• Horticultural Science & Technology
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• v.34 no.1
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• pp.77-83
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• 2016

The measurement of total fresh weight of plants provides an essential indicator of crop growth for monitoring production. To measure fresh weight without damaging the vegetation, image-based methods have been developed, but they have limitations. In addition, the total plant fresh weight is difficult to measure directly in hydroponic cultivation systems because of the amount of nutrient solution. This study aimed to develop a real-time, precise method to measure the total fresh weight of Romaine lettuce (Lactuca sativa L. cv. Asia Heuk Romaine) with growth stage in a plant factory using a nutrient film technique. The total weight of the channel, amount of residual nutrient solution in the channel, and fresh shoot and root weights of the plants were measured every 7 days after transplanting. The initial weight of the channel during nutrient solution supply (Wi) and its weight change per second just after the nutrient solution supply stopped were also measured. When no more draining occurred, the final weight of the channel (Ws) and the amount of residual nutrient solution in the channel were measured. The time constant (${\tau}$) was calculated by considering the transient values of Wi and Ws. The relationship of Wi, Ws, ${\tau}$, and fresh weight was quantitatively analyzed. After the nutrient solution supply stopped, the change in the channel weight exponentially decreased. The nutrient solution in the channel slowly drained as the root weight in the channel increased. Large differences were observed between the actual fresh weight of the plant and the predicted value because the channel included residual nutrient solution. These differences were difficult to predict with growth stage but a model with the time constant showed the highest accuracy. The real-time fresh weight could be calculated from Wi, Ws, and ${\tau}$ with growth stage.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.105-115
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• 2021

The geological CO2 sequestration in underground geological formation such as deep saline aquifers and depleted hydrocarbon reservoirs is one of the most promising options for reducing the atmospheric CO2 emissions. The process in geological CO2 sequestration involves injection of supercritical CO2 (scCO2) into porous media saturated with pore water and initiates CO2 flooding with immiscible displacement. The CO2 migration and distribution, and, consequently, the displacement efficiency is governed by the interaction of fluids. Especially, the viscous force and capillary force are controlled by geological formation conditions and injection conditions. This study aimed to estimate the effects of surfactant on interfacial tension between the immiscible fluids, scCO2 and porewater, under high pressure and high temperature conditions by using a pair of proxy fluids under standard conditions through pendant drop method. It also aimed to observe migration and distribution patterns of the immiscible fluids and estimate the effects of surfactant concentrations on the displacement efficiency of scCO2. Micromodel experiments were conducted by applying n-hexane and deionized water as proxy fluids for scCO2 and porewater. In order to quantitatively analyze the immiscible displacement phenomena by n-hexane injection in pore network, the images of migration and distribution pattern of the two fluids are acquired through a imaging system. The experimental results revealed that the addition of surfactants sharply reduces the interfacial tension between hexane and deionized water at low concentrations and approaches a constant value as the concentration increases. Also it was found that, by directly affecting the flow path of the flooding fluid at the pore scale in the porous medium, the surfactant showed the identical effect on the displacement efficiency of n-hexane at equilibrium state. The experimental observation results could provide important fundamental information on immiscible displacement of fluids in porous media and suggest the potential to improve the displacement efficiency of scCO2 by using surfactants.

• Journal of Korea Entertainment Industry Association
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• v.15 no.2
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• pp.169-178
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• 2021

The Korean girl group "Kara" has suffered the trilemma of its de facto failure to debut, the crisis of team breakup, and the CEO crisis of the agency. But the group has made an outstanding achievement in the history of Korean pop music after overcoming all odds. Their success strategy has never been disclosed by insiders involved in Kara's total music projects. This study has been carried out in the analysis of the strategy to provide academic implications and to honor the contribution of the late CEO Ho-yeon Lee and Kara's key member Ha-ra Gu. Therefore, between Nov. and Dec. 2020, we conducted in-depth interviews with managers, composers, stylists and Ha-ra Gu(Only in 2019, before her death) who took part in the project. The research model is set up by combining Porter's Competitive Advantage Strategy and the music value chain model into categories of "Product Innovation Differentiation (PD)" (producing, album production, performance activities) and "Marketing Differentiation (MD)" (market targeting, image specialization, promotion and communication). The analysis showed that the PD focused on complete rediscovered harmonization and revalued members' personality and sincerity with peppy songs and dainty dances as well as emission of "bright energy" which caused healing effects instead of mimicking other star singers recklessly. In terms of MD, they selected Japan's 10-20s as their main market, increasing intimacy with fans and media with the image of cute+pretty+classy+sexy. The result suggests that Poter's differentiation can function as a meaningful strategy frame in the fostering, hit, and revival of idol groups. In addition, it reaffirmed that spontaneous and passionate activities of early-stage or celebrity fan may serve as a valid catalyst for realizing differentiation, as Kara's caller of Japanese actor Gekidan Hitori caused a strong "priming effect" that drove Kara's unexpected wonderful success in Japan.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.21-35
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• 2023

