• Smart Structures and Systems
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• v.8 no.1
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• pp.139-155
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• 2011

Polymeric thin-film assemblies whose bulk electrical conductivity and mechanical performance have been enhanced by single-walled carbon nanotubes are proposed for measuring strain and corrosion activity in metallic structural systems. Similar to the dermatological system found in animals, the proposed self-sensing thin-film assembly supports spatial strain and pH sensing via localized changes in electrical conductivity. Specifically, electrical impedance tomography (EIT) is used to create detailed mappings of film conductivity over its complete surface area using electrical measurements taken at the film boundary. While EIT is a powerful means of mapping the sensing skin's spatial response, it requires a data acquisition system capable of taking electrical impedance measurements on a large number of electrodes. A low-cost wireless impedance analyzer is proposed to fully automate EIT data acquisition. The key attribute of the device is a flexible sinusoidal waveform generator capable of generating regulated current signals with frequencies from near-DC to 20 MHz. Furthermore, a multiplexed sensing interface offers 32 addressable channels from which voltage measurements can be made. A wireless interface is included to eliminate the cumbersome wiring often required for data acquisition in a structure. The functionality of the wireless impedance analyzer is illustrated on an experimental setup with the system used for automated acquisition of electrical impedance measurements taken on the boundary of a bio-inspired sensing skin recently proposed for structural health monitoring.

• Smart Structures and Systems
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• v.8 no.1
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• pp.119-137
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• 2011

A bio-inspired two-mode structural health monitoring (SHM) system based on the Na$\ddot{i}$ve Bayes (NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

• Smart Structures and Systems
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• v.5 no.2
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• pp.139-152
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• 2009

Globally, civil infrastructures are deteriorating at an alarming rate caused by overuse, overloading, aging, damage or failure due to natural or man-made hazards. With such a vast network of deteriorating infrastructure, there is a growing interest in continuous monitoring technologies. In order to provide a true distributed sensor and control system for civil structures, we are developing a Structural Nervous System that mimics key attributes of a human nervous system. This nervous system is made up of building blocks that are designed based on mechanoreceptors as a fundamentally new approach for the development of a structural health monitoring and diagnostic system that utilizes the recently developed piezo-fibers capable of sensing and actuation. In particular, our research has been focused on producing a sensory nervous system for civil structures by using piezo-fibers as sensory receptors, nerve fibers, neuronal pools, and spinocervical tract to the nodal and central processing units. This paper presents up to date results of our research, including the design and analysis of the structural nervous system.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.392-400
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• 2008

This paper proposes a novel optimization algorithm inspired by bacteria behavior patterns for foraging. Most bacteria can trace attractant chemical molecules for foraging. This tracing capability of bacteria called chemotaxis might be optimized for foraging because it has been evolved for few millenniums. From this observation, we developed a new optimization algorithm based on the chemotaxis of bacteria in this paper. We first define behavior and decision rules based on the behavior patterns of bacteria and then devise an optimization algorithm with these behavior and decision rules. Generally bacteria have a quorum sensing mechanism that makes it possible to effectively forage, but we leave its implementation as a further work for simplicity. Thereby, we call our algorithm a simple bacteria cooperative optimization (BCO) algorithm. Our simple BCO is tested with four function optimization problems on various' parameters of the algorithm. It was found from experiments that the simple BCO can be a good framework for optimization.

• Smart Structures and Systems
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• v.8 no.1
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• pp.107-117
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• 2011

Biological sensory systems continuously monitor and analyze changes in real-world environments that are relevant to an animal's specific behavioral needs and goals. Understanding the sensory mechanisms and information processing principles that biological systems utilize for efficient sensory data acquisition may provide useful guidance for the design of smart-sensing systems in engineering applications. Weakly electric fish, which use self-generated electrical energy to actively sense their environment, provide an excellent model system for studying biological principles of sensory data acquisition. The electrosensory system enables these fish to hunt and navigate at night without the use of visual cues. To achieve reliable, real-time task performance, the electrosensory system implements a number of smart sensing strategies, including efficient stimulus encoding, multi-scale virtual sensor arrays, task-dependent filtering and online subtraction of sensory expectation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.320-326
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• 2008

In this paper, we propose a new algorithm emulating the DNA characteristics for noise-tolerant pattern matching problem on digital system. The digital pattern matching becomes core technology in various fields, such as, robot vision, remote sensing, character recognition, and medical diagnosis in particular. As the properties of natural DNA strands allow hybridization with a certain portion of incompatible base pairs, DNA-inspired data structure and computation technique can be adopted to bio-signal pattern classification problems which often contain imprecise data patterns. The key feature of noise-tolerance of DNA computing comes from control of reaction temperature. Our hardware system mimics such property to diagnose cardiovascular disease and results superior classification performance over existing supervised learning pattern matching algorithms. The hardware design employing parallel architecture is also very efficient in time and area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1042-1062
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• 2010

