• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.57 no.4
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• pp.316-333
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• 2021

In order to understand basic data for improving the fishing system and fishing vessel structure in coastal improved stow net fishery, a questionnaire survey and on-site hearing were conducted from May 10 to June 11, 2019 to analyze opinions on the improvement of operation status and fishing vessel structure. The questionnaire survey consisted of ten questions on the operation status of coastal improved stow net fishery and six questions on the improvement of fishing vessel structure, and the results of each question were analyzed by the region, the captain's age, the captain's career and the age of fishing vessel. As a result of analyzing opinions on the operation status of the coastal improved stow net fishery, it was found that the average time required for casting net was 32.8 to 33.0 minutes and that the average time required for hauling net was 41.0 to 42.2 minutes which took 10 to 12 minutes more than for casting net. The most important work requiring improvement during fishing operation (the first priority) were 'hauling net operation,' 'readjustment and storage of fishing gear,' and 'fish handling' and the hardest factor in fishing management were in the order of 'reduction of catch,' 'labor shortage' and 'rising labor costs.' The most institutional improvement that is most needed in coastal improved stow net fishery was an 'using fine mesh nets.' Most of the respondent to the questions on the experience in hiring foreign crews was 'either hiring or willing to hire foreign crews,' and the average number of foreign crews employed was found to be 2.3 to 2.4 persons. The most important reason for hiring (or considering employment) foreign crews was 'high labor costs.' The degree of communication with foreign crews during fishing operation were 'moderate' or 'difficult to direct work.' The most important problem in hiring foreign crews (the first priority) was an 'illegal departure.' As the survey results on the opinion of structural improvement of coastal improved stow net fishing vessel, the degree of satisfaction with fishing vessel structure related to fishing operation was found to be somewhat unsatisfactory, with an average of 3.3 points on a five-point scale. The inconvenient structure of fishing vessel in possession (the first priority), the space needed most for the construction of new fishing vessel (the first priority) and the space considered important for the construction of new fishing vessel (the first prioprity) was a 'fish warehouse.' The most preferred equipment for the construction of new fishing vessel were 'engine operation monitoring' and 'navigation safety devices.' The average size (tonnage class), the average horse power and the average total length of fishing vessel for proper profit and safety fishing operation was between 13.8 and 14.0 tonnes, 808.3 to 819.5 H.P. and 23.4 to 23.5 meters, respectively. The results of the operation status of coastal improved stow net fishery and the requirement for improving the fishing vessel structure are expected to be provided as basic data for reference when we build or improve the fishing vessel.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.476-484
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• 2022

This study was conducted to investigate the effect of closed cultivation and open cultivation method and substrate type on the nutrient ion change pattern and growth of sweet pepper (Capsicum annuum L.) 'Scirocco' according to the reuse of drainage in hydroponics. The sowing, transplanting, and application of the closed and open cultivation method were carried out on August 19 and September 16, and October 21, 2021, respectively. As a result of the analysis of nutrients in the drainage, Na⁺ and Cl^- are representative ions that crops do not absorb properly, and as the growth progresses, they are accumulated in the closed method. In addition, since the content of NH₄-N in the drainage is significantly lower than that of NO₃-N, it is thought that NH₄-N is preferentially absorbed rather than NO₃-N due to the ion selectivity of sweet pepper. The growth and fruit characteristics of sweet pepper did not differ significantly between treatments according to the drainage reuse and the type of substrate. In conclusion, if you take care of poor fruiting due to the weakening of power after the middle period in hydroponic cultivation of sweet pepper according to the cultivation method of closed and open, and the substrate type of coir and rock wool, the difference between treatments is not large, so the sweet pepper can be produced by selecting the cultivation methods and substrate types suitable for the conditions of grower. However, as interest in environmental pollution has recently increased, it is judged that there is no need to worry about a decrease in quantity or quality, even if a closed cultivation method is adopted under the assumption that pathogen infection due to drainage reuse is well managed. It is expected that if coir is applied instead of rock wool, which causes a problem of disposal, it will further contribute to the reduction of environmental pollution.

• Journal of the Korea Organic Resources Recycling Association
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• v.29 no.4
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• pp.67-76
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• 2021

