• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 춘계종합학술대회
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• pp.275-278
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• 2007

이 논문에서는 곤충의 발자국을 인식하기 위한 전 단계로서 인식대상이 되는 의미 있는 단위의 발자국 영역을 자동 추출하고자 한다. 인식의 대상이 되는 곤충들은 크기와 종류에 따라 남겨지는 발자국 패턴의 크기 및 간격이 다르게 나타난다. 따라서 이 논문에서는 패턴의 크기와 간격에 관계 없이 인식의 기본 단위가 되는 영역을 추출할 수 있도록 하기위해 개선된 군집화 알고리듬을 제안하였다. 제안한 알고리듬에서는 군집화를 위한 임계값이 군집화의 대상이 되는 모든 패턴들의 거리를 축적한 그래프의 형태에 따라 자동으로 설정되도록 하였다. 이러한 제안된 기법으로 군집화 된 영역은 의미 있는 주변 정보에 의해 곤충 인식에 기본 단계의 세그먼트를 자동 추출하게 된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.520-541
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• 2023

In CRNs, SS is of utmost significance. Every CR user generates a sensing report during the training phase beneath various circumstances, and depending on a collective process, either communicates or remains silent. In the training stage, the fusion centre combines the local judgments made by CR users by a majority vote, and then returns a final conclusion to every CR user. Enough data regarding the environment, including the activity of PU and every CR's response to that activity, is acquired and sensing classes are created during the training stage. Every CR user compares their most recent sensing report to the previous sensing classes during the classification stage, and distance vectors are generated. The posterior probability of every sensing class is derived on the basis of quantitative data, and the sensing report is then classified as either signifying the presence or absence of PU. The ISVM technique is utilized to compute the quantitative variables necessary to compute the posterior probability. Here, the iterations of SVM are tuned by novel GO-PSA by combining GOA and PSO. Novel GO-PSA is developed since it overcomes the problem of computational complexity, returns minimum error, and also saves time when compared with various state-of-the-art algorithms. The dependability of every CR user is taken into consideration as these local choices are then integrated at the fusion centre utilizing an innovative decision combination technique. Depending on the collective choice, the CR users will then communicate or remain silent.

• 예술인문사회 융합 멀티미디어 논문지
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• 제7권2호
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• pp.331-339
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• 2017

본 논문에서는 임베디드 시스템에 적용 가능한 웹소켓 서버의 스레드 풀 관리 기법을 제안한다. 웹소켓은 동적인 웹을 구성하기 위하여 제안된 기술로서, HTML5와 jQuery를 이용하여 구성한다. 동적인 웹을 구성하기 위하여 Apache, Oracle등에서 다양한 연구가 진행되어 오고 있다. 기존의 웹 서비스 시스템은 대용량, 고성능의 하드웨어 사양을 필요로 하며, 임베디드 시스템에 적용하기엔 부적합하다. HTML5와 jQuery로 구성된 Node.js는 오픈소스로 구성된 대표적인 웹소켓 서버이며, 단일 스레드로 이루어진 자바스크립트 기반의 웹 어플리케이션이다. 이러한 Node.js는 임베디드 시스템에 적용하여 고속의 데이터를 처리하기에는 성능상의 한계가 있다. 본 논문에서는 이러한 문제점을 해결하기 위하여 스레드 풀로 운영되는 웹소켓 서버를 구성한다. 제안하는 웹소켓 서버의 스레드 풀은 비트 레지스터를 기반으로 관리되며, 임베디드 시스템에 적합하도록 구성한다. 제안하는 알고리즘의 성능을 평가하기 위하여 네트워크 성능 테스트 도구인 JMeter를 이용한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.742-762
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• 2015

In this study, we propose a distance metric learning approach called discriminant metric learning (DML) for face verification, which addresses a binary-class problem for classifying whether or not two input images are of the same subject. The critical issue for solving this problem is determining the method to be used for measuring the distance between two images. Among various methods, the large margin nearest neighbor (LMNN) method is a state-of-the-art algorithm. However, to compensate the LMNN's entangled data distribution due to high levels of appearance variations in unconstrained environments, DML's goal is to penalize violations of the negative pair distance relationship, i.e., the images with different labels, while being integrated with LMNN to model the distance relation between positive pairs, i.e., the images with the same label. The likelihoods of the input images, estimated using DML and LMNN metrics, are then weighted and combined for further analysis. Additionally, rather than using the k-nearest neighbor (k-NN) classification mechanism, we propose a verification mechanism that measures the correlation of the class label distribution of neighbors to reduce the false negative rate of positive pairs. From the experimental results, we see that DML can modify the relation of negative pairs in the original LMNN space and compensate for LMNN's performance on faces with large variances, such as pose and expression.

