• Transactions on Electrical and Electronic Materials
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• v.18 no.3
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• pp.136-140
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• 2017

The state of charge (SOC) is an important parameter in a battery-management system (BMS), and is very significant for accurately estimating the SOC of a battery. Li-ion batteries boast of excellent performance, and can only remain at their best working state by means of accurate SOC estimation that gives full play to their performances and raises their economic benefits. This paper summarizes some measures taken in SOC estimation, including the discharge experiment method, the ampere-hour integral method, the open circuit voltage method, the Kalman filter method, the neural network method, and electrochemical impedance spectroscopy (EIS. The principles of the various SOC estimation methods are introduced, and their advantages and disadvantages, as well as the working conditions adopted during these methods, are discussed and analyzed.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.24-31
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• 2023

The solid-state drive (SSD) possesses higher input and output speeds, more resistance to physical shock, and lower latency compared with regular hard disks; hence, it is an increasingly popular storage device. However, tiny components on an internal printed circuit board (PCB) hinder the manual detection of malfunctioning components. With the rapid development of artificial intelligence technologies, automatic detection of components through convolutional neural networks (CNN) can provide a sound solution for this area. This study proposes applying the YOLOv5 model to SSD PCB component detection, which is the first step in detecting defective components. It achieves pioneering state-of-the-art results on the SSD PCB dataset. Contrast experiments are conducted with YOLOX, a neck-and-neck model with YOLOv5; evidently, YOLOv5 obtains an mAP@0.5 of 99.0%, essentially outperforming YOLOX. These experiments prove that the YOLOv5 model is effective for tiny object detection and can be used to study the second step of detecting defective components in the future.

• Applied Microscopy
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• v.50
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• pp.14.1-14.6
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• 2020

Scanning electron microscopy (SEM) plays a central role in analyzing structures by imaging a large area of brain tissue at nanometer scales. A vast amount of data in the large area are required to study structural changes of cellular organelles in a specific cell, such as neurons, astrocytes, oligodendrocytes, and microglia among brain tissue, at sufficient resolution. Array tomography is a useful method for large-area imaging, and the osmium-thiocarbohydrazide-osmium (OTO) and ferrocyanide-reduced osmium methods are commonly used to enhance membrane contrast. Because many samples prepared using the conventional technique without en bloc staining are considered inadequate for array tomography, we suggested an alternative technique using post-staining conventional samples and compared the advantages.

• Smart Structures and Systems
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• v.29 no.5
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• pp.693-703
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• 2022

This study investigates an effective approach to stabilize nonlinear systems. To ensure the asymptotic nonlinear stability in nonlinear discrete-time systems, the present study presents controller for an EBA (Evolved Bat Algorithm) NN (fuzzy neural network) in the algorithm. In fuzzy evolved NN modeling, the auxiliary circuit with high frequency LDI (linear differential inclusions) and NN model representation is developed for the nonlinear arbitrary dynamics. An example is utilized to demonstrate the system more robust compared with traditional control systems.

• Korean Journal of Biological Psychiatry
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• v.12 no.2
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• pp.123-135
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• 2005

Objectives:EEG coherence could imply the connectivity between two different areas of the brain, which is known to be important in the pathophysiology of bipolar I disorder(BPD I) and schizophrenia. The authors investigated EEG coherence in patients with BPD I and schizophrenia to examine the connectivity of the neural circuit. Methods:EEGs were recorded in 15 schizophrenia and 14 bipolar disorder patients, and 14 age-matched normal control subjects from 16 electrodes with linked-ear reference. Spectral parameters and coherence were calculated for the alpha bandwidth(8-13Hz) by a multi-channel autoregressive model using 20 artifact-free 2-seconds epochs and the differences were compared among three groups by two different statistical methods;F-test and Kruskal-Wallis test. Furthermore, when there were significant differences among three groups, Scheffe's multiple comparison tests were provided and Jonckheere-Terpstra tests for the ordered alternative were given. Results:In the intra-hemispheric comparison, left frontal coherence was increased in order of control, BPD I and schizophrenia. In the inter-hemispheric comparison, 1) inter-prefrontal coherence in BPD I was signifi- cantly higher than in normal controls, and 2) inter-prefrontal coherence in schizophrenia was significantly lower than in controls. Conclusion:These results suggest that 1) both schizophrenia and BPD I are diseases having the abnormality of neural circuit connectivity in both frontal and prefrontal lobes, and 2) the abnormality is more severe in schizophrenia than in BPD I. Furthermore, the data support that a common pathogenetic process may reside in both schizophrenia and BPD I.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.409-418
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• 2021

