Is LOHAS-related Lifestyle able to Reveal Korean-food Chinese Consumption Patterns in Jeju.

Yun Lu,Cho,Moon-Soo,Jamil,A,Chaudhry 세계문화관광학회 2011 Conference Proceedings Vol.12 No.-

Jeju Island known as the "Island of the Gods " is a popular vacation spot for not only Koreans but also for Chinese. Jeju Island off the southern coast of Korea has become the first Korean natural heritage site honoured by UNESCO through listing on its World Heritage List. It remains one of the top honeymoon destinations for Korean newlyweds. The island's mixture of volcanic rock frequent rains and temperate climate sets up the LOHAS image which is further enriched through its natural environs food offerings such as fresh fish squid octopus sea cucumber a number of restaurants whites beaches all ingredients of perfect healthy life styles delivered in n excellent setting especially for visitors coming from China looking for LOHAS-life style vacations at place away from home . Recently with added health consciousness Chinese consumer has developed a higher standard and requirement of food. Therefore there are a large number of challenges in offering truly LOHAS-related Korean food products at Jeju Island. This research takes "China LOHAS Food Tourism" as a core and extends and explores "LOHAS lifestyle" effects on consumer behaviors especially on the Korean food purchase activity in Jeju Island. The object of survey studies is Chinese Permanent Residents who are over 15 years of age and of mix gender both male and female without any prejudice. In order to carryon investigation this study firstly uses Factor Analysis method through SPSS. Two variables "LOHAS-Life Style and Korean Food Consumers' Behavior" are specially investigated as part of the study along withthrird element of LOHAS food purchasing factor." Second by using some relevant analyzing approaches this report also pays attention on discussing as how a specific factor affects "Activities of Purchasing Korean Food." For the purpose it compartmentalizes Chinese consumers into various "LOHAS Life Style" groups and then by using ANOVA and Crossable Analysis it analyzes the problem analytical. The purpose of this study is to help develop and promote LOHAS Food Tourism Industry in Jeju Island and raise the Jeju LOHAS Image among people patronizing this very island and related tourism industries.

Penalized weighted composite quantile regression in the linear regression model with heavy-tailed autocorrelated errors

Yunlu Jiang,Hong Li 한국통계학회 2014 Journal of the Korean Statistical Society Vol.43 No.4

In this paper, a penalized weighted composite quantile regression estimation procedureis proposed to estimate unknown regression parameters and autoregression coefficientsin the linear regression model with heavy-tailed autoregressive errors. Under some conditions,we show that the proposed estimator possesses the oracle properties. In addition, weintroduce an iterative algorithm to achieve the proposed optimization problem, and usea data-driven method to choose the tuning parameters. Simulation studies demonstratethat the proposed new estimation method is robust and works much better than the leastsquares based method when there are outliers in the dataset or the autoregressive errordistribution follows heavy-tailed distributions. Moreover, the proposed estimator workscomparably to the least squares based estimator when there are no outliers and the erroris normal. Finally, we apply the proposed methodology to analyze the electricity demanddataset.

Two-dimensional Harmonic Modelling for Electro-magnetic Solution in Cartesian Coordinates

Yunlu Du,Baocheng Guo,Z. Djelloul-khedda,Fei Peng,Yunkai Huang 한국자기학회 2022 Journal of Magnetics Vol.27 No.3

This paper first presents a general two-dimension (2D) harmonic analytical solution for the magnetic field of electric machines in the Cartesian coordinates. In this solution, the relative permeance is directly considered in Laplace and Poisson’s equations, and the particular solutions in Cartesian coordinates are solved. By applying the complex Fourier separation method, with the boundary and interface conditions, the magnetic field in the inhomogeneous region is solved from system equations. Numerical examples validate the presented method and the obtained results have a satisfactory agreement with the finite element analysis. The proposed model in this paper has a significant value for modelling electric machines, such as linear permanent magnet (PM) machines and axial flux PM machines.

Intrusion Detection System Combining Misuse Detection and Anomaly Detection Using Genetic Network Programming

Yunlu Gong,Shingo Mabu,Ci Chen,Yifei Wang,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

In this paper, a class association rule mining approach based on Genetic Network Programming(GNP) for detecting network intrusion combining misuse detection and anomaly detection is proposed. The proposed approach is an extension of the intrusion detection approach using GNP, so it can detectand distinguish normal, known in trusion and unknown intrusion. The simulation result shows that the detection rate is improved compared with traditional intrusion detection approach, and normal, known intrusion and unknown intrusion are distinguished with high accuracy.

A Hybrid Filtering Stage Based Quasi-type-1 PLL under Distorted Grid Conditions

Yunlu Li,Dazhi Wang,Wei Han,Zhenao Sun,Tianqing Yuan 전력전자학회 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.3

For three-phase synchronization applications, the synchronous reference frame phase-locked loop (SRF-PLL) is probably the most widely used technique due to its ease of implementation and satisfactory phase tracking performance under ideal grid conditions. However, under unbalanced and distorted grid conditions, its performance tends to worsen. To deal with this problem, a variety of filtering stages have been proposed and used in SRF-PLLs for the rejection of disturbance components at the cost of degrading the dynamic performance. In this paper, to improve dynamic performance without compromising the filtering capability, an effective hybrid filtering stage is proposed and incorporated into the inner loop of a quasi-type-1 PLL (QT1-PLL). The proposed filtering stage is a combination of a moving average filter (MAF) and a modified delay signal cancellation (DSC) operator in cascade. The time delay caused by the proposed filtering stage is smaller than that in the conventional MAF-based and DSC-based PLLs. A small-signal model of the proposed PLL is derived. The stability is analyzed and parameters design guidelines are given. The effectiveness of the proposed PLL is confirmed through experimental results.

