• Korean Chemical Engineering Research
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• v.57 no.6
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• pp.874-882
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• 2019

In this study, the gasification characteristics of four types of unused woody biomass and one waste wood in a lab-scale bubbling fluidized bed gasifier (Diameter: 0.11 m, Height: 0.42 m) were investigated. Effect of equivalence ratio (ER) of 0.15-0.3 and gas velocity of $2.5-5U_0/U_{mf}$ are determined at the constant temperature of $800^{\circ}C$ and fuel feeding rate of 1 kg/h. The silica sand particle having an average particle size of $287{\mu}m$ and olivine with an average particle size of $500{\mu}m$ were used as the bed material, respectively. The average product gas composition of samples is as follows; $H_2$ 3-4 vol.%, CO 15-16 vol.%, $CH_4$ 4 vol.% and $CO_2$ 18-19 vol.% with a lower heating value (LHV) of $1193-1301kcal/Nm^3$ and higher heating value (HHV) of $1262-1377kcal/Nm^3$. In addition, it was found that olivine reduced most of C2 components and increased $H_2$ content compared to silica sand, resulting in cracking reaction of tar. The non-condensable tar decreases by 72% ($1.24{\rightarrow}0.35g/Nm^3$) and the condensable tar decreases by 27% ($4.4{\rightarrow}3.2g/Nm^3$).

• Korean Chemical Engineering Research
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• v.57 no.6
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• pp.868-873
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• 2019

In this study, the evaluation of performance of AORFB using methyl viologen and TEMPOL as organic active materials in neutral supporting electrolyte (NaCl) with various membrane types was performed. Using methyl viologen and TEMPOL as active materials in neutral electrolyte solution, the cell voltage is 1.37V which is relatively high value for AORFB. Two types of membranes were examined for performance comparison. First, when using Nafion 117 membrane which is commercial cation exchange membrane, only the charge process occurred in the first cycle and the single cell couldn't work because of its high resistance. However, when using Fumasep anion exchange membrane (FAA-3-50) instead of Nafion 117 membrane, the result was obtained as the totally different charge-discharge graphs. When current density was $40mA{\cdot}cm^{-2}$ and cut off voltage range was from 0.55 V to 1.7 V, the charge efficiency (CE) was 97% and voltage efficiency (VE) was 78%. In addition, the discharge capacity was $1.44Ah{\cdot}L^{-1}$ which was 54% of theoretical capacity ($2.68Ah{\cdot}L^{-1}$) at $10^{th}$ cycle and the capacity loss rate was $0.0015Ah{\cdot}L^{-1}$ per cycle during 50 cycles. Through cyclic voltammetry test, it seems that this difference in the performance between the full cell using Nafion 117 membrane and Fumasep anion exchange membrane came from increasing resistance due to chemical reaction between membrane and active material, not the capacity loss due to cross-over of active material through membrane.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.875-882
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• 2021

To minimize occupational noise induced hearing loss, it is recommended that workers should not be exposed to noise levels exceeding 85 dBA for over 8 h. In the present study, noise exposure levels were measured for seven workers based on their tasks on a training ship. The A-weighted noise exposure level (L_ex,24h) was measured by taking into account the A-weighted equivalent continuous sound level (L_Aeq,i), duration (h) and noise contribution (L_ex,24h,i) from the workers' locations. Results are thus obtained for different job categories as follows: officer group L_ex,24h=56.1 dB, navigation crew L_ex,24h=58.9 dB, navigation cadet L_ex,24h=62.0 dB, ship's cook L_ex,24h=64.3 dB, engine cadet L_ex,24h=91.1 dB, engineer L_ex,24h=91.1 dB, and engine crew L_ex,24h=95.1 dB. It was determined that the engineers, engine crews, and engine cadets in charge of machinery must wear hearing protection devices. By wearing hearing protection devices when working in highly noisy engine rooms, it is estimated that the noise expose levels could be reduced by the following amounts: engineer L_ex,24h=23.1 dB, engine Crew L_ex,24h=24.4 dB, and engine cadet L_ex,24h=21.5 dB. Moreover, if the no. 2 lecture room and mess room bottom plates in the cadets accommodations were improved to the 64 mm A-60-class floating plates, then further reductions are possible as follows: navigation cadet L_ex,24h=4.3 dB and engine cadet L_ex,24h=1.8 dB.

