• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.335-347
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• 2013

There are various types of real world signals. For example, an electrocardiogram(ECG) represents myocardium activities (contraction and relaxation) according to the beating of the heart. ECG can be expressed as the fluctuation of ampere ratings over time. A signal is a composite of various types of signals. An orchestra (which boasts a beautiful melody) consists of a variety of instruments with a unique frequency; subsequently, each sound is combined to form a perfect harmony. Various research on how to to decompose mixed stationary signals have been conducted. In the case of non-stationary signals, there is a limitation to use methodologies for stationary signals. Huang et al. (1998) proposed empirical mode decomposition(EMD) to deal with non-stationarity. EMD provides a data-driven approach to decompose a signal into intrinsic mode functions according to local oscillation through the identification of local extrema. However, due to the repeating process in the construction of envelopes, EMD algorithm is not efficient and not robust to a noise, and its computational complexity tends to increase as the size of a signal grows. In this research, we propose a new method to extract a local oscillation embedded in a signal by utilizing the second derivative.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.7
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• pp.601-608
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• 2015

The static aeroelastic analysis and optimization of flexible wings are conducted for steady state conditions while both aerodynamic and structural parameters can be used as optimization variables. The system of multidisciplinary design optimization as a robust methodology to couple commercial codes for a static aeroelastic optimization purpose to yield a convenient adaptation to engineering applications is developed. Aspect ratio, taper ratio, sweepback angle are chosen as optimization variables and the skin thickness of the wing. The real-coded adaptive range multi-objective genetic algorithm code, which represents the global multi-objective optimization algorithm, was used to control the optimization process. The support vector regression(SVR) is applied for optimization, in order to reduce the time of computation. For this multi-objective design optimization problem, numerical results show that several useful Pareto optimal designs exist for the flexible wing.

• Journal of KIISE
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• v.41 no.11
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• pp.982-991
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• 2014

The integrity assurance of recorded video by car black boxes are necessary as the car black box is becoming more popular. In this paper, we propose a video data integrity assurance scheme reflecting the features of car black box. The proposed method can detect any kind of deletion, insertion, modification of frames by cyclic chaining using inter block relation. And, it provides the integrity assurance function consistently even in cases of file overwriting because of no more free space in storage, partial file data lost. And non-repudiation is supported. Experimental results with a car black box embedded system with A8 application processor show that our method has a feasible computational overhead to process full HD resolution video at 30 frames per second in a real time.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.74-78
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• 1992

This paper describes an intelligent system to automatic evaluation of eddy current(EC) signal for Inspection of steam generator(SG) tubes in nuclear power plant. Some features of the intelligent system design in the proposed system are : (1) separation of representation scheme ,or event capturing knowledge in EC signal and for structural inspection knowledge in SG tubes inspection; (2) each representation scheme is implemented in different methods, one is syntactic pattern grammar and the other is rule based production. This intelligent system also includes an data base system and an user interface system to support integration of the hybrid knowledge processing methods. The intelligent system based on the proposed concept is useful in simplifying the knowledge elicitation process of the rule based production system, and in increasing the performance in real time signal inspection application.

• Journal of Advanced Navigation Technology
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• v.22 no.3
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• pp.213-219
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• 2018

In this paper, we propose a design method and process for hardware and software of hybrid V2X communication module that supports both C-ITS communication protocol designed for vehicle environment and Legacy LTE communication technology. C-ITS is suitable for safety service applications due to its low latency characteristics, and Legacy LTE is a technology suitable for non-safety applications such as traffic information and infotainment due to high latency and high capacity. The hybrid V2X communication module supports multiple communication technologies of WAVE and LTE, in which WAVE supports multiple channels, so that it is designed to transmit road information such as LDM and positioning correction information to an autonomous vehicle in real time. The main design results presented in this paper will be applied to the implementation of future hybrid V2X communication terminals for vehicles.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• Journal of the Korea Institute of Building Construction
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• v.18 no.1
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• pp.81-87
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• 2018

