• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.453-463
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• 2018

An analysis including ASE model accessing based on the intention of behavior performance of online university students is a new approach to improve academic persistence considering the characteristics of students with extensive personal variables, a uniqueness of learning environment. This study aimed to identify the relationship between ASE model including academic stress and academic persistence, and the effect of these factors on academic persistence of online university students. Data were collected from 181 sophomores in K open university from March to June, 2018. Frequency analysis, ${\chi}^2-test$, t-test, F-test, Pearson's correlation analysis, and multiple regression analysis used for data analysis. For factors affecting academic persistence, academic stress (${\beta}=-.16$, p=.016), online learning attitude (${\beta}=.44$, p<.001), and social support among social influential factors (${\beta}=.16$, p=.045) were statistically significant and the prediction model of academic persistence showed 29% explanation power (F=15.76, p<.001). To enhance academic persistence of online university students, it is needed to develop programs to reduce academic stress, improve attitude toward online learning, and improve social support.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.1-8
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• 2020

The need for storage, management, and retrieval techniques for alternative data has emerged as technologies based on data generated from business activities conducted by enterprises have emerged as the key to business success in recent years. Existing big data platform systems must load a large amount of data generated in real time without delay to process unstructured data, which is an alternative data, and efficiently manage storage space by utilizing a deduplication system of different storages when redundant data occurs. In this paper, we propose a multi-layer distributed data deduplication process system using the similarity of the Cuckoo hashing filter technique considering the characteristics of big data. Similarity between virtual machines is applied as Cuckoo hash, individual storage nodes can improve performance with deduplication efficiency, and multi-layer Cuckoo filter is applied to reduce processing time. Experimental results show that the proposed method shortens the processing time by 8.9% and increases the deduplication rate by 10.3%.

• Journal of Service Research and Studies
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• v.3 no.2
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• pp.53-60
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• 2013

Datamining from the large data in the form of various techniques for obtaining information have been developed. In recent years one of the most sought areas of pattern recognition and machine learning method is created with most of existing learning algorithms based on categorical attributes to a rule or decision model. However, the real-world data, it may consist of numeric attributes in many cases. In addition it contains attributes with numerical values to the normal categorical attribute. In this case, therefore, it is required processes in order to use the data to learn an appropriate value for the type attribute. In this paper, the domain of the numeric attributes are divided into several segments using learning algorithm techniques of discritization. It is described Clustering with other data mining techniques. Large amount of first cluster with characteristics is similar records from the database into smaller groups that split multiple given finite patterns in the pattern space. It is close to each other of a set of patterns that together make up a bunch. Among the set without specifying a particular category in a given data by extracting a pattern. It will be described similar grouping of data clustering technique to classify the data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.110-123
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• 2019

Traffic data is the most basic element necessary for transportation planning and traffic system operation. Recently, a method of estimating traffic flow characteristics using distance to a leading vehicle measured by an ADAS camera has been attempted. This study investigated the feasibility of the ADAS vehicle reflecting the distance error of image-based vehicle identification technology as a means to estimate the traffic flow through the normalized root mean square error (NRMSE) based on the number of lanes, traffic demand, penetration rate of probe vehicle, and time-space estimation area by employing the microscopic simulation model, VISSIM. As a result, the estimate of low density traffic flow (i.e., LOS A, LOS B) is unreliable due to the limitation of the maximum identification distance of ADAS camera. Although the reliability of the estimates can be improved if multiple lanes, high traffic demands, and high penetration rates are implemented, artificially raising the penetration rates is unrealistic. Their reliability can be improved by extending the time dimension of the estimation area as well, but the most influential one is the driving behavior of the ADAS vehicle. In conclusion, although it is not possible to accurately estimate the traffic flow with the ADAS camera, its applicability will be expanded by improving its performance and functions.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.29-38
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• 2012

