• Journal of Biosystems Engineering
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• v.42 no.3
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• pp.227-233
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• 2017

Purpose: Morphological properties of soybean roots are important indicators of the vigor of the seed, which determines the survival rate of the seedlings grown. The current vigor test for soybean seeds is manual measurement with the human eye. This study describes an application of a machine vision technique for rapid measurement of soybean seed vigor to replace the time-consuming and labor-intensive conventional method. Methods: A CCD camera was used to obtain color images of seeds during germination. Image processing techniques were used to obtain root segmentation. The various morphological parameters, such as primary root length, total root length, total surface area, average diameter, and branching points of roots were calculated from a root skeleton image using a customized pixel-based image processing algorithm. Results: The measurement accuracy of the machine vision system ranged from 92.6% to 98.8%, with accuracies of 96.2% for primary root length and 96.4% for total root length, compared to manual measurement. The correlation coefficient for each measurement was 0.999 with a standard error of prediction of 1.16 mm for primary root length and 0.97 mm for total root length. Conclusions: The developed machine vision system showed good performance for the morphological measurement of soybean roots. This image analysis algorithm, combined with a simple color camera, can be used as an alternative to the conventional seed vigor test method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.211-220
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• 2017

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.452-455
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• 2017

As the number of high - rise buildings has increased recently, this research society has become interested in the method of cleaning the external wall required. When cleaning these areas, an accident occurs every year when a person uses dangerous manual labor using a gondola. The main goal of this work is to enable people to manipulate the robot with simple operation without dangerous manual operation when working in a vertical structure. As the altitude increases, the concept of the mechanism attaching to the vertical structure while enduring the external resistance in the increase of the wind strength is applied, and the additional attachment device is attached to the end of the leg based on the existing four- According to the control, the development result of the robot having the function of detachable to the glass is presented.

• Korean Journal of Construction Engineering and Management
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• v.7 no.5
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• pp.149-158
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• 2006

Current removing process is labor intensive and time consuming, employing a conventional grinding type manual machine. This manual tasks trigger various dangers such as unexpected traffic accidents or explosions of propane gas used for finalizing removing process, leading to health damage and environment pollution by dusts and noxious gases. Accordingly, it is necessity for the development of new alternative equipment. In this paper, we have developed a prototype equipment for road stripe removing made up with a high-pressure water-jet system as a mobile type system. The following shows the results. First, an analysis of the current road stripe removal process showed that it can be divided into a) preparation, b) removal and c) ground finishing. It also showed that the b) removal process requires equipment which can cover the whole process. Second, the study compared between the productivity of the developed equipment and conventional methods, and it obtained 280% productivity improvement compared to the conventional equipment.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.172-179
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• 2022

Unlike other processes such as washing, the ironing process in the laundry process is difficult to standardize and thus relies on manual labor. Unlike upper garments, pants have pleats at the waist as well as a crease line below the waist, Therefore, two separate ironing devices have been developed and used. However, in this method, problems such as additional worker input, space loss, and wrinkling of pants occur due to manual movement between processes, Consequently, a pants ironing device that combines the two equipments is required. The all-in-one pants ironing system described in this paper automatically sequentially irons the upper part and side of the pants regardless of the length, shape, and upper pleats of the pants. It also performs a self-diagnosis function while displaying the ironing progress on the user's monitor. As a result of this study, it became possible to double the amount of ironing and reduce power consumption by more than 20% compared to the case of using two independent equipment.

• Applied Microscopy
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• v.51
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• pp.4.1-4.12
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• 2021

Fluorescence in situ hybridization (FISH) is a technique to visualize specific DNA/RNA sequences within the cell nuclei and provide the presence, location and structural integrity of genes on chromosomes. A confocal Whole Slide Imaging (WSI) scanner technology has superior depth resolution compared to wide-field fluorescence imaging. Confocal WSI has the ability to perform serial optical sections with specimen imaging, which is critical for 3D tissue reconstruction for volumetric spatial analysis. The standard clinical manual scoring for FISH is labor-intensive, time-consuming and subjective. Application of multi-gene FISH analysis alongside 3D imaging, significantly increase the level of complexity required for an accurate 3D analysis. Therefore, the purpose of this study is to establish automated 3D FISH scoring for z-stack images from confocal WSI scanner. The algorithm and the application we developed, SHIMARIS PAFQ, successfully employs 3D calculations for clear individual cell nuclei segmentation, gene signals detection and distribution of break-apart probes signal patterns, including standard break-apart, and variant patterns due to truncation, and deletion, etc. The analysis was accurate and precise when compared with ground truth clinical manual counting and scoring reported in ten lymphoma and solid tumors cases. The algorithm and the application we developed, SHIMARIS PAFQ, is objective and more efficient than the conventional procedure. It enables the automated counting of more nuclei, precisely detecting additional abnormal signal variations in nuclei patterns and analyzes gigabyte multi-layer stacking imaging data of tissue samples from patients. Currently, we are developing a deep learning algorithm for automated tumor area detection to be integrated with SHIMARIS PAFQ.

