• Journal of the Korean Geotechnical Society
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• v.24 no.8
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• pp.5-12
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• 2008

Biot proposed the frequency dependent formulation for the propagation of elastic waves in saturated media based on the coupled theory mixtures. Based on Biot theory, a special frequency called 'the characteristic frequency' contains unique information of the permeability of soils. The characteristic frequency is measured from I/Q (inverse quality factor) versus frequency curve by an acoustic sweep test, and the permeability of soils is computed from Biot equation. In this paper, laboratory tests are performed at The University of Mississippi using a large test box. The measured characteristic frequency is consistently obtained at 3500 Hz for mortar sands. The computed permeability of mortar sands based on Biot equation turned out 2.01 $10^{-4}m/sec$, while the permeability from the laboratory constant head test turned out 1.49 $10^{-4}m/sec$. This paper addresses the theoretical background and experimental procedure of this technique.

• Composites Research
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• v.21 no.5
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• pp.9-14
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• 2008

Carbon nanofibers (CNFs) were used as dielectric lossy materials and NiFe particles were used as magnetic lossy materials. Total twelve specimens for the three types such as dielectric, magnetic and mixed radar absorbing materials (RAMs) were fabricated. Their complex permittivities and permeabilities in the range of $2{\sim}18$ GHz were measured using the transmission line technique. The parametric studios for reflection loss characteristics of each specimen to design the single-layered RAMs were performed. The mixed RAMs generally showed the improved absorbing characteristics with thinner matching thickness. One of the mixed RAMs, MD3with the thickness of 2.00 mm had the 10 dB absorbing bandwidth of 4.0 GHz in the X-band ($8.2{\sim}12.4$ GHz). It also showed very broad 10 dB absorbing bandwidth as wide as 6.0 GHz in the Ku-band ($12.0{\sim}18.0$ GHz) with the thickness tuning to 1.49 mm. The experimental results for selected several specimens were in very good agreements with simulation ones in terms of the overall reflection loss characteristics and 10 dB absorbing bandwidth.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.15-22
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• 2022

In order to improve the accuracy of the deep learning object detection technique, the effect of magnification rate conditions and seasonal factors on detection accuracy in aerial photographs and drone images was analyzed through experiments. Among the deep learning object detection techniques, Mask R-CNN, which shows fast learning speed and high accuracy, was used to detect the vehicle to be detected in pixel units. Through Seoul's aerial photo service, learning images were captured at different screen magnifications, and the accuracy was analyzed by learning each. According to the experimental results, the higher the magnification level, the higher the mAP average to 60%, 67%, and 75%. When the magnification rates of train and test data of the data set were alternately arranged, low magnification data was arranged as train data, and high magnification data was arranged as test data, showing a difference of more than 20% compared to the opposite case. And in the case of drone images with a seasonal difference with a time difference of 4 months, the results of learning the image data at the same period showed high accuracy with an average of 93%, confirming that seasonal differences also affect learning.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.29-39
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• 2017

Various electronic warfare situations drive the need to develop an integrated electronic warfare simulator that can perform electronic warfare modeling and simulation on radar threats. In this paper, we analyze the components of a simulation system to reversely model the radar threats that emit electromagnetic signals based on the parameters of the electronic information, and propose a method to gradually maintain the reverse extrapolation model of RF threats. In the experiment, we will evaluate the effectiveness of the incremental model update and also assess the integration method of reverse extrapolation models. The individual model of RF threats are constructed by using decision tree, naive Bayesian classifier, artificial neural network, and clustering algorithms through Euclidean distance and cosine similarity measurement, respectively. Experimental results show that the accuracy of reverse extrapolation models improves, while the size of the threat sample increases. In addition, we use voting, weighted voting, and the Dempster-Shafer algorithm to integrate the results of the five different models of RF threats. As a result, the final decision of reverse extrapolation through the Dempster-Shafer algorithm shows the best performance in its accuracy.

• Korean Journal of Environmental Agriculture
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• v.42 no.2
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• pp.112-120
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• 2023

Methane (CH₄) and nitrous oxide (N₂O) are significant contributors to greenhouse gas (GHG) emissions from rice fields. Mid-summer drainage is a commonly practiced water management technique that reduces CH₄ emissions from rice fields. Slow-release fertilizers gradually release nutrients over an extended period and have been shown to reduce N₂O emissions. However, the combined effect of slow-release fertilizer and water management on GHG emissions remains unclear. This study compared GHG emissions from a rice paddy subjected to mid-summer drainage for 10 days (control) with that of a rice paddy subjected to prolonged mid-summer drainage for 20 days combined with slow-release fertilizer (W+S). Gas sampling was conducted weekly using a closed chamber method. During the rice cultivation period, cumulative CH₄ and N₂O emissions were reduced by 12.3% and 16.2%, respectively, in the W+S treatment compared to the control. Moreover, the W+S treatment exhibited a 1.9% increase in grain yield compared to the control. Under experimental conditions, slow-release fertilizers, in combination with prolonged mid-summer drainage, proved to be the optimal approach for achieving high crop yield while reducing GHG emissions. This represents an effective strategy to mitigate GHG emissions from rice paddy fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• Convergence Security Journal
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• v.22 no.3
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• pp.25-32
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• 2022

