• Journal of Navigation and Port Research
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• v.33 no.10
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• pp.671-678
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• 2009

Recently, there have been some researches in the equipment fault detection based on shock and vibration information. Most research of them is based on shock and vibration monitoring to determine the equipment fault or not. Different with engine fault detection based on shock and vibration information we focus on detection of engine for boat and system control. First, it use the duplicated-checking method for shock and vibration information to determine the engine fault or not. If there is a fault happened, we use the integral to determine the error engine shock wave width and detect the fault area. On the other hand, we use the engine trend analysis and standard of safety engine to implement the shock and vibration information database. Our simulation results show that the probability of engine fault determination is 98% and the probability of engine fault detection is 72%

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.41-48
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• 2023

The presence of abnormalities in the subgrade of roads poses safety risks to users and results in significant maintenance costs. In this study, we aimed to experimentally evaluate the temperature distributions in abnormal areas of subgrade materials using infrared cameras and analyze the data with machine learning techniques. The experimental site was configured as a cubic shape measuring 50 cm in width, length, and depth, with abnormal areas designated for water and air. Concrete blocks covered the upper part of the site to simulate the pavement layer. Temperature distribution was monitored over 23 h, from 4 PM to 3 PM the following day, resulting in image data and numerical temperature values extracted from the middle of the abnormal area. The temperature difference between the maximum and minimum values measured 34.8℃ for water, 34.2℃ for air, and 28.6℃ for the original subgrade. To classify conditions in the measured images, we employed the image analysis method of a convolutional neural network (CNN), utilizing ResNet-101 and SqueezeNet networks. The classification accuracies of ResNet-101 for water, air, and the original subgrade were 70%, 50%, and 80%, respectively. SqueezeNet achieved classification accuracies of 60% for water, 30% for air, and 70% for the original subgrade. This study highlights the effectiveness of CNN algorithms in analyzing subgrade properties and predicting subsurface conditions.

• Journal of IKEEE
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• v.28 no.3
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• pp.474-478
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• 2024

In this paper, we proposes a deep learning network for quality inspection in a multi-camera inline inspection system for pharmaceutical containers. The proposed deep learning network is specifically designed for pharmaceutical containers by using data produced in real manufacturing environments, leading to more accurate quality inspection. Additionally, the use of an inline-capable deep learning network allows for an increase in inspection speed. The development of the deep learning network for quality inspection in the multi-camera inline inspection system consists of three steps. First, a dataset of approximately 10,000 images is constructed from the production site using one line camera for foreign substance inspection and three area cameras for dimensional inspection. Second, the pharmaceutical container data is preprocessed by designating regions of interest (ROI) in areas where defects are likely to occur, tailored for foreign substance and dimensional inspections. Third, the preprocessed data is used to train the deep learning network. The network improves inference speed by reducing the number of channels and eliminating the use of linear layers, while accuracy is enhanced by applying PReLU and residual learning. This results in the creation of four deep learning modules tailored to the dataset built from the four cameras. The performance of the proposed deep learning network for quality inspection in the multi-camera inline inspection system for pharmaceutical containers was evaluated through experiments conducted by a certified testing agency. The results show that the deep learning modules achieved a classification accuracy of 99.4%, exceeding the world-class level of 95%, and an average classification speed of 0.947 seconds, which is superior to the world-class level of 1 second. Therefore, the effectiveness of the proposed deep learning network for quality inspection in a multi-camera inline inspection system for pharmaceutical containers has been demonstrated.

• Clinics in Shoulder and Elbow
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• v.18 no.4
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• pp.248-253
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• 2015

Background: Scapular notching can happen at diverse location depending on implant design or operative technique, therefore, it is easily misdiagnosed. Thus, this study purposed to suggest a method helpful to assess scapular notching. Methods: The subjects were 73 cases of reverse shoulder arthroplasty (RSA) for cuff tear arthropathy during the period from May 2009 to April 2014 and followed-up for over a year. There was medialized RSA in 22 cases, bone increased offset RSA (BIO-RSA) in 36 cases, and metal increased offset RSA (metal-RSA) in 15 cases. Scapular notching was not determined by bone defect at the inferior of glenosphere as Sirveaux's classification, but scapular notching at the site where the rotational route of the polyethylene of humeral implant met the scapular neck were examined. The results were compared with conventional method. Results: By conventional method, scapular notching was observed in 10 cases (45.5%) in medialized RSA, 12 cases (33.3%) in BIO-RSA, and none in metal-RSA. By new method, it was observed in 9 cases (40.9%) in medialized RSA, 10 cases (27.8%) in BIO-RSA, and none of metal-RSA. The site of scapular notching was apart from glenoshpere in 18 cases, and at inferior of glenosphere in 1 case. Absorption of bone graft was observed in 4 (11.1%) out of 36 cases of BIO-RSA. Conclusions: It is hard to distinguish scapular notching from absorption of bone graft in BIO-RSA, and bone absorption at the lateral lower end of glenoid in medialized RSA. Thus, it is considered useful to assess scapular notching at the site where the rotational route of the polyethylene insert meets scapular neck.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

