, which is governed by adaptive constraints to ensure reliability and accuracy in scale estimation. The proposed structural enhancement is tested on VTBv1.0 benchmark for its accuracy and robustness.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.275-284
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• 2016

Accurate electricity demand forecasts is essential in reducing energy spend and preventing imbalance of the power supply. In forcasting electricity demand, we considered double seasonal Holt-Winters model and TBATS model with sliding window. We selected a specific time zone as the reference line of daily electric demand because it is least likely to be influenced by external factors. The forecasting performance have been evaluated in terms of RMSE and MAPE criteria. We used the observations ranging January 4, 2009 to December 31 for testing data. For validation data, the records has been used between January 1, 2012 and December 29, 2012.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.116-119
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• 2005

As to the synthetical estimation of land covering parameters or the compounded land covering classification for multi-resolution satellite data, former researches mainly adopted linear or nonlinear regression models to describe the regression relationship of land covering parameters caused by the degradation of spatial resolution, in order to improve the retrieval accuracy of global land covering parameters based on 1;he lower resolution satellite data. However, these methods can't authentically represent the complementary characteristics of spatial resolutions among different satellite data at arithmetic level. To resolve the problem above, a new compounded land covering classification method at arithmetic level for multi-resolution satellite data is proposed in this .paper. Firstly, on the basis of unsupervised clustering analysis of the higher resolution satellite data, the likelihood distribution scatterplot of each cover type is obtained according to multiple-to-single spatial correspondence between the higher and lower resolution satellite data in some local test regions, then Parzen window approach is adopted to derive the real likelihood functions from the scatterplots, and finally the likelihood functions are extended from the local test regions to the full covering area of the lower resolution satellite data and the global covering area of the lower resolution satellite is classified under the maximum likelihood rule. Some experimental results indicate that this proposed compounded method can improve the classification accuracy of large-scale lower resolution satellite data with the support of some local-area higher resolution satellite data.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.210-213
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• 1988

A new and accurate expression to derive a window area is presented with a sequence for daylighting design using Daylight Factor Method process not in its classical point--by-point method but in lumen method as in artificial lighting design process to consider daylight in the early stage of a building design process. Accepting CIE Overcast Sky as the worst state with the lowest sky luminance, a user of a room can have more available daylight in his or her room. In the design process uniformity is checked to ensure reasonably even daylighting by comparing the depth of the room with the computed limiting depth. After these steps the shape and position of window is altered, of which the Sky Component of Daylight Factor under an Overcast Sky, SCo, is investigated and computed in Composite Simpson Multiple Integral so that a building designer or an analyst can choose the best shape and location that satisfies his/her taste and purpose of the room.