• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.277-284
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• 2022

In this paper, we proposes a method of optimizing the processing order of skids to minimize the span time in a conveyor environment of the shipbuilding subassembly process. The subassembly process consists of a series of fixed tasks where the required work time is varied according to the skid type. The loading order of skids on a conveyor which determines the span time should be properly optimized and the problem size exponentially increases with the number of skids. In this regard, we propose a novel method called UniDev by defining a measure of the mean-absolute-deviation about the time difference among simultaneously processed tasks and iteratively improving it. Through simulations with various numbers of skids and processes, it was observed that our proposed method can efficiently reduce the overall work time compared with the multi-start and the 2-OPT methods.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.905-910
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• 2022

Drowsy driving requires a lot of social attention because it increases the incidence of traffic accidents and leads to fatal accidents. The number of accidents caused by drowsy driving is increasing every year. Therefore, in order to solve this problem all over the world, research for measuring various biosignals is being conducted. Among them, this paper focuses on non-contact biosignal analysis. Various noises such as engine, tire, and body vibrations are generated in a running vehicle. To measure the driver's heart rate and respiration rate in a driving vehicle with a piezoelectric sensor, a sensor plate that can cushion vehicle vibrations was designed and noise generated from the vehicle was reduced. In addition, we developed a system for classifying whether the driver is sleeping or not by extracting the model using the CNN-LSTM ensemble learning technique based on the signal of the piezoelectric sensor. In order to learn the sleep state, the subject's biosignals were acquired every 30 seconds, and 797 pieces of data were comparatively analyzed.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.5
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• pp.98-104
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• 2022

When processing parts, the cutting oil can improve the cooling performance of the workpiece and tool to increase the precision of the workpiece or extend the life of the tool and facilitate chip extraction. Since such cutting oil has a harmful effect on the environment and the human body due to additives such as sulfur, research on a minimum lubrication supply method using an eco-friendly oil is recently underway. The minimum lubrication supply method minimizes the amount of cutting oil used during processing and processes it, which can reduce the amount of cutting oil used, but has a problem in that cooling performance efficiency is poor. Therefore, this study conducted a study on mist cooling of lubricants to reduce the amount of cutting oil used and maximize the cooling effect of processing heat generated during tapping processing. Spray pressure, processing speed, direction, and lubricant spray amount, which are considered to have an effect on cooling performance, were set as process conditions, and the effect on temperature was analyzed by performing an experiment using the box benquin method among experiments were analyzed. Through the experimental analysis results, the optimal conditions for mist and processing that maximize the cooling effect were derived, and the validity of the results derived through additional experiments was verified. In the case of processing by applying the mist lubrication method verified through this study, it is considered that high-precision processing is possible by improving the cooling effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2529-2551
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• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.7 no.2
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• pp.85-91
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• 2021

Selective amino acid conjugation of bulky antibodies is a valuable asset for real-time diagnosis and therapy. However, selective conjugation incorporating a chelate-bearing radioactive atom into an antibody without affecting its immunoreactivity is a challenging task. A bifunctional chelator (BFC), a selective amino acid-targeting probe, and a linker have been developed to overcome this problem. Here, we report the synthesis of a novel propylene cross-bridged chelator (PCB)-1,8-N,N'-bis-(carboxymethyl)-1,4,8,11-tetraazacyclotetradecane (TE2A)-luminol BFC via a click reaction and radiolabel it with a ⁶⁴Cu ion for tyrosine-selective conjugation of trastuzumab. In the initial optimization study, we tried different oxidative addition conditions such as electro-oxidation, hemin, horseradish peroxidase, iodogen tube, chloramine-T, and iodo beads. In this study, up to 82% of ⁶⁴Cu-PCB-TE2A-luminol was conjugated with the antibody in an iodo bead-catalyzed oxidative addition reaction with an isolated yield of 24.4%.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.4
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• pp.135-142
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• 2023

Recently, many companies are using public cloud services or building their own data center because digital transformation is expanding. The software-defined storage is a key solution for storing data on the cloud platform and its use is expanding worldwide. Software-defined storage has the advantage of being able to virtualize and use all storage resources as a single storage device and supporting flexible scale-out. On the other hand, since the size of an object is variable, an imbalance occurs in the use of the disk and may cause a failure. In this study, a method of redistributing objects by optimizing disk weights based on storage state information was proposed to solve the imbalance problem of disk use, and the experimental results were presented. As a result of the experiment, it was confirmed that the maximum utilization rate of the disk decreased by 10% from 89% to 79%. Failures can be prevented, and more data can be stored by optimizing the use of disk.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.260-262
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• 2021

In recent years, many studies are being conducted to reduce the damage to humans in the event of a fire. In case of fire in large cities, evacuation route guidance services are provided using Mobile GIS (geographic information system). However, among the algorithms used in the existing evacuation route system, Dijkstra Algorithm has a problem that when the cost is negative, it cannot obtain an infinite loop or an accurate result value, and does not help to select an appropriate shortest route by searching all routes. For this reason, in this paper, we propose the shortest route guidance system based on A^* Algorithm. In case of fire, the shortest route is searched and the shortest route is visualized and provided using a map service on a mobile device using mobile GIS.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2A
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• pp.285-292
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• 2006

In this study, a method determining the local bond-slip model from pure shear test results of CFRP sheet-concrete adhesive joints is proposed and local bond-slip models are presented. Adhesive joints with a specific bond-slip model, which is assumed as multi-linear curve in order to represent arbitary function, are solved numerically. The difference between the solution and test results are minimized for finding the bond-slip model. The model with bilinear curve is also optimized to verify the improvement of multi-linear model. The selected test results are ultimate load-adhesive length curves from a series of adhesive joints and load-displacement curves for each joint. The optimization problem is formulated by physical programming, and the optimized bond-slip model is found using genetic algorithm.