• Journal of Technology Innovation
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• v.17 no.2
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• pp.55-80
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• 2009

Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.

• Korean Journal of Food Science and Technology
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• v.30 no.4
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• pp.908-915
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• 1998

By using Korean native soybean, traditional meju was prepared in Chuncheon, Kangweondo according to the traditional process. Analysis of physico-chemical, enzymatic and microbiological changes during meju fermentation were carried out in order to obtain a basic information for industrial scale production of meju. The enviroments for natural meju fermentation were $10{\sim}15^{\circ}C$ and $60{\sim}70%{\;}RH$. Moisture content decreased from 59% to 11% (exterior section) and 19% (interior section). the pH of meju rapidly increased up to 8.5 at $33^{rd}{\;}day$ of fermentation and thereafter decreased down to 7.9 at $70^{th}{\;}day$ of fermentation. Souble protein content was 1.47% at initial stage and increased up to $6.31{\sim}7.34%$ at $33^{rd}{\;}day$ of fermentation. Amino nitrogen content was $460{\sim}770{\;}mg%$ at $70^{th}{\;}day$ of fermentation. the color of meju became gradually black and decreased in redness and yellowness. During the process, protease and lipase seemed to play an important role in the digestion of soy protein and fat. Acidic protease activity increased up to $135.9{\sim}152.4{\;}unit/g$ at $33^{rd}{\;}day$ of fermentation and were $181.3{\sim}272.6{\;}unit/g$ at $70^{th}{\;}day$ of fermentation. Lipase activity increased up to 6 unit/g (interior section) and 15 unit/g (exterior section) at $70^{th}{\;}day$ of fermentation. the viable cell count of meju was at the level of $10^8{\;}CFU/g$ during the overall fermentation period. Aerobic halophilic count was $1.51{\times}10^7{\;}CFU/g$ at initial stage and maintained $10^8{\;}CFU/g$ level during the process. Initial anaerobic cell count was $2.0^9{\times}10^4{\;}CFU/g$ and increased up to $10^5{\;}CFU/g$ level at 47 days. Yeast and mold counts were $10^4{\sim}10^5{\;}CFU/g$ for the fermentation period.

• Korean Journal of Food Science and Technology
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• v.50 no.3
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• pp.331-338
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• 2018

The purpose of this study was to determine the optimum sterilization conditions for the production of retorted steamed egg using response surface methodology. Sterilization processes for eighteen conditions using varying sterilization temperature ($X_1$), time ($X_2$), and method ($X_3$) as the independent variables were carried out through a $3^2{\times}2$ experimental factorial design. Quality evaluations after sterilization included measurements of $F_0$ value ($Y_1$), peak stress ($Y_2$), pH ($Y_3$), color value ($Y_{4-6}$), and organoleptic test [preference for appearance ($Y_7$), overall acceptability ($Y_8$), and preference for texture ($Y_9$) and egg taste ($Y_{10}$)]. Dependent variables ($Y_{1-10}$) of eighteen conditions were more affected by temperature and time than by the sterilization method. Eight factors were selected among the dependent variables as significant factors related to the quality of the steamed egg. Finally, by establishing an optimum range of each dependent variable and contour analysis, the optimum sterilization conditions for the production of steamed egg were determined to be $120^{\circ}C$ for 25 min using a 2-step sterilization process.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.495-505
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• 2006

