• 한국정밀공학회지
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• 제33권9호
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• pp.761-768
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• 2016

Weight bearing is effective during rehabilitation of gait, in the elderly and disabled people. Various training devices using weight bearing function were developed along with treadmill walking; however, no device has been developed in conjunction to walking on the ground. Here, we designed a rail type frame of a gait rehabilitation system for body-weight support (BWS) function, and analyzed its mechanical safety in the static weight bearing condition of a vertical axis. Computational simulations were performed to analyze structure of the driving parts, which are connected with a rail and driving rollers and the lower plate of the BWS. Structural analyses showed the drivers and BWS were safe, when simulated at 135kg weight under static conditions. Thus, this rail type rehabilitation system can be used for gait training of the elderly and disabled.

• 대한기계학회논문집A
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• 제41권8호
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• pp.691-701
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• 2017

본 연구에서는 스미스 머신을 이용한 스쿼트 운동에 대한 가상 테스트 프레임웍을 제안하고 구현하였다. 이를 위하여 인체 및 제품의 통합 모델을 개발하고, 실험 데이터를 바탕으로 자세의 변화에 따른 새로운 동작을 생성하는 알고리즘을 개발한 후, 가상의 조건에서 동작을 생성하여 인체의 관절에 걸리는 토크와 근육에 걸리는 힘을 시뮬레이션 하는 전체 시스템을 개발하였다. 이 시스템을 검증하기 위하여 동작 수집과 더불어 EMG와 지면반력에 대한 데이터를 수집하여 시뮬레이션 결과가 실제와 잘 맞는지 확인하였다. 이 시스템을 확장시켜 사용한다면 다양한 조건에서 운동하였을 때 신체 근육과 관절에 어떤 영향을 끼치는 지 시뮬레이션 해봄으로써 적절한 운동 프로그램을 효과적으로 개발할 수 있을 것으로 기대된다.

• 한국산학기술학회논문지
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• 제12권9호
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• pp.3834-3842
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• 2011

본 연구에서는 선천적으로 관절 느슨함과 낮은 근 장력을 갖고 있는 다운증후군아동 보행의 운동학적 특성을 알아보고자 한다. 특히 자료수집의 편리성과 재현성이 높고, 유용한 보행훈련도구로서 그 사용이 증가되는 추세인 트레드밀에서 보행을 실시하였다. 다른 중복장애가 없는 다운증후군 남자아동 10명을 대상으로 적외선카메라로 3차원 보행 자료를 수집하여 시공간적 변인과 운동학적 변인을 산출하여 이들의 보행특성을 분석하였다. 그 결과, 다운증후군아동들은 선천적인 근골격계 특성으로 인한 보행불안정성을 극복하기 위해 엉덩, 무릎, 발목관절 모두를 구부리고 걷는 구부정 걸음(crouch gait)형태를 보였으며, 모든 다리관절의 신전을 작게 하여 결과적으로 추진력이 자제하였다. 이는 더 크게 신체를 추진할수록 발뒤꿈치 착지 시에 체중수용의 부담이 증가하기 때문인 것으로 판단된다. 이 연구 결과는 다운증후군의 관절불안정성을 개선하기 위해 효과적인 근 골격계 강화훈련 프로그램을 마련하는데 유용한 자료를 제공할 것으로 기대되며, 다운증후군뿐만 아니라 선천적 또는 후천적 근골격계 장애를 개선하거나 극복할 수 있는 운동프로그램 개발에도 참고 자료가 될 것으로 예상된다.

