• Advanced Industrial SCIence
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• v.3 no.1
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• pp.23-29
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• 2024

In this scholarly investigation, the focus is placed on the transformative potential of edge computing in enhancing Intelligent Transportation Systems (ITS) for the facilitation of autonomous driving. The intrinsic capability of edge computing to process voluminous datasets locally and in a real-time manner is identified as paramount in meeting the exigent requirements of autonomous vehicles, encompassing expedited decision-making processes and the bolstering of safety protocols. This inquiry delves into the synergy between edge computing and extant ITS infrastructures, elucidating the manner in which localized data processing can substantially diminish latency, thereby augmenting the responsiveness of autonomous vehicles. Further, the study scrutinizes the deployment of edge servers, an array of sensors, and Vehicle-to-Everything (V2X) communication technologies, positing these elements as constituents of a robust framework designed to support instantaneous traffic management, collision avoidance mechanisms, and the dynamic optimization of vehicular routes. Moreover, this research addresses the principal challenges encountered in the incorporation of edge computing within ITS, including issues related to security, the integration of data, and the scalability of systems. It proffers insights into viable solutions and delineates directions for future scholarly inquiry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.699-707
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• 2016

This paper proposes a low-complexity central processing unit (CPU) that is suitable for deeply embedded systems, including Internet of things (IoT) applications. The core features a 16-bit instruction set architecture (ISA) that leads to high code density, as well as a multicycle architecture with a counter-based control unit and adder sharing that lead to a small hardware area. A co-processor, instruction cache, AMBA bus, internal SRAM, external memory, on-chip debugger (OCD), and peripheral I/Os are placed around the core to make a system-on-a-chip (SoC) platform. This platform is based on a modified Harvard architecture to facilitate memory access by reducing the number of access clock cycles. The SoC platform and CPU were simulated and verified at the C and the assembly levels, and FPGA prototyping with integrated logic analysis was carried out. The CPU was synthesized at the ASIC front-end gate netlist level using a $0.18{\mu}m$ digital CMOS technology with 1.8V supply, resulting in a gate count of merely 7700 at a 50MHz clock speed. The SoC platform was embedded in an FPGA on a miniature board and applied to deeply embedded IoT applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.719-730
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• 2021

We have demonstrated a sandwich-type ZnO-based piezoelectric energy harvesting nanogenerator, namely ZCZ-NG device, composed of symmetrically stacked layers of ZnO/carbon tape/ZnO structure. Especially, we have adopted a conductive double-sided adhesive carbon tape in an effort to fabricate a high-quality ZCZ-NG device, leading to its superior output performance in terms of the peak-to-peak output voltage. Effects of the device size, ZnO layer thickness, and bending strain rate on the device performance have been investigated by measuring the output voltage. Moreover, to evaluate the effectiveness of the fabricated ZCZ-NG devices, we have experimentally implemented a sensor network testbed which can utilize the output voltages of ZCZ-NG devices. This sensor network testbed consists of several components such as Arduino-based transmitter and receiver nodes, wirelessly transmitting the sensed information of each node. We hope that this research combining the ZnO-based energy harvesting devices and IoT sensor networks will contribute to the development of more advanced energy harvester-driven IoT sensor networks in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.7
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• pp.659-664
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• 2016

In this paper, we suggest operational trans-conductance amplifier(OTA) for current-mode FIR filter that can be used in a digital circuit system requiring high operating frequency and low power consumption. The current-mode signal processing is one of the very innovative design method for a low power consumption system with high operating frequency because it shows a constant power regardless of frequency. From the simulation result using 0.35um CMOS process, when Vdd is 2V, it is confirmed that the proposed circuit showed the dynamic range of the about 1V, about 50% of supply voltage and output current swing of about 0~200uA. Also, the power consumption was evaluated with about 21uW and the active size for an integration was measured with $71um{\times}166um$.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.1-10
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• 2017

The high costs of electric vehicle supply equipment (EVSE) and installation are currently a stumbling block to the proliferation of electric vehicles (EVs). The cost-effective solutions are needed to support the expansion of charging infrastructure. In this paper, we develope EV charging system based on the big data analysis of the power consumption patterns. The developed EV charging system is consisted of the smart EV outlet, gateways, powergates, the big data management system, and mobile applications. The smart EV outlet is designed to low costs of equipment and installation by replacing the existing 220V outlet. We can connect the smart EV outlet to household appliances. Z-wave technology is used in the smart EV outlet to provide the EV power usage to users using Apps. The smart EV outlet provides 220V EV charging and therefore, we can restore vehicle driving range during overnight and work hours.