Biogasification is a technology that produces environmentally friendly fuel using methane gas generated in the process of stably decomposing and processing organic waste. Biogasification is the most used method for energy conversion of organic waste with high moisture content, and is a useful method for organic waste treatment following the prohibition of direct landfill (2005) and marine dumping (2013). Due to African Swine Fever (ASF), which recently occurred in Korea, recycling of wet feed is prohibited, and consumers such as dry feed and compost are negatively recognized, making it difficult to treat food waste. Accordingly, biogasification is attracting more attention for the treatment and recycling of food waste. Korea's energy consumption amounted to 268.41 106toe, ranking 9th in the world. However, it is an energy-poor country that depends on foreign imports for about 95.8% of its energy supply. Therefore, in Korea, the Renewable Energy Portfolio Standard (RPS) is being introduced. The domestic RPS system sets the weight of the new and renewable energy certificate (REC, Renewable energy certificate) of waste energy lower than that of other renewable energy. Therefore, an additional incentive system is required for the activation of waste-to-energy. In this study, the operation of an anaerobic digester that treats food waste, food waste Leachate and various organic wastes was confirmed. It was intended to be used as basic data for preparing the waste-to-energy incentive system through precise monitoring for a certain period of time. Four sites that produce biogas from organic waste and use them for power generation and heavy gas were selected as target facilities, and field surveys and sampling were conducted. Basic properties analysis was performed on the influent sample of organic waste and the effluent sample according to the treatment process. As a result of the analysis of the properties, the total solids of the digester influent was an average of 12.11%, and the volatile solids of the total solids were confirmed to be 85.86%. BOD and CODcr removal rates were 60.8% and 64.8%. The volatile fatty acids in the influent averaged 55,716 mg/L. It can be confirmed that most of the volatile fatty acids were decomposed and removed with an average reduction rate of 92.3% after anaerobic digestion.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Clean Technology
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• v.29 no.3
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• pp.193-199
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• 2023

Biomass has been used to secure renewable energy certificates (REC) in domestic and overseas coal-fired power plants. In recent years, biofuel has been diversified from traditional wood pellets to non-woody biomass. Non-woody biomass has a higher content of alkaline metals such as K and Na than wood-based biomass, resulting in a lower melting point and an increase in slagging on boiler tubes, which reduces boiler efficiency. This study analyzed the effect of kaolin, an additive commonly used to increase melting points, on biomass co-firing to coal through thermochemical equilibrium calculations. In a previous experiment on biomass co-firing to coal conducted at 80 kW_th, it was interpreted that the use of kaolin actually increased the amount of fouling. In this study, analysis showed that when kaolin was added, aluminosilicate compounds were generated due to Al₂O₃, which is abundant in coal, and mullite was formed. Thus, it was confirmed that the amount of slag increased when more kaolin was used. Further analysis was conducted by increasing the biomass co-firing rate from 0% to 100% at 10% intervals, and the results showed non-linear liquid slag generation. As a result, it was found that the least amount of liquid slag was generated when the biomass co-firing rate was between 50 and 60%. The phase diagram analysis showed that high melting point compounds such as leucite and feldspar were most abundantly generated under these conditions.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.421-433
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• 2023

The final disposal of spent nuclear fuel(SNF) from nuclear power plants takes place in a deep geological repository. The metal canister encasing the SNF is made of cast iron and copper, and is engineered to effectively isolate radioactive isotopes for a long period of time. The SNF is further shielded by a multi-barrier disposal system comprising both engineering and natural barriers. The deep disposal environment gradually changes to an anaerobic reducing environment. In this environment, sulfide is one of the most probable substances to induce corrosion of copper canister. Stress-corrosion cracking(SCC) triggered by sulfide can carry substantial implications for the integrity of the copper canister, potentially posing a significant threat to the long-term safety of the deep disposal repository. Sulfate can exist in various forms within the deep disposal environment or be introduced from the geosphere. Sulfate has the potential to be transformed into sulfide by sulfate-reducing bacteria(SRB), and this converted sulfide can contribute to the corrosion of the copper canister. Bentonite, which is considered as a potential material for buffering and backfilling, contains oxidized sulfate minerals such as gypsum(CaSO4). If there is sufficient space for microorganisms to thrive in the deep disposal environment and if electron donors such as organic carbon are adequately supplied, sulfate can be converted to sulfide through microbial activity. However, the majority of the sulfides generated in the deep disposal system or introduced from the geosphere will be intercepted by the buffer, with only a small amount reaching the metal canister. Pyrite, one of the potential sulfide minerals present in the deep disposal environment, can generate sulfates during the dissolution process, thereby contributing to the corrosion of the copper canister. However, the quantity of oxidation byproducts from pyrite is anticipated to be minimal due to its extremely low solubility. Moreover, the migration of these oxidized byproducts to the metal canister will be restricted by the low hydraulic conductivity of saturated bentonite. We have comprehensively analyzed and summarized key research cases related to the presence of sulfates, reduction processes, and the formation and behavior characteristics of sulfides and pyrite in the deep disposal environment. Our objective was to gain an understanding of the impact of sulfates and sulfides on the long-term safety of high-level radioactive waste disposal repository.