In order to classify aerosol type, Aerosol Optical Thickness (AOT) and Fine mode Fraction (FF), which is the optical thickness ratio of small particles$(<1{\mu}m)$ to total particles, data from MODIS (MODerate Imaging Spectraradiometer) aerosol products were analyzed over North-East Asia during one year period of 2005. A study area was in the ocean region of $20^{\circ}N\sim50^{\circ}N$ and $110^{\circ}E\simt50^{\circ}E$. Three main atmospheric aerosols such as dust, sea-salt, and pollution can be classified by using the relationship between AOT and FF. Dust aerosol has frequently observed over the study area with relatively high aerosol loading (AOT>0.3) of large particles (FF<0.65) and its contribution to total AOT in spring was up to 24.0%. Pollution aerosol, which is originated from anthropogenic sources as well as a natural process like biomass burning, has observed in the regime of high FF (>0.65) with wide AOT variation. Average pollution AOT was $0.31{\pm}0.05$ and its contribution to total AOT was 79.8% in summer. Characteristic of sea-salt aerosol was identified with low AOT (<0.3), almost below 0.1, and slightly higher FF than dust and lower FF than pollution. Seasonal analysis results show that maximum AOT $(0.33{\pm}0.11)$ with FF $(0.66{\pm}0.21)$ in spring and minimum AOT $(0.19{\pm}0.05)$, FF $(0.60{\pm}0.14)$ in fall were observed in the study area. Spatial characteristic was that AOT increasing trend is observed as closing to the eastern part of China due to transport of aerosols from China by the prevailing westerlies.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.209-213
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• 2019

Recently, demand for high energy density and long cycling stability of energy storage system has increased for application using with frequency regulation (F/R) in power grid. Supercapacitor have long lifetime and high charge and discharge rate, it is very adaptable to apply a frequency regulation in power grid. Supercapacitor can complement batteries to reduce the size and installation of batteries. Because their utilization in a system can potentially eliminate the need for short-term frequent replacement as required by batteries, hence, saving the resources invested in the upkeep of the whole system or extension of lifecycle of batteries in the long run of power grid. However, low energy density in supercapacitor is critical weakness to utilization for huge energy storage system of power grid. So, it is still far from being able to replace batteries and struggle in meeting the demand for a high energy density. But, today, LIC (Lithium Ion Capacitor) considered as an attractive structure to improve energy density much more than EDLC (Electric double layer capacitor) because LIC has high voltage range up to 3.8 V. But, many aspects of the electrochemical performance of LIC still need to be examined closely in order to apply for commercial use. In this study, in order to improve the capacitance of LIC related with energy density, we designed new method of pre-doping in anode electrode. The electrode in cathode were fabricated in dry room which has a relative humidity under 0.1% and constant electrode thickness over $100{\mu}m$ was manufactured for stable mechanical strength and anode doping. To minimize of contact resistance, fabricated electrode was conducted hot compression process from room temperature to $65^{\circ}C$. We designed various pre-doping method for LIC structure and analyzing the doping mechanism issues. Finally, we suggest new pre-doping method to improve the capacitance and electrochemical stability for LIC.

• Journal of Technology Innovation
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• v.29 no.3
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• pp.1-31
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• 2021

With the growing importance of technology innovation as a key factor for firms' competitive advantage, 'innovation persistence' became also an important research subject. 'Innovation Persistence' is a concept that indicates whether or not firms' innovation activity or performance continues. However, the data used for innovation studies are carried out as cross-sectional surveys in most countries. For this reason, studies dealing with longitudinal aspect of innovation persistence are rare. In particular, there is almost no research on innovation persistence using Korean innovation survey data. This study reviews the concepts and characteristics of innovation persistence based on extant literature, and perform an empirical analysis on the status and features of Korean firms' technology innovation persistence. Based on the data of the Korean Innovation Survey (KIS) conducted every other year from 2012 to 2018, panel data on 3,379 firms which observed multiple times are constructed. As a result, only part of the firms with persistent innovation were observed (for innovation performance 10~12%, for innovation activity 15~17%), and it was found that the persistence of non-innovation was remarkable(about 52~57%). And it was confirmed that the persistence of innovation activities is stronger than that of innovation performance. Besides, some features by sub-types of innovation appeared. Product innovation showed higher persistence than process innovation, and internal R&D also showed higher persistence than joint/external R&D. As a result of additional logit analysis to identify factors, it was found that radical or gradual product innovation is the most influential factor in persisting innovation in the next period. Since the sample selection bias due to a limitations of raw data might exist in the panel data constructed in this study, it should be noted that faulty generalization of the results are not allowed. Nevertheless, this is the first study to examine the technology innovation persistence targeting Korean firms and is expected to be a starting point for follow-up studies. It is anticipated that advanced research results will be drawn through the establishment of official panel data and improved methodologies.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Food Engineering Progress
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• v.14 no.2
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• pp.85-91
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• 2010

