• 유체기계공업학회:학술대회논문집
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• 2001.11a
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• pp.83-90
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• 2001

Experimental and numerical investigations of the off·design performance of a simple channel-wedge diffuser in a small centrifugal compressor are presented. Surge and choke conditions as well as design point are considered using somewhat limited range of experimental data and also supplementary 3D CFD results. Some critical meanline design parameters' behavior is investigated numerically, to render the basis for improved modelings in the meanline performance prediction.

• ETRI Journal
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• v.20 no.1
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• pp.16-27
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• 1998

In this paper, we consider the acquisition performance of an IS-95 reverse link access channel slot as a function of system design parameters such as postdetection integration length and the number of access channel message block repetitons. The uncertainty region of the reverse link spreading codes compared to that of forward link is very small, since the uncertainty region of the reverse link is determined by a cell radius. Thus, the parallel acquisiton technique in the reverse link is more efficient than a serial acquisition technique in terms of implementation and of acquisition time. The parallel acquisition is achieved by a bank of N parallel I/Q noncoherent correlator are analyzed for band-limited noise and the Rayleigh fast fading channel. The detection probability is derived for multiple correct code-phase offsets and multipath fading. The probability of no message error is derived when rake combining, access channel message block combining, and Viterbi decoding are applied. Numerical results provide the acquisition performance for system design parameters such as postdetection integration length and number of access channel message block repetitions in case of a random access on a mobile station.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.58-67
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• 2020

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.108-112
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• 2021

For smart mobile devices, the wireless communication module is one of the hardware modules that consume the most energy. If we can build a multi-channel multi-interface environment using heterogeneous communication modules and operate them dynamically, data transmission performance can be highly improved by increasing the parallelism. Also, because these heterogeneous modules have different data rates, transmission ranges, and power consumption, we can save energy by exploiting a power efficient and low speed wireless interface module to transmit/receive sporadic small data. In this paper, we propose a power efficient data transmission method using heterogeneous communication networks. We also compared the performance of our proposed scheme to a conventional scheme, and proved that our proposed scheme can save energy while guaranteeing reasonable data delivery time.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.9
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• pp.1219-1226
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• 2001

Pressure drops were measured for the flow boiling process within horizontal rectangular channels. The gap between the upper and the lower plates of each channel ranges from 0.4 to 2mm while the channel width being fixed to 20mm. Refrigerant 113 was used as the test fluid. The mass flux ranges from 50 to 200kg/㎡s and the channel walls were uniformly heated up to 15kW/㎡. The quality range covers from 0.15 to 0.75. The present experimental conditions coincide with the operating conditions of compact heat exchangers in which the liquid and gas flows are laminar and turbulent. The measured results were well represented by the two-phase frictional multiplier of Lee (2001) which has been developed for air-water two-phase flows within the deviation of $\pm$20%.

• Journal of Communications and Networks
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• v.5 no.1
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• pp.43-54
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• 2003

Due to insufficient available bandwidth resources and the continuously growing demand for cellular communication services, the channel assignment problem has become increasingly important. To trace the optimal assignment, several heuristic strategies have been proposed. So far, most of them focus on the small-scale systems containing no more than 25 cells and they use an anachronistic cost model, which does not satisfy the requirements ity. Solving the small-scale channel assignment problems could not be applied into existing large scale cellular networks' practice. This article proposes a decomposition approach to solve the fixed channel assignment problem (FCAP) for large-scale cellular networks through partitioning the whole cellular network into several smaller sub-networks and then designing a sequential branch-and-bound algorithm that is made to solve the FCAP for them sequentially. The key issue of partition is to minimize the dependences of the sub-networks so that the proposed heuristics for solving smaller problems will suffer fewer constraints in searching for better assignments. The proposed algorithms perform well based on experimental results and they were applied to the Taiwan Cellular Cooperation (TCC) in ChungLi city to find better assignments for its network.

