• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.50-60
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• 2010

For the last half a century, the personal computer and software industries have been prosperous due to the incessant evolution of computer systems. In the 21st century, the embedded system market has greatly increased as the market shifted to the mobile gadget field. While a lot of multimedia gadgets such as mobile phone, navigation system, PMP, etc. are pouring into the market, most industrial control systems still rely on 8-bit micro-controllers and simple application software techniques. Unfortunately, the technological barrier which requires additional investment and higher quality manpower to overcome, and the business risks which come from the uncertainty of the market growth and the competitiveness of the resulting products have prevented the companies in the industry from taking advantage of such fancy technologies. However, high performance, low-power and low-cost hardware and software platforms will enable their high-technology products to be developed and recognized by potential clients in the future. This paper presents such a platform for industrial embedded systems. The platform was designed based on Telechips TCC8300 multimedia processor which embedded a variety of parallel hardware for the implementation of multimedia functions. And open-source Embedded Linux, TinyX and GTK+ are used for implementation of GUI to minimize technology costs. In order to estimate the expected performance and power consumption, the performance improvement and the power consumption due to each of enabled hardware sub-systems including YUV2RGB frame converter are measured. An analytic model was devised to check the feasibility of a new application and trade off its performance and power consumption. The validity of the model has been confirmed by implementing a real target system. The cost can be further mitigated by using the hardware parts which are being used for mass production products mostly in the cell-phone market.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.65-76
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• 2023

In the field of technology entrepreneurship and startups, the development of Artificial Intelligence(AI) has emerged as a key topic for business model innovation. As a result, venture firms are making various efforts centered on AI to secure competitiveness(Kim & Geum, 2023). The purpose of this study is to analyze the relationship between the development of GenAI technology and the startup ecosystem by analyzing domestic news articles to identify trends in the technology startup field. Using BIG Kinds, this study examined the changes in GenAI-related news articles, major issues, and trends in Korean news articles from 1990 to August 10, 2023, focusing on the emergence of ChatGPT before and after, and visualized the relevance through network analysis and keyword visualization. The results of the study showed that the mention of GenAI gradually increased in the articles from 2017 to 2023. In particular, OpenAI's ChatGPT service based on GPT-3.5 was highlighted as a major issue, indicating the popularization of language model-based GenAI technologies such as OpenAI's DALL-E, Google's MusicLM, and VoyagerX's Vrew. This proves the usefulness of GenAI in various fields, and since the launch of ChatGPT, Korean companies have been actively developing Korean language models. Startups such as Ritten Technologies are also utilizing GenAI to expand their scope in the technology startup field. This study confirms the connection between GenAI technology and startup entrepreneurship activities, which suggests that it can support the construction of innovative business strategies, and is expected to continue to shape the development of GenAI technology and the growth of the startup ecosystem. Further research is needed to explore international trends, the utilization of various analysis methods, and the possibility of applying GenAI in the real world. These efforts are expected to contribute to the development of GenAI technology and the growth of the startup ecosystem.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.139-151
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• 2018

The National Base Map construction consists of the regular correction work of dividing the whole country into two regions and carrying out the modification Plotting by aerial photographs every two years as well as the real time updating work of correcting the major change feature within two weeks by the field survey and the As-Built Drawing. In the case of the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS) used for real time updating work of the National base map, the coordinate transformation error is included in the positional error when applied to the National Base Map based on the World Geodetic Reference System as the coordinate system based on the Regional Geodetic Reference System. In addition, National Base Map is registered based on the outline(eaves line) of the building in the Digital Topographic Map, and the Cadastral and Architecture are registered based on the building center line. Therefore, the Building Object management standard is inconsistent. In order to investigate the improvement method, the network RTK survey was conducted directly on a location of the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS) and the problems were analyzed by comparing with the plane plotting position reference in National Base Map. In the case of the general structure with the difference on the Building center line and the eaves line, beside the location information was different also the difference in the ratio of the building object was different between Building center line and the eave. In conclusion, it is necessary to provide the Base data of the double layer of the Building center line and the outline of the building(eaves line) in order to utilize the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS). In addition, it is necessary to study an organic map update process that can acquire the up-to-dateness and the accuracy at the same time.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Restorative Dentistry and Endodontics
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• v.24 no.1
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• pp.100-115
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• 1999

