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prediction algorithm for settlement amplitude of Taizhou's building foundation

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY 2018年第2期21卷 233-237页

作者： Xue, Lingya He, Caikao Taizhou Vocat & Tech Coll Taizhou 318000 Peoples R China

The traditional prediction algorithm of settlement amplitude has some problems such as poor stability and large error etc. To this end, based on least absolute deviations, the settlement amplitude of Taizhou's building foundation is predicted. The stability of Taizhou's building foundation is analyzed by the relative error of least absolute deviations. Under the principle of zero error, the ultimate settlement of the building foundation is calculated, and the prediction of the foundation settlement is realized. The experimental results show that the proposed prediction algorithm is better than the traditional prediction algorithm, and the prediction results are more accurate.

关键词： Taizhou's residence Building foundation Settlement range prediction algorithm

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prediction algorithm for amino acid types with their secondary structure in proteins (PLATON) using chemical shifts

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JOURNAL OF BIOMOLECULAR NMR 2003年第1期25卷 41-53页

作者： Labudde, D Leitner, D Krüger, M Oschkinat, H Forschungsinst Mol Pharmakol D-13125 Berlin Germany

The algorithm PLATON is able to assign sets of chemical shifts derived from a single residue to amino acid types with its secondary structure (amino acid species). A subsequent ranking procedure using optionally two different penalty functions yields predictions for possible amino acid species for the given set of chemical shifts. This was demonstrated in the case of the alpha-spectrin SH3 domain and applied to 9 further protein data sets taken from the BioMagRes database. A database consisting of reference chemical shift patterns (reference CSPs) was generated from assigned chemical shifts of proteins with known 3D-structure. This reference CSP database is used in our approach for extracting distributions of amino acid types with their most likely secondary structure elements (namely alpha-helix, beta-sheet, and coil) for single amino acids by comparison with query CSPs. Results obtained for the 10 investigated proteins indicates that the percentage of correct amino acid species in the first three positions in the ranking list, ranges from 71.4% to 93.2% for the more favorable penalty function. Where only the top result of the ranking list for these 10 proteins is considered, 36.5% to 83.1% of the amino acid species are correctly predicted. The main advantage of our approach, over other methods that rely on average chemical shift values is the ability to increase database content by incorporating newly derived CSPs, and therefore to improve PLATON's performance over time.

关键词： chemical shift pattern PLATON prediction algorithm protein NMR secondary structure statistics PLATON

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prediction algorithm of Bridge Construction Cost Based on Regression Analysis

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JOURNAL OF COASTAL RESEARCH 2020年第Sup1期103卷 979-982页

作者： Yang, Zhongxuan Qiu, Hejia Zhongyuan Univ Technol Syst & Ind Engn Technol Res Ctr Zhengzhou 450000 Peoples R China

In the prediction of bridge construction cost, the correlation between the variables that affect the construction cost is an important factor that affects the prediction results. In the previous prediction algorithm, there is the problem of low Pearson correlation coefficient. Therefore, the prediction algorithm of bridge construction cost based on regression analysis is designed. According to the structure of the bridge entity and the various costs in the project, the influencing factors of the construction cost of the bridge project are determined, and the determinable coefficients among the influencing factors are calculated by regression analysis, then the predicted value of the construction cost of the bridge project is calculated, and the average relative error method and the mean square deviation ratio method are set to ensure the reliability of the predicted value. The experimental results show that the Pearson correlation coefficient of the designed prediction algorithm based on regression analysis is higher than that of the traditional prediction algorithm, which shows that the algorithm is suitable for practical bridge engineering projects.

关键词： Regression analysis bridge engineering construction cost prediction algorithm

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prediction algorithm of the Cat Spinal Segments Lengths and Positions in Relation to the Vertebrae

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ANATOMICAL RECORD-ADVANCES IN INTEGRATIVE ANATOMY AND EVOLUTIONARY BIOLOGY 2019年第9期302卷 1628-1637页

作者： Shkorbatova, Polinay Lyakhovetskii, Vsevolod A. Merkulyeva, Natalia S. Veshchitskii, Alexandr A. Bazhenova, Elena Y. Laurens, Jean Pavlova, Natalia, V Musienko, Pavel E. Russian Acad Sci Pavlov Inst Physiol St Petersburg Russia Minist Hlth RF Russian Res Ctr Radiol & Surg Technol St Petersburg Russia St Petersburg State Univ Inst Translat Biomed St Petersburg Russia Baylor Coll Med Dept Neurosci Houston TX 77030 USA Inst Phthysiopulmonol Dept Nonpulm TB Childrens Surg & Orthoped Clin St Petersburg Russia

