• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.2
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• pp.232-237
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• 2008

A method to generate electric power from small scale water tank. For this purpose, manufacturing tank is investigated, measuring water level change at any time, and finally comparing experimental and theoretical value, are performed. Inner and outer tank are made to simulate flood and ebb generation. Two sets of pipe are connected between tanks, and experiments are performed under varying flowrate. Coefficients of flowrate are calculated comparing water level change data and theoretical value. Measured and theoretical water levels are highly correlated, and this ascertains that analytical equation simulates real water level changes well. Flowrate change depending on the existence of propeller and valve, on flood and ebb generation, shows the necessity of experiments in the process of manufacturing electric power system. Moreover, total energy calculated from experimental data agrees well with that of theoretical equation. In spite of small tidal power output, this generating system with optimum water tank can be applied to any place where high water level change occurs, and can make a contribution to producing new and renewable energy consequently.

• Journal of Environmental Science International
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• v.23 no.5
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• pp.771-780
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• 2014

Tetragonia tetragonides is a medicinal plant native to ocean sand soil of southern provinces and has significant effects on the prevention and curing of gastroenteric disorders. Despite of its popularity, supply of the plant has never met the level of demand because of the absence of an adequate culturing method. The present study, thereby, was conducted for classifying the plants with geographically different characteristics, studying growth habits, developing a new culturing method and establishing a large scale propagation system of selected superior individual plants. The study was also aimed for revealing optimum conditions for seed treatment, fertilization, and efficient culturing system and thereby, for utilizing the plant as a new income source for rural communities. The seed was elongated with size of 2.6 mm (width) ${\times}$ 1.8 mm (length). No difference in seed size was observed depending on different inhabitate. Each flower produced about 4.5~4.8 seeds. Germination rate was high for seeds matured for 40 days after fertilization, but deceased to 50% for seeds matured only for 20 or 30 days. Seed dormancy lasted 6 months and seed storage at humid $5^{\circ}C$ facilitated germination. Mechanical obstruct of seed germination was due to seed coat and removal of seed coat enhanced the germination rate. Optimum temp. for seed storage was $5^{\circ}C$, and high germination rate was maintained for 350 days. However, for stratification condition or at room temperature, germination was significantly reduced as storage time increased Optimum treatment of plant growth regulators was soaking in $GA_3$ 250 mg/L for 1 hr. The priming treatment with 50 mM $Ca(NO_3)_2$ at $20^{\circ}C$ for two days improved the seed germination with 10% compared to non-treated control. The treatment of 20% NaOCl for 3 hr. improved the seed germination rate up to 10% and 1 day ahead.

• Korean Journal of Heritage: History & Science
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• v.48 no.2
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• pp.4-29
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• 2015

This article considered generally gradational change aspect, character of community for the tombs of the Three kingdoms prepared by some certain homogenous groups. It was completed by tracing the transfering process to grasp character of social structure and organizing System in the community. For this reason, it's analyzed that Wooden chamber tomb was formed in some certain Time-Space of the Yaksadong Buk-dong archaeological site in the Three Kingdom Period. Constructed group was distinguished by the five classes. So perhaps scale of Wooden chamber and sort, material of burial relics, the top layer and the upper layer were established two status those are active the ruling class led the Wooden chamber tomb's society, the general public hold a large majority from the middle class to the lowest class. And it depends on age(nonage-adult) that differed in relation of arrangement, character. It's changed with function or roles, property, purpose etc. of social community in accordance with these individual position, age. Received signification, value of social status were considered with social worth, faith. Members in a community had been conducted under premeditated, organised system for common target, relatedness. Also it seems to attempt organization's own maintenance, persistiveness in norms that given at common law. To do that, relation for combination between members who consisted systems had to be organic. The relation for combination was considered to form relation for the much larger community in based on blood relationship that most vital part. One social structure had established family(house, household) community in based on individual blood relationship, family communities gather up to develop into relative(blood relative, same race) community gradually. Furthermore, relative(blood relative, same race) communities, that focus on the public good gather up to develop into social community in based on regionalism. It's considered to go through the Development Process of family(house, household) community - relative(blood relative, same race) community - social community.

