• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.49-54
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• 2016

Regarding global and local factors of a set of features, a given single image or multiple images is a common approach in image processing. This paper introduces an application of an adaptive version of non-local filter whose original version searches non-local similarity for removing noise. Since most images involve texture partner in both foreground and background, extraction of signified regions with texture is a challenging task. Aiming to the detection of visual attention regions for images with texture, we present the contrast analysis of image patches located in a whole image but not nearby with assistance of the adaptive filter for estimation of non-local divergence. The method allows extraction of signified regions with texture of images of wild life. Experimental results for a benchmark demonstrate the ability of the proposed method to deal with the mentioned challenge.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.1
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• pp.35-43
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• 2015

Over the past couple of years, tremendous progress has been made in applying deep learning (DL) techniques to computer vision. Especially, deep convolutional neural networks (DCNNs) have achieved state-of-the-art performance on standard recognition datasets and tasks such as ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). Among them, GoogLeNet network which is a radically redesigned DCNN based on the Hebbian principle and scale invariance set the new state of the art for classification and detection in the ILSVRC 2014. Since there exist various deep learning techniques, this review paper is focusing on techniques directly related to DCNNs, especially those needed to understand the architecture and techniques employed in GoogLeNet network.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.761-771
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• 2011

The sensitivity of microwave scattering to the dielectric properties and the geometric structure of soil surfaces makes radar remote sensing a challenge for a wide range of environmental issues directly related to the condition of natural surfaces. Especially, the potential for retrieving soil moisture with a high spatial and/or temporal resolution represents a significant contribution to hydrological and ecological modeling. This paper aims to review the current state of the art in SAR technology and methodological issues towards the discovery of a new potential accurate monitoring of soil moisture changes. In this paper, important parameters or constraints significantly affect the sensitivity of the measurements to soil moisture, such as roughness statistics, spatial resolution, and local topography, are discussed to improve the applicability of SAR remote sensing techniques. This study particularly intends to discuss important notes for developing smart and reliable methods capable of retrieving geophysical information.

• Smart Structures and Systems
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• v.6 no.2
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• pp.135-146
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• 2010

This paper presents the perspective of the Structural Mechanics program of the Air Force Office of Scientific Research (AFOSR) on the damage assessment of structures for the period 2006-2009 when the author was serving as Program Manager at AFOSR. It is found that damage assessment of structures plays a very important role in assuring the safety and operational readiness of US Air Force fleet. The current fleet has many aging aircraft, which poses a considerable challenge for the operators and maintainers. The nondestructive evaluation technology is rather mature and able to detect damage with considerable reliability during the periodic maintenance inspections. The emerging structural health monitoring methodology has great potential, because it will use on-board damage detection sensors and systems, will be able to offer on-demand structural health bulletins. Considerable fundamental and applied research is still needed to enable the development, implementation, and dissemination of structural health monitoring technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6133-6151
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• 2017

The field of periocular biometrics has gained wide attention as an alternative or supplemental means to conventional biometric traits such as the iris or the face. Periocular biometrics provide intermediate resolution between the iris and the face, which enables it to support both. We have developed a periocular recognition system by using uniform Multiscale Local Binary Pattern (uMLBP) and attribute features. The proposed system has been evaluated in terms of major factors that need to be considered on a mobile platform (e.g., distance and facial pose) to assess the feasibility of the use of periocular biometrics on mobile devices. Experimental results showed 98.7% of rank-1 identification accuracy on a subset of the Face Recognition Grand Challenge (FRGC) database, which is the best performance among similar studies.

• Proceeding of EDISON Challenge
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• 2017.03a
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• pp.506-510
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• 2017

Based on the Jansen mechanism theory, a walking robot is developed, which is able to trace a line. In order to find the optimized legs, GL(Ground Length), GAC(Ground Angle Coefficient) and Grashof criteria are utilized in m.sketch program as well as EdisonDesign program. Many types of design are applied to sensors and controls, and the functionality is checked. Finally, a prototype line tracer robot is manufactured using aduino parts and smart boards. The prototype robot is test run to check the validity of the design, and modifications are applied to improve the performance according to each test result until the best design is achieved.

• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.491-495
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• 2015

Currently, selfie stick, a monopod take selfie photographs, got its popularity. Cradle used to connect selfie stick and smartphone usually uses electric force of spring to get smartphone fixed. But, using electric forces makes it hard to attach and detach smartphone from the cradle and gives possibility of smartphone falling down. CoQue suggests new solution of smartphone cradle by using cable tie (patent number US 8407863 B2) instead of electric force. It will give more easy and stable way of using selfie stick.

• Smart Structures and Systems
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• v.5 no.4
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• pp.427-442
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• 2009

In this paper, the acceptability of system identification results for health monitoring of instrumented bridges is addressed. This is conducted by comparing the confidence intervals of identified modal parameters for a bridge in California, namely Truckee I80/Truckee river bridge, with the change of these parameters caused by several damage scenarios. A challenge to the accuracy of the identified modal parameters involves consequences regarding the damage detection and health monitoring, as some of the identified modal information is essentially not useable for acquiring a reliable damage diagnosis of the bridge system. Use of strong motion data has limitations that should not be ignored. The results and conclusions underline these limitations while presenting the opportunities offered by system identification using strong motion data for better understanding and monitoring the health of bridge systems.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1448-1458
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• 2018

Vehicular Ad-Hoc Networks (VANETs) have become one of the active areas of research, standardization, and development because they have tremendous potentials to improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. However, message trustfulness is a challenge because the propagation of false message by malicious vehicles induces unreliable and ineffectiveness of VANETs, Therefore, we need a reliable reputation method to ensure message trustfulness. In this paper, we consider a vulnerability against the Sybil attack of the previous reputation systems based on blockchain and suggest a new reputation system which resists against Sybil attack on the previous system. We propose an initial authentication process as a countermeasure against a Sybil attack and provide a reliable reputation with a cooperative message delivery to cope with message omission. In addition, we use Homomorphic Commitment to protect the privacy breaches in VANETs environment.

• ETRI Journal
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• v.44 no.2
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.