• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.794-807
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• 2022

Although registration of dogs is mandatory according to the revision of the Animal Protection Act, the registration rate is low due to the inconvenience of the current registration method. In this paper, a performance improvement study was conducted on the dog face recognition technology, which is being reviewed as a new registration method. Through deep learning learning, an embedding vector for facial recognition of a dog was created and a method for identifying each dog individual was experimented. We built a dog image dataset for deep learning learning and experimented with InceptionNet and ResNet-50 as backbone networks. It was learned by the triplet loss method, and the experiments were divided into face verification and face recognition. In the ResNet-50-based model, it was possible to obtain the best facial verification performance of 93.46%, and in the face recognition test, the highest performance of 91.44% was obtained in rank-5, respectively. The experimental methods and results presented in this paper can be used in various fields, such as checking whether a dog is registered or not, and checking an object at a dog access facility.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.913-924
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• 2022

In this paper, we presented a database construction of undersea images for the Habitats of Ecklonia cava and Sargassum and conducted an experiment for semantic segmentation using state-of-the-art (SOTA) models such as High Resolution Network-Object Contextual Representation (HRNet-OCR) and Shifted Windows-L (Swin-L). The result showed that our segmentation models were superior to the existing experiments in terms of the 29% increased mean intersection over union (mIOU). Swin-L model produced better performance for every class. In particular, the information of the Ecklonia cava class that had small data were also appropriately extracted by Swin-L model. Target objects and the backgrounds were well distinguished owing to the Transformer backbone better than the legacy models. A bigger database under construction will ensure more accuracy improvement and can be utilized as deep learning database for undersea images.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.293-301
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• 2023

Unstandardized medical data collection and management are still being conducted manually, and studies are being conducted to classify CT data using deep learning to solve this problem. However, most studies are developing models based only on the axial plane, which is a basic CT slice. Because CT images depict only human structures unlike general images, reconstructing CT scans alone can provide richer physical features. This study seeks to find ways to achieve higher performance through various methods of converting CT scan to 2D as well as axial planes. The training used 1042 CT scans from five body parts and collected 179 test sets and 448 with external datasets for model evaluation. To develop a deep learning model, we used InceptionResNetV2 pre-trained with ImageNet as a backbone and re-trained the entire layer of the model. As a result of the experiment, the reconstruction data model achieved 99.33% in body part classification, 1.12% higher than the axial model, and the axial model was higher only in brain and neck in contrast classification. In conclusion, it was possible to achieve more accurate performance when learning with data that shows better anatomical features than when trained with axial slice alone.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• Journal of the Korean Geotechnical Society
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• v.26 no.1
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• pp.45-54
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• 2010

It is important to calculate the natural frequency of a piled structure in the design stage in order to prevent resonance-induced damage to the pile foundation and analyze the dynamic behavior of the piled structure during an earthquake. In this paper, a simple but relatively accurate method employing a mass-spring model is presented for the evaluation of the natural frequency of a pile-soil system. Greatly influencing the calculation of the natural frequency of a piled structure, the spring stiffness between a pile and soil was evaluated by using the coefficient of subgrade reaction, the p-y curve, and the subsoil elastic modulus. The resulting natural frequencies were compared with those of 1-g shaking table tests. The comparison showed that the natural frequency of the pile-soil system could be most accurately calculated by constructing a stiffness matrix with the spring stiffness of the Reese (1974) method, which utilizes the coefficient of the subgrade reaction modulus, and Yang's (2009) dynamic p-y backbone curve method. The calculated natural frequencies were within 5% error compared with those of the shaking table tests for the pile system in dry dense sand deposits and 5% to 40% error for the pile system in saturated sand deposits depending on the occurrence of excess pore water pressure in the soil.