• Journal of Bio-Environment Control
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• v.32 no.2
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• pp.106-114
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• 2023

With rising concerns about pesticide spray drift by aerial application, this study attempt to evaluate aerodynamic property and collection efficiency of spray drift according to the leaf area index (LAI) of crop for preventing undesirable pesticide contamination by the spray-drift tunnel experiment. The collection efficiency of the plant with 'Low' LAI was measured at 16.13% at a wind speed of 1 m·s^-1. As the wind speed increased to 2 m·s^-1, the collection efficiency of plant with the same LAI level increased 1.80 times higher to 29.06%. For the 'Medium' level LAI, the collection efficiency was 24.42% and 43.06% at wind speed of 1 m·s^-1 and 2 m·s^-1, respectively. For the 'High' level LAI, it also increased 1.24 times higher as the wind speed increased. The measured results indicated that the collection of spray droplets by leaves were increased with LAI and wind speed. This also implied that dense leaves would have more advantages for preventing the drift of airborne spray droplets. Aerodynamic properties also tended to increase as the LAI increased, and the regression analysis of quadric equation and power law equation showed high explanatory of 0.96-0.99.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.563-576
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• 2023

In this study nitrogen dioxide (NO₂), formaldehyde (HCHO) from the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI), OMI/ Microwave Limb Sounder (MLS) tropospheric column ozone (TCO), and Airkorea ground-based O₃ data were analyzed to examine the photochemical reaction relationship between tropospheric ozone and its precursors nitrogen oxides (NOx) and volatile organic compounds (VOCs). As a result of analyzing the trend of long-term changes from 2006 to 2020 using OMI satellite data, TCO showed an increasing trend, NO₂ steadily decreased, and HCHO continued to increase in Northeast Asia. In addition, formaldehyde nitrogen dioxide ratio (FNR; HCHO/NO₂ ratio), an indicator of ozone sensitivity, is gradually increasing, which means that the VOC-limited regime is decreasing. This study conducted a sensitivity analysis of ozone generation using TROPOMI FNR and ground-based ozone (O₃) over the recent years (2019~2022) to identify the possible cause for the continuous increase of ozone in Korea. Similar to the previous studies, VOC-limited and transitional regimes appeared in megacities, and VOC-limited regimes also appeared in areas where major power plants were located. In VOC-limited regimes, in other words, areas where NOx is excessively saturated, the reduction in NOx emissions may have weakened the ozone titration and thus led to the increase of ozone. Therefore, VOC emissions should be reduced in the short term rather than NOx emissions to reduce ozone concentrations under the VOC-limited regime.

• Explosives and Blasting
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• v.9 no.1
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• pp.3-13
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• 1991

The cautious blasting works had been used with emulsion explosion electric M/S delay caps. Drill depth was from 3m to 6m with Crawler Drill ${\phi}70mm$ on the calcalious sand stone (soft -modelate -semi hard Rock). The total numbers of test blast were 88. Scale distance were induced 15.52-60.32. It was applied to propagation Law in blasting vibration as follows. Propagtion Law in Blasting Vibration $V=K(\frac{D}{W^b})^n$ were V : Peak partical velocity(cm/sec) D : Distance between explosion and recording sites(m) W : Maximum charge per delay-period of eight milliseconds or more (kg) K : Ground transmission constant, empirically determind on the Rocks, Explosive and drilling pattern ets. b : Charge exponents n : Reduced exponents where the quantity $\frac{D}{W^b}$ is known as the scale distance. Above equation is worked by the U.S Bureau of Mines to determine peak particle velocity. The propagation Law can be catagorized in three groups. Cubic root Scaling charge per delay Square root Scaling of charge per delay Site-specific Scaling of charge Per delay Plots of peak particle velocity versus distoance were made on log-log coordinates. The data are grouped by test and P.P.V. The linear grouping of the data permits their representation by an equation of the form ; $V=K(\frac{D}{W^{\frac{1}{3}})^{-n}$ The value of K(41 or 124) and n(1.41 or 1.66) were determined for each set of data by the method of least squores. Statistical tests showed that a common slope, n, could be used for all data of a given components. Charge and reduction exponents carried out by multiple regressional analysis. It's divided into under loom over loom distance because the frequency is verified by the distance from blast site. Empirical equation of cautious blasting vibration is as follows. Over 30m ------- under l00m ${\cdots\cdots\cdots}{\;}41(D/sqrt[2]{W})^{-1.41}{\;}{\cdots\cdots\cdots\cdots\cdots}{\;}A$ Over 100m ${\cdots\cdots\cdots\cdots\cdots}{\;}121(D/sqrt[3]{W})^{-1.66}{\;}{\cdots\cdots\cdots\cdots\cdots}{\;}B$ where ; V is peak particle velocity In cm / sec D is distance in m and W, maximLlm charge weight per day in kg K value on the above equation has to be more specified for further understaring about the effect of explosives, Rock strength. And Drilling pattern on the vibration levels, it is necessary to carry out more tests.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.