• 전자공학회논문지
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• 제51권7호
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• pp.3-8
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• 2014

본 논문에서는 다중 안테나 밀리미터파 시스템에서 하이브리드 빔포밍의 동작 원리를 이해하고, 피드백 에너지를 줄이기 위한 채널 추정 방식 및 하이브리드 빔포밍 방식에 대한 연구를 진행하였다. 밀리미터파 채널에서 각 경로의 에너지는 경로 이득에 의하여 결정된다는 점을 이용하여, 이를 기준으로 제한된 경로의 수를 기반으로 한 채널 피드백 방식을 제안하였다. 또한, 제한된 피드백 시스템에 적합한 하이브리드 빔포밍 방식에 대한 연구도 진행하였다. 모의실험 결과를 통하여 제안한 기법이 기존의 빔포밍 방식에 비하여 데이터 전송율 측면에서 비슷한 성능을 보이면서도 피드백 에너지를 크게 절감시키는 효과를 확인하였다.

• 웰빙융합연구
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• 제7권2호
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• pp.31-37
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• 2024

Purpose: Pop-up stores have emerged in the retail industry in recent years, offering consumers a new shopping experience for a limited time and location, and are used for a variety of purposes, including driving purchase behavior. In particular, they have become an important marketing tool among Gen MZ consumers who are quick to acquire information and sensitive to trends. Therefore, this study aims to analyze the impact of pop-up store characteristics on the purchasing behavior of MZ consumers. Research design, data and methodology: Based on a qualitative research approach, the study analyzed successful pop-up stores in Korea to closely examine how the limited operating period and experience-oriented marketing strategy of pop-up stores affect the perceptual attitudes and purchase decision process of Generation MZ. Results: The results of the case study revealed that selling limited edition items, maximizing customer experience factors, and differentiated concepts are the main factors that positively influence the purchase behavior of Gen MZ consumers. These factors contribute to the enhanced purchasing behavior of Gen MZ, making pop-up stores an effective marketing strategy. Conclusions: Pop-up stores are more than just a sales space, but an important communication channel that can strengthen the emotional connection with Gen MZ and effectively communicate brand values. This study provides useful insights for brands and companies to develop marketing strategies for MZ.

• Journal of Microbiology and Biotechnology
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• 제16권7호
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• pp.1139-1143
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• 2006

Various techniques and strategies have been developed for the identification of intracellular metabolic conditions, and among them, isotope balance-based flux analysis with gas chromatography/mass spectrometry (GC/ MS) has recently become popular. Even though isotope balance-based flux analysis allows a more accurate estimation of intracellular fluxes, its application has been restricted to relatively small metabolic systems because of the limited number of measurable metabolites. In this paper, a strategy for incorporating isotope balance-based flux data obtained for a small network into metabolic flux analysis was examined as a feasible alternative allowing more accurate quantification of intracellular flux distribution in a large metabolic system. To impose GC/MS based data into a large metabolic network and obtain optimum flux distribution profile, data reconciliation procedure was applied. As a result, metabolic flux values of 308 intracellular reactions could be estimated from 29 GC/ MS based fluxes with higher accuracy.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 A
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• pp.122-124
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• 2000

This study is focused on developing a program which can edit different formats of power system data used by system operators PSS/E program, which has been widely used as a tool for power system analysis. Provides only a limited function of editing PSS/E input data. Considering that more and more power system analysers will be developed and applied for power system planning and operation in the near future, unified handling of multi-types of power system data format, such as conversion of one input data format into another, is indispensible. In this paper, a new power system data editor, functionally augmented from PSS/E editor, is introduced. The new editing program was developed in GUI environment for users to conveniently edit input data for EUROSTAG program without running several editors. Considerable savings in time and manpower are expected.

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2003년도 Proceedings of the international symposium on the fusion technology
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• pp.459-463
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• 2003

Waveform inversion requires extracting a reliable low frequency content of seismic data for estimating of the low wave number velocity model. The low frequency content of the seismic data is usually discarded or neglected because of the band-limited response of the source and the receivers. In this study, however small the spectral of the low frequency seismic data is, we assume that it is possible to extract a reliable phase information of the low frequency from the seismic data and use it in waveform inversion. To this end, we exploit the frequency domain finite element modeling and source-receiver reciprocity to calculate the $Frech\`{e}t$ derivative of the phase of the seismic data with respect to the earth model parameter such as velocity, and then apply a damped least squares method to invert the phase of the seismic data. Through numerical example, we will attempt to demonstrate the feasibility of our method in estimating the correct velocity model for prestack depth migration.

• Biotechnology and Bioprocess Engineering:BBE
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• 제11권1호
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• pp.67-71
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• 2006

This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

• 한국IT서비스학회지
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• 제10권1호
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• pp.135-149
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• 2011

In this paper, we propose the high-risk group detection model for patients with high blood pressure using case-based reasoning. The proposed model can be applied for public health maintenance organizations to effectively manage knowledge related to high blood pressure and efficiently allocate limited health care resources. Especially, the focus is on the development of the model that can handle constraints such as managing large volume of data, enabling the automatic learning to adapt to external environmental changes and operating the system on a real-time basis. Using real data collected from local public health centers, the optimal high-risk group detection model was derived incorporating optimal parameter sets. The results of the performance test for the model using test data show that the prediction accuracy of the proposed model is two times better than the natural risk of high blood pressure.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.25-33
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• 2021

Energy-harvesting wireless sensor networks(EH-WSNs) can collect energy from the environment and overcome the technical limitations of existing power. Since the transmission distance in a wireless sensor network is limited, the data are delivered to the destination node through multi-hop routing. In EH-WSNs, the routing protocol should consider the power situations of nodes, which is determined by the remaining power and energy-harvesting rate. In addition, in applications such as environmental monitoring, when there are urgent data, the routing protocol should be able to transmit it stably and quickly. This paper proposes an adaptive routing protocol that satisfies different requirements of normal and urgent data. To extend network lifetime, the proposed routing protocol reduces power imbalance for normal data and also minimizes transmission latency by controlling the transmission power for urgent data. Simulation results show that the proposed adaptive routing can improve network lifetime by mitigating the power imbalance and greatly reduce the transmission delay of urgent data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.938-957
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• 2023

The limited computing power of sensor nodes in wireless sensor networks (WSNs) and data tampering during wireless transmission are two important issues. In this paper, we propose a scheme for independent individual authentication of WSNs data based on digital watermarking technology. Digital watermarking suits well for WSNs, owing to its lower computational cost. The proposed scheme uses independent individual to generate a digital watermark and embeds the watermark in current data item. Moreover, a sink node extracts the watermark in single data and compares it with the generated watermark, thereby achieving integrity verification of data. Inherently, individual validation differs from the grouping-level validation, and avoids the lack of grouping robustness. The improved performance of individual integrity verification based on proposed scheme is validated through experimental analysis. Lastly, compared to other state-of-the-art schemes, our proposed scheme significantly reduces the false negative rate by an average of 5%, the false positive rate by an average of 80% of data verification, and increases the correct verification rate by 50% on average.