• Korean Chemical Engineering Research
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• v.59 no.4
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• pp.548-556
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• 2021

This study was conducted to investigate the applicability of lignocellulosic fiber and coconut shell activated carbon (CSA) for the production of a particulate matter (PM)-reducing air-filter as raw materials to solve the environmental problems of non-woven fabrics. CSA had a good potential to use as a raw material of air-filter for reducing volatile organic compounds as well as noxious metals, and reduction capability of the CSA was 5 times higher than that of wood fiber. Natural adhesives formulated with proteinaceous wastes mostly were applied successfully to fabricate air-filters with the shape of fiberboard. The air-filter fabricated with the minimum target density of 200 kg/m³ and the maximum CSA-content of 40 wt% in fiberboard had a good manageable strength. However, the fiberboard filters was required to make vent-holes for improving an air-permeability of the filters. Size of the CSA particles was adjusted to greater than 2 mesh with the consideration of strength and formability of the fiberboard. Three-layers fiberboard that only wood fiber and the mixture of wood fiber and CSA were formed in the surface and middle layers, respectively, was determined to the optimal condition for the production of air-filters. In addition, traditional Korean paper handmade from mulberry trees (TKP) showed a good PM-reducing property as an air-filter. It is concluded that air-filtering set composed of fiberboard with vent-holes and TKP instead of conventional air-filters made with non-woven fabrics can be used as a filter for reducing the concentrations of PM, VOC and noxious metals existed in indoor and outdoor spaces.

• Korean Chemical Engineering Research
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• v.59 no.4
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• pp.557-564
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• 2021

Sr_0.92Y_0.08Ti_1-xV_xO_3-δ (SYTV) with perovskite structure was investigated as an alternative anode to utilize H₂S containing fuels in solid oxide fuel cells. To improve the electrochemical performance of Sr_0.92Y_0.08TiO_3-δ (SYT), vanadium(V) was substituted to titanium(Ti) at the B-site of the SYT perovskites. The SYTV synthesized by the Pechini method was chemically compatible with the YSZ electrolyte without additional by-products formation under the cell fabricating conditions. As increasing V substitution amounts, the oxygen vacancies increased, resulting to increasing ionic conductivity of the anode. The cell performance in pure H₂ at 850 ℃ is 19.30 mW/cm² and 34.87 mW/cm² for a 1 mol.% and 7 mol.% of V substituted anodes, respectively. The cell performance using H₂ fuel containing 1000 ppm of H₂S at 850 ℃ was 23.37 mW/cm² and 73.11 mW/cm² for a 1 mol.% and 7 mol.% of V substituted anodes, respectively.

• 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.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.453-462
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• 2021

A lot of license plate data is required for car number recognition. License plate data needs to be balanced from past license plates to the latest license plates. However, it is difficult to obtain data from the actual past license plate to the latest ones. In order to solve this problem, a license plate recognition study through deep learning is being conducted by creating a synthetic license plates. Since the synthetic data have differences from real data, and various data augmentation techniques are used to solve these problems. Existing data augmentation simply used methods such as brightness, rotation, affine transformation, blur, and noise. In this paper, we apply a style transformation method that transforms synthetic data into real-world data styles with data augmentation methods. In addition, real license plate data are noisy when it is captured from a distance and under the dark environment. If we simply recognize characters with input data, chances of misrecognition are high. To improve character recognition, in this paper, we applied the DeblurGANv2 method as a quality improvement method for character recognition, increasing the accuracy of license plate recognition. The method of deep learning for license plate detection and license plate number recognition used YOLO-V5. To determine the performance of the synthetic license plate data, we construct a test set by collecting our own secured license plates. License plate detection without style conversion recorded 0.614 mAP. As a result of applying the style transformation, we confirm that the license plate detection performance was improved by recording 0.679mAP. In addition, the successul detection rate without image enhancement was 0.872, and the detection rate was 0.915 after image enhancement, confirming that the performance improved.

