• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.90-97
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• 2003

This paper proposes a speech recognition based on HMM(Hidden Markov Model) using FSVQ(First Section Vector Quantization) and fuzzy concept. In the proposed paper, we generate codebook of First Section, and then obtain multi-observation sequences by order of large propabilistic values based on fuzzy rule from the codebook of the first section. Thereafter, this observation sequences of first section from codebooks is trained and in case of recognition, a word that has the most highest probability of first section is selected as a recognized word by same concept. Train station names are selected as the target recognition vocabulary and LPC cepstrum coefficients are used as the feature parameters. Besides the speech recognition experiments of proposed method, we experiment the other methods under same conditions and data. Through the experiment results, it is proved that the proposed method based on HMM using FSVQ and fuzzy concept is superior to tile others in recognition rate.

• Genomics & Informatics
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• v.5 no.3
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• pp.102-106
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• 2007

Radiofrequency (RF) radiation at the frequency of mobile phones has been not reported to induce cellular responses in in vitro and in vivo models. We exposed HEI-OC1, conditionally-immortalized mouse auditory cells, to RF radiation to characterize cellular responses to 1763 MHz RF radiation. While we could not detect any differences upon RF exposure, whole-genome expression profiling might provide the most sensitive method to find the molecular responses to RF radiation. HEI-OC1 cells were exposed to 1763 MHz RF radiation at an average specific absorption rate (SAR) of 20 W/kg for 24 hr and harvested after 5 hr of recovery (R5), alongside sham-exposed samples (S5). From the whole-genome profiles of mouse neurons, we selected 9 differentially-expressed genes between the S5 and R5 groups using information gain-based recursive feature elimination procedure. Based on support vector machine (SVM), we designed a prediction model using the 9 genes to discriminate the two groups. Our prediction model could predict the target class without any error. From these results, we developed a prediction model using biomarkers to determine the RF radiation exposure in mouse auditory cells with perfect accuracy, which may need validation in in vivo RF-exposure models.

• Journal of the Korean association of regional geographers
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• v.22 no.2
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• pp.424-438
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• 2016

In an effort to prevent urban decline and hollowing-out phenomenon and to vitalize stagnant local economy, a new urban regeneration paradigm is on the rise. This study aims to analyze urban decline characteristics using the spatial statistical method and GIS on the basis of decline standards in the Urban Regeneration Special Act, and spatial autocorrelation technique. The Gwangju Metropolitan City was set as a research target, and the decline standards in the Urban Regeneration Special Act - population reduction, business declines, and outworn buildings - were applied as the indicator to secure the objectivity. In particular, this study has a distinctive feature from the other existing ones, as applying GIS and the spatial statistical technique, in a sense to make urban decline characteristics analysis by the spatial autocorrelation technique. The overall analysis procedure was carried out by applying the standards of designating urban regeneration regions, and following the spatial exploratory procedure step by step. Therefore, the spatial statistical method procedure and the urban decline characteristics analysis data being presented in this study, as the results, are expected to contribute to the urban decline diagnosis at the level of metropolitan city, as well as to provide useful information for spatial decision making in accordance with urban regeneration.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.1013-1021
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• 2020

A combination of simultaneous thermal analysis, evolved gas analysis and non-ambient XRD techniques was used to characterise and investigate the conversion reactions of ammonium uranates into uranium oxides. Two solid phases of the ternary system NH₃ - UO₃ - H₂O were synthesised under specified conditions. Microspheres prepared by the sol-gel method via internal gelation were identified as 3UO₃·2NH₃·4H₂O, whereas the product of a typical ammonium diuranate precipitation reaction was associated to the composition 3UO₃·NH₃·5H₂O. The thermal decomposition profile of both compounds in air feature distinct reaction steps towards the conversion to U₃O₈, owing to the successive release of water and ammonia molecules. Both compounds are converted into α-U₃O₈ above 550 ℃, but the crystallographic transition occurs differently. In compound 3UO₃·NH₃·5H₂O (ADU) the transformation occurs via the crystalline β-UO₃ phase, whereas in compound 3UO₃·2NH₃·4H₂O (microspheres) an amorphous UO₃ intermediate was observed. The new insights obtained on these uranate systems improve the information base for designing and synthesising minor actinide-containing target materials in future applications.

• Journal of KIISE
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• v.42 no.4
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• pp.475-482
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• 2015

Developing a high-performance Semantic Role Labeling (SRL) system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Performances of Korean SRL are degraded by almost 15% or more, when it is directly applied to another domain with relatively small training data. This paper proposes two techniques to minimize performance degradation in the domain transfer. First, a domain adaptation algorithm for Korean SRL is proposed which is based on the prior model that is one of domain adaptation paradigms. Secondly, we proposed to use simplified features related to morphological and syntactic tags, when using small-sized target domain data to suppress the problem of data sparseness. Other domain adaptation techniques were experimentally compared to our techniques in this paper, where news and Wikipedia were used as the sources and target domains, respectively. It was observed that the highest performance is achieved when our two techniques were applied together. In our system's performance, F1 score of 64.3% was considered to be 2.4~3.1% higher than the methods from other research.

