• Journal of Communications and Networks
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• v.15 no.2
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• pp.164-172
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• 2013

We consider the problem of secure and robust clustering for quantized target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose a new method for jointly activating the best group of candidate sensors that participate in data aggregation, detecting the malicious sensors and estimating the target position. Firstly, we select the appropriate group in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Secondly, we detect the malicious sensor nodes based on the information relevance of their measurements. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The selection of the candidate sensors group is based on multi-criteria function, which is computed by using the predicted target position provided by the QVF algorithm, while the malicious sensor nodes detection is based on Kullback-Leibler distance between the current target position distribution and the predicted sensor observation. The performance of the proposed method is validated by simulation results in target tracking for WSN.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.594-597
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• 2010

This paper proposes a fire detection algorithm using low-cost camera to detect visual features of fire. In the previous work sensor cameras were used, but here we use very simple cameras. This method uses YCbCr and YIQ color model to detect candidate regions of fire. The candidate areas are extracted from the boundaries of the fire. noise removal elimination is performed. Regardless of environmental changes around the fire area, the results of the proposed algorithm are very satisfactory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.1951-1972
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• 2018

Wi-Fi Access Point (AP) optimization approaches are used in indoor positioning systems for signal coverage enhancement, as well as positioning precision improvement. Although the huge power consumption of the AP optimization forms a serious problem due to the signal coverage requirement for large-scale indoor environment, the conventional approaches treat the problem of power consumption independent from the design of indoor positioning systems. This paper proposes a new Fast Water-filling algorithm Group Power Constraint (FWA-GPC) based Wi-Fi AP optimization approach for indoor positioning in which the power consumed by the AP optimization is significantly considered. This paper has three contributions. First, it is not restricted to conventional concept of one AP for one candidate AP location, but considered spare APs once the active APs break off. Second, it utilizes the concept of water-filling model from adaptive channel power allocation to calculate the number of APs for each candidate AP location by maximizing the location fingerprint discrimination. Third, it uses a fast version, namely Fast Water-filling algorithm, to search for the optimal solution efficiently. The experimental results conducted in two typical indoor Wi-Fi environments prove that the proposed FWA-GPC performs better than the conventional AP optimization approaches.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.42.3-42.3
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• 2020

We investigate the observational properties of Wolf-Rayet stars, suggest the constraint of their mass-loss rate and apply our results to the observed progenitor candidates of Type Ib/Ic supernovae (iPTF13bvn and SN 2017ein). For this purpose, we adopt the WR star models with various mass-loss rates and wind terminal velocities. We obtain the high resolution spectra of those models at the pre-supernova phase using the radiative transfer code CMFGEN. We verify the optically faint property of SN Ic progenitors and show that the optical faintness is mainly originated by the high effective temperature at the photosphere. We also show that a simple analytic model for WR winds using a constant opacity can roughly predict the photospheric parameters. We show that the change of the mass-loss rate and the terminal wind velocity critically affects the optical luminosity. We find the optical luminosities of SN Ic progenitor models with our fiducial mass-loss rate prescription are fainter than the detection limits. We also suggest the mass-loss rate of WR stars may not exceed 2 times of our fiducial value by comparing our predictions with the detection limit of SN Ib/Ic progenitors. The directly observed progenitor candidate of iPTF13bvn can be explained by our SN Ib progenitor models. We find that the SN 2017ein progenitor candidate is too bright and too blue to be a SN Ic progenitor.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1186-1191
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• 2003

The 40% of wall criterion, which is generally used for the plugging of steam generator tubes, may be applied only to a single crack. In the previous study, a total of 9 failure models were introduced to estimate the local failure of the ligament between cracks and the optimum coalescence model of multiple collinear cracks was determined among these models. It is, however, known that parallel axial cracks are more frequently detected during an in-service inspection than collinear axial cracks. The objective of this study is to determine the plastic collapse model which can be applied to the steam generator tube containing two parallel axial through-wall cracks. Nine previously proposed local failure models were selected as the candidates. Subsequently interaction effects between two adjacent cracks were evaluated to screen them. Plastic collapse tests for the plate with two parallel through-wall cracks and finite element analyses were performed for the determination of the optimum plastic collapse model. By comparing the test results with the prediction results obtained from the candidate models, a plastic zone contact model was selected as an optimum model.

• Nuclear Engineering and Technology
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• v.36 no.4
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• pp.316-326
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• 2004

The $40\%$ of wall criterion, which is generally used for the plugging of steam generator tubes, is applied only to a single crack. In a previous study, a total number of 9 failure models were proposed to estimate the local failure of the ligament between cracks, and the optimum coalescence model of multiple collinear cracks was determined among these models. It is, however known that parallel axial cracks are more frequently detected than collinear axial cracks during an in-service inspection. The objective of this study is to determine the plastic collapse model that can be applied to steam generator tubes containing two parallel axial through-wall cracks. Three previously proposed local failure models were selected as the candidates. Subsequently, the interaction effects between two adjacent cracks were evaluated to screen them. Plastic collapse tests for the plate with two parallel through-wall cracks and finite element analyses were performed to determine the optimum plastic collapse model. By comparing the test results with the prediction results obtained from the candidate models, a COD base model was selected as an optimum model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1062-1070
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• 2018

This paper presents a simple identification method for discrete-time low-order model of unknown delay process from pulse response. The key idea is to find the parameters of the model such that the first N moments of the unknown process and the model are equal. We first show that the k-th moment of a process can be determined by the moments of the input and output. The parameters and delay are estimated separately. It is shown that for a given delay, the parameters of the low-order model can be determined by solving linear equations in a matrix form. Delay of the model is estimated such that the integral of the absolute errors (IAE) of the candidate models with possible delays minimizes. The illustrative example shows that the proposed method can directly identify low-order models without order reduction process from a single pulse response.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.115-121
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• 2012

This paper, being the second in a two-part series, presents the robust performance of the proposed design method which can enhance a reliability-based design optimization(RBDO) under the uncertainties of probabilistic models. The robust performances of the solutions obtained by the proposed method, described in the Part 1, are investigated through the parametric studies. A 10-bar truss example is considered, and the uncertain parameters include the number of data observed, and the variations of applied loadings and allowable stresses. The numerical results show that the proposed method can produce a consistent result despite of the large variations in the parameters. Especially, even with the relatively small data set, the analysis results show that the exact probabilistic model can be successfully predicted with optimized design sections. This consistency of estimating appropriate probability model is also observed in the case of the variations of other parameters, which verifies the robustness of the proposed method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.217-228
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• 2020

The ROK Army must detect the enemy's location and the type of artillery weapon to respond effectively at wartime. This paper proposes a radar positioning model by applying a scenario-based robust optimization method i.e., binary integer programming. The model consists of the different types of radar, its available quantity and specification. Input data is a combination of target, weapon types and enemy position in enemy's attack scenarios. In this scenario, as the components increase by one unit, the total number increases exponentially, making it difficult to use all scenarios. Therefore, we use partial scenarios to see if they produce results similar to those of the total scenario, and then apply them to case studies. The goal of this model is to deploy an artillery locating radar that maximizes the detection probability at a given candidate site, based on the probability of all possible attack scenarios at an expected enemy artillery position. The results of various experiments including real case study show the appropriateness and practicality of our proposed model. In addition, the validity of the model is reviewed by comparing the case study results with the detection rate of the currently available radar deployment positions of Corps. We are looking forward to enhance Korea Artillery force combat capability through our research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.