Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies

Using eye-tracking into decision makers evaluation in evolutionary interactive UA-FLP algorithms

Unequal area facility layout problem is an important issue in the design of industrial plants, as well as other fields such as hospitals or schools, among others. While participating in an interactive designing process, the human user is required to evaluate a high number of proposed solutions, which produces them fatigue both mental and physical. In this paper, the use of eye-tracking to estimate user’s evaluations from gaze behavior is investigated. The results show that, after a process of training and data taking, it is possible to obtain a good enough estimation of the user’s evaluations which is independent of the problem and of the users as well. These promising results advice to use eye-tracking as a substitute for the mouse during users’ evaluations

Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, K(trans), plasma volume fraction, v(p), and extravascular, extracellular space, v(e), directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, v(p), K(trans), and v(e), respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches

Simulación y predicción de indicadores de gestión financiera en PYMES mediante el uso de Redes Neuronales Artificiales

[ES] En este trabajo se realiza un análisis de un modelo que facilita la evaluación y predicción de indicadores financieros y de gestión de las PYMES. El modelo se basa en el uso de Redes Neuronales Artificiales como herramienta de minería de datos que, con base en los estados financieros de una empresa, contribuye a la evaluación y predicción de indicadores de su liquidez, deuda, rendimiento, eficiencia y rentabilidad. Así mismo, se incluye un análisis Monte Carlo del comportamiento del modelo. Todo ello se realiza en el marco del sistema DuPont.[EN] In this work an analysis of a model that facilitates the evaluation and prediction of financial and management indicators of Small and Medium Enterprises (SMEs) is carried out. The model is based on the use of Artificial Neural Networks as a data mining tool that, based on the financial statements of a company, contributes to the evaluation and prediction of indicators of its liquidity, debt, performance, efficiency and profitability. Likewise, a Monte Carlo analysis of the behavior of the model is included. All this is done within the framework of the DuPont system.Torriente García, I. (2021). Simulación y predicción de indicadores de gestión financiera en PYMES mediante el uso de Redes Neuronales Artificiales. Universitat Politècnica de València. http://hdl.handle.net/10251/173994TFG

A Machine Learning Classification Framework for Early Prediction of Alzheimer’s Disease

People today, in addition to their concerns about getting old and having to go through watching themselves grow weak and wrinkly, are facing an increasing fear of dementia. There are around 47 million people affected by dementia worldwide and the cost associated with providing them health and social care support is estimated to reach 2 trillion by 2030 which is almost equivalent to the 18th largest economy in the world. The most common form of dementia with the highest costs in health and social care is Alzheimer’s disease, which gradually kills neurons and causes patients to lose loving memories, the ability to recognise family members, childhood memories, and even the ability to follow simple instructions. Alzheimer’s disease is irreversible, unstoppable and has no known cure. Besides being a calamity to affected patients, it is a great financial burden on health providers. Health care providers also face a challenge in diagnosing the disease as current methods used to diagnose Alzheimer’s disease rely on manual evaluations of a patient’s medical history and mental examinations such as the Mini-Mental State Examination. These diagnostic methods often give a false diagnosis and were designed to identify Alzheimer’s after stage two when the part of all symptoms are evident. The problem is that clinicians are unable to stop or control the progress of Alzheimer’s disease, because of a lack of knowledge on the patterns that triggered the development of the disease. In this thesis, we explored and investigated Alzheimer’s disease from a computational perspective to uncover different risk factors and present a strategic framework called Early Prediction of Alzheimer’s Disease Framework (EPADf) that would give a future prediction of early-onset Alzheimer’s disease. Following extensive background research that resulted in the formalisation of the framework concept, prediction approaches, and the concept of ranking the risk factors based on clinical instinct, knowledge and experience using mathematical reasoning, we carried out experiments to get further insight and investigate the disease further using machine learning models. In this study, we used machine learning models and conducted two classification experiments for early prediction of Alzheimer’s disease, and one ranking experiment to rank its risk factors by importance. Besides these experiments, we also presented two logical approaches to search for patterns in an Alzheimer’s dataset, and a ranking algorithm to rank Alzheimer’s disease risk factors based on clinical evaluation. For the classification experiments we used five different Machine Learning models; Random Forest (RF), Random Oracle Model (ROM), a hybrid model combined of Levenberg-Marquardt neural network and Random Forest, combined using Fischer discriminate analysis (H2), Linear Neural Networks (LNN), and Multi-layer Perceptron Model (MLP). These models were deployed on a de-identified multivariable patient’s data, provided by the ADNI (Alzheimer’s disease Neuroimaging Initiative), to illustrate the effective use of data analysis to investigate Alzheimer’s disease biological and behavioural risk factors. We found that the continues enhancement of patient’s data and the use of combined machine learning models can provide an early cost-effective prediction of Alzheimer’s disease, and help in extracting insightful information on the risk factors of the disease. Based on this work and findings we have developed the strategic framework (EPADf) which is discussed in more depth in this thesis

