• 대한안전경영과학회지
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• 제21권4호
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• pp.67-74
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• 2019

Visual inspection is regarded as one of the critical activities for quality control in a manufacturing company. it is thus important to improve the performance of detecting a defective part or product. There are three probable working modes for visual inspection: fully automatic (by automatic machines), fully manual (by human operators), and semi-automatic (by collaboration between human operators and automatic machines). Most of the current studies on visual inspection have been focused on the improvement of automatic detection performance by developing a better automatic machine using computer vision technologies. However, there are still a range of situations where human operators should conduct visual inspection with/without automatic machines. In this situation, human operators'performance of visual inspection is significant to the successful quality control. However, visual inspection of components assembled into a mobile camera module belongs to those situations. This study aims to investigate human performance issues in visual inspection of the components, paying more attention to human errors. For this, Abstraction Hierarchy-based work domain modeling method was applied to examine a range of direct or indirect factors related to human errors and their relationships in the visual inspection of the components. Although this study was conducted in the context of manufacturing mobile camera modules, the proposed method would be easily generalized into other industries.

• Physical Therapy Rehabilitation Science
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• 제11권1호
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• pp.58-65
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• 2022

Objective: The purpose of this study was to investigate the effect of visual feedback bicycle training on running performance, maximal oxygen uptake and quadriceps muscle strength. Design: A randomized controlled trial. Methods: Fifteen healthy adult men with no musculoskeletal or nervous system disease and capable of bicycle training were included. After the pretest, subjects were randomly assigned to visual feedback bicycle training group and general fixed bicycle training group. Both groups were trained two times a week for three weeks, each week for a fixed time and number of repetitions, followed by a six week washout period and then crossing the training method. visual feedback bicycle training provides visual feedback of heart rate in real time using a monitor and a heart rate meter during bicycle training, and general fixed bicycle training performed general bicycle training without visual feedback. After training, each item was measured using a wearable technology, gas analyzer, isokinetic equipment. Results: The results of this study was significant differences in running performance, maximal oxygen uptake and quadriceps muscle strength in visual feedback bicycle training group (p<0.05). The differential effect was found between visual feedback bicycle training group and general fixed bicycle training group in running performance, maximal oxygen uptake, and quadriceps muscle strength (p<0.05). Conclusions: This study suggested that that visual feedback bicycle training can be applied as a useful training method to improve running performance, maximal oxygen uptake and quadriceps muscle strength.

• 로봇학회논문지
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• 제17권3호
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• pp.334-338
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• 2022

In visual simultaneous localization and mapping (vSLAM), the correct recognition of a place benefits in relocalization and improved map accuracy. However, its performance is significantly affected by the environmental conditions such as variation in light, viewpoints, seasons, and presence of dynamic objects. This research addresses the problem of feature occlusion caused by interference of dynamic objects leading to the poor performance of visual place recognition algorithm. To overcome the aforementioned problem, this research analyzes the role of scene semantics in correct detection of a place in challenging environments and presents a semantics aided visual place recognition method. Semantics being invariant to viewpoint changes and dynamic environment can improve the overall performance of the place matching method. The proposed method is evaluated on the two benchmark datasets with dynamic environment and seasonal changes. Experimental results show the improved performance of the visual place recognition method for vSLAM.

• 말소리와 음성과학
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• 제3권2호
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• pp.65-70
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• 2011

Recently, audio-visual speech recognition (AVSR) has been studied to cope with noise problems in speech recognition. In this paper we propose a novel method of deciding weighting factors for audio-visual information fusion. We adopt the particle swarm optimization (PSO) to weighting factor determination. The AVSR experiments show that PSO-based normalized confidence measures (NCM) improve the rejection performance of mis-recognized words by 33%.

• 대한인간공학회지
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• 제21권1호
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• pp.95-112
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• 2002

Two experiments were carried out to compare the suitability in visual tasks between cathode-ray tube (CRT) and thin film transistor-liquid crystal display (TFT-LCD). In the first experiment, the subjects were requested to detect pre-assigned target words or icons among distracters presented under time-invariant (static) image mode. The subjects' visual performance and fatigue were assessed while carrying out search tasks with dim and bright ambient light conditions. Significant interaction effects were found among displays, task types, and ambient light conditions. Due to visual fatigue, the subjects' accommodative power decreased in the end of task and the degradation was more significant for the CRT users and under bright ambient light. IN the second experiment, the subjects performed information processing task with time-varying road signs at a driving simulator to assess interaction effects between display types and changing speed of dynamic image. The perception time using TFT-TCD was shorter under slow image change while that of CRT was shorter rapid image change. Findings from this study suggest that, to improve visual task performance, users should carefully select their visual display type depending on the task to be performed.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권1호
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• pp.41-47
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• 2000

A better tracking performance can be achieved, if visual sensors such as CCD cameras are used in controling a robot manipulator, than when only relative sensors such as encoders are used. However, for precise visual servoing of a robot manipulator, an expensive vision system which has fast sampling rate must be used. Moreover, even if a fast vision system is implemented for visual servoing, one cannot get a reliable performance without use of robust and stable inner joint servo-loop. In this paper, we propose a dynamic control scheme for robot manipulators with eye-in-hand camera configuration, where a dynamic learning controller is designed to improve the tracking performance of robotic system. The proposed control scheme is implemented for tasks of tracking moving objects and shown to be robust to parameter uncertainty, disturbances, low sampling rate, etc.

• 한국정밀공학회지
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• 제20권10호
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• pp.82-88
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• 2003

Many visual servoing algorithms have been recently developed by the robot vision researchers. They do not, however, consider the stability of servoing system. The camera calibration is the most important factor to the control stability and performance of position based visual servoing. In this article we describe the ECL(End Point Closed Loop) servoing can make no steady state error for the control of 6-DOF robot of which accuracy is dependent on the camera calibration and kinematics. And we propose a dynamic calibration algorithm, which can improve stability and performance of ECL visual servoing. To verify the potential of our approach, we run assembly experiments and present our finding.

• Smart Structures and Systems
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• 제30권3호
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• pp.287-301
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• 2022

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

• Journal of Construction Engineering and Project Management
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• 제5권4호
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• pp.16-22
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. Several case studies were conducted to experimentally evaluate their impact on BIM-based energy analysis to calculate energy load. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.532-536
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.