• Korean Chemical Engineering Research
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• v.56 no.6
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• pp.871-877
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• 2018

Storing the surplus energy from renewable energy resource is one of the challenges related to intermittent and fluctuating nature of renewable energy electricity production. $CO_2$ methanation is well known reaction that as a renewable energy storage system. $CO_2$ methanation requires a catalyst to be active at relatively low temperatures ($250-500^{\circ}C$) and selectivity towards methane. In this study, the catalytic performance test was conducted using a pressurized bubbling fluidized bed reactor (Diameter: 0.025 m and Height: 0.35 m) with $Ni/{\gamma}-Al_2O_3$ (Ni70%, and ${\gamma}-Al_2O_3$30%) catalyst. The range of the reaction conditions were $H_2/CO_2$ mole ratio range of 4.0-6.0, temperature of $300-420^{\circ}C$, pressure of 1-9 bar, and gas velocity ($U_0/U_{mf}$) of 1-5. As the $H_2/CO_2$ mole ratio, temperature and pressure increased, $CO_2$ conversion increases at the experimental temperature range. However, $CO_2$ conversion decreases with increasing gas velocity due to poor mixing characteristics in the fluidized bed. The maximum $CO_2$ conversion of 99.6% was obtained with the operating condition as follows; $H_2/CO_2$ ratio of 5, temperature of $400^{\circ}C$, pressure of 9 bar, and $U_0/U_{mf}$ of 1.4-3.

• Korean Chemical Engineering Research
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• v.57 no.1
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• pp.90-104
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• 2019

In this study, CALPUFF modeling was performed, using a real surface and upper air meterological data to predict trustworthy modeling-results. Pollutant-releases from windscreen chambers of enclosed poultry farms, P1 and P2, and from a open poultry farm, P3, and their diffusing behavior were modeled by CALPUFF modeling with volume sources as well as by finally-adjusted CALPUFF modeling where a linear velocity of upward-exit gas averaged with the weight of each directional-emitting area was applied as a model-linear velocity ($u^M_y$) at a stack, with point sources. In addition, based upon the scenario of poultry farm-releasing odor and particulate matter (PM) removal efficiencies of 0, 20, 50 and 80% or their corresponding emission rates of 100, 80, 50 and 20%, respectively, CALPUFF modeling was performed and concentrations of odor and PM were predicted at the region as a discrete receptor where civil complaints had been frequently filed. The predicted concentrations of ammonia, hydrogen sulfide, $PM_{2.5}$ and $PM_{10}$ were compared with those required to meet according to the offensive odor control law or the atmospheric environmental law. Subsequently their required removal efficiencies at poultry farms of P1, P2 and P3 were estimated. As a result, a priori assumption that pollutant concentrations at their discrete receptors are reduced by the same fraction as pollutant concentrations at P1, P2 and P3 as volume source or point source, were controlled and reduced, was proven applicable in this study. In case of volume source-adopted CALPUFF modeling, its required removal efficiencies of P1 compared with those of point source-adopted CALPUFF modeling, were predicted similar each other. However, In case of volume source-adopted CALPUFF modeling, its required removal efficiencies of both ammonia and $PM_{10}$ at not only P2 but also P3 were predicted higher than those of point source-adopted CALPUFF modeling. Nonetheless, the volume source-adopted CALPUFF modeling was preferred as a safe approach to resolve civil complaints. Accordingly, the required degrees of pollution prevention against ammonia, hydrogen sulfide, $PM_{2.5}$ and $PM_{10}$ at P1 and P2, were estimated in a proper manner.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.101-116
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• 2019

Technology-based start-up is important in that it encourages innovation, facilitates the development of new products and services, and contributes to job creation. Technology-based start-up activates entrepreneurship when appropriate support is provided within the ecosystem. Thus, understanding the technology-based start-up ecosystem is crucial. The purpose of this study is as follows. First, in Herrmann et al.'s(2015) study, we compare and analyze the ecosystem of each country by selecting representative regions such as Silicon Valley, Tel Aviv, London and Singapore which have the highest ranking in the start-up ecosystem. Second, we try to deeply understand the start-up ecosystem based on in-depth interviews with various stakeholders such as VC investors, start-ups, support organizations, and professors related to the Korean start-up ecosystem. Finally, based on the results of the study, we suggest development and activation of Korean technology-based start-up ecosystem. As a result, the Seoul start-up ecosystem showed a positive evaluation of government support compared to other advanced countries. In addition, it was confirmed that the ratio of tele-work and start-up company working experience of employees was higher than other countries. On the other hand, in Seoul, It was confirmed that overseas market performance, human resource diversity, attracting investment, hiring technological engineers, and the ratio of female entrepreneurs were lower than those of overseas advanced countries. In addition, according to the results of the interview analysis, Seoul was able to find that start-up ecosystems such as individual angel investors, accelerators, support institution, and media are developing thanks to the government's market-oriented policy support. However, in order for this development to continue, it is necessary to improve the continuous investment system, expansion of diversity, investment return system, and accessibility to the global market. A discussion on this issue is presented.