• Korean Journal of Fisheries and Aquatic Sciences
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• v.19 no.2
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• pp.93-99
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• 1986

A longline is made of many snoods with baited hooks which are connected to mainline at constant intervals. Hauling the mainline, removing the unused baits and the hooked fish, and the arrangement of hooks are dependent on mainly manual labour as compared with mechanized other fishing gear in fishing operation. The mechanization for longline operation is needed in order to eliminate the manual handling and to shorten the labour time. The magnetic hook splitting apparatus which consists of the hook separator and the guide leading to storage magazine rail was devised for the mechanization of hauling operation. The experiments were carried out in order to measure the splitting rate of hooks in accordance with the hauling speed of mainline and magnetic flux density of splitting apparatus from February to November, 1985. The splitting rate was $94\%$ for the Alaska pollack (Theragra chalcogramma) hook and $96\%$ for the halibut (Paralichthys olivaceus) hook at the hauling speed 24 m/min and magnetic flux density 482 gauss. The unsplitting of hooks was caused by entangling snood in the mainline and low magnetic flux density. The rate is greater the faster hauling speed and the lower magnetic flux density, with an average of about $6\%$, The magnetic flux density needed to hook splitting becomes increased with the increasing hauling speed. When the practical hauling speed is from 20 to 35m/min, the magnetic flux density is needed from 400 to 850 gauss.

• IE interfaces
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• v.17 no.2
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• pp.242-248
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• 2004

Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots to allow the overlapping of operations between successive machines in a multi-stage production system. A new genetic algorithm (NGA) is proposed for minimizing the mean weighted absolute deviation of job completion times from due dates when jobs are scheduled in a no-wait lot-streaming flow shop. In a no-wait flow shop, each sublot must be processed continuously from its start in the first machine to its completion in the last machine without any interruption on machines and without any waiting in between the machines. NGA replaces selection and mating operators of genetic algorithms (GAs), which often lead to premature convergence, by new operators (marriage and pregnancy operators) and adopts the idea of inter-chromosomal dominance. The performance of NGA is compared with that of GA and the results of computational experiments show that NGA works well for this type of problem.

• Korean Journal of Materials Research
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• v.33 no.5
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• International Journal of CAD/CAM
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• v.3 no.1_2
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• pp.85-95
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• 2003

During the design of dies and molds, the cavity of the object is obtained by subtracting it from a surrounding rectangular block. This box is subsequently split into two halves by the parting surface. Similar problems also occur in some RP processes such as LOM, SGC, SLS and 3DP where the machine produces a block inside which the prototype is buried. Determining the orientation of the object inside the box and the corresponding parting surface taking appropriate constraints into account have been addressed by several researchers. However, given the parting surface, the problem of splitting the box development of a software package called OptiLOM (now a module of an RP software Magics 8.0), the authors realized non-triviality of this problem since the loop can spread over as many as 5 faces of the box. In this paper, the authors have tried to bring out the importance of this problem and have presented their algorithm to solve it.

• Advances in nano research
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• v.15 no.6
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• pp.533-539
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• 2023

This study aims to examine four machine learning (ML)-based models for their potential to estimate the splitting tensile strength (STS) of manufactured sand concrete (MSC). The ML models were trained and tested based on 310 experimental data points. Stone nanopowder content (SNPC), curing age (CA), and water-to-cement (W/C) ratio were also studied for their impacts on the STS of MSC. According to the results, the support vector regression (SVR) model had the highest correlation with experimental data. Still, all of the optimized ML models showed promise in estimating the STS of MSC. Both ML and laboratory results showed that MSC with 10% SNPC improved the STS of MSC.

• Explosives and Blasting
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• v.27 no.1
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• pp.7-20
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• 2009

In this study, 5 steps of different delay intervals are applied to a plasma rock-breaking machine that is driven by electric shocks in order to improve the workability of the traditional single-shot type plasma rock-breaking operation. The sequential steps use the electrolyte volume per delay of 1, 2, 3, 4, 5 kg and it has been analyzed to measure the delay time and level of the ground vibration and noise according to exploding. The delay time of the rock-breaking machine by an electric shock of 5 steps has used about 40~50ms at the electrolyte connected from 1 to 3 holes, about 70~80ms at the electrolyte connected from 4 to 5 holes. It is identified that the extents of the ground vibration is low to 1 over 3~6 compared with that of the emulsion explosives.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.203-215
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• 2023

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10a
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• pp.489-494
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• 1998

This paper describes the possibility to reuse concrete waste produced by demolition of reinforced concrete structures as aggregate for concrete from the viewpoint of strength. Concrete rubble obtained from the demolished buildings at Taejon were crushing machine to reuse as coarse aggregate. The strength properties, such as compressive strength, splitting tensile strength, bending strength and shear strength, of recycled and normal concrete were examined and compared experimentally when water cement ratio was varied. From the results of this study, it was thought that in case of non-washed aggregate concrete, strength properties of recycled coarse aggregate is similar to that of normal concrete, In W/C 55%~45%, stress-strain curve of recycled concrete shows more stable than that of normal concrete, while in W/C 35%, it shows brittle behavior.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.2
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• pp.159-164
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• 2004

Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots and then scheduling these sublots in order to accelerate the completion of jobs in a multi-stage production system. Anew genetic algorithm (NGA) is proposed for an-job, m-machine, equal-size sublot lot-streaming flow shop scheduling problem with delivery windows in which the objective is to minimize the mean weighted absolute deviation of job completion times from due dates. The performance of NGA is compared with that of an adjacent pairwise interchange (API) method and the results of computational experiments show that NGA works well for this type of problem.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.22-28
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• 2007

The welded defects are usually called user's unsatisfaction for appearance and functional usage. For checking these defects effectively without time loss, setup of weldability estimation system is an important for detecting whole specimen quality. In this study, after catching a rawdata on welded specimen profiles and treating vision processing with these data, the qualitative defects are estimated from getting these information by laser vision camera at first. At the same time, the weldability estimation for whole specimen is produced. For user friendly, the weldability estimation results are shown each profiles, final reports and visual graphics method. So, user can easily determined weldability. By applying these system to welding fabrication, these technologies are contribution to on-line setup of weldability estimation system.