• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.660-665
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• 2018

Research into underwater wireless networks that enable the monitoring and controlling of the ocean environments has been continuing for disaster prevention and military proposes, as well as for the exploitation of ocean resources throughout the world. A research group led by Hoseo university has been studying a distributed underwater monitoring and controlling network. In this study, we developed an interpolator for acoustic communication between an underwater base station controller and underwater base station, which is included in this network. The underwater acoustic communication provided by this network defines four links whose sampling rates are different. Low power consumption is one of the most important requirements. Therefore, we adopted CIC interpolators, which are known to act as filters with a low power consumption, and some CIC interpolators with an appropriate changing rate were selected depending on the link. However, these interpolators have a large passband drop and wide transition region. To solve these problems, we added a compensator and half-band filter. After verifying the algorithm by using Matlab, we designed and verified it with Verilog-HDL in a ModelSim environment.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.104-110
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• 2019

In this paper, a complementary metal oxide semiconductor(CMOS) power amplifier(PA) integrated circuit operating in the 900 MHz band for long-term evolution(LTE) communication systems is presented. The output matching network based on a transformer was implemented on a printed circuit board for low loss. Simultaneously, to achieve high efficiency of the PA, the second harmonic impedances are controlled. The CMOS PA was fabricated using a $0.18{\mu}m$ CMOS process and measured using an LTE uplink signal with a bandwidth of 10 MHz and peak to average power ratio of 7.2 dB for verification. The implemented CMOS PA module exhibits a power gain of 24.4 dB, power-added efficiency of 34.2%, and an adjacent channel leakage ratio of -30.1 dBc at an average output power level of 24.3 dBm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1165-1171
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• 2018

CNN requires a large amount of computation and memory in the process of extracting the feature of the object. Also, It is trained from the network that the user has configured, and because the structure of the network is fixed, it can not be modified during training and it is also difficult to use it in a mobile device with low computing power. To solve these problems, we apply a pruning method to the pre-trained weight file to reduce computation and memory requirements. This method consists of three steps. First, all the weights of the pre-trained network file are retrieved for each layer. Second, take an absolute value for the weight of each layer and obtain the average. After setting the average to a threshold, remove the weight below the threshold. Finally, the network file applied the pruning method is re-trained. We experimented with LeNet-5 and AlexNet, achieved 31x on LeNet-5 and 12x on AlexNet.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.183-189
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• 2018

This paper proposes distributed file placement and coverage expansion techniques for mitigating the traffic bottleneck in backhaul for small-cell networks. In order to minimize the backhaul load with limited memory space, the proposed scheme controls the coverage and file placement of base station according to file popularity distribution and memory space of base stations. In other words, since the cache hit ratio is low when there is small memory capacity or widespread file popularity distribution, the base stations expand its coverage and cache different set of files for the user located in overlapped area to exploit multiple cached file sets of base stations. Our simulation results show that the proposed scheme outperforms the conventional cache strategy in terms of network throughput when there is small memory capacity or widespread file popularity distribution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.395-400
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• 2017

The paper discussed the air-to-fuel ratio control of automotive fuel-injection systems using the cerebellar model articulation controller(CMAC) neural network. Because of the internal combustion engines and fuel-injection's dynamics is extremely nonlinear, it leads to the discontinuous of the fuel-injection and the traditional method of control based on table look up has the question of control accuracy low. The advantages about CMAC neural network are distributed storage information, parallel processing information, self-organizing and self-educated function. The unique structure of CMAC neural network and the processing method lets it have extensive application. In addition, by analyzing the output characteristics of oxygen sensor, calculating the rate of fuel-injection to maintain the air-to-fuel ratio. The CMAC may easily compensate for time delay. Experimental results proved that the way is more good than traditional for petrol control and the CMAC fuel-injection controller can keep ideal mixing ratio (A/F) for engine at any working conditions. The performance of power and economy is evidently improved.

• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.1-14
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• 2016

This paper proposes four general wireless gateway types, which are distinguished by their own processing ways to connect between a wireless sub-network of things and the mobile internet that links mobile network to internet step by step. In this paper, we also design general processing procedures of these four types using the state transition model. Gateways of each types were developed on the basis of the resulted state transition models and their performances were evaluated through several tests, analyzed, and compared each other. As the results of our evaluation, compared with the other types, the type, which combines both of a low-power Sleep-interrupt way and polling ways for receiving data or responses in all the waiting states of a gateway, shows the best performance in all of data transmission real-timeliness, data loss and energy consumption.

• Journal of IKEEE
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• v.23 no.3
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• pp.858-864
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• 2019

A clock system is proposed to eliminate data loss due to frequency difference between sensor nodes in a sensor utility network. The proposed clock system for each sensor node consists of a bust clock-data recovery (CDR) circuit, a digital phase-locked loop outputting a 32-phase clock, and a digital frequency synthesizer using a programmable open-loop fractional divider. A CMOS oscillator using an active inductor is used instead of a burst CDR circuit for the first sensor node. The proposed clock system is designed by using a 65 nm CMOS process with a 1.2 V supply voltage. When the frequency error between the sensor nodes is 1%, the proposed burst CDR has a time jitter of only 4.95 ns with a frequency multiplied by 64 for a data rate of 5 Mbps as the reference clock. Furthermore, the frequency change of the designed digital frequency synthesizer is performed within one period of the output clock in the frequency range of 100 kHz to 320 MHz.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.355-366
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• 2022

This paper proposes a microcode-based neural network accelerator controller for artificial intelligence accelerators that can be reconstructed using a programmable architecture and provide the advantages of low-power and ultra-small chip size. In order for the target accelerator to support various neural network models, the neural network model can be converted into microcode through microcode compiler and mounted on accelerator to control the operators of the accelerator such as datapath and memory access. While the proposed controller and accelerator can run various CNN models, in this paper, we tested them using the YOLOv2-Tiny CNN model. Using a system clock of 200 MHz, the Controller and accelerator achieved an inference time of 137.9 ms/image for VOC 2012 dataset to detect object, 99.5ms/image for mask detection dataset to detect wearing mask. When implementing an accelerator equipped with the proposed controller as a silicon chip, the gate count is 618,388, which corresponds to 65.5% reduction in chip area compared with an accelerator employing a CPU-based controller (RISC-V).

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.817-826
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• 2023

In this paper, a fault determination methodology based on an artificial neural network was proposed to protect the system from faults on the lines in the smart grid distribution system. In the proposed methodology, first, it was designed to determine whether there is a low impedance line fault (LIF) based on the magnitude of the current RMS value, and if it is determined to be a normal current, it was designed to determine whether a high impedance ground fault (HIF) is present using Normal/HIF classifier based on artificial neural network. Among repetitive DSP module-based algorithm verification tests, the normal/HIF classifier recognized the current waveform as normal and did not show reclosing operation for the cases of normal state current waveform simulation test where the RMS value was smaller than the minimum operating current value. On the other hand, for the cases of LIF where RMS value is greater than the minimum operating current value, the validity of the proposed methodology could be confirmed by immediately recognizing it as a fault state and showing reclosing operation according to the prescribed procedure.

• The Journal of Bigdata
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• v.8 no.2
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• pp.141-148
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• 2023

Recent advancements in the integration of satellite technology and the Internet of Things (IoT) have led to the development of a sophisticated network ecosystem, capable of generating and utilizing vast amounts of big data across various sectors. However, this integrated network faces significant security challenges, primarily due to constraints like limited latency, low power requirements, and the incorporation of diverse heterogeneous devices. Addressing these security concerns, this paper explores the construction of a satellite-IoT network through the application of Software Defined Networking (SDN). While SDN offers numerous benefits, it also inherits certain inherent security vulnerabilities. To mitigate these issues, we propose a novel approach that incorporates blockchain technology within the SDN framework. This blockchain-based SDN environment enhances security through a distributed controller system, which also facilitates the authentication of IoT terminals and nodes. Our paper details the implementation plan for this system and discusses its validation through a series of tests. Looking forward, we aim to expand our research to include the convergence of artificial intelligence with satellite-IoT devices, exploring new avenues for leveraging the potential of big data in this context.