• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1277-1283
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• 2021

It is important for operators of poly-pipelines in petroleum industry to continuously monitor characteristics of transferred fluid such as its type and amount. To achieve this aim, in this study a dual energy gamma attenuation technique in combination with artificial neural network (ANN) is proposed to simultaneously determine type and amount of four different petroleum by-products. The detection system is composed of a dual energy gamma source, including americium-241 and barium-133 radioisotopes, and one 2.54 cm × 2.54 cm sodium iodide detector for recording the transmitted photons. Two signals recorded in transmission detector, namely the counts under photo peak of Americium-241 with energy of 59.5 keV and the counts under photo peak of Barium-133 with energy of 356 keV, were applied to the ANN as the two inputs and volume percentages of petroleum by-products were assigned as the outputs.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.124-132
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• 2022

With growth in intelligence of mobile robots, interaction with humans is emerging as a very important issue for mobile robots and the pedestrian tracking technique following the designated person is adopted in many cases in a way that interacts with humans. Among the existing multi-object tracking techniques for pedestrian tracking, Simple Online and Realtime Tracking (SORT) is suitable for small mobile robots that require real-time processing while having limited computational performance. However, SORT fails to reflect changes in object detection values caused by the movement of the mobile robot, resulting in poor tracking performance. In order to solve this performance degradation, this paper proposes a more stable pedestrian tracking algorithm by correcting object tracking errors caused by robot movement in real time using wheel odometry information of a mobile robot and dynamically managing the survival period of the tracker that tracks the object. In addition, the experimental results show that the proposed methodology using data collected from actual mobile robots maintains real-time and has improved tracking accuracy with resistance to the movement of the mobile robot.

• Journal of Biomedical Engineering Research
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• v.44 no.1
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• pp.19-24
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• 2023

Due to the spread of COVID-19, many patients with severe respiratory diseases have occurred worldwide, and accordingly, the use of mechanical ventilators has exploded. However, hospitals do not have systematic risk management, and the Medical Device Regulation also provides medical device risk management standards for manufacturers, but does not apply to devices in use. In this paper, we applied the Failure Mode Effects Analysis (FMEA) risk analysis technique based on the International Standard ISO 14971 (Medical Devices-Application of risk management to medical devices) for 85 mechanical ventilators of a specific model in use in hospitals. Failure modes and effects of each parts were investigated, and risk priority was derived through multiplication of each score by preparing criteria for severity, occurrence, and detection for each failure mode. As a result, it was confirmed that the microprocessor-based Patient Unit/Monitoring board in charge of monitoring scored the highest score with 36 points, and that reliability management is possible through systematic risk management according to priority.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.1-8
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• 2023

In this paper, we developed a mid-range automotive radar systems based on the performance requirements and test procedures of the intelligent transport systems, that is lane change decision aid systems (LCDAS). The mid-range automotive radar has the maximum detection range up to 80m and an update time within 50ms. The computational loads of a signal processing were reduced by using ROI preprocessing technique. Considering actual driving environments, radar performance evaluations were conducted in two driving scenarios at an automotive proving ground.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.199-214
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• 2020

An important technique of the present invention is primarily to parallel light detection, self-pulse therapy after diagnosis. Herpes zoster is a disease caused by varicella zoster virus, and the virus that has been latent in the dorsal root ganglion that controls the skin segment loses its immune system and physically damages it. It is an acute skin disease in which acute pain and bullous rash occur along the sensory ganglia, which are rehab by inducers such as malignant tumors. Dorsal root ganglion after complete recovery of varicella, relapsed after incubation in brain ganglion, latent virus sometimes suppressed activity by cell mediated immunity, and in cell ganglion with reduced cellular immunity. It proliferates and destroys neurons, causing pain while forming a rash and blisters. This can reduce cell necrosis and increase the phagocytosis and enzymatic activity through the movement of ions through the cell membrane, depolarization and membrane potential change, growth factor secretion, calcium ion transfer, chondrocyte synthesis, etc., And may offer treatment options for lesions of herpes zoster and post-herpetic neuralgia (PHN).Therefore, according to the present research, the diagnosis and treatment device of treating paing for herpes zoster and post-herpetic pain can be implemented in the early stage of herpes zoster, and conventional analgesic regulation, anti-inflammatory effect, post-herpetic neuralgia.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.107-127
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• 2022

Structural health monitoring (SHM) has been related to damage identification with either operational loads or other environmental loading playing a significant complimentary role in terms of structural safety. In this study, a non-parametric method of time frequency analysis on the measurement is used to address the time-frequency representation for modal parameter estimation and system damage identification of structure. The method employs the wavelet decomposition of dynamic data by using the modified complex Morlet wavelet with variable central frequency (MCMW+VCF). Through detail discussion on the selection of model parameter in wavelet analysis, the method is applied to study the dynamic response of both steel structure and reinforced concrete frame under white noise excitation as well as earthquake excitation from shaking table test. Application of the method to building earthquake response measurement is also examined. It is shown that by using the spectrogram generated from MCMW+VCF method, with suitable selected model parameter, one can clearly identify the time-varying modal frequency of the reinforced concrete structure under earthquake excitation. Discussions on the advantages and disadvantages of the method through field experiments are also presented.

• Current Optics and Photonics
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• v.6 no.2
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• pp.137-142
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• 2022

Leak detection is one of the most important challenges in condition monitoring of water pipelines. Fiber Bragg grating (FBG) sensors offer an attractive technique to detect leak signals. In this paper, leak measurements were conducted on a water distribution pilot plant with a length of 270 m and a diameter of 100 mm. FBG sensors were installed on the pipeline surface and used to detect leak vibration signals. The leak was demonstrated with 1-, 2-, 3-, and 4-mm diameter leak holes in four different pipe types. The frequency response of leak signals was analyzed by fast Fourier transform analysis in real time. In the experiment, the frequency range of leak signals was approximately 340-440 Hz. The frequency shifts of leak signals according to the pipe type and the size of the leak hole were demonstrated at a pressure of 1.8 bar and a flow rate of 25.51 m³/h. Results show that frequency shifts detected by FBG sensors can be used to detect leaks in pipelines.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.51-73
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• 2023

Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNetB0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

• ETRI Journal
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• v.45 no.3
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• pp.448-461
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• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.