• Progress in Medical Physics
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• v.21 no.3
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• pp.261-273
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• 2010

For the use of Indirect-conversion CMOS (complementary metal-oxide-semiconductor) detectors for digital x-ray radiography and their better designs, we have theoretically evaluated the spatial-frequency-dependent detective quantum efficiency (DQE) using the cascaded linear-systems transfer theory. In order to validate the developed model, the DQE was experimentally determined by the measured modulation-transfer function (MTF) and noise-power spectrum, and the estimated incident x-ray fluence under the mammography beam quality of W/Al. From the comparison between the theoretical and experimental DQEs, the overall tendencies were well agreed. Based on the developed model, we have investigated the DQEs values with respect to various design parameters of the CMOS x-ray detector such as phosphor quantum efficiency, Swank noise, photodiode quantum efficiency and the MTF of various scintillator screens. This theoretical approach is very useful tool for the understanding of the developed imaging systems as well as helpful for the better design or optimization for new development.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.33-43
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• 2011

Routing technologies considering QoS-based hypermedia services have been seen as a crucial network property in next generation optical Internet (NGOI) networks based on IP/dense-wavelength division multiplexing (DWDM). The huge potential capacity of one single fiber. which is in Tb/s range, can be exploited by applying DWDM technology which transfers multiple data streams (classified and aggregated IP traffics) on multiple wavelengths (classified with QoS-based) simultaneously. So, DWDM-based optical networks have been a favorable approach for the next generation optical backbone networks. Finding a qualified path meeting the multiple constraints is a multi-constraint optimization problem, which has been proven to be NP-complete and cannot be solved by a simple algorithm. The majority of previous works in DWDM networks has viewed heuristic QoS routing algorithms (as an extension of the current Internet routing paradigm) which are very complex and cause the operational and implementation overheads. This aspect will be more pronounced when the network is unstable or when the size of network is large. In this paper, we propose a flooding-based unicast and multicast QoS routing methodologies(YS-QUR and YS-QMR) which incur much lower message overhead yet yields a good connection establishment success rate. The simulation results demonstrate that the YS-QUR and YS-QMR algorithms are superior to the previous routing algorithms.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.446-452
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• 2016

The aircraft has to be considered for safety very importantly because of peculiarity of flight in the air, so it should be retained through proper inspection and maintenance not only in production phase but also in operating phase. Recently, it is using the latest technology as engineering approach not depending on human factor to determine on maintenance needs, and domestic production rotary aircraft also has the health & usage monitoring system to measure and to monitor major components. However, continued vibration exceedance phenomenon occurred in production and operation phase because of inappropriate thresholds, and it confirmed as false alarm which is not necessary to repair. In this paper, it is described that operational concept of HUMS, and especially it contains a study result for efficiency of aircraft operation and ultimately the improvement of flight safety by optimizing HUMS thresholds to determine efficiently necessity of maintenance under limited conditions and by establishing inspection/maintenance procedures when the re-designated thresholds exceedance occurred.

• Steel and Composite Structures
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• v.34 no.5
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• pp.743-767
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• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.336-349
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• 2003

Histograms have been getting a lot of attention recently. Histograms are commonly utilized in commercial database systems to capture attribute value distributions for query optimization Recently, in the advent of researches on approximate query answering and stream data, the interests in histograms are widely being spread. The simplest approach assumes that the attributes in relational tables are independent by AVI(Attribute Value Independence) assumption. However, this assumption is not generally valid for real-life datasets. To alleviate the problem of approximation on multi-dimensional data with multiple one-dimensional histograms, several techniques such as wavelet, random sampling and multi-dimensional histograms are proposed. Among them, GENHIST is a multi-dimensional histogram that is designed to approximate the data distribution with real attributes. It uses overlapping buckets that allow more efficient approximation on the data distribution. In this paper, we propose a scheme, OPT that can determine the optimal frequencies of overlapped buckets that minimize the SSE(Sum Squared Error). A histogram with overlapping buckets is first generated by GENHIST and OPT can improve the histogram by calculating the optimal frequency for each bucket. Our experimental result confirms that our technique can improve the accuracy of histograms generated by GENHIST significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.21-32
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• 1996

The performance of the energy density control algorithm for controlling a broadband noise is evaluated in a one-dimensional enclosure. To avoid noncausality problem of a control filter, which often happens in a frequency domain optimization, analyses presented in this paper are undertaken in the time domain. This approach provides the form of the causally constrained optimal controller. Numerical results are presented to predict the performance of the active noise control system, and indicate that imp개ved global attenuation of the broadband noise can be achieved by minimizing the energy density, rather than the squared pressure. It is shown that minimizing the energy density at a single location yields global attenuation results that are comparable to minimizing the potential energy. Furthermore, unlike the squared pressure control, the energy density control does not demonstrate any dependence on the error sensor location for this one-dimensional field. A practical implementation of the energy-based control algorithm is presented. Results show that the energy density control can be implemented using the two sensor technique with a tolerable margin of performance degradation.

• Journal of the Korea Institute of Building Construction
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• v.10 no.5
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• pp.45-54
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• 2010

Recently more and more construction projects have become high-rise, complex and intelligent. Accordingly, such projects require an integrated management system for tasks, with a lean approach to construction with work processes for management and productivity. In particular, Construction Information Technology (CIT) fields are concerned with Building Information Modeling (BIM), which represents the process of generating and managing building data during its life cycle. Constructability research has progressed for the project goal which is a cost-time-quality of optimization by integrated construction knowledge and experience. However, the current constructability process has not been performed efficiently, as the existing 2D drawings and papers lack consistent and accurate information, it is difficult to share the contents of work, and the use of information is inefficient. This study proposes that the reformation and enhancement of BIM-based constructability work process can lead to brilliant performance in the framework of the construction phase through achieving collaboration between the design team and the workers at the site.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.657-664
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• 2003

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classily individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.354-359
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• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.