Cooperative spectrum sensing (CSS) is an effective technology for alleviating the unreliability of local spectrum sensing due to fading/shadowing effects. Unlike most existing solutions, this paper considers the use of CSS technology in decentralized networks where a fusion center is not available. In such a decentralized network, some attackers may sneak into the ranks of cooperative users. On the basis of recent advances in bio-inspired consensus algorithms, an attack-proof, decentralized CSS scheme is proposed in which all secondary users can maintain cooperative sensing by exchanging information locally instead of requiring centralized control or data fusion. Users no longer need any prior knowledge of the network. To counter three potential categories of spectrum sensing data falsification (SSDF) attacks, some anti-attack strategies are applied to the iterative process of information exchange. This enables most authentic users to exclude potentially malicious users from their neighborhood. As represented by simulation results, the proposed scheme can generally ensure that most authentic users reach a consensus within the given number of iterations, and it also demonstrates much better robustness against different SSDF attacks than several existing schemes.

• Smart Structures and Systems
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• v.20 no.3
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• pp.311-328
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• 2017

The characteristics of boundary layers have significant effects on the aerodynamic forces and vibration of the wind turbine blade. The incorporation of active trailing edge flaps (ATEF) into wind turbine blades has been proven as an effective control approach for alleviation of load and vibration. This paper is aimed at investigating the effects of external trailing edge flaps on the flow pattern and velocity distribution within a boundary layer of a NREL 5MW reference wind turbine, as well as designing a new type of velocity sensors for future validation measurements. An aeroelastic-aerodynamic simulation with FAST-AeroDyn code was conducted on the entire wind turbine structure and the modifications were made on turbine blade sections with ATEF. The results of aeroelastic-aerodynamic simulations were combined with the results of two-dimensional computational fluid dynamic simulations. From these, the velocity profile of the boundary layer as well as the thickness variation with time under the influence of a simplified load case was calculated for four different blade-flap combinations (without flap, with $-5^{\circ}$, $0^{\circ}$, and $+5^{\circ}$ flap). In conjunction with the computational modeling of the characteristics of boundary layers, a bio-inspired hair flow sensor was designed for sensing the boundary flow field surrounding the turbine blades, which ultimately aims to provide real time data to design the control scheme of the flap structure. The sensor element design and performance were analyzed using both theoretical model and finite element method. A prototype sensor element with desired bio-mimicry responses was fabricated and validated, which will be further refined for integration with the turbine blade structures.

• Journal of Sensor Science and Technology
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• v.25 no.3
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• pp.223-228
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• 2016

In this work, we prepared porous WO3 nanofibers (NFs) functionalized by bio-inspired catalytic $Cr_2O_3$ and $Co_3O_4$ nanoparticles as highly sensitive and selective $H_2S$ gas sensing layers. Highly porous 3-dimensional (3D) NFs networks decorated by well-dispersed catalyst NPs exhibited superior $H_2S$ gas response ($R_{air}/R_{gas}$ = 46 at 5 ppm) in high humidity environment (95 %RH). In particular, the sensors showed outstanding $H_2S$ selectivity against other interfering analytes (such as acetone, toluene, CO, $H_2$, ethanol). Exhaled breath sensors using $Cr_2O_3$ and $Co_3O_4$ catalysts-loaded $WO_3$ NFs are highly promising for the accurate detection of halitosis.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.229.1-229.1
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• 2014

Tactile sensors have widely been researched in the areas of electronics, robotic system and medical tools for extending to the form of bio inspired devices that generate feeling of touch mimicking those of humans. Recent efforts in adapting the tactile sensor have included the use of novel materials with both scalability and high sensitivity [1]. Graphene, a 2-D allotrope of carbon, is a prospective candidate for sensor technology, having strong mechanical properties [2] and flexibility, including recovery from mechanical stress. In addition, its truly 2-D nature allows the formation of continuous films that are intrinsically useful for realizing sensing functions. However, very few investigations have been carrier out to investigate sensing characteristics as a device form with the graphene subjected to strain/stress and pressure effects. In this study, we present a sensor of vertical forces based on single-layer graphene, with a working range that corresponds to the pressure of a gentle touch that can be perceived by humans. In spite of the low gauge factor that arises from the intrinsic electromechanical character of single-layer graphene, we achieve a resistance variation of about 30% in response to an applied vertical pressure of 5 kPa by introducing a pressure-amplifying structure in the sensor. In addition, we demonstrate a method to enhance the sensitivity of the sensor by applying resistive single-layer graphene.