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3rd IEEE Student Conference on Electrical Machines and Systems (SCEMS)

作者： Fan, Huijing Zhen, Zhao Liu, Jiaming Wang, Fei Mi, Zengqiang North China Elect Power Univ Dept Elect Engn Baoding Peoples R China

ISBN: (纸本)9781728156224

At present, wind energy has become one of the most promising renewable energy sources. However, large-scale wind power integration will adversely affect the power grid's safe and stable operation. So accurate wind power forecast is critically crucial for the power system to operate reliably and economically. Still, there are also many challenges obstructing it, such as the diversity of wind power fluctuation patterns. Focusing on this problem, a new forecast model based on fluctuation pattern clustering and prediction is proposed in this paper. First, wavelet decomposition is used to filter the original power time series. Then, according to swinging door algorithm and K-means clustering algorithm, the power fluctuation patterns are divided and clustered into three categories. The artificial neural network is used to predict the clustering labels of the fluctuation patterns. According to this result, the neural network is trained with different training sets classified by the tags to establish the power prediction model. The forecast accuracy of this model is better than that of direct prediction by the neural network. Finally, the actual data of five wind farms is used for experiments to verify the proposed model's feasibility and effectiveness.

关键词： ultra-short-term wind power forecasting fluctuation pattern clustering artificial neural network

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Grouped Data Analysis of H2O and H2O Mixed with NaOH on Multi Spectral High fluctuation pattern

Grouped Data Analysis of H2O and H2O Mixed with NaOH on Mult...

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International Conference on Electrical Engineering and Informatics (ICELTICs)

作者： Melinda, Melinda Tanjung, Alfiqie Tamsir, Agus Santoso Basari, Basari Gunawan, Dadang Univ Indonesia Fac Engn Dept Elect Depok 16424 Jawa Barat Indonesia Univ Syiah Kuala Fac Engn Dept Elect Banda Aceh 23111 Indonesia

ISBN: (纸本)9781538629345

Grouping of data is entailed to facilitate the process of data analysis in the great numbers, especially the data whose has fluctuated condition and multi spectral properties. This study aims to create a concept of grouping data with several new approaches in the analysis of its data grouping. The signal fluctuation used is HF (high fluctuation). There will be a way of approach to represent the pattern into one value that can represent data from the pattern. Furthermore, a representative point for each set of data is set and a unique code is made to compare between one data set in 1 material with data sets contained on different materials. The materials objected to the study are H2O and H2O mixed with NaOH. We analyze the data set by performing the mean values, standard deviation values and variance to mean ratio (VMR) values. Then, their goals are to gain, which dataset groups have the fairly stable value and also can represent the data set for subsequent processing. The results show that the groupings of data sets per 200 data sets are sufficient to represent a good percentage value. In other world, it is noticeable to say that we can obtain the characteristics of a material based on its fluctuation pattern.

关键词： Data Grouping fluctuation pattern mean value

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Self Comparison Performance Analysis of H2O on Multi Spectral fluctuation pattern

Self Comparison Performance Analysis of H2O on Multi Spectra...

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International Conference on Electrical Engineering and Informatics (ICELTICs)

作者： Tamsir, Agus Santoso Melinda, Melinda Hariadi, Michael Basari, Basari Gunawan, Dadang Univ Indonesia Fac Engn Dept Elect Depok 16424 Jawa Barat Indonesia Univ Syiah Kuala Fac Engn Dept Elect Banda Aceh 23111 Indonesia

ISBN: (纸本)9781538629345

This study aims to obtain the performance of a measured material (H2O) in a multi spectral fluctuation pattern by applying self-comparison method approach. This is done because the data obtained is quite large and also fluctuated. The fluctuation pattern used is HF (high fluctuation) and HHF (high high fluctuation). We propose several stages to exhibit the performance of this fluctuation design. It is started by forming of the data grouping stages that is useful for grouping each range of data. Then, we apply the coding of data set to provide the identity of data. Lastly, self comparison stage is able to analyze the fluctuation pattern activity in the data set obtained. Consequently, we divide this method into two parts, namely: ISC (inner self comparison) and OSC (outer self comparison). Furthermore, we analyze the data by comparing the two methods to the available sub of data sets. So it will indicate which of the sub data sets that have the expected standard values. Besides, there is 2D (dimension) graphics that represent the ISC result for each group. While, for the OSC result, there will be the comparison value to determine among the groups. Thus, we have found that the results obtained from the application of this method are the value of the amplitude percentage ratio that is approximately of 100% for grouping of 200 data sets.

