• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Korean Journal of Computational Design and Engineering
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• v.19 no.1
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• pp.29-40
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• 2014

Conceptual Modeling is the process of abstracting a model from a real or proposed system. It is probably the most important aspect of a simulation study. Relate works show that the elementary developers devoted little time to understanding how the systems actually worked, namely they didn't build appropriate conceptual model. Thus, the result of simulation is inconsistent because it depends on developer's competence. Although many researchers suggested various techniques enabling developer to build conceptual model, there were several limitations. In this study, to overcome the limitations of existing techniques, we proposed COMBINE-DES (COnceptual Model BuildINg framEwork using ontology for Discrete Event Simulation). The COM-BINE-DES supports expediting the conceptual modeling with Solution ontology generated by Domain ontology and Simulation ontology. Moreover, it provides consistent simulation result regardless of repeated modeling.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.123-128
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• 2021

Liquid dispensing systems are extensively used in various industries such as display, semiconductor, and battery manufacturing. Of the many types of dispensers, drop-on-demand piezoelectric jetting systems are widely used in semiconductor industries because of their ability to dispense minute volumes with high precision. However, due to the problems of nozzle clogging and undesirable dispensing behavior in these dispensers, which often result in device failure, the use of highly viscous fluids is limited. Accordingly, we studied the behaviors of droplet formation based on changes in viscosity. The effects of surface energy and the inner diameters of needle-type nozzles were also studied. Results showed that nozzles with lower surface energies reduced the ejection volume of droplets when a smaller nozzle diameter (0.21 mm in this study) was applied. These results indicate that the hydrophobic treatment of nozzle surfaces and the use of smaller nozzle diameters are critical factors enabling the use of highly viscous fluids in precision dispensing applications.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.10
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• pp.40-44
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• 2010

Laser drilling is an enabling technology for Through Silicon Via (TSV) interconnect applications. Recent advances in picoseconds laser drilling of blind, micron sized vias in silicon is presented here highlighting some of the attractive features of this approach such as excellent sidewall quality. In this study, we dealt with comparison of heat affection around drilled hole between a picosecond laser and a nanosecond laser process under the UV wavelength. Points which special attention should be paid are that picosecond laser process lowered experimentally recast layer, surface debris and micro-crack around hole in comparison with nanosecond laser process. These finding suggests that laser TSV process has possibility to drill under $10{\mu}m$ via. Finally, the laser drilling platform was constructed successfully.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.3
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• pp.76-88
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• 2008

The purpose of the research is to propose an earned-value indicator for technical performance measurements of an ongoing defense R&D project and the associated measurement, analysis and the implementation process for data collection and usages. Furthermore, the study demonstrates the evidences of benefits and validity of the proposed approach through the enabling tool development and its application examples.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.67-72
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• 2023

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.1
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• pp.141-148
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• 2017

Recently, localized heavy rain storms have been occurring frequently due to global warming, and it is difficult to shield a large number of facilities against disaster with limited manpower. The unmanned water treatment system uses five senses to analyze various judgment criteria, which are set according to field situations such as machine vibrations, the temperature of bearings, the sound of the operating machines, and the hydraulic pressure, current, and voltage of the hydraulic floodgates. It thus judges normal or abnormal operation status and conducts unmanned control of such machines. It automatically applies a system to the interruption of communications and therefore improves the reliability of its unmanned irrigation facilities. It maximizes the operational efficiency of managers responsible for various fields, enabling them to discharge water before the situation escalates to a crisis within the golden time, and to protect against damage to humans and property.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.2
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• pp.408-427
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• 2011

Intensive computational complexity is an obstacle of enabling multiview video coding for real-time applications. In this paper, we present a fast macroblock (MB) mode selection algorithm for B frames which are based on the computational complexity analyses between the MB mode selection and reference frame selection. Three strategies are proposed to reduce the coding complexity jointly. First, the temporal correlation of MB modes between current MB and its temporal corresponding MBs is utilized to reduce computational complexity in determining the optimal MB mode. Secondly, Lagrangian cost of SKIP mode is compared with that of Inter $16{\times}16$ modes to early terminate the mode selection process. Thirdly, reference frame correlation among different Inter modes is exploited to reduce the number of reference frames. Experimental results show that the proposed algorithm can promote the encoding speed by 3.71~7.22 times with 0.08dB PSNR degradation and 2.03% bitrate increase on average compared with the joint multiview video model.

• Journal of the Korean Society of Safety
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• v.35 no.6
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• pp.54-60
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• 2020

Recently, the issue of construction safety is growing. In the construction industry, accidents have continued to increase since 2000. In particular, the number of accident deaths at small and medium-sized construction sites accounts for 72.11% of the total number of accident deaths in the construction industry. For construction safety, prior safety evaluation systems such as Design for safety and safety management plan preparation are in place. However, at construction sites, these systems are recognized as formal legal documents, and their effectiveness is greatly reduced. Therefore, in this study, a linkage model that links design safety review information and safety management plan information was presented so that the safety management plan can be efficiently established. In addition, the effectiveness of the proposed process was verified as an example of actual work. The linkage model will contribute to improving the safety management environment at the site by increasing the productivity of safety management work by enabling easy sharing of risk factor information in the construction stage safety management work. The results of this study will be used as basic information for the development of the integrated safety management system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.