• The Journal of Sustainable Design and Educational Environment Research
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• v.18 no.1
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• pp.38-48
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• 2019

The purpose of this study is to present a more effective daily checklist than the formal routine check before the experiment to prevent accidents in the university laboratory. To do this, we reconstructed the current daily checklist and previous research data and conducted a second Delphi survey. As a result, there were four general safeties such as arranging the laboratory, three mechanical safeties such as abnormal condition of machine and tool tightening parts, three electric safeties such as prohibition of loading around the electric distribution panel, six chemical safeties such as handling and managing harmful factors, three items of fire safety such as fire extinguisher inspection, five cases of gas safety gas container inspection, one item of biological safety such as the state of hand sterilizer management and one other item, which were provided in the daily checklist as twenty six categories in total. According to the opinions of related experts, it is necessary to have an easy and simplified daily checklist for actual daily checkups.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.93-102
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• 2019

As interest in wellness grows, There is a lot of research about monitoring individual health using wearable devices. Accordingly, a variety of methods have been studied to distinguish exercise from daily activities using wearable devices. Most of these existing studies are machine learning methods. However, there are problems with over-fitting on individual person's learning, data discontinuously recognition by independent segmenting and fake activity. This paper suggests a detection method for exercise activity based on the physiological response principle of heart rate up and down during exercise. This proposed method calculates activity intensity and heart rate from triaxial and photoplethysmography sensor to determine a heart rate recovery, then detects exercise by estimating activity intensity or detecting a heart rate rising state. Experimental results show that our proposed algorithm has 98.64% of averaged accuracy, 98.05% of averaged precision and 98.62% of averaged recall.

• The Journal of Advanced Prosthodontics
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• v.13 no.1
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• pp.1-11
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• 2021

PURPOSE. The study compared the color change, lightness, and translucency of hybrid resin ceramics exposed to toothbrush abrasion and surface treatment. MATERIALS AND METHODS. Four hybrid ceramics [Lava Ultimate (LU), Vita Enamic (EN), Shofu HC (SH), and Crystal Ultra (CU)] were compared with a glass-ceramic (Vita Mark II) control. One hundred and twenty specimen blocks were prepared using a precision saw machine. Specimens in each material were divided into four subgroups based on the surface treatment (polishing or staining) and a storage medium (water or citric acid). Simulated tooth brushing with a mixture of 100 RDA (radioactive abrasives) with 0.3 ml distilled water was used for 3650 cycles (7300 strokes) for each specimen. Measurements for the color change, lightness, and translucency were measured after toothbrushing using a spectrophotometer. Statistical analysis compared outcomes using paired t-test, ANOVA, and Tukey post hoc test. RESULTS. The maximum color change was identified in SH (stained acid) [1.44 (0.40)], whereas the lowest was identified in EN (polished water) [0.66 (0.16)] material. The maximum and minimum loss of surface translucency was observed in SH (polished water) [12.3 (0.52)] and EN (stained acid) [6.5 (0.55)] specimens, respectively. Lastly, loss of lightness was the highest in VM (polished acid) [69 (0.95)], whereas the lowest was observed in CU (stained water) [56.7 (0.86)]. CONCLUSION. The comparison presented a significant effect of toothbrush abrasion on translucency and lightness of the hybrid resin ceramics. Color change was not significantly influenced irrespective of the storage medium employed. Surface staining demonstrated the preservation and stability of color and optical properties under the influence of toothbrush abrasion and chemical trauma.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.319-328
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• 2021

Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.41-47
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• 2020

