• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.6
• /
• pp.1098-1115
• /
• 2010

An inseparable challenge associated with every random access network is the design of an efficient Collision Resolution Algorithm (CRA), since collisions cannot be completely avoided in such network. To maximize the collision resolution efficiency of a popular CRA, namely Binary Exponential Backoff (BEB), we propose a reactive backoff algorithm. The proposed backoff algorithm is reactive in the sense that it updates the contention window based on the previously selected backoff value in the failed contention stage to avoid a typical type of collision, referred as cross-collision. Cross-collision would occur if the contention slot pointed by the currently selected backoff value appeared to be present in the overlapped portion of the adjacent (the previous and the current) windows. The proposed reactive algorithm contributes to significant performance improvements in the network since it offers a supplementary feature of Cross Collision Exclusion (XCE) and also retains the legacy collision mitigation features. We formulate a Markovian model to emulate the characteristics of the proposed algorithm. Based on the solution of the model, we then estimate the throughput and delay performances of WLAN following the signaling mechanisms of the Distributed Coordination Function (DCF) considering IEEE 802.11b system parameters. We validate the accuracy of the analytical performance estimation framework by comparing the analytically obtained results with the results that we obtain from the simulation experiments performed in ns-2. Through the rigorous analysis, based on the validated model, we show that the proposed reactive cross collision exclusionary backoff algorithm significantly enhances the throughput and reduces the average packet delay in the network.

• Journal of the Institute of Convergence Signal Processing
• /
• v.11 no.3
• /
• pp.209-214
• /
• 2010

This paper proposes an algorithm of segmentation and title detection for document image. The automated title detection method that we have developed is composed of two phases, segmentation and title area detection. In the first phase, we extract and segment the document image. To perform this operation, the binary map is segmented by combination of morphological operation and CCA(connected component algorithm). The first phase provides segmented regions that would be detected as title area for the second stage. Candidate title areas are detected using geometric information, then we can extract the title region that is performed by removing non-title regions. After classification step that removes non-text regions, projection is performed to detect a title region. From the fact that usually the largest font is used for the title in the document, horizontal projection is performed within text areas. In this paper, we proposed a method of segmentation and title detection for various forms of document images using geometric features and projection profile analysis. The proposed system is expected to have various applications, such as document title recognition, multimedia data searching, real-time image processing and so on.

• The Bulletin of The Korean Astronomical Society
• /
• v.36 no.2
• /
• pp.147.2-147.2
• /
• 2011

We have initiated a long-term identification campaign of supernova remnant candidates in X-ray regime. In the short-listed unidentified sources from the ROSAT All Sky Survey, we have chosen the brightest candidate, G308.3-1.4, as our pilot target for a dedicated investigation with Chandra X-ray Observatory. Our observation has revealed an incomplete shell-like X-ray structure which well-correlated with the radio feature. Together with the spectral properties of a shocked heated plasma, we confirm that G308.3-1.4 is indeed a supernova remnant. A bright X-ray point source which locates close to the remnant center is also uncovered in this observation. Its spectral behavior conform with those observed in a rare class of neutron stars. The properties of its optical/infrared counterpart suggests the evidence for a late-type companion star. Interestingly, possible excesses in B-band and H-alpha have been found which indicate this can be an accretion-powered system. With the further support from the putative periodicity of ~1.4 hrs, this source can possibly provide the direct evidence of a binary system survived in a supernova explosion for the first time.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.5
• /
• pp.467-472
• /
• 2002

In this paper, we propose a Fuzzy Cellular Neural Network(FCNN) that is based on a-Least Trimmed Square Hausdorff distance(a-LTSHD) which applies Hausdorff distance(HD) to the FCNN structure in order to remove the impulse noise of images effectively and also improve the speed of operation. FCNN incorporates Fuzzy set theory to Cellular Neural Network(CNN) structure and HD is used as a scale which computes the distance between set or two pixels in binary images without confrontation of the feature object. This method has been widely used with the adjustment of the object. For performance evaluation, our proposed method is analyzed in comparison with the conventional FCNN, with the Opening-Closing(OC) method, and the LTSHD based FCNN by using Mean Square Error(MSE) and Signal to Noise Ratio(SNR). As a result, the performance of our proposed network structure is found to be superior to the other algorithms in the removal of impulse noise.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.34 no.11
• /
• pp.1681-1689
• /
• 2010

Several studies on the use of Support Vector Machines (SVMs) for diagnosing rotating machinery have been successfully carried out, but the fault classification depends on the input features as well as a multi-classification scheme, binary optimizer, kernel function, and the parameter to be used in the kernel function. Most of the published papers on multiclass SVM applications report the use of the same features to classify the faults. In this study, simple statistical features are determined on the basis of time domain vibration signals for various fault conditions, and the optimal features for each fault condition are selected. Then, the optimal features are used in the SVM training and in the classification of each fault condition. Simulation results using experimental data show that the results of the proposed stepwise classification approach with a relatively short training time are comparable to those for a single multi-class SVM.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.288-296
• /
• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Journal of Internet Computing and Services
• /
• v.23 no.1
• /
• pp.87-93
• /
• 2022

