• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.159-166
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• 2023

Humanities are as crucial as the technology itself in the intelligent information society. Human-centered convergence information technology (IT), which reflects emotional and human nature, can be considered a unique technology with an optimistic outlook in the unpredictable future. Based on this research background, this paper proposed an education model that can improve the IT humanities capabilities of various learners, including elementary and secondary students, prospective teachers, incumbent teachers, school managers, and the general public, through analysis of previous studies on convergence education models. Furthermore, the practical aspects of the proposed model were closely examined so that the proposed education model could be stably incorporated and utilized in the educational field. There are seven strategies for implementing the education model proposed in this paper, including research on textbooks, teaching and learning materials, activation of research results, maker space creation, global joint research, online education operation, developing living lab governance, and diversification of self-sustaining platforms for sustainable and practical education. In the future, validity verification through expert Delphi is required as a follow-up study.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• Fire Science and Engineering
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• v.33 no.6
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• pp.28-34
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• 2019

The purpose of this study is to examine the influence of occupant's path selection on the shape of the pictogram and the opening/closing of the door. This study was carried out through a simulation experiment using computer virtual reality. Exit light pictogram for exit door and exit light pictogram for passage were arranged for each scenario in type T corridor and type + corridor. The computer graphic was used to carry out the simulation. In addition, we analyzed the response of human behavior according to the two directions (left and right) of exit light pictogram for exit door and the effect of opening direction of doorway. In addition, the change of decision-making according to the presence or absence of exit light pictogram for passage was confirmed. The results of the direction selection response were as follows. First, in the case of the T-shaped corridor, if the exit light was not installed on the door, it was influenced by the opening direction of the door. Second, when the exit light is attached to the door, the selectivity in the direction that matches the exit light pictogram direction is high. As a result, it was confirmed that the pictogram direction of the exit light influenced the evacuation route selection of the occupants.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.429-441
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• 2002

This paper concerns extracting 3D motion information from a 3D input device in real time focused to enabling effective human-computer interaction. In particular, we develop a novel algorithm for extracting 6 degrees-of-freedom motion information from a 3D input device by employing an epipolar geometry of stereo camera, color, motion, and structure information, free from requiring the aid of camera calibration object. To extract 3D motion, we first determine the epipolar geometry of stereo camera by computing the perspective projection matrix and perspective distortion matrix. We then incorporate the proposed Motion Adaptive Weighted Unmatched Pixel Count algorithm performing color transformation, unmatched pixel counting, discrete Kalman filtering, and principal component analysis. The extracted 3D motion information can be applied to controlling virtual objects or aiding the navigation device that controls the viewpoint of a user in virtual reality setting. Since the stereo vision-based 3D input device is wireless, it provides users with a means for more natural and efficient interface, thus effectively realizing a feeling of immersion.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.147-153
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• 2011

In this paper, we present a new video management technique using forward timeline to efficiently store and delete the videos on a local server. The proposed method is based on capturing the changing preference of the videos according to recentness, frequency, and playback length of the requested videos. For this purpose, we utilize the forward timeline which represents the time area within a number of predefined intervals. The local server periodically measures time popularity and request segment of all videos. Based on the measured data, time popularity and request segment, the local server calculates the mean time popularity and mean request segment of a video using forward timeline. Using mean time popularity and mean request segment of video, we estimate the ranking and allocated storage space of a video. The ranking represents the priority of deletion when the storage area of local server is running out of space and the allocated storage space means the maximum size of storage space to be allocated to a video. In addition, we propose an efficient storage space partitioning technique in order to stably store videos and present a time based free-up storage space technique using the expected variation of video data in order for avoiding the overflow on a local server in advance. The simulation results show that the proposed method performs better than other methods in terms of hit rate and number of deletion. Therefore, our video management technique for local server provides the lowest user start-up latency and the highest bandwidth saving significantly.

• Journal of the Korea Computer Graphics Society
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• v.15 no.4
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• pp.1-11
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• 2009

In this paper, we present a prism-based mesh culling method to improve effectiveness of continuous collision detection which is a major bottleneck in a simulation using polygonal mesh models. A prism is defined based on two matching triangles between a sequence of times m a polygonal model. In order to detect potential colliding set(PCS) of prism between two polygonal models in a unit time, we apply the visibility test based on the occlusion query to two sets of prisms which are defined from two polygonal models in a unit time. Moreover, we execute the narrow band culling based on SAT(Separating Axis Test) to define potential colliding prism pairs from PCS of prisms extracted as a result of the visibility test. In the SAT, we examine one axis to be perpendicular to a plane which divides a 3D space into two half spaces to include each prism. In the experiments, we applied the proposed culling method to pairs of polygonal models with the different size and compared the number of potential colliding prism pairs with the number of all possible prism pairs of two polygonal models. We also compared effectiveness and performance of the visibility test-based method with those of the SAT-based method as the second narrow band culling. In an experiment using two models to consist of 2916 and 2731 polygons, respectively, we got potential colliding prism pairs with 99 % of culling rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of Dental Rehabilitation and Applied Science
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• v.37 no.2
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• pp.73-80
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• 2021

Purpose: The purpose of this study is to compare the color stability of provisional restorative materials fabricated by subtractive and additive manufacturing. Materials and Methods: PMMA specimens by subtractive manufacturing and conventional method and bis-acryl specimens by additive manufacturing were fabricated each 20. After immersing specimens in the coffee solution and the wine solution, the color was measured as CIE Lab with a colorimeter weekly for 4 weeks. Color change was calculated and data were analyzed with one-way ANOVA and the Tukey multiple comparisons test (α = 0.05). Results: PMMA provisional prosthetic materials by subtractive manufacturing showed superior color stability compared to bis-acryl provisional prosthetic materials by additive manufacturing (P < 0.05), and showed similar color stability to the PMMA provisional prosthetic materials by conventional method (P > 0.05). Conclusion: It is recommended to fabricate provisional restorations by subtractive manufacturing in areas where esthetics is important, such as anterior teeth, and consideration of the color stability will be required when making provisional prosthetic using additive manufacturing.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.345-353
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• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.1-8
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• 2023

Modeling of artificial targets in Synthetic Aperture radar (SAR) mainly simulates radar signals reflected from the faces and edges of the 3D Computer Aided Design (CAD) model with a ray-tracing method, and modeling of the clutter on the Earth's surface uses a method of distinguishing types with similar distribution characteristics through statistical analysis of the SAR image itself. In this paper, man-made targets on the surface and background clutter on the terrain are integrated and made into a three-dimensional (3D) point cloud scatterer model, and SAR image were created through computational signal processing. The results of the SAR Stripmap image generation of the actual automobile based SAR radar system and the results analyzed using EM modeling or statistical distribution models are compared with this 3D point cloud scatterer model. The modeling target is selected as an bridge because it has the characteristic of having both water surface and ground terrain around the bridge and is also a target of great interest in both military and civilian use.