Due to the rapid development of network environment and wireless communication technology, the distribution of digital video has made easily and the importance of the protection for digital video has been increased. This paper proposes the digital video encryption system based on multiple chaos maps for MPEG-2 video encoding process. The proposed method generates secret hash key of having 128-bit characteristics from hash chain using Tent map as a basic block and generates $8{\times}8$ lattice cipher by applying this hash key to Logistic map and Henon map. The method can reduce the encryption overhead by doing selective XOR operations between $8{\times}8$ lattice cipher and some coefficient of low frequency in DCT block and it provides simple and randomness characteristic because it uses the architecture of combining chaos maps. Experimental results show that PSNR of the proposed method is less than or equal to 12 dB with respect to encrypted video, the time change ratio, compression ratio of the proposed method are 2%, 0.4%, respectively so that it provides good performance in visual security and can be applied in real time.

• Journal of IKEEE
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• v.17 no.4
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• pp.505-510
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• 2013

In this paper, a method of estimating hand and elbow movements using electrocorticogram (ECoG) signals is proposed. Using multiple channels, surface electromyogram (EMG) signals and ECoG signals were obtained from patients simultaneously. The estimated movements were those to close and then open the hand and those to bend the elbow inward. The patients were encouraged to perform the movements in accordance with their free will instead of after being induced by external stimuli. Surface EMG signals were used to find movement time points, and ECoG signals were used to estimate the movements. To extract the characteristics of the individual movements, the ECoG signals were divided into a total of six bands (the entire band and the ${\delta}$, ${\Theta}$, ${\alpha}$, ${\beta}$, and ${\gamma}$ bands) to obtain the information entropy, and the maximum likelihood estimation method was used to estimate the movements. The results of the experiment showed the performance averaged 74% when the ECoG of the gamma band was used, which was higher than that when other bands were used, and higher estimation success rates were shown in the gamma band than in other bands. The time of the movements was divided into three time sections based on movement time points, and the "before" section, which included the readiness potential, was compared with the "onset" section. In the "before" section and the "onset" section, estimation success rates were 66% and 65%, respectively, and thus it was determined that the readiness potential could be used.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.1
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• pp.1-12
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• 2004

In this paper, we take advantage of the characteristics of optical burst switching (OBS) to support service-differentiation in optical networks. With the offset time between control packet and burst data, the proposed scheme uses different offset time of each service class. As contrasted with the Previous method, in which the high Priority service use only long offset time, it derives the burst loss rate as a QoS parameter in consideration of conservation law and given service-differential ratios and decides a reasonable offset time for this QoS finally Firstly proposed method classifies services into one of high or low class and is an algorithm deciding the offset time for supporting the required QoS of high class. In order to consider the multi-classes environment, we expand the analysis method of first algorithm and propose the second algorithm. It divides services into one of high or low group according to their burst loss rate and decides the offset time for high group, and lastly cumulates the offset time of each class. The proposed algorithms are evaluated through simulation. The result of simulation is compared with that of analysis to verify the proposed scheme.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.164-173
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• 2014

In underwater robotics, vision would be a key element for recognition in underwater environments. However, due to turbidity an underwater optical camera is rarely available. An underwater imaging sonar, as an alternative, delivers low quality sonar images which are not stable and accurate enough to find out natural objects by image processing. For this, artificial landmarks based on the characteristics of ultrasonic waves and their recognition method by a shape matrix transformation were proposed and were proven in Part 1. But, this is not working properly in undulating and dynamically noisy sea-bottom. To solve this, we propose a framework providing a selection phase of likelihood candidates, a selection phase for final candidates, recognition phase and tracking phase in sequence images, where a particle filter based selection mechanism to eliminate fake candidates and a mean shift based tracking algorithm are also proposed. All 4 steps are running in parallel and real-time processing. The proposed framework is flexible to add and to modify internal algorithms. A pool test and sea trial are carried out to prove the performance, and detail analysis of experimental results are done. Information is obtained from tracking phase such as relative distance, bearing will be expected to be used for control and navigation of underwater robots.

