• International conference on construction engineering and project management
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• 2015.10a
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• pp.255-258
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• 2015

Scaffolds are frequently used in construction projects. Despite the impact on the entire safety, scaffolds are rarely analyzed as part of the safety planning. While recent advances in BIM (Building Information Modeling) provides opportunity to address potential safety issues in the early planning stages, it is still labor-intensive and challenging to incorporate scaffolds into current manual jobsite safety analysis which is time-consuming and error-prone. Consequently, potential safety hazards related to scaffolds are identified and presented during the construction phase. The objective of this research is to integrate scaffolds into automated safety analysis using BIM. A safety checking system was created to simulate the movements of scaffolds along the paths of crews using the scaffolds. Algorithms in the system automatically identify safety hazards related to activities working on scaffolds. Then, the system was implemented in a commercially available BIM software program for case studies. The results show that the algorithms successfully identified safety hazards that were not noticed by project managers of the projects. The results were visualized in BIM to facilitate early safety communications.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.167-168
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• 2023

Construction of reinforced concrete structures required massive amounts of concrete and steel rebar. The current procedure to estimate the quantity of rebar requires tedious and time-consuming manual labor. Consequently, this circumstance made the engineers vulnerable to error and mistake, which led to the rebar waste. No system that is capable of automatically calculating rebar length has yet been developed Thus, this study proposes a preliminary investigation of automatic rebar length estimation of beam element by using BIM-based shape codes drawn in Revit. Beam is chosen due to its complexity in the rebar arrangement. In addition, the development of this study could assist engineers on the construction site and effectively contribute to the minimization of rebar waste in the future.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.181-189
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• 2023

In this paper, we present a case study of developing MVIS (Machine Vision Inspection System) designed for exterior quality inspection of stamping dies used in the production of automotive exterior components in a small to medium-sized factory. While the primary processes within the factory, including machining, transportation, and loading, have been automated using PLCs, CNC machines, and robots, the final quality inspection process still relies on manual labor. We implement the MVIS with general-purpose industrial cameras and Python-based open-source libraries and frameworks for rapid and low-cost development. The MVIS can play a major role on improving throughput and lead time of stamping dies. Furthermore, the processed inspection images can be leveraged for future process monitoring and improvement by applying deep learning techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.61-69
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• 2016

Since 2000, the market of convenient stores in Korea has developed rapidly due to the explosive growth in single households but it still consists mainly of manual work due to the nature of the domestic industry. Hence the explosive increase in demand in the market is mostly due to workers. Therefore, the research aimed at encouraging efficiency via automation, which is carried out in manufacturing, such as electronic, cars and so on, is inadequate. This study performed a feasibility analysis of investment for introducing an automated system on brand A, which is domestic famous convenience store company. Productivity growth according to the introduction of an automated guided vehicle and the cost-benefits was studied with using a simulation for the picking process, which is most personnel and time consuming. As a result, the simulation showed that the equipment AGV introduced for choosing the process has the effects of cost saving and increased time efficiency for performing manual labor. Furthermore, appropriate numbers of AGV were forecasted considering the capacity of the distribution Center in the brand A convenient store, which has been growing steadily. There are increasing numbers of worker labor costs in the distribution industry these days. Before building a large new automate center, it is expected to provide a good information to investors who are considering increasing productivity through partial automation of each of unit process to achieve some cost reduction.

• Journal of Appropriate Technology
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• v.5 no.2
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• pp.70-81
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• 2019

The garment industry is one of the most labor-intensive manufacturing industries, with its sewing process relying almost entirely on manual labor. Its costs highly depend on the efficiency of this production line and thus is crucial to determine the production rate in real-time for line balancing. However, current production tracking methods are costly and make it difficult for many Small and Medium-sized Enterprises (SMEs) to implement them. As a result, their reliance on manual counting of finished products is both time consuming and prone to error, leading to high manufacturing costs and inefficiencies. In this paper, a production tracking system that uses the sewing machines' energy consumption data to track and count the total number of sewing tasks completed through Convolutional Neural Network (CNN) classifiers is proposed. This system was tested on two target sewing tasks, with a resulting maximum classification accuracy of 98.6%; all sewing tasks were detected. In the developing countries, the garment sewing industry is a very important industry, but the use of a lot of capital is very limited, such as applying expensive high technology to solve the above problem. Applied with the appropriate technology, this system is expected to be of great help to the garment industry in developing countries.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.169-179
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• 2005

Standard industry and occupation code are usually assigned manually in Korean census. The manual coding is very labor intensive and expensive task. Furthermore, inconsistent coding is resulted from the ability of human experts and their working environments. This paper proposes an automatic code classification system which converts natural language responses on survey questionnaires into corresponding numeric codes by using manually constructed rule base and example-based machine learning. The system was trained with 400,000 records of which standard codes was assigned. It was evaluated with 10-fold cross validation and was tested with three code sets: population occupation set, industry set, and industry survey set. The proposed system showed 76.63%, 82.24 and 99.68% accuracy for each code set.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.1
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• pp.77-86
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• 2000

Even manual materials handling tasks(MMHT) is decreasing by adopt of automatic manufacturing system & transportation supporting machine because of increase of productivity, wage lack of labor, safety, in fact working at inclined & complicated slope such as farm, orchard, harbor loading & unloading, logging place and mining place can't be substituted by machine perfectly. So, workers should do MMHT at this place by themselves, lifting on slope can cause much of hazard, include falling. Keeping balance net to slip can be a reason of low back pain(LBP) by overloaded musculoskeletal system but, there's no enough study about lift on slope. Therefore, In this study, we assessed and analyzed change of center of pressure(COP) when lifting on slope by force platform. The result showed that the length lengthen as increasing angle of slope. Especially, the length extremely increased over 15°. Through These basic result, present proper angle boundary, prevent industrial accidents and give proper data not only lifting but also pushing and pulling on slope someday.

• Journal of Korean Society of Forest Science
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• v.90 no.3
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• pp.388-397
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• 2001

In forestry, where improvement of labor environment is quite impossible, improved posture would result in direct effects by preventing waste of physical strength, prevention of accidental injury caused by fatigue accumulated on certain body parts, and prevention of human error by inattentiveness due to weakened body. Therefore, this research carried on analysis of working posture in manual forest work(thinning using chain-saw, salvage cutting using chain-saw, clearing using hand saw, clearance of twiner using sickle, pruning using saw with a long handle, and tending of young growth using sickle) using OWAS analysis system. According to the OWAS method, percentage of OWAS action categories III and IV in the tasks using chain-saw and sickle was higher than another tasks. For the compared middle skillful worker group and low skillful worker group at felling work using chain-saw, percentage of OWAS action categories IV in middle skillful worker group was 5.1%, and low skillful worker group was 14.1%.

• 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.