Forecasting Dose and Dose Rate from Solar Particle Events Using Locally Weighted Regression Techniques

Continued human exploration of the solar system requires the mitigating of radiation effects from the Sun. Doses from Solar Particle Events (SPE) pose a serious threat to the health of astronauts. A method for forecasting the rate and total severity of such events would give time for the astronauts to take actions to mitigate the effects from an SPE. The danger posed from an SPE depends on dose received and the temporal profile of the event. The temporal profile describes how quickly the dose will arrive (dose rate). Previously deployed methods used neural networks to predict the total dose from the event. Later work added the ability to predict the temporal profiles using the neural network approach. Locally weighted regression (LWR) techniques were then investigated for use in forecasting the total dose from an SPE. That work showed that LWR methods could forecast the total dose from an event. This previous research did not calculate the uncertainty in a forecast. The present research expands the LWR model to forecast dose and temporal profile from an SPE along with the uncertainty in these forecasts. Forecasts made with LWR method are able to make forecasts at a time early in an event with results that can be beneficial to operators and crews. The forecasts in this work are all made at or before five hours after the start of the SPE. For 58 percent of the events tested, the dose-rate profile is within the uncertainty bounds. Restricting the data set to only events less than 145 cGy, 86 percent of the events are within the uncertainty bounds. The uncertainty in the forecasts are large, however the forecasts are being made early enough into an SPE that very little of the dose will have reached the crew. Increasing the number of SPEs in the data set increases the accuracy of the forecasts and reduces the uncertainty in the forecasts

Using Artificial Intelligence Methods to Predict Doses From Large Solar Particle Events in Space

Space exploration presents mankind with an opportunity to investigate and discover the nature of our solar system, galaxy, perhaps even the universe. The accomplishment of space exploration will only be achieved if the multitude of problems inherent in space travel are solved. One such problem is protecting humans from radiation. The astronauts are able to protect themselves by surrounding themselves with a radiation shield. For the radiation shield to be effective, the astronauts must have advanced warning of incoming radiation in order to seek shelter in a timely manner. The parameterization of a time-dose profile from an SPE reveals that a non-linear 3 parameter Wiebull curve fits the data very well. Neural networks excel at predicting non-linear functions and their processing in a time period that is much shorter than traditional algorithms used to solve non-linear relationships. Locally weighted regression (LWR), is able to handle non-linear events by performing linear regression on a region locally to the query. Both methods are able to forecast the maximum potentially absorbed dose from a SPE. Currently only the neural network approach has been expanded to forecast the entire dose-profile of a SPE. The neural networks are able to produce reasonable forecasts within 10 hours from the start of a SPE. The dose received in the first 8 hours is on average around 5 cGy which is not consider a significant health risk to the Astronauts. The error in the prediction of all three wiebull parameters is normally reduced to around 10% within the first 10 hours of an event. The LWR is also able to predict the maximum received dose before a dangerous level of radiation would reach the space craft. On average though, the received dose was around 10 cGy and the time into the event before an accurate forecast is made was longer than when using the neural networks. The neural networks are able to forecast the dose-time profile in a timely fashion. The forecasts occur before a significant dose would have time to reach the astronauts in a near Earth situation. This is accomplished using a sliding time delayed neural network technique. In the same time frame the LWR technique is unable to produce forecasts that are as accurate as the neural networks. However, the forecasts using the LWR are within a reasonable amount of time to provide adequate warning and the method tends to always converge to the correct maximum received dose from a particular SPE

A forecasting of indices and corresponding investment decision making application

Student Number : 9702018F - MSc(Eng) Dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built EnvironmentDue to the volatile nature of the world economies, investing is crucial in ensuring an individual is prepared for future financial necessities. This research proposes an application, which employs computational intelligent methods that could assist investors in making financial decisions. This system consists of 2 components. The Forecasting Component (FC) is employed to predict the closing index price performance. Based on these predictions, the Stock Quantity Selection Component (SQSC) recommends the investor to purchase stocks, hold the current investment position or sell stocks in possession. The development of the FC module involved the creation of Multi-Layer Perceptron (MLP) as well as Radial Basis Function (RBF) neural network classifiers. TCategorizes that these networks classify are based on a profitable trading strategy that outperforms the long-term “Buy and hold” trading strategy. The Dow Jones Industrial Average, Johannesburg Stock Exchange (JSE) All Share, Nasdaq 100 and the Nikkei 225 Stock Average indices are considered. TIt has been determined that the MLP neural network architecture is particularly suited in the prediction of closing index price performance. Accuracies of 72%, 68%, 69% and 64% were obtained for the prediction of closing price performance of the Dow Jones Industrial Average, JSE All Share, Nasdaq 100 and Nikkei 225 Stock Average indices, respectively. TThree designs of the Stock Quantity Selection Component were implemented and compared in terms of their complexity as well as scalability. TComplexity is defined as the number of classifiers employed by the design. Scalability is defined as the ability of the design to accommodate the classification of additional investment recommendations. TDesigns that utilized 1, 4 and 16 classifiers, respectively, were developed. These designs were implemented using MLP neural networks, RBF neural networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4 classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of concern. It has also been determined that the neural network architecture as well as the Fuzzy Inference System implementation of this design performed equally well

