• Smart Structures and Systems
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• v.17 no.6
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• pp.957-980
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• 2016

Structural damage detection (SDD) is a challenging task in the field of structural health monitoring (SHM). As an exploring attempt to the SDD problem, a hybrid self-adaptive Firefly-Nelder-Mead (SA-FNM) algorithm is proposed for the SDD problem in this study. First of all, the basic principle of firefly algorithm (FA) is introduced. The Nelder-Mead (NM) algorithm is incorporated into FA for improving the local searching ability. A new strategy for exchanging the information in the firefly group is introduced into the SA-FNM for reducing the computation cost. A random walk strategy for the best firefly and a self-adaptive control strategy of three key parameters, such as light absorption, randomization parameter and critical distance, are proposed for preferably balancing the exploitation and exploration ability of the SA-FNM. The computing performance of the SA-FNM is evaluated and compared with the basic FA by three benchmark functions. Secondly, the SDD problem is mathematically converted into a constrained optimization problem, which is then hopefully solved by the SA-FNM algorithm. A multi-step method is proposed for finding the minimum fitness with a big probability. In order to assess the accuracy and the feasibility of the proposed method, a two-storey rigid frame structure without considering the finite element model (FEM) error and a steel beam with considering the model error are taken examples for numerical simulations. Finally, a series of experimental studies on damage detection of a steel beam with four damage patterns are performed in laboratory. The illustrated results show that the proposed method can accurately identify the structural damage. Some valuable conclusions are made and related issues are discussed as well.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1318-1320
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• 2007

Fan noise prediction method is presented for air conditioning and/or cooling system applications where fan acts as an internal equipment having very complicated flow interaction with other various system components. The internal flow paths and distribution in the fan-applied systems such as computer or air conditioner are analyzed by using the FNM(Flow Network Modeling) with the flow resistances for flow elements of the system. Based on the fan operation point predicted from the FNM analysis results, the present fan noise model predicts overall sound power, pressure levels and spectrum. The predictions of the flow distribution, the fan operation and the noise level in electronic system by the present method are well agreed with 3-D CFD and actual noise test results.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.9
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• pp.952-960
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• 2008

Fan noise prediction method is presented for air conditioning, automobile and electronic cooling system applications where fan acts as an internal equipment having very complicated flow interaction with other various system components. The internal flow paths and distribution in the fan-applied systems such as computer or air conditioner are analyzed by using the FNM(flow network modeling). Fan noise prediction method comprises two models for the discrete frequency noise due to rotating steady aerodynamic lift and blade interaction and for the broadband noise due to turbulent boundary layer and wake vortex shedding. Based on the fan operation point predicted from the FNM analysis results and fan design parameters, the present far noise model predicts overall sound pressure level and spectrum. The predictions for the flow distribution, the fan operation and the noise level in air cooling system by the present method are well agreed with 3-D CFD and actual noise test results.

• The KSFM Journal of Fluid Machinery
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• v.10 no.3 s.42
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• pp.33-38
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• 2007

Fan selection procedure and fan noise evaluation method are presented for the system electronic cooling by combining FNM(Flow Network Model) and fan noise correlation model. Internal flow paths and distribution in electronic system we analyzed by using the FNM with the flow resistances for flow elements of the system. Based on the fan operation point predicted from the FNM analysis results, the present fan noise model predicts overall sound power, pressure levels and spectrum. The predictions of the flow distribution, the fan operation and the noise level in electronic system by the present method are well agreed with 3-D CFD and actual test results.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.48 no.4
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• pp.207-218
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• 2022

Objectives: This study aimed to define the prevalence and characteristics of skull base anomalies and the features of sphenoid sinus pneumatization (SSP). Materials and Methods: Five hundred cone-beam computed tomography scans were evaluated retrospectively for the presence of fossa navicularis magna (FNM), canalis basilaris medianus (CBM), sphenoid emissary foramen (SEF), and/or Onodi cells (OC). Patterns of the SSP and sphenoid sinus mucosa dimensions (SSMD) were also recorded. Results: The prevalence of FNM, CBM, SEF, and OC was 26.0%, 22.4%, 47.4%, and 18.4%, respectively. Two hundred sixty-two (52.4%) sellar-type SSP were defined, followed by post-sellar 191 (38.2%), pre-sellar 31 (6.2%), and conchal 16 (3.2%) types. The frequency of SSMD less than 1 mm, 1-3 mm, and greater than 3 mm was 40.6%, 38.4%, and 21.0%, respectively. An SEF was detected more frequently in females, while SSMD greater than 3 mm was more frequent in males. An FNM was more prevalent in the 18-29 and 30-39 age groups and SEF was significantly less frequent in patients over 60 years of age compared to other age groups. A sinus mucosa larger than 3 mm was more common in the younger than 18 year group. The frequency of post-sellar-type pneumatization was lower in patients younger than 18 years. Conclusion: Skull-base anomalies are common and may be detected incidentally during imaging procedures. The sphenoid sinus, its variations, and pneumatization patterns should also be taken into consideration in imaging procedures performed for various purposes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1648-1653
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• 2006

In this paper, transport characteristics have been investigated using analytical current-voltage model for double gate MOSFET(DGMOSFET). Scaling down to 100nm of gate length for MOSFET can bring about various problems such as a threshold voltage roll-off and increasing off current by tunneling since thickness of oxide is down by 1.fnm and doping concentration is increased. A current-voltage characteristics have been calculated according to changing of channel length,using analytical current-voltage relation. The analytical model has been verified by calculating I-V relation according to changing of oxide thickness and channel thickness as well as channel length. A current-voltage characteristics also have been compared and analyzed for operating temperature. When gate voltage is 2V, it is shown that a current-voltage characteristic in 77K is superior to in room temperature.