Detailed knowledge of the topographic organization and precise access to the spinal cord segments is crucial for the neurosurgical manipulations as well as in vivo neurophysiological investigations of the spinal networks involved in sensorimotor and visceral functions. Because of high individual variability, accurate identification of particular portion of the lumbosacral enlargement is normally possible only during postmortem dissection. Yet, it is often necessary to determine the precise location of spinal segments prior to in vivo investigation, targeting spinal cord manipulations, neurointerface implantations, and neuronal activity recordings. To solve this problem, we have developed an algorithm to predict spinal segments locations based on their relation to vertebral reference points. The lengths and relative positions of the spinal cord segments (T13-S3) and the vertebrae (VT13-VL7) were measured in 17 adult cats. On the basis of these measurements, we elaborated the estimation procedure: the cubic regression of the ratio of the segment's length to the lengths of the VL2 vertebra was used for the determination of segment's length;and the quadratic regression of the ratio of their positions in relation to the VL2 rostral part was used to determine the position of the segments. The coefficients of these regressions were calculated at the training sample (nine cats) and were then confirmed at the testing sample (eight cats). Although the quality of the prediction is decreased in the caudal direction, we found high correlations between the regressions and real data. The proposed algorithm can be further translated to other species including human. Anat Rec, 302:1628-1637, 2019. (c) 2018 American Association for Anatomy

关键词： spinal cord segments vertebrae dorsal roots cat prediction algorithm

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EOF-based linear prediction algorithm: Examples

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JOURNAL OF CLIMATE 1999年第7期12卷 2076-2092页

作者： Kim, KY North, GR Texas A&M Univ Dept Meteorol Climate Syst Res Program College Stn TX 77843 USA

Considered here are examples of statistical prediction based on the algorithm developed by Kim and North. The predictor is constructed in terms of space-time EOFs of data and prediction domains. These EOFs are essentially a different representation of the covariance matrix, which is derived From past observational data. The two sets of EOFs contain information on how to extend the data domain into prediction domain (i.e., statistical prediction) with minimum error variance. The performance of the predictor is similar to that of an optimal autoregressive model since both methods are based on the minimization of prediction error variance. Four different prediction techniques-canonical correlation analysis (CCA), maximum covariance analysis (MCA), principal component regression (PCR), and principal oscillation pattern (POP)-have been compared with the present method. A comparison shows that oscillation patterns in a dataset can faithfully be extended in terms of temporal EOFs, resulting in a slightly better performance of the present method than that of the predictors based on the maximum pattern correlations (CCA, MCA, and PCR) or the POP predictor One-dimensional applications demonstrate the usefulness of the predictor The NINO3 and the NINO3.4 sea surface temperature time series (3-month moving average) were forecasted reasonably up to the lead time of about 6 months. The prediction skill seems to be comparable to other more elaborate statistical methods. Two-dimensional prediction examples also demonstrate the utility of the new algorithm. The spatial patterns of SST anomaly field (3-month moving average) were forecasted reasonably up to about 6 months ahead. All these examples illustrate that the prediction algorithm is useful and computationally efficient for routine prediction practices.

关键词： .Regression EOFs mCA PCR predictors EOF-based essentially prediction error prediction algorithm data domain statistical prediction

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Operation data prediction algorithm of information system based on discrete second-order difference

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WIRELESS NETWORKS 2022年第6期28卷 2765-2774页

作者： Sun, Beining Yunnan Power Grid Co Ltd Minist Sci & Technol Kunming 650000 Yunnan Peoples R China

This study proposes an algorithm for predicting the running data of information systems based on discrete second-order difference clustering. The wide stationary time series model of information system operation data is established, and the association rules mining and feature distributed transmission sequence fitting of information system operation data are conducted by binary semantic information representation method. The principal component feature detection and matching of information system operation data are carried out. High-order spectral feature analysis and extraction of information system operation data is realized based on big data analysis, and the prediction algorithm is improved. The proposed method has high accuracy, good convergence and high real-time performance, which can improve the scheduling ability of information system operation data.

