• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.759-766
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• 2022

In this paper, we propose a multi-band small antenna with CPW(Coplanar Waveguide) feeding structure WLAN(Wireless Local Area Network) and WiMAX (Worldwide Interoperability for Microwave Access) bands. The proposed antenna is designed two slit in the modified monopole type radiator and FR-4 substrate, which is thickness 1.0 mm, and the dielectric constant is 4.4. The size of proposed antenna is 15.1 mm⨯16.41 mm, and total size of proposed antenna is 17.5 mm⨯16.4 mm. From the fabrication and measurement results, From the fabrication and measurement results, bandwidths of 439 MHz (2.06 to 2.499 GHz), 840 MHz (3.31 to 4.25) and 1,315 MHz (5.23 to 6.545 GHz) were obtained on the basis of -10 dB impedance bandwidth. Also, 3D radiation pattern characteristics of the proposed antenna are displayed and measured gains 2.24 dBi, 2.83 dBi, and 2.0 dBi shown in the three frequency band, respectively.

• Journal of the Korean Electrochemical Society
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• v.24 no.4
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• pp.93-99
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• 2021

In order to decrease the weight of stretchable energy storage devices, interest in developing lightweight materials to replace metal current collectors is increasing. In this study, nanofibers prepared by electrospinning a conductive polymer, PEDOT:PSS, were used as current collectors for lithium ion batteries. The nanofiber showed improved electrical conductivity by using DMSO, a dopant, and indicated a stretch rate of 30% or more from the elasticity evaluation result. In addition, the use of the nanofiber current collector facilitates penetration of the liquid electrolyte and exhibits the effect of increasing the electronic conductivity through the nanofiber network. The lithium-ion battery using the DMSO-doped PEDOT:PSS@PAM nanofiber current collector indicated a high discharge capacity of 135mAh g^-1, and indicated a high capacity retention rate of 73.5% after 1000 cycles. Thus, the excellent electrochemical stability and mechanical properties of conductive nanofibers showed that they can be used as lightweight current collectors for stretchable energy storage devices.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.23-31
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• 2022

Cloud services, which started in the early 2000s as a method of using idle servers, are more active with the advent of the 4th industrial revolution, and are being used in many fields as an optimal platform that can be used for business by collecting and analyzing data. On the other hand, the Internet of Things is an environment in which all surrounding objects can freely connect to the Internet network anytime and anywhere to transmit sensed data. In the Internet of Things, data is transmitted in real time, so BaaS, that is, a cloud service for data only has been added. In this paper, among BaaS services for the Internet of Things, a back-end service method that manages data based on Parse Server is explained, and a service that helps patients in rehabilitation is presented using this. For this, a Raspberry Pi is used as a hardware environment, and it is connected to the Internet, collects patient movement information in real time, and manages it through the Parse Server.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2257-2285
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• 2022

Cyber-physical systems (CPS) have been growing exponentially due to improved cloud-datacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling large-scale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the non-recursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.29-38
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• 2023

Although the biosignal is the best way to represent the human condition, it is difficult to acquire the biosignal of a driver driving for detecting driver's condition. As one of the methods to overcome this limitation, this paper proposes a driving stress monitoring system based on information provided by OBD-II(on-board diagnostics version II). The driving information and EDA(Electrodermal activity) data are obtained through the OBD-II scanner and E4 wristband, respectively. EDA data is used as ground truth to distinguish whether driver is stressed or not. MLP(multi-layer perceptron) neural network is used as a model to detect driving stress and is trained using driving data for about a month. To evaluate the proposed system, we used about 1 hour of driving data and the accuracy is 92%.

• Journal of Biomedical Engineering Research
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• v.44 no.4
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• pp.255-263
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• 2023

Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, capturing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios. Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer's motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learning (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through extensive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.563-570
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• 2023

The Safety Transmission Unit(STU) is a critical device used in railway systems to ensure safe and efficient operations by providing communication between trains and railway infrastructure. It is responsible for transmitting vital information and commands, allowing for the control and coordination of train movements. The STU plays a crucial role in maintaining the safety of passengers, crew, and the overall railway network. This paper presents the design and testing of a STU for the next-generation wireless-based train control system. An analysis of european and domestic standards was conducted to review requirements and ensure the design of a STU for the train control system meets international standards. Based on this analysis, hardware and software designs were developed to create an internationally recognized level of safety for the communication device. To verify the functionality of the STU, a simulator was developed, and it was confirmed that the designed features were successfully implemented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.322-323
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• 2022

The most necessary thing for the optimal operation of a water purification plant is to accurately predict the pattern and amount of tap water used by consumers. The required amount of tap water should be delivered to the drain using a pump and stored, and the required flow rate should be supplied in a timely manner using the minimum amount of electrical energy. The short-term demand forecasting required from the point of view of energy optimization operation among water purification plant volume predictions has been made in consideration of seasons, major periods, and regional characteristics using time series analysis, regression analysis, and neural network algorithms. In this paper, we analyzed energy management methods through AI-based complex sensor applicability analysis such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), which are types of cyclic neural networks.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.88-94
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• 2023

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.