Indentation Curve Prediction and Inverse Material Parameters Identification of Hyperfoam Materials Based on Intelligent ANN Method

In this work, an ANN program has been developed to predict the indentation P-h curves with known properties. An interactive parametric FE model and python programming based data extracting program has been developed and used to develop data for the ANN program. Two approaches have been proposed and evaluated to represent the P-h curve. One is using 2nd order polynomial trendline approach, the other is to use the forces at different indentation depth. The performance of the ANN based on the trendline approach is evaluated with MSE and relative error of the curve coefficient and the average error in forces over different depths. A frequency method is used to analyse the data, which provided important data/base to further enhanced the accuracy of the P-h curve based on averaging multiple ANN tests. This approach effectively taking use of the fact that ANN prediction is not continuous around any property point. The ANN program with the depth based approach showed similar accuracy in predicting P-h curves of hyperfoam materials. The program was validated in blind tests with numerical data and experimental data on two EVA foams with known properties. Comparison with other approaches (including surface mapping and direct date space fitting process) showed that the ANN program is accurate and much quicker than some other approaches and direct FE modeling. The feasibility of using ANN to directly predict the material properties is evaluated including assessing its capacity to predict trained data and untrained data. The use of single indenter approach and dual indenter approach is assessed. It was found that the approach with 2nd order polynomial fitting of the P-h curves is not able to predict the material parameters. Using 3rd order fitting showed much improvement and it is able to predict the trained data accurately but could not be used to predict untrained data. Works on dual indenter approach with R4 and R6 showed some improvement in predicting untrained data but could not produce data with reasonable accuracy of the full dataset. A new approach utilising the direct ANN program for P-h curve prediction is developed. A computerised program (with Web based interface) has been developed including data generation through ANN, data storage, interface for input and viewing results. A searching program is developed which enables the identification of any possible materials property sets that produce P-h curves matching the experiment data within a predefined error range. The approach is applied to analysis single and dual indenter methods through blind tests with model materials (with known material properties). A new approach using foams of different thickness is also proposed. The results showed that in a single indenter approach, there are multiple materials property sets that can produce similar P-h curves, thus the results are not unique. Dual indenter size approach showed a significant improvement in mapping out all potential material sets matching the tetsign data. The new program successfully identify addition material property sets that can produce P-h curve that match both R4 and R6 data, which was not identified previously with other inverse programs. The new approach proposed of using the tested data on samples of different thickness showed that the uniqueness of the prediction can be improved. The accuracy and validity of the program is firstly assessed with blind tests (using numerical data as input/target) then used to predict the properties of the EVA foam samples. Some key results of the real foam data is compared to the target and prediction results from other programs and data processing method, the comparison results showed that the new ANN base computer program has clear improvement in accuracy, robustness and efficiency in predicting the parameters of EVA foams. Future work is to transfer the program and methodology developed to other material system and testing conditions and further develop the computer program for material developments and research

Sinteza algoritama navigacije i vođenja projektila zasnovanih na mašinskom učenju

This thesis discusses the use of machine learning to design guidance, navigation, and control algorithms as an alternative to traditional algorithms for a missile system. The machine learning algorithm used in this thesis is the neural network. It is trained using the Neuro Evolution of Augmenting Topologies algorithm. Furthermore, the missile system and its environment have been modeled in order to simulate and compare the missile performances. The terminal guidance neural network will be compared to the proportional navigation algorithm. In addition, the neural network GPS/INS integration will be compared to the Kalman filter GPS/INS integration. Moreover, the neural network roll, pitch, and yaw autopilots will be compared to the traditional PID roll, pitch, and yaw autopilots. The goal of this thesis is to design neural network guidance, navigation, and control solutions which is expected to perform similar or better than their traditional counterparts. Thereby, the viability of the neural network designs as a guidance, navigation, or control solution will be verified.У овој докторској тези се разматра употреба машинског учења у синтези алгоритама навигације, управљања и вођења ракете, као алтернативи традиционалним алгоритмима. Алгоритам машинског учења који се користи у овој докторској тези је заснован на примени неуронских мрежа. Неуронска мрежа се обучава Неуро еволуционим алгоритмом са приширеном топологијом. Осим тога, извршено је математичко моделовање вођеног пројектила и његовог окружења како би се извршиле нумеричке симулације и упоредиле његове перформансе. Извршено је поређење неуронске мреже алгоритма вођења терминалне фазе са алгоритмом пропорционалне навигације. Осим тога, интеграција GPS/INS-а на бази неуронских мрежа је упоређена са Калмановим филтром. На крају је дато поређење аутопилота по каналима ваљања, пропињања и скретања реализованих неуронским мрежама насупрот традиционалним аутопилотима са ПИД управљачким алгоритмима. Циљ ове докторске тезе је синтеза алгоритма вођења и управљања пројектила применом неуронских мрежа које треба да покаже слично или боље понашање од традиционалних решења. Притом, верификује се одрживост решења примене неуронских мрежа у синтези алгоритама управљања и вођења

