• Journal of Drive and Control
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• v.11 no.3
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• pp.31-40
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• 2014

This paper proposes a multi absorbing wave energy converter design, in which a hydrostatic transmission is used to transfer wave energy to electric energy. The most important feature of this system is its combination of the pressure coupling principle with the use of a hydraulic accumulator to eliminate the effects of wave power fluctuation; this maintains a constant speed of the hydraulic motor. Tilt motion of a floating buoy was employed as the power take-off mechanism. Furthermore, a PID controller was designed to carry out the speed control of the hydraulic motor. The design offers some advantages such as extending the life of the hydraulic components, increasing the amount of energy harvested, and stabilizing the output speed.

• International Journal of Oral Biology
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• v.44 no.3
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• pp.77-80
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• 2019

Clear cell odontogenic carcinoma (CCOC), a very rare neoplasm located mostly in the mandible, has been regarded as a benign tumor. However, due to the accumulation of case reports, CCOC has been reclassified as a malignant entity by the World Health Organization. Patients with CCOC present with regional swelling and periodontal indications with variable pain, often remaining misdiagnosed for a long period. CCOC has slow growth but aggressive behavior, requiring radical resection. Histologic analysis revealed the monophasic, biphasic, and ameloblastic types of CCOC with clear cells and a mixed combination of polygonal and palisading cells. At the molecular level, CCOC shows the expression of cytokeratin and epithelial membrane antigen, along with markers that assign CCOC to the sarcoma family. At the genetic level, Ewing sarcoma breakpoint region 1-activating transcription factor 1 fusion is regarded as the key feature for identification. Nevertheless, the scarcity of cases and dependence on histological data delay the development of an efficient therapy. Regarding the high recurrence rate and the potential of distant metastasis, further characterization of CCOC is necessary for an early and accurate diagnosis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5765-5781
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• 2018

Extreme learning machine (ELM) is emerging as a powerful machine learning method in a variety of application scenarios due to its promising advantages of high accuracy, fast learning speed and easy of implementation. However, how to select the optimal hidden layer of ELM is still an open question in the ELM community. Basically, the number of hidden layer nodes is a sensitive hyperparameter that significantly affects the performance of ELM. To address this challenging problem, we propose to adopt multiple kernel learning (MKL) to design a multi-hidden-layer-kernel ELM (MHLK-ELM). Specifically, we first integrate kernel functions with random feature mapping of ELM to design a hidden-layer-kernel ELM (HLK-ELM), which serves as the base of MHLK-ELM. Then, we utilize the MKL method to propose two versions of MHLK-ELMs, called sparse and non-sparse MHLK-ELMs. Both two types of MHLK-ELMs can effectively find out the optimal linear combination of multiple HLK-ELMs for different classification and regression problems. Experimental results on seven data sets, among which three data sets are relevant to classification and four ones are relevant to regression, demonstrate that the proposed MHLK-ELM achieves superior performance compared with conventional ELM and basic HLK-ELM.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.144-150
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• 2021

This study was designed to evaluate the prophylactic efficacy of a combinational patch system containing physostigmine and procyclidine against sarin and soman using guinea pig. The median lethal dose values of two nerve agents were calculated by a probit analysis of deaths occurring within 24 h. In this study, the values of median lethal dose of sarin and soman were determined to be 33.0 and 26.7 ㎍/kg in guinea-pigs, respectively. The guinea pigs treated with a prophylactic patch(4×5 ㎠) for 24 h were 100 % protected against a challenge of 1.5 LD50. The combinational KMARK-1(atropine and 2-PAM) and prophylactic patch were more effective than a single KMARK-1, a combination of pyridostigmine and KMARK-1 significantly. Epileptiform seizures in the guinea pigs treated with the combinational antidotes led to neuropathological changes, in comparison with intact feature of brain of the animal treated with the patch.

• The Research Journal of the Costume Culture
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• v.29 no.6
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• pp.932-948
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• 2021

Tartan, the woven, checked, and wool textile considered by many to be originally from Scotland, has in fact been in use in a range of forms across numerous cultures and during various historical periods. The characteristic checked feature is due to the assembly of different coloured threads in both warp and weft directions which intersect at 90 degrees in a combination known as a sett. For well over one hundred years, different setts and thus different colour combinations have been associated closely with different geographical regions within Scotland, as well as different clans or families. Tartan-type textiles have reached popularity at various times and those have often been a predicted fashion trend suggested, for example, by contributors to fashion gatherings such as Premier Vision in Paris. Often proposed designs are best considered based on tartan combinations rather than simple reproductions. Promotional terms such as "patched checks" or "textured checks" have been common, and often these have been derived from tartan-type constructions. This paper explores novel pattern design methods by identifying the underlying grid structures and proportions exhibited by various well-known tartan setts. The possibility of pattern development from tartan grids and their manipulations is thus the focus of attention. An insight into the methodology associated with the production of original pattern designs is thus provided.

• Journal of Animal Science and Technology
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• v.64 no.5
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• pp.830-841
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• 2022

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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• pp.235-242
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• 2022

As ship condition inspection technology has been developed, research on collecting, analyzing, and diagnosing condition information has become active. In ships, related research has been conducted, such as analyzing, detecting, and classifying major hull failures such as cracks and corrosion using 2D and 3D data information. However, for geometric deformation such as indents and bulges, 2D data has limitations in detection, so 3D data is needed to utilize spatial feature information. In this study, we aim to detect hull structural deformation positions. It builds a specimen based on actual hull structure deformation and acquires a point cloud from a model scanned with a 3D scanner. In the obtained point cloud, deformation(outliers) is found with a combination of RANSAC algorithms that find the best matching model in the Octree data structure and dataset.

• Geomechanics and Engineering
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• v.31 no.5
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• pp.453-460
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• 2022

It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R² value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1689-1705
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• 2023

To deal with the potential XSS vulnerabilities in the source code of the power communication network, an XSS vulnerability detection method combining the static analysis method with the dynamic testing method is proposed. The static analysis method aims to analyze the structure and content of the source code. We construct a set of feature expressions to match malignant content and set a "variable conversion" method to analyze the data flow of the code that implements interactive functions. The static analysis method explores the vulnerabilities existing in the source code structure and code content. Dynamic testing aims to simulate network attacks to reflect whether there are vulnerabilities in web pages. We construct many attack vectors and implemented the test in the Selenium tool. Due to the combination of the two analysis methods, XSS vulnerability discovery research could be conducted from two aspects: "white-box testing" and "black-box testing". Tests show that this method can effectively detect XSS vulnerabilities in the source code of the power communication network.