• The Journal of Korean Institute of Communications and Information Sciences
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• 제31권5B호
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• pp.456-477
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• 2006

Due to advances in wireless communication technology and increasing demand for various types of wireless access, cellular, WLAN, and portable internet(such as WiBro and IEEE 802.16) systems are likely to be integrated into a unified wireless access system. This expectation premises the availability of multi-mode handsets and cooperative interworking of heterogenous wireless access networks allied by roaming contracts. Under such environments, a user may lie in the situation where more than one wireless accesses are available at his/her location, and he/she will want to choose the 'best' access among them. In this paper, we define the 'best' access(es) as the access(es) that charges minimum cost while fulfilling the required QoS of wireless access, and address the problem of choosing the optimal set of accesses theoretically by introducing a graph representation of service environment. Two optimal selection algorithms are proposed, which individually consider cases where single or multiple wireless access can be supported by multi-mode handsets.

• KIPS Transactions on Software and Data Engineering
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• 제9권4호
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• pp.137-144
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• 2020

Delirium is among the most common mental disorders encountered in patients with a temporary cognitive impairment such as consciousness disorder, attention disorder, and poor speech, particularly among those who are older. Delirium is distressing for patients and families, can interfere with the management of symptoms such as pain, and is associated with increased elderly mortality. The purpose of this paper is to generate useful clinical knowledge that can be used to distinguish the outcomes of patients with delirium in long-term care facilities. For this purpose, we extracted the clinical classification knowledge associated with delirium using a local covering rule acquisition approach with the rough lower approximation region. The clinical applicability of the proposed method was verified using data collected from a prospective cohort study. From the results of this study, we found six useful clinical pieces of evidence that the duration of delirium could more than 12 days. Also, we confirmed eight factors such as BMI, Charlson Comorbidity Index, hospitalization path, nutrition deficiency, infection, sleep disturbance, bed scores, and diaper use are important in distinguishing the outcomes of delirium patients. The classification performance of the proposed method was verified by comparison with three benchmarking models, ANN, SVM with RBF kernel, and Random Forest, using a statistical five-fold cross-validation method. The proposed method showed an improved average performance of 0.6% and 2.7% in both accuracy and AUC criteria when compared with the SVM model with the highest classification performance of the three models respectively.

• Journal of the Korean Institute of Intelligent Systems
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• 제22권2호
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• pp.192-197
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• 2012

With the advent of DNA microarray technologies, researches for disease diagnosis has been actively in progress. In typical experiments using microarray data, problems such as the large number of genes and the relatively small number of samples, the inherent measurement noise and the heterogeneity across different samples are the cause of the performance decrease. To overcome these problems, a new method using functional modules (e.g. signaling pathways) used as markers was proposed. They use the method using an activity of pathway summarizing values of a member gene's expression values. It showed better classification performance than the existing methods based on individual genes. The activity calculation, however, used in the method has some drawbacks such as a correlation between individual genes and each phenotype is ignored and characteristics of individual genes are removed. In this paper, we propose a method based on the ensemble classifier. It makes weak classifiers based on feature vectors using subsets of genes in selected pathways, and then infers the final classification result by combining the results of each weak classifier. In this process, we improved the performance by minimize the search space through a filtering process using gene-gene interaction information and the mRMR filter. We applied the proposed method to a classifying the lung cancer, it showed competitive classification performance compared to existing methods.

• The KIPS Transactions:PartD
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• 제11D권6호
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• pp.1259-1268
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• 2004

To organize training in limited training area for an actuai combat, realistic training simulation plugged in by various battle conditions is essential. In this paper, we propose a virtual target overlay technique which does not use a virtual image, but Projects a virtual target on ground-based CCD image by appointed scenario for a realistic training simulation. In the proposed method, we create a realistic 3D model (for an instructor) by using high resolution Geographic Tag Image File Format(GeoTIFF) satellite image and Digital Terrain Elevation Data (DTED), and extract the road area from a given CCD image (for both an instructor and a trainee). Satellite images and ground-based sensor images have many differences in observation position, resolution, and scale, thus yielding many difficulties in feature-based matching. Hence, we propose a moving synchronization technique that projects the target on the sensor image according to the marked moving path on 3D satellite image by applying Thin-Plate Spline(TPS) interpolation function, which is an image warping function, on the two given sets of corresponding control point pair. To show the experimental result of the proposed method, we employed two Pentium4 1.8MHz personal computer systems equipped with 512MBs of RAM, and the satellite and sensor images of Daejoen area are also been utilized. The experimental result revealed the effective-ness of proposed algorithm.

• Journal of Korean Society of Transportation
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• 제20권7호
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• pp.177-183
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• 2002

Current state-of-the-art of VMS control is based upon simple knowledge-based inference engine with message set and each message's priority. And R&Ds of the VMS control are focused on the accurate detection and estimation of traffic condition of the subject roadways. However VMS display itself cannot achieve a desirable traffic allocation among alternative routes in the network In this context, VMS display strategy is the most crucial part in the VMS control. VMS itself has several limitations in its nature. It is generally known that VMS causes overreaction and concentration problems, which may be more serious in urban network than highway network because diversion should be more easily made in urban network. A feedback control algorithm is proposed in this paper to address the above-mentioned issues. It is generally true that feedback control approach requires low computational effort and is less sensitive to models inaccuracy and disturbance uncertainties. Major features of the proposed algorithm are as follows: Firstly, a regulator is designed to attain system optimal traffic allocation among alternative routes for each VMS in the network. Secondly, strategic messages should be prepared to realize the desirable traffic allocation, that is, output of the above regulator. VMS display strategy module is designed in this context. To evaluate Probable control benefit and to detect logical errors of the Proposed feedback algorithm, a offline simulation test is performed using real network in Daejon, Korea.

