An Efficient Microcontroller Based Sun Tracker Control for Solar Cell Systems

The solar energy is fast becoming a different means of electricity resource. Now in world Fossil fuels are seriously depleting thus the need for another energy source is a necessity. To create effective utilization of its solar, energy efficiency must be maximized. An attainable way to deal with amplifying the power output of sun-powered exhibit is by sun tracking. This paper presents the control system for a solar cell orientation device which follows the sun in real time during daytime

AUTO-CDD: automatic cleaning dirty data using machine learning techniques

Cleaning the dirty data has become very critical significance for many years, especially in medical sectors. This is the reason behind widening research in this sector. To initiate the research, a comparison between currently used functions of handling missing values and Auto-CDD is presented. The developed system will guarantee to overcome processing unwanted outcomes in data Analytical process; second, it will improve overall data processing. Our motivation is to create an intelligent tool that will automatically predict the missing data. Starting with feature selection using Random Forest Gini Index values. Then by using three Machine Learning Paradigm trained model was developed and evaluated by two datasets from UCI (i.e. Diabetics and Student Performance). Evaluated outcomes of accuracy proved Random Forest Classifier and Logistic Regression gives constant accuracy at around 90%. Finally, it concludes that this process will help to get clean data for further analytical process

Evaluation of Machine Learning Algorithms for Emotions Recognition using Electrocardiogram

In recent studies, researchers have focused on using various modalities to recognize emotions for different applications. A major challenge is identifying emotions correctly with only electrocardiograms (ECG) as the modality. The main objective is to reduce costs by using single-modality ECG signals to predict human emotional states. This paper presents an emotion recognition approach utilizing the heart rate variability features obtained from ECG with feature selection techniques (exhaustive feature selection (EFS) and Pearson’s correlation) to train the classification models. Seven machine learning (ML) models: multi-layer perceptrons (MLP), Support Vector Machine (SVM), Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Logistic Regression, Adaboost and Extra Tree classifier are used to classify emotional state. Two public datasets, DREAMER and SWELL are used for evaluation. The results show that no particular ML works best for all data. For DREAMER with EFS, the best models to predict valence, arousal, and dominance are Extra Tree (74.6%), MLP and DT (74.6%), and GBDT and DT (69.8%), respectively. Extra tree with Pearson’s correlation are the best method for the ECG SWELL dataset and provide 100% accuracy. The usage of Extra tree classifier and feature selection technique contributes to the improvement of the model accuracy. Moreover, the Friedman test proved that ET is as good as other classification models for predicting human emotional state and ranks highest. Doi: 10.28991/ESJ-2023-07-01-011 Full Text: PD

Potencial aplicabilidade de compósitos poliméricos com resíduos minerais e da construção civil em revestimentos internos / Potential applicability of polymeric composites with mineral waste and civil construction in internal coatings

A fabricação de materiais compósitos a partir do reaproveitamento de resíduos industriais vem sendo estudado com o objetivo de desenvolver materiais com boas propriedades físico-mecânicas aliado ao desenvolvimento sustentável. Com isso, foram confeccionados compósitos poliméricos com reforços de resíduo de mármore e granito, resíduo de construção e demolição e resíduo de minério de ferro, nas proporções de 10 e 20 % na granulometria de 100 mesh da série Tyler. O método utilizado para a fabricação das placas compósitas foi o hand lay-up. A matriz polimérica utilizada foi a poliéster isoftálica insaturada com proporções de 1,5 % de acelerador de cobalto e 1 % de catalisador MEK-P (Butanox M-50). Foram realizados ensaios de caracterização física de massa específica aparente (ASTM D-792), absorção de água (ASTM D-570) e porosidade aparente (ASTM D-2734) e ensaios de flamabilidade horizontal, seguindo a norma ASTM D-635. Os resultados obtidos mostram que a composição com 20 % de minério de ferro obteve menores valores de porosidade aparente e absorção de água. Todos os compósitos apresentaram retardo de chama, em destaque os compósitos com 20 % de resíduo de minério de ferro com retardo à chama em aproximadamente 85 % em relação à matriz plena. Portanto, esses materiais mostram-se viáveis para revestimentos internos na indústria automobilística e revestimentos internos na área da construção civil, além de reduzir os custos de produção e minimizar os impactos ambientais

Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study

Background: Mobile technology may help to better understand the adherence to treatment. MASK-rhinitis (Mobile Airways Sentinel NetworK for allergic rhinitis) is a patient-centred ICT system. A mobile phone app (the Allergy Diary) central to MASK is available in 22 countries. Objectives: To assess the adherence to treatment in allergic rhinitis patients using the Allergy Diary App. Methods: An observational cross-sectional study was carried out on all users who filled in the Allergy Diary from 1 January 2016 to 1 August 2017. Secondary adherence was assessed by using the modified Medication Possession Ratio (MPR) and the Proportion of days covered (PDC) approach. Results: A total of 12143 users were registered. A total of 6949 users reported at least one VAS data recording. Among them, 1887 users reported >= 7 VAS data. About 1195 subjects were included in the analysis of adherence. One hundred and thirty-six (11.28%) users were adherent (MPR >= 70% and PDC = 70% and PDC = 1.50) and 176 (14.60%) were switchers. On the other hand, 832 (69.05%) users were non-adherent to medications (MPR Conclusion and clinical relevance: Adherence to treatment is low. The relative efficacy of continuous vs on-demand treatment for allergic rhinitis symptoms is still a matter of debate. This study shows an approach for measuring retrospective adherence based on a mobile app. This also represents a novel approach for analysing medication-taking behaviour in a real-world setting.Peer reviewe

Tool wear monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network

Turning is one of the important machining processes in manufacturing industries. Tools wear during turning, is one of the major problems which may lead to production loss and machine down time. An effective tool wear monitoring method is therefore required to minimise the above. The work done in this thesis is related to the development of a new method for tool wear monitoring using tool-emitted sound signal in conjunction with newly reported Hilbert Huang Transform (HHT). In the proposed method, the condition of the cutting tool insert is monitored to classify its state into three different categories, namely, fresh, slightly worn and severely worn by analysing the tool-emitted audible sound. A trained competitive neural network is employed for this purpose. The network is trained by using the instantaneous amplitudes and instantaneous frequencies extracted from the tool-emitted sound using HHT. The novelty of the present work is the use of HHT to extract the instantaneous amplitudes and the frequencies of the tool-emitted sound to determine the condition of the cutting tool insert based on its flank wear. HHT is a recently developed signal processing technique more suitable for analysing nonstationary and nonlinear signals such as tool-emitted sound

Tool Condition Monitoring using Competitive Neural Network and Hilbert-Huang Transform

One of the major problems in fully automated manufacturing systems is the breakage and deterioration of the tools. Efficient tool condition monitoring systems are required to address such problem. In this study, a new method is proposed for tool condition monitoring for turning operation. The proposed method monitors the condition of the tool flank wear by classifying the tool into any one of the three states; initial wear, medium wear and severe wear. This classifying is done by a trained competitive neural network. The network is trained by using the instantaneous frequencies and amplitudes extracted from the audible emitted tool sound signal by using the new signal processing technique Hilbert-Huang transform. The proposed new method is tested by the audible sound signals collected from a turning machine while machining carbon steel with new, slightly worn and severely worn carbide inserts coated with Aluminum titanium nitride. From the marginal spectrum of Hilbert-Huang Transform analysis it is found that the amplitude of the emitted sound is increasing staidly as the tool flank wear is progressing with time. This correlation between the amplitude of the tool sound and tool flank wear enabled the trained competitive neural network to perform tool wear classification with 80% of accuracy. Hence, the new method can be implemented in tool condition monitoring of turning machines

Tool Flank Wear Classification using Hilbert Huang Transform and Competitive Neural Network

In this study, the relationship between emitted sound signal and the growth of tool wear was investigated and a new method is proposed for tool flank wear classification during turning operation. For this purpose, experiments were conducted in a turning machine in the university mechanical workshop by using fresh, slightly worn and severely worn carbide inserts while machining steel work piece. The emitted sound signal data was obtained by using a microphone. Tool wear was measured by a toolmaker’s microscope. The features namely, the instantaneous frequencies and their amplitudes, required for the competitive neural network to classify the state of the tool, were extracted from each emitted sound signal by using the new signal processing technique Hilbert Huang Transform. From the marginal spectrum plots, it is found that the increase in tool flank wear resulted in an increase of the sound pressure amplitude. This correlation enabled the competitive neural network to perform tool wear classification with 83.3% of accuracy

A study on the effect of electromigration on solder alloy joint on copper with nickel surface finish

The purpose of this study is to investigate the effect of electromigration (EM) on solder alloy joint on copper with nickel surface finish. Sn-Bi solder alloy has been used in this research. The EM process was completed with the duration of 0, 24, 48, 72 and 96 h under direct current (DC) of 1,000 mA. Tensile stress on the substrates was assessed after EM at a tension rate of 0.1 mm/min. Microscopy was used to observe the formation and size of voids and conduct an analysis between copper and nickel substrates. Findings – Four types of intermetallic compounds (IMCs), namely, Cu-Sn, Cu3Sn, Cu6Sn5, and Sn-Bi, were detected between the Sn-Bi/Co solder joint. Voids appear to be at the anode and the cathode for 96 h of EM for Sn-Bi/Ni solder join; however, there seem to be more voids at the cathode. Originality/value – EM is one of the crucial keys to produce a good integrated circuit (IC). When the current density is extremely high and will cause the metal ions to move into the electron direction flow, it will be characterised based on the ion flux density. In this research, the effect of EM on the Sn-Bi solder alloy joint on copper with nickel surface finish was studied