• Computers and Concrete
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• v.32 no.1
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• pp.61-74
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• 2023

This article investigates the wave propagation analysis of the imperfect functionally graded (FG) sandwich plates based on a novel simple four-variable integral quasi-3D higher-order shear deformation theory (HSDT). The thickness stretching effect is considered in the transverse displacement component. The presented formulation ensures a parabolic variation of the transverse shear stresses with zero-stresses at the top and the bottom surfaces without requiring any shear correction factors. The studied sandwich plates can be used in several sectors as areas of aircraft, construction, naval/marine, aerospace and wind energy systems, the sandwich structure is composed from three layers (two FG face sheets and isotropic core). The material properties in the FG faces sheet are computed according to a modified power law function with considering the porosity which may appear during the manufacturing process in the form of micro-voids in the layer body. The Hamilton principle is utilized to determine the four governing differential equations for wave propagation in FG plates which is reduced in terms of computation time and cost compared to the other conventional quasi-3D models. An eigenvalue equation is formulated for the analytical solution using a generalized displacements' solution form for wave propagation. The effects of porosity, temperature, moisture concentration, core thickness, and the material exponent on the plates' dispersion relations are examined by considering the thickness stretching influence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.39-47
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• 2024

Time series analysis is widely employed by many organizations to solve business problems, as it extracts various information and insights from chronologically ordered data. Among its applications, measuring time series similarity is a step to identify time series with similar patterns, which is very important in time series analysis applications such as time series search and clustering. In this study, we propose an efficient method for measuring time series similarity that focuses on anomalies rather than the entire series. In this regard, we validate the proposed method by measuring and analyzing the rank correlation between the similarity measure for the set of subsets extracted by anomaly detection and the similarity measure for the whole time series. Experimental results, especially with stock time series data and an anomaly proportion of 10%, demonstrate a Spearman's rank correlation coefficient of up to 0.9. In conclusion, the proposed method can significantly reduce computation cost of measuring time series similarity, while providing reliable time series search and clustering results.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.361-370
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• 2024

In this paper, a method was studied for applying a series resonant type fault current limiter that can be manufactured at low cost to the smart grid distribution system. First, the impact of the harmonic components of the short-circuit fault current injected into the series resonance circuit of the fault current limiter on the peak value of the transient response was analyzed, and a methodology for determining the steady-state response was studied using percent impedance-based fault current computation method. Next, the effectiveness of the method was verified by applying it to a test distribution line. The test distribution system using the designed current limiter was modeled using EMTP_RV, and a three-phase short-circuit fault was simulated. In the fault simulation results, it was confirmed that the steady-state response of the fault current accurately followed the design target value after applying the fault current limiter. In addition, by comparing the fault current waveform before and after applying the fault current limiter, it was confirmed that the fault current was greatly suppressed, confirming the effect of applying the series resonance type current limiter to the distribution system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.289-299
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• 2024

Blockchains are not efficient for real-time processing because the growing number of transactions and users delays many computations and network communications. This study proposes a cloud proxy server, so that legitimate users can use blockchain as well as reduce network latency. To proceed with a blockchain transaction, the blockchain copy server verifies all transaction-related data, but the cloud proxy server verifies legitimate users with a simple zero-knowledge proof algorithm, enabling efficient blockchain real-time processing. The cloud proxy server can support blockchain anonymity, security, and scalability that can verify legitimate users with the proposed zero-knowledge proof by receiving the registered key pair of the blockchain user. In the proposed research analysis, blockchain-based cloud proxy server reduces network latency compared to previous studies and key processing on cloud proxy servers reduces the cost of key computation compared to previous studies.

• Journal of Broadcast Engineering
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• v.20 no.2
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• pp.340-347
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• 2015

HEVC (High Efficiency Video Coding) achieves high coding efficiency by employing a quadtree-based coding unit (CU) block partitioning structure and various prediction units (PUs), and the determination of the best CU partition structure and the best PU mode based on rate-distortion (R-D) cost. However, the computation complexity of encoding also dramatically increases. In this paper, to reduce such encoding computational complexity, we propose three fast PU mode decision methods based on encoding information of upper depth as follows. In the first method, the search of PU mode of the current CU is early terminated based on the sub-CBF (Coded Block Flag) of upper depth. In the second method, the search of intra prediction modes of PU in the current CU is skipped based on the sub-Intra R-D cost of upper depth. In the last method, the search of intra prediction modes of PU in the lower depth's CUs is skipped based on the sub-CBF of the current depth's CU. Experimental results show that the three proposed methods reduce the computational complexity of HM 14.0 to 31.4%, 2.5%, and 23.4% with BD-rate increase of 1.2%, 0.11%, and 0.9%, respectively. The three methods can be applied in a combined way to be applied to both of inter prediction and intra prediction, which results in the complexity reduction of 34.2% with 1.9% BD-rate increase.