Recently, the image processing industry has been activated as deep learning-based technology is introduced in the image recognition and detection field. With the development of deep learning technology, learning model vulnerabilities for adversarial attacks continue to be reported. However, studies on countermeasures against poisoning attacks that inject malicious data during learning are insufficient. The conventional countermeasure against poisoning attacks has a limitation in that it is necessary to perform a separate detection and removal operation by examining the training data each time. Therefore, in this paper, we propose a technique for reducing the attack success rate by applying modifications to the training data and inference data without a separate detection and removal process for the poison data. The One-shot kill poison attack, a clean label poison attack proposed in previous studies, was used as an attack model. The attack performance was confirmed by dividing it into a general attacker and an intelligent attacker according to the attacker's attack strategy. According to the experimental results, when the proposed defense mechanism is applied, the attack success rate can be reduced by up to 65% compared to the conventional method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2C
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• pp.133-142
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• 2006

Many methods have been tried to more correctly measure rock joint roughness. However, true roughness may be distorted and underestimated due to the sampling interval and measurement method. Thus, currently used measurement methods produce a dead zone and distort roughness profiles. The purpose of this study is to suggest new roughness measurement method by a camera-type 3D scanner as an alternative of currently used methods. First, the underestimation of artificial roughness is analyzed by using the current measurement method such as laser profilometry. Second, we replicate eight specimens from two rock joint surfaces, and digitize by a 3D scanner. Then, the roughness coefficient values obtained from eight numbers of 3D surface data and from three hundred twenty numbers of 2D profiles data are analyzed by using current and new measurement methods. The artificial simulation confirms that the sampling interval is one of main factors for the distortion of roughness and shows that inclination of waviness may not be considered any current methods. The experimental results show that the camera-type 3D scanner produces 10% larger roughness values than current methods. As the proposed new method is a fast, high precision and more accurate method for the roughness measurement, it should be a promising technique in this area.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5A
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• pp.887-899
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• 2006

The aim of this study is to investigate a correlation between fundamental data on aerodynamic characteristics of bridge girder cross-sections, such as aerodynamic force coefficients and flutter derivatives, and their aerodynamic behaviour. The section model tests were carried out in three stages. In the first stage, seven deck configurations were studied, namely; Six 2-edge girders and one box girder. In this stage, changes in aerodynamic force coefficients due to geometrical shape of girders, incidence angle of flow, wind directions and turbulence intensities were studied by static section model tests. In the second stage, the dynamic section model tests were carried out to investigate the relativity of static coefficients to dynamic responses. And finally, the two-dimensional (lift-torsion) aerodynamic derivatives of three bridge deck configurations were investigated by dynamic section model tests. The aerodynamic derivatives can be best described as a representation of the aerodynamic damping and the aerodynamic stiffness provided by the wind for a given deck geometry. The method employed here to extract these unsteady aerodynamic properties is known as the initial displacement technique. It involves the measurement of the decay in amplitude with time of an initial displacement of the deck in heave and torsion, for various wind speeds, in smooth flow. It is suggested that the proposed aerodynamic force coefficients and flutter derivatives of bridge girder sections will be potentially useful for the aeroelastic analysis and buffeting analysis.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.787-799
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• 2023

As IoT devices are widely used at home, IoT automation system that is integrate IoT devices for users' demand are gaining populrity. There is automation rule in IoT automation system that is collecting event and command action. But attacker delay the packet and make time that real state is inconsistent with state recongnized by the system. During the time, the system does not work correctly by predefined automation rule. There is proposed some detection method for delay attack, they have limitations for application to IoT systems that are sensitive to traffic volume and battery consumption. This paper proposes a practical packet delay attack detection technique that can be applied to IoT systems. The proposal scheme in this paper can recognize that, for example, when a sensor transmits an message, an broadcast packet notifying the transmission of a message is sent to the Server recognized that event has occurred. For evaluation purposes, an IoT system implemented using Raspberry Pi was configured, and it was demonstrated that the system can detect packet delay attacks within an average of 2.2 sec. The experimental results showed a power consumption Overhead of an average of 2.5 mA per second and a traffic Overhead of 15%. We demonstrate that our method can detect delay attack efficiently compared to preciously proposed method.