• Journal of the Korean Society of Safety
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• v.34 no.6
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• pp.29-37
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• 2019

Since construction sites are exposed to outdoor environments, working conditions are significantly dangerous. Thus, wearing of the personal protective equipments such as safety helmet is very important for worker safety. However, construction workers are often wearing-off the helmet as inconvenient and uncomportable. As a result, a small mistake may lead to serious accident. For this, checking of wearing safety helmet is important task to safety managers in field. However, due to the limited time and manpower, the checking can not be executed for every individual worker spread over a large construction site. Therefore, if an automatic checking system is provided, field safety management should be performed more effectively and efficiently. In this study, applicability of deep learning based computer vision technology is investigated for automatic checking of wearing safety helmet in construction sites. Faster R-CNN deep learning algorithm for object detection and classification is employed to develop the automatic checking model. Digital camera images captured in real construction site are used to validate the proposed model. Based on the results, it is concluded that the proposed model may effectively be used for automatic checking of wearing safety helmet in construction site.

• Journal of the Korea Safety Management & Science
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• v.10 no.3
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• pp.253-265
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• 2008

In this paper, we developed a heuristic algorithm to assign job to workers in parallel line inspection process without sequence. Objective of assigning job in inspection process is only to assign job to workers evenly. But this objective needs much time and effort since there are many cases in assigning job and cases increase geometrically if the number of job and worker increases. In order to solve this problem, we proposed heuristic algorithm to assign job to workers evenly. Experiments of assigning job are performed to evaluate performance of this heuristic algorithm. The result shows that heuristic algorithm can find the optimal solution to assign job to workers evenly in many type of cases. Especially, in case there are more than two optimal solutions, this heuristic algorithm can find the optimal solution with 98% accuracy.

• Proceedings of the Safety Management and Science Conference
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• 2002.05a
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• pp.91-97
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• 2002

In this thesis presents line balancing problems of two-sided and mixed model assembly line widely used in practical fields using genetic algorithm for reducing throughput time, cost of tools and fixtures and improving flexibility of assembly lines. Two-sided and mixed model assembly line is a special type of production line where variety of product similar in product characteristics are assembled in both sides. This thesis proposes the genetic algorithm adequate to each step in tow-sided and mixed model assembly line with suitable presentation, individual, evaluation function, selection and genetic parameter. To confirm proposed genetic algorithm, we apply to increase the number of tasks in case study. And for evaluation the performance of proposed genetic algorithm, we compare to existing algorithm of one-sided and mixed model assembly line. The results show that the algorithm is outstanding in the problems with a larger number of stations or larger number of tasks.

• Journal of the Korea Safety Management & Science
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• v.11 no.3
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• pp.157-166
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• 2009

In this study, we developed a heuristic algorithm to get better efficiency of clustering than conventional algorithms. Conventional clustering algorithm had lower efficiency of clustering as there were no solid method for selecting initial center of cluster and as they had difficulty in search solution for clustering. EMC(Expanded Moving Center) heuristic algorithm was suggested to clear the problem of low efficiency in clustering. We developed algorithm to select initial center of cluster and search solution systematically in clustering. Experiments of clustering are performed to evaluate performance of EMC heuristic algorithm. Squared-error of EMC heuristic algorithm showed better performance for real case study and improved greatly with increase of cluster number than the other ones.

• Journal of the Korea Safety Management & Science
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• v.4 no.2
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• pp.83-101
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• 2002

In this thesis presents line balancing problems of two-sided and mixed model assembly line widely used in practical fields using genetic algorithm for reducing throughput time, cost of tools and fixtures and improving flexibility of assembly lines. Two-sided and mixed model assembly line is a special type of production line where variety of product similar in product characteristics are assembled in both sides. This thesis proposes the genetic algorithm adequate to each step in tow-sided and mixed model assembly line with suitable presentation, individual, evaluation function, selection and genetic parameter. To confirm proposed genetic algorithm, we apply to increase the number of tasks in case study. And for evaluation the performance of proposed genetic algorithm, we compare to existing algorithm of one-sided and mixed model assembly line. The results show that the algorithm is outstanding in the problems with a larger number of stations or larger number of tasks.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.1-8
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• 2023

This paper is proposing a novel machine scheduling model for the unrelated parallel machine scheduling problem without setup times to minimize the total completion time, also known as "makespan". This problem is a NP-complete problem, and to date, most approaches for real-life situations are based on the operator's experience or simple heuristics. The new model based on the Memetic Algorithm, which was proposed by P. Moscato in 1989, is a hybrid algorithm that includes genetic algorithm and local search optimization. The new model is tested on randomly generated datasets, and is compared to optimal solution, and four scheduling models; three rule-based heuristic algorithms, and a genetic algorithm based scheduling model from literature; the test results show that the new model performed better than scheduling models from literature.

• International Journal of Contents
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• v.7 no.4
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• pp.30-34
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• 2011

A Leader is a Coordinator that supports a set of processes to cooperate a given task. This concept is used in several domains such as distributed systems, parallelism and cooperative support for cooperative work. In completely asynchronous systems, there is no solution for the election problem satisfying both of safety and liveness properties in asynchronous distributed systems. Therefore, to solve the election problem in those systems, one property should be weaker than the other property. If an election algorithm strengthens the safety property in sacrifice of liveness property, it would not nearly progress. But on the contrary, an election algorithm strengthening the liveness property in sacrifice of the safety property would have the high probability of violating the safety property. In this paper, we presents a safety strengthened Leader Election protocol with an unreliable failure detector and analyses it in terms of safety and liveness properties in asynchronous distributed systems.

• Journal of the Korean Society of Safety
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• v.23 no.5
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• pp.1-6
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• 2008

`SOS', Simulator Of Safety assessment for fire, was developed to simulate fire safety assessment for a structure which is geometrically complex. The program(SOS) is intended to use for searching as well as designing tools to analyse the evacuation safety through a wide range of structure conditions. The computer program has a function which importing FDS's calculating results to each individual resident in the structure. These attributes include a walking speed reduction by producing visibility reduction for each person on the fire. $A^*$ pathfinding algorithm is adopted to calculate the simulation of escape movement, overtaking, route deviation, and adjustments individual speeds in accordance with the proximity of crowd members. This SOS program contributes to a computer package that evaluates the fire safety assessment of individual occupants as they walk towards, and through the exits especially for building, underground spaces like a subway or tunnel.

• Proceedings of the Safety Management and Science Conference
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• 2000.05a
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• pp.249-263
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• 2000

This paper considers an algorithm using Johnson transformation to calculate process capability index for non-normal distribution. Johnson transformation is well known as one of methods transforming the data with non-normal distribution to normal data.

• Journal of Auto-vehicle Safety Association
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• v.5 no.2
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• pp.24-31
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• 2013

This paper describes a coordinated control algorithm for rear-side collision avoidance. In order to assist driver actively and increase driver's safety, the proposed coordinated control algorithm is designed to combine lateral control using a steering torque overlay by Motor Driven Power Steering (MDPS) and differential braking by Vehicle Stability Control (VSC). The main objective of a combined control strategy is twofold. The one is to prevent the collision between the subject vehicle and approaching vehicle in the adjacent lanes. The other is to limit actuator's control inputs and vehicle dynamics to safe values for the assurance of the driver's comfort. In order to achieve these goals, the Lyapunov theory and LMI optimization methods has been employed. The proposed coordinated control algorithm for rear-side collision avoidance has been evaluated via simulation using CarSim and MATLAB/Simulink.