• 농업과학연구
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• 제50권3호
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• pp.373-382
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• 2023

The task of sorting and excluding blemished apples and others that lack commercial appeal is currently performed manually by human eye sorting, which not only causes musculoskeletal disorders in workers but also requires a significant amount of time and labor. In this study, an automated apple-sorting machine was developed to prevent musculoskeletal disorders in apple production workers and to streamline the process of sorting blemished and non-marketable apples from the better quality fruit. The apple-sorting machine is composed of an arm-rest, a main body, and a height-adjustable part, and uses object detection through a machine learning technology called 'You Only Look Once (YOLO)' to sort the apples. The machine was initially trained using apple image data, RoboFlow, and Google Colab, and the resulting images were analyzed using Jetson Nano. An algorithm was developed to link the Jetson Nano outputs and the conveyor belt to classify the analyzed apple images. This apple-sorting machine can immediately sort and exclude apples with surface defects, thereby reducing the time needed to sort the fruit and, accordingly, achieving cuts in labor costs. Furthermore, the apple-sorting machine can produce uniform quality sorting with a high level of accuracy compared with the subjective judgment of manual sorting by eye. This is expected to improve the productivity of apple growing operations and increase profitability.

• 대한산업공학회지
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• 제41권2호
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• pp.121-127
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• 2015

A reduction of motor performance due to brain disorders can be screened by evaluating force tracking capabilities (FTCs). Existing studies have examined FTCs mainly using simple sinusoidal waves, of which repeated profiles have a critical limitation due to a learning effect in force tracking. The present study examined the effects of personal factors (age and gender) and sinusoidal wave factors (central force and complexity) on FTCs of healthy adults using composite sinusoidal wave profiles (CSWPs). FTCs were measured using Finger $Touch^{TM}$ for 30 seconds and quantified in terms of time within the target range (TWR, accuracy measure) and relative RMSE (RRMSE, variability measure). A total of 90 healthy adults in 20s to 70s with the equal gender ratio participated in the experiment consisting of combinations of 2 central force levels (6 N and 10 N) and 2 complexity levels (approximate entropy, ApEn = 0.03 and 0.06) of CSWPs. Significantly decreased FTCs (lower TWR and higher RRMSE) were found in aged adults, females, the low central force, and the high complexity. The detailed FTC decrements include a 43% reduced TWR and a 85% increased RRMSE of older adults in 70s as compared to those in 20s, a 17% reduced TWR and a 17% increased RRMSE of female as compared to those of male, a 30% reduced TWR and a 108% increased RRMSE at central force = 6N when compared to those at central force = 10N, and a 19% reduced TWR and a 30% increased RRMSE at ApEn = 0.06 as compared to those at ApEn = 0.03. The characteristics of FTCs for CSWPs can be of use in establishing an assessment protocol of motor performance for screening brain disorders.

• 재활복지
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• 제21권2호
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• pp.233-246
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• 2017

본 연구는 디지털 세계와 물리적 세계가 접목하는 증강현실을 언어치료에 적용하기 위한 기초 연구이다. 특히, 아동들에게 동사를 지도할 때 증강현실로 구현한 동사 목록이 해당 움직임을 정확하게 나타내고 있는지와 같은 동작 타당도, 그리고 해당 동사 목록이 어휘 지도 목표로 적절한 것인지에 대한 어휘 타당도를 살펴본 연구이다. 선행 연구들을 참고하여 45개의 동사를 어휘 지도 목록으로 선정하고 이 동사들에 대하여 1급 언어재활사 자격증을 소지한 언어치료학과 교수 3명으로 하여금 어휘 타당도를 평가하도록 하였다. 그 결과, 39개 동사에서 높은 어휘 타당도를 얻었다. 높은 어휘 타당도를 얻은 39개 동사에 대한 동작 타당도를 살펴보기 위하여 언어치료 전공 석사과정 대학원생들에게 각 동사를 증강현실로 구현하여 보여주고 생각나는 동사를 기록하게 하였다. 증강현실로 구현된 동작 애니메이션을 보고 난 후 50% 이상의 대학원생이 해당 동사로 기록한 동사는 32개였다. 이차적으로는 이 32개 동사만 증강현실로 구현하여 87명의 언어치료 전공 대학생들에게 보여주고 Likert 5점 척도로 각 동사와 구현된 동작의 일치도를 평가하게 하였다. 최종적으로 30개 동사가 증강현실로 구현하여 지도하기에 타당한 동사 목록으로 선정되었다. 이 연구 결과를 바탕으로 향후 공인 타당도 및 적용 타당화 연구를 지속하여 증강현실을 활용한 어휘 평가 및 지도가 언어치료 임상 현장에서 활용되기를 기대한다.

