• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1402-1408
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• 2017

Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.771-791
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• 2022

Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.623-625
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• 2021

Rubber o-rings are produced by conventional injection molding methods. In this case, products that are not normally molded are determined to be defective. However, if images acquired during image-based reading are read as original, there is a problem of poor accuracy. We have thus learned from convolutional neural networks using adaptive binarization and Convex Hull algorithms by extracting only rubber oring parts from the original images through pre-processing. During the test process, it was confirmed that the defect detection performance of the learning method applied pre-processing was better than the standard suggested.

• Journal of IKEEE
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• v.19 no.3
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• pp.455-459
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• 2015

This paper proposes a dispatch unit of GP-GPU with SIMT architecture to support the acceleration of general-purpose operation as well as graphics processing. If all the information of an operand used instructions issued from the warp scheduler is decoded, an unnecessary operand load occurs, resulting in register loads. To resolve this problem, this paper proposes a method that can reduce the operand load and the load on the resister by decoding only the information of the operand using a pre-decoding method. The operand information from the dispatch unit is passed to the operand selection unit with preventing register bank collisions. Thus the overall performance are improved. In the simulation test, the total clock cycles required by processing 10,000 arbitrary instructions issued from the wrap scheduler using ModelSim SE 10.0b are measured. It shows that the application of the dispatch unit equipped with the pre-decoding function proposed in this paper can make an improvement of about 12% in processing performance compared to the conventional method.

• BMB Reports
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• v.39 no.6
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• pp.717-721
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• 2006

In vitro analyses of type I signal peptidase activities require protein precursors as substrates. Usually, these pre-proteins are expressed in vitro and cleavage of the signal sequence is followed by SDS polyacrylamide gel electrophoresis coupled with autoradiography. Radioactive amino acids have to be incorporated in the expressed protein, since the amount of the in vitro expressed protein is usually very low and processing of the signal peptide cannot be followed by SDS polyacrylamide gel electrophoresis alone. Here we describe a rapid and simple method to express large amounts of a protein precursor in E. coli. We have analyzed the effect of ionophors as well as of azide on the accumulation of expressed protein precursors. Azide blocks the function of SecA and the ionophors dissipate the electrochemical gradient across the cytoplasmic membrane of E. coli. Addition of azide ions resulted in the formation of inclusion bodies, highly enriched with pre-apo-plastocyanine. Plastocyanine is a soluble copper protein, which can be found in the periplasmic space of cyanobacteria as well as in the thylakoid lumen of cyanobacteria and chloroplasts, and the pre-protein contains a cleavable signal sequence at its N-terminus. After purification of cyanobacterial pre-apo-plastocyanine, its signal sequence can be cleaved off by the E. coli signal peptidase, and protein processing was followed on Coomassie stained SDS polyacrylamide gels. We are optimistic that the presented method can be further developed and applied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.84-89
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• 2023

In water resource management, data prediction is performed using artificial intelligence, and companies, governments, and institutions continue to attempt to efficiently manage resources through this. LSTM is a model specialized for processing time series data, which can identify data patterns that change over time and has been attempted to predict groundwater level data. However, groundwater level data can cause sen-sor errors, missing values, or outliers, and these problems can degrade the performance of the LSTM model, and there is a need to improve data quality by processing them in the pretreatment stage. Therefore, in pre-dicting groundwater data, we will compare the LSTM model with the MSE and the model after normaliza-tion through distribution, and discuss the important process of analysis and data preprocessing according to the comparison results and changes in the results.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.271-276
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• 2003

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the Earth Observing System (EOS) of Terra and Aqua satellites, launched in December 1999 and May 2002, has been directly received by Korea Aerospace Research Institute (KARI) ground station facility from July 2002. MODIS scans a swath width of 2330 km that is sufficiently wide to cover Korean peninsular, Yellow and East Sea at once. The MODIS has 36 spectral bands between 0.415 $\mu\textrm{m}$ and 14.235 $\mu\textrm{m}$, i.e., through the visible into the thermal infrared. MODIS has been observed active fires, floods, smoke transport, dust storms, severe storms since February of 2000. The satellite imagery obtained through the MODIS will be utilized for many application such as national territorial management, agriculture, natural environment, atmosphere and ocean, etc. In this study is to introduce various application field of MODIS imagery and data processing system.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1113-1116
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• 2006

Security in wireless sensor networks is very pressing especially when sensor nodes are deployed in hostile environments. To obtain security purposes, it is essential to be able to encrypt and authenticate messages sent amongst sensor nodes. Keys for encryption and authentication must be agreed upon by communicating nodes. Due to resource limitations and other unique features, obtaining such key agreement in wireless sensor network is extremely complex. Many key agreement schemes used in general networks, such as trusted server, Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks [1], [2], [5], [7], [8]. In that situation, key pre-distribution scheme has been emerged and considered as the most appropriate scheme [2], [5], [7]. Based on that sense, we propose a new resource-optimal key pre-distribution scheme utilizing merits of the two existing key pre-distribution schemes [3], [4]. Our scheme exhibits the fascinating properties: substantial improvement in sensors' resource usage, rigorous guarantee of successfully deriving pairwise keys between any pair of nodes, greatly improved network resiliency against node capture attack. We also present a detailed analysis in terms of security and resource usage of the scheme.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.1 s.145
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• pp.87-95
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• 2006

There are hundreds of ship blocks which are under the block assembly, painting, and outfitting assembly works in the pre-erection shops of shipyard. Generally, each block is planned to be processed in a pre-erection shop according to the block type by the long-term production-scheduling before six months. However, many blocks can't be processed in the planned time and the planned shop since the before and after block-processing changes or delays the planned sequential works in pre-erection shops. Therefore, it is essential to monitor the current location of each block and work in process to cope with the changed situation of pre-erection shops. Present study integrates PDA, GPS, and CDMA not only to chase the location of each block but also to exchange the pre-erection work order and the work report between the production-scheduling server and the production managers in the pre-erection shops. This study shows a prototype for the block tracing and process monitoring in the pre-erection shops.