Conformal coating is a technology that protects PCB(Printed Circuit Board) and minimizes PCB failures. Since the defects in the coating are linked to failure of the PCB, the coating surface is examined for air bubbles to satisfy the successful conditions of the conformal coating. In this paper, we propose an algorithm for detecting problematic bubbles in high-risk groups by applying image signal processing. The algorithm consists of finding candidates for problematic bubbles and verifying candidates. Bubbles do not appear in visible light images, but can be visually distinguished from UV(Ultra Violet) light sources. In particular the center of the problematic bubble is dark in brightness and the border is high in brightness. In the paper, these brightness characteristics are called valley and mountain features, and the areas where both characteristics appear at the same time are candidates for problematic bubbles. However, it is necessary to verify candidates because there may be candidates who are not bubbles. In the candidate verification phase, we used convolutional neural network models, and ResNet performed best compared to other models. The algorithms presented in this paper showed the performance of precision 0.805, recall 0.763, and f1-score 0.767, and these results show sufficient potential for bubble test automation.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.2
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• pp.125-130
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• 2012

The nematode Caenorhabditis elegans is a widely used model organism in biological research. Thanks to the availability of well-established knowledge about its neural connectivity, a wide range of studies have been attempted to uncover the relationship between behaviors and the responsible neurons. In our research, the adaptive behavior of C. elegans in solid environments with different surface rigidities is investigated, where the worm adapts to different mechanical stiffnesses by modulating its crawling waveform. The amplitude and wavelength of the crawling waveform decrease as the environment becomes more rigid. Interestingly, the mechanosensation-defective mutant shows different responses to the surface rigidity compared to those of the wild-type worm. To explain the adaptation process in mechanically different environments, we suggest a plausible neural circuit model.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.73-83
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• 2014

Thanks to the development of diverse audio editing Technology, audio file can be easily revised. As a result, diverse social problems like forgery may be caused. Digital forensic technology is actively studied to solve these problems. In this paper, a hand-held device identification method, an area of digital forensic technology is proposed. It uses the noise features of devices caused by the design and the integrated circuit of each device but cannot be identified by the audience. Wiener filter is used to get the noise sounds of devices and their acoustic features are extracted via MIRtoolbox and then they are trained by multi-layer neural network. To evaluate the proposed method, we use 5-fold cross-validation for the recorded data collected from 6 mobile devices. The experiments show the performance 99.9%. We also perform some experiments to observe the noise features of mobile devices are still useful after the data are uploaded to UCC. The experiments show the performance of 99.8% for UCC data.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1385-1393
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• 2014

Gas insulated switchgear (GIS) is widely used in the power systems, however, the contacts overheating of the inside circuit breaker or disconnector may be a potential cause of developing accidents. As the temperature of the contacts cannot be directly acquired due to existence of the metallic shield, an infrared sensor is adopted to directly measure the temperature of the shield and then the contacts temperature can be indirectly obtained by data fitting, based on which the on-line temperature monitoring technology specifically for GIS contacts based on infrared sensing is proposed in this paper. A real GIS test platform is constructed and experimental studies are carried out to account for the influential factors that affect the accuracy of the infrared temperature measurement. A heat transfer model of the GIS module is also developed, together with experimental studies, the nonlinear temperature relationship among the contacts, the metallic shield and the environment based on a neural network algorithm is established. Finally, an integrated on-line temperature monitoring system for the GIS contacts is developed for on-site applications.

• BMB Reports
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• v.56 no.3
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• pp.178-183
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• 2023

Huntington's disease (HD) is a neurodegenerative disorder, of which pathogenesis is caused by a polyglutamine expansion in the amino-terminus of huntingtin gene that resulted in the aggregation of mutant HTT proteins. HD is characterized by progressive motor dysfunction, cognitive impairment and neuropsychiatric disturbances. Histone deacetylase 6 (HDAC6), a microtubule-associated deacetylase, has been shown to induce transport- and release-defect phenotypes in HD models, whilst treatment with HDAC6 inhibitors ameliorates the phenotypic effects of HD by increasing the levels of α-tubulin acetylation, as well as decreasing the accumulation of mutant huntingtin (mHTT) aggregates, suggesting HDAC6 inhibitor as a HD therapeutics. In this study, we employed in vitro neural stem cell (NSC) model and in vivo YAC128 transgenic (TG) mouse model of HD to test the effect of a novel HDAC6 selective inhibitor, CKD-504, developed by Chong Kun Dang (CKD Pharmaceutical Corp., Korea). We found that treatment of CKD-504 increased tubulin acetylation, microtubule stabilization, axonal transport, and the decrease of mutant huntingtin protein in vitro. From in vivo study, we observed CKD-504 improved the pathology of Huntington's disease: alleviated behavioral deficits, increased axonal transport and number of neurons, restored synaptic function in corticostriatal (CS) circuit, reduced mHTT accumulation, inflammation and tau hyperphosphorylation in YAC128 TG mouse model. These novel results highlight CKD-504 as a potential therapeutic strategy in HD.