• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.8-15
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• 2003

In Korea, quality evaluation of dried oak mushrooms are done first by classifying them into more than 10 different categories based on the state of opening of the cap, surface pattern, and colors. And mushrooms of each category are further classified into 3 or 4 groups based on its shape and size, resulting into total 30 to 40 different grades. Quality evaluation and sorting based on the external visual features are usually done manually. Since visual features of mushroom affecting quality grades are distributed over the entire surface of the mushroom, both front (cap) and back (stem and gill) surfaces should be inspected thoroughly. In fact, it is almost impossible for human to inspect every mushroom, especially when they are fed continuously via conveyor. In this paper, considering real time on-line system implementation, image processing algorithms utilizing artificial neural network have been developed for the quality grading of a mushroom. The neural network based image processing utilized the raw gray value image of fed mushrooms captured by the camera without any complex image processing such as feature enhancement and extraction to identify the feeding state and to grade the quality of a mushroom. Developed algorithms were implemented to the prototype on-line grading and sorting system. The prototype was developed to simplify the system requirement and the overall mechanism. The system was composed of automatic devices for mushroom feeding and handling, a set of computer vision system with lighting chamber, one chip microprocessor based controller, and pneumatic actuators. The proposed grading scheme was tested using the prototype. Network training for the feeding state recognition and grading was done using static images. 200 samples (20 grade levels and 10 per each grade) were used for training. 300 samples (20 grade levels and 15 per each grade) were used to validate the trained network. By changing orientation of each sample, 600 data sets were made for the test and the trained network showed around 91 % of the grading accuracy. Though image processing itself required approximately less than 0.3 second depending on a mushroom, because of the actuating device and control response, average 0.6 to 0.7 second was required for grading and sorting of a mushroom resulting into the processing capability of 5,000/hr to 6,000/hr.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.224-226
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• 2016

With the technical development and rapid increase of private demand, the new market for unmanned vehicle combined with the characteristics of 'unmanned automation' and 'vehicle' is rapidly growing. Even though the pilot driving is currently allowed in some countries, there is no country that has institutionalized the formal driving of self-driving cars. In case of the existing vehicles, safety incidents are frequently happening due to the frequent malfunction of the rear sensor, blind spot of the rear camera, or drivers' carelessness. Once such minor flaws are complemented, the relevant regulations for the commercialization of self-driving car and small drone could be relieved. Contrary to the ultrasonic and laser sensors used for the existing vehicles, this paper aims to attempt the distance measurement by using the depth sensor. A depth camera calculates the distance data based on the TOF method calculating the time difference by lighting laser or infrared light onto an object or area and then receiving the beam coming back. As this camera can obtain the depth data in the pixel unit of CCD camera, it can be used for collecting depth data in real-time. This paper suggests to solve problems mentioned above by using depth data in real-time and also to design the obstacle avoidance system through distance measurement.

• Journal of Korean Ophthalmic Optics Society
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• v.14 no.4
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• pp.65-69
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• 2009

Purpose: Recently incidence of VDT syndrome has gradually increased as extensive use of computers. VDT syndrome reported by VDT workers include musculoskeletal disorder, neuropsychiatric disoders and eye symptoms such as eye strain, tired eyes, irritation and blurred vision. The environmental factors of VDT syndrome include electromagnetic waves, size, brightness and lighting of computer screen, height of a monitor and a worktable, working hours, kind of task, distance between screen and workers, indoor humidity and temperature, indoor air contamination and ventilation. In this study, we investigated the environmental factors related to body symptoms and health effects included in VDT syndrome. Methods: Study subjects were total 120 persons (54 male, 66 female) with age from 19 to 28. We surveyed the body symptoms and physical discomfort when doing an activity in a short distance such as reading book or paper, computer work. The questionnaire included main body symptoms, self-consciousness symptoms of eye, satisfaction of working environment, pain of the wrist when using keyboard and mouse. Results: Most of people (70%) felt physical pain from long time work of computer, paper, electrical apparatus. They mainly complained pain of neck and low back (57.1%), eye (45.2%) and head (31%). With the environmental factors, 78.3% of the subjects complaint pain of eye from inappropriate illumination. Most of the symptoms included 'eye fatigue'(38.3%), 'dryness of eye'(31.9%) and 'blurred vision'(23.7%). Subjects in this study complained discomfort of their chairs and most of them experienced pain in the wrist when using keyboard or mouse. Conclusions: When people use electrical apparatus or work with paper, people would get their eye fatigue and feeling of physical fatigue because of not harmonizing various environmental factors such as light, space, posture, worktable with theirselves. Therefore, workers should develop preventive method such as self-control of adequate break time to avoid fatigue while VDT work. Work environment should be changed to ergonomic design for optimal visual environment to prevent musculoskeletal disorder through constant research.