Iluminação natural e consumo energético de edificações não residenciais: aplicação de redes neurais artificiais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Civil, Florianópolis, 2015.Raras ferramentas simplificadas oferecem a possibilidade de avaliar o impacto do aproveitamento da luz natural no consumo energético de edificações de forma completa devido à dificuldade de modelar a sua dupla influência: no sistema de iluminação artificial e condicionamento de ar. Este trabalho teve por objetivo investigar as possibilidades e as limitações da aplicação de redes neurais artificiais (RNAs) para estimar o potencial energético do aproveitamento da iluminação natural em edificações não residenciais por meio da metamodelagem de suas variáveis-chave. O método adotado apresentou duas etapas: a primeira, de abordagem sistemática investigativa; e a segunda, de abordagem propositiva. Na etapa investigativa, identificou-se a necessidade de avaliar o desempenho das RNAs diante de técnicas estatísticas já estudadas, adotando-se a regressão linear multivariada (RLM), e de avaliar o potencial das RNAs para a metamodelagem das principais variáveis-chave da iluminação natural referentes: a descrição, a localização e o desempenho do edifício. Para a comparação entre a precisão dos modelos RNA e RLM, repetiu-se um estudo anterior que propôs uma equação baseada em RLM para modelar uma zona termoluminosa, propondo-se uma RNA e comparando-se a precisão das técnicas. A investigação das variáveis da descrição do edifício teve como principal norteador a verificação do potencial das RNAs para modelar variáveis que operam em diferentes escalas. A investigação das variáveis do contexto do edifício avaliou o potencial das redes para modelar o comportamento de edificações em diferentes climas. Para a investigação das variáveis de desempenho do edifício, as redes foram testadas diante de diferentes agrupamentos de variáveis de desempenho de iluminação natural e de consumo energético. Essas avaliações foram desenvolvidas conforme: a) a seleção das variáveis; b) a amostragem dos dados (direcionada ou aleatória através de Hipercubo Latino); c) a simulação energética (EnergyPlus e plug-in DIVA do programa Rhinoceros, conforme a etapa do estudo priorizasse tempo ou precisão da simulação); d) o treinamento da RNA; e e) a análise dos resultados e a definição da ação seguinte. Para a realização das simulações integradas no DIVA, propôs-se um algoritmo de parametrização desenvolvido no plug-in Grasshopper, que permite a simulação de mais de 10 milhões de casos diferentes. A metamodelagem baseou-se em uma única zona termoluminosa, partindo-se da rede mais simples que pudesse aprender e generalizar soluções. A acurácia das RNAs foi verificada através da utilização de novos modelos, com características diferentes das conhecidas pela rede. A abordagem propositiva do método dispôs um método simplificado para determinar a redução do consumo energético de edifícios devido ao aproveitamento da iluminação natural. A variável de saída da RNA foi a Densidade de Potência de Iluminação em Uso (DPU), que é ponderada pela área de zonas de iluminação natural para determinar a DPU do edifício. Para a definição da área das zonas, foram propostas equações que consideraram o dinamismo do clima. Como resultado, 799 redes foram testadas diante de 19 variáveis da descrição, 14 do contexto e 15 do desempenho do edifício. Foram simulados 12.041 casos (21.187 simulações de iluminação natural e termoenergéticas). Os resultados da etapa investigativa apontaram as RNAs como método mais preciso que a RLM, a ponto de melhorar o coeficiente de determinação de 0,62 para 0,99. Quanto à investigação das variáveis da descrição do edifício, as RNAs foram capazes de modelar a influência dos parâmetros orientação, percentual de abertura da fachada e transmissão visível com erro percentual inferior a 5%. Quanto ao contexto do edifício, os melhores resultados foram obtidos agrupando-se variáveis de localização geográfica, características térmicas e disponibilidade de luz e considerando-se cidades de diferentes hemisférios na mesma rede, num total de 11 climas. Quanto às variáveis de desempenho, as redes apresentaram maior facilidade na predição das variáveis de consumo energético isoladamente do que de medidas de iluminação natural isoladamente ou de ambos na mesma rede. Quanto à etapa propositiva, o método simplificado resultou em erros inferiores a 5% quando comparado à simulação computacional. Como conclusão geral, as RNAs apresentaram elevado potencial para serem utilizadas em métodos simplificados de iluminação natural sob o enfoque energético para diversas localidades com precisão superior à RLM. Os objetivos do trabalho foram cumpridos, visto que se obteve um panorama das potencialidades da metamodelagem da iluminação natural utilizando RNAs diante de suas principais variáveis-chave e das estruturas de RNAs mais difundidas para a representação de funções. Como principal contribuição social e prática do trabalho, destacam-se um método passível de ser aplicado em todo o território nacional e a contribuição para códigos e normas locais. Como contribuição científica e teórica, destaca-se o avanço nos estudos de aplicação de inteligência artificial