• International Journal of Aeronautical and Space Sciences
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• v.8 no.1
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• pp.10-20
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• 2007

For spacecraft attitude control, reaction wheel (RW) steering laws with more than three wheels for three-axis attitude control can be derived by using a control allocation (CA) approach.1-2 The CA technique deals with a problem of distributing a given control demand to available sets of actuators.3-4 There are many references for CA with applications to aerospace systems. For spacecraft, the control torque command for three body-fixed reference frames can be constructed by a combination of multiple wheels, usually four-wheel pyramid sets. Multi-wheel configurations can be exploited to satisfy a body-axis control torque requirement while satisfying objectives such as minimum control energy.1-2 In general, the reaction wheel steering laws determine required torque command for each wheel in the form of matrix pseudo-inverse. In general, the attitude control command is generated in the form of a feedback control. The spacecraft body angular rate measured by gyros is used to estimate angular displacement also.⁵ Combination of the body angular rate and attitude parameters such as quaternion and MRPs(Modified Rodrigues Parameters) is typically used in synthesizing the control command which should be produced by RWs.¹ The attitude sensor signals are usually corrupted by noise; gyros tend to contain errors such as drift and random noise. The attitude determination system can estimate such errors, and provide best true signals for feedback control.⁶ Even if the attitude determination system, for instance, sophisticated algorithm such as the EKF(Extended Kalman Filter) algorithm⁶, can eliminate the errors efficiently, it is quite probable that the control command still contains noise sources. The noise and/or other high frequency components in the control command would cause the wheel speed to change in an undesirable manner. The closed-loop system, governed by the feedback control law, is also directly affected by the noise due to imperfect sensor characteristics. The noise components in the sensor signal should be mitigated so that the control command is isolated from the noise effect. This can be done by adding a filter to the sensor output or preventing rapid change in the control command. Dynamic control allocation(DCA), recently studied by Härkegård, is to distribute the control command in the sense of dynamics⁴: the allocation is made over a certain time interval, not a fixed time instant. The dynamic behavior of the control command is taken into account in the course of distributing the control command. Not only the control command requirement, but also variation of the control command over a sampling interval is included in the performance criterion to be optimized. The result is a control command in the form of a finite difference equation over the given time interval.⁴ It results in a filter dynamics by taking the previous control command into account for the synthesis of current control command. Stability of the proposed dynamic control allocation (CA) approach was proved to ensure the control command is bounded at the steady-state. In this study, we extended the results presented in Ref. 4 by adding a two-step dynamic CA term in deriving the control allocation law. Also, the strict equality constraint, between the virtual and actual control inputs, is relaxed in order to construct control command with a smooth profile. The proposed DCA technique is applied to a spacecraft attitude control problem. The sensor noise and/or irregular signals, which are existent in most of spacecraft attitude sensors, can be handled effectively by the proposed approach.

• Journal of Hospice and Palliative Care
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• v.17 no.2
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• pp.57-65
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• 2014

Purpose: Since most terminally ill cancer patients die of multiple organ failure, plasma endotoxin concentration levels may be used to predict the life expectancy. This study was performed to evaluate the clinical significance of endotoxin level in plasma as a prognostic factor for survival in patients with terminal cancer. Methods: This study was conducted with 56 terminally ill cancer patients, above 20 years old, from April 2009 through October 2009. Demographic characteristics, Karnofsky performance status, and survival time were evaluated. We analyzed blood levels of white blood cell hemoglobin, hematocrit, aspartate aminotransferase, alanine aminotransferase, c-reactive protein, total bilirubin and endotoxin in each patient. Results: We considered following variable for univariate analysis: plasma endotoxin level, sex, age, WBC, hemoglobin, hematocrit, AST, ALT, total bilirubin, CRP and severity of pain. Univariate analysis did not show a significant association between plasma endotoxin level and survival time. However, in a multivariate analysis with factors that were found to be significantly associated with survival sex, WBC count and total bilirubin level in univariate analysis, high levels of plasma endotoxin and short survival time were significantly related. Conclusion: Plasma endotoxin level could be used as a prognostic factor to predict the life expectancy of terminally ill cancer patients.