The purpose of this study was to investigate the distribution of calcitonin gene-related peptide containing nerve fibers in rat pulp after dentinl injury by means of immunohistochemistry and confocal laser scanning microscope. The Spague-Dawley rats weighing about 250-300gm were used. The animals were devided into normal control and experimental groups. Experimental animals were sacrified 1, 2, 4, 7, 10, 21days after dentinal injury (dentin cutting, and then acid etching with 35% phosphoric acid) on the maxillary molar teeth. The maxillary teeth and alveolar bone were removed and immersed in the 4% paraformaldehyde in 0.1M phosphate buffer (pH 7.4), then were decalcified with 15% formic acid for 10 days. Serial frozen $50{\mu}m$ thick sections were cut on a cryostat. The rabbit CGRP antibody was used as a primary antibody with a dilution of 1:2000 in 0.01M PB. The sections were incubated for 48 hours at $4^{\circ}C$, and placed into biotinylated antirabbit Ig G as a secondary anti body with dilution of 1:200 in 0.01M PB and incubated in ABC(avidin-biotin complex). The peroxidase reaction was visualized by incubating the sections in 0.05% 3,3 diaminobenzidine tetrahydrochloride containing 0.02% $H_2O_2$. For the confocal laser scanning microscopic examination, Primary antibody reaction was same as immunoperoxidase stainning, but fluorescein isothiocyanate(FITC)-conjugate antirabbit IgG as a secondary antibody was used. The confocal laser scanning microscope was used for the examination. A series of images of optical sections was collected with a 20x objective at $3{\mu}m$ intervals throughout the depth of specimen. FITC fluerescence was registrated through a 488nm and 568nm excitation filter, and images were saved on optical disk. The stereoscopic images and three dimentionnal images were reconstructed by computer software, and then were analyzed. The results were as follows : 1. In normal control group, CGRP containing nerve fibers were coursed through the root with very little branching, and then formed a dense network of terminals in coronal pulp. 2. A slight increase in CGRP containing nerve fibers at 1 and 2day postinjury was noted subjacent to the injury site. In the 4day group, there were an extensive increase in the number of reactive fibers, followed by a partial return toward normal levels at 7~10 day postinjury, and return by 21days. 3. The sprouting of the CGRP containing nerve fibers was evident within 2day after dentinal injury, and by 4days there was a maximal increased, but was decreased at 7days and returned to normal 10~21 day postinjury. 4. In confocal laser scanning microscopic exammination, the distinct distribution pattern and sprouting reaction of CGRP containing nerve fibers were observed in stereoscopic images and three dimentional images. These results suggest that CGRP containing nerve fiber can be important role in the response to dental injury and pain regulation.

• Korean Journal of Poultry Science
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• v.44 no.1
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• pp.1-9
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• 2017

The objective of the present study was to determine the expression of genes associated with lipopolysaccharide (LPS)-induced stressor in two breeds of chickens: the Korean native chicken (KNC) and the White Leghorn chicken (WLH). Forty chickens per breed, aged 40 weeks, were randomly allotted to the control (CON, administered the saline vehicle) and LPS-injected stress groups. Samples were collected at 0 and 48 h post-LPS injection, and total RNA was extracted from the chicken livers for RNA microarray and quantitative real-time polymerase chain reaction (qRT-PCR) analyses. In response to LPS, 1,044 and 1,193 genes were upregulated, and 1,000 and 1,072 genes were downregulated in the KNC and WLH, respectively, using a ${\geq}2$-fold cutoff change. A functional network analysis revealed that stress-related genes were downregulated in both KNC and WLH after LPS infection. The results obtained from the qRT-PCR analysis of mRNA expression of heat shock 90 (HSP90), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), activating transcription factor 4 (ATF4), sterol regulatory element-binding protein 1 (SREBP1), and X-box binding protein 1 (XBP1) were confirmed by the results of the microarray analysis. There was a significant difference in the expression of stress-associated genes between the control and LPS-injected KNC and WLH groups. The qRT-PCR analysis revealed that the stress-related $HSP90{\alpha}$ and HMGCR genes were downregulated in both LPS-injected KNC and WLH groups. However, the HSP70 and $HSP90{\beta}$ genes were upregulated only in the LPS-injected KNC group. The results suggest that the mRNA expression of stress-related genes is differentially affected by LPS stimulation, and some of the responses varied with the chicken breed. A better understanding of the LPS-induced infective stressors in chicken using the qRT-PCR and RNA microarray analyses may contribute to improving animal welfare and husbandry practices.

