• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.679-686
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• 2023

According to the PSPC (Performance Standard for Protective coatings) rule, the edge preparation must keep 2R or 3-pass grinding. The stiffener plate edge grinding of the ship inside is manually progressed by worker and worked with just one pass grinding. In addition, the poor working condition cause grinding workers to avoid working in them, and the quality is determined by the skill-level of a worker. This research developed optimal tool for edge milling. In order to milling various collar plates edge, this research developed vision system that can recognize the edge points and it developed a program that operator can adjust the amount of cutting and speed, and add various features so that milling quality would be improved. So, this research focused on overcoming the difficulties in working condition and development of automated milling machine for ship stiffener plate.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.155-156
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• 2022

In the process of developing an industrial robot, various simulations should be conducted to evaluate the driving, movement, and performance of the robot. Space and time constraints exist to manufacture existing robots and implement various simulations, and efficiency is reduced due to high costs. To solve this problem, many simulations can be conducted by implementing the same movement and working environment as the real environment in virtual reality using digital twin technology. This study proposes a process for establishing a digital twin model of automated layout robots. Using the digital twin model, it is expected that it will not only evaluate the hardware performance of the robot in the future, but also verify the robot's algorithms such as motion planning and work process, identify and solve potential problems in advance, and prevent problems caused by software.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.185-199
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• 2024

In this paper, an improved automated spectral clustering (IASC) algorithm is proposed to address the limitations of the traditional spectral clustering (TSC) algorithm, particularly its inability to automatically determine the number of clusters. Firstly, a cluster number evaluation factor based on the optimal clustering principle is proposed. By iterating through different k values, the value corresponding to the largest evaluation factor was selected as the first-rank number of clusters. Secondly, the IASC algorithm adopts a density-sensitive distance to measure the similarity between the sample points. This rendered a high similarity to the data distributed in the same high-density area. Thirdly, to improve clustering accuracy, the IASC algorithm uses the cosine angle classification method instead of K-means to classify the eigenvectors. Six algorithms-K-means, fuzzy C-means, TSC, EIGENGAP, DBSCAN, and density peak-were compared with the proposed algorithm on six datasets. The results show that the IASC algorithm not only automatically determines the number of clusters but also obtains better clustering accuracy on both synthetic and UCI datasets.

• Analytical Science and Technology
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• v.12 no.2
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• pp.1017-1024
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• 1999

• Korean Management Science Review
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• v.11 no.3
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• pp.55-65
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• 1994

Given the flow matrix, plant size and department sizes, the algorithms in this paper provide the layout with rectilinear distance. To construct automated facility design, cut tree approach is employed. A branch and bound computer code developed by Tillinghast is modifided to find the feasible fits of departments without shape distortion in the plant rectangle.

• Transportation Technology and Policy
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• v.13 no.4
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• pp.30-41
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• 2016

• Journal of Information Management
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• v.2 no.4
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• pp.6-8
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• 1964

This first article in a series of descriptions of devices and methods in the field of automated information processing depicts two machines representing a. certain type of documentation systems which use micro-images and coding of documents in an unalterable sequence, namely on 16mm filmrolls.

• Advances in robotics research
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• v.2 no.3
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• pp.201-217
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• 2018

Agriculture production is a critical human intensive task, which takes place in all regions of the world. The process to grow and harvest crops is labor intensive in many countries due to the lack of automation and advanced technology. Much of the difficult, dangerous and dirty labor of crop production can be automated with intelligent and robotic platforms. We propose an intelligent, agent-oriented robotic team, which can enable the process of harvesting, gathering and collecting crops and fruits, of many types, from agricultural fields. This paper describes a novel robotic organization enabling humans, robots and agents to work together for automation of gathering and collection functions. The focus of the research is a model, called HARMS, which can enable Humans, software Agents, Robots, Machines and Sensors to work together indistinguishably. With this model, any capability-based human-like organization can be conceived and modeled, such as in manufacturing or agriculture. In this research, we model, design and implement a technology application of knowledge-based robot-to-robot and human-to-robot collaboration for an agricultural gathering and collection function. The gathering and collection functions were chosen as they are some of the most labor intensive and least automated processes in the process acquisition of agricultural products. The use of robotic organizations can reduce human labor and increase efficiency allowing people to focus on higher level tasks and minimizing the backbreaking tasks of agricultural production in the future. In this work, the HARMS model was applied to three different robotic instances and an integrated test was completed with satisfactory results that show the basic promise of this research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.416-424
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• 2017

This study was conducted to examine the awareness of workers in public institutions of automated external defibrillators, their usage intention and related educational experience in an effort to increase the efficiency of education and the rate of automated external defibrillator usage in emergency situations. The subjects in this study were 243 selected. male and female adults who worked in public institutions in Gangwon Province. A survey was conducted from June 13 to August 21, 2016. The statistical analysis was based on the frequency analysis and the $X^2$-test. There were significant differences among the public institution workers awareness of how to use an automated external defibrillator according to gender (${\chi}^2=6.32$, p<0.05) and academic credentials (${\chi}^2=5.91$, p<0.05). Additionally, there were significant differences in usage intention according to academic background (${\chi}^2=5.70$, p<0.05). The most common reason for having no intention to use an automated external defibrillator for patients with cardiac arrest was that the respondents did not know how to use it (31.5%). There were also significant differences in educational experience with automated external defibrillators according to age (${\chi}^2=6.15$, p<0.05), academic credentials (${\chi}^2=11.56$, p<0.01) and career (${\chi}^2=11.92$, p<0.01). The most common form of education that the workers received pertaining to automated external defibrillators was a combination of theoretical and practical education, which represented 55.9%. The findings of the study seem to suggest that workers in public institutions used by many people must receive compulsory standard education regarding automated external defibrillators and try to boost the revival rate of patients with cardiac arrest in real emergency situations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.99-118
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• 2013

Electrocardiograms (ECGs) are widely used by clinicians to identify the functional status of the heart. Thus, there is considerable interest in automated systems for real-time monitoring of arrhythmia. However, intra- and inter-patient variability as well as the computational limits of real-time monitoring poses significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is therefore a promising new intelligent diagnostic tool.