Solar radiation forecasting by Pearson correlation using LSTM neural network and ANFIS method: application in the west-central Jordan

none6siSolar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.Topical Collection "Computer Vision, Deep Learning and Machine Learning with Applications"openHossam Fraihat, Amneh A. Almbaideen, Abdullah Al-Odienat, Bassam Al-Naami, Roberto De Fazio, Paolo ViscontiFraihat, Hossam; Almbaideen, Amneh A.; Al-Odienat, Abdullah; Al-Naami, Bassam; DE FAZIO, Roberto; Visconti, Paol

Assessment of Dual-Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

none6siThe research paper proposes a novel denoising method to improve the outcome of heartsound (HS)-based heart-condition identification by applying the dual-tree complex wavelet transform (DTCWT) together with the adaptive neuro-fuzzy inference System (ANFIS) classifier. The method consists of three steps: first, preprocessing to eliminate 50 Hz noise; second, applying four successive levels of DTCWT to denoise and reconstruct the time-domain HS signal; third, to evaluate ANFIS on a total of 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The results show that the signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was an 11% to many decibel (dB)-fold increase in SNR after DTCWT, representing a significant improvement in denoising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc compared to other attempts on the same dataset. Therefore, DTCWT is a successful technique in removing noise from biosignals such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.Special Issue “Biomedical Signal Processing”, Section BioelectronicsopenBassam Al-Naami, Hossam Fraihat, Jamal Al-Nabulsi, Nasr Y. Gharaibeh, Paolo Visconti, Abdel-Razzak Al-HinnawiAl-Naami, Bassam; Fraihat, Hossam; Al-Nabulsi, Jamal; Gharaibeh, Nasr Y.; Visconti, Paolo; Al-Hinnawi, Abdel-Razza

Contribution to the visual perception multi-resolution of the 3D environment : application to autonomous robotics

Le travail de recherche effectué dans le cadre de cette thèse concerne le développement d’un système de perception de la saillance en environnement 3D en tirant l’avantage d’une représentation pseudo-3D. Notre contribution et concept issue de celle-ci part de l'hypothèse que la profondeur de l’objet par rapport au robot est un facteur important dans la détection de la saillance. Sur ce principe, un système de vision saillante de l’environnement 3D a été proposé, conçu et validée sur une plateforme comprenant un robot équipé d’un capteur pseudo-3D. La mise en œuvre du concept précité et sa conception ont été d’abord validés sur le système de vision pseudo-3D KINECT. Puis dans une deuxième étape, le concept et les algorithmes mis aux points ont été étendus à la plateforme précitée. Les principales contributions de la présente thèse peuvent être résumées de la manière suivante : A) Un état de l'art sur les différents capteurs d'acquisition de l’information de la profondeur ainsi que les différentes méthodes de la détection de la saillance 2D et pseudo 3D. B) Etude d’un système basé sur la saillance visuelle pseudo 3D réalisée grâce au développement d’un algorithme robuste permettant la détection d'objets saillants dans l’environnement 3D. C) réalisation d’un système d’estimation de la profondeur en centimètres pour le robot Pepper. D) La mise en œuvre des concepts et des méthodes proposés sur la plateforme précitée. Les études et les validations expérimentales réalisées ont notamment confirmé que les approches proposées permettent d’accroitre l’autonomie des robots dans un environnement 3D réelThe research work, carried out within the framework of this thesis, concerns the development of a system of perception and saliency detection in 3D environment taking advantage from a pseudo-3D representation. Our contribution and the issued concept derive from the hypothesis that the depth of the object with respect to the robot is an important factor in the detection of the saliency. On this basis, a salient vision system of the 3D environment has been proposed, designed and validated on a platform including a robot equipped with a pseudo-3D sensor. The implementation of the aforementioned concept and its design were first validated on the pseudo-3D KINECT vision system. Then, in a second step, the concept and the algorithms have been extended to the aforementioned robotic platform. The main contributions of the present thesis can be summarized as follow: A) A state of the art on the various sensors for acquiring depth information as well as different methods of detecting 2D salience and pseudo 3D. B) Study of pseudo-3D visual saliency system based on benefiting from the development of a robust algorithm allowing the detection of salient objects. C) Implementation of a depth estimation system in centimeters for the Pepper robot. D) Implementation of the concepts and methods proposed on the aforementioned platform. The carried out studies and the experimental validations confirmed that the proposed approaches allow to increase the autonomy of the robots in a real 3D environmen

