• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.21 no.2
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• pp.131-136
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• 1985

The calculation of the resulting fluid motion is an important problem of ship hydrodynamics. For a partially immersed body the condition of constant pressure at the free surface can be linearized. The resulting linear boundary-value problem for the velocity potential is the Neumann-Kelvin problem. The two-dimensional Neumann-Kelvin problem is studied for the half-immersed circular cylinder by Ursell. Maruo introduced a slender body approach to simplify the Neumann-Kelvin problem in such a way that the integral equation which determines the singularity distribution over the hull surface can be solved by a marching procedure of step by step integration starting at bow. In the present pater for the two-dimensional Neumann-Kelvin problem, it has been suggested that any solution of the problem must have singularities in the corners between the body surface and free surface. There can be infinitely many solutions depending on the singularities in the coroners.

• Korean Chemical Engineering Research
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• v.56 no.2
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• pp.261-268
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• 2018

To prepare mesoporous $TiO_2$ ($meso-TiO_2$) with anatase and rutile crystal structures, hydrothermal and template synthesis were used. $Meso-TiO_2$ with anatase structure was obtained by hydrothermal synthesis. The crystal structure of $meso-TiO_2$ by hydrothermal synthesis converted from anatase to rutile by simple heating at $600^{\circ}C$ and above. However, their mesopore structure collapsed due to phase transition. To prepare $meso-TiO_2$ with rutile structure, template synthesis method was applied using mesoporous silica KIT-6 as a template. Once we incorporated anatase $TiO_2$ inside mesopores of silica, the phase transition temperature of $TiO_2$ confined inside KIT-6 was much higher ($900^{\circ}C$) than that of free-standing $TiO_2$ ($600^{\circ}C$). The suppression of $TiO_2$ phase transition inside mesopores of KIT-6 is closely related with the interaction between $TiO_2$ surface and silica walls in KIT-6 because oxygen vacancy in $TiO_2$ is regarded as the starting point for phase transition. After removal of silica template by NaOH solution washing, $meso-TiO_2$ with mixed phase between anatase and rutile was obtained.

• Journal of Practical Agriculture & Fisheries Research
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• v.17 no.1
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• pp.57-71
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• 2015

This study was conducted to develop the fertigation system with a peristaltic hose pump and brushless DC motor. The fertigation system was consisted of sensor, main controller, motor control unit, peristaltic pump, water supply pump, control panel, and filter. The peristaltic pump discharges liquid by squeezing the tube with rollers. Rollers attached to the external circumference of the rotor compresses the flexible tube. The fluid is contained within a flexible tube fitted inside a circular pump casing. The developed fertigation system has no mixing tank but instead injects directly a concentrated nutrient solution into a water supply pipe. The revolution speed of the peristaltic pump is controlled by PWM (Pulse width modulation) method. When the revolution speed of the peristaltic pump was 300rpm, the flow rate of the 3.2, 4.8, 6.3mm diameter tube was 202, 530, 857mL/min, respectively. As increasing revolution speed, the flow rate of the peristaltic pump linearly increased. As the inner diameter of a tube larger, a slope of graph is more steep. Flow rate of three roller was more than that of four roller. Flow rate of a norprene tube with good restoring force was more than that of a pharmed tube. As EC sensor probe was installed in direct piping in comparison with bypass piping showed good performance. After starting the system, it took 16~17 seconds to stabilize EC. The maximum value of EC was 1.44~1.7dS/m at a setting value of 1.4dS/m. The developed fertigation system showed ±0.06dS/m deviation from the setting value of EC. In field test, Cucumber plants generally showed good growth. From these findings, this fertigation system can be appropriately suitable for fertigation culture for crops.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.