• Convergence Security Journal
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• v.19 no.2
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• pp.91-104
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• 2019

In this paper, we present a novel local descriptor, Local Prominent Directional Pattern (LPDP), to represent the description of facial images for gender recognition purpose. To achieve a clearly discriminative representation of local shape, presented method encodes a target pixel with the prominent directional variations in local structure from an analysis of statistics encompassed in the histogram of such directional variations. Use of the statistical information comes from the observation that a local neighboring region, having an edge going through it, demonstrate similar gradient directions, and hence, the prominent accumulations, accumulated from such gradient directions provide a solid base to represent the shape of that local structure. Unlike the sole use of gradient direction of a target pixel in existing methods, our coding scheme selects prominent edge directions accumulated from more samples (e.g., surrounding neighboring pixels), which, in turn, minimizes the effect of noise by suppressing the noisy accumulations of single or fewer samples. In this way, the presented encoding strategy provides the more discriminative shape of local structures while ensuring robustness to subtle changes such as local noise. We conduct extensive experiments on gender recognition datasets containing a wide range of challenges such as illumination, expression, age, and pose variations as well as sketch images, and observe the better performance of LPDP descriptor against existing local descriptors.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.73-90
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• 2014

The Officially Announced Land Price System has a limit, that is required to be able to reflect changes in land constantly every year, to implement Land Characteristic Survey for calculating land price during a specific period and human resources with limited. The purpose of this study is to apply the 'National Territory Space Usage status Survey' method to survey part of the territorial feature status information inside of selected the target sites, considering the core survey items (land category, the state of land use, altitude difference, standard site inclusion, etc) in the areas surrounding Yeonshinnae Station in which three dongs (Galhyeon-dong, Daejo-dong, Bulgwang-dong) of Eunpyeong-gu, Seoul share borders with. Based on the given budget, the manpower and period was taken into consideration to sort a total of 2,041 lots and conduct surveys on all sites. This study will be able to diagnose the efficient idle human resource utilization and work process construction plan through pilot projects specialized for providing real estate information services in preparation for cases in which national territory information survey projects that provide various business model, as well as major future core projects of the corporation will be carried out.

• Korean Journal of Cognitive Science
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• v.28 no.2
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• pp.111-131
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• 2017

The connectionist model is one approach to studying language processing from a computational perspective. And building a representation in the connectionist model study is just as important as making the structure of the model in that it determines the level of learning and performance of the model. The connectionist model has been constructed in two different ways: localist representation and distributed representation. However, the localist representation used in the previous studies had limitations in that the unit of the output layer having a rare target activation value is inactivated, and the past distributed representation has the limitation of difficulty in confirming the result by the opacity of the displayed information. This has been a limitation of the overall connection model study. In this paper, we present a new method to induce distributed representation with local representation using abstraction of information, which is a feature of restricted Boltzmann machine, with respect to the limitation of such representation of the past. As a result, our proposed method effectively solves the problem of conventional representation by using the method of information compression and inverse transformation of distributed representation into local representation.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Journal of Korean Neurosurgical Society
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• v.29 no.12
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• pp.1606-1611
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• 2000

Objective : To obtain more reliable sample in stereotactic biopsy, authors adopted proton chemical shift imaging ($^1H$-CSI)-directed biopsy. Until now, proton single voxel spectroscopy($^1H$-SVS) technique has been reported as a technique using metabolic information in stereotactic biopsy. The authors performed $^1H$-CSI with a stereotactic headframe in place and evaluated the pathologic results obtained from local metabolic information through $^1H$-CSI. Methods : $^1H$ CSI-directed stereotactic biopsy was performed in four patients. $^1H$-CSI and conventional Gd-enhancement stereotactic MRI was done simultaneously after application of the stereotatic frame. After reconstruction of metabolic maps of NAA/Cr, Cho/Cr, and Lactate/Cr ratios, the focal areas of increased Cho/Cr ratios and decreased NAA/Cr ratios were selected for target sites in the MR images Results : There was no difficulty in performing $^1H$-CSI with the stereotactic headframe in place. In pathologic examinations, the samples taken in area of increased Cho/Cr ratios and decreased NAA/Cr ratios showed the features of increased cellularity, mitoses and cellular atypism, thus facilitated the diagnosis. The pathologic samples taken from the area of increased Lactate/Cr ratios showed prominent feature of necrosis. Conclusion : $^1H$-CSI was feasible with stereotactic head frame in place. The final pathologic results obtained in our samples were concordant with the local metabolic informations from $^1H$-CSI. Authors believe that $^1H$ CSI-directed stereotactic biopsy may provide us advantages in obtaining more reliable tissue specimen in stereotactic biopsy.