• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.5
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• pp.644-653
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• 2019

With the development of themal observatory device(TOD), thermal camouflage system has been applied not only to the weapon system but also to the combat suit for soldiers. In this paper, the characteristic of thermal radiation of human body depending on the clothing material properties was analyzed through numerical simulations. The bioheat equation with thermoregulatory model was solved to obtain the realistic surface temperature of human body and these results are combined with the emissivity of human skin and clothing in order to calculate the thermal signature from the human body. According to each thermal resistance of clothing, the optimal background radiance which makes contrast radiance intensity(CRI) be lowest is different. Also, the average CRI variation per thermal resistance change is about twice as much as the case of evaporative resistance change.

• Smart Structures and Systems
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• v.24 no.6
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• pp.783-791
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• 2019

Prefabricated reinforced-concrete shear walls are used extensively in building structures because they are convenient to construct and environmentally sustainable. To make large walls easier to transport, they are divided into smaller segments and then assembled at the construction site using a variety of connection methods. The present paper proposes a precast shear wall assembled using steel shear keys, wherein the shear keys are fixed on the embedded steel plates of adjacent wall segments by combined plug and fillet welding. The anchoring strength of shear keys is known to affect the mechanical properties of the wall segments. Loading tests were therefore performed to observe the behavior of precast shear wall specimens with different anchoring components for shear keys. The specimen with insufficient strength of anchoring components was found to have reduced stiffness and lateral resistance. Conversely, an extremely high anchoring strength led to a short-column effect at the base of the wall segments and low deformation ability. Finally, for practical engineering purposes, a design approach involving the safety coefficient of anchoring components for steel shear keys is suggested.

• Ceramist
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• v.21 no.4
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• pp.312-330
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• 2018

Energy storage/conversion has become crucial not only to meet the present energy demand but also more importantly to sustain the modern society. Particularly, electrical energy storage is critical not only to support electronic, vehicular and load-levelling applications but also to efficiently commercialize renewable energy resources such as solar and wind. While Li-ion batteries are being intensely researched for electric vehicle applications, there is a pressing need to seek for new battery chemistries aimed at stationary storage systems. In this aspect, Zn-ion batteries offer a viable option to be utilized for high energy and power density applications since every intercalated Zn-ion yields a concurrent charge transfer of two electrons and thereby high theoretical capacities can be realized. Furthermore, the simplicity of fabrication under open-air conditions combined with the abundant and less toxic zinc element makes aqueous Zn-ion batteries one of the most economical, safe and green energy storage technologies with prospective use for stationary grid storage applications. Also, Zn-ion batteries are very safe for next-generation technologies based on flexible, roll-up, wearable implantable devices the portable electronics market. Following this advantages, a wide range of approaches and materials, namely, cathodes, anodes and electrolytes have been investigated for Zn-ion batteries applications to date. Herein, we review the progresses and major advancements related to aqueous. Zn-ion batteries, facilitating energy storage/conversion via $Zn^{2+}$ (de)intercalation mechanism.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.161-161
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• 2019

Water security issues, stimulated by increasing population and changing climate, are growing and pausing major challenges for water resources managers around the world. Proper utilization, management and distribution of all available water resources is key to sustainable development for achieving water security To alleviate the water shortage, most of the current research on multi-sources combined water supplies depends on an overall generalization of regional water supply systems, which are seldom broken down into the detail required to address specific research objectives. This paper proposes the concept of optimization framework on multi water sources selection. A multi-objective water allocation model with four objective functions is introduced in this paper. Harmony search algorithm is employed to solve the applied model. The objective functions addresses the economic, environmental, and social factors that must be considered for achieving a sustainable water allocation to solve the issue of water security.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.36-43
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• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.

• Smart Structures and Systems
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• v.2 no.2
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• pp.189-208
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• 2006

Monitoring large-scale civil engineering structures such as offshore platforms and high-large buildings requires a large number of sensors of different types. Innovative sensor data information technologies are very extremely important for data transmission, storage and retrieval of large volume sensor data generated from large sensor networks. How to obtain the optimal sensor set and placement is more and more concerned by researchers in vibration-based SHM. In this paper, a method of determining the sensor location which aims to extract the dynamic parameter effectively is presented. The method selects the number and place of sensor being installed on or in structure by through the tolerance domain statistical inference algorithm combined with second order sensitivity technology. The method proposal first finds and determines the sub-set sensors from the theoretic measure point derived from analytical model by the statistical tolerance domain procedure under the principle of modal effective independence. The second step is to judge whether the sorted out measured point set has sensitive to the dynamic change of structure by utilizing second order characteristic value sensitivity analysis. A 76-high-building benchmark mode and an offshore platform structure sensor optimal selection are demonstrated and result shows that the method is available and feasible.

