• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.153-159
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• 2022

Plain radiographic analysis is the initial imaging modality for suspected small bowel obstruction. Among the many features that affect the diagnosis of small bowel obstruction (SBO), the presence of gas-filled or fluid-filled small bowel loops is the most salient feature that can be automatized by computer vision algorithms. In this study, we compare three frequently applied pixel-clustering algorithms for extracting gas-filled areas without human intervention. In a comparison involving 40 suspected SBO cases, the Possibilistic C-Means and Fuzzy C-Means algorithms exhibited initialization-sensitivity problems and difficulties coping with low intensity contrast, achieving low 72.5% and 85% success rates in extraction. The Adaptive Resonance Theory 2 algorithm is the most suitable algorithm for gas-filled region detection, achieving a 100% success rate on 40 tested images, largely owing to its dynamic control of the number of clusters.

• 드라이브 ㆍ 컨트롤
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• 제20권4호
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• pp.54-63
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• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

• 드라이브 ㆍ 컨트롤
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• 제21권1호
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• pp.53-58
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• 2024

With the recent development of intelligence and automation technologies for construction machinery, the demand for safety and efficiency of road-cutting operations has continued to increase. In response to this, a double-blade road cutter has been developed that can automatically cut roads. However, a double-blade road cutter has a load difference between the two blades due to the ground and wear conditions of the cutting blades. The difference in load between the two blades distorts the direction of travel of the cutter. In this study, a vision sensor-based driving guide technology was developed to correct the driving path of road cutters. In addition, we developed a load-sensing technology that detects blade loads in real-time and controls driving speed in the event of overload.

• The Journal of Korean Physical Therapy
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• 제30권5호
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• pp.193-198
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• 2018

Purpose: The purpose of this study was to analysis of effectiveness between cognitive function assessment scores and gaming cognitive rehabilitation system in children with intellectual impairment. Methods: Five children (male=5, $age=10.00{\pm}0.80$) with intellectual impairment participated in this study and were randomly assigned to the experiment that played (received) gaming cognitive rehabilitation system (Neuroworld). The children were applied 2 times a week for 30 minutes during 3 months. The children were assessed K-WSIC-VI (Korean-Wechsler intelligence scale for children-fourth edition) and recorded that gained score in gaming cognitive rehabilitation system before and after intervention. K-WSIC-VI contained five primary index scores: verbal comprehension index, visual spatial index, fluid reasoning index, working memory index, and processing speed index. Gaming cognitive rehabilitation system scoring was composed visual recall, target recall, sequence recall, selective attention, continuous attention, and exploration. Results: In the intelligence quotient (IQ) of K-WSIC-VI, there were significant increased in all children. The visual recall item was highest effective in all children. However, sequential recall showed the lowest improvement in all children. The performance speed of selective attention item was decreased, this means that children's skills have improved. Also, their ability to explore has improved significantly. Conclusion: In conclusion, gaming cognitive rehabilitation system was significant effectiveness in cognitive function in some categories for children with intellectual impairment. However, the visual recall and performance speed don't represent of all cognitive function. Therefore, further studies will need to verify by applying more subject and longer duration.

• 정신신체의학
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• 제10권1호
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• pp.55-60
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• 2002

중심성 뇌교 수초용해 (CPM) 및 뇌교의 수초용해(EPM) 은 대사 이상을 수반하는 여러 질환에서 뇌 세포 내외의 삼투질농도의 급속한 변화와 관련하여 발생하는 신경학적 질환이다. 저자들은 당뇨병성 신중에 의한 만성 신부전으로 신장이식을 받은 43세 남자 환자에서 발현한 CPM과 EPM 증례를 보고하였다. 환자는 망상, 연상이완, 환각, 부적절한 정동, 공격성, 기억장애 등을 수반한 정산병적 증상과 언어실조를 특징적으로 보인 경우로서, CPM과 EPM에서 비교적 드물게 발생하는 정신증상, 특히 정신병적 증상을 보인 증례이기에, 정선과적으로 중요한 임상적 의의를 가진다고 판단하여 문헌고찰과 함께 보고하는 바이다.

