Abstract Protecting user privacy is essential in machine learning research, especially in the context of data collection. Federated learning (FL), which trains models across decentralized devices without sharing raw data, has emerged as a promising solution. However, FL is still vulnerable to security threats, including inference attacks, which have been underexplored in comparison to poisoning and backdoor attacks that have received more attention in existing research. To address these vulnerabilities, this paper proposes a novel aggregation framework called homomorphic and polymorphic federated learning aggregation of parameters (HP_FLAP). HP_FLAP integrates both homomorphic and polymorphic encryption to enhance the security and privacy of FL. Homomorphic encryption allows the server to perform aggregation on encrypted parameters without decrypting them, ensuring that sensitive information is protected during the aggregation process. Polymorphic encryption further strengthens security by using different encryption keys for each set of parameters, mitigating the risk of system-wide compromise in case a key is leaked. This dual encryption approach effectively counters inference attacks while maintaining robust protections against other security threats. The framework is evaluated using multiple models, including logistic regression, Gaussian Naive Bayes, Stochastic Gradient Descent, and Multi-Layer Perceptron, demonstrating HP_FLAP’s ability to enhance both security and privacy in FL environments.

Smart Grid (SG) technology utilizes advanced network communication and monitoring technologies to manage and regulate electricity generation and transport. However, this increased reliance on technology and connectivity also introduces new vulnerabilities, making SG communication networks susceptible to large-scale attacks. While previous surveys have mainly provided high-level overviews of SG architecture, our analysis goes further by presenting a comprehensive architectural diagram encompassing key SG components and communication links. This holistic view enhances understanding of potential cyber threats and enables systematic cyber risk assessment for SGs. Additionally, we propose a taxonomy of various cyberattack types based on their targets and methods, offering detailed insights into vulnerabilities. Unlike other reviews focused narrowly on protection and detection, our proposed categorization covers all five functions of the National Institute of Standards and Technology cybersecurity framework. This delivers a broad perspective to help organizations implement balanced and robust security. Consequently, we have identified critical research gaps, especially regarding response and recovery mechanisms. This underscores the need for further investigation to bolster SG cybersecurity. These research needs, among others, are highlighted as open issues in our concluding section.

In this paper, the bacterial foraging optimization (BFO) method is developed to track the Global Maximum Power Point (GMPP) of a partially shaded PV system for a standalone PV system. This heuristic method emulates the behavior of E.coli bacteria and can be used to solve various complex problems. Voltage and current measured data are used as input to evaluate the operation point of the PV system and the BFO method can reach the GMPP within an appropriate time. Simulation results confirm the performance of the BFO method for GMMPT for different partial shading profiles of a PV system.

Ambient Assisted Living (AAL) aims at assisting people in their Activities of Daily Living (ADL). We have seen an increased interest in their applicability to the rural seniors who are slowly losing their autonomy due to aging and chronic diseases. By deploying intelligent devices in the environment of an individual performing their ADLs, we can gather data in the form of a time series. One research venue is to seek to use forecasting techniques to discover trends and predict future trends that could be used to analyze the health of these individuals. With the recent advances in computational power new deep learning forecasting algorithms have been developed. In this paper, we compare a univariate one-dimensional CNN model and a LSTM model that performs multi-step forecasting for one week ahead. The novel dataset used comes from a set of activity and health related sensors deployed in a small apartment that uses our previously designed analytics architecture. We compare these to a forecasting baseline strategy. Both deep learning approaches increase the forecasting accuracy significantly.

Anew control strategy to achieve high performance from solar photovoltaic system using single stage inverter interfacing grid without maximum power point tracking (MPPT) techniques is investigated in this paper. Sinusoid-tracking algorithm and adaptive filter is proposed to improve the power quality and to regulate constant AC voltage at the point of common coupling (PCC). Proportional integral controllers for the both outer loops are reinforced by antiwindup strategy to prevent saturation issue when system is subjected to sudden variation. The effectiveness of the developed control algorithm and the proposed configuration are tested using Matlab/Simulink.

In this paper control design for safe and effective operation of small-scale hybrid wind/diesel/Battery standalone power generation system (WT-DG HSPGS) employing fixed speed generators is investigated. To compensate the fluctuation of wind power, reduce the run-time of the diesel generator (DG), regulate the AC voltage and the system frequency, as well as, to improve the power quality at the PCC, STATCOM supported by battery energy storage system (BESS) is controlled employing synchronous reference frame (SRF) theory and estimated in-phase and quadrature unit AC voltage templates. To synchronize the DG to the PCC, breaker switch is controlled using the estimated voltage phase angle and the system frequency. To protect BESS from overcharging, dump loads is controlled using simple approach based on BESS limit charging voltage. To test the performance of the proposed WT-DG HSPGS and their developed control algorithms under presence of balanced/unbalanced, linear/nonlinear loads and change in climate conditions, simulations are carried out using MATLAB/Simulink.

A control strategy developed for hybrid wind-diesel system (HWDS) is presented in this paper. The proposed system consists of the induction generator based wind turbine generation (WTG) system, IGBT based power electronics converters, diesel generator (DG) unit and dump load (DL) system. Developed control strategy shares the required power generation between the WTG and DG unit. The control system operates the wind turbine to achieve maximum power extraction using maximum power point tracking (MPPT) scheme, and simultaneously controls the dump load to absorb excessive power generation, in turn regulates the frequency of power supply. The DG unit is controlled for supplementing the generated power from WTG to supply the complete load power demand. The complete system is modeled and simulated using SimPowerSystems toolbox from Matlab-Simulink software package.

Abstract This paper examines the applicability of fine-grained tree-structured SIMD machines, which are amenable to highly efficient VLSI implementation, to several low-level image understanding tasks. Algorithms are presented for histogramming, thresholding, image correlation, connected component labeling, and computing Euler number. A particular massively parallel machine called NON-VON is used for purposes of explication and performance evaluation. Only NON-VON tree-structured communication capabilities and its SIMD mode of execution are considered in this paper. Novel algorithmic techniques are described, such as vertical pipelining, subproblem partitioning, associative matching, and data duplication, that effectively exploit the massive parallelism available in fine-grained SIMD tree machines while avoiding communication bottlenecks. Simulation results are presented and compared with results obtained or forecast for other highly parallel machines. The relative advantages and limitations of the class of machines under consideration are outlined; except for some types of image correlation, the fine-grained SIMD tree is exceptionally fast.