As modern industries are highly being developed, it is required that mechanical parts have to be manufactured with a high precision. In order to have precise parts, error-free designs have to be done before manufacturing with accuracy. For this intention being fulfilled, a mechanical analysis is essential for design proof. Nowadays, FEM simulation is a popular tool for verifying a machine design. In this paper, an impeller, being utilized in a compressor or an oil mixer as an actuator, is studied for an evaluation. The purpose of this study is to present a safety of an impeller for a proof of its mechanical stability. A static analysis for stress, strain, and deformation within a regular usage is examined. This simulation test shows 357.26×106 Pa for maximum equivalent stress and 0.207mm for total deformation. A fatigue test is carried to provide durability and its result shows that minimum safety factor is 3.2889, which guarantees that it runs without a fatigue failure in 106 cycles. The natural frequencies for the impeller is ranged from 228.09Hz to 1,253.6Hz for the 1st to the 6th mode. Total deformations at these natural frequencies are shown from 6.84mm to 12.631mm. Furthermore, Campbell diagram reveals that a critical speed is not found throughout regular rotational speeds. From the test results for the analysis, this paper concludes that the suggested impeller is proved for its mechanical safety and good to utilize at industries.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.1-11
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• 2021

Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.208-219
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• 2021

In the era of the 4th industrial revolution driven by the convergence of ICT(information and communication technology) and manufacturing, research on smart factories is being actively conducted. In particular, the manufacturing industry prefers smart factories that autonomously connect and analyze data. For the efficient implementation of smart factories, it is essential to have an integrated production system that vertically integrates separately operated production equipment and heterogeneous S/W systems such as ERP, MES. In addition, it is necessary to double-verify production data by using automatic data collection technology so that the production process can be traced transparently. In this study, we want to show a case of data-centered integration of a large aircraft parts processing factory that requires high precision, takes a long time, and has the characteristics of processing large raw materials. For this, the components of the data-oriented integrated production system were identified and the connection structure between them was explained. And we would like to share the experience gained through the design and implementation case. The integrated production system proposed in this study integrates internal components based on data, which is expected to serve as a basis for SMEs to develop into an advanced stage, and traces materials with RFID technology.

• Plant Journal
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• v.17 no.4
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• pp.53-60
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• 2021

The purpose of this study was to develop a portable vibration analyzer that is effective for acquiring and analyzing vibration data of rotating equipment of a power plant and a domestic vibration monitoring system manufacturer Nada Co., Ltd. The hardware of the developed portable vibration analyzer minimizes measurement errors by calibrating the measured values obtained through measurement uncertainty for calibration of the measuring devices in the system, and is composed of a signal processing device with high resolution through high speed data processing. The software structure implements a variety of vibration plots to execute a detailed analysis program, and applies algorithms to measure and remove noise caused by disturbances while operating a rotating machine. The developed product contributed greatly to increase the user's mobility and performance, as well as to reduce the purchase cost due to localization.

• The Journal of Advanced Prosthodontics
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• v.13 no.4
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• pp.191-204
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• 2021

PURPOSE. The purpose of this study is to present a methodology to evaluate the accuracy of intraoral scanners (IOS) used in vivo. MATERIALS AND METHODS. A specific feature-based gauge was designed, manufactured, and measured in a coordinate measuring machine (CMM), obtaining reference distances and angles. Then, 10 scans were taken by an IOS with the gauge in the patient's mouth and from the obtained stereolithography (STL) files, a total of 40 distances and 150 angles were measured and compared with the gauge's reference values. In order to provide a comparison, there were defined distance and angle groups in accordance with the increasing scanning area: from a short span area to a complete-arch scanning extension. Data was analyzed using software for statistical analysis. RESULTS. Deviations in measured distances showed that accuracy worsened as the scanning area increased: trueness varied from 0.018 ± 0.021 mm in a distance equivalent to the space spanning a four-unit bridge to 0.106 ± 0.08 mm in a space equivalent to a complete arch. Precision ranged from 0.015 ± 0.03 mm to 0.077 ± 0.073 mm in the same two areas. When analyzing angles, deviations did not show such a worsening pattern. In addition, deviations in angle measurement values were low and there were no calculated significant differences among angle groups. CONCLUSION. Currently, there is no standardized procedure to assess the accuracy of IOS in vivo, and the results show that the proposed methodology can contribute to this purpose. The deviations measured in the study show a worsening accuracy when increasing the length of the scanning area.