• Journal of Biosystems Engineering
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• v.33 no.1
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• pp.30-37
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• 2008

Dynamic excitation and response analysis is an acceptable method to determine some of physical properties of agricultural product for quality evaluation. There is a difference in the internal viscoelasticity between sound and defective fruits due to the difference of geometric structures, thereby showing different vibration characteristics. This study was carried out to develop a portable piezoelectric film-based glove sensor system that can separate internally damaged watermelons from sound ones using an acoustic impulse response technique. Two piezoelectric sensors based on polyvinylidene fluoride (PVDF) films to measure an impact force and vibration response were separately mounted on each glove. Various signal parameters including number of peaks, energy ratio, standard deviation of peak to peak distance, zero-crossing rate, and integral value of peaks were examined to develop a regression-estimated model. When using SMLR (Stepwise Multiple Linear Regression) analysis in SAS, three parameters, i.e., zeros value, number of peaks, and standard deviation of peaks were selected as usable factors with a coefficient of determination ($r^2$) of 0.92 and a standard error of calibration (SEC) of 0.15. In the validation tests using twenty watermelon samples (sound 9, defective 11), the developed model provided good capability showing a classification accuracy of 95%.

• Journal of Advanced Navigation Technology
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• v.19 no.5
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• pp.370-378
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• 2015

The data acquisition unit, signal acquiring and processing equipment, processes and provides major data of an aircraft such as engine system, power train system, hydraulic system, etc. However, it had lots of failure related to the system during production test flight, and it is necessary to fix them perfectly as soon as possible because of the significance of the equipment. In this paper, it contains failure classification and analysis for each defect element to improve whole software as well as electrical circuit. Particularly, utilizing Fault Injection Method based interworking test, more efficient improvement activity was performed for not only equipment level but also aircraft level, and as a result, it is achieved that considerable betterment of Surion quality and flight safety.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2037-2042
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• 2017

The manufacturing facility is generally operated by a pre-set program under the existing factory automation system. On the other hand, the manufacturing facility must decide how to operate autonomously in Industry 4.0. Determining the operation mode of the production facility itself means, for example, that it detects the abnormality such as the deterioration of the facility at the shop-floor, prediction of the occurrence of the problem, detection of the defect of the product, In this paper, we propose a manufacturing process modeling using a queue for detection of manufacturing process abnormalities at the shop-floor, and detect abnormalities in the modeling using SVM, one of the machine learning techniques. The queue was used for M / D / 1 and the conveyor belt manufacturing system was modeled based on ${\mu}$, ${\lambda}$, and ${\rho}$. SVM was used to detect anomalous signs through changes in ${\rho}$.

• Journal of Dental Rehabilitation and Applied Science
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• v.26 no.1
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• pp.33-38
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• 2010

Maxillectomy is a treatment option for maxillary cancer, which leaves the patient with a palatal defect. It may cause problems with facial deformation, swallowing, mastication, and speech. These functional problems and changes in appearance may result in psychological problems. To control these deficits after maxillectomy, surgical reconstruction or prosthodontic treatment can be chosen as a treatment option. Obturator prosthesis has been used as a preferred method of rehabilitation for most maxillectomy patients. This case is a patient who was classified Aramany classification II hemi-maxillectomy patient with residual teeth from #11-25, whose teeth had substantial labioversion and clinically lengthened from alveolar bone involution, thus making it hard to select proper framework design and resist to the rotational dislodging force of the obturator. Therefore we selected swing-lock attachment design to remain pre-existing crown and bridges and obtain retention and stability of obturator. The swing-lock RPD is economical than the conventional RPD because we can remain pre-existing crown and bridges. And residual teeth which have mobility and poor prognosis can be successfully retained through properly designed swing-lock RPD as it is functioning as a removable splint on the teeth.

• Archives of Plastic Surgery
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• v.44 no.2
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• pp.117-123
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• 2017

Background Upper extremity soft tissue defects with complete circumferential involvement are not common. Coupled with the unique anatomy of the upper extremity, the underlying etiology of such circumferential soft tissue defects represent additional reconstructive challenges that require treatment to be tailored to both the patient and the wound. The aim of this study is to review the various options for soft tissue reconstruction of complete circumferential defects in the upper extremity. Methods A literature review of PubMed and MEDLINE up to December 2016 was performed. The current study focuses on forearm and arm defects from the level at or proximal to the wrist and were assessed based on Tajima's classification (J Trauma 1974). Data reviewed for analysis included patient demographics, causality, defect size, reconstructive technique(s) employed, and postoperative follow-up and functional outcomes (when available). Results In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 14 unique articles were identified for a total of 50 patients (mean=28.1 years). Underlying etiologies varied from extensive thermal or electrical burns to high impact trauma leading to degloving or avulsion, crush injuries, or even occur iatrogenically after tumor extirpation or extensive debridement. Treatment options ranged from the application of negative pressure wound dressings to the opposite end of the spectrum in hand transplantation. Conclusions With the evolution of reconstructive techniques over time, the extent of functional and aesthetic rehabilitation of these complex upper extremity injuries has also improved. The proposed management algorithm comprehensively addresses the inherent challenges associated with these complex cases.