para a modelagem dos fenômenos físicos dinâmicos no ambiente construído (luminoso e térmico).Abstract : Few simplified tools offer the possibility of evaluating the impact of comprehensively using daylighting on energy consumption, since it is difficult to model its dual influence: on the artificial lighting system and on air conditioning. This work aimed at investigating the possibilities and the limitations of applying artificial neural networks (ANN) to predict daylight harvesting in non-residential buildings through metamodeling its key variables. There are two stages in the adopted method: the first one used a systematic investigative approach and the second one used a purposeful approach. In the investigative stage, it was identified the necessity of evaluating the ANN performance by adopting multivariate linear regression (MLR) facing other traditional statistical techniques, and to assess the ANNs potential for metamodeling the main daylighting key variables referring to: building description, building location and building performance. In order to compare the accuracy of ANN and MLR models, a previous study that had proposed a RLM-based equation to model a luminous-thermal zone was repeated, by proposing an ANN and comparing the accuracy of the techniques. The investigation of the building description variables was mainly guided by the verification of the ANNs potential, in order to model variables that operate at different scales. The investigation of the building context variables aimed at verifying the networks potential to model the buildings behavior in different climates. In order to research the variables related to building performance, different grouping parameters connected to daylighting and energy consumption were tested. The evaluation actions were developed according to: a) variables selection; b) sampling data (directed or random by Latin Hypercube); c) energy simulation (EnergyPlus and plug-in DIVA Rhinoceros software, when the study stage prioritized time or simulation accuracy); d) ANN training and e) results analysis and next actions definition. In order to perform the integrated simulations into DIVA, a parameterization algorithm developed at the Grasshopper plug-in that allows the simulation of more than 10 million different cases was proposed. The metamodel experiments were based on interactions using only one luminous-thermal zone and it started from the simplest network that could learn and generalize solutions. The accuracy of the ANNs was verified by using new models, whose characteristics were different from the ones that were already known by the network. The purposeful approach of the method disposed a simplified process for determining the reduction of buildings energy consumption due to the daylighting harvesting. The ANN output variable was the Lighting Density Power in Use (LDPU), which is weighted by the daylit to determine the building LDPU. Aiming at computing the daylit zones area, equations that consider the weather dynamism were proposed. As a result, 799 networks were tested facing 19 building description, 14 building context and 15 building performance key variables. 12,041 cases were simulated (21,187 daylighting, thermal and energy simulations). The investigative stage results indicated that the ANNs were a more accurate method than the linear regression, since the determination coefficient improved from 0.62 to 0.99. Regarding the investigation of the building description variables, the ANNs were able to model the influence of the following parameters: orientation, window to wall ratio and visible transmission, showing a percentage error lower than 5%. Concerning the building context, the best results were obtained by grouping variables regarding: geographic location, thermal characteristics and daylight availability. It considered cities from different hemispheres in the same ANN, adding up 11 different climates. The percentage error of the best network solution was lower than 10%, thus it was higher than 30% for the test set individually. On the subject of building performance parameters, the networks showed better results when predicting the energy consumption parameters separately than when predicting daylighting parameters independently, or when both of them were in the same network. Respecting the purposeful approach stage, the simplified method error was lower than 5% when compared to the computer simulation. As a general conclusion, it can be stated that the ANN technique shows a potential for being applied to develop simplified daylighting methods in line with the energy approach for multiple locations with greater precision than the MLR. The research goals have been met, since it was possible to obtain an overview of the potential using ANNs to daylighting metamodeling facing its main key variables and the most widespread ANNs structures. The main social / practical contribution of this research is the possibility of applying the method across the country and contributing to local codes and standards. As a scientific / theoretical contribution, it can be highlighted the progress in studies on artificial intelligence application for metamodeling dynamic physical phenomena in the built environment (luminous and thermal)