• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.229-240
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• 2012

Advances in MEMS and CMOS technologies have motivated the development of low cost/power sensors and wireless multimedia sensor networks (WMSN). The WMSNs were created to ubiquitously harvest multimedia content. Such networks have allowed researchers and engineers to glimpse at new Machine-to-Machine (M2M) Systems, such as remote monitoring of biosignals for telemedicine networks. These systems require the acquisition of a large number of data streams that are simultaneously generated by multiple distributed devices. This paradigm of data generation and transmission is known as event-streaming. In order to be useful to the application, the collected data requires a preprocessing called data fusion, which entails the temporal alignment task of multimedia data. A practical way to perform this task is in a centralized manner, assuming that the network nodes only function as collector entities. However, by following this scheme, a considerable amount of redundant information is transmitted to the central entity. To decrease such redundancy, data fusion must be performed in a collaborative way. In this paper, we propose a collaborative data alignment approach for event-streaming. Our approach identifies temporal relationships by translating temporal dependencies based on a timeline to causal dependencies of the media involved.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.2
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• pp.7-16
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• 2019

Neural networks can be implemented in variety of ways, and specialized chips is being developed for hardware improvement. In order to apply such neural networks to wearable devices, the compactness and the low power operation are essential. In this point of view, a suitable implementation method is a digital circuit design using field programmable gate array (FPGA). To implement this system, the learning algorithm which takes up a large part in neural networks must be implemented within FPGA for better performance. In this paper, a back propagation algorithm among various learning algorithms is implemented using FPGA, and this neural network is verified by OR gate operation. In addition, it is confirmed that this neural network can be used to analyze various users' bio signal measurement results by learning algorithm.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.295-302
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• 2022

The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.

• Annual Conference of KIPS
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• 2008.11a
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• pp.806-809
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• 2008

온칩(on-chip) 캐쉬는 외부 메모리로의 접근을 감소시키는 중요한 역할을 한다. 본 연구에서는 내장형 시스템에 맞추어 설계된 2-레벨 캐쉬 메모리 구조를 제안하고자 한다. 레벨1(L1) 캐쉬의 구성으로 작은 크기, 직접사상(direct-mapped) 그리고 바로쓰기(write-through)를 채용한다. 대조적으로 레벨2(L2) 캐쉬는 일반적인 캐쉬 크기와 집합연관(Set-associativity) 그리고 나중쓰기(write-back) 정책을 채용한다. 결과적으로 L1캐쉬는 한 사이클 이내에 접근될 수 있고 L2캐쉬는 전체 캐쉬의 미스율(global miss rate)을 낮추는데 효과적이다. 두 캐쉬 계층간 바로쓰기(write-thorough) 정책에서 오는 빈번한 L2 캐쉬 접근으로 인한 에너지 소비를 줄이기 위해 본 연구에서는 One-way 접근 기법을 제안하였다. 본 연구에서 제안한 2-레벨 캐쉬 메모리 구조는 평균적으로 26%의 성능향상과 43%의 에너지 소비 그리고 77%의 에너지-지연 곱에서 이득을 보여주었다.

• Journal of Ocean Engineering and Technology
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• v.38 no.3
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• pp.103-114
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• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.12
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• pp.1155-1164
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• 2011

In this paper, we proposed the transmit beam measurement method of active phased array antenna, which is installed in Korea's first developed naval medium range radar, using near-field measurement facility. The pulse-mode high power characteristics of active phased array antenna's trasmit-beam make it difficult to measure with general near-field measurement facilities where low power continuous RF signals are used. Thus, in this paper, the measurement method of active phased array antenna's transmit beam in conjunction with the near-field measurement facility, which is suitable for the high-power transmit beam measurement, and PNA-X network analyzer(Agilent Technologies), which can support pulse-mode measurement, was proposed and measured by near-field measurement techniques. And the EIRP(Effective Isotropic Radiated Power), the transmit characteristic of active phased array antenna, was measured by the near field measurement techniques and compared to numerical estimation which was nearly equal with small difference of 0.1 dB.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.43-49
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• 2015

In this paper, we propose enhanced wireless power transfer system based on magnetic resonance for portable electronic device charging. Resonators were designed and fabricated for efficiency improvement and miniaturization through electromagnetism simulation using HFSS(High Frequency Structure Simulator). Impedance matching network is employed to minimize reflections that is caused by difference between input impedance and output impedance. Receiver IC that consist of rectifier and Low Drop Out(LDO) regulator were designed and fabricated to reduce power loss. This chip is implemented in $0.35{\mu}m$ BCD technology. A maximum overall efficiency of 73.8% is determined for the system through experimental verification.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1479-1485
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• 2012

The parking management system can increase driver's convenience with detailed parking information service in the parking lot. At the same time, parking management system consumes non-negligible electrical energy with large amount of sensors, displays and control modules. With the increase in the demand for green and sustainable building design all over the world, it becomes a meaningful issue for parking management system to reduce operating power. This paper presents the preliminary design and estimated results of a parking management system which is optimized to reduce the power consumption mainly on detectors and displays. The system design is based on pre-developed wireless parking detectors, Park Tile and Park Disk. The system has a number of parking space detectors, vehicle count detectors, information displays, guidance terminals and other control units. We have performed system architecture design, communication network design, parking information service scenario planning, battery life regulation and at last operating power estimation. The estimated operating power was 0.93KW per parking-slot, which is 20% of traditional systems. The estimated annual maintenance cost was 18% of traditional systems.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.387-396
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• 2016

Multiuser communication has been an important research area of underwater acoustic communications and networking. This paper studies the use of adaptive orthogonal frequency-division multiple access (OFDMA) in a downlink scenario, where a central node sends data to multiple distributed nodes simultaneously. In practical implementations, the instantaneous channel state information (CSI) cannot be perfectly known by the central node in time-varying underwater acoustic (UWA) channels, due to the long propagation delays resulting from the low sound speed. In this paper, we explore the CSI feedback for resource allocation. An adaptive power-bit loading algorithm is presented, which assigns subcarriers to different users and allocates power and bits to each subcarrier, aiming to minimize the bit error rate (BER) under power and throughput constraints. Simulation results show considerable performance gains due to adaptive subcarrier allocation and further improvement through power and bit loading, as compared to the non-adaptive interleave subcarrier allocation scheme. In a lake experiment, channel feedback reduction is implemented through subcarrier clustering and uniform quantization. Although the performance gains are not as large as expected, experiment results confirm that adaptive subcarrier allocation schemes based on delayed channel feedback or long term statistics outperform the interleave subcarrier allocation scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1202-1208
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• 2020

In line with the current era of the 4th Industrial Revolution, it is necessary to prepare for the future by integrating AI elements in the ship sector. In addition, it is necessary to respond to this in the field of power management for the appearance of autonomous ships. In this study, we propose a battery-linked electric propulsion system (BLEPS) algorithm using machine learning's DNN. For the experiment, we learned the pattern of ship power consumption for each operation mode based on the ship data through LabView and derived the battery status through Python to check the flexibility of the generator and battery interlocking. As a result of the experiment, the low load operation of the generator was reduced through charging and discharging of the battery, and economic efficiency and reliability were confirmed by reducing the fuel consumption of 1% of LNG.