• Journal of Navigation and Port Research
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• v.47 no.3
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• pp.167-173
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• 2023

The manufacturing industry is the backbone of the Korean economy. Among them, the petrochemical industry is a strategic growth industry, which makes a profit through reexports based on eminent technology in South Korea which imports all of its crude oil. South Korea imports whole amount of crude oil, which is the raw material for many manufacturing industries, by sea transportation. Therefore, it must respond swiftly to a highly volatile tanker freight market. This study aimed to make an early warning model of crude oil shipping market using a signal approach. The crisis of crude oil shipping market is defined by BDTI. The overall leading index is made of 38 factors from macro economy, financial data, and shipping market data. Only leading correlation factors were chosen to be used for the overall leading index. The overall leading index had the highest correlation coefficient factor of 0.499 two months ago. It showed a significant correlation coefficient five months ago. The QPS value was 0.13, which was found to have high accuracy for crisis prediction. Furthermore, unlike other previous time series forecasting model studies, this study quantitatively approached the time lag between economic crisis and the crisis of the tanker ship market, providing workers and policy makers in the shipping industry with an framework for strategies that could effectively deal with the crisis.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.91-98
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• 2023

Recently, according to development of artificial intelligence, a wide range of industry being automatic and optimized. Also we can find out some research of using supervised learning for deteceting defect of railway in domestic rail industry. However, there are structures other than rails on the track, and the fastener is a device that binds the rail to other structures, and periodic inspections are required to prevent safety accidents. In this paper, we present a method of reducing cost for labeling using semi-supervised and transfer model trained on rail fastener data. We use Resnet50 as the backbone network pretrained on ImageNet. At first we randomly take training data from unlabeled data and then labeled that data to train model. After predict unlabeled data by trained model, we adopted a method of adding the data with the highest probability for each class to the training data by a predetermined size. Futhermore, we also conducted some experiments to investigate the influence of the number of initially labeled data. As a result of the experiment, model reaches 92% accuracy which has a performance difference of around 5% compared to supervised learning. This is expected to improve the performance of the classifier by using relatively few labels without additional labeling processes through the proposed method.

• Applied Chemistry for Engineering
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• v.35 no.4
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• pp.296-302
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• 2024

An ionic conjugated polymer-iron (III) chloride composite was prepared via in-situ quaternization polymerization of 2-ethynylpyridine (2EP) using iron (III) chloride. Various instrumental methods revealed that the chemical structure of the resulting conjugated polymer (P2EP)-iron (III) chloride composite has the conjugated backbone system having the designed pyridinium ferric chloride complexes. The polymerization mechanism was assumed to be that the activated triple bond of 2-ethynylpyridinium salt, formed at the first reaction step, is easily susceptible to the step-wise polymerization, followed by the same propagation step that contains the propagating macroanion and monomeric 2-ethynylpyridinium salts. The electro-optical and electrochemical properties of the P2EP-FeCl₃ composite were studied. In the UV-visible spectra of P2EP-FeCl₃ composite, the absorption maximum values were 480 nm and 533 nm, and the PL maximum value was 598 nm. The cyclic voltammograms of the P2EP-FeCl₃ composite exhibited irreversible electrochemical behavior between the oxidation and reduction peaks. The kinetics of the redox process of composites were found to be very close to a diffusion-controlled process from the plot of the oxidation current density versus the scan rate.

• Polymer(Korea)
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• v.24 no.4
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• pp.505-512
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• 2000

The mixture density data for poly(lactide-co-glycolide) [PLGA] with supercritical $CO_2$, CHF$_3$ and CHClF$_2$ were obtained in the temperature range of 27 to 10$0^{\circ}C$ and at pressures as high as 3000 bar (PLGA$_{x}$, Where the molar concentration of glycolide in the backbone, x, range from 0 to 50 mol%). The PLA-$CO_2$, PLA-CHF$_3$, and PLA-CHClF$_2$ systems dissolve in the pressure less than 1430 below 700, and below 100 bar, respectively. The mixture density shows from 1.084 to 1.334 g/cm$^3$ at temperatures from 27 to 93$^{\circ}C$. The PLGA$_{15}$ -$CO_2$ mixture dissolves at pressures of below 1900 bar and the mixture density is in the range of 1.158 to 1.247 g/cm$^3$ at temperatures between 37 and 92$^{\circ}C$. The solubilities of the PLGA$_{25}$ for $CO_2$, CHF$_3$, and CHClF$_2$ are shown to pressure as high as 2390, 1470, and 118 bar, respectively, and the mixture density exhibits iron 1.154 to 1.535 g/cm$^3$ at temperatures from 29 to 81$^{\circ}C$. The PLGA$_{50}$-$CO_2$ system does not dissolve at 24$0^{\circ}C$ and 3000 bar while the PLGA$_{50}$-CHCIF$_2$ does easily at 5$0^{\circ}C$ and 100 bar. The mixture density for the PLGA-CHClF$_2$ system increases even at low pressures as the glycolide molar concentration increases.es.es.