• Journal of Information Processing Systems
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• v.17 no.3
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• pp.645-657
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• 2021

Along with the rapid development of the economy, the urban scale has extended rapidly, leading to the formation of different types of urban function districts (UFDs), such as central business, residential and industrial districts. Recognizing the spatial distributions of these districts is of great significance to manage the evolving role of urban planning and further help in developing reliable urban planning programs. In this paper, we propose an automatic UFD division method based on big data analysis of point of interest (POI) data. Considering that the distribution of POI data is unbalanced in a geographic space, a dichotomy-based data retrieval method was used to improve the efficiency of the data crawling process. Further, a POI spatial feature analysis method based on the mean shift algorithm is proposed, where data points with similar attributive characteristics are clustered to form the function districts. The proposed method was thoroughly tested in an actual urban case scenario and the results show its superior performance. Further, the suitability of fit to practical situations reaches 88.4%, demonstrating a reasonable UFD division result.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.519-526
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• 2022

Identifying and searching for characters appearing in scenes during multimedia video editing is an arduous and time-consuming process. Applying artificial intelligence to labor-intensive media editing tasks can greatly reduce media production time, improving the creative process efficiency. In this paper, a method is proposed which combines existing artificial intelligence based techniques to automate character recognition and search tasks for video editing. Object detection, face detection, and pose estimation are used for character localization and face recognition and color space analysis are used to extract unique representation information.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.983-990
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• 2022

In this paper, we implemented a hierarchical encryption system using two-step phase-shifting digital holography(PSDH) technology based on XOR and scramble operations. The proposed encryption system is a system that authenticates access through the issuance of an encryption key for access to individual laboratories, department offices, and universities. In the encryption process, we proposed a double encryption method using XOR and scramble operation with digital technology and two-step phase-shifting digital holography with optical technology. In the two-step PSDH process, an new method of determining the reference wave intensity without measuring it by using random common object image gererated from digital encryption process was also proposed. In the decryption process, the process is performed in the reverse order of encryption process. And only when the various key information used in the encryption process is correct, the encrypted information can be decrypted, so that the user can access the desired place. That is, there is a feature that can hierarchically control the space that can be accessed according to the type of key issued in the proposed encryption system. Through the computer simulation, the feasibility of the proposed hierarchical encryption system was confirmed.

• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.651-664
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• 2022

A precise prediction of the ultimate bond strength between rebar and surrounding concrete plays a major role in structural design, as it effects the load-carrying capacity and serviceability of a member significantly. In the present study, Gaussian models are employed for modelling bond strength of ribbed steel bars embedded in concrete. Gaussian models offer a non-parametric method based on Bayesian framework which is powerful, versatile, robust and accurate. Five different Gaussian models are explored in this paper-Gaussian Process (GP), Variational Heteroscedastic Gaussian Process (VHGP), Warped Gaussian Process (WGP), Sparse Spectrum Gaussian Process (SSGP), and Twin Gaussian Process (TGP). The effectiveness of the models is also evaluated in comparison to the numerous design formulae provided by the codes. The predictions from the Gaussian models are found to be closer to the experiments than those predicted using the design equations provided in various codes. The sensitivity of the models to various parameters, input feature space and sampling is also presented. It is found that GP, VHGP and SSGP are effective in prediction of the bond strength. For large data set, GP, VHGP, WGP and TGP can be computationally expensive. In such cases, SSGP can be utilized.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2002.09a
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• pp.219-225
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• 2002

This paper presents a novel "bumpless flip chip package"for cost! performance driven devices. Using the conventional electroplating and etching processes, this package enables the production of fine pitch BGA up to 256 I/O with single layer routing. An array of circuitry down to $25-50{\mu}{\textrm}{m}$ line/space is fabricated to fan-in and fan-out of the bond pads without using bumps or substrate. Various types of joint methods can be applied to connect the fine trace and the bond pad directly. The resin-filled terminal provides excellent compliancy between package and the assembled board. More interestingly, the thin film routing is similar to wafer level packaging whereas the fan-out feature enables high lead count devices to be accommodated in the BGA format. Details of the design concepts and processing technology for this novel package are discussed. Trade offs to meet various cost or performance goals for selected applications are suggested. Finally, the importance of design integration early in the technology development cycle with die-level and system-level design teams is highlighted as critical to an optimal design for performance and cost.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.217-223
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• 2009

Vocabulary recognition system of Mobile terminal is executed statistical method for vocabulary recognition and used statistical grammar recognition system using N-gram. If limit arithmetic processing capacity in memory of vocabulary to grow then vocabulary recognition algorithm complicated and need a large scale search space and many processing time on account of impossible to process. This study suggest vocabulary recognition optimize using MLHF System. MLHF separate acoustic search and lexical search system using FLaVoR. Acoustic search feature vector of speech signal extract using HMM, lexical search recognition execution using Levenshtein distance algorithm. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%, represent recognition speed of 1.61 second.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.36.2-36.2
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• 2019

Fe II emission is a prominent and ubiquitous feature in the spectra of broad-line Active Galactic Nuclei (AGN) by producing a pseudo-continuum from UV to optical with complex and strong blends of the numerous emission lines themselves, other emission lines, and continuum. Since theoretical modeling of such intricate Fe II emission is very difficult and still far from able to reproduce observed data in detail, an empirical iron emission template, derived from observations of a narrow-line Seyfert 1 galaxy, is an essential and practical tool to obtain accurate measurements of all the emission lines and continuum in AGN spectra. However, the existing iron templates, based on the single prototypical strong Fe II emitter I Zw 1, are suffering from inadequate S/N and non-simultaneous, inconsistent data with limited wavelength coverage, which consequently limit the accuracy of all the spectral measurements. To overcome the limitations and construct an improved iron template with wide spectral coverage, high-quality UV and optical spectra for the new and better identified template galaxy, Mrk 493, were successfully obtained from our HST STIS program (GO-14744). We will show the preliminary results for multicomponent spectral decomposition of the data and template construction with application tests to various AGN spectra and comparison with previous templates.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.540-542
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• 2023

Due to the serious issues posed by facial manipulation technologies, many researchers are becoming increasingly interested in the identification of face forgeries. The majority of existing face forgery detection methods leverage powerful data adaptation ability of neural network to derive distinguishing traits. These deep learning-based detection methods frequently treat the detection of fake faces as a binary classification problem and employ softmax loss to track CNN network training. However, acquired traits observed by softmax loss are insufficient for discriminating. To get over these limitations, in this study, we introduce a novel discriminative feature learning based on Vision Transformer architecture. Additionally, a separation-center loss is created to simply compress intra-class variation of original faces while enhancing inter-class differences in the embedding space.