• Journal of Navigation and Port Research
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• v.48 no.4
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• pp.274-283
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• 2024

Climate change is currently one of the most pressing environmental issues, primarily caused by carbon emissions from fossil fuel usage. As a result, alternative fuels that effectively reduce carbon emissions are garnering more attention. Among these alternatives, hydrogen has numerous advantages, such as its ability for large-scale storage and transport. However, it is crucial to prioritize safety measures, particularly in facilities that handle hydrogen, due to its highly flammable and fast-spreading nature. This study aims to compare and analyze the placement of supply and exhaust vents to efficiently release hydrogen in the event of a leak in an enclosed space. The experiments involved six different scenarios, each with various combinations of supply and exhaust vents. To ensure the experimental process's safety, helium, which shares similar physical properties with hydrogen, was used to analyze the internal oxygen concentration during ventilation system operations. The results revealed that among the six scenarios, Case 2, which employed a lower side supply vent and an upper side exhaust vent, exhibited the shortest ventilation time of 4 minutes and 30 seconds. Additionally, the decrease rate in oxygen concentration was examined in the upper, middle, and lower areas. Ventilation utilizing an upper surface supply vent and two exhaust vents on the upper surface and upper side (Case 6), showed lower oxygen concentration values in the upper area, while Case 2 yielded lower values in the middle and lower areas. Therefore, it is crucial to select an appropriate supply and exhaust vent configuration considering the space's characteristics and usage environment.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.35 no.2
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• pp.93-101
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• 1999

A simple experimental method was used in an attempt to realize the elevation of the fishing ability of purse seine in the sea area of Cheju Island, the changes of seine volume and tension in the purseline during pursing. Experiments carried out on the six types simplified reduced model seines which were made of knotless nettings. The nettings were woven in different leg length 4.3, 5.0, 5.5, 6.0, 6.6 and 7.7mm of polyester 28 tex two threads two-ply twine, and each of the seines were named I, II, III, IV, V and Ⅵ seine. Dimension of seine models were 450cm for corkline and 85cmfor seine depth, each seines rigged up 160g of float for a floatline and 50g (underwater weight) of lead for a leadline. These model purse seines were made of the scale of 1/200 of its full scale, a 120 ton in the near sea of Cheju Island. Designing and testing for the model purse seines were based on the Tauti's law. Experiments were measured in the observation channel of a flume tank at the static conditions set up shooting and pursing equipments. Motion of purse seine during purse line was recorded by the two sets video camera for VTR which were placed in top and front of the model seine. The reading coordinate of seine volume carried out by the video digitization system, disk data for the purseline tension. An analysis were performed on the changes seine volume and tension in the purseline during pursing. The results obtained were as follows: 1. The seine volume during pursing was largest for Ⅵ seine with smallest d/l followed by V, IV, III, II and I seines, and tension in the purseline was small. 2. Seine volume during pursing can be expressed by the following equation; CVt=l-EXP[｛2.79 (d/l）＋0.35｝t-33.37 (d/l) ＋ 0.57] Where CVt is volume ratio, d is twine diameter, l is leg length and t is pursing time (sec). 3. Tension in the purse line during pursing can be expressed by the following equation; T= 1- EXP ｛0.57t ＋ 13.36 (d/l)+2.97｝ Where T is tension (kg) in the purseline during pursing.

• 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.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of The Korean Society of Grassland and Forage Science
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• v.29 no.2
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• pp.137-152
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• 2009

Present experiment was conducted at the field of Gyeongnam Agricultural Research and Extension Services in Jinju city for two continuous cropping seasons to develop several adaptable and valuable year-round forage-producing system for elevating self-sufficiency and dollar-saving by reduced importing of crude forage. Twenty cropping systems were tested in experiment using whole crop barley (WCB), oat, rye, Italian ryegrass (IRG), and triticale in winter season and com, sorghum, sorghum ${\times}$ sudangrass hybrid, and oat in summer time. Sorghum ${\times}$ sudangrass hybrid showed highest fresh forage yield among experimented summer season crops, and followed com. Com produced the most dry matter yield, and followed sorghum${\times}$sudangrass hybrid, sorghum and oat in order. There was no significant effect of former winter crops on fresh and dry matter production succeeding summer time crops. Among winter season forage crops tested, oat showed the highest fresh and dry matter when clipped on mid-May, and followed triticale, IRG, rye and WCB. Winter-time cultivated crops showed no clear effect on the growth and forage (fresh and dry matter) producing ability of following summer crops. There was the most protein content in oat plant among summer season planted crops, and in sorghum for acid detergent fiber (ADF) and in sorghum ${\times}$ sudangrass hybrid for neutral detergent fiber (NDF), respectively. While, com showed highest value of relative feed value (RFV) and total digestive nutrients (TDN) among those crops. Among winter crops, the highest crude protein was in oat plant showing no significant differences of ADF and NDF, while, relatively higher value of RFV was recognized with rye and triticale. Also, triticale contained more TDN as compare to other forage crops. The cropping combinations such as com followed by (fb) rye and maize fb triticale were regarded as promising systems having higher dry matter producing ability among tested combinations. Considering TDN producing potential, the combinations with sorghum ${\times}$ sudangrass hybrid fb triticale andlor rye were would be suitable ones, coincidently. There was a tendency which elevating pH, electric conductivity (EC) and organic matter (OM) contents in soil after experiment comparing to before planting. More crude protein content in plant was shown at mid-May clipping as compared to the forage at April cut in all winter season grown crops. ADF and NDF contents were increased by delayed clipping showing decreased tendency of RFV and TDN in plant. In conclusion, many cropping systems would be available using above mentioned forage crops according to farmer's conditions and scale, etc.

