Establishing the Technical Activities and Technical Committees of IEEE Consumer Technology Society

The IEEE Consumer Technology Society (CTSoc) is the oldest technical society: it was part of IRE 1920, which merged with AIEE to form IEEE in 1963. As CTSoc claims to be an IEEE Technical Society and is actually one of the 39 IEEE Societies operating under the IEEE Technical Activities Board, it was essential for its recent organizational restructure to include a Technical Activities (TAs) area. This article summarizes the efforts that have been put recently in place over slightly more than two years (since September 2019) by a group of volunteers under the guidance of CTSoc's President and with the help of VP of TAs to establish the TAs area and its 15 technical committees (TCs)

A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques

CDPS-IoT: Cardiovascular Disease Prediction System Based on IoT using Machine Learning

Internet of Things, Machine learning, and Cloud computing are the emerging domains of information communication and technology. These techniques can help to save the life of millions in the medical assisted environment and can be utilized in health-care system where health expertise is less available. Fast food consumption increased from the past few decades, which makes up cholesterol, diabetes, and many more problems that affect the heart and other organs of the body. Changing lifestyle is another parameter that results in health issues including cardio-vascular diseases. Affirming to the World Health Organization, the cardiovascular diseases, or heart diseases lead to more death than any other disease globally. The objective of this research is to analyze the available data pertaining to cardiovascular diseases for prediction of heart diseases at an earlier stage to prevent it from occurring. The dataset of heart disease patients was taken from Jammu and Kashmir, India and stored over the cloud. Stored data is then pre-processed and further analyzed using machine learning techniques for the prediction of heart diseases. The analysis of the dataset using numerous machines learning techniques like Random Forest, Decision Tree, Naive based, K-nearest neighbors, and Support Vector Machine revealed the performance metrics (F1 Score, Precision and Recall) for all the techniques which shows that Naive Bayes is better without parameter tuning while Random Forest algorithm proved as the best technique with hyperparameter tuning. In this paper, the proposed model is developed in such a systematic way that the clinical data can be obtained through the use of IoT with the help of available medical sensors to predict cardiovascular diseases on a real-time basis

sCrop: A Novel Device for Sustainable Automatic Disease Prediction, Crop Selection, and Irrigation in Internet-of-Agro-Things for Smart Agriculture

Accepted Manuscript version of an article introducing the innovative idea of the Internet-of-Agro-Things (IoAT) with an explanation of the automatic detection of plant disease for the development of Agriculture Cyber-Physical System (ACPS). An accuracy of 99.24% is achieved by the proposed plant disease prediction framework

Evaluation of Metaverse Traffic Safety Implementations using fuzzy Einstein based logarithmic methodology of additive weights and TOPSIS method

As the Metaverse’s popularity grows, its effect on everyday problems is beginning to be discussed. The upcoming Metaverse world will influence the transportation system as cross-border lines blur due to rapid globalization. The purpose of this paper is to investigate the capabilities of the Metaverse and its alternatives to traffic safety, as well as to prioritize its advantages. The case study is based on a densely populated metropolis with an extensive education system. The city’s decision-makers will have to weigh the pros and cons of the Metaverse’s effect on traffic safety. To illustrate the complex forces that drive the decision-making process in traffic safety, we create a case study with four alternatives to Metaverse’s integration into the traffic system. Alternatives are evaluated using twelve criteria that reflect the decision problem’s rules and regulations, technology, socioeconomic, and traffic aspects. In this study, fuzzy Einstein based logarithmic methodology of additive weights (LMAW) is applied to calculate the weights of the criteria. We present a new framework that combines Einstein norms and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the alternatives. The findings of this study show that public transportation is the most appropriate area for implementing the Metaverse into traffic safety because of its practical opportunities and broad usage area

Long-term experimental study of price responsive predictive control in a real occupied single-family house with heat pump

The continuous introduction of renewable electricity and increased consumption through electrification of the transport and heating sector challenges grid stability. This study investigates load shifting through demand side management as a solution. We present a four-month experimental study of a low-complexity, hierarchical Model Predictive Control approach for demand side management in a near-zero emission occupied single-family house in Denmark. The control algorithm uses a price signal, weather forecast, a single-zone building model, and a non-linear heat pump efficiency model to generate a space-heating schedule. The weather-compensated, commercial heat pump is made to act smart grid-ready through outdoor temperature input override to enable heat boosting and forced stops to accommodate the heating schedule. The cost reduction from the controller ranged from 2-33% depending on the chosen comfort level. The experiment demonstrates that load shifting is feasible and cost-effective, even without energy storage, and that the current price scheme provides an incentive for Danish end-consumers to shift heating loads. However, issues related to controlling the heat pump through input-manipulation were identified, and the authors propose a more promising path forward involving coordination with manufacturers and regulators to make commercial heat pumps truly smart grid-ready

Microgrids

Microgrids are a growing segment of the energy industry, representing a paradigm shift from centralized structures toward more localized, autonomous, dynamic, and bi-directional energy networks, especially in cities and communities. The ability to isolate from the larger grid makes microgrids resilient, while their capability of forming scalable energy clusters permits the delivery of services that make the grid more sustainable and competitive. Through an optimal design and management process, microgrids could also provide efficient, low-cost, clean energy and help to improve the operation and stability of regional energy systems. This book covers these promising and dynamic areas of research and development and gathers contributions on different aspects of microgrids in an aim to impart higher degrees of sustainability and resilience to energy systems

Méthode inverse pour estimer les paramètres électrochimiques et thermophysiques des batteries aux ions lithium composées de différents matériaux pour l’électrode positive