• 한국운동역학회지
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• 제29권2호
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• pp.105-112
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• 2019

Objective: The main purpose of this study was to investigate the effects of wearing an ankle weight belt while performing gait in water by focusing on the effect of using ankle weights have on the gait kinematics and the muscle activities for developing optimum training strategies. Method: A total of 10 healthy male university students were recruited for the study. Each participant was instructed to perform 3 gait conditions; normal walking over ground, walking in water chest height, and walking in water chest height while using ankle weights. All walking conditions were set at control speed of $4km/h{\pm}0.05km/h$. The depth of the swimming pool was at 1.3 m, approximately chest height. The motion capture data was recorded using 6 digital cameras and the EMG was recorded using waterproof Mini Wave. From the motion capture data, the following variables were calculated for analysis; double and single support phase (s), swing phase (s), step length (%height), step rate (m/s), ankle, knee, and hip joint angles ($^{\circ}$). From the electromyography the %RVC of the lower limb muscles medial gastrocnemius, rectus femoris, erector spinae, semitendinosus, tibialis anterior, vastus lateralis oblique was calculated. Results: The results show significant differences between the gait time, and step length between the right and left leg. Additionally, the joint angular velocities and gait velocity were significantly affected by the water resistance. As expected, the use of the ankle weights increased all of the lower leg maximum muscle activities except for the lower back muscle. Conclusion: In conclusion, the ankle weights can be shown to stimulate more muscle activity during walking in chest height water and therefore, may be useful for rehabilitation purposes.

• 한국정보통신학회논문지
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• 제23권9호
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• pp.1049-1059
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• 2019

기존의 영역 병합을 통한 이미지 분할 방법에서는 이웃한 두 영역 사이의 정보만을 이용하여 병합 모델을 학습한다. 학습 과정에서는 두 영역 사이의 지역적인 정보뿐만 아니라 물체 정보와 같은 전역적인 정보 또한 활용 가능하므로 주어진 모든 정보를 활용하여 병합 모델의 성능을 높이는 것이 바람직하다. 본 논문에서는 학습 기반의 이미지 분할 알고리즘에서 학습 시에만 사용 가능한 특권정보를 활용하는 SVM+ 방법을 제안한다. 특권정보는 학습 시에만 사용 가능한 정보이므로 전통적인 지도학습 방법으로는 학습이 불가하다. SVM+와 같은 특권정보를 학습할 수 있는 구조를 통해 지역 정보뿐만 아니라 물체 정보를 포함하여 영역 간의 병합 여부를 결정하는 모델을 학습하였다. BSDS 500 데이터 세트와 VOC 2012 데이터 세트에서 벤치마크를 수행하였으며 대부분의 평가 지표에서 개선된 성능을 보여 주었다. 특히 학습 데이터 세트가 작은 경우에 기존의 알고리즘에 비해서 월등히 뛰어난 성능을 보인다.

• Computers and Concrete
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• 제27권4호
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• pp.319-332
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• 2021

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

• 한국운동역학회지
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• 제32권2호
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• pp.43-48
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• 2022

Objective: The purpose of this study was to verify the effectiveness among simulating ski jumping trainings by comparing with actual ski jump. Method: Three healthy youth national athletes were recruited for this study (age: 13.70 ± 0.9 yrs, height: 169.30 ± 0.9 cm, jumping caree: 5.3 ± 0.9 yrs). Participants were asked to performed ski jumping with 3 simulating and one actual situation. A 3-dimensional motion analysis with 5 channels of EMG was performed in this study. Muscle activations of Rectus Femoris [RF], Tibialis Anterior [TA], Thoracis [TH], Gluteus maximus [GM], and Gastronemius [GL] were achieved with sampling rate of 2,000 Hz during each jump. Results: In the case of S1 in the actual jumping motion, the deviation of the muscle activity peak did not appear each trial, and the jump timing was consistent. For S2, the timing of the muscles peak activation which can maintain the posture of the upper body and ankles appeared at the beginning. In the case of S3, the part maintaining the ankle posture at the beginning appeared, but it could be expected that it would progress in the vertical direction due to the activation of GL at the time of jumping. Conclusion: The muscle activation peak before the take-off point showed a different pattern for each athlete, and individual differences were large. In addition, it was attempted to confirm the actual jump with simulation jump, and it was found that not only the difference in patterns but also the fluctuations in the timing of each muscle activation peak were large.

