• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.71-77
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• 2021

An impeller identification technique that is essential for adding viscosity measurement functions to overhead stirrers is presented in this study. Previous studies have revealed that using magnets facing the same poles arranged in a row can aid in distinguishing the types of impellers by detecting the number of magnets in a non-contact manner. However, as these previous studies measured the magnetic fields using analog Hall sensors, a converting circuit for the digital signals is required that can interface with the MCU. In this study, it was demonstrated that the number of magnets can be distinguished without using a separate conversion circuit by using a Hall sensor with a digital output. Owing to the unique hysteresis characteristics of digital Hall sensors, it was confirmed through experiments that the complex and diverse outputs appear depending on the direction of the magnetic field, the arrangement of magnetic poles, and the moving direction of the magnet. The measurement of the magnetic field showed that an edge signal equal to the number of magnets inserted into the impeller was detected when the radial direction was used, and the south pole was first approached.

• Smart Structures and Systems
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• v.24 no.2
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• pp.223-231
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• 2019

The properties of Electroactive Polymer (EAP) materials are attracting the attention of engineers and scientists from many different disciplines. From the point-of-view of robotics, Ionic Polymer Metal Composites (IPMC) belong to the most developed group of the EAP class. To allow effective design of IPMC-actuated mechanisms with large induced strains, it is necessary to have adequate analytical tools for predicting the behavior of IPMC actuators as well as simulating their response as part of prototyping methodologies. This paper presents a novel IPMC motor construction. To simulate the bending behavior that is the dominant phenomenon of motor movement process, a nonlinear model is used. To accomplish the motor design, the IPMC model was identified via a series of experiments. In the proposed model, the curvature output and current transient fields accurately track the measured responses, which is verified by measurements. In this research, a three-dimensional Finite Element Method (FEM) model of the IPMC motor, composed of IPMC actuators, simultaneously determines the mechanical and electrical characteristics of the device and achieves reliable analysis results. The principle of the proposed drive and the output signals are illustrated in this paper. The proposed modelling approach can be used to design a variety of controllers and motors for effective micro-robotic applications, where soft and complex motion are required.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.363-369
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• 2019

Various studies have been conducted for the diagnosis of noise condition of complex rotary machines. In this study, diagnosis index using vibration signal is developed for the efficient and objective assessment of noise condition of a blower motor. The noise most commonly caused by the abnormal blower motor are Tick/Click noise and Tone noise. According to cause and noise characteristics, time-frequency analysis is used to diagnose Tick/Click noise, and smoothing in frequency domain is used to diagnose tone noise condition. The noise condition of the blower motors were diagnosed using the developed index and these results are compared with the diagnostic results by the experts. As a result, the agreement rate was about 95 %.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.216-239
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• 2021

The present work addresses the challenging problem of coordinating power allocation with interference management in multi-robot networks by applying the promising expansion capabilities of multiple-input multiple-output (MIMO) and full duplex systems, which achieves it for maximizing the throughput of networks under the impacts of Doppler frequency shifts and external jamming. The proposed power allocation with interference coordination formulation accounts for three types of the interference, including cross-tier, co-tier, and mixed-tier interference signals with cluster head nodes operating in different full-duplex modes, and their signal-to-noise-ratios are respectively derived under the impacts of Doppler frequency shifts and external jamming. In addition, various optimization algorithms, including two centralized iterative optimization algorithms and three decentralized optimization algorithms, are applied for solving the complex and non-convex combinatorial optimization problem associated with the power allocation and interference coordination. Simulation results demonstrate that the overall network throughput increases gradually to some degree with increasing numbers of MIMO antennas. In addition, increasing the number of clusters to a certain extent increases the overall network throughput, although internal interference becomes a severe problem for further increases in the number of clusters. Accordingly, applications of multi-robot networks require that a balance should be preserved between robot deployment density and communication capacity.