Machine Learning for Decision-Support in Distributed Networks

Student Number : 9801145J - MSc dissertation - School of Electrical and Information Engineering - Faculty of EngineeringIn this document, a paper is presented that reports on the optimisation of a system that assists in time series prediction. Daily closing prices of a stock are used as the time series under which the system is being optimised. Concepts of machine learning, Artificial Neural Networks, Genetic Algorithms, and Agent-Based Modeling are used as tools for this task. Neural networks serve as the prediction engine and genetic algorithms are used for optimisation tasks as well as the simulation of a multi-agent based trading environment. The simulated trading environment is used to ascertain and optimise the best data, in terms of quality, to use as inputs to the neural network. The results achieved were positive and a large portion of this work concentrates on the refinement of the predictive capability. From this study it is concluded that AI methods bring a sound scientific approach to time series prediction, regardless of the phenomena that is being predicted

Обґрунтування ефективності моніторингу гірничих комплексів на основі нейронних мереж

Хмура О. А. Обґрунтування ефективності моніторингу гірничих комплексів на основі нейронних мереж : дипломна робота магістра спеціальності 121 Інженерія програмного забезпечення. - Дніпро, 2018Explanatory note:135 р., 32 fig., 3 applications, 72 sources. Object of research: is mine hoisting installations. The purpose of the degree project: Rationale for the effectiveness of monitoring of mountain complexes based on neural networks. Methods of research. When solving this problem, scientific achievements were used in the fields of data analysis, simulation of artificial neural networks, Data Science. The scientific novelty is expected to analyze and identify shortcomings in the traditional approach to the development of emergency protection systems. The practical value of work is to develop techniques for creating, deploying and scaling systems for forecasting accidents in time series. The scope. The developed technique can be applied at mining and industrial enterprises. The value of the work and conclusions. The developed system allows you to design systems that can warn the threat is confirmed by the developed software product in this master's work. Projections on development research. On the basis of the developed project it is possible to create systems of protection and forecasting of breakdowns not only of lifting machines, but also of all mine equipment in general, which will significantly improve labor safety in the industry. In section "Economics" calculated the complexity of software development, the cost of creating the software and the duration of its development, and marketing studies market created by the software.Пояснювальна записка: 135 с., 32 рис., 3 додатків., 72 джерела. Об'єкт дослідження: є шахтні підйомні установки. Мета магістерської роботи: Обґрунтування ефективності моніторингу гірничих комплексів на основі поєднання нейронних мереж та методу групового урахування аргументів. Методи дослідження. При рішенні поставленої задачі використовувалися наукові досягнення в областях аналізу даних, моделювання штучних нейронних мереж, Data Science. Наукова новизна результатів, що очікуються, полягає у проведені аналізу та виявленні недоліків традиційного підходу до розробки систем захисту від аварійних ситуацій. Практична цінність результатів полягає у розробленні методик для створення, розгортання та масштабування систем прогнозування аварій в часових рядах. Область застосування. Розроблена методика може застосовуватися на гірничо-промислових підприємствах. Значення роботи та висновки. Розроблена система дозволяє проектувати системи які можуть попереджувати загрозу, що підтверджується розробленим програмним продуктом в даній магістерській роботі. Прогнози щодо розвитку досліджень. На основі розробленого проекту можна створити системи захисту та прогнозування поламок не тільки підйомних машин, а й усього шахтного устаткування взагалі, що значно підвищить безпеку праці в галузі. У розділі «Економіка» проведені розрахунки трудомісткості розробки програмного забезпечення, витрат на створення ПЗ й тривалості його розробки, а також провести маркетингові дослідження ринку збуту створеного програмного продукту

