Numerical Computation, Data Analysis and Software in Mathematics and Engineering

The present book contains 14 articles that were accepted for publication in the Special Issue “Numerical Computation, Data Analysis and Software in Mathematics and Engineering” of the MDPI journal Mathematics. The topics of these articles include the aspects of the meshless method, numerical simulation, mathematical models, deep learning and data analysis. Meshless methods, such as the improved element-free Galerkin method, the dimension-splitting, interpolating, moving, least-squares method, the dimension-splitting, generalized, interpolating, element-free Galerkin method and the improved interpolating, complex variable, element-free Galerkin method, are presented. Some complicated problems, such as tge cold roll-forming process, ceramsite compound insulation block, crack propagation and heavy-haul railway tunnel with defects, are numerically analyzed. Mathematical models, such as the lattice hydrodynamic model, extended car-following model and smart helmet-based PLS-BPNN error compensation model, are proposed. The use of the deep learning approach to predict the mechanical properties of single-network hydrogel is presented, and data analysis for land leasing is discussed. This book will be interesting and useful for those working in the meshless method, numerical simulation, mathematical model, deep learning and data analysis fields

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Development of Features for Recognition of Handwritten Odia Characters

In this thesis, we propose four different schemes for recognition of handwritten atomic Odia characters which includes forty seven alphabets and ten numerals. Odia is the mother tongue of the state of Odisha in the republic of India. Optical character recognition (OCR) for many languages is quite matured and OCR systems are already available in industry standard but, for the Odia language OCR is still a challenging task. Further, the features described for other languages can’t be directly utilized for Odia character recognition for both printed and handwritten text. Thus, the prime thrust has been made to propose features and utilize a classifier to derive a significant recognition accuracy. Due to the non-availability of a handwritten Odia database for validation of the proposed schemes, we have collected samples from individuals to generate a database of large size through a digital note maker. The database consists of a total samples of 17, 100 (150 × 2 × 57) collected from 150 individuals at two different times for 57 characters. This database has been named Odia handwritten character set version 1.0 (OHCS v1.0) and is made available in http://nitrkl.ac.in/Academic/Academic_Centers/Centre_For_Computer_Vision.aspx for the use of researchers. The first scheme divides the contour of each character into thirty segments. Taking the centroid of the character as base point, three primary features length, angle, and chord-to-arc-ratio are extracted from each segment. Thus, there are 30 feature values for each primary attribute and a total of 90 feature points. A back propagation neural network has been employed for the recognition and performance comparisons are made with competent schemes. The second contribution falls in the line of feature reduction of the primary features derived in the earlier contribution. A fuzzy inference system has been employed to generate an aggregated feature vector of size 30 from 90 feature points which represent the most significant features for each character. For recognition, a six-state hidden Markov model (HMM) is employed for each character and as a consequence we have fifty-seven ergodic HMMs with six-states each. An accuracy of 84.5% has been achieved on our dataset. The third contribution involves selection of evidence which are the most informative local shape contour features. A dedicated distance metric namely, far_count is used in computation of the information gain values for possible segments of different lengths that are extracted from whole shape contour of a character. The segment, with highest information gain value is treated as the evidence and mapped to the corresponding class. An evidence dictionary is developed out of these evidence from all classes of characters and is used for testing purpose. An overall testing accuracy rate of 88% is obtained. The final contribution deals with the development of a hybrid feature derived from discrete wavelet transform (DWT) and discrete cosine transform (DCT). Experimentally it has been observed that a 3-level DWT decomposition with 72 DCT coefficients from each high-frequency components as features gives a testing accuracy of 86% in a neural classifier. The suggested features are studied in isolation and extensive simulations has been carried out along with other existing schemes using the same data set. Further, to study generalization behavior of proposed schemes, they are applied on English and Bangla handwritten datasets. The performance parameters like recognition rate and misclassification rate are computed and compared. Further, as we progress from one contribution to the other, the proposed scheme is compared with the earlier proposed schemes

Shape based classification and functional forecast of traffic flow profiles

This dissertation proposes a methodology for traffic flow pattern analysis, its validation, and forecasting. The shape of the daily traffic flows are directly related to the commuter’s traffic behavior which merit analysis based on their shape characteristics. As a departure from the traditional approaches, this research proposed a methodology based on shape for traffic flow analysis. Specifically, Granulometric Size Distributions (GSDs) were used to achieve classification of daily traffic flow patterns. A mathematical morphology method was used that allows the clustering of shapes. The proposed methodology leads to discovery of interesting daily traffic phenomena such as five normal daily traffic shapes beside abnormal shapes representing accidents, congestion behavior, peak time fluctuations, and malfunctioning sensors. To ascertain the significance of shape in traffic analysis, the proposed methodology was validated through a comparative classification analysis of the original data and GSD transformed data using the Back Prorogation Neural Network (BPNN). Results demonstrated that through shape based clustering more appropriate grouping can be accomplished that can result in better estimates of model parameters. Lastly, a functional time series approach was proposed to forecast traffic flow for short and medium-term horizons. It is based on functional principal components decomposition to forecast three different traffic scenarios. Real-time forecast scenarios of partially observed traffic profiles through Penalized Least squares (PLS) technique were also demonstrated. Functional methods outperform the conventional ARIMA model in both short and medium-term forecast horizons. In addition, performance of functional methods in forecasting beyond one hour was also found to be robust and consistent. --Abstract, page iii

Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Optimisation of welding parameters to mitigate the effect of residual stress on the fatigue life of nozzle–shell welded joints in cylindrical pressure vessels.