• Smart Structures and Systems
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• v.15 no.1
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• pp.227-244
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• 2015

This study develops a two stage procedure to identify the structural damage based on the optimized artificial neural networks. Initially, the modal strain energy index (MSEI) is established to extract the damaged elements and to reduce the computational time. Then the genetic algorithm (GA) and artificial neural networks (ANNs) are combined to detect the damage severity. The input of the network is modal strain energy index and the output is the flexural stiffness of the beam elements. The principal component analysis (PCA) is utilized to reduce the input variants of the neural network. By using the genetic algorithm to optimize the parameters, the ANNs can significantly improve the accuracy and convergence of the damage identification. The influence of noise on damage identification results is also studied. The simulation and experiment on beam structures shows that the adaptive parameter selection neural network can identify the damage location and severity of beam structures with high accuracy.

• Ocean Systems Engineering
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• v.5 no.3
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• pp.161-179
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• 2015

An emergency response system (ERS) for vessel oil spills is a complex and dynamic system comprising a number of subsystems and activities. Failures may occur during the emergency response operations, this has negative impacts on the effectiveness of the ERS. Of the classes of problems in analyzing failures, the lack of quantitative data is fundamental. In fact, most of the empirical data collected via questionnaire survey is subjective in nature and is inevitably associated with uncertainties caused by the human being's inability to provide complete judgement. In addition, incomplete information and/or vagueness of the meaning about the failures add difficulties in evaluating the effectiveness of the system. Therefore this paper proposes a framework to evaluate the ERS effectiveness by using the combination of fuzzy reasoning and evidential synthesis approaches. Based on analyzing the procedure of ERS for oil spills, the failures in the system could be identified, using Analytic Hierarchy Process(AHP)to determine the relative weight of identified failures. Fuzzy reasoning combined with evidential synthesis is applied to evaluate the effectiveness of ERS for oil spills under uncertainties last. The proposed method is capable of dealing with uncertainties in data including ignorance and vagueness which traditional methods cannot effectively handle. A case study is used to illustrate the application of the proposed method.

• Structural Engineering and Mechanics
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• v.35 no.4
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• pp.387-407
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• 2010

In this paper, using perturbation and Galerkin method, the response of a resonant viscoelastic microbeam to an electric actuation is obtained. The microbeam is under axial load and electrical load. It is assumed that midplane is stretched, when the beam is deflected. The equation of motion is derived using the Newton's second law. The viscoelastic model is taken to be the Kelvin-Voigt model. In the first section, the static deflection is obtained using the Galerkin method. Exact linear symmetric mode shape of a straight beam and its deflection function under constant transverse load are used as admissible functions. So, an analytical expression that describes the static deflection at all points is obtained. Comparing the result with previous research show that using deflection function as admissible function decreases the computation errors and previous calculations volume. In the second section, the response of a microbeam resonator system under primary and secondary resonance excitation has been obtained by analytical multiple scale perturbation method combined with the Galerkin method. It is shown, that a small amount of viscoelastic damping has an important effect and causes to decrease the maximum amplitude of response, and to shift the resonance frequency. Also, it shown, that an increase of the DC voltage, ratio of the air gap to the microbeam thickness, tensile axial load, would increase the effect of viscoelastic damping, and an increase of the compressive axial load would decrease the effect of viscoelastic damping.

• Animal cells and systems
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• v.5 no.2
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• pp.139-143
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• 2001

Alzheimer's disease is the most common form of dementia and no cure is known so far. Extensive genetic works and in vitro experiments combined with clinical observations link amyloid $\beta$--protein (A$\beta$-) to the pathogenesis of Alzheimer's disease (AD). It was hypothesized that $A\beta$- becomes toxic when it adopts a fibrillar conformation. Recently, non-fibrillar form of $A\beta$- was observed and the potential role in the pathogenesis of AD became an interesting subject. In this study, the cytotoxicity of non-fibrillar $A\beta$- and fibrillar $A\beta$- was compared on oxidative stress, membrane damage, or nucleosome break down. Non-fibrillar $A\beta$- was not toxic in peripheral nervous system-derived cells but significantly toxic in central nervous system-derived cells while fibrillar $A\beta$- was non-selectively toxic in both cell culture. The neurotoxicity of non-fibrillar $A\beta$- was reproduced in semi-in vivo culture of mouse brain slice. In conclusion, non-fibrillar $A\beta$- could be more relevant to the selective neurodegeneration in Alzheimer's brains than fibrillar $A\beta$- and further research needs to be done for identification of the cause of AD.