Prediction of dose-time profiles for solar particle events using neural networks

When planning long duration space missions, radiation effects due to large solar particle events (SPEs) can become a major concern. As time in space increases, the chance that a measurable amount of dose is received from a SPE also increases. Therefore, a prediction mechanism for SPEs needs to be in place, which allows spacecraft operators to estimate the time until certain doses are reached following the onset of one of these events. Typical dose-time profiles of these events exhibit a Weibull functional form, which can be described by three fitting parameters. Since the profiles are nonlinear, the use of neural networks to approximate the profiles is ideal. The purpose of this research is to use neural networks to forecast the dose-time profiles of SPEs. A network set comprised of three networks is used to forecast each of the three Weibull parameters based on doses during the early stages of the SPE. The networks either utilize sliding or conventional time delay techniques. Once all three parameters have been forecasted, profiles are determined and compared to actual profiles. Sometimes a second, or even third event occurs before the first event is complete; therefore, the network set also has the ability to determine when one of these multiple-rise events occurs and can determine the profile for each subsequent event. From these profiles, radiation doses from a particular event and the length of time until applicable dose limits are reached can be forecasted. This research showed that neural networks do have the ability to forecast the Weibull parameters necessary for describing dose-time profiles of SPEs, both single and multiple-rise. Typically the forecasts were within thirty percent error of the actual profile before half of the event dose was received. Sometimes one or more of the parameters was not adequately forecasted, which caused the event to be either over- or under-predicted. However, when comparing times and doses exceeding particular dose limits, forecasts and actual values were always within a few percent of each other

Artificial Neural Network-Based Flood Forecasting: Input Variable Selection and Peak Flow Prediction Accuracy

Floods are the most frequent and costly natural disaster in Canada. Flow forecasting models can be used to provide an advance warning of flood risk and mitigate flood damage. Data-driven models have proven to be suitable for flow forecasting applications, yet there are several outstanding challenges associated with model development. Firstly, this research compares four methods for input variable selection for data-driven models, which are used to minimize model complexity and improve performance. Next, methods for reducing the temporal error for data-driven flood forecasting models are investigated. Two procedures are proposed to minimize timing error: error weighting and least-squares boosting. A class of performance measures called visual measures is used to discriminate between timing and amplitude errors, and hence quantifying the impacts of each correction procedure. These studies showcase methods for improving the performance of flow forecasting models, more reliable flood risk predictions, and better preparedness for flood events

Long-term Time Series Prediction Using Wrappers For Variable Selection And Clustering For Data Partition

In an attempt to implement long-term time series prediction based on the recursive application of a one-step-ahead multilayer neural network predictor, we have considered the eleven short time series provided by the organizers of the Special Session NN3 Neural Network Forecasting Competition, and have proposed a joint application of a variable selection technique and a clustering procedure. The purpose was to define unbiased partition subsets and predictors with high generalization capability, based on a wrapper methodology. The proposed approach overcomes the performance of the predictor that considers all the lags in the regression vector. After obtaining the eleven long-term predictors, we conclude the paper presenting the eighteen multi-step predictions for each time series, as requested in the competition. ©2007 IEEE.30683073Puma-Villanueva W.J. & Von Zuben, F.J. Data partition and variable selection for time series prediction using wrappers. IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, July 16-21, 2006Box, G.E.P., Jenkins, G.M., Time Series Analysis: Forecasting, and Control. Holden Day, San Francisco, CA. 1976Guyon, I., Elisseeff, A., An introduction to variable and feature selection (2003) Journal of Machine Learning Research, 3, pp. 1157-1182Kohavi, R., John, G., Wrappers for Feature Subset Selection (1997) Artificial Intelligence, 97 (1-2), pp. 273-324Bonnlander, B.V., (1996) Nonparametric selection of input variables for connectionist learning, , PhD thesis, University of ColoradoCover, T.M., Thomas, J.A., (1991) Elements of Information Theory, , Wiley, New YorkFast, F.F., Binary Feature Selection with Conditional Mutual Information (2004) Journal of Machine Learning Research, 5, pp. 1531-1555Wang, G., Lochovsky, F.H., Feature selection with conditional mutual information maximin in text categorization (2004) Conference on Information and Knowledge Management, pp. 342-349Leray, P., Gallinari, P., Feature selection with neural networks (1999) Behaviormetrika (special issue on Analysis of Knowledge Representation in Neural Network Models), 26 (1), pp. 145-166Conway, A.J., Macpherson, K.P., Brown, J.C., Delayed time series predictions with neural networks (1998) Neurocomputing, 18 (1-3), pp. 81-89Nelson, M., Hill, T., Remus, T., O'Connor, M., Time series forecasting using NNs: Should the data be deseasonalized first (1999) Journal of Forecasting, 18, pp. 359-367Ripley, B., (1993) Statistical aspects of neural networks. In Chaos and Networks - Statistical and Probabilistic Aspects, pp. 40-123. , eds O. Barnorff-Nielsen, J. Jensen and W. Kendall, London: Chapman and HallSharda, R., Patil, R.B., Conectionist approach to time series prediction: An empirical test (1992) Journal of Intelligent Manufacturiong, 3, pp. 317-323Cherkassky, V., Mulier, F., (1998) Learning from data, concepts, theory and methods, , John Wiley & Sons, New YorkHornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators (1989) Neural Networks, 2, pp. 359-366Foster, W.R., Collopy, F., Ungar, L.H., Neural network forecasting of short, noisly time series (1992) Comput. Chem. Engng, 16, pp. 293-297Lima, C.A.M., Puma-Villanueva, W.J., dos Santos, E.P., Von Zuben, F.J., Mixture of experts applied to financial time series prediction (2004) Proceedings of the XIII Brazilian Symposium on Neural Networks, , in Portuguese, paper no. 3708Refenes, A.N., Azema-Barac, M., Karousssos, S.A., Currency exchange rate forecasting by error backpropagation (1992) Proceedings of the Twenty-Fifth Annual Hawaii International Conference on System Sciences, 4, pp. 504-515Tang, Z., de Almeida, C., Fishwick, P.A., Time series forecasting using neural networks vs. Box-Jenkins methodology (1991) Simulation, 57 (5), pp. 303-310Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982) Journal of Forecasting, 1, pp. 111-153Makridakis, S., Forecasting Accuracy and System Complexity (1995) RAIRO, 29 (3), pp. 259-283Hartigan, J., Wang, M., A K-means clustering algorithm (1979) Applied Statistics, 28, pp. 100-108Bishop, C.M., (1995) Neural Networks for Pattern Recognition, , Clarendon Press, OxfordTumer, K. and Ghosh, J. Theoretical foundations of linear and order statistics combiners for neural pattern classifiers, IEEE Transactions on Neural Networks, March 1995Cellucci, C.J.Albano, A. M.Rapp, P. E. Statistical validation of mutual information calculations: Comparison of alternative numerical algorithms. Physical Review E 71, pp.066208-1-14, 2005Hansen, L.K., Salamon, P., Neural network ensembles (1990) IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (10), pp. 993-1001Hashem, S., Schmeiser, B., Yih, Y., Optimal linear combinations of neural networks: An overview (1994) Proceedings of the 1994 IEEE International Conference on Neural Networks, , Orlando, F