• Journal of Chest Surgery
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• v.39 no.7 s.264
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• pp.505-510
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• 2006

Background: Small animal cardiopulmonary bypass (CPB) model would be a valuable tool for investigating path-ophysiological and therapeutic strategies on bypass. The main advantages of a small animal model include the reduced cost and time, and the fact that it does not require a full scale operating environment. However the rat CPB models have a number of technical limitations. Effective maintenance and control of core temperature by a heat exchanger is among them. The purpose of this study is to confirm the effect of rectal temperature maintenance using a heat exchanger of cardioplegia system in cardiopulmonary bypass model for rats. Material and Method: The miniature circuit consisted of a reservoir, heat exchanger, membrane oxygenator, roller pump, and static priming volume was 40 cc, Ten male Sprague-Dawley rats (mean weight 530 gram) were divided into two groups, and heat exchanger (HE) group was subjected to CPB with HE from a cardioplegia system, and control group was subjected to CPB with warm water circulating around the reservoir. Partial CPB was conducted at a flow rate of 40 mg/kg/min for 20 min after venous cannulation (via the internal juglar vein) and arterial cannulation (via the femoral artery). Rectal temperature were measured after anesthetic induction, a ter cannulation, 5, 10, 15, 20 min after CPB. Arterial blood gas with hematocrit was also analysed, 5 and 15 min after CPB. Result: Rectal temperature change differed between the two groups (p<0.01). The temperatures of HE group were well maintained during CPB, whereas control group was under progressive hypothermia, Rectal temperature 20 min after CPB was $36.16{\pm}0.32^{\circ}C$ in the HE group and $34.22{\pm}0.36^{\circ}C$ in the control group. Conclusion: We confirmed the effect of rectal temperature maintenance using a heat exchanger of cardioplegia system in cardiopulmonary bypass model for rats. This model would be a valuable tool for further use in hypothermic CPB experiment in rats.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.511-519
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• 2022

In order to develop an air-stripping based remediation process to remove the TOC (Total Organic Carbon) in groundwater around the underground LPG storage cavern, the laboratory scale experiments at various conditions (change of air injection volume and temperature, the application of ultrasonic treatment, etc.) for two types of groundwater (initial TOC concentration of 608 mg/L and 153 mg/L, respectively). From results of experiment, as the air injection rate for stripping into groundwater increased from 2 L/min to 11 L/min and as the air-stripping time increased from 1 hour to 24 hour, the TOC removal efficiency of air-stripping increased. However, the TOC concentration of treated groundwater was higher than the discharge tolerance limit (100 mg/L) even after 24 hour stripping at the maximum air injection rate of 11 L/min. The main compounds of the TOC in groundwater were identified as methanol and propane and the long stripping time (more than 24 hour) was needed to separate the methanol from groundwater because of the affinity between water and methanol. At 20℃ and 4 L/min of air injection, the TOC removal efficiency increased to 59.1% after 24 hour air-stripping. When the temperature of groundwater increased to 30℃ and 40℃, the TOC removal efficiency increased up to 80.0% and 82.8%, suggesting that more than 24 hour air-stripping at 40℃ is needed to lower the TOC concentration to below 100 mg/L and the additional TOC removal process as well as the air-stripping is necessary. When the temperature increased to 60℃ and the ultrasonic treatment was conjugated with the air-stripping, the TOC removal efficiency increased to 87.8% within 5 hour stripping and the final TOC concentration (72.4 mg/L) was satisfied with the TOC discharge tolerance limit. The TOC removal efficiency for groundwater having low TOC concentration (153 mg/L) also showed similar removal efficiency of 89.7% (the final TOC concentration: 18.9 mg/L). Results in this study supported that the air-stripping conjugated with the ultrasonic treatment could remove successfully the TOC in groundwater around the underground LPG strorage cavern.