La sécurité de plusieurs systèmes électriques est fortement dépendante de la fiabilité de leur bloc-batterie à base de piles aux ions lithium (Li-ion). Par conséquent, ces batteries doivent être suivis et contrôlés par un système de gestion des batteries (BMS). Le BMS interagit avec toutes les composantes du bloc-batterie de façon à maintenir leur intégrité. La principale composante d’un BMS est un modèle représentant le comportement des piles Liion et capable de prédire ses différents points d’opération. Dans les industries de l’électronique et de l’automobile, le BMS repose habituellement sur des modèles empiriques simples. Ceux-ci ne sont cependant pas capables de prédire les paramètres de la batterie lorsqu’elle vieillit. De plus, ils ne sont applicables que pour des piles spécifiques. D’un autre côté, les modèles électrochimiques sont plus sophistiqués et plus précis puisqu’ils sont basés sur la résolution des équations de transport et de cinétique électrochimique. Ils peuvent être utilisés pour simuler les caractéristiques et les réactions à l’intérieur des piles aux ions lithium. Pour résoudre les équations des modèles électrochimiques, il faut connaître les différents paramètres électrochimiques et thermo-physiques de la pile. Les variables les plus significatives des piles Li-ion peuvent être divisées en 3 catégories : les paramètres géométriques, ceux définissant les matériaux et les paramètres d’opération. Les paramètres géométriques et de matériaux peuvent être facilement obtenus à partir de mesures directes ou à partir des spécifications du manufacturier. Par contre, les paramètres d’opération ne sont pas faciles à identifier. De plus, certains d’entre eux peuvent dépendre de la technique de mesure utilisée et de l’âge. Finalement, la mesure de certains paramètres requiert le démantèlement de la pile, une procédure risquée et destructive. Plusieurs recherches ont été réalisées afin d’identifier les paramètres opérationnels des piles aux ions lithium. Toutefois, la plupart de ces études ont porté sur l’estimation d’un nombre limité de paramètres et se sont attardées sur un seul type de matériau pour l’électrode positive utilisé dans la fabrication des piles Li-ion. De plus, le couplage qui existe entre les paramètres électrochimiques et thermo-physiques est complètement ignoré. Le but principal de cette thèse est de développer une méthode générale pour identifier simultanément différents paramètres électrochimiques et thermo-physiques et de prédire la performance des piles Li-ion à base de différents matériaux d’électrodes positives. Pour atteindre ce but, une méthode inverse efficace a été introduite. Des modèles directs représentatifs des piles Li-ion à base de différents matériaux d’électrodes positives ont également été développés. Un modèle rapide et précis simulant la performance de piles Li-ion avec des électrodes positives à base de LiMn2O4 ou de LiCoO2 est présenté. Également, deux modèles ont été développés pour prédire la performance des piles Li-ion avec une électrode positive de LiFePO4. Le premier, appelé modèle mosaïque modifié (MM), est basé sur une approche macroscopique alors que le deuxième, appelé le modèle mésoscopique, est plutôt basé sur une approche microscopique. Des études d’estimation de paramètres ont été conduites en utilisant les modèles développés et des données expérimentales fournies par Hydro-Québec. Tous les paramètres électrochimiques et thermo-physiques des piles Li-ions ont été simultanément identifiés et appliqués à la prédiction de la performance des piles. Finalement, une technique en temps réel reposant sur des réseaux de neurones est introduite dans la méthode d’estimation des paramètres intrinsèques au piles Li-ion.Abstract : The safety of many electrical systems is strongly dependent on the reliable operation of their lithium-ion (Li-ion) battery packs. As a result, the battery packs must be monitored by a battery management system (BMS). The BMS interacts with all the components of the system so as to maintain the integrity of the batteries. The main part of a BMS is a Li-ion battery model that simulates and predicts its different operating points. In the electronics and in the automobile industries, the BMS usually rests on simple empirical models. They are however unable to predict the battery parameters as it ages. Furthermore, they are only applicable to a specific cell. Electrochemical-based models are, on the other hand, more sophisticated and more precise. These models are based on chemical/electrochemical kinetics and transport equations. They may be used to simulate the Li-ion battery characteristics and reactions. In order to run the electrochemical-based mathematical models, it is imperative to know the different electrochemical and thermophysical parameters of the battery. The significant variables of the Li-ion battery can be classified into three groups: geometric, material and operational parameters. The geometric and material parameters can be easily obtained from direct measurements or from the datasheets provided by the manufacturer. The operational properties are, on the other hand, not easily available. Furthermore, some of them may vary according to the measurement techniques or the battery age. Sometimes, the measurement of these parameters requires the dismantling of the battery itself, which is a risky and destructive procedure. Many investigations have been conducted to identify the operational parameters of Li-ion batteries. However, most of these studies focused on the estimation of limited parameters, or considered only one type of the positive electrode materials used in Li-ion batteries. Moreover, the coupling of the thermophysical parameters to the electrochemical variables is ignored in all of them. The main goal of this thesis is to develop a general method to simultaneously identify different electrochemical and thermophysical parameters and to predict the performance of Li-ion batteries with different positive electrode materials. To achieve this goal, an effective inverse method is introduced. Also, direct models representative of Li-ion batteries are developed, applicable for all of the positive electrode materials. A fast and accurate model is presented for simulating the performance of the Li-ion batteries with the LiMn2O4 and LiCoO2 positive electrodes. Moreover, two macro- and micro-based models are developed for predicting the performance of Li-ion battery with the LiFePO4 positive electrode, namely the Modified Mosaic (MM) and the mesoscopic-based models. The parameter estimation studies are then implemented by means of the developed direct models and experimental data provided by Hydro-Québec. All electrochemical and thermophysical parameters of the Li-ion batteries are simultaneously identified and applied for the prediction of the battery performance. Finally, a real-time technique resting on neural networks is used for the estimation of the Li-ion batteries intrinsic parameters

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader