• Structural Engineering and Mechanics
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• 제31권5호
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• pp.507-526
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• 2009

Based on numerical and experimental methods, a systematic structural evaluation of a steel natatorium in service was carried out in detail in this paper. Planning of inspection tasks was proposed firstly according to some national codes in China in order to obtain the economic and reliable results. The field visual inspections and static computation were conducted in turn under in-service environmental conditions. Further a three-dimensional finite element model was developed according to its factual geometry properties obtained from the field inspection. An analytical modal analysis was performed to provide the analytical modal properties. The field vibration tests on the natatorium were conducted and then two different system identification methods were used to obtain the dynamic characteristics of the natatorium. A good correlation was achieved in results obtained from the two system identification methods and the finite element method (FEM). The numerical and experimental results demonstrated that the main structure of the natatorium in its present status is safe and it still satisfies the demand of the national codes in China. But the roof system such as purlines and skeletons must be removed and rebuilt completely. Moreover the system identification results showed that field vibration test is sufficient to identify the reliable dynamic properties of the natatorium. The constructive suggestion on structural evaluation of the natatorium is that periodic assessment work must be maintained to ensure the natatorium's safety in the future.

• 대한상한금궤의학회지
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• 제5권1호
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• pp.19-29
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• 2013

Objective : The purpose of this study is to analyze the naming of 'A diagnostic system based on Shanghanlun six meridian patterns and provisions' and to suggest an alternative naming. Methods : 1. The meaning of 'Six meridian(六經)' was reviewed on existing theories and Shanghanlun provisions. 2. Comparing the name of diangostic system with the term in 'Korean Standard Classification of Diseases-6(KCD-6)' and term in 'WHO international standard terminologies on traditional medicine in the western pacific region' was done. Results : 'Six meridian' is customary used in the Shagnhanlun study but its meaning is not match with original Shanghanlun system and could possibly make misunderstanding. So 'Disease pattern identification' is suitable than 'Six meridian' for this diagnostic system. Conclusions : This study suggests that using 'A disease pattern identification diagnostic system based on Shanghanlun provisions.'is more appropriate instead of using the name of the six meridian diagnostic system.

• 대한의생명과학회지
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• 제25권1호
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• pp.40-53
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• 2019

Of the Acinetobacter spp., A. baumannii (genospecies 2) is the most clinically significant in terms of hospital-acquired infections worldwide. It is difficult to perform Acinetobacter-related taxonomy using phenotypic characteristics and routine laboratory methods owing to clusters of closely related species. The ability to accurately identify Acinetobacter spp. is clinically important because antimicrobial susceptibility and clinical relevance differs significantly among the different genospecies. Based on the medical importance of pathogenic Acinetobacter spp., the distribution and characterization of Acinetobacter spp. isolates from 123 clinical samples was determined in the current study using four typically applied bacterial identification methods; partial rpoB gene sequencing, amplified rRNA gene restriction analysis (ARDRA) of the intergenic transcribed spacer (ITS) region of the 16~23S rRNA, the $VITEK^{(R)}$ 2 system (an automated microbial identification system) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). A. baumannii isolates (74.8%, 92/123) were the most common species, A. nosocomialis (10.6%, 13/123) and A. pittii isolates (7.5%, 9/123) were second and third most common strains of the A. calcoaceticus-A. baumannii (ACB) complex, respectively. A. soli (5.0%, 6/123) was the most common species of the non-ACB complex. RpoB gene sequencing and ARDRA of the ITS region were demonstrated to lead to more accurate species identification than the other methods of analysis used in this study. These results suggest that the use of rpoB genotyping and ARDRA of the ITS region is useful for the species-level identification of Acinetobacter isolates.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 춘계학술대회논문집
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• pp.1334-1337
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• 2006

System identification is the field of modeling dynamic systems from experimental data. As a modeling technique, we can mention finite element method (FEM). In addition, we are able to measure modal data as the experimental data. The system can be generally categorized into a gray box and black box. In the gray box, we know mathematical model of a system, but we don't know structural parameters exactly, so we need to estimate structural parameters. In the black box, we don't know a system completely, so we need to identify system from nothing. To date, various system identification methods have been developed. Among them, we introduce system realization theory which uses Hankel matrix and Eigensystem Realization Algorithm (ERA) that enable us to identify modal parameters from noisy measurement data. Although we obtain noise-free data, however, we are likely to face difficulties in identifying a structure with hidden modes. Hidden modes can be occurred when the input or output position comes to a nodal point. If we change a system using a mode decoupling controller, the hidden modes can be revealed. Because we know the perturbation quantities in a closed loop system with the controller, we can realize an original system by subtracting perturbation quantities from the closed loop system. In this paper, we propose a novel method to identify a structure with hidden modes using the mode decoupling controller and the associated example is given for illustration.