• Journal of Distribution Science
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• v.20 no.8
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• pp.33-46
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• 2022

Purpose: This study aims to understand the impact of e-commerce's advantage among SME's digital channel in Malaysia. To achieve the objective, this study used Technology, Organization, Environment (TOE) framework, Diffusion of Innovation (DOI) theory and Resource-Based View (RBV) theory. Research design, data and methodology: This study is quantitative method involve 195 SMEs online business in Malaysia. The integrated framework highlights the conceptual insight in multidimensional technology context (i.e. relative advantage, compatibility, and complexity); organizational context (i.e. top management support, innovation capability, branding capability, market-sensing capability, and human and organization capability); and environmental context (i.e. government support and supplier pressure). Results: The findings revealed that complexity, branding capability, human and organization capability, government support, and supplier pressure have significant positive relationship with e-commerce advantage. Meanwhile, relative advantage, compatibility, innovation capability, and market-sensing capability have significant negative relationship with e-commerce advantage. However, top management support has found no significant relationship with e-commerce advantage. Conclusions: The extended theoretical framework dimension improves existing knowledge on e-commerce advantage as well as the prospect of SMEs digital channel.

• Journal of Communications and Networks
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• v.6 no.1
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• pp.19-25
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• 2004

In this paper, we present a trellis-based blind channel estimation and equalization technique coupling two kinds of adaptive Viterbi algorithms. First, the initial blind channel estimation is accomplished by incorporating the list parallel Viterbi algorithm with the least mean square (LMS) updating approach. In this operation, multiple trellis mappings are preserved simultaneously and ranked in terms of path metrics. Equivalently, multiple channel estimates are maintained and updated once a single symbol is received. Second, the best channel estimate from the above operation will be adopted to set up the whole trellis. The conventional adaptive Viterbi algorithm is then applied to detect the signal and further update the channel estimate alternately. A small delay is introduced for the symbol detection and the decision feedback to smooth the noise impact. An automatic switch between the above two operations is also proposed by exploiting the evolution of path metrics and the linear constraint inherent in the trellis mapping. Simulation has shown an overall excellent performance of the proposed scheme in terms of mean square error (MSE) for channel estimation, robustness to the initial channel guess, computational complexity, and channel equalization.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.42.3-42.3
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• 2019

We translate Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA) ultraviolet (UV) multi-channel images into another UV single-channel image using a deep learning algorithm based on conditional generative adversarial networks (cGANs). The base input channel, which has the highest correlation coefficient (CC) between UV channels of AIA, is 193 Å. To complement this channel, we choose two channels, 1600 and 304 Å, which represent upper photosphere and chromosphere, respectively. Input channels for three models are single (193 Å), dual (193+1600 Å), and triple (193+1600+304 Å), respectively. Quantitative comparisons are made for test data sets. Main results from this study are as follows. First, the single model successfully produce other coronal channel images but less successful for chromospheric channel (304 Å) and much less successful for two photospheric channels (1600 and 1700 Å). Second, the dual model shows a noticeable improvement of the CC between the model outputs and Ground truths for 1700 Å. Third, the triple model can generate all other channel images with relatively high CCs larger than 0.89. Our results show a possibility that if three channels from photosphere, chromosphere, and corona are selected, other multi-channel images could be generated by deep learning. We expect that this investigation will be a complementary tool to choose a few UV channels for future solar small and/or deep space missions.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.253-257
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• 2007

Channel-aquifer interaction is one of the key hydrological processes that determine water flows in the stream/river channel. Field measurements of channel-aquifer interaction, however, is very difficult and costly, particularly when one intends to understand its variations across a catchment for a long period. Hydrological simulations using a catchment model are a relatively easier and cheaper alternative provided the model structure is appropriate for describing channel-aquifer interaction. In this study, a catchment model called CAMEL (Chemicals from Agricultural Management and Erosion Losses) is used for estimating channel-aquifer interaction over time and space. CAMEL is a distributed catchment model to simulate transformation and transport processes of sediment and pollutants as well as water flows at the catchment scale. In the model, a catchment is represented using a network of square columns each of which is comprised of various storages of water. CAMEL explicitly simulates both surface and subsurface processes including channel-aquifer interaction. This paper presents an application study results of CAMEL for the Tarland Burn Catchment, a small (catchment area $52\;km^2$) rural catchment in Scotland, UK, demonstrating some of the channel-aquifer interaction dynamics across the catchment during a 2-year period.