Contribution à la perception visuelle multi-résolution de l’environnement 3D : application à la robotique autonome

The research work, carried out within the framework of this thesis, concerns the development of a system of perception and saliency detection in 3D environment taking advantage from a pseudo-3D representation. Our contribution and the issued concept derive from the hypothesis that the depth of the object with respect to the robot is an important factor in the detection of the saliency. On this basis, a salient vision system of the 3D environment has been proposed, designed and validated on a platform including a robot equipped with a pseudo-3D sensor. The implementation of the aforementioned concept and its design were first validated on the pseudo-3D KINECT vision system. Then, in a second step, the concept and the algorithms have been extended to the aforementioned robotic platform. The main contributions of the present thesis can be summarized as follow: A) A state of the art on the various sensors for acquiring depth information as well as different methods of detecting 2D salience and pseudo 3D. B) Study of pseudo-3D visual saliency system based on benefiting from the development of a robust algorithm allowing the detection of salient objects. C) Implementation of a depth estimation system in centimeters for the Pepper robot. D) Implementation of the concepts and methods proposed on the aforementioned platform. The carried out studies and the experimental validations confirmed that the proposed approaches allow to increase the autonomy of the robots in a real 3D environmentLe travail de recherche effectué dans le cadre de cette thèse concerne le développement d’un système de perception de la saillance en environnement 3D en tirant l’avantage d’une représentation pseudo-3D. Notre contribution et concept issue de celle-ci part de l'hypothèse que la profondeur de l’objet par rapport au robot est un facteur important dans la détection de la saillance. Sur ce principe, un système de vision saillante de l’environnement 3D a été proposé, conçu et validée sur une plateforme comprenant un robot équipé d’un capteur pseudo-3D. La mise en œuvre du concept précité et sa conception ont été d’abord validés sur le système de vision pseudo-3D KINECT. Puis dans une deuxième étape, le concept et les algorithmes mis aux points ont été étendus à la plateforme précitée. Les principales contributions de la présente thèse peuvent être résumées de la manière suivante : A) Un état de l'art sur les différents capteurs d'acquisition de l’information de la profondeur ainsi que les différentes méthodes de la détection de la saillance 2D et pseudo 3D. B) Etude d’un système basé sur la saillance visuelle pseudo 3D réalisée grâce au développement d’un algorithme robuste permettant la détection d'objets saillants dans l’environnement 3D. C) réalisation d’un système d’estimation de la profondeur en centimètres pour le robot Pepper. D) La mise en œuvre des concepts et des méthodes proposés sur la plateforme précitée. Les études et les validations expérimentales réalisées ont notamment confirmé que les approches proposées permettent d’accroitre l’autonomie des robots dans un environnement 3D rée

Automated Detection of Left Bundle Branch Block from ECG Signal Utilizing the Maximal Overlap Discrete Wavelet Transform with ANFIS

Left bundle branch block (LBBB) is a common disorder in the heart’s electrical conduction system that leads to the ventricles’ uncoordinated contraction. The complete LBBB is usually associated with underlying heart failure and other cardiac diseases. Therefore, early automated detection is vital. This work aimed to detect the LBBB through the QRS electrocardiogram (ECG) complex segments taken from the MIT-BIH arrhythmia database. The used data contain 2655 LBBB (abnormal) and 1470 normal signals (i.e., 4125 total signals). The proposed method was employed in the following steps: (i) QRS segmentation and filtration, (ii) application of the Maximal Overlapped Discrete Wavelet Transform (MODWT) on the ECG R wave, (iii) selection of the detailed coefficients of the MODWT (D2, D3, D4), kurtosis, and skewness as extracted features to be fed into the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier. The obtained results proved that the proposed method performed well based on the achieved sensitivity, specificity, and classification accuracies of 99.81%, 100%, and 99.88%, respectively (F-Score is equal to 0.9990). Our results showed that the proposed method was robust and effective and could be used in real clinical situations