• Advances in robotics research
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• 제2권3호
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• pp.219-236
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• 2018

This research focuses on controlling robots and their formations using rough mereology as a means for spatial reasoning. The authors present the state of the art theory behind path planning, robot cooperation domains and ways of creating robot formations. Furthermore, the theory behind Rough Mereology as a way of implementing mereological potential field based path creation and navigation for single and multiple robots is described. An implementation of the algorithm is shown in simulation using RoboSim simulator. Five formations are tested (Line, Rhomboid, Snake, Circle, Cross) along with three decision systems (First In, Leader First, Horde Mode) as compared to other methods.

• Journal of Information Processing Systems
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• 제13권2호
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• pp.385-399
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• 2017

Nowadays, with the development of signal processing technique, the protection to the integrity and authenticity of images has become a topic of great concern. A blind image authentication technology with high tamper detection accuracy for different common attacks is urgently needed. In this paper, an improved fragile watermarking method based on local binary pattern (LBP) is presented for blind tamper location in images. In this method, a binary watermark is generated by LBP operator which is often utilized in face identification and texture analysis. In order to guarantee the safety of the proposed algorithm, Arnold transform and logistic map are used to scramble the authentication watermark. Then, the least significant bits (LSBs) of original pixels are substituted by the encrypted watermark. Since the authentication data is constructed from the image itself, no original image is needed in tamper detection. The LBP map of watermarked image is compared to the extracted authentication data to determine whether it is tampered or not. In comparison with other state-of-the-art schemes, various experiments prove that the proposed algorithm achieves better performance in forgery detection and location for baleful attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.272-287
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• 2016

Recently, various and effective contrast based salient object detection models to focus on a single target have been proposed. However, there is a lack of research on detection of multiple objects, and also it is a more challenging task than single target process. In the multiple target problem, we are confronted by new difficulties caused by distinct difference between properties of objects. The characteristic of existing models depending on the global maximum distribution of data point would become a drawback for detection of multiple objects. In this paper, by analyzing limitations of the existing methods, we have devised three main processes to detect multiple salient objects. In the first stage, regional features are extracted from over-segmented regions. In the second stage, the regional features are categorized into homogeneous cluster using the mean-shift algorithm with the kernel function having various sizes. In the final stage, we compute saliency scores of the categorized regions using only spatial features without the contrast features, and then all scores are integrated for the final salient regions. In the experimental results, the scheme achieved superior detection accuracy for the SED2 and MSRA-ASD benchmarks with both a higher precision and better recall than state-of-the-art approaches. Especially, given multiple objects having different properties, our model significantly outperforms all existing models.

• Journal of Computational Design and Engineering
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• 제2권1호
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• pp.26-37
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• 2015

A lot of researchers have been investigating interactive portable projection systems such as a mini-projector. In addition, in exhibition halls and museums, there is a trend toward using interactive projection systems to make viewing more exciting and impressive. They can also be applied in the field of art, for example, in creating shadow plays. The key idea of the interactive portable projection systems is to recognize the user's gesture in real-time. In this paper, a vision-based shadow gesture recognition method is proposed for interactive projection systems. The gesture recognition method is based on the screen image obtained by a single web camera. The method separates only the shadow area by combining the binary image with an input image using a learning algorithm that isolates the background from the input image. The region of interest is recognized with labeling the shadow of separated regions, and then hand shadows are isolated using the defect, convex hull, and moment of each region. To distinguish hand gestures, Hu's invariant moment method is used. An optical flow algorithm is used for tracking the fingertip. Using this method, a few interactive applications are developed, which are presented in this paper.

• 로봇학회논문지
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• 제11권4호
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• pp.300-309
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• 2016

This paper proposes a method for motion estimation of consecutive cameras using 3-D straight lines. The motion estimation algorithm uses two non-parallel 3-D line correspondences to quickly establish an initial guess for the relative pose of adjacent frames, which requires less correspondences than that of current approaches requiring three correspondences when using 3-D points or 3-D planes. The estimated motion is further refined by a nonlinear optimization technique with inlier correspondences for higher accuracy. Since there is no dominant line representation in 3-D space, we simulate two line representations, which can be thought as mainly adopted methods in the field, and verify one as the best choice from the simulation results. We also propose a simple but effective 3-D line fitting algorithm considering the fact that the variance arises in the projective directions thus can be reduced to 2-D fitting problem. We provide experimental results of the proposed motion estimation system comparing with state-of-the-art algorithms using an open benchmark dataset.

