• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.7
• /
• pp.15-24
• /
• 2021

In this paper, to analyze the types of surface morphology error according to factors that cause machining error, the experiments were conducted in the ultra-precision diamond machine using a diamond tool. The factors causing machining error were classified into the pressure variation of compressed air, external shock, tool errors, machining conditions (rotational speed and feed rate), tool wear, and vibration. The pressure variation of compressed air causes a form accuracy error with waviness. An external shock causes a ring-shaped surface defect. The installed diamond tool for machining often has height error, feed-direction position error, and radius size error. The types of form accuracy error according to the tool's errors were analyzed by CAD simulation. The surface roughness is dependent on the tool radius, rotational speed, and feed rate. It was confirmed that the surface roughness was significantly affected by tool wear and vibration, and the surface roughness of Rz 0.0105 ㎛ was achieved.

• Journal of Information Processing Systems
• /
• v.19 no.6
• /
• pp.745-755
• /
• 2023

The wire rope is an indispensable production machinery in coal mines. It is the main force-bearing equipment of the underground traction system. Accurate detection of wire rope defects and positions exerts an exceedingly crucial role in safe production. The existing defect detection solutions exhibit some deficiencies pertaining to the flexibility, accuracy and real-time performance of wire rope defect detection. To solve the aforementioned problems, this study utilizes the camera to sample the wire rope before the well entry, and proposes an object based on YOLOv5. The surface small-defect detection model realizes the accurate detection of small defects outside the wire rope. The transfer learning method is also introduced to enhance the model accuracy of small sample training. Herein, the enhanced YOLOv5 algorithm effectively enhances the accuracy of target detection and solves the defect detection problem of wire rope utilized in mine, and somewhat avoids accidents occasioned by wire rope damage. After a large number of experiments, it is revealed that in the task of wire rope defect detection, the average correctness rate and the average accuracy rate of the model are significantly enhanced with those before the modification, and that the detection speed can be maintained at a real-time level.

• Journal of Electrochemical Science and Technology
• /
• v.15 no.2
• /
• pp.299-313
• /
• 2024

Electrochemical machining (ECM) is an effective manufacturing method for difficult-to-machine materials and is widely used in the precision manufacturing of aerospace components. In recent years, the requirements for the machining accuracy and surface integrity of ECM have become increasingly stringent. To further improve the machining quality, this work investigated the intricate laws between electrolyte filtration accuracy and machining quality. Electrolytes with different filtration accuracies were compared, and a numerical simulation was used to evaluate the change in temperature and bubble rate of the flow field in the machining area. Experiments were conducted on ECM of Ti-6Al-4V (TC4) alloy workpieces using electrolytes with different filtration accuracy. The workpiece machining accuracy and surface quality were analyzed, and the repetition accuracy of the workpiece was evaluated. The intricate laws between electrolyte filtration accuracy and machining quality were explored. It was found that when the electrolyte filtration accuracy is improved, so too is the machining quality of the ECM. However, once the filtration accuracy has reached a certain value, the machining quality has extremely limited improvement. By evaluating the repetition accuracy of processed workpieces in electrolytes with different filtration accuracies, it was found that when the filtration accuracy reaches a certain value, there is no positive correlation between the repetition accuracy and filtration accuracy. The result shows that, for the workpiece material and conditions considered in this paper, an electrolyte with 0.5㎛ filtration accuracy is suitable for the wide application of precision ECM.

• 유체기계공업학회:학술대회논문집
• /
• 1999.12a
• /
• pp.109-113
• /
• 1999

Thermal mass flow meter (TMF) is used measuring the small mass flow rate of gases. Generally, flow rate measuring accuracy of TMF is $\pm2{\%}$ of full scale. TMF is manufactured for specified working pressure and specified working gas by customer. If it were applied for different working pressure and gases, flow rate measurement accuracy decreased dramatically. In this study, a TMF tested with three different gases and pressure range of 0.2 MPa to 1.0 MPa. Effect of specific heat cause to increase flow measurement error as much as ratio of specific heat compare with reference gas. Pressure change cause to increase flowrate measurement deviation about $-0.2{\%}$ as the working pressure decreased 0.1 MPa.

• Journal of Korea Multimedia Society
• /
• v.21 no.12
• /
• pp.1467-1472
• /
• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

• Journal of Sensor Science and Technology
• /
• v.33 no.3
• /
• pp.119-124
• /
• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

• The Journal of the Korea Contents Association
• /
• v.16 no.10
• /
• pp.393-402
• /
• 2016

To compensate for limited the satisfaction survey currently conducted by Korea Metrological Administration (KMA), a sentiment analysis via a social networking service (SNS) can be utilized. From 2011 to 2014, with the sentiment analysis, Twitter who had commented 'KMA' had collected, then, using $Na{\ddot{i}}ve$ Bayes classification, we were classified into three sentiments: positive, negative, and neutral sentiments. An additional dictionary was made with morphemes appeared only in the positive, negative, and neutral sentiments of basic $Na{\ddot{i}}ve$ Bayes classification, thus the accuracy of sentiment analysis was improved. As a result, when sentiments were classified with a basic $Na{\ddot{i}}ve$ Bayes classification, the training data were reproduced about 75% accuracy rate. Whereas, when classifying with the additional dictionary, it showed 97% accuracy rate. When using the additional dictionary, sentiments of verification data was classified with about 75% accuracy rate. Lower classification accuracy rate would be improved by not only a qualified dictionary that has increased amount of training data, including diverse keywords related to weather, but continuous update of the dictionary. Meanwhile, contrary to the sentiment analysis based on dictionary definition of individual vocabulary, if sentiments are classified into meaning of sentence, increased rate of negative sentiment and change in satisfaction could be explained. Therefore, the sentiment analysis via SNS would be considered as useful tool for complementing surveys in the future.