• 대한의용생체공학회:의공학회지
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• 제44권3호
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• pp.211-217
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• 2023

Recently, many studies have introduced artificial intelligence systems in the surgical process to reduce the incidence and mortality of complications in patients. Bleeding is a major cause of operative mortality and complications. However, there have been few studies conducted on detecting bleeding in surgical videos. To advance the development of deep learning models for detecting intraoperative hemorrhage, three models have been trained and compared; such as, YOLOv5, RetinaNet50, and RetinaNet101. We collected 1,016 bleeding images extracted from five surgical videos. The ground truths were labeled based on agreement from two specialists. To train and evaluate models, we divided the datasets into training data, validation data, and test data. For training, 812 images (80%) were selected from the dataset. Another 102 images (10%) were used for evaluation and the remaining 102 images (10%) were used as the evaluation data. The three main metrics used to evaluate performance are precision, recall, and false positive per image (FPPI). Based on the evaluation metrics, RetinaNet101 achieved the best detection results out of the three models (Precision rate of 0.99±0.01, Recall rate of 0.93±0.02, and FPPI of 0.01±0.01). The information on the bleeding detected in surgical videos can be quickly transmitted to the operating room, improving patient outcomes.

• 대한원격탐사학회지
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• 제27권6호
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• pp.637-651
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• 2011

This study presents a rule-based urban image classification method for time series analysis of changes in the vicinity of Asan-si and Cheonan-si in Chungcheongnam-do, using Landsat satellite images (1991-2006). The area has been highly developed through the relocation of industrial facilities, land development, construction of a high-speed railroad, and an extension of the subway. To determine the yearly changing pattern of the urban area, eleven classes were made depending on the trend of development. An algorithm was generalized for the rules to be applied as an unsupervised classification, without the need of training area. The analysis results show that the urban zone of the research area has increased by about 1.53 times, and each correlation graph confirmed the distribution of the Built Up Index (BUI) values for each class. To evaluate the rule-based classification, coverage and accuracy were assessed. When Optimal allowable factor=0.36, the coverage of the rule was 98.4%, and for the test using ground data from 1991 to 2006, overall accuracy was 99.49%. It was confirmed that the method suggested to determine the maximum allowable factor correlates to the accuracy test results using ground data. Among the multiple images, available data was used as best as possible and classification accuracy could be improved since optimal classification to suit objectives was possible. The rule-based urban image classification method is expected to be applied to time series image analyses such as thematic mapping for urban development, urban development, and monitoring of environmental changes.

• 수산해양기술연구
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• 제34권3호
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• pp.294-301
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• 1998

Towing performance of a midwater trawl system was examined aboard the training ship KAYA(2900ps) at the East Sea using the midwater trawl gear that had been designed and manufactured in accordance with the vessel. In this experiment, the trawl system data, the towing speed, the length and tension of the warp, net mouth height, and the depth of otter boards and net were measured and analyzed. The results are as follows: 1. In case of heaving in the warp with constant towing speed, the tension was suddenly increased and then again was reduced and after returned to the original steady state tension. At this time, net height was reduced a bit by ascension of ground rope, but returned to it’s original value. In the case where the warp was paid out, the tension was suddenly decreased and after increased and then returned to the tension of the original state, and the net height was greatly increased instantly by the sinking of the ground rope and then returned to the steady state 2. In the case of increased towing speed mm constant warp length, the tension was increased, and reducing the net height, the gear depth was decreased. On the other hand, in the case where towing speed was reduced, the tension was reduced and the gear depth and net height was increased. 3. Otter boards show a swing motion in the scope of 5~ 10m continuously. Otter boards responded to the state change of the trawl system at first, and then the motion of the net appeared. 4. The depth of net center was about 20m deeper than that of the otter boards, it shows about 0.4 times the warp length at the 4knots towing speed.