• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.93-115
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• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

• Nuclear Engineering and Technology
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• v.50 no.7
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• pp.1006-1016
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• 2018

Since the early forties, researchers from around the world have been studying the phenomenon of deadtime in radiation detectors. Many have attempted to develop models to represent this phenomenon. Two highly idealized models; paralyzable and non-paralyzable are commonly used by most individuals involved in radiation measurements. Most put little thought about the operating conditions and applicability of these ideal models for their experimental conditions. So far, there is no general agreement on the applicability of any given model for a specific detector under specific operating conditions, let alone a universal model for all detectors and all operating conditions. Further the related problem of pile-up is often confused with the deadtime phenomenon. Much work, is needed to devise a generalized and practical solution to these related problems. Many methods have been developed to measure and compensate for the detector deadtime count loss, and many researchers have addressed deadtime and pulse pile-up. The goal of this article is to summarize the state of science of deadtime; measurement and compensation techniques as proposed by some of the most significant work on these topics and to review the deadtime correction models applicable to present day radiation detection systems.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• Journal of the Microelectronics and Packaging Society
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• v.28 no.4
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• pp.57-61
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• 2021

A formation process of indium bump arrays on micro-pillar structures is proposed. The space to form indium bump on the narrow structures can be secured applying the benzocyclobutene (BCB) planarization and its etch-back process. We exhibit a detailed overview of the process steps involved in the fabrication of 320×256 hybrid camera sensor for short-wavelength infrared (SWIR) detection. The shear strength of the BCB, which has undergone the different processes, is extracted by quartz crystal microbalance measurement. The shear strength of the BCB is three orders of magnitude higher than that of the indium bump itself. The measured dark current distribution of the fabricated SWIR camera sensor indicates the suggested process of indium bumps can be useful for embodying highly sensitive infared camera sensors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.295-311
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• 2023

Cloud computing has evolved significantly, intending to provide users with fast, dependable, and low-cost services. With its development, malicious users have become increasingly capable of attacking both its internal and external security. To ensure the security of cloud services, encryption, authorization, firewalls, and intrusion detection systems have been employed. However, these single monitoring agents, are complex, time-consuming, and they do not detect ransomware and zero-day vulnerabilities on their own. An innovative Record and Replay-based hybrid Honeynet (R2NET) system has been developed to address this issue. Combining honeynet with Record and Replay (RR) technology, the system allows fine-grained analysis by delaying time-consuming analysis to the replay step. In addition, a machine learning algorithm is utilized to cluster the logs of attackers and store them in a database. So, the accessing time for analyzing the attack may be reduced which in turn increases the efficiency of the proposed framework. The R2NET framework is compared with existing methods such as EEHH net, HoneyDoc, Honeynet system, and AHDS. The proposed system achieves 7.60%, 9.78%%, 18.47%, and 31.52% more accuracy than EEHH net, HoneyDoc, Honeynet system, and AHDS methods.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.537-546
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• 2004

Adiponectin is adipocyte complement-related protein which is highly specialized to play important roles in metabolic and honnonal processes. This protein, called GBP-28, AdipoQ, and Acrp30, is encoded by the adipose most abundant gene transcript 1 (APM1) which locates on human chromosome 3q27 and mouse chromosome 16. In order to determine chromosomal localization of the porcine APM1, we carried out PCR analysis using somatic cell hybrid panel as well as porcine whole genome radiation hybrid (RH) panel. The result showed that the porcine APM1 located on chromosome 13q41 or 13q46-49. These locations were further investigated with the two point analysis of RH panel, revealed the most significant linked marker (LOD score 20.29) being SIAT1 (8 cRs away), where the fat-related QTL located. From the SSCP analysis of APM1 using 8 pig breeds, two distinct SSCP types were detected from K~ native and Korean wild pigs. The determined sequences in Korean native and Korean wild pigs showed that two nucleotide positions (T672C and C705G) were substituted. The primary sequence of the porcine APM1 has 79 to 87% identity with those of human, mouse, and bovine APM1. The domain structures of the porcine APM1 such as signal sequence, hypervariable region, collagenous region. and globular domain are also similar to those of mammalian genes.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.5
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• pp.508-517
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• 2004

