• Electronics and Telecommunications Trends
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• v.34 no.5
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• pp.14-25
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• 2019

As the Internet of Things, artificial intelligence and big data have received a lot of attention as key growth engines in the era of the fourth industrial revolution, data acquisition and utilization in mobile, automotive, robotics, manufacturing, agriculture, health care and national defense are becoming more important. Due to numerous data-based industrial changes, demand for sensor technologies is exploding, especially for intelligent sensor technologies that combine control, judgement, storage and communication functions with the sensors's own functions. Intelligent sensor technology can be defined as a convergence component technology that combines intelligent sensor units, intelligent algorithms, modules with signal processing circuits, and integrated plaform technologies. Intelligent sensor technology, which can be applied to variety of smart IT convergence services such as smart devices, smart homes, smart cars, smart factory, smart cities, and others, is evolving towards intelligent and convergence technologies that produce new high-value information through recognition, reasoning, and judgement based on artificial intelligence. As a result, development of intelligent sensor units is accelerating with strategies for miniaturization, low-power consumption and convergence, new form factor such as flexible and stretchable form, and integration of high-resolution sensor arrays. In the future, these intelligent sensor technologies will lead explosive sensor industries in the era of data-based artificial intelligence and will greatly contribute to enhancing nation's competitiveness in the global sensor market. In this report, we analyze and summarize the recent trends in intelligent sensor technologies, especially those for four core technologies.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.192-198
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• 2021

The battery's State of Health (SOH) is a critical parameter in the process of battery use, as it represents the Remaining Useful Life (RUL) of the battery. Electrochemical Impedance Spectroscopy (EIS) is a widely used technique in observing the state of the battery. The measured impedance at certain frequencies can be used to evaluate the state of the battery, as it is intimately tied to the underlying chemical reactions. In this work, a low-cost portable EIS instrument is developed on the basis of the ARM Cortex-M4 Microcontroller Unit (MCU) for measuring the impedance spectrum of Li-ion battery packs. The MCU uses a built-in DAC module to generate the sinusoidal sweep perturbation signal. Moreover, it performs the dual-channel acquisition of voltage and current signals, calculates impedance using a Digital Lock-in Amplifier (DLA), and transmits the result to a PC. By using LabVIEW, an interface was developed with the real-time display of the EIS information. The developed instrument was suitable for measuring the impedance spectrum of the battery pack up to 1000 V. The measurement frequency range of the instrument was from 1 hz to 1 Khz. Then, to prove the performance of the developed system, the impedance of a Samsung SM3 battery pack and a Bexel pouch module were measured and compared with those obtained by the commercial instrument.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2444-2452
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• 2022

The field-programmable gate array (FPGA) is gaining popularity in industrial automation such as nuclear power plant instrumentation and control (I&C) systems due to the benefits of having non-existence of operating system, minimum software errors, and minimum common reason failures. Separate functions can be processed individually and in parallel on the same integrated circuit using FPGAs in comparison to the conventional microprocessor-based systems used in any plant operations. The use of FPGAs offers the potential to minimize complexity and the accompanying difficulty of securing regulatory approval, as well as provide superior protection against obsolescence. Wireless sensor networks (WSNs) are a new technology for acquiring and processing plant data wirelessly in which sensor nodes are configured for real-time signal processing, data acquisition, and monitoring. ZigBee (IEEE 802.15.4) is an open worldwide standard for minimum power, low-cost machine-to-machine (M2M), and internet of things (IoT) enabled wireless network communication. It is always a challenge to follow the specific topology when different Zigbee nodes are placed in a large network such as a plant. The research article focuses on the hardware chip design of different topological structures supported by ZigBee that can be used for monitoring and controlling the different operations of the plant and evaluates the performance in Vitex-5 FPGA hardware. The research work presents a strategy for configuring FPGA with ZigBee sensor nodes when communicating in a large area such as an industrial plant for real-time monitoring.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.308-318
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• 2022

Noise is a serious issue. While sending images via electronic communication, Impulse noise, which is created by unsteady voltage, is one of the most common noises in digital communication. During the acquisition process, pictures were collected. It is possible to obtain accurate diagnosis images by removing these noises without affecting the edges and tiny features. The New Average High Noise Density Median Filter. (HNDMF) was proposed in this paper, and it operates in two steps for each pixel. Filter can decide whether the test pixels is degraded by SPN. In the first stage, a detector identifies corrupted pixels, in the second stage, an algorithm replaced by noise free processed pixel, the New average suggested Filter produced for this window. The paper examines the performance of Gaussian Filter (GF), Adaptive Median Filter (AMF), and PHDNF. In this paper the comparison of known image denoising is discussed and a new decision based weighted median filter used to remove impulse noise. Using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structure Similarity Index Method (SSIM) metrics, the paper examines the performance of Gaussian Filter (GF), Adaptive Median Filter (AMF), and PHDNF. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the HNDMF model has reached to a better performance with the maximum picture quality. images affected by various amounts of pretend salt and paper noise, as well as speckle noise, are calculated and provided as experimental results. According to quality metrics, the HNDMF Method produces a superior result than the existing filter method. Accurately detect and replace salt and pepper noise pixel values with mean and median value in images. The proposed method is to improve the median filter with a significant change.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.379-386
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• 2023

