• Journal of Information Processing Systems
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• 제16권1호
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• pp.155-170
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• 2020

Passive ranging is a critical part of machine vision measurement. Most of passive ranging methods based on machine vision use binocular technology which need strict hardware conditions and lack of universality. To measure the distance of an object placed on horizontal plane, we present a passive ranging method based on monocular vision system by smartphone. Experimental results show that given the same abscissas, the ordinatesis of the image points linearly related to their actual imaging angles. According to this principle, we first establish a depth extraction model by assuming a linear function and substituting the actual imaging angles and ordinates of the special conjugate points into the linear function. The vertical distance of the target object to the optical axis is then calculated according to imaging principle of camera, and the passive ranging can be derived by depth and vertical distance to the optical axis of target object. Experimental results show that ranging by this method has a higher accuracy compare with others based on binocular vision system. The mean relative error of the depth measurement is 0.937% when the distance is within 3 m. When it is 3-10 m, the mean relative error is 1.71%. Compared with other methods based on monocular vision system, the method does not need to calibrate before ranging and avoids the error caused by data fitting.

• Tribology and Lubricants
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• 제36권6호
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• pp.297-306
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• 2020

This paper reviews studies on the types and functions of oil sensors used for machine condition monitoring. Machine condition monitoring is essential for maintaining the reliability of machines and can help avoid catastrophic failures while ensuring the safety and longevity of operation. Machine condition monitoring involves several components, such as compliance monitoring, structural monitoring, thermography, non-destructive testing, and noise and vibration monitoring. Real-time monitoring with oil analysis is also utilized in various industries, such as manufacturing, aerospace, and power plants. The three main methods of oil analysis are off-line, in-line, and on-line techniques. The on-line method is the most popular among these three because it reduces human error during oil sampling, prevents incipient machine failure, reduces the total maintenance cost, and does not need complicated setup or skilled analysts. This method has two advantages over the other two monitoring methods. First, fault conditions can be noticed at the early stages via detection of wear particles using wear particle sensors; therefore, it provides early warning in the failure process. Second, it is convenient and effective for diagnosing data regardless of the measurement time. Real-time condition monitoring with oil analysis uses various oil sensors to diagnose the machine and oil statuses; further, integrated oil sensors can be used to measure several properties simultaneously.

• 한국산업융합학회 논문집
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• 제23권6_2호
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• pp.979-987
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• 2020

This study used the Deep Learning models used in previous studies, we selected the basic model. The selected model was selected as ZFNet among ZFNet, Googlenet and ResNet, and the object was detected using a ZFNet based FRCNN. In order to reduce the detection error rate of FRCNN, location of four types of objects detected inside the image was designed by SVM classifier and location-based filtering was applied. As simulation results, it showed similar performance to the lane marking classification method with conventional 경계 detection, with an average accuracy of about 88.8%. In addition, studies using the Linear-parabolic Model showed a processing speed of 165.65ms with a minimum resolution of 600 × 800, but in this study, the resolution was treated at about 33ms with an input resolution image of 1280 × 960, so it was possible to classify lane marking at a faster rate than the previous study by CNN-based End to End method.

• 한국건축친환경설비학회 논문집
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• 제12권6호
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• Imaging Science in Dentistry
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• 제51권3호
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• pp.299-306
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• 2021

Purpose: This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. Materials and Methods: In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure-a region of interest machine and a detection machine-each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation. Results: The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability. Conclusion: This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.

• 전기전자학회논문지
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• 제24권2호
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• pp.475-485
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• 2020

최근, 별도의 센서를 설치하지 않고 센서가 포함된 사용자의 기기로부터 제공되는 실시간 센싱 데이터를 가지고 새로운 센싱 서비스를 제공하는 크라우드센싱(Crowdsensing) 시스템이 주목받고 있다. 크라우드센싱 시스템에서는 사용자의 조작실수나 통신 문제로 인해 의미 없는 데이터가 제공되거나 보상을 얻기 위해 거짓 데이터를 제공할 수 있어 해당 이상 데이터의 탐지 및 제거가 크라우드센싱 서비스의 질을 결정짓는다. 이러한 이상데이터를 탐지하기 위해 제안되었던 방법들은 크라우드센싱의 빠른 변화 환경에 효율적이지 않다. 본 논문은 머신러닝 기술을 활용하여 지속적이고 빠르게 변화하는 센싱 데이터의 특징을 추출하고 적절한 알고리즘을 통해 모델링하여 이상데이터를 탐지하는 방법을 제안한다. 지도학습의 딥러닝 이진 분류 모델과 비지도학습의 오토인코더 모델을 사용하여 제안 시스템의 성능 및 실현 가능성을 보인다.

• 한국정밀공학회지
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• 제14권11호
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• pp.118-125
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• 1997

Non-contacting optical microscopes are increasingly used in recent industrial applications of probes for coordinate measuring machines. They have been found more efficient than conventional touch trigger porbes with ball tips especially in inspecting small-sized objects. There are two major factors affecting measuring accuracy: (1) geometric relations between coordinate systems, (2) magnification ratios of a microscope. In order to determine the magnification ratios exactly, optical imaging of edge was theroretically analyzed and practically adopted to image processing for edge detection. In addition, this paper proposes a geometric calibration method to obtain exact coordinates of measured points from the relations between the machine coordinate system and the image. In the method, the error according to the squareness between the machine axises was also removed. The method was practically adopted to a real coordinate measuring machine. An ultraprecision measurement of 0.2 um uncertainty can be practically achieved.

• 한국기계가공학회지
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• 제19권12호
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• pp.80-86
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• 2020

With the exception of welding activities, it is forbidden to use electricity in shipyards, owing to safety concerns such as the possibility of fire, explosions, and short circuits. In this paper, an automatic chamfering machine using pneumatics is proposed for use in such environments. Customers specify their requirements and the machine derives the corresponding theoretical design conditions. The proposed machine was used to perform 3D modeling, and its suitability and performance were confirmed via cutting experiments of the manufactured device. Two types of sensors may be used in this system: contact and non-contact. In the case of the contact type, an end-stop switch that can recognize the end of the material is installed, and when the machine reaches the end of the material, the end-stop switch is operated to cut off the air pressure. In the non-contact type, four sensors were used: photonic, ultrasonic, metal detection, and encoder. The use of the four sensors was repeated 30 times, and the average error determined. Thus, the optimum sensor was identified.

• 센서학회지
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• 제31권2호
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제1권1호
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• pp.19-31
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• 2007

RFID tag is detected by an RFID antenna and information is read from the tag detected, by an RFID reader. RFID tag detection by an RFID reader is very important at the deployment stage. Tag detection is influenced by factors such as tag direction on a target object, speed of a conveyer moving the object, and the contents of an object. The water content of the object absorbs radio waves at high frequencies, typically approximately 900 MHz, resulting in unstable tag signal power. Currently, finding the best conditions for factors influencing the tag detection requires very time consuming work at deployment. Thus, a quick and simple RFID tag detection scheme is needed to improve the current time consuming trial-and-error experimental method. This paper proposes a back-propagation learning-based RFID tag detection prediction scheme, which is intelligent and has the advantages of ease of use and time/cost savings. The results of simulation with the proposed scheme demonstrate a high prediction accuracy for tag detection on a water content, which is comparable with the current method in terms of time/cost savings.