• Journal of Practical Agriculture & Fisheries Research
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• v.13 no.1
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• pp.47-69
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• 2011

The purpose of this study is to suggest strategies for vitalizing Jeju citrus export-specialized organization. To achieve the goal, the research has been focused on the establishment and standardization for fruit production practices that can keep a steady supply of high quality citrus fruit for export market, and followed by systematization of farms using the practices. According to the research findings, it is desirable that the high quality fruit production manual such as the Global GAP must be established, developed and prepared to train farm households. The establishment of the high quality fruit production manual is focused on the items as follows : 1) labor productivity enhancement by structural improvement of farm layout, 2) the practices for high-sugar-fruit production such as thinning out, irrigation and drainage system, and mulching with a poromeric sheet 3) productivity enhancement and quality improvement of fruit by cultivar improvement and virus-free nursery trees, 4) standardization of cultural practices throughout the citrus fruit production.

• Journal of Biosystems Engineering
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• v.30 no.2 s.109
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• pp.81-88
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• 2005

Manual transplanting Chinese cabbage needs 184 hours per ha in Korea. Mechanization of Chinese cabbage transplanting operation has been highly required because it needs highly intensive labor during peak season. This study was conducted to developed walking-type Chinese cabbage transplanter. In order to find out design factor of the transplanter, a kinematic analysis software, RecurDyn, was used. The prototype was tested in the circular soil bin and its operating motion was captured and analyzed using high speed camera system. Prototype was one row type which utilized original parts of engine, transmission and etc. from walking-type rice transplanter in order to save the manufacturing cost. Success ratio of pick-up device of hole-pin type and latch type were $96.0\%$ and $99.2\%$, respectively. which was highly affected by feeding accuracy of feeding device of seedling. Transplanting device of the prototype produced a elliptic loci which were coincident with those produced by the computer simulation. Prototype proved good performance in transplanting with mulching and without mulching operation, either. Working performance of prototype was 22 hours per ha and operation cost of the prototype was 961,757 won per ha. So, it would reduce $88\%$ of the labor and $29\%$ of operation cost.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.1-7
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• 2021

In this paper, We focused the issue of creating a socially problematic nurse schedule. The nurse schedule should be prepared in consideration of three shifts, appropriate placement of experienced workers, the fairness of work assignment, and legal work standards. Because of the complex structure of the nurse schedule, which must reflect various requirements, in most hospitals, the nurse in charge writes it by hand with a lot of time and effort. This study attempted to automatically create an optimized nurse schedule based on legal labor standards and fairness. We developed an I/O Q-Learning algorithm-based model based on Python and Web Application for automatic nurse schedule. The model was trained to converge to 100 by creating an Fairness Indicator Score(FIS) that considers Labor Standards Act, Work equity, Work preference. Manual nurse schedules and this model are compared with FIS. This model showed a higher work equity index of 13.31 points, work preference index of 1.52 points, and FIS of 16.38 points. This study was able to automatically generate nurse schedule based on reinforcement Learning. In addition, as a result of creating the nurse schedule of E hospital using this model, it was possible to reduce the time required from 88 hours to 3 hours. If additional supplementation of FIS and reinforcement Learning techniques such as DQN, CNN, Monte Carlo Simulation and AlphaZero additionally utilize a more an optimized model can be developed.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.373-382
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• 2023

The task of sorting and excluding blemished apples and others that lack commercial appeal is currently performed manually by human eye sorting, which not only causes musculoskeletal disorders in workers but also requires a significant amount of time and labor. In this study, an automated apple-sorting machine was developed to prevent musculoskeletal disorders in apple production workers and to streamline the process of sorting blemished and non-marketable apples from the better quality fruit. The apple-sorting machine is composed of an arm-rest, a main body, and a height-adjustable part, and uses object detection through a machine learning technology called 'You Only Look Once (YOLO)' to sort the apples. The machine was initially trained using apple image data, RoboFlow, and Google Colab, and the resulting images were analyzed using Jetson Nano. An algorithm was developed to link the Jetson Nano outputs and the conveyor belt to classify the analyzed apple images. This apple-sorting machine can immediately sort and exclude apples with surface defects, thereby reducing the time needed to sort the fruit and, accordingly, achieving cuts in labor costs. Furthermore, the apple-sorting machine can produce uniform quality sorting with a high level of accuracy compared with the subjective judgment of manual sorting by eye. This is expected to improve the productivity of apple growing operations and increase profitability.