• Journal of the Korean Wood Science and Technology
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• 제45권6호
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• pp.797-808
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• 2017

Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.

• 대한치과교정학회지
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• 제51권2호
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• pp.77-85
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• 2021

Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 PA-cephalograms synthesized from cone-beam computed tomography scans (CBCT-PA) were selected as samples. Twenty-three landmarks used for Tweemac analysis were manually identified on all CBCT-PA images by a single examiner. Intra-examiner reproducibility was confirmed by repeating the identification on 85 randomly selected images, which were subsequently set as test data, with a two-week interval before training. For initial learning stage of the multi-stage CNN model, the data from 345 of 430 CBCT-PA images were used, after which the multi-stage CNN model was tested with previous 85 images. The first manual identification on these 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors in manual identification and artificial intelligence (AI) prediction. Results: The AI showed an average MRE of 2.23 ± 2.02 mm with an SDR of 60.88% for errors of 2 mm or lower. However, in a comparison of the repetitive task, the AI predicted landmarks at the same position, while the MRE for the repeated manual identification was 1.31 ± 0.94 mm. Conclusions: Automated identification for CBCT-synthesized PA cephalometric landmarks did not sufficiently achieve the clinically favorable error range of less than 2 mm. However, AI landmark identification on PA cephalograms showed better consistency than manual identification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3458-3481
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• 2021

Existing methods for blind identification of linear block codes without a candidate set are mainly built on the Gauss elimination process. However, the fault tolerance will fall short when the intercepted bit error rate (BER) is too high. To address this issue, we apply the reverse algebra approach and propose a novel "two-step-screening" algorithm by solving the linear error equations on the binary Galois field, or GF(2). In the first step, a recursive matrix partition is implemented to solve the system linear error equations where the coefficient matrix is constructed by the full codewords which come from the intercepted noisy bitstream. This process is repeated to derive all those possible parity-checks. In the second step, a check matrix constructed by the intercepted codewords is applied to find the correct parity-checks out of all possible parity-checks solutions. This novel "two-step-screening" algorithm can be used in different codes like Hamming codes, BCH codes, LDPC codes, and quasi-cyclic LDPC codes. The simulation results have shown that it can highly improve the fault tolerance ability compared to the existing Gauss elimination process-based algorithms.

• Journal of Electrical Engineering and Technology
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• 제8권2호
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• pp.197-205
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• 2013

Real time transient stability assessment mainly depends on real-time prediction. Unfortunately, conventional techniques based on offline analysis are too slow and unreliable in complex power systems. Hence, fast and reliable stability prediction methods and simple stability criterions must be developed for real time purposes. In this paper, two new methods for real time determining critical clearing time based on clustering identification are proposed. This article is covering three main sections: (i) clustering generators and recognizing critical group; (ii) replacing the multi-machine system by a two-machine dynamic equivalent and eventually, to a one-machine-infinite-bus system; (iii) presenting a new method to predict post-fault trajectory and two simple algorithms for calculating critical clearing time, respectively established upon two different transient stability criterions. The performance is expected to figure out critical clearing time within 100ms-150ms and with an acceptable accuracy.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.589-594
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• 2003

The paper proposes a methodology of identifying a substructure model of an existing structure when correct sectional and material properties of the structure are not known. A substructure model is identified by estimating boundary spring constants and stiffness properties of the substructure. Both of static and modal system identification methods have been applied using responses measured at limited locations within the substructure. In defining a substructure model it is required that computed structural responses be consistent with the actual behavior of the part of the structure. Simulation studies on a continuous beam structure and an application to an actual bridge have been carried with static and modal responses. The results and associated problems are discussed in the paper

• ETRI Journal
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• 제40권5호
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• pp.643-652
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• 2018

Dysarthria is a degenerative disorder of the central nervous system that affects the control of articulation and pitch; therefore, it affects the uniqueness of sound produced by the speaker. Hence, dysarthric speaker recognition is a challenging task. In this paper, a feature-extraction method based on deep belief networks is presented for the task of identifying a speaker suffering from dysarthria. The effectiveness of the proposed method is demonstrated and compared with well-known Mel-frequency cepstral coefficient features. For classification purposes, the use of a multi-layer perceptron neural network is proposed with two structures. Our evaluations using the universal access speech database produced promising results and outperformed other baseline methods. In addition, speaker identification under both text-dependent and text-independent conditions are explored. The highest accuracy achieved using the proposed system is 97.3%.