关键词： fluctuation pattern self comparison statistical approach

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Comparison Analysis of Data Groupings of fluctuation patterns based on the Amplitude Representation Value (ARV) 2

Comparison Analysis of Data Groupings of Fluctuation Pattern...

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2nd International Conference on Electrical Engineering and Informatics (ICELTICs)

作者： Melinda, Melinda Tamsir, Agus Santoso Bastian, Andrew Gunawan, Dadang Univ Indonesia Engn Fac Elect Dept Depok 16424 Jawa Barat Indonesia Syiah Kuala Univ Engn Fac Elect & Comp Dept Darussalam 23111 Banda Aceh Indonesia

ISBN: (纸本)9781538661406

In this study, we developed an analysis of the data grouping application based on the amplitude representation values (ARV). We use MSCS (multi spectral capacitive sensor) to facilitate the data acquisition process. Furthermore, we compared the 3 objects study in the initial process, such as: H2O, H2O mixed with NaOH and H2O mixed HCl. Here, we apply the quite new method of Tamsir statistical transformation (TST) approach for data processing, which produces 3 fluctuation patterns for each material, namely: MF (Mean fluctuation), HF (High fluctuation) and HHF (High High fluctuation). In the next step, we use the data grouping method at a later stage with the approach of the ARV that are close together at the data processing stage. Hereafter, at the analysis stage, we try to compare the three patterns of fluctuations with several numbers of data sets based on the ARV. Whereas, the results show the value of a small ARV that is found in the 100 data set for each object. While the large ARV value is in the grouping of 200 data set on each object. Hence, we expect this study to be a potential fluctuations reference condition of the material based on the data grouping.

关键词： fluctuation pattern Data Set Amplitude Representative Value

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Music Perception Analysis on Hearing Impaired Listeners 3

Music Perception Analysis on Hearing Impaired Listeners

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IEEE International Conference on Wireless Communications, Signal Processing and Networking (IEEE WiSPNET)

作者： John, Alka Rajan, Rajeev Sajeev, Karun Rajiv Gandhi Inst Technol Dept Elect & Commun Engn Kottayam Kerala India

ISBN: (纸本)9781538636244

A hearing aid designed for speech is not optimally set for music. Hearing impaired persons suffer from many problems such as difficulty in understanding melodies, distinguishing timbre, meter, harmony and other musical features. In the proposed experiment, a music perception test with components namely, meter, timbre, texture, fluctuation pattern and melody perception in noise is used for the evaluation. The test is performed on normal hearing and hearing-impaired listeners. The test is conducted on 20 respondents. A statistical analysis is performed using ANalysis Of VAriance (ANOVA) test. The results show that hearing-impaired participants (HI) have lower perception in understanding meter, musical texture and melody in noise than normal hearing participants (NH). It is observed that the perception variation of timbre and modulation is less between both the categories as compared to other attributes.