• Journal of Drive and Control
• /
• v.21 no.2
• /
• pp.30-35
• /
• 2024

Domestic rice is more expensive than imported products, so it is necessary to reduce production costs to secure competitiveness. Low-density planting developed in Japan is a cultivation technology that reduces labor and production costs without yield loss. The area of low-density cultivation is continuously increasing. However, research on how rice transplanters adapt to low-density planting has not been conducted. Therefore, this study was carried out to determine the optimal working conditions of a rice transplanter for low-density planting. Three types of rice transplanters were used and treated based on 3 conveying distance levels. The number of picked seedlings, pick missing rate, the number of planted seedlings, and the mis-planted rate were investigated to evaluate planting accuracy according to the transfer distance to the seedling tray. The results showed that the number of planted seedlings was 4.31~4.95 EA with an L1 seedling tray transfer distance (horizontal 9 mm, vertical 8 mm), but the mis-planted rate was higher than in other conditions. At L2 (horizontal 9 mm, vertical 10 mm) and L3 (horizontal 11 mm, vertical 8 mm) transfer distance conditions, the number of planted seedlings were 4.89-5.68 EA and 4.69-5.66 EA, respectively, with a low mis-planted rate of less than 3%. The results showed that if the transfer distance is adjusted properly, a rice transplanter can be used for low-density planting with high planting accuracy.

• Journal of Biomedical Engineering Research
• /
• v.30 no.5
• /
• pp.428-437
• /
• 2009

The Performance research about EEG-BCI algorithm in BCI-naive subjects is very important for evaluating the applicability to the public. We analyzed the result of the performance evaluation experiment about the EEG-BCI algorithm in BCI-naive subjects on three different aspects. The EEG-BCI algorithm used in this paper is composed of the common spatial pattern(CSP) and the least square linear classifier. CSP is used for obtaining the characteristic of event related desynchronization, and the least square linear classifier classifies the motor imagery EEG data of the left hand or right hand. The performance evaluation experiments about EEG-BCI algorithm is conducted for 40 men and women whose age are 23.87${\pm}$2.47. The performance evaluation about EEG-BCI algorithm in BCI-naive subjects is analyzed in terms of the accuracy, the relation between the information transfer rate and the accuracy, and the performance changes when the different types of cue were used in the training session and testing session. On the result of experiment, BCI-naive group has about 20% subjects whose accuracy exceed 0.7. And this results of the accuracy were not effected significantly by the types of cue. The Information transfer rate is in the inverse proportion to the accuracy. And the accuracy shows the severe deterioration when the motor imagery is less then 2 seconds.

• Clinical and Experimental Reproductive Medicine
• /
• v.32 no.3
• /
• pp.223-230
• /
• 2005

Objective: This study was performed to evaluate the accuracy of hysterosalpingography (HSG) for evaluating female infertility patients by comparison with hysteroscopic and laparoscopic examination. Methods and Material: Total 219 infertile patients were retrospectively analyzed between January 1, 2002 and December 31, 2003. Ninety seven patients (44.3%) were primary infertility, 122 patients (55.7%) were secondary infertility. We performed hysteroscopic and laparoscopic examination on next cycle when HSG revealed any abnormal finding, and 3~6 cycles later if HSG was normal. Results: The accuracy of HSG was 65.2% compared with hysteroscopic examination (sensitivity 88.4%, specificity 46.4%, false positive rate 53.6%, false negative rate 11.6%). The most common abnormal finding of hysteroscopy was uterine synechia (67.4%) followed by endometrial polyp, uterine anomaly (e.g. uterine septum), endometrial hyperplasia. Compared with laparoscopic examination, the accuracy of HSG was 76.9% (sensitivity 98.9%, specificity 70.6%, +LR 3.36, -LR 0.02). The positive predictive value of normal patent tube was excellent (99.6%) but that of proximal tubal blockage was only 46.7%. The unilateral tubal obstruction of HSG was poor accuracy (+LR 3.85 -LR 0.68) and 70% of those was patent by laparoscopic examination. Laparoscopic examination also revealed that 53% of patients had peritubal adhesion and 37% of patients has additional pelvic findings, especially endometriosis. Among the patients had normal HSG, 53.5% patients with normal ultrasonography was diagnosed endometriosis (25.6% of them had endometriosis stage I-II). Conclusion: Normal HSG shows a high negative predictive value. Nevertheless, the incidence of associated pelvic disease in the normal HSG group is high enough to warrant diagnostic laparoscopy if nonsurgical treatment is unsuccessful. Because HSG has poor accuracy in predicting distal tubal blockage and peritubal adhesion, and poor positive predictive value of proximal tubal blockage, laparoscopic examination could be considered in abnormal HSG group.