Expectations surrounding generative AI technology and its profound ramifications are sweeping across various industrial domains. Given the anticipated pivotal role of the startup ecosystem in the utilization and advancement of generative AI technology, it is imperative to cultivate a deeper comprehension of the present state and distinctive attributes characterizing venture capital (VC) investments within this domain. The current investigation delves into South Korea's landscape of VC investment deals and prognosticates the projected VC investments by juxtaposing these against the United States, the frontrunner in the generative AI industry and its associated ecosystem. For analytical purposes, a compilation of 286 investment deals originating from 117 U.S. generative AI startups spanning the period from 2008 to 2023, as well as 144 investment deals from 42 South Korean generative AI startups covering the years 2011 to 2023, was amassed to construct new datasets. The outcomes of this endeavor reveal an upward trajectory in the count of VC investment deals within both the U.S. and South Korea during recent years. Predominantly, these deals have been concentrated within the early-stage investment realm. Noteworthy disparities between the two nations have also come to light. Specifically, in the U.S., in contrast to South Korea, the quantum of recent VC deals has escalated, marking an augmentation ranging from 285% to 488% in the corresponding developmental stage. While the interval between disparate investment stages demonstrated a slight elongation in South Korea relative to the U.S., this discrepancy did not achieve statistical significance. Furthermore, the proportion of VC investments channeled into generative AI enterprises, relative to the aggregate number of deals, exhibited a higher quotient in South Korea compared to the U.S. Upon a comprehensive sectoral breakdown of generative AI, it was discerned that within the U.S., 59.2% of total deals were concentrated in the text and model sectors, whereas in South Korea, 61.9% of deals centered around the video, image, and chat sectors. Through forecasting, the anticipated VC investments in South Korea from 2023 to 2029 were derived via four distinct models, culminating in an estimated average requirement of 3.4 trillion Korean won (ranging from at least 2.408 trillion won to a maximum of 5.919 trillion won). This research bears pragmatic significance as it methodically dissects VC investments within the generative AI domain across both the U.S. and South Korea, culminating in the presentation of an estimated VC investment projection for the latter. Furthermore, its academic significance lies in laying the groundwork for prospective scholarly inquiries by dissecting the current landscape of generative AI VC investments, a sphere that has hitherto remained void of rigorous academic investigation supported by empirical data. Additionally, the study introduces two innovative methodologies for the prediction of VC investment sums. Upon broader integration, application, and refinement of these methodologies within diverse academic explorations, they stand poised to enhance the prognosticative capacity pertaining to VC investment costs.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.9
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• pp.1388-1398
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• 2015

Quality Function Deployment (QFD) is the most complete and comprehensive method for translating what customers need from a product. This study utilized QFD to develop sauces made from Gochujang and to determine how to fulfill international customers' requirements. A customer survey and expert opinion survey were conducted from May 13 to August 22, 2014 and targeted 220 consumers and 20 experts in the U.S. and China. Finally, a total of 208 (190 consumers and 18 experts) useable data were selected. The top three customer requirements for Gochujang sauces were identified as fresh flavor (4.40), making better flavor (3.99), and cooking availability (3.90). Thirty-three engineering characteristics were developed. The results from the calculation of relative importance of engineering characteristics identified that 'cooking availability', 'free sample and food testing', 'unique concept', and 'development of brand' were the highest. The relative importance of engineering characteristics, correlation, and technical difficulties are ranked, and this result could contribute to the development Korean sauces based on customer needs and engineering characteristics.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1021-1028
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• 2005

The general solution for classification and regression problems can be found by matching and modifying matrices with the information in real world and then these matrices are teaming in neural networks. This paper treats primary space as a real world, and dual space that Primary space matches matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Further more the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-Curve, which are well known for getting regularization parameter, and kernel methods. Both GCV and L-Curve have excellent performance to get regularization parameters, and the performances are similar although they show little bit different results from the different condition of problems. However, these methods are two-step solution because both have to calculate the regularization parameters to solve given problems, and then those problems can be applied to other solving methods. Compared with UV and L-Curve, kernel methods are one-step solution which is simultaneously teaming a regularization parameter within the teaming process of pattern weights. This paper also suggests dynamic momentum which is leaning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV and L-Curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problems.

• 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.

• Science of Emotion and Sensibility
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• v.15 no.3
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• pp.389-398
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• 2012

Virtual worlds have pursued reality as if they actually exist. In order to evaluate the sense of reality in the computer-simulated worlds, several subjective questionnaires, which include specific independent variables, have been proposed in the literature. However, the questionnaires lack reliability and validity necessary for defining and measuring the virtual realization. Few studies have been conducted to investigate the effect of visual factors on the sense of reality experienced by exposing to a virtual environment. Therefore, this study was aimed at reinvestigating the variables and proposing a more reliable and advisable questionnaire for evaluating the virtual realization, focusing on visual factors. Twenty-one questions were gleaned from the literature and subjective interviews with focused groups. Exploratory factor analysis with oblique rotation was performed on the data obtained from 200 participants(females: 100) after exposing to a virtual character image described in an extreme way. After removing poorly loading items, remained subsets were subjected to confirmatory factor analysis on the data obtained from the same participants. As a result, 3 significant factors were determined to efficiently measure the virtual realization. The determined factors included visual presence(3 subset items), visual immersion(7 subset items), and visual interactivity(4 subset items). The proposed factors were verified by conducting a subjective evaluation in which participants were asked to evaluate a 3D virtual eyeball model based on the visual presence. The results implicated that the measurement method was suitable for evaluating the degree of the virtual realization. The proposed method is expected to reasonably measure the degree of the virtual realization.