• 전기학회논문지
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• 제65권9호
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• pp.1557-1563
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• 2016

According to recent studies, poor sitting posture of the spine has been shown to lead to a variety of spinal disorders. For this reason, it is important to measure the sitting posture. We proposed a strategy for classification of sitting posture using machine learning. We retrieved acceleration data from single tri-axial accelerometer attached on the back of the subject's neck in 5-types of sitting posture. 6 subjects without any spinal disorder were participated in this experiment. Acceleration data were transformed to the feature vectors of principle component analysis. Support vector machine (SVM) and K-means clustering were used to classify sitting posture with the transformed feature vectors. To evaluate performance, we calculated the correct rate for each classification strategy. Although the correct rate of SVM in sitting back arch was lower than that of K-means clustering by 2.0%, SVM's correct rate was higher by 1.3%, 5.2%, 16.6%, 7.1% in a normal posture, sitting front arch, sitting cross-legged, sitting leaning right, respectively. In conclusion, the overall correction rates were 94.5% and 88.84% in SVM and K-means clustering respectively, which means that SVM have more advantage than K-means method for classification of sitting posture.

• 대한한방소아과학회지
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• 제15권2호
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• pp.69-73
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• 2001

The neurofibromatosis (NF) are a set of genetic disorders which cause tumors to grow along various types of nerves and, in addition, can affect the development of non-nervous tissues such as bones and skin. NF causes tumors to grow anywhere on or in the body. It also leads to developmental abnormalities. For example, individuals with NF have a higher incidence of learning disabilities. Neurofibromatosis(NF) has been classified into two distinct types: NF-I and NF-II. neurofibromatosis 1(NF-I), also known as von Recklinghausen NF or Peripheral NF, occurring in 1:4,000 births, is characterized by multiple cafe-au-lait spots and neurofibromas on or under the skin. Enlargement and deformation of bones and curvature of the spine (scoliosis) may also occur. Occasionally, tumors may develop in the brain, on cranial nerves, or on the spinal cord. About 50% of people with NF also have learning disabilities. Neurofibromatosis 2(NF-II), also known as Bilateral Acoustic NF(BAN), is much rarer occurring in 1:50,000 births. NF-II is characterized by multiple tumors on the cranial and spinal nerves, and by other lesions of the brain and spinal cord. Tumors affecting both of the auditory nerves are the hallmark. Hearing loss beginning in the teens or early twenties is generally the first symptom. We reported a 10-year-old female patient with NF-I, she has pain and edema in left leg, no symptoms of NF.

• Journal of Ginseng Research
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• 제40권3호
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• pp.211-219
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• 2016