Mixture Of Heterogeneous Experts Applied To Time Series: A Comparative Study

Prediction models for time series generally include preprocessing followed by the synthesis of an input-output mapping. Neural network models have been adopted to perform both steps, by means of unsupervised and supervised learning, respectively. The flexibility and the generalization capability are the most relevant attributes in favor of connectionist approaches. However, even though time series prediction can be roughly interpreted as learning from data, high levels of performance will solely be achieved if some peculiarities of each time series are properly considered in the design, particularly the existence of trend and seasonality. Instead of directly adopting detrend and/or deseasonality treatments, this paper proposes a novel paradigm for supervised learning based on a mixture of heterogeneous experts. Some mixture models have already been proved to produce good performance as predictors, but the present approach will be devoted to a hybrid mixture composed of a set of distinct experts. The purpose is not only to further explore the "divide-and-conquer" principle, but also to compare the performance of mixture of heterogeneous experts with the standard mixture of experts approach, using ten distinct time series. The obtained results indicate that mixture of heterogeneous experts generally requires a more elaborate gating device and performs better in the case of more challenging time series. © 2005 IEEE.211601165Box, G.E.P., Jenkins, G.M., (1976) Time Series Analysis: Forecasting, and Control, , Holden Day, San Francisco, CABridle, J.S., Probabilistic interpretation of feedforward classification network outputs with relationships to statistical pattern recognition (1990) Neurocomputing: Algorithms. Architectures, and Applications, pp. 227-236. , F. Fogelman Soulié and J. 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Kendall), London: Chapman and HallSharda, R., Patil, R.B., Conectionist approach to time series prediction: An empirical test (1992) Journal of Intelligent Manufacturiong, 3, pp. 317-323Tang, Z., De Almeida, C., Fishwick, P.A., Time series forecasting using neural networks vs. Box-Jenkins methodology (1991) Simulation, 57 (5), pp. 303-310Van Der Vaar, H.R., An example of the performance of time series methods with respect to a known model (1997) Time Series and Ecological Processes, Proceeding of a SIMS Conference, , Alta, Utha, sponsored by SIAM Institute for Mathematics and SocietyWaterhouse, S.R., Robinson, A.J., Classification using hierarchical mixtures of experts (1994) Procs. 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