Since 99% of PCs operating in the defense domain use the Windows operating system, detection and response of Window-based malware is very important to keep the defense cyberspace safe. This paper proposes a model capable of detecting malware in a Windows PE (Portable Executable) format. The detection model was designed with an emphasis on rapid update of the training model to efficiently cope with rapidly increasing malware rather than the detection accuracy. Therefore, in order to improve the training speed, the detection model was designed based on a Bidirectional LSTM (Long Short Term Memory) network that can detect malware with minimal sequence data without complicated pre-processing. The experiment was conducted using the EMBER2018 dataset, As a result of training the model with feature sets consisting of three type of sequence data(Byte-Entropy Histogram, Byte Histogram, and String Distribution), accuracy of 90.79% was achieved. Meanwhile, it was confirmed that the training time was shortened to 1/4 compared to the existing detection model, enabling rapid update of the detection model to respond to new types of malware on the surge.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.2
• /
• pp.181-192
• /
• 2022

The emergence of new malware is incapacitating existing signature-based malware detection techniques., and applying various anti-analysis techniques makes it difficult to analyze. Recent studies related to signature-based malware detection have limitations in that malware creators can easily bypass them. Therefore, in this study, we try to build a machine learning model that can detect and classify the anti-analysis techniques of packers applied to malware, not using the characteristics of the malware itself. In this study, the n-gram opcodes are extracted from the malicious binary to which various anti-analysis techniques of the commercial packers are applied, and the features are extracted by using TF-IDF, and through this, each anti-analysis technique is detected and classified. In this study, real-world malware samples packed using The mida and VMProtect with multiple anti-analysis techniques were trained and tested with 6 machine learning models, and it constructed the optimal model showing 81.25% accuracy for The mida and 95.65% accuracy for VMProtect.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2021.11a
• /
• pp.139-141
• /
• 2021

In the maritime digital forensic part, it is very important and difficult process that analysis of data and information with vessel navigation system's binary log data for situation awareness of maritime accident. In recent years, anaysis of vessel's navigation system's trajectory information is an essential element of maritime accident investigation for vessel digital forensic process. So, we analysis of maritime navigation systems of vessel and feature of device and environments. In the future, we will research on information of ship's trajectory and movement for useful forensic service.

• Economic and Environmental Geology
• /
• v.8 no.1
• /
• pp.1-23
• /
• 1975

The subject of this study deals with phase relations between stannite ($Cu_2FeSnS_4$) and sphalerite (${\beta}-ZnS$)/wurtzite (${\alpha}-ZnS$). The phase relations were systematically investigated from liquidus temperature to $400^{\circ}C$ under controlled conditions. ${\beta}-stannite$ (tetragonal) is stable up to $706{\pm}5^{\circ}C$, where it inverts to a high-temperature polymorph ${\alpha}-stannite$ (cubic) melting congruently at $867{\pm}5^{\circ}C$. Sphalerite (cubic, ${\beta}-ZnS$) inverts at $1013{\pm}3^{\circ}C$ to wurtzite, which is the hexagonal hightemperature polymorph of ZnS. Between ${\alpha}-stannite$ and sphalerite a complete solid solution series exists above approximately $870^{\circ}C$ up to solidus temperature. The melting temperature of ${\alpha}-stannite$ rises towards sphalerite and reaches a maximum at $1074{\pm}3^{\circ}C$, which is the peritectic with the composition of 91 wt. % sphalerite and 9 wt. % ${\alpha}-stannite$. At this temperature, wurtzite takes only 5wt. % ${\alpha}-stannite$ in solid solution which decreases with increasing temperature. The inverson temperature of ${\alpha}/{\beta}-stannite$ is lowered with increasing amounts of sphalerite in solid solution down to $614{\pm}7^{\circ}C$, which is the eutectoid with the composition of 13 wt. % sphalerite and 87 wt. % ${\alpha}-stannite$. Here, ${\beta}-stannite$ contains only 10wt. % sphalerite in solid solution. With decreasing temperature, the ranges of the solid solution on both sides of the system narrow. The phase relations in the above pure system changed due to the FeS impurities in the sphalerite solid solution. The eutectoid increased from $614{\pm}7^{\circ}C$ up to $695{\pm}5^{\circ}C$ (5 wt. % FeS) and $700{\pm}5^{\circ}C$ (10wt. % FeS), while the peritectic decreased from $1074{\pm}3^{\circ}C$ down to $1036{\pm}3^{\circ}C$ (wt. %FeS) and $987{\pm}3^{\circ}C$ (10wt. %FeS). A most notable change is the appearance of non-binary regions. An important feature is the combination of this study system with the experimental results reported by Sprinfer (1972). If a stannite-kesterite solid solution is used in the place of stannite as a bulk composition, the inversion temperature is lowered to less than $400^{\circ}C$ which belongs to temperatures of the hydrothermal region.