Doctoral Degree. University of KwaZulu-Natal, Durban.The process of welding steel structures inadvertently causes residual stress as a result of thermal cycles that the material is subjected to. These welding-induced residual stresses have been shown to be responsible for a number of catastrophic failures in critical infrastructure installations such as pressure vessels, ship’s hulls, steel roof structures, and others. The present study examines the relationship between welding input parameters and the resultant residual stress, fatigue properties, weld bead geometry and mechanical properties of welded carbon steel pressure vessels. The study focuses on circumferential nozzle-to-shell welds, which have not been studied to this extent until now. A hybrid methodology including experimentation, numerical analysis, and mathematical modelling is employed to map out the relationship between welding input parameters and the output weld characteristics in order to further optimize the input parameters to produce an optimal welded joint whose stress and fatigue characteristics enhance service life of the welded structure. The results of a series of experiments performed show that the mechanical properties such as hardness are significantly affected by the welding process parameters and thereby affect the service life of a welded pressure vessel. The weld geometry is also affected by the input parameters of the welding process such that bead width and bead depth will vary depending on the parametric combination of input variables. The fatigue properties of a welded pressure vessel structure are affected by the residual stress conditions of the structure. The fractional factorial design technique shows that the welding current (I) and voltage (V) are statistically significant controlling parameters in the welding process. The results of the neutron diffraction (ND) tests reveal that there is a high concentration of residual stresses close to the weld centre-line. These stresses subside with increasing distance from the centre-line. The resultant hoop residual stress distribution shows that the hoop stresses are highly tensile close to the weld centre-line, decrease in magnitude as the distance from the weld centre-line increases, then decrease back to zero before changing direction to compressive further away from the weld centre-line. The hoop stress distribution profile on the flange side is similar to that of the pipe side around the circumferential weld, and the residual stress peak values are equal to or higher than the yield strength of the filler material. The weld specimens failed at the weld toe where the hoop stress was generally highly tensile in most of the welded specimens. The multiobjective genetic algorithm is successfully used to produce a set of optimal solutions that are in agreement with values obtained during experiments. The 3D finite element model produced using MSC Marc software is generally comparable to physical experimentation. The results obtained in the present study are in agreement with similar studies reported in the literature

The simulation and optimization of steady state process circuits by means of artificial neural networks