• The KIPS Transactions:PartC
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• v.13C no.1 s.104
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• pp.83-94
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• 2006

This paper describes ATM cell encipherment method using Rijndael Algorithm adopted as an AES(Advanced Encryption Standard) by NIST in 2001. ISO 9160 describes the requirement of physical layer data processing in encryption/decryption. For the description of ATM cell encipherment method, we implemented ATM data encipherment equipment which satisfies the requirements of ISO 9160, and verified the encipherment/decipherment processing at ATM STM-1 rate(155.52Mbps). The DES algorithm can process data in the block size of 64 bits and its key length is 64 bits, but the Rijndael algorithm can process data in the block size of 128 bits and the key length of 128, 192, or 256 bits selectively. So it is more flexible in high bit rate data processing and stronger in encription strength than DES. For tile real time encryption of high bit rate data stream. Rijndael algorithm was implemented in FPGA in this experiment. The boundary of serial UNI cell was detected by the CRC method, and in the case of user data cell the payload of 48 octets (384 bits) is converted in parallel and transferred to 3 Rijndael encipherment module in the block size of 128 bits individually. After completion of encryption, the header stored in buffer is attached to the enciphered payload and retransmitted in the format of cell. At the receiving end, the boundary of ceil is detected by the CRC method and the payload type is decided. n the payload type is the user data cell, the payload of the cell is transferred to the 3-Rijndael decryption module in the block sire of 128 bits for decryption of data. And in the case of maintenance cell, the payload is extracted without decryption processing.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.931-939
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• 2022

In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.59-88
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• 2008