• Earthquakes and Structures
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• v.10 no.1
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• pp.105-123
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• 2016

In this paper, using the probabilistic methods, the seismic demand of buckling restrained braced frames subjected to earthquake was evaluated. In this regards, 4, 6, 8, 10, 12 and 14-storybuildings with different buckling restrained brace configuration (including diagonal, split X, chevron V and Inverted V bracings) were designed. Because of the inherent uncertainties in the earthquake records, incremental dynamical analysis was used to evaluate seismic performance of the structures. Using the results of incremental dynamical analysis, the "capacity of a structure in terms of first mode spectral acceleration", "fragility curve" and "mean annual frequency of exceeding a limit state" was determined. "Mean annual frequency of exceeding a limit state" has been estimated for immediate occupancy (IO) and collapse prevention (CP) limit states using both Probabilistic Seismic Demand Analysis (PSDA) and solution "based on displacement" in the Demand and Capacity Factor Design (DCFD) form. Based on analysis results, the inverted chevron (${\Lambda}$) buckling restrained braced frame has the largest capacity among the considered buckling restrained braces. Moreover, it has the best performance among the considered buckling restrained braces. Also, from fragility curves, it was observed that the fragility probability has increased with the height.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.334-337
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• 2019

In this study, we built the data collection system to learn user's habit data by deep learning and to create an indoor environment according to the situation. The system consists of a data collection server and several sensor nodes, which creates the environment according to the data collected. We used Google Inception v3 network to analyze the photographs and hand-designed second DNN (Deep Neural Network) to infer behaviors. As a result of the DNN learning, we gained 98.4% of Testing Accuracy. Through this results, we were be able to prove that DNN is capable of extrapolating the situation.

• Smart Media Journal
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• v.11 no.7
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• pp.94-103
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• 2022

Weeds bring disadvantages to crops since they can damage them, and a clean treatment with less pollution and contamination should be developed. Artificial intelligence gives new hope to agriculture to achieve smart farming. This study delivers an automated weeds growth point detection using deep learning. This study proposes a combination of semantic graphics for generating data annotation and U-Net with pre-trained deep learning as a backbone for locating the growth point of the weeds on the given field scene. The dataset was collected from an actual field. We measured the intersection over union, f1-score, precision, and recall to evaluate our method. Moreover, Mobilenet V2 was chosen as the backbone and compared with Resnet 34. The results showed that the proposed method was accurate enough to detect the growth point and handle the brightness variation. The best performance was achieved by Mobilenet V2 as a backbone with IoU 96.81%, precision 97.77%, recall 98.97%, and f1-score 97.30%.

• Broadcasting and Media Magazine
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• v.22 no.4
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• pp.34-40
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• 2017

현재 멀티미디어는 사용자의 감성을 자극하며 몰입감과 현장감을 자아내고 극대화할 수 있는 실감 미디어(Immersive media)로 발전하고 있다. 이러한 실감 미디어 내 시청각 정보의 저장과 표현을 위한 데이터 템플릿과 인터페이스들은 상당히 많은 부분에서 표준화되어 왔지만, 이와 달리 후각, 미각, 촉각 등의 실감 정보들은 관련 표준들의 부재로 인해 많은 연구 개발에도 불구하고 해당 서비스들의 상용화가 지연되고 있는 중이다. 이 문제를 해결하기 위해 이 논문에서는 후각 미디어에 초점을 맞추어 MPEG-V 표준과 현재 진행 중인 MPEG-IoMT(Internet of Media Thing) 표준을 사용하여 미디어 사물 인터넷 내 후각 인터렉션을 위해 필요한 표준 데이터 템플릿과 인터페이스를 기술하였다.

• Bulletin of the Korean Chemical Society
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• v.11 no.2
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• pp.124-126
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• 1990

The potentiometric response behavior of a polypyrrole(PPy) coated Pt electrode to nitrate ion has been studied. The electrode shows a nernstian behavior with a slope of 59 mV over 0.50 M to $1.0{\times}10^{-3}M\;NO_3\;^-$ and a detection limit of $1.0{\times}10^{-4}M\;NO_3\;^-$. The response of the electrode is fast and the selectivities for $I^-,\;ClO_4\;^-,\;and\;IO_4\;^-$ are found to be improved. The effect of pH on the potential response to $NO_3\;^-$ is compared with the existing nitrate ion selective electrodes.