Enhancing hybrid renewable system performance through load shifting: A multi-objective optimization and forecasting approach

• Epistemic uncertainty based reliability assessment of reconfigurable manufacturing systems • Reliability performance models built by optimistic and pessimistic assessment approaches • Numerical analysis of the optimistic and pessimistic based reliability models The importance of assessing the reliability of a complex system paradigm such as a reconfigurable manufacturing system arises from its relationship with quality, efficiency, flexibility, complexity, safety and application. However, during practical reliability analysis of a reconfigurable manufacturing system, epistemic uncertainty can emerge since the degradation information of the complex system can be insufficient, making it critical to develop a response approach. Therefore, this study develops an approach which takes into account the two extreme possibilities - optimistic and pessimistic - to set the boundary for the reliability performance of the system. The approach is proposed specifically for a reconfigurable manufacturing system with variable configuration orders. The optimistic assessment assumes that the system does not have a usage history when reconfigured with a new tool. In contrast, the pessimistic assessment assumes that the usage history has full impact on the newly reconfigured system. An algorithm is developed for the optimistic and pessimistic assessment model based on Monte Carlo simulation. The simulation result demonstrates the ability of the approach to analyze different levels of optimism and pessimism, and to quantify the uncertainty of reliability performance of a reconfigurable manufacturing system.

Conventional vibration demodulation typically utilizes nearby sample points in conjunction with pulses having relatively wide width for demodulation, which has drawbacks, including inconsistent spatial positions corresponding to adjacent sampling points and limited spatial resolution. This research proposes a novel quantitative vibration demodulation technique based on direct detection Φ-OTDR to address this issue. At a set repetition frequency, this technique alternately emits optical pulses with comparable but different pulse lengths. To precisely demodulate the vibration phase, it obtains two optical intensity signals at the same fiber position, which have different initial phases but virtually similar vibration-induced phase shifts. Compared with the conventional demodulation method, the proposed technique achieves a signal-to-noise ratio (SNR) improvement of 9.4 dB. By not relying on the data acquisition card's (DAQ) sampling rate, this technique reduces the acquisition system's cost and performance requirements while avoiding the spatial resolution deterioration associated with conventional demodulation using nearby sampling points.

Optimizing static interphase power controllers (SIPC) placement: A comprehensive multi-objective strategy for enhanced performance of the electric transmission networks

Model for predicting photovoltaic soiling loss and degradation of PV electrical performance using optimized machine learning

Algal-bacterial and bacterial aerobic granular sludge under mixed antibiotic exposure: System performance and resistance-related dynamics

Operational insights and analyses of an integrated subsea CO2 injection system

The dawn of Reconfigurable Intelligent Surfaces (RIS) promises to revolutionize wireless communication by enabling dynamic control of the propagation of electromagnetic waves. However, the practical implementation of RIS demands sophisticated configuration strategies to unlock their full potential. This paper presents a hardware implementation of a Deep Learning (DL) approach to the configuration of RIS, addressing both the complexity and efficiency of the configuration process. A deep learning-based algorithm, designed to optimize the phase shifts of RIS elements and shown to enhance signal quality and system performance, is implemented on two dedicated hardware platforms based on Field-Programmable Gate Arrays (FPGA), which leads to real-time processing and adaptability. This approach leverages the inherent parallelism of FPGAs to accelerate the computationally intensive tasks associated with deep learning inference. As a matter of fact, it is possible to achieve more than 18.000 configurations per second, thus ensuring rapid and efficient RIS configuration with a novel approach within this field. • FPGA implementation of hardware accelerators of CNNs for RIS configuration on the edge. • CNN tailored for FPGA-based acceleration. • Resource and performance analysis of several architectures on Altera® and AMD FPGA devices. • Novel approach to computation of RIS configurations.

Multi-unit residential buildings represent an emerging archetype that requires energy for cooling, heating, and power typically supplied from the local grid. Renewable energy-based solutions meet this energy demand in a sustainable manner by reducing pressure on existing grid infrastructure. In this study, a transient numerical model of a building-based photovoltaic/thermal driven combined cooling, heating and power system with two latent heat thermal energy storage tanks is developed in TRNSYS and C++. The proposed system is assessed for its capability to offset the electrical, space conditioning and domestic hot water requirements of a low-energy multi-unit residential building comprising 12 individual units. This combination demonstrates a novel integration between the proposed system and emerging residential building archetype in North America. A parametric analysis is conducted in which four annual simulations are conducted. Two North American case study locations representing a heating dominated (Ottawa, Canada) and a cooling dominated (Albuquerque, United States of America) climate are examined. For each location, two scenarios of solar collector array area are considered. System performance is assessed via calculation of the annual solar fraction, electricity fraction, and grid electricity consumption. Results show that the total grid electricity consumption varies between 373.6 GJ and 427.5 GJ depending on the local climate conditions, and also operating the system in Albuquerque relative to Ottawa results in an increase in solar fraction and electricity fraction of up to 62% and 40%, respectively. However, increasing the solar collector array area in Ottawa corresponds to an increase in solar fraction, whereas in Albuquerque the solar fraction remains relatively constant, highlighting the proposed system's sensitivity to the local climate. • A novel dual tank control algorithm is integrated into a low energy residential building. • The low energy multi-unit residential building consists of 12 units. • The climate location and collector area are varied in the analysis. • The solar and electricity fractions, and grