• IE interfaces
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• v.17 no.spc
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• pp.37-45
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• 2004

Human factors engineering design guidelines for main control rooms(MCR) in nuclear power plants(NPP) have been applied to optimize human-machine interface(HMI) between operators and their equipment on the basis of physical, physiological and cognitive aspects. However, the HMI design for MCR is not found to be sufficient to maximize operators' performance since the operators in the MCR experience excessive stress due to the environmental factors such as inappropriate interiors and illumination. Therefore, well-designed environment of the MCR may be equally important to improve human performance in the MCR. The objectives of the study are two-fold: (1) to propose an interior design of SHIN-KORI 1 2 for pleasant and comfortable working environments, and (2) to design indirect lighting system to enhance visibility and productivity. The human factors engineering checklists were developed to examine whether or not the proposed human-environment design for SHIN-KORI 1 2 satisfies the regulations and guidelines presented by NUREG-0700 Revision 1. The implications of the human-environment design are discussed in detail.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.68-70
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• 1990

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.84-95
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• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.

• Advances in robotics research
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• v.2 no.2
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• pp.129-140
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• 2018

With the increasing demand for the development of human pose estimation, such as human-computer interaction and human activity recognition, there have been numerous approaches to detect the 2D poses of people in images more efficiently. Despite many years of human pose estimation research, the estimation of human poses with images remains difficult to produce satisfactory results. In this study, we propose a robust 2D human body pose estimation method using an RGB camera sensor. Our pose estimation method is efficient and cost-effective since the use of RGB camera sensor is economically beneficial compared to more commonly used high-priced sensors. For the estimation of upper-body joint positions, semantic segmentation with a fully convolutional network was exploited. From acquired RGB images, joint heatmaps accurately estimate the coordinates of the location of each joint. The network architecture was designed to learn and detect the locations of joints via the sequential prediction processing method. Our proposed method was tested and validated for efficient estimation of the human upper-body pose. The obtained results reveal the potential of a simple RGB camera sensor for human pose estimation applications.

• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.743-755
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• 2005

Human Sensibility Engineering System (HSES) has been applied to product development for customer's satisfaction based on ergonomic technology. The system is composed of three parts such as human sensibility analysis, inference mechanism, and presentation technologies. Inference mechanism translating human sensibility into design elements plays an important role in the HSES. In this paper, we propose a rule-based inference model for HSES. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected human sensibility words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customer's feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.31
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• pp.33-42
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• 1994

This paper aims to analyse and appraise the effect of human performance to the variation of quality through constructing the dynamic recursive control model considering the human factor in a single production system. The model studied in this paper has a great advance from the point of combinating three technologies(quality control, automatic control theory, human engineering) and can also be expanded in several applications.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.299-309
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• 2022

Human factor represents a very challenging issue to organizations. Human factor is responsible for many cybersecurity incidents by noncompliance with the organization security policies. In this paper we conduct a comprehensive review of the literature to identify strategies to address human factor. Security awareness, training and education program is the main strategy to address human factor. Scholars have consistently argued that importance of security awareness to prevent incidents from human behavior.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.416-422
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• 2017

The robot control with safety consideration is required since robots and human work together in the same space more frequently in these days. For safety, robots must have compliance to human force and robust tracking performance with high impednace for the nonhuman disturbances. The novel idea is proposed to achieve the compliance and high impedance with one controller structure. For the compliance, the ISMC(Integral Sliding Mode Control) and HDOB(Human Disturbance Observer) The human force is identified by using the human band pass filter and its output is sent to the sliding surface. The sliding mode dynamic is affected by human disturbance and the compliance for human is achieved. The disturbances besides human frequencies are decoupled by the ISMC and the robust tracking is achieved. The additional LDOB(Low Frequency Disturbance Observer) decreases the maxim nonlinear gain and leads low chattering. The introduction of human disturbance into the sliding mode dynamic is the main novel idea of this paper.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.1
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• pp.62-69
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• 2014

In this paper, WBAN(Wireless Body Area Network) On-body system using the surface-oriented antenna about the impact of human internal organs were analyzed through experiments. The received signal strength is measured for effect of human using the human model and the phantom of torso. Experiments are performed in anechoic chamber without moving and measured by Vector Network Analyzer. This paper confirms the effect of human body by comparing the human model and the phantom of torso. And also know the human internal organs effect on the antennas loss of received signal strength by measured data.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.328-335
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• 2022

With the advance of robot capabilities and functionalities, construction robots assisting construction workers have been increasingly deployed on construction sites to improve safety, efficiency and productivity. For close-proximity human-robot collaboration in construction sites, robots need to be aware of the context, especially construction worker's behavior, in real-time to avoid collision with workers. To recognize human behavior, most previous studies obtained 3D human poses using a single camera or an RGB-depth (RGB-D) camera. However, single-camera detection has limitations such as occlusions, detection failure, and sensor malfunction, and an RGB-D camera may suffer from interference from lighting conditions and surface material. To address these issues, this study proposes a novel method of 3D human pose estimation by extracting 2D location of each joint from multiple images captured at the same time from different viewpoints, fusing each joint's 2D locations, and estimating the 3D joint location. For higher accuracy, the probabilistic representation is used to extract the 2D location of the joints, considering each joint location extracted from images as a noisy partial observation. Then, this study estimates the 3D human pose by fusing the probabilistic 2D joint locations to maximize the likelihood. The proposed method was evaluated in both simulation and laboratory settings, and the results demonstrated the accuracy of estimation and the feasibility in practice. This study contributes to ensuring human safety in close-proximity human-robot collaboration by providing a novel method of 3D human pose estimation.