• Journal of electromagnetic engineering and science
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• v.14 no.3
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• pp.284-292
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• 2014

This paper presents a characteristic evaluation of commercial gallium nitride (GaN) transistors having two different gate-lengths of $0.4-{\mu}m$ and $0.25-{\mu}m$ in the design of a class-S power amplifier (PA). Class-S PA is operated by a random pulse-width input signal from band-pass delta-sigma modulation and has to deal with harmonics that consider quantization noise. Although a transistor having a short gate-length has an advantage of efficient operation at higher frequency for harmonics of the pulse signal, several problems can arise, such as the cost and export license of a $0.25-{\mu}m$ transistor. The possibility of using a $0.4-{\mu}m$ transistor on a class-S PA at 955 MHz is evaluated by comparing the frequency characteristics of GaN transistors having two different gate-lengths and extracting the intrinsic parameters as a shape of the simplified switch-based model. In addition, the effectiveness of the switch model is evaluated by currentmode class-D (CMCD) simulation. Finally, device characteristics are compared in terms of current-mode class-S PA. The analyses of the CMCD PA reveal that although the efficiency of $0.4-{\mu}m$ transistor decreases more as the operating frequency increases from 955 MHz to 3,500 MHz due to the efficiency limitation at the higher frequency region, it shows similar power and efficiency of 41.6 dBm and 49%, respectively, at 955 MHz when compared to the $0.25-{\mu}m$ transistor.

• The Mathematical Education
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• v.61 no.2
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• pp.359-373
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• 2022

This study aims to examine the differences in the longitudinal relationship between academic stress, mathematics self-efficacy, and engagement in mathematics class according to the math achievement level. According to the results, academic stress, math self-efficacy, and math class engagement were stable over time for the high and low groups. Also, In the high group, math self-efficacy had a negative longitudinal mediation effect in the influence of academic stress to math class engagement. Whereas, in the low group math class engagement had a positive longitudinal mediation effect in the influence of academic stress to math self-efficacy. This means that the academic stress affects differently according to the math achievement level, and mathematics teachers should reflect these results in their teaching/learning strategies so that students can increase their mathematics self-efficacy along with their engagement in mathematics classes.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.13-22
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• 2021

Recently network based attack technologies are rapidly advanced and intelligent, the limitations of existing signature-based intrusion detection systems are becoming clear. The reason is that signature-based detection methods lack generalization capabilities for new attacks such as APT attacks. To solve these problems, research on machine learning-based intrusion detection systems is being actively conducted. However, in the actual network environment, attack samples are collected very little compared to normal samples, resulting in class imbalance problems. When a supervised learning-based anomaly detection model is trained with such data, the result is biased to the normal sample. In this paper, we propose to overcome this imbalance problem through One-Class Anomaly Detection using an auto encoder. The experiment was conducted through the NSL-KDD data set and compares the performance with the supervised learning models for the performance evaluation of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.391-411
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• 2023

Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.632-657
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• 2022

Detecting web attacks is a major challenge, and it is observed that the use of simple models leads to low sensitivity or high false positive problems. In this study, we aim to develop a robust two-stage deep learning based stacked ensemble web application firewall. Normal and abnormal classification is carried out in the first stage of the proposed WAF model. The classification process of the types of abnormal traffics is postponed to the second stage and carried out using an integrated stacked ensemble model. By this way, clients' requests can be served without time delay, and attack types can be detected with high sensitivity. In addition to the high accuracy of the proposed model, by using the statistical similarity and diversity analyses in the study, high generalization for the ensemble model is achieved. Within the study, a comprehensive, up-to-date, and robust multi-class web anomaly dataset named GAZI-HTTP is created in accordance with the real-world situations. The performance of the proposed WAF model is compared to state-of-the-art deep learning models and previous studies using the benchmark dataset. The proposed two-stage model achieved multi-class detection rates of 97.43% and 94.77% for GAZI-HTTP and ECML-PKDD, respectively.