A Hybrid Filtering Stage Based Quasi-type-1 PLL under Distorted Grid Conditions

Li, Yunlu,Wang, Dazhi,Han, Wei,Sun, Zhenao,Yuan, Tianqing The Korean Institute of Power Electronics 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.3

For three-phase synchronization applications, the synchronous reference frame phase-locked loop (SRF-PLL) is probably the most widely used technique due to its ease of implementation and satisfactory phase tracking performance under ideal grid conditions. However, under unbalanced and distorted grid conditions, its performance tends to worsen. To deal with this problem, a variety of filtering stages have been proposed and used in SRF-PLLs for the rejection of disturbance components at the cost of degrading the dynamic performance. In this paper, to improve dynamic performance without compromising the filtering capability, an effective hybrid filtering stage is proposed and incorporated into the inner loop of a quasi-type-1 PLL (QT1-PLL). The proposed filtering stage is a combination of a moving average filter (MAF) and a modified delay signal cancellation (DSC) operator in cascade. The time delay caused by the proposed filtering stage is smaller than that in the conventional MAF-based and DSC-based PLLs. A small-signal model of the proposed PLL is derived. The stability is analyzed and parameters design guidelines are given. The effectiveness of the proposed PLL is confirmed through experimental results.

Chemical Vapor Deposition Growth of Graphene Domains Across the Cu Grain Boundaries

Yang Wang,Yu Cheng,Yunlu Wang,Shuai Zhang,Chen Xu,Xuewei Zhang,Miao Wang,Yang Xia,Qunyang Li,Pei Zhao,Hongtao Wang 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2018 NANO Vol.13 No.08

Many aspects in the chemical vapor deposition (CVD) growth of graphene remain unclear such as its behavior near the catalyst grain boundaries. Here we investigate the CVD growth mechanism of graphene across the Cu grain boundaries using unidirectional aligned graphene domains, which simplifies the analysis of both graphene and Cu to a large extent. We found that for a graphene domain grown across the Cu grain boundary, the domain orientation is determined by the Cu grain where the domain nucleation center is located, and the Cu grain boundary will not change the growth behavior for this graphene domain. This growth mechanism is consistent with the Custep-attached nucleation and edge-attachment-limited growth mechanism for H-terminated graphene domains and will provide more guidance for the synthesis of high-quality graphene with less domain boundaries.

Robust variable selection for the varying index coefficient models

Zou Hang,Jiang Yunlu 한국통계학회 2023 Journal of the Korean Statistical Society Vol.52 No.4

Recently, the statistical inference of the varying index coefficient model has been widely concerned. However, to the best of our knowledge, there has no existing robust variable selection method for the varying index coefficient model in the presence of outliers in the response and covariates. To overcome this difficulty, we develop a robust variable selection method for the varying index coefficient model via the exponential squared loss (ESL) function in this article. We first approximate nonparametric functions by B-spline basis functions and then apply the minorization-maximization (MM) algorithm and the Fisher scoring algorithm to calculate the proposed estimators. Under some mild conditions, the theoretical properties of the proposed estimators are established. Furthermore, we propose a data-driven procedure to select the tuning parameters. Some numerical simulations are conducted to illustrate the finite sample performance of the proposed method. Finally, the analysis of New Zealand workforce data reveals the merit of the proposed method.

Extracting Attributes of Named Entity from Unstructured Text with Deep Belief Network

Bei Zhong,Jin Liu,Yuanda Du,Yunlu Liaozheng,Jiachen Pu 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.5

Entity attribute extraction is a challenging research topic with broad application prospects. Many researchers had proposed rule based or statistic based approaches to deal with the extraction task in a variety of application areas. Recently, deep learning had shown its capacity to model high-level abstractions in data by using multiple processing layers network with complex structures. However there has no research reported to conduct entity attribute extraction with deep learning method. In this paper, we propose a new approach to extract the entities’ attributes from unstructured text corpus that was gathered from Web. The proposed method is an unsupervised machine learning method that extracts the entity attributes utilizing deep belief network (DBN). Experiment results show that, with our method, entity attributes can be extracted accurately and manual intervention can be reduced when compared with tradition methods.

On the Application of Channel Characteristic-Based Physical Layer Authentication in Industrial Wireless Networks

( Qiuhua Wang ),( Mingyang Kang ),( Lifeng Yuan ),( Yunlu Wang ),( Gongxun Miao ),( Kim-kwang Raymond Choo ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6

Channel characteristic-based physical layer authentication is one potential identity authentication scheme in wireless communication, such as used in a fog computing environment. While existing channel characteristic-based physical layer authentication schemes may be efficient when deployed in the conventional wireless network environment, they may be less efficient and practical for the industrial wireless communication environment due to the varying requirements. We observe that this is a topic that is understudied, and therefore in this paper, we review the constructions and performance of several commonly used test statistics and analyze their performance in typical industrial wireless networks using simulation experiments. The findings from the simulations show a number of limitations in existing channel characteristic-based physical layer authentication schemes. Therefore, we believe that it is a good idea to combine machine learning and multiple test statistics for identity authentication in future industrial wireless network deployment. Four machine learning methods prove that the scheme significantly improves the authentication accuracy and solves the challenge of choosing a threshold.