Dissertation (Ph.D.) -- University of Stellenbosch, 1993.ENGLISH ABASTRACT: Since the advent of modern process industries engineers engaged in the modelling and simulation of chemical and metallurgical processes have had to contend with two important dilemmas. The first concerns the ill-defined nature of the processes they have to describe, while the second relates to the limitations of prevailing computational resources. Current process simulation procedures are based on explicit process models in one form or another. Many chemical and metallurgical processes are not amenable to this kind of modelling however, and can not be incorporated effectively into current commercial process simulators. As a result many process operations do not benefit from the use of predictive models and simulation routines and plants are often poorly designed and run, ultimately leading to considerable losses in revenue. In addition to this dilemma, process simulation is in a very real way constrained by available computing resources. The construction of adequate process models is essentially meaningless if these models can not be solved efficiently - a situation occurring all too often. In the light of these problems, it is thus not surprising that connectionist systems or neural network methods are singularly attractive to process engineers, since they provide a powerful means of addressing both these dilemmas. These nets can form implicit process models through learning by example, and also serve as a vehicle for parallel supercomputing devices. In this dissertation the use of artificial neural networks for the steady state modelling and optimization of chemical and metallurgical process circuits is consequently investigated. The first chapter is devoted to a brief overview of the simulation of chemical and metallurgical plants by conventional methods, as well as the evolution and impact of computer technology and artificial intelligence on the process industries. Knowledge of the variance covariance matrices of process data is of paramount importance to data reconciliation and gross error detection problems, and although various methods can be employed to estimate these often unknown variances, it is shown in the second chapter that the use of feedforward neural nets can be more efficient than conventional strategies. In the following chapter the important problem of gross error detection in process data is addressed. Existing procedures are statistical and work well for systems subject to linear constraints. Non-linear constraints are not handled well by these methods and it is shown that back propagation neural nets can be trained to detect errors in process systems, regardless of the nature of the constraints. In the fourth chapter the exploitation of the massively parallel information processing structures of feedback neural nets in the optimization of process data reconciliation problems is investigated. Although effective and sophisticated algorithms are available for these procedures, there is an ever present demand for computational devices or routines that can accommodate progressively larger or more complex problems. Simulations indicate that neural nets can be efficient instruments for the implementation of parallel strategies for the optimization of such problems. In the penultimate chapter a gold reduction plant and a leach plant are modelled with neural nets and the models shown to be considerably better than the linear regression models used in practice. The same technique is also demonstrated with the modelling of an apatite flotation plant. Neural nets can also be used in conjunction with other methods and in the same chapter the steady state simulation and optimization of a gravity separation circuit with the use of two linear programming models and a neural net are described.AFRIKAANSE OPSOMMING: Sedert die ontstaan van prosesingenieurswese, het ingenieurs gemoeid met die modellering en simulasie van chemiese en metallurgiese prosesse met twee belangrike dilemmas te kampe gehad. Die eerste het te make met die swakgedefinieerde aard van chemiese prosesse, wat die beskrywing en dus ook die beheer daarvan kompliseer, terwyl die tweede verband hou met die beperkinge van huidige berekeningsmiddele. Die prosesse wat tans gebruik word om chemiese prosesse te simuleer is gebaseer op eksplisiete prosesmodelle van een of ander aard. Baie chemiese en metallurgiese prosesse kan egter nie op 'n eksplisiete wyse gemodelleer word nie, en kan gevolglik ook nie doeltreffendheid deur kommersiële prosessimulators beskryf word nie. Die bedryf van baie prosesse vind derhalwe nie baat by die gebruik van voorspellende modelle en simulasie-algoritmes nie en aanlegte word dikwels suboptimaal ontwerp en bedryf, wat uiteindelik tot aansienlike geldelike verliese kan lei. Prosessimulasie word op die koop toe ook beperk deur die beskikbaarheid van berekeningsfasiliteite. Die konstruksie van geskikte prosesmodelle hou geen voordeel in as hierdie modelle nie doeltreffendheid opgelos kan word nie. Teen die agtergrond van hierdie probleme is dit nie verrassend dat neurale netwerke 'n besondere bekoring vir prosesingenieurs inhou nie, aangesien hulle beide hierdie dilemmas aanspreek. Hierdie nette kan implisiete prosesmodelle konstrueer deur te leer van voorbeelde en dien ook as 'n raamwerk vir parallelle superrekenaars. In hierdie proefskrif word die gebruik van kunsmatige neurale netwerke vir gestadigde toestandsmodellering en optimering van chemiese en metallurgiese prosesse gevolglik ondersoek. Die eerste hoofstuk word gewy aan 'n kort oorsig oor die simulasie van chemiese en metallurgiese aanlegte met konvensionele tegnieke, asook die ontwikkeling en impak van rekenaartegnologie en skynintelligensie in die prosesnywerhede. Kennis van die variansie-kovariansie-matrikse van prosesdata is van kardinale belang vir datarekonsiliasie en die identifikasie en eliminasie van sistematiese foute en alhoewel verskeie metodes aangewend kan word om hierdie onbekende variansies te beraam, word daar in die tweede hoofstuk getoon dat die gebruik van neurale netwerke meer doeltreffend is as konvensionele strategieë. In die volgende hoofstuk word die belangrike probleem van sistematiese foutopsporing in prosesdata ondersoek. Bestaande prosedures is statisties van aard en werk goed vir stelsels onderworpe aan lineêre beperkinge. Nie-lineêre beperkinge kan nie doeltreffend deur hierdie prosedures hanteer word nie en daar word gewys dat terugwaarts-propagerende nette geleer kan word om sulke foute in prosessisteme op te spoor, ongeag die aard van die beperkinge. In die vierde hoofstuk word die rekonsiliasie van prosesdata met behulp van massiewe parallelle dataverwerkingstrukture soos verteenwoordig deur terugvoerende neurale nette, ondersoek. Alhoewel doeltreffende en gesofistikeerde algoritmes beskikbaar is vir die optimering van die tipe probleme, is daar 'n onversadigbare aanvraag na rekenaars wat groter en meer komplekse stelsels kan akkommodeer. Simulasie dui aan dat neurale nette effektief aangewend kan word vir die implementering van parallelle strategieë vir dié tipe optimeringsprobleme. In die voorlaaste hoof stuk word die konneksionistiese modellering van 'n goudreduksie- en 'n logingsaanleg beskryf en daar word aangetoon dat die neurale netwerk-modelle aansienlik beter resultate lewer as die linneêre regressie modelle wat in die praktyk gebruik word. Dieselfde tegnieke vir die modellering van 'n flottasie-aanleg vir apatiet word ook bespreek. Neural nette kan ook saam met ander metodes aangewend word en in dieselfde hoofstuk word die gebruik van twee lineêre programmeringsmodelle en 'n neural net om 'n gravitasieskeidingsbaan onder gestadigde toestande te simuleer en te optimeer, beskryf