Digital Convergence means integration between industry, technology, and contents, and in marketing, it usually comes with creation of new types of product and service under the base of digital technology as digitalization progress in electro-communication industries including telecommunication, home appliance, and computer industries. One can see digital convergence not only in instruments such as PC, AV appliances, cellular phone, but also in contents, network, service that are required in production, modification, distribution, re-production of information. Convergence in contents started around 1990. Convergence in network and service begins as broadcasting and telecommunication integrates and DMB(digital multimedia broadcasting), born in May, 2005 is the symbolic icon in this trend. There are some positive and negative expectations about DMB. The reason why two opposite expectations exist is that DMB does not come out from customer's need but from technology development. Therefore, customers might have hard time to interpret the real meaning of DMB. Time is quite critical to a high tech product, like DMB because another product with same function from different technology can replace the existing product within short period of time. If DMB does not positioning well to customer's mind quickly, another products like Wibro, IPTV, or HSPDA could replace it before it even spreads out. Therefore, positioning strategy is critical for success of DMB product. To make correct positioning strategy, one needs to understand how consumer interprets DMB and how consumer's interpretation can be changed via communication strategy. In this study, we try to investigate how consumer perceives a new product, like DMB and how AD strategy change consumer's perception. More specifically, the paper segment consumers into sub-groups based on their DMB perceptions and compare their characteristics in order to understand how they perceive DMB. And, expose them different printed ADs that have messages guiding consumer think DMB in specific ways, either cellular phone or personal TV. Research Question 1: Segment consumers according to perceptions about DMB and compare characteristics of segmentations. Research Question 2: Compare perceptions about DMB after AD that induces categorization of DMB in direction for each segment. If one understand and predict a direction in which consumer perceive a new product, firm can select target customers easily. We segment consumers according to their perception and analyze characteristics in order to find some variables that can influence perceptions, like prior experience, usage, or habit. And then, marketing people can use this variables to identify target customers and predict their perceptions. If one knows how customer's perception is changed via AD message, communication strategy could be constructed properly. Specially, information from segmented customers helps to develop efficient AD strategy for segment who has prior perception. Research framework consists of two measurements and one treatment, O1 X O2. First observation is for collecting information about consumer's perception and their characteristics. Based on first observation, the paper segment consumers into two groups, one group perceives DMB similar to Cellular phone and the other group perceives DMB similar to TV. And compare characteristics of two segments in order to find reason why they perceive DMB differently. Next, we expose two kinds of AD to subjects. One AD describes DMB as Cellular phone and the other Ad describes DMB as personal TV. When two ADs are exposed to subjects, consumers don't know their prior perception of DMB, in other words, which subject belongs 'similar-to-Cellular phone' segment or 'similar-to-TV' segment? However, we analyze the AD's effect differently for each segment. In research design, final observation is for investigating AD effect. Perception before AD is compared with perception after AD. Comparisons are made for each segment and for each AD. For the segment who perceives DMB similar to TV, AD that describes DMB as cellular phone could change the prior perception. And AD that describes DMB as personal TV, could enforce the prior perception. For data collection, subjects are selected from undergraduate students because they have basic knowledge about most digital equipments and have open attitude about a new product and media. Total number of subjects is 240. In order to measure perception about DMB, we use indirect measurement, comparison with other similar digital products. To select similar digital products, we pre-survey students and then finally select PDA, Car-TV, Cellular Phone, MP3 player, TV, and PSP. Quasi experiment is done at several classes under instructor's allowance. After brief introduction, prior knowledge, awareness, and usage about DMB as well as other digital instruments is asked and their similarities and perceived characteristics are measured. And then, two kinds of manipulated color-printed AD are distributed and similarities and perceived characteristics for DMB are re-measured. Finally purchase intension, AD attitude, manipulation check, and demographic variables are asked. Subjects are given small gift for participation. Stimuli are color-printed advertising. Their actual size is A4 and made after several pre-test from AD professionals and students. As results, consumers are segmented into two subgroups based on their perceptions of DMB. Similarity measure between DMB and cellular phone and similarity measure between DMB and TV are used to classify consumers. If subject whose first measure is less than the second measure, she is classified into segment A and segment A is characterized as they perceive DMB like TV. Otherwise, they are classified as segment B, who perceives DMB like cellular phone. Discriminant analysis on these groups with their characteristics of usage and attitude shows that Segment A knows much about DMB and uses a lot of digital instrument. Segment B, who thinks DMB as cellular phone doesn't know well about DMB and not familiar with other digital instruments. So, consumers with higher knowledge perceive DMB similar to TV because launching DMB advertising lead consumer think DMB as TV. Consumers with less interest on digital products don't know well about DMB AD and then think DMB as cellular phone. In order to investigate perceptions of DMB as well as other digital instruments, we apply Proxscal analysis, Multidimensional Scaling technique at SPSS statistical package. At first step, subjects are presented 21 pairs of 7 digital instruments and evaluate similarity judgments on 7 point scale. And for each segment, their similarity judgments are averaged and similarity matrix is made. Secondly, Proxscal analysis of segment A and B are done. At third stage, get similarity judgment between DMB and other digital instruments after AD exposure. Lastly, similarity judgments of group A-1, A-2, B-1, and B-2 are named as 'after DMB' and put them into matrix made at the first stage. Then apply Proxscal analysis on these matrixes and check the positional difference of DMB and after DMB. The results show that map of segment A, who perceives DMB similar as TV, shows that DMB position closer to TV than to Cellular phone as expected. Map of segment B, who perceive DMB similar as cellular phone shows that DMB position closer to Cellular phone than to TV as expected. Stress value and R-square is acceptable. And, change results after stimuli, manipulated Advertising show that AD makes DMB perception bent toward Cellular phone when Cellular phone-like AD is exposed, and that DMB positioning move towards Car-TV which is more personalized one when TV-like AD is exposed. It is true for both segment, A and B, consistently. Furthermore, the paper apply correspondence analysis to the same data and find almost the same results. The paper answers two main research questions. The first one is that perception about a new product is made mainly from prior experience. And the second one is that AD is effective in changing and enforcing perception. In addition to above, we extend perception change to purchase intention. Purchase intention is high when AD enforces original perception. AD that shows DMB like TV makes worst intention. This paper has limitations and issues to be pursed in near future. Methodologically, current methodology can't provide statistical test on the perceptual change, since classical MDS models, like Proxscal and correspondence analysis are not probability models. So, a new probability MDS model for testing hypothesis about configuration needs to be developed. Next, advertising message needs to be developed more rigorously from theoretical and managerial perspective. Also experimental procedure could be improved for more realistic data collection. For example, web-based experiment and real product stimuli and multimedia presentation could be employed. Or, one can display products together in simulated shop. In addition, demand and social desirability threats of internal validity could influence on the results. In order to handle the threats, results of the model-intended advertising and other "pseudo" advertising could be compared. Furthermore, one can try various level of innovativeness in order to check whether it make any different results (cf. Moon 2006). In addition, if one can create hypothetical product that is really innovative and new for research, it helps to make a vacant impression status and then to study how to form impression in more rigorous way.