• Journal of Korean Society of Forest Science
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• v.105 no.1
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• pp.115-121
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• 2016

This study was conducted to provide the basic policy information for establishing efficient forest-road networks. Synthetic forest-road networks that consist of main and spur roads and forest-road networks with only main road (hereafter called "main-road network") were planned for the five forest-road experimental districts of Korea Forest Service in this study. Road density of the synthetic forest-road networks was calculated and compared with the road density of the main-road networks. The results showed that the optimum road density of the synthetic forest-road networks was 10.1~15.9 m/ha, and the road density of the main-road networks was 8.4~12.4 m/ha. The construction cost of the synthetic forest-road networks was estimated about 1~8% lower than the main-road networks, while the road density was 20~30% greater than the main-road networks. As timber volume and hauling cost increased, the optimum road density of the synthetic forest-road networks increased, within which the road density of highstandard main road rapidly increased. On the other hand, the spur road density increased with slope gradient.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.4
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• pp.437-444
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• 2020

A lunar rover's optical camera is used to provide navigation and terrain information in an exploration zone. However, due to the scant presence of atmosphere, the Moon has homogeneous terrain with dark soil. Also, in extreme environments, the rover has limited data storage with low computation capability. Thus, for successful exploration, it is required to examine feature detection and matching methods which are robust to lunar terrain and environmental characteristics. In this research, SIFT, SURF, BRISK, ORB, and AKAZE are comparatively analyzed with lunar terrain images from a lunar rover. Experimental results show that SIFT and AKAZE are most robust for lunar terrain characteristics. AKAZE detects less quantity of feature points than SIFT, but feature points are detected and matched with high precision and the least computational cost. AKAZE is adequate for fast and accurate navigation information. Although SIFT has the highest computational cost, the largest quantity of feature points are stably detected and matched. The rover periodically sends terrain images to Earth. Thus, SIFT is suitable for global 3D terrain map construction in that a large amount of terrain images can be processed on Earth. Study results are expected to provide a guideline to utilize feature detection and matching methods for future lunar exploration rovers.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.3
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• pp.249-255
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• 2017

The finger vein recognition technology is a method to acquire a finger vein image by illuminating infrared light to the finger and to authenticate a person through processes such as feature extraction and matching. In order to recognize a finger vein, a 2D mask-based two-dimensional convolution method can be used to detect a finger edge but it takes too much computation time when it is applied to a low cost micro-processor or micro-controller. To solve this problem and improve the recognition rate, this study proposed an extraction method for the region of interest based on virtual core points and moving average filtering based on the threshold and absolute value of difference between pixels without using 2D convolution and 2D masks. To evaluate the performance of the proposed method, 600 finger vein images were used to compare the edge extraction speed and accuracy of ROI extraction between the proposed method and existing methods. The comparison result showed that a processing speed of the proposed method was at least twice faster than those of the existing methods and the accuracy of ROI extraction was 6% higher than those of the existing methods. From the results, the proposed method is expected to have high processing speed and high recognition rate when it is applied to inexpensive microprocessors.

• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.71-80
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• 2001

Object-Oriented (OO) methodology to use the concept like encapsulation, inheritance, polymorphism, and message passing demands metrics that are different from structured methodology. There are many studies for OO software metrics such as program complexity or design metrics. But the metrics for the analysis class need to decrease the complexity in the analysis phase so that greatly reduce the effort and the cost of system development. In this paper, we propose new metrics to measure the complexity of analysis classes which draw out in the analysis phase based on Unified Process. By the collaboration complexity, is denoted by CC, we mean the maximum number of the collaborations can be achieved with each of the collaborator and detennine the potential complexity. And the interface complexity, is denoted by IC, shows the difficulty related to understand the interface of collaborators each other. We prove mathematically that the suggested metrics satisfy OO characteristics such as class size and inheritance. And we verify it theoretically for Weyuker' s nine properties. Moreover, we show the computation results for analysis classes of the system which automatically respond to questions of the it's user using the text mining technique. As we compared CC and IC to CBO and WMC, the complexity can be represented by CC and IC more than CBO and WMC. We expect to develop the cost-effective OO software by reviewing the complexity of analysis classes in the first stage of SDLC (Software Development Life Cycle).