When patients suffer from finger injuries, their finger joints can become stiff and inflexible due to decreased ability to exercise the finger tendons. This can lead to a loss of strength and difficulty using their hands. To address this, it is important to provide patients with consistent rehabilitation treatment that can help restore finger flexibility and strength simultaneously. In this study, we propose wearable gloves that use FSRs (force sensitive resistors) for finger strength training. The glove is designed to be adjustable using rubber bands and a custom PCB is designed for signal acquisition. For the evaluation of finger strength training, the result was analyzed in four cases. We suggest a vector that represents the center of five finger forces, and the result shows that the vector can indicate the level of force balance.

• Investigative Magnetic Resonance Imaging
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• v.5 no.1
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• pp.38-42
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• 2001

Dark band artifacts are often observed in angiograms of arteries obtained by 2D time-of-flight (TOF) angiography with saturation of veins by presaturation RF pulses. At some arteries the arterial blood velocity varies in a triphasic pattern during a cardiac cycle. The arterial blood, that is saturated by presaturation RF pulses in the saturation band, can flow back into the imaging slice during the retrograde flow phase of the triphasic variation. When such saturated retrograde flow occurs during the acquisition of the central part of the K space, a signal void can result in base images and consequently dark band artifacts can appear in angiograms. This phenomenon is experimentally demonstrated by varying the gap between the imaging slice and the saturation band. Furthermore, a new pulse sequence is proposed to eliminate the dark band artifacts by changing the profile of the saturation band front a rectangle to a ramp.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.315-321
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• 2023

Long-span bridges are flexible structures with low natural frequencies and damping ratios, making them susceptible to vibrational serviceability problems. However, the current design guideline of South Korea assumes a uniform threshold of wind speed or vibrational amplitude to assess the occurrence of harmful vibrations, potentially overlooking the complex vibrational patterns observed in long-span bridges. In this study, we propose a pointwise vortex-induced vibration (VIV) detection method using a deep-learning-based signalsegmentation model. Departing from conventional supervised methods of data acquisition and manual labeling, we synthesize training data by generating sinusoidal waves with an envelope to accurately represent VIV. A Fourier synchrosqueezed transform is leveraged to extract time-frequency features, which serve as input data for training a bidirectional long short-term memory model. The effectiveness of the model trained on synthetic VIV data is demonstrated through a comparison with its counterpart trained on manually labeled real datasets from an actual cable-supported bridge.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• Journal of the Korean Society of Radiology
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• v.81 no.1
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• pp.81-100
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• 2020

Magnetic resonance neurography (MRN) has been increasingly used in recent years for the assessment of peripheral neuropathies. Fat suppression T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) have typically been used to provide high contrast MRN. Isotropic 3-dimensional (3D) sequences with fast spin echo, post-processing imaging techniques, and fast imaging methods, among others, allow good visualization of peripheral nerves that have a small diameter, complex anatomy, and oblique course within a reasonable scan time. However, there are still several issues when performing high contrast and high resolution MRN including standard sequence; fat saturation techniques; balance between resolution, field of view, and slice thickness; post-processing techniques; 2D vs. 3D image acquisition; different T2 contrasts between proximal and distal nerves; high T2 signal intensity of adjacent veins or joint fluid; geometric distortion; and appropriate p-values on DWI. The proper understanding of these issues will help novice radiologists evaluate peripheral neuropathies using MRN.

• Korean Journal of Radiology
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• v.25 no.1
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• pp.24-32
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• 2024

Despite improvements in operative techniques and perioperative care, post-hepatectomy liver failure (PHLF) remains the most serious cause of morbidity and mortality after surgery, and several risk factors have been identified to predict PHLF. Although volumetric assessment using imaging contributes to surgical simulation by estimating the function of future liver remnants in predicting PHLF, liver function is assumed to be homogeneous throughout the liver. The combination of volumetric and functional analyses may be more useful for an accurate evaluation of liver function and prediction of PHLF than only volumetric analysis. Gadoxetic acid is a hepatocyte-specific magnetic resonance (MR) contrast agent that is taken up by hepatocytes via the OATP1 transporter after intravenous administration. Gadoxetic acid-enhanced MR imaging (MRI) offers information regarding both global and regional functions, leading to a more precise evaluation even in cases with heterogeneous liver function. Various indices, including signal intensity-based methods and MR relaxometry, have been proposed for the estimation of liver function and prediction of PHLF using gadoxetic acid-enhanced MRI. Recent developments in MR techniques, including high-resolution hepatobiliary phase images using deep learning image reconstruction and whole-liver T1 map acquisition, have enabled a more detailed and accurate estimation of liver function in gadoxetic acid-enhanced MRI.