Since February 2017, site managers in small architectural projects are required by the revised building act. In order to address issues for site managers and to improve project efficiency, system improving methods are surveyed and proposed in this study. The key inspection items in the existing surveillance guidance manual were investigated, and the checklist for site managers for a small to medium sized construction site was analyzed. This study also tries to propose using web-based site management systems for improvement of the utilization of checklists. It is expected that these on-site management systems, along with BIM technology, will be able to monitor the small size building construction project in real time and effectively monitor the various problems occurring on-site.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.231-238
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• 2002

As a modeling method where the merits of fuzzy inference system and evolutionary computation are put together, evolutionary fuzzy modeling performs global approximate optimization. The paper proposes fuzzy clustering as fuzzy rule generation process which is one of the most important steps in evolutionary fuzzy modeling. With application of fuzzy clustering into the experiment or simulation results, fuzzy rules which properly describe non-linear and complex design problem can be obtained. The efficiency of evolutionary fuzzy modeling can be improved utilizing the membership degrees of data to clusters from the results of fuzzy clustering. To ensure the validity of the proposed method, the real design problem of an automotive inner trim is applied and the global approximation is achieved. Evolutionary fuzzy modeling is performed for several cases which differ in the number of clusters and the criterion of rule selection and their results are compared to prove that the proposed method can provide proper fuzzy rules for a given system and reduce computation time while maintaining the errors of modeling as a satisfactory level.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.289-296
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• 2021

The Korean construction industry has been implementing G-SEED, a certification system that evaluates the environmental properties of buildings for the purpose of reducing environmental burdens such as energy and resource consumption and pollutant emissions. Also, creating a pleasant environment in general is one more purpose of G-SEED certification system. However, G-SEED certification in practice is difficult and time consuming due to the complexity of the certification acquisition process coupled with little economic consideration for the materials of each certification item. Therefore, in this study, we present a model for the optimal selection of materials and economic assessment using a genetic algorithm. The development of the model involves building a material database based on life-cycle costing (LCC) targeted at "Application of Indoor Air Pollutant Low Emission Material" from G-SEED. Next, the model was validated using a real non-residential building case study. The result shows an average cost reduction rate of 74.5 % compared with the existing cost. This model is expected to be used as an economically efficient tool in G-SEED.

• Journal of Korean Neurosurgical Society
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• v.65 no.2
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• pp.236-244
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• 2022

Objective : To evaluate the interactions among differentially expressed autophagy and mitophagy markers in subarachnoid hemorrhage (SAH) patients with delayed cerebral ischemia (DCI). Methods : The expression data of autophagy and mitophagy-related makers in the cerebrospinal fluid (CSF) cells was analyzed by real-time reverse transcription-polymerase chain reaction and Western blotting. The markers included death-associated protein kinase (DAPK)-1, BCL2 interacting protein 3 like (BNIP3L), Bcl-1 antagonist X, phosphatase and tensin homolog-induced kinase (PINK), Unc-51 like autophagy activating kinase 1, nuclear dot protein 52, and p62. In silico functional analyses including gene ontology enrichment and the protein-protein interaction network were performed. Results : A total of 56 SAH patients were included and 22 (38.6%) of them experienced DCI. The DCI patients had significantly increased mRNA levels of DAPK1, BNIP3L, and PINK1, and increased expression of BECN1 compared to the non-DCI patients. The most enriched biological process was the positive regulation of autophagy, followed by the response to mitochondrial depolarization. The molecular functions ubiquitin-like protein ligase binding and ubiquitin-protein ligase binding were enriched. In the cluster of cellular components, Lewy bodies and the phagophore assembly site were enriched. BECN1 was the most connected gene among the differentially expressed markers related to autophagy and mitophagy in the development of DCI. Conclusion : Our study may provide novel insight into mitochondrial dysfunction in DCI pathogenesis.