• Journal of Food Hygiene and Safety
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• 제35권6호
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• pp.607-617
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• 2020

Human bio-monitoring (HBM) data is a very important resource for tracking total exposure and concentrations of a parent chemical or its metabolites in human biomarkers. However, until now, it was difficult to execute the integration of different types of HBM data due to incompatibility problems caused by gaps in study design, chemical description and coding system between different sources in Korea. In this study, we presented a standardized code system and HBM knowledge model (KM) based on relational database modeling methodology. For this purpose, we used 11 raw datasets collected from the Ministry of Food and Drug Safety (MFDS) between 2006 and 2018. We then constructed the HBM database (DB) using a total of 205,491 concentration-related data points for 18,870 participants and 86 chemicals. In addition, we developed a summary report-type statistical analysis program to verify the inputted HBM datasets. This study will contribute to promoting the sustainable creation and versatile utilization of big-data for HBM results at the MFDS.

• Journal of the Korean Academy of Esthetic Dentistry
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• 제30권2호
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• pp.71-90
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• 2021

The virtual patient dataset is a collection of diagnostic data from various sources acquired from a single patient into a coordinate system of three-dimensional visualization. Virtual patient dataset makes it possible to establish a treatment plan, simulate various treatment procedures, and create a treatment planning delivery device. Clinicians can design and simulate a patient's smile on the virtual patient dataset and select the optimal result from the diagnostic process. The selected treatment plan can be delivered identically to the patient using manufacturing techniques such as 3D printing, milling, and injection molding. The delivery of this treatment plan can be linked to the final prosthesis through mockup confirmation through provisional restoration fabrication and delivery in the patient's mouth. In this way, if the diagnostic data superimposition and processing accuracy during the manufacturing process are guaranteed, 3D digital smile design simulated in 3D visualization can be accurately delivered to the real patient. As a clinical application method of the virtual patient dataset, we suggest a decision-making method that can exclude occlusal adjustment treatment from the treatment plan through the digital occlusal pressure analysis. A comparative analysis of whole-body scans before and after temporomandibular joint treatment was suggested for adolescent idiopathic scoliosis patients with temporomandibular joint disease. Occlusal plane and smile aesthetic analysis based on the virtual patient dataset was presented when treating patients with complete dentures.

• Advanced Industrial SCIence
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• 제3권1호
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• pp.23-29
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• 2024

In this scholarly investigation, the focus is placed on the transformative potential of edge computing in enhancing Intelligent Transportation Systems (ITS) for the facilitation of autonomous driving. The intrinsic capability of edge computing to process voluminous datasets locally and in a real-time manner is identified as paramount in meeting the exigent requirements of autonomous vehicles, encompassing expedited decision-making processes and the bolstering of safety protocols. This inquiry delves into the synergy between edge computing and extant ITS infrastructures, elucidating the manner in which localized data processing can substantially diminish latency, thereby augmenting the responsiveness of autonomous vehicles. Further, the study scrutinizes the deployment of edge servers, an array of sensors, and Vehicle-to-Everything (V2X) communication technologies, positing these elements as constituents of a robust framework designed to support instantaneous traffic management, collision avoidance mechanisms, and the dynamic optimization of vehicular routes. Moreover, this research addresses the principal challenges encountered in the incorporation of edge computing within ITS, including issues related to security, the integration of data, and the scalability of systems. It proffers insights into viable solutions and delineates directions for future scholarly inquiry.

• Journal of the Korean Society of Food Science and Nutrition
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• 제44권12호
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• pp.1881-1894
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• 2015

The objective of this study was to provide information on difficulty of performing tasks, degree of task satisfaction, job satisfaction, organizational commitment, and turnover intention as well as investigate correlations among these factors. Data were collected on employees working at Centers for Children's Foodservice Management, which had been operating for over 6 months until December 2013. The recruitment period was from December 16, 2013 to January 30, 2014. A total of 228 employees (79.7%) participated in the study, and 227 completed questionnaires were analyzed. Statistical analyses were performed on the data utilizing the SPSS V20.0 and AMOS V21.0 programs. The main results of this study were as follows: task satisfaction of employees in charge of 'visiting-teaching' for children was highest (4.24 points), whereas that of employees in charge of financial management was lowest (2.92 points). In terms of evaluation of job satisfaction factors, the score of 'co-worker' was highest (3.99 points) while that of 'payment' was lowest (2.45 points). Average scores of general job satisfaction, organizational commitment, and turnover intention were 3.56 points, 3.54 points, and 3.07 points, respectively. Job achievement was the most significant influencing factor on general job satisfaction, organizational commitment, and turnover intention. According to the path analysis results, the degree of task satisfaction affected job satisfaction. Organizational commitment had a more significant effect on turnover intention than job satisfaction and mediate both job satisfaction and turnover intention. Although employees of CCFSMs endeavor to improve the quality of child-care facility foodservice, some facilities do not. Controlling turnover intention of employees is especially critical for CCFSMs since it is important for each employees to form strong bonds with child-care facilities as well as to shorten the time required to train new employees. Thus, job satisfaction, which is related to organizational commitment and turnover intention, can be improved by considering poorly scored job satisfaction factors such as wage or workload.

• Journal of Korean Society of Transportation
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• 제22권1호
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• pp.43-62
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• 2004

Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.