Background: Panax ginseng root is used in traditional oriental medicine for human health. Its main active components such as saponins and polysaccharides have been widely evaluated for treating diseases, but secondary active components such as oligosaccharides have been rarely studied. This study aimed to assess the impact of water-soluble ginseng oligosaccharides (WGOS), which were isolated from the warm-water extract of Panax ginseng root, on scopolamine-induced cognitive impairment in mice and its antineuroinflammatory mechanisms. Methods: We investigated the impact of WGOS on scopolamine-induced cognitive impairment in mice by using Morris water maze and novel object recognition task. We also analyzed the impact of WGOS on scopolamine-induced inflammatory response (e.g., the hyperexpression of proinflammatory cytokines IL-$1{\beta}$ and IL-6 and astrocyte activation) by quantitative real-time polymerase chain reaction and glial fibrillary acid protein (GFAP) immunohistochemical staining. Results: WGOS pretreatment protected against scopolamine-induced learning and memory deficits in the Morris water maze and in the novel object recognition task. Furthermore, WGOS pretreatment downregulated scopolamine-induced hyperexpression of proinflammatory cytokines interleukin (IL)-$1{\beta}$ and IL-6 mRNA and astrocyte activation in the hippocampus. These results indicate that WGOS can protect against scopolamine-induced alterations in learning and memory and inflammatory response. Conclusion: Our data suggest that WGOS may be beneficial as a medicine or functional food supplement to treat disorders with cognitive deficits and increased inflammation.

• Safety and Health at Work
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• 제12권3호
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• pp.304-310
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• 2021

Background: Healthcare settings have been recognized among the most hazardous places to work. Based on the five categories of occupational hazards that were identified by the ILO and WHO, this study aimed to analyze policy framework relevant to occupational health protection of health workers (HWs) in public health service in China, then discussed how to share the experience of the National Health Service (NHS) England for improvement. Methods: Based on policy learning theories, policy analysis and qualitative interview were used in this study. Results: In the Chinese public health service, at least five laws related to the regulation of occupational health protection for HWs; however, enforcement of relevant laws was separated and multi-centered; the national monitoring system, which targeted to occupational hazards and health outcome for HWs in China, had yet to be developed; the top three priorities were workplace violence, bloodborne pathogens, and musculoskeletal disorders; national strategies included Security Hospital, and Healthy China 2030. In NHS England, three laws were fundamental; several monitoring systems had been set up, including NHS Staff Survey, Commissioning for Quality and Innovation incentive scheme; mental health, musculoskeletal problem, and nutrition disorder and overweight were raised great concern; Health and Safety, and NHS Healthy Workforce Program were critical nationwide strategies. Conclusion: There were several similarities as well as differences between the Chinese public health system and NHS England, which laid foundation of learning by China. Recommendations of improving occupational health policies in China were provided, based on the lessons learned from the NHS England.

• Imaging Science in Dentistry
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• 제52권4호
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• pp.393-398
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• 2022

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint(TMJ) magnetic resonance imaging (MRI) protocol. Materials and Methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient. Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement(ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement(ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion: The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

• Safety and Health at Work
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• 제14권1호
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• pp.66-70
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• 2023

Background: Online teaching and learning extend the duration of using gadgets such as mobile phones and tablets. A prolonged usage of these gadgets in a static position can lead to musculoskeletal disorders (MSD). Therefore, this study aims to identify the risk factors related to musculoskeletal symptoms while using gadgets during work from home due to the COVID-19 pandemic. Method: A cross-sectional survey with online-based questionnaires was collected from the University of Indonesia, consisting of lecturers, students, and managerial staff. The minimum number of respondents was 1,080 and was defined by stratified random sampling. Furthermore, the dependent variable was musculoskeletal symptoms, while the independent were age, gender, job position, duration, activity when using gadgets, and how to hold them. Result: Most of the respondents had mobile phones but only 16% had tablets. Furthermore, about 56.7% have used a mobile phone for more than 10 years, while about 89.7% have used a tablet for less than 10 years. A multivariate analysis found factors that were significantly associated with MSD symptoms while using a mobile phone, such as age, gender, web browsing activity, work, or college activities. These activities include doing assignments and holding the phone with two hands with two thumbs actively operating. The factors that were significantly associated with MSD symptoms when using tablets were gender, academic position, social media activity, and placing the tablet on a table with two actively working index fingers. Conclusion: Therefore, from the results of this study it is necessary to have WFH and e-learning policies to reduce MSD symptoms and enhance productivity at work.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.