• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.8
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• pp.1024-1033
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• 1997

The past several years have witnessed an ever-increasing acceptance and adoption of parallel processing, both for high performance scientific computing as well as for more general purpose applications. Furthermore with increasing needs to perform the complex flow calculations in an efficient manner, the use of the message passing model on distributed networks has emerged as an important alternative to the expensive supercomputers. This work attempts to provide a generic framework to enable the parallelization of all CFD-related works using the master-slave model. This framework consists of (1) input geometry, (2) domain decomposition, (3) grid generation, (4) flow computations, (5) flow visualization, and (6) output display as the sequential components, but performs computations for (2) to (5) in parallel on the workstation clustering. The flow computations are parallized by having multiple copies of the flow-code to solve a PDE on different spatial regions on different processors, while their flow data are exchanged across the region boundaries, and the solution is time-stepped. The Parallel Virtual Machine (PVM) is used for distributed communication in this work.

• ETRI Journal
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• v.37 no.6
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• pp.1199-1210
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• 2015

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components - multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

• Journal of Communications and Networks
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• v.12 no.3
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• pp.266-274
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• 2010

The efficient network resource management is one of the important topics in a real-time system. In this paper, we present a practical network resource management framework, control-theoretic packet scheduler (CPS) system. Using our framework, an operating system can schedule both input and output streams accurately and efficiently. Our framework adopts very portable feedback control theory for efficiency and accuracy. The CPS system is able to operate independent of the internal network protocol state, and it is designed to schedule packet streams in fine-grained time intervals to meet the resource requirement. This approach simplifies the design of the CPS system, and leads us to obtain the intended output bandwidth. We implemented our prototype system in Linux, and measured the performance of the network resource management system under various network QoS constraints. The distinctive features of our principles are as follows: It is robust and accurate, and its operation is independent of internal network protocols.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.191-203
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• 2009

Simulation is the work that identifies the characteristics of some problem through the simulated experiments. During the experiments it is frequently required to change or replace the simulation models, algorithms, or input/output data. Especially, in the case of the simulation works performed by replacing algorithms, if a replaceable component that implements a specific algorithm is not correct with respect to its functionality it is very difficult to carry out the simulation works successfully. In this paper, we suggest a test framework that verifies functional correctness of the replaceable component in the software-based GNSS (Global Navigation Satellite System) simulation environments. When a component is replaced, this framework enables us to properly execute the functional test for the component according to its context.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.2 s.140
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• pp.165-174
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• 2005

Recently, world leading companies on manufacturing field are trying to adopt a PLM methodology, which is a new production paradigm, for a survival and strengthening the competitiveness. Some projects for a digital shipyard including a methodology of a digital simulation framework are going on by Seoul national university and Samsung heavy industry. A Database methodology for a scheduling data, an interfacing methodology for a simulation input and output, and a synchronized simulation related methodology are required for enhancing the value of the digital simulation for shipbuilding. In this paper, such a methodologies and a related case study for a fabrication factory and an assembly factory are presented.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• The Journal of Sustainable Design and Educational Environment Research
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• v.9 no.3
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• pp.1-11
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• 2010

In the construction industry, IT based information system has been diversely applied to increase productivity. Although IT device such as PDA, RFID, Barcode, wireless network and web camera has been introduced to gather information in construction site, the effect of the IT device is limited, because of bringing about additional works of engineer. In this paper, we proposed a defect management system which is based on handwritten document digitalization framework for introducing applicability of new IT device, digital pen. By the proposed system, we can effectively gather and input defect information to defect management system by using digital pen and paper like conventional way. Applying the data gathering device, digital pen to defect management, it is able to increase productivity by improving work process, building up and utilizing defect information database of good quality.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.3
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• pp.562-568
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• 1998

During the past three decades, several techniques have been suggested for robust performance design of multivariable systems within the framework of Quantitative Feedback Theory. They are all characterized and limited by the use of loop transmission inversion. A new approach within framework is described which leads to a design tehnique without loop transmission inversion. Complete sequential design algorithms are derived for performance specifications in terms of plant input disturbance, sensitivity, complimentary sensitivity and control effort.

• ETRI Journal
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• v.36 no.1
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• pp.167-170
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• 2014

In this letter, we propose an unsupervised framework for speech noise reduction based on the recent development of low-rank and sparse matrix decomposition. The proposed framework directly separates the speech signal from noisy speech by decomposing the noisy speech spectrogram into three submatrices: the noise structure matrix, the clean speech structure matrix, and the residual noise matrix. Evaluations on the Noisex-92 dataset show that the proposed method achieves a signal-to-distortion ratio approximately 2.48 dB and 3.23 dB higher than that of the robust principal component analysis method and the non-negative matrix factorization method, respectively, when the input SNR is -5 dB.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.884-892
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• 2006

This paper describes a framework for multimodal object manipulation in virtual environments. The gist of the proposed framework is the semantic integration of multimodal input using spatial ontology and user context to integrate the interpretation results from the inputs into a single one. The spatial ontology, describing the spatial relationships between objects, is used together with the current user context to solve ambiguities coming from the user's commands. These commands are used to reposition the objects in the virtual environments. We discuss how the spatial ontology is defined and used to assist the user to perform object placements in the virtual environment as it will be in the real world.