• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3712-3729
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• 2020

Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.42 no.2
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• pp.111-114
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• 2016

The detection of foreign bodies in the upper-aerodigestive tract is a fairly frequent event and can occur in various areas and for various reasons. In rare cases, foreign bodies can simulate a neoplasia. We evaluated similar cases during emergency regimen with an oral cavity mucosal lesion, causing lockjaw, sore throat, dysphagia, and swelling of the submandibular and laterocervical region. Physical examination revealed an extensive mucosal ulceration in the floor of the mouth and the lateral surface of the tongue, comparable to oral cancer. During a second, more accurate assessment, a partially deteriorated iron supplement tablet was found embedded in a mucosal pocket. After removing the tablet, gradual normalization of the tissue was observed without any sequelae. This is one of the many reasons why it is advisable and useful in cases of oral lesions to collect a detailed medical history and to perform an accurate clinical evaluation, including inspection and palpation of the lesion, before proceeding to further diagnostic assessments, especially in elderly patients taking many medications. However unlikely, it is possible that difficulty in swallowing pills or tablets could generate tumorlike lesions.

• Proceedings of the KSRS Conference
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• v.2
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• pp.852-855
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• 2006

There is an increasing need to use data from different sensors in order to maximize the chances of obtaining a cloud-free image and to meet timely requirements for information. However, the use of data from multiple sensor systems is depending on comprehensive relationships between sensors of different types. Indeed, a study of inter-sensor relationships is well advanced in the effective use of remotely sensed data from multiple sensors. This paper was concerned with relationships between sensors of different types for vegetation indices (VI). The study was conducted using IKONOS and Landsat-7 ETM+ images. IKONOS and Landsat-7 ETM+ image of the same or about the same dates were acquired. The Landsat-7 ETM+ images were resampled in order to make them coincide with the pixel sizes of IKONOS. Inter-relationships of vegetation indices between images were performed using at-satellite reflectance obtained by converting image digital number (DN). All images were applied to topographic normalization method in order to reduce topographic effect in digital imagery. Also, Inter-sensor model equations between two sensors were developed and applied to other study region. In the result, the relational equations can be used to compute or interpret VI of one sensor using the VI of another sensor.

• Biomolecules & Therapeutics
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• v.25 no.4
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• pp.390-395
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• 2017

The present study investigated the sedative effects of Sophora flavescens (SF) and its bioactive compound, matrine through performing locomotor activity test and the electroencephalography (EEG) analysis in the rat. The underlying neural mechanism of their beneficial effects was determined by assessing c-Fos immunoreactivity and serotonin (5-HT) in the brain utilizing immunohistochemical method and enzyme-linked immunosorbent assay. The results showed that SF and matrine administration had an effect on normalization of caffeine-induced hyperactivity and promoting a shift toward non-rapid eye movement (NREM) sleep. c-Fos-immunoreactivity and 5-HT level in the ventrolateral preoptic nucleus (VLPO), a sleep promoting region, were increased in the both SF and matrine-injected groups. In conclusion, SF and its bioactive compound, matrine alleviated caffeine-induced hyperactivity and promoted NREM sleep by activating VLPO neurons and modulating serotonergic transmission. It is suggested that SF might be a useful natural alternatives for hypnotic medicine.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.52-55
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• 1996

The spectra of whole blood EDTA samples from two people were generated using a CARY 5E (UV-VIS-NIR) spectrophotometer from 400 to 1000nm which contain visible and NIR region. Only the data between 400 and 800nm were used to analyze the components of blood. Using the same spectrophotometer, the spectra of Water, normal saline, plasma were generated These spectra were subtracted from each blood sample, and then the first derivative of each of the subtracted data was taken to minimize baseline variations and indicated the wavelength-shift of peak and valley. Normalization and division between two blood samples were used to correlate the quantity ratio of specific components with feature of spectra. Samples were controlled at $30^{\circ}C,\;37^{\circ}C$, ambient temperature.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.325-333
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• 2010

Image registration involves overlapping images of an identical region and assigning the data into one coordinate system. Image registration has proved important in remote sensing, enabling registered satellite imagery to be used in various applications such as image fusion, change detection and the generation of digital maps. The image descriptor, which extracts matching points from each image, is necessary for automatic registration of remotely sensed data. Using contrast enhancement algorithms such as histogram equalization and image stretching, the normalized data are applied to the image descriptor. Drawing on the different spectral characteristics of high resolution satellite imagery based on sensor type and acquisition date, the applied normalization method can be used to change the results of matching interest point descriptors. In this paper, the matching points by scale invariant feature transformation (SIFT) are extracted using various contrast enhancement algorithms and injection of Gaussian noise. The results of the extracted matching points are compared with the number of correct matching points and matching rates for each point.

• The Journal of Economics, Marketing and Management
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• v.10 no.3
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• pp.21-36
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• 2022

Purpose: The purpose of this study is to compare and analyze the enterprise's score index calculated from atypical data and corrected data. Research design, data, and methodology: In this study, news articles which are non-financial information but qualitative data were collected from 2,432 SMEs that has been extracted "square proportional stratification" out of 18,910 enterprises with fixed data and compared/analyzed each enterprise's score index through text mining analysis methodology. Result: The analysis showed that qualitative data can be quantitatively evaluated by region, industry and period by collecting news from SMEs, and that there are concerns that it could be an element of alternative credit evaluation. Conclusion: News data cannot be collected even if one of the small businesses is self-employed or small businesses has little or no news coverage. Data normalization or standardization should be considered to overcome the difference in scores due to the amount of reference. Furthermore, since keyword sentiment analysis may have different results depending on the researcher's point of view, it is also necessary to consider deep learning sentiment analysis, which is conducted by sentence.

• Journal of Distribution Science
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• v.20 no.2
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• pp.55-64
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• 2022

Purpose: This research is aimed to study the level of the intellectual potential distribution, as well as the correlation between economic growth and key indicators of intellectual potential in each region of Kazakhstan. A review of the conceptual framework shows that there is a large body of research evaluating the level of intellectual potential in different ways based on different factors. Research design, data, and methodology: The research methodology is divided into two groups the integral index method using the normalization of indicators, weighting, and ranking; the method of correlation analysis. By the proposed methodological approaches, were calculated a set of factors affect the distribution of the intellectual potential. Statistics are taken for indicators of development of the intellectual potential for 2011-2020 from the Bureau of National Statistics. Results: Ranking results showed gaps between regions in Kazakhstan by the level of intellectual potential. Correlation analysis results revealed a statistically significant relationship on expenditures on R&D, computer literacy, innovative products, number of PhD students, and cultural and leisure indicators. Conclusions: Based on the obtained results of the intellectual potential level development there were given recommendations for the reproduction and regulation of the intellectual potential in the future.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.45-50
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• 2004

In this paper, we propose a new PCA-based LDA Mixture Algorithm(PLMA) for real-time face recognition system. This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction part we applied subtraction image, color filtering, eyes and mouth region detection, and normalization method, and in the face recognition part we used the method mixing PCA and LDA in extracted face candidate region images. The existing recognition system using only PCA showed low recognition rates, and it is hard in the recognition system using only LDA to apply LDA to the input images as it is when the number of image pixels ire small as compared with the training set. To overcome these shortcomings, we reduced dimension as we apply PCA to the normalized images, and apply LDA to the compressed images, therefore it is possible for us to do real-time recognition, and we are also capable of improving recognition rates. We have experimented using self-organized DAUface database to evaluate the performance of the proposed system. The experimental results show that the proposed method outperform PCA, LDA and ICA method within the framework of recognition accuracy.