• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.1-14
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• 1998

In this paper, Antenna, LNA, Mixer, VCO, and Modulation/Demodulation in Baseband processor which are the RF main components in Wireless LAN system for ultra high-speed communications network are studied. Antenna bandwidth and selective fading due to multipath can be major obstacles in high speed digital communications. To solve this problem, wide band MSA which has loop-structure magnetic antenna characteristics is designed. Distributed mixer using dual-gate GaAs MESFET can achieve over 10dB LO/RF isolation without hybrid, and minimize circuit size. As linear mixing signal is produced, distortions can be decreased at baseband signals. Conversion gain is achieved by mixing and amplification simultaneously. Mixer is designed to have wide band characteristics using distributed amplifier. In VCO design, Oscillator design method by large signal analysis is used to produce stable signal. Modulation/Demodulation system in baseband processor, DS/SS technique which is robust against noise and interference is used to eliminate the effect of multipath propagation. DQPSK modulation technique with M-sequences for wideband PN spreading signals is adopted because of BER characteristic and high speed digital signal transmission.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.649-655
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• 2020

This study determined effective educational strategies by investigating and analyzing the related educational demands for SMEs (small and medium-sized enterprises) in the 4th Industrial Revolution based area of smart farms. In order to derive the approprate educational strategies, Importance-Performance Analysis (IPA) and Borich's Needs Assessment Model were conducted based on the smart farm technological field. As a result, the education demand survey showed high demand for production systems and intelligent farm machinery. In detail, Borich's analysis showed the need for pest prevention and diagnosis technology (8.03), network and analysis SW linkage technology (7.83), and intelligent farm worker-agricultural power system-electric energy hybrid technology (7.43). In contrast, smart plant factories (4.09), lighting technology for growth control (4.46) and structure construction technology (4.62) showed low demands. Based on this, the IPA portfolio shows that the network and analysis SW linkage technology and the CAN-based complex center are urgently needed. However, the technology that has already been developed, such as smart factory platform development, growth control lighting technology and structure construction technology, was oversized. Based on these results, it is possible to strategically suggest the customized training programs for industrial sectors of SMEs that reflect the needs for efficiently operating smart farms. This study also provides effective ways to operate the relevant training programs.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.32-38
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• 2012

Each sensor node can know its location in several ways, if the node process the information based on its geographical position in sensor networks. In the localization scheme using GPS, there could be nodes that don't know their locations because the scheme requires line of sight to radio wave. Moreover, this scheme is high costly and consumes a lot of power. The localization scheme without GPS uses a sophisticated mathematical algorithm estimating location of sensor nodes that may be inaccurate. AHLoS(Ad Hoc Localization System) is a hybrid scheme using both GPS and location estimation algorithm. In AHLoS, the GPS node, which can receive its location from GPS, broadcasts its location to adjacent normal nodes which are not GPS devices. Normal nodes can estimate their location by using iterative triangulation algorithms if they receive at least three beacons which contain the position informations of neighbor nodes. But, there are some cases that a normal node receives less than two beacons by geographical conditions, network density, movements of nodes in sensor networks. We propose an indirect localization scheme for low-density sensor nodes which are difficult to receive directly at least three beacons from GPS nodes in wireless network.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.7-13
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• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

• Journal of IKEEE
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• v.21 no.4
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• pp.388-396
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• 2017

Since there are many variables such as various poses, illuminations and occlusions in a face detection problem, a high performance detection system is required. Although CNN is excellent in image classification, CNN operatioin requires high-performance hardware resources. But low cost low power environments are essential for small and mobile systems. So in this paper, the CPU-FPGA integrated system is designed based on 3-stage cascade CNN architecture using small size FPGA. Adaptive Region of Interest (ROI) is applied to reduce the number of CNN operations using face information of the previous frame. We use a Field Programmable Gate Array(FPGA) to accelerate the CNN computations. The accelerator reads multiple featuremap at once on the FPGA and performs a Multiply-Accumulate (MAC) operation in parallel for convolution operation. The system is implemented on Altera Cyclone V FPGA in which ARM Cortex A-9 and on-chip SRAM are embedded. The system runs at 30FPS with HD resolution input images. The CPU-FPGA integrated system showed 8.5 times of the power efficiency compared to systems using CPU only.