• Current Optics and Photonics
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• v.5 no.3
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• pp.322-328
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• 2021

In this paper, a pretreatment method for a matrix in convex optimization is proposed to optimize the spectral reconstruction process of a disordered dispersion spectrometer. Unlike the reconstruction process of traditional spectrometers using Fourier transforms, the reconstruction process of disordered dispersion spectrometers involves solving a large-scale matrix equation. However, since the matrices in the matrix equation are obtained through measurement, they contain uncertainties due to out of band signals, background noise, rounding errors, temperature variations and so on. It is difficult to solve such a matrix equation by using ordinary nonstationary iterative methods, owing to instability problems. Although the smoothing Tikhonov regularization approach has the ability to approximatively solve the matrix equation and reconstruct most simple spectral shapes, it still suffers the limitations of reconstructing complex and irregular spectral shapes that are commonly used to distinguish different elements of detected targets with mixed substances by characteristic spectral peaks. Therefore, we propose a special pretreatment method for a matrix in convex optimization, which has been proved to be useful for reducing the condition number of matrices in the equation. In comparison with the reconstructed spectra gotten by the previous ordinary iterative method, the spectra obtained by the pretreatment method show obvious accuracy.

• Journal of IKEEE
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• v.25 no.1
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• pp.53-63
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• 2021

As the types of radio equipment becomes more complex and diversified in various fields, radio interference occurs frequently. In the domestic situation where the territory is narrow, many systems are deployed in a specific highland with a good radio wave environment. Radar systems that transmit high power signals of the same band are sometimes deployed and operating at close distance. In this paper, the type of interference was classified for the actual radio wave interference phenomenon and appropriate signal interference parameters were derived. The power density of the interference signal was predicted using the analysis method and the effectiveness was verified through measurement. And, we propose a method to minimize interference by analyzing operating scenarios of interferer radar and victim radar.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.20-24
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• 2021

The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.108-117
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• 2022

The recent development of information and communication technology has a great impact on the audio/video industry. In particular, IP-based AoIP transmission technology and AVB technology are making changes in the audio/video market. Video signal transmission technology has been introduced to the market through a network, but it has not replaced the video switcher function. Video signals in the conference room or classroom are still controlled by the switching device. In order to switch input/output video devices, a cable that is not limited by distance must be connected to the switcher. In addition, the control of the switching device must be performed by a person who has received professional training. In this paper, it is a technology that can be operated even by non-experts by replacing complex video cables (RGB, DVI, HDMI, DP) with LAN cables and enabling IP-based video switching and transmission (Video Mirroring over IP: VMoIP) to replace video switcher equipment. We are going to do this study, I/O videos were controlled in the form of matrix and high-definition videos were transmitted without distortion, and VMoIP is expected to become the standard for video switching systems in the future.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.208-212
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• 2009

Ground wave is an acoustic wave propagating at a sediment sound speed in the case that sediment sound speed is constant with depth, which is explained by modal dispersion effects. In this paper, the ground wave in time domain is simulated using the ray-based approach, which is possible because the modal dispersion can be explained by the guiding of energy caused by reflection and refraction in the waveguide geometry. For a Pekeris waveguide, the ground wave can be interpreted as a sequence of head waves, called a head wave sequence [Choi and Dahl, J. Acoust. Soc. Am. 119, 3660-3668 (2006)]. The ground wave is simulated by convolution of the source signal with a channel impulse response of the head wave sequence, which is compared with simulated signals obtained via a Fourier synthesis of a complex parabolic equation (PE) field.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.1-6
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• 2024

Recently, artificial neural networks have been playing a crucial role and advancing across various fields. Artificial neural networks are typically categorized into feedforward neural networks and recurrent neural networks. However, feedforward neural networks are primarily used for processing static spatial patterns such as image recognition and object detection. They are not suitable for handling temporal signals. Recurrent neural networks, on the other hand, face the challenges of complex training procedures and requiring significant computational power. In this paper, we propose memristors suitable for an advanced form of recurrent neural networks called reservoir computing systems, utilizing a mask processor. Using the characteristic equations of Ti/TiOx/TaOy/Pt, Pt/TiOx/Pt, and Ag/ZnO-NW/Pt memristors, we generated current-voltage curves to verify their memristive behavior through the confirmation of hysteresis. Subsequently, we trained and inferred reservoir computing systems using these memristors with the NIST TI-46 database. Among these systems, the accuracy of the reservoir computing system based on Ti/TiOx/TaOy/Pt memristors reached 99%, confirming the Ti/TiOx/TaOy/Pt memristor structure's suitability for inferring speech recognition tasks.