• 한국전자통신학회논문지
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• 제18권4호
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• pp.641-648
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• 2023

최근 4차 산업 혁명 중에서 인공지능의 급성장은 반도체의 성능 향상 및 회로의 집적을 기반으로 진보하였다. 전자기기 및 장비의 내부에서 연산을 돕는 트랜지스터는 고도화 및 소형화 되어 가며 발열의 제어 및 방열의 효율 개선이 새로운 성능의 지표로 대두되었다. DUT(Device Under Test) Shell은 트랜지스터의 검수를 위하여 정격 전류를 인가한 후, 임의의 발열 지점에서 전원을 차단한 상태에서, 방열을 통하여 트랜지스터의 내구도를 평가하여 불량 트랜지스터를 검출하는 장비이다. DUT Shell은 장비 내부의 방열 구조에 따라 동시에 더 많은 트랜지스터를 테스트할 수 있기 때문에 방열 효율은 불량 트랜지스터 검출 효율과 직접적인 관계를 갖는다. 이에 본 논문에서는 DUT Shell의 방열 최적화를 위하여 배치구조의 다양한 방법을 제안하고 전산유체역학을 이용하여 최적의 DUT Shell의 다양한 변형과 열 해석을 제안하였다.

• Advances in aircraft and spacecraft science
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• 제3권2호
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• pp.149-170
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• 2016

Multi-Disciplinary Optimization (MDO) is widely used to handle the advanced design in several engineering applications. Such applications are commonly simulation-based, in order to capture the physics of the phenomena under study. This framework demands fast optimization algorithms as well as trustworthy numerical analyses, and a synergic integration between the two is required to obtain an efficient design process. In order to meet these needs, an adaptive Computational Fluid Dynamics (CFD) solver and a fast optimization algorithm have been developed and combined by the authors. The CFD solver is based on a high-order discontinuous Galerkin discretization while the optimization algorithm is a high-performance version of the Artificial Bee Colony method. In this work, they are used to address a typical aero-mechanical problem encountered in turbomachinery design. Interesting achievements in the considered test case are illustrated, highlighting the potential applicability of the proposed approach to other engineering problems.

• 드라이브 ㆍ 컨트롤
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• 제18권1호
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• Nuclear Engineering and Technology
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• 제53권4호
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• pp.1277-1283
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• 2021

It is important for operators of poly-pipelines in petroleum industry to continuously monitor characteristics of transferred fluid such as its type and amount. To achieve this aim, in this study a dual energy gamma attenuation technique in combination with artificial neural network (ANN) is proposed to simultaneously determine type and amount of four different petroleum by-products. The detection system is composed of a dual energy gamma source, including americium-241 and barium-133 radioisotopes, and one 2.54 cm × 2.54 cm sodium iodide detector for recording the transmitted photons. Two signals recorded in transmission detector, namely the counts under photo peak of Americium-241 with energy of 59.5 keV and the counts under photo peak of Barium-133 with energy of 356 keV, were applied to the ANN as the two inputs and volume percentages of petroleum by-products were assigned as the outputs.

• 한국가시화정보학회지
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• 제20권3호
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• pp.109-116
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• 2022

The optimized design of airfoils is essential to increase the performance and efficiency of wind turbines. The aerodynamic characteristics of airfoils near the stall show large deviation from experiments and numerical simulations. Hence, it is needed to perform repetitive analysis of various shapes near the stall. To overcome this, the artificial intelligence is used and combined with numerical simulations. In this study, three types of airfoils are chosen, which are S809, S822 and SD7062 used in wind turbines. A convolutional neural network model is proposed in the combination of VGG16 and U-Net. Learning data are constructed by extracting pressure fields and aerodynamic characteristics through numerical analysis of 2D shape. Based on these data, the pressure field and lift coefficient of untrained airfoils are predicted. As a result, even in untrained airfoils, the pressure field is accurately predicted with an error of within 0.04%.