In the food process, twist screen is widely used to divide particles on the basis of size. As screen equipped in the twist screen perfoms an important part in the particle size distribution mechanism, the contact area of screen and particles, retention time of particles on the screen, mesh and string thickness of screen and the flow pattern of particles on the screen are major points of the separation efficiency. To improve the separation efficiency, increase the retention time and control the flow pattern of particles, screen frame dam and spiral blockage are installed on the sieve of twist screen ${\emptyset}$ 1200 and ${\emptyset}$ 1500. Twist screen ${\emptyset}$ 1500 with frame dam treated similar separation capacity, 37% higher separation ratio and less non-separated particles of product output 1 than general twist screen. Twist screens with frame dam and spiral blockage showed less treatment capacity, three times higher division ratio and entire separation than general twist screen.

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.1-12
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• 2019

The health benefits associated with consumption of fresh produce have been clearly demonstrated and encouraged by international nutrition and health authorities. However, since fresh produce is usually minimally processed, increased consumption of fresh fruits and vegetables has also led to a simultaneous escalation of foodborne illness cases. According to the report by the World Health Organization (WHO), 1 in 10 people suffer from foodborne diseases and 420,000 die every year globally. In comparison to other processed foods, fresh produce can be easily contaminated by various routes at different points in the supply chain from farm to fork. This review is focused on the identification and characterization of possible sources of foodborne illnesses from chemical, biological, and physical hazards and the applicable methodologies to detect potential contaminants. Agro-chemicals (pesticides, fungicides and herbicides), natural toxins (mycotoxins and plant toxins), and heavy metals (mercury and cadmium) are the main sources of chemical hazards, which can be detected by several methods including chromatography and nano-techniques based on nanostructured materials such as noble metal nanoparticles (NMPs), quantum dots (QDs) and magnetic nanoparticles or nanotube. However, the diversity of chemical structures complicates the establishment of one standard method to differentiate the variety of chemical compounds. In addition, fresh fruits and vegetables contain high nutrient contents and moisture, which promote the growth of unwanted microorganisms including bacterial pathogens (Salmonella, E. coli O157: H7, Shigella, Listeria monocytogenes, and Bacillus cereus) and non-bacterial pathogens (norovirus and parasites). In order to detect specific pathogens in fresh produce, methods based on molecular biology such as PCR and immunology are commonly used. Finally, physical hazards including contamination by glass, metal, and gravel in food can cause serious injuries to customers. In order to decrease physical hazards, vision systems such as X-ray inspection have been adopted to detect physical contaminants in food, while exceptional handling skills by food production employees are required to prevent additional contamination.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.65-73
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• 2019

In order to increase the production efficiency of the ship and shorten the production cycle, it is important to evaluate the accuracy of the ship components efficiently during the drying cycle. The accuracy control of the block is important for shortening the ship process, reducing the cost, and improving the accuracy of the ship. Some systems have been developed and used mainly in large shipyards, but in some cases, they are measured and managed using conventional measuring instruments such as tape measure and beam, optical instruments as optical equipment, In order to perform accuracy control, these tools and equipment as well as equipment for recording measurement data and paper drawings for measuring the measurement position are inevitably combined. The measured results are managed by the accuracy control system through manual input or recording device. In this case, the measurement result is influenced by the work environment and the skill level of the worker. Also, in the measurement result management side, there are a human error about the lack of the measurement result creation, the lack of the management sheet management, And costs are lost in terms of efficiency due to consumption. The purpose of this study is to improve the working environment in the existing accuracy management process by using the augmented reality technology to visualize the measurement information on the actual block and to obtain the measurement information And a smart management system based on augmented reality that can effectively manage the accuracy management data through interworking with measurement equipment. We confirmed the applicability of the proposed system to the accuracy control through the prototype implementation.