Accuracy Estimation of Digital Map Series Data Sets Published by the Geographical Survey Institute<sup>*</sup>

This paper documents the set-up and validation of nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine. The considered gas turbine is a General Electric PG 9351FA located in Italy. The data used for model training are time series data sets of several different maneuvers taken experimentally during the start-up procedure and refer to cold, warm and hot start-up. The trained NARX models are used to predict other experimental data sets and comparisons are made among the outputs of the models and the corresponding measured data. Therefore, this paper addresses the challenge of setting up robust and reliable NARX models, by means of a sound selection of training data sets and a sensitivity analysis on the number of neurons. Moreover, a new performance function for the training process is defined to weigh more the most rapid transients. The final aim of this paper is the set-up of a powerful, easy-to-build and very accurate simulation tool which can be used for both control logic tuning and gas turbine diagnostics, characterized by good generalization capability.

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Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

10.1101/170027 ◽

2017 ◽

Author(s):

Anthony Szedlak ◽

Spencer Sims ◽

Nicholas Smith ◽

Giovanni Paternostro ◽

Carlo Piermarocchi

Keyword(s):

Neural Network ◽

Gene Expression ◽

Cell Cycle ◽

Time Series ◽

Time Series Data ◽

Series Data ◽

Data Sets ◽

Expression Data ◽

Time Series Gene Expression ◽

Human Cervical Cancer

AbstractModern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.Author SummaryCell cycle – the process in which a parent cell replicates its DNA and divides into two daughter cells – is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

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Longitudinal Measurement Invariance of Beck Depression Inventory–II in Early Adolescents

Assessment ◽

10.1177/1073191115608941 ◽

2016 ◽

Vol 24 (3) ◽

pp. 337-345 ◽

Cited By ~ 11

Author(s):

Pei-Chen Wu

Keyword(s):

High School Students ◽

Measurement Invariance ◽

Beck Depression Inventory ◽

Factor Model ◽

Early Adolescents ◽

Series Data ◽

Data Sets ◽

Longitudinal Measurement Invariance ◽

Longitudinal Measurement ◽

Three Factor

This study explored the longitudinal measurement invariance in the Beck Depression Inventory–II (BDI-II) in early adolescents (junior high school students). The participants were 730 early adolescents (330 boys and 400 girls), who were followed up over 3 years (in six waves). To reduce the size of longitudinal model and verify the stability of the findings, the Fall and Spring series data sets were analyzed separately. Each series includes three waves of data with about 1-year apart. It was found that the three-factor model (Negative Attitude, Performance Difficulty, and Somatic Elements) best fitted the data. Results of both data sets provided support for the longitudinal measurement invariance (threshold invariance) of the three-factor model, suggesting that the BDI-II measured the same construct over 3 years. The study also examined the category function of the BDI-II on the basis of the pattern of threshold estimates. Finally, the implications of the findings on the continuing use of the BDI-II are discussed.

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An Efficient Method for Forecasting Using Fuzzy Time Series

Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-0914-1.ch013 ◽

2017 ◽

pp. 287-304 ◽

Cited By ~ 3

Author(s):

Pritpal Singh

Keyword(s):

Time Series ◽

Time Series Data ◽

Weather Forecasting ◽

Small Error ◽

Fuzzy Time Series ◽

Series Data ◽

Data Sets ◽

Proposed Model ◽

Temperature Forecasting ◽

The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

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Growth Limits: A Conceptual Analysis for Sustainable Development in Nigeria

SAGE Open ◽

10.1177/2158244020918277 ◽

2020 ◽

Vol 10 (2) ◽

pp. 215824402091827

Author(s):

Oluwabunmi O. Adejumo

Keyword(s):

Time Series Data ◽

Optimal Growth ◽

Series Data ◽

Data Sets ◽

African Countries ◽

Limits To Growth ◽

Development Indicators ◽

Long Run ◽

Sub Saharan

In the school of development thought, growth has been identified as a viable alternative to the challenge of poverty and economic backwardness. However, the ecologists have continuously challenged the growth position in relation to environmental degradation and depletion. It is against this background; this study examined the limits to growth in Nigeria beyond which there will be inimical consequences for the environment. The study employed time series data that spanned between 1970 and 2014. These data sets were sourced from the World Development Indicators. Based on the assimilation model, threshold estimates were used to identify optimal growth regions, whereas regression estimates were used to measure growth effects. It was discovered that below the identified growth limit, there are currently significant negative impacts on the quality of the environment in Nigeria via economic growth. This study is a single-country case, that is, Nigeria; hence, the study can be expanded to include other sub-Saharan African countries. The study adds to knowledge by establishing the prospects for sustainability in the quality of the environment in the long run; therefore, policies designed in this areas have higher likelihood of attaining sustainability.