关键词： Music perception meter melody timbre texture fluctuation pattern

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Main Diseases and Pests of Lonicerae japonica in Guangxi Province and fluctuation pattern of Their Natural Enemies

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Plant Diseases and Pests 2011年第3期2卷 31-34,38页

作者：欧善生苏桂花谢恩倍覃连红简峰黄雪兰石化玉黄奉茂陈彩贤王小欣黄艳花广西农业职业技术学院广西南宁530007 广西壮族自治区忻城县科学技术情报研究所广西忻城546200

[ Objective ] The paper was to study the main diseases and pests in Lonicerae japonica in Guangxi Province and fluctuation pattern of their natural ene- mies. [Method] The population change of main diseases and pests, and main predatory naturaI enemies of pests in different species of Lonicerae japonica in Xincheng County of Guangxi Province were systematically investigated during 2008 -2010. [ Result ] The variation cycle of population number of Semiaphis heraclei （Takahashi） belongs to single peak type, and it has large occurrence in April. The variation cycle of population number of Franklinlella sp. and Haptonchus luteo- ius （Erichson） belongs to double peak type, and they have large occurrence at April and September. The variation cycle of disease severity of Microsphaera lonicerae （ Wint. ） belongs to double peak type, and the disease index will be larger at April and September. The variation cycle of diseased shoot rate belongs to single peak type; diseased shoot rote of Lonicera dasystyla Rehd. and Loniccra hvpolzlauca Miquel is relatively high during April to June, while the index of Lonice- ra confusa （Sweet） DC. is relatively high during February to May. The variation cycle of population number of main natural enemies such as Lemnia biplagiata （ Swartz）, Coccinella septcmpunctata （ Linnaeus ） and Harmonia axyridis （Pallas） belongs to double peak type, and they have large occurrence at April and Sep- tember. [ Conclusion] The results could provide scientific basis for determination of optimum control period against major diseases and pests in Loniceraejaponica and development of integrated control programs.

关键词： Lonicerae japonica Pests and diseases Enzyme fluctuation pattern China

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Jiang, Shan Wang, Shuofeng Pei, Xin Li, Zhiheng Guo Weiwei Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China North China Univ Technol Beijing Key Lab Urban Rd Intelligent Traff Contro Beijing 100144 Peoples R China

ISBN: (纸本)9781467365963

Traffic time series analysis is important because of its use in traffic control and travel time prediction. In this paper, we discuss how to cluster traffic time series that have similar fluctuation patterns. We use simple average detrending method and only study the residual time series. Second, we use principle component analysis (PCA) on raw data and use the weight of the first d-components as the features of the time series. Third, we use k-means algorithm to cluster the traffic time series. Finally, we study the results of the clustering algorithm and discuss the origins of the clusters. In summary, the most important factors of clustering results are urban/rural area, direction and in/not in ramp entrance.

关键词： traffic time series fluctuation pattern detrending PCA k-means

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A pattern fusion model for multi-step-ahead CPU load prediction

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JOURNAL OF SYSTEMS AND SOFTWARE 2013年第5期86卷 1257-1266页

作者： Yang, Dingyu Cao, Jian Fu, Jiwen Wang, Jie Guo, Jianmei Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Stanford Univ Dept Civil & Environm Engn Stanford CA 94305 USA Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

In distributed systems, resource prediction is an important but difficult topic. In many cases, multiple prediction is needed rather than only performing prediction at a single future point in time. However, traditional approaches are not sufficient for multi-step-ahead prediction. We introduce a pattern fusion model to predict multi-step-ahead CPU loads. In this model, similar patterns are first extracted from the historical data via calculating Euclidean distance and fluctuation pattern distance between historical patterns and current sequence. For a given pattern length, multiple similar patterns of this length can often be found and each of them can produce a prediction. We also propose a pattern weight strategy to merge these prediction. Finally, a machine learning algorithm is used to combine the prediction results obtained from different length pattern sets dynamically. Empirical results on four real-world production servers show that this approach achieves higher accuracy on average than existing approaches for multi-step-ahead prediction. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Time series CPU load Multi-step-ahead prediction fluctuation pattern

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Temporal and spatial correlation in chick production of willow grouse Lagopus lagopus in Sweden and Norway

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WILDLIFE BIOLOGY 2006年第4期12卷 347-355页

作者： Hornell-Willebrand, Maria Marcstrom, Vidar Brittas, Rolf Willebrand, Tomas Int Inst Appl Syst Anal A-2361 Laxenburg Austria Dept Physiol & Dev Biol SE-75236 Uppsala Sweden Swedish Assoc Hunting & Wildlife Management SE-79155 Falun Sweden Swedish Univ Agr Sci Dept Anim Ecol SE-90183 Umea Sweden