• Steel and Composite Structures
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• 제30권3호
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• pp.293-304
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• 2019

The optimum cost of a reinforced concrete plane and space frames have been found by using the Genetic Algorithm (GA) method. The design procedure is subjected to many constraints controlling the designed sections (beams and columns) based on the standard specifications of the American Concrete Institute ACI Code 2011. The design variables have contained the dimensions of designed sections, reinforced steel and topology through the section. It is obtained from a predetermined database containing all the single reinforced design sections for beam and columns subjected to axial load, uniaxial or biaxial moments. The designed optimum beam sections by using GAs have been unified through MATLAB to satisfy axial, flexural, shear and torsion requirements based on the designed code. The frames' functional cost has contained the cost of concrete and reinforcement of steel in addition to the cost of the frames' formwork. The results have found that limiting the dimensions of the frame's beams with the frame's columns have increased the optimum cost of the structure by 2%, declining the re-analysis of the optimum designed structures through GA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2140-2156
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• 2023

The Wireless Sensor Network (WSN), is constructed out of teeny-tiny sensor nodes that are very low-cost, have a low impact on the environment in terms of the amount of power they consume, and are able to successfully transmit data to the base station. The primary challenges that are presented by WSN are those that are posed by the distance between nodes, the amount of energy that is consumed, and the delay in time. The sensor node's source of power supply is a battery, and this particular battery is not capable of being recharged. In this scenario, the amount of energy that is consumed rises in direct proportion to the distance that separates the nodes. Here, we present a Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) guided routing strategy for preserving WSNs' low power footprint. An efficient fitness function based on firefly optimization is used to select the Cluster Head (CH) in this procedure. It aids in minimising power consumption and the occurrence of dead sensor nodes. After a cluster head (CH) has been chosen, the Glow-Worm Swarm Optimization (GSO) algorithm is used to figure out the best path for sending data to the sink node. Power consumption, throughput, packet delivery ratio, and network lifetime are just some of the metrics measured and compared between the proposed method and methods that are conceptually similar to those already in use. Simulation results showed that the proposed method significantly reduced energy consumption compared to the state-of-the-art methods, while simultaneously increasing the number of functioning sensor nodes by 2.4%. Proposed method produces superior outcomes compared to alternative optimization-based methods.

• 방송공학회논문지
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• 제21권2호
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• pp.200-209
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• 2016

본 논문에서는 촬영 시 발생하는 블러 현상을 효율적으로 제거하기 위해 IMU 센서와 노출시간이 길고, 짧은 두 영상을 이용한 비균일 디블러 알고리즘을 제안한다. 종래 센서 정보를 이용한 블러 커널 추정 기법들은 센서 정보의 한계로 인해 성능이 만족스럽지 못하다. 그 한계를 극복하기 위해 우리는 노출시간이 서로 다른 여러 영상들을 이용한 커널 개선 과정을 제안하여, 추정된 커널의 정확도를 향상시킨다. 또한 종래 비균일 디블러 기법들이 블러 커널이 커질수록 심한 화질 열화를 겪는 문제점을 해결하기 위해 본 논문은 호모그래피 기반 잔여 디콘볼루션을 제안하여 디콘볼루션 과정에서 발생하는 링형 현상과 같은 화질 열화를 최소화한다. 실험 결과를 통해 제안 알고리즘의 화질이 기존 기법에 비해 주관적/객관적으로 현저하게 우수함을 볼 수 있다.

• 한국컴퓨터정보학회논문지
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• 제19권2호
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• pp.21-30
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• 2014

최근 산업 현장에서 유도 전동기의 사용이 증대되고 있으며, 유도 전동기는 산업 현장에서 중요한 역할을 하고 있다. 따라서 유도 전동기의 결함으로 인한 피해를 최소화하기 위해 유도 전동기의 결함 검출 및 분류 시스템의 개발이 중요한 문제로 대두되고 있다. 따라서 본 논문에서는 유도전동기의 결함을 조기에 식별하기 위해 선형예측 코딩(LPC)기법과 Expectation Maximization(EM) 알고리즘을 이용하여 각각의 유도 전동기 고장의 스펙트럼 포락처리 모델을 추정한다. 앞서 두 기법을 사용하여 추정된 고장 유형 모델과 마할라노비스 거리(MD) 기법을 사용하여 유도전동기의 결합을 분류한다. 또한 제안된 알고리즘 성능을 평가하기 위해 기존에 제안된 진동 신호의 특징을 이용한 유도 전동기 결함 분류 알고리즘과 분류 정확도 측면에서 성능을 검증하였다. 실험 결과, 제안하는 알고리즘은 잡음이 없는 환경 및 잡음이 섞인 환경에서도 높은 분류 성능을 보였다.