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Practical Constraint K-Segment Principal Curve Algorithms for Generating Railway GPS Digital Map

Mathematical Problems in Engineering ◽

10.1155/2013/258694 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11

Author(s):

Dewang Chen ◽

Long Chen

Keyword(s):

Data Storage ◽

Expert Knowledge ◽

Low Cost ◽

Large Data ◽

Gps Data ◽

Small Data ◽

Data Sets ◽

Digital Map ◽

Digital Maps ◽

Practical Algorithms

In order to obtain a decent trade-off between the low-cost, low-accuracy Global Positioning System (GPS) receivers and the requirements of high-precision digital maps for modern railways, using the concept of constraint K-segment principal curves (CKPCS) and the expert knowledge on railways, we propose three practical CKPCS generation algorithms with reduced computational complexity, and thereafter more suitable for engineering applications. The three algorithms are named ALLopt, MPMopt, and DCopt, in which ALLopt exploits global optimization and MPMopt and DCopt apply local optimization with different initial solutions. We compare the three practical algorithms according to their performance on average projection error, stability, and the fitness for simple and complex simulated trajectories with noise data. It is found that ALLopt only works well for simple curves and small data sets. The other two algorithms can work better for complex curves and large data sets. Moreover, MPMopt runs faster than DCopt, but DCopt can work better for some curves with cross points. The three algorithms are also applied in generating GPS digital maps for two railway GPS data sets measured in Qinghai-Tibet Railway (QTR). Similar results like the ones in synthetic data are obtained. Because the trajectory of a railway is relatively simple and straight, we conclude that MPMopt works best according to the comprehensive considerations on the speed of computation and the quality of generated CKPCS. MPMopt can be used to obtain some key points to represent a large amount of GPS data. Hence, it can greatly reduce the data storage requirements and increase the positioning speed for real-time digital map applications.

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AstroCatR: a mechanism and tool for efficient time series reconstruction of large-scale astronomical catalogues

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa1413 ◽

2020 ◽

Vol 496 (1) ◽

pp. 629-637

Author(s):

Ce Yu ◽

Kun Li ◽

Shanjiang Tang ◽

Chao Sun ◽

Bin Ma ◽

...

Keyword(s):

Time Series ◽

High Performance ◽

Large Scale ◽

Extrasolar Planets ◽

Time Series Data ◽

Series Data ◽

Data Sets ◽

Observation Data ◽

Data Volume ◽

And Performance

ABSTRACT Time series data of celestial objects are commonly used to study valuable and unexpected objects such as extrasolar planets and supernova in time domain astronomy. Due to the rapid growth of data volume, traditional manual methods are becoming extremely hard and infeasible for continuously analysing accumulated observation data. To meet such demands, we designed and implemented a special tool named AstroCatR that can efficiently and flexibly reconstruct time series data from large-scale astronomical catalogues. AstroCatR can load original catalogue data from Flexible Image Transport System (FITS) files or data bases, match each item to determine which object it belongs to, and finally produce time series data sets. To support the high-performance parallel processing of large-scale data sets, AstroCatR uses the extract-transform-load (ETL) pre-processing module to create sky zone files and balance the workload. The matching module uses the overlapped indexing method and an in-memory reference table to improve accuracy and performance. The output of AstroCatR can be stored in CSV files or be transformed other into formats as needed. Simultaneously, the module-based software architecture ensures the flexibility and scalability of AstroCatR. We evaluated AstroCatR with actual observation data from The three Antarctic Survey Telescopes (AST3). The experiments demonstrate that AstroCatR can efficiently and flexibly reconstruct all time series data by setting relevant parameters and configuration files. Furthermore, the tool is approximately 3× faster than methods using relational data base management systems at matching massive catalogues.

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