We analysed the spatial and temporal dynamics of chick production of willow grouse Lagopus lagopus in the Swedish and Norwegian mountain region using harvest data covering 24-38 years and line-transect counts covering 6-10 years from the period 1960-2003. Juvenile and adult grouse were counted in the bag of harvested willow grouse in late August and throughout September at six sites in Sweden and Norway. In addition, numbers of juvenile and adult willow grouse were obtained from line-transect counts at 21 sites in Sweden just before the hunting season started on 25 August. The juvenile: adult ratio from the harvest data, estimated as number of juveniles per two adults, showed sin-filar long-term averages and distributions between all six sites. The results from the line-transect counts revealed an overall higher average and a greater range of production between sites. We suggest that the difference between estimates based on line-transect counts and harvest data are caused by hunter behaviour. Hunters did not sample the juvenile and adult grouse in proportion to what was present in the population, e.g., productivity estimates derived from harvest samples will underestimate the proportion of juveniles in autumn in most years. We suggest that it is the harvest process that acts to adjust the juvenile to old bird ratio in the harvest data to a similar distribution in different sites. We found little evidence of regular fluctuations/cycles in annual production of juveniles from either harvest data or line-transect data. We conclude that chick production in willow grouse appears to fluctuate more irregularly than was previously believed based on harvest data and can not be used as a potential forecasting tool in willow grouse management as suggested earlier. Spatial correlation was weak and investigating only a limited number of sites to predict the proportion of juveniles in autumn should not be considered as a management tool for large areas such as the state-owned lan

关键词： chick production fluctuation pattern spatial temporal willow grouse

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Identification of gene expression patterns using planned linear contrasts

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BMC BIOINFORMATICS 2006年第1期7卷 245-245页

作者： Li, Hao Wood, Constance L. Liu, Yushu Getchell, Thomas V. Getchell, Marilyn L. Stromberg, Arnold J. Univ Kentucky Dept Stat Lexington KY 40536 USA Coll Med Dept Physiol Lexington KY 40536 USA Coll Med Dept Anat & Neurobiol Lexington KY 40536 USA Univ Kentucky Med Ctr Sanders Brown Ctr Aging 309 Lexington KY 40536 USA

Background: In gene networks, the timing of significant changes in the expression level of each gene may be the most critical information in time course expression profiles. With the same timing of the initial change, genes which share similar patterns of expression for any number of sampling intervals from the beginning should be considered co-expressed at certain level(s) in the gene networks. In addition, multiple testing problems are complicated in experiments with multi-level treatments when thousands of genes are involved. Results: To address these issues, we first performed an ANOVA F test to identify significantly regulated genes. The Benjamini and Hochberg (BH) procedure of controlling false discovery rate (FDR) at 5% was applied to the P values of the F test. We then categorized the genes with a significant F test into 4 classes based on the timing of their initial responses by sequentially testing a complete set of orthogonal contrasts, the reverse Helmert series. For genes within each class, specific sequences of contrasts were performed to characterize their general 'fluctuation' shapes of expression along the subsequent sampling time points. To be consistent with the BH procedure, each contrast was examined using a stepwise Studentized Maximum Modulus test to control the gene based maximum family-wise error rate (MFWER) at the level of a new determined by the BH procedure. We demonstrated our method on the analysis of microarray data from murine olfactory sensory epithelia at five different time points after target ablation. Conclusion: In this manuscript, we used planned linear contrasts to analyze time-course microarray experiments. This analysis allowed us to characterize gene expression patterns based on the temporal order in the data, the timing of a gene's initial response, and the general shapes of gene expression patterns along the subsequent sampling time points. Our method is particularly suitable for analysis of microarray experiments in whi

关键词： False Discovery Rate Microarray Experiment Olfactory Epithelium Olfactory Sensory Neuron fluctuation pattern

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