• Informatization Policy
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• v.18 no.3
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• pp.88-107
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• 2011

This paper proposes a prepaid system promotion policy for the 3G(WCDMA) MVNO with regard to the newly included paragraphs 3, 4 and 5 of Article 32 (User Protection) and Article 38 (Provision of Wholesale Telecommunications Services) of the Telecommunication Business Act, which was revised on March 22, 2010. As of June 2011, there are only a few prepaid system subscribers to the mobile communications service due to various limitations, including prepay, interconnection, carrier selection, the MVNO policy, and number portability. However, overseas communications service regulatory agencies and service providers are increasingly presenting policies and strategies for mobile prepaid plans, in order to accommodate the various customer demands that are increasing the use of smart phones and data. This paper advances various proposals concerning promotion of the prepaid system by the 3G MVNO under the current Telecommunication Business Act, including separation of the prepaid data system and the mobile network; introduction of a monthly fixed-rate hybrid prepaid system, a top-up system and USIM system; introduction of mobile network carrier selection; differentiated retail discounts between prepaid and post-paid prices; revision of the retail discount policy for wholesale provision; increase in the number of mandatory service providers; and in-depth consideration of the introduction of a number portability policy for the prepaid and post-paid systems.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.319-343
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• 2009

This paper considers some kinds of socio-spatial constraints and strategies for overcoming them which immigrant workers in Korea have experienced in their work-place and life-space, with an analysis of questionnaire data and of direct interview materials on them. Though they appear somewhat satisfactory or positive with their work-place, this can be seen as a hypocritical or false attitude rather than a real one: they are forced to work with long hours (more than 70 hours per week) and rigid controls in the other' territory. Their daily life-spaces also are severe: they can be hardly embedded in an existing community with a sense of place due to serious institutional and interaction constraints, even though they seem to have a basic mobility to survive in life-spaces. In order to escape or alleviate such local constraints, they try to constitute multi-scalar (local, trans-regional, and transnational) networks, and to find informations and means to resolve or cope with them. However, this kind of endeavors of immigrant workers to make a trans-national network and social space has a limitation for them to be free entirely from constraints, which might be strengthened with a lack of geographical knowledge of them. Then immigrant workers in Korea live ineluctably with not only hybrid national identity but also with disturbed local identity in an aliened workplace and life-spaces.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.3-11
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• 2015

An advanced receiver which can manage inter-cell interference is required to cope with the explosively increasing mobile data traffic. 3rd Generation Partnership Project (3GPP) has discussed network assisted interference cancellation and suppression (NAICS) to improve signal-to-noise-plus-interference ratio (SINR) and receiver performance by suppression or cancellation of interference signal from inter-cells. In this paper, we propose the advanced receiver based on soft decision to reduce the interference from neighbor cell in LTE-Advanced downlink system. The proposed receiver can suppress and cancel the interference by calculating the unbiased estimation value of interference signal using minimum mean square error (MMSE) or interference rejection combing (IRC) receiver. The interference signal is updated using soft information expressed by log-likelihood ratio (LLR). We perform the system level simulation based on 20MHz bandwidth of 3GPP LTE-Advanced downlink system. Simulation results show that the proposed receiver can improve SINR, throughput, and spectral efficiency of conventional system.

• Phonetics and Speech Sciences
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• v.11 no.1
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• pp.41-49
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• 2019

An end-to-end speech recognition model consisting of a single integrated neural network model was recently proposed. The end-to-end model does not need several training steps, and its structure is easy to understand. However, it is difficult to understand how the model recognizes speech internally. In this paper, we visualized and analyzed the attention-based end-to-end model to elucidate its internal mechanisms. We compared the acoustic model of the BLSTM-HMM hybrid model with the encoder of the end-to-end model, and visualized them using t-SNE to examine the difference between neural network layers. As a result, we were able to delineate the difference between the acoustic model and the end-to-end model encoder. Additionally, we analyzed the decoder of the end-to-end model from a language model perspective. Finally, we found that improving end-to-end model decoder is necessary to yield higher performance.