关键词： Discrete second-order difference Information system Running data prediction algorithm

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An epileptic seizure prediction algorithm based on second-order complexity measure

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PHYSIOLOGICAL MEASUREMENT 2005年第5期26卷 609-625页

作者： Jia, WY Kong, N Li, F Gao, XR Gao, SK Zhang, GJ Wang, YP Yang, FS Tsinghua Univ Sch Med Dept Biomed Engn Beijing 100084 Peoples R China Capital Univ Med Sci Xuanwu Hosp Dept Funct Neurosurg Beijing 100053 Peoples R China Capital Univ Med Sci Xuanwu Hosp Dept Neurol Beijing 100053 Peoples R China

The quality of life of many epilepsy patients may be improved significantly if the occurrence of epileptic seizures can be successfully forecasted and clinical intervention, such as electrical stimulation or drug delivery, can then be used to suppress their emergence, or warn the patient of the forthcoming events. In this paper, a prediction algorithm based on the second-order complexity measure was proposed to predict the impending seizures. Through the analysis of long-term intracranial EEG recordings from two frontal lobe epilepsy patients, the results indicated that the sensitivity of prediction was 77.8% (14/18) and 66.7% (4/6) and the number of false warnings was 3 and 2 for the two patients, respectively. Because only the information of past seizures was utilized to predict the current seizure and the computation load was low, the prediction algorithm could possibly be applied to clinical practice.

关键词： epileptic seizure prediction algorithm Lempel-Ziv complexity second-order complexity

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SPEED: An Inhabitant Activity prediction algorithm for Smart Homes

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2012年第4期42卷 985-990页

作者： Alam, Muhammad Raisul Reaz, M. B. I. Ali, M. A. Mohd Univ Kebangsaan Malaysia Inst Microengn & Nanoelect Bangi 43600 Malaysia Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Fac Engn & Built Environm Bangi 43600 Malaysia

This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the ON-OFF states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3% prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5.

关键词： Activity prediction Markov model prediction algorithm prediction by partial matching (PPM) smart home

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EOF-based linear prediction algorithm: Theory

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JOURNAL OF CLIMATE 1998年第11期11卷 3046-3056页

作者： Kim, KY North, GR Texas A&M Univ Climate Syst Res Program College Stn TX 77843 USA

This study considers the theory of a general three-dimensional (space and time) statistical prediction/extrapolation algorithm. The predictor is in the form of a linear data filter. The prediction kernel is based on the minimization of prediction error and its construction requires the covariance statistics of a predict and field. The algorithm is formulated in terms of the spatiotemporal EOFs of the predict and field. This EOF representation facilitates the selection of useful physical modes for prediction. Limited tests have been conducted concerning the sensitivity of the prediction algorithm with respect to its construction parameters and the record length of available data for constructing a covariance matrix. Tests reveal that the performance of the predictor is fairly insensitive to a wide range of the construction parameters. The accuracy of the filter, however. depends strongly on the accuracy of the covariance matrix, which critically depends on the length of available data. This inaccuracy implies suboptimal performance of the prediction filter. Simple examples demonstrate the utility of the new algorithm.

关键词： .EOFs implies covariance matrix predictand constructing insensitive available data prediction error record length prediction algorithm prediction kernel construction parameters prediction filter data domain

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Simulation of English part-of-speech recognition based on machine learning prediction algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期40卷 2409-2419页

作者： Pan Lanlan Hu Liangyu Li Zhengya Jiangsu Vocat Coll Med Sch Publ Fdn Yancheng Jiangsu Peoples R China

English part-of-speech intelligent recognition is the scientific and technological basis for the development of intelligent speech systems. The difficulty in the current English speech recognition system lies in the recognition of English parts of speech. In order to improve the effect of English part-of-speech recognition, this study builds the language rules and morphological models of English morphological forms based on machine learning algorithms. Moreover, this study proposes a stemming extraction algorithm and a syllable division algorithm based on English characteristic rules. By studying basic phrases in English, this study analyzes the compositional structure of phrases, and determines the basic phrase structure and composition rules of English such as noun, verb, and adjective. In addition, this research studies the basic English phrase recognition algorithm based on the rule method and the analysis of basic phrase ambiguity resolution. Finally, this study designs a control experiment to analyze the performance of the algorithm proposed in this paper model and confirm the classification algorithm. The research results show that the algorithm proposed in this paper has a certain practical effect.

关键词： Machine learning prediction algorithm English part-of-speech recognition algorithm improvement

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