• 품질경영학회지
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• 제21권2호
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• pp.1-16
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• 1993

Early generations of products had little to no inherent capability to test themselves. The technologies involved often required only visual inspection and limited probing to troubleshoot the system once it was turned over to maintenance personnel. However, as the complexity of military and commercial systems grew, symptoms of failure became less noticeable to the operator. Therefore, the procedure to access, inspect, repair and replace a component became complicated, the requirements for personnel skill and testing equipment increased. and it took too long of a time to maintain a system. Meanwhile, the need for availability became more mission-critical and maintenance become very expensive. The obvious solution was to design in-system circuits or devices to self-test the primary system, the Built-In-Test(BIT) was born. This approach has continued right on up through present systems and is an integral part of systems now being designed. The object of this paper is to present a state-of-the-art research for filtering out the BIT diagnosis mistakes using Bayesian analysis and develop the algorithm for Redundant systems with BIT to improve BIT diagnosis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1359-1375
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• 2015

Energy efficiency is critical for Wireless Sensor Networks (WSNs) since sensor nodes usually have very limited energy supply from battery. Sleep scheduling and nodes cooperation are two of the most efficient methods to achieve energy conservation in WSNs. In this paper, we propose an adaptive energy optimization approach for target tracking applications, called Energy-Efficient Node Coordination (EENC), which is based on the grid structure. EENC provides an unambiguous calculation and analysis for optimal the nodes cooperation theoretically. In EENC, the sleep schedule of sensor nodes is locally synchronized and globally unsynchronized. Locally in each grid, the sleep schedule of all nodes is synchronized by the grid head, while globally the sleep schedule of each grid is independent and is determined by the proposed scheme. For dynamic sleep scheduling in tracking state we propose a multi-level coordination algorithm to find an optimal nodes cooperation of the network to maximize the energy conservation while preserving the tracking performance. Experimental results show that EENC can achieve energy saving of at least 38.2% compared to state-of-the-art approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.302-320
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• 2017

In this paper, an innovative robust feature detection and matching strategy for visual odometry based on stereo image sequence is proposed. First, a sparse multiscale 2D local invariant feature detection and description algorithm AKAZE is adopted to extract the interest points. A robust feature matching strategy is introduced to match AKAZE descriptors. In order to remove the outliers which are mismatched features or on dynamic objects, an improved random sample consensus outlier rejection scheme is presented. Thus the proposed method can be applied to dynamic environment. Then, geometric constraints are incorporated into the motion estimation without time-consuming 3-dimensional scene reconstruction. Last, an iterated sigma point Kalman Filter is adopted to refine the motion results. The presented ego-motion scheme is applied to benchmark datasets and compared with state-of-the-art approaches with data captured on campus in a considerably cluttered environment, where the superiorities are proved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3657-3682
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• 2018

This paper presents a Bluetooth and pedestrian dead reckoning (PDR)-based indoor fusion localization approach (BtPDR) using smartphones. A Bluetooth and PDR-based indoor fusion localization approach can localize the initial position of a smartphone with the received signal strength (RSS) of Bluetooth. While a smartphone is moving, BtPDR can track its position by fusing the localization results of PDR and Bluetooth RSS. In addition, BtPDR can adaptively modify the parameters of PDR. The contributions of BtPDR include: a Bluetooth RSS-based Probabilistic Voting (BRPV) localization mechanism, a probabilistic voting-based Bluetooth RSS and PDR fusion method, and a heuristic search approach for reducing the complexity of BRPV. The experiment results in a real scene show that the average positioning error is < 2m, which is considered adequate for indoor location-based service applications. Moreover, compared to the traditional PDR method, BtPDR improves the location accuracy by 42.6%, on average. Compared to state-of-the-art Wireless Local Area Network (WLAN) fingerprint + PDR-based fusion indoor localization approaches, BtPDR has better positioning accuracy and does not need the same offline workload as a fingerprint algorithm.