• Journal of Internet Technology
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• v.22 no.4
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• pp.903-911
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• 2021

As cyber-attacks on Cyber-Physical System (CPS) become more diverse and sophisticated, it is important to quickly detect malicious behaviors occurring in CPS. Since CPS can collect sensor data in near real time throughout the process, there have been many attempts to detect anomaly behavior through normal behavior learning from the perspective of data-driven security. However, since the CPS datasets are big data and most of the data are normal data, it has always been a great challenge to analyze the data and implement the anomaly detection model. In this paper, we propose and evaluate the Clustered Deep One-Class Classification (CD-OCC) model that combines the clustering algorithm and deep learning (DL) model using only a normal dataset for anomaly detection. We use auto-encoder to reduce the dimensions of the dataset and the K-means clustering algorithm to classify the normal data into the optimal cluster size. The DL model trains to predict clusters of normal data, and we can obtain logit values as outputs. The derived logit values are datasets that can better represent normal data in terms of knowledge distillation and are used as inputs to the OCC model. As a result of the experiment, the F1 score of the proposed model shows 0.93 and 0.83 in the SWaT and HAI dataset, respectively, and shows a significant performance improvement over other recent detectors such as Com-AE and SVM-RBF.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1285-1296
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• 2013

The fundamental concerns of this paper are to analyze the effects of student course evaluation using subject characteristic and student characteristic variables. We use a 2-level hierarchical linear model since the data structure of subject characteristic and student characteristic variables is multilevel. Four models we consider are as follows; (1) null model, (2) random coefficient model, (3) mean as outcomes model, (4) intercepts and slopes as outcomes model. The results of the analysis were given as follows. First, the result of null model was that subject characteristics effects on course evaluation had much larger than student characteristics. Second, the result of conditional model specifying subject and student level predictors revealed that class size, grade, tenure, mean GPA of the class, native class for level-1, and sex, department category, admission method, mean GPA of the student for level-2 had statistically significant effects on course evaluation. The explained variance was 13% in subject level, 13% in student level.

• Earthquakes and Structures
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• v.26 no.5
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• pp.337-348
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• 2024

This paper presents a new seismic isolation design for liquid storage tank (LST). The seismic isolation system includes: LST, flexible membrane, sand mat and rolling seismic isolation devices. Based on the mechanical equilibrium theory, the symmetric concave rolling restoring force model of the isolation device is derived. Based on the elasticity theory and restoring force model of the seismic isolation, a simplified mechanical model of LST with the new seismic isolation is established. The rationality of the seismic isolation design of LST is explored. Meanwhile, the seismic response of the new seismic isolation LST is investigated by numerical simulation. The results show that the new seismic isolation tank can effectively reduce the seismic response, especially the control of base shear and overturning moment, which greatly reduces the risk of seismic damage. The seismic reduction rate of the new seismic isolation storage tanks in Class I, II, and III sites is better than that in Class IV sites. Moreover, the seismic isolation device can effectively control the ground vibration response of storage tanks with different liquid heights. The new seismic isolation LST design provides better isolation for slender LSTs than for broad LSTs.

• The korean journal of orthodontics
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• v.11 no.2
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• pp.101-108
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• 1981

This investigation was designed to compare the calcification degree of maxillary second permanent molar to mandibular second permanent molar in skeletal Class III Malocclusion. The material selected for this study consisted in standand lateral cephalogram study model and orthopantomogram of two hundred fifty seven Korean Children, one hundred twenty one boys and one hundred twenty four girls, aged 6 through 12 years, having skeletal Class III Malocclusion. On the basis of findigs of this study, the following results were obtained 1. In the stage of completion of crown, there was no significant difference in calcification degree between maxillary second molar and mandibular second molar of both boys and girls in skeletal Class III Malocclusion. 2. From 8 years of age at the stage of beginning root formation to 12 years of age, the calcification degree of mandibular second molar was more advanced than Maxillary second molar of both boys and girls in skeletal Class III Malocclusion.