We described a near-field scanning microwave microscope which uses a high-quality dielectric resonator with a tunable screw. The operating frequency is f=4.5 5GHz. The probe tip is mounted in a cylindrical resonant cavity coupled to a dielectric resonator We developed a hybrid tip combining a reduced length of the tapered part with a small apex. In order to understand the function of the probe, we fabricated three different tips using a conventional chemical etching technique and observed three different NSMM images for patterened Cr films on glass substrates. We measured the reflection coefficient of different metal thin film samples with the same thickness of 300m and compared with theoretical impedance respectly. By tuning the tunable screw coming through the top cover, we could improve sensitivity, signal-to-noise ratio, and spatial resolution to better than $1{\mu}m$. To demonstrate the ability of local microwave characterization, the surface resistance of metallic thin films has been mapped.

• Journal of Korea Spatial Information System Society
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• v.4 no.2 s.8
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• pp.55-64
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• 2002

VEfficient generation of road data is one of the most important issues in GIS (Geographic Information System). In this paper, we propose a hybrid approach for automatic generation of road data by combining mobile mapping and image processing techniques. Mobile mapping systems have a form of vehicle equipped with CCD camera, GPS, and INS. They can calculate absolute position of objects that appear in acquired image by photogrammetry, but it is labor-intensive and time-consuming. Automatic road detection methods have been studied also by image processing technology. However, the methods are likely to fail because of obstacles and exceptive conditions in the real world. To overcome the problems, we suggest a hybrid method for automatic road generation, by exploiting both GPS/INS data acquired by mobile mapping system and image processing algorithms. We design an estimator to estimate 3-D coordinates of road line and corresponding location in an image. The estimation process reduces complicated image processing operations that find road line. The missing coordinates of road line due to failure of estimation are obtained by cubic spline interpolation. The interpolation is done piecewise, separated by rapid change such as road intersection. We present experimental results of the suggested estimation and interpolation methods with image sequences acquired by mobile mapping system, and show that the methods are effective in generation of road data.

• Korean Journal of Agricultural Science
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• v.48 no.2
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• pp.299-310
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• 2021

Glycerol is a non-volatile compound with no aromatic properties that contributes significantly to the quality of wine by providing sweetness and richness of taste. In addition, it is also the third most significant byproduct of alcoholic fermentation in terms of quantity after ethanol and carbon dioxide. In this study, Fourier transform infrared (FT-IR) spectroscopy was employed as a fast non-destructive method in conjugation with multivariate regression analysis to build a model for the quantitative analysis of glycerol concentration in wine samples. The samples were prepared by using three varieties of red wine samples (i.e., Shiraz, Merlot, and Barbaresco) that were adulterated with glycerol in concentration ranges from 0.1 to 15% (v·v^-1), and subjected to analysis together with pure wine samples. A net analyte signal (NAS)-based methodology, called hybrid linear analysis in the literature (HLA/GO), was applied for predicting glycerol concentrations in the collected FT-IR spectral data. Calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results exhibited a high coefficient of determination (R²) of 0.987 and a low root mean square error (RMSE) of 0.563% for the calibration set, and a R² of 0.984 and a RMSE of 0.626% for the validation set. Further, the model was validated in terms of sensitivity, selectivity, and limits of detection and quantification, and the results confirmed that this model can be used in most applications, as well as for quality assurance.

• Composites Research
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• v.19 no.1
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• pp.29-35
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• 2006

The avoidance of enemy's radar detection is very important issue in the modem electronic weapon system. Researchers have studied to minimize reflected signals of radar. In this research, two types of radar absorbing structure (RAS), 'C'-type shell and 'U'-type shell, were fabricated using fiber-reinforced composite materials and their radar cross section (RCS) were evaluated. The absorption layer was composed of glass fiber reinforced epoxy and nano size carbon-black, and the reflection layer was fabricated with carbon fiber reinforced epoxy. During their manufacturing process, undesired thermal deformation (so called spring-back) was observed. In order to reduce spring-back, the bending angle of mold was controlled by a series of experiments. The spring-back of parts fabricated by using compensated mold was predicted by finite element analysis (ANSYS). The RCS of RAS shells were measured by compact range and predicted by physical optics method. The measured RCS data was well matched with the predicted data.