• Proceedings of the Korean Ceranic Society Conference
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• 2002.10a
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• pp.12-12
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• 2002

• Polymer Science and Technology
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• v.25 no.5
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• pp.411-416
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• 2014

• Journal of Aerospace System Engineering
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• v.12 no.5
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• pp.16-23
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• 2018

In the paper, a robot control technique by employing Biomimetics is described. Rhythmic movements of the diving beetle's leg were analyzed and the formulated equations on the motion were drawn by applying Fourier least mean square fitting method. Simple control parameters were defined by comparing the observed locomotion through a motion capture system and reproduced motions according to changes in the values in the equation. Subsequently, the correlation of each parameter was discovered and expressed in a parameter space. Apparently, it was confirmed that various bio-mimicking motions can simply be generated for controlling the robot. Additionally, robot designing based on adopting structural advantages which the living organism possess have been briefly introduced. The proposed bio-mimicking motion generating technique was observed to be applicable to robot system developments under various environmental conditions.

• 한국생물공학회:학술대회논문집
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• 2005.04a
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• pp.18-18
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• 2005

Photoinduced electron transport process in nature such as photoelectric conversion and long-range electron transfer in photosynthetic organisms are known to occur not only very efficiently but also unidirectionally through the functional groups of biomolecules. The basic principles in the development of new functional devices can be inspired from the biological systems such as molecular recognition, electron transfer chain, or photosynthetic reaction center. By mimicking the organization of the biological system, molecular electronic devices can be realized $artificially^{1)}$. The nano-fabrication technology of biomolecules was applied to the development of nano-protein chip for simultaneously analyzing many kinds of proteins as a rapid tool for proteome research. The results showed that the self-assembled protein layer had an influence on the sensitivity of the fabricated bio-surface to the target molecules, which would give us a way to fabricate the nano-protein chip with high sensitivity. The results implicate that the biosurface fabrication using self-assembled protein molecules could be successfully applied to the construction of nanoscale bio-photodiode and nano-protein chip based on electrical detection.

• Toxicological Research
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• v.32 no.2
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• pp.95-102
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• 2016

In the recent years, noble nanoparticles have attracted and emerged in the field of biology, medicine and electronics due to their incredible applications. There were several methods have been used for synthesis of nanoparticles such as toxic chemicals and high energy physical procedures. To overcome these, biological method has been used for the synthesis of various metal nanoparticles. Among the nanoparticles, silver nanoparticles (AgNPs) have received much attention in various fields, such as antimicrobial activity, therapeutics, bio-molecular detection, silver nanocoated medical devices and optical receptor. Moreover, the biological approach, in particular the usage of natural organisms has offered a reliable, simple, nontoxic and environmental friendly method. Hence, the current article is focused on the biological synthesis of silver nanoparticles and their application in the biomedical field.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.294-297
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• 2009

다방면적인 과학기술의 발달은 우리에게 대량의 데이터와 또한 새로운 영역으로의 접근 가능성을 열어주었다. 유전자 정보와 같은 대량의 정보를 다루는 시대가 열리면서 바이오 데이터를 분석하여 새로운 연관성과 정보를 찾아내는 바이오인포매틱스가 고부가가치 창출을 위한 학문으로 특히 부각되고 있다. 본 논문에서는 이러한 연구의 일환으로 보다 효율적인 바이오 데이터 분석을 위해 BNP에 내장된 생태계 모방 알고리즘의 특성을 연구하고, 이를 분류화에 접목시킨 방법에 대해 논하고자 한다.

• 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.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.1-9
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• 2017

Innovations from current researches on cloud computing such as applying bio-inspired computing techniques have brought new level solutions in offloading mechanisms. With the growing trend of mobile devices, mobile cloud computing can also benefit from applying bio-inspired techniques. Energy-efficient offloading mechanisms on mobile cloud systems are needed to reduce the total energy consumption but previous works did not consider energy consumption in the decision-making of task distribution. This paper proposes the Particle Swarm Optimization (PSO) as an offloading strategy of cloudlet to data centers where each task is represented as a particle during the process. The collected tasks are classified using K-means clustering on the cloudlet before applying PSO in order to minimize the number of particles and to locate the best data center for a specific task, instead of considering all tasks during the PSO process. Simulation results show that the proposed PSO excels in choosing data centers with respect to energy consumption, while it has accumulated a little more processing time compared to the other approaches.

• 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.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.9-20
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• 2014

Making good predictions about the outcome of one's actions would seem to be essential in the context of social interaction and decision-making. This paper proposes a computational model for learning articulated motion patterns for action recognition, which mimics biological-inspired visual perception processing of human brain. Developed model of cortical architecture for the unsupervised learning of motion sequence, builds upon neurophysiological knowledge about the cortical sites such as IT, MT, STS and specific neuronal representation which contribute to articulated motion perception. Experiments show how the model automatically selects significant motion patterns as well as meaningful static snapshot categories from continuous video input. Such key poses correspond to articulated postures which are utilized in probing the trained network to impose implied motion perception from static views. We also present how sequence selective representations are learned in STS by fusing snapshot and motion input and how learned feedback connections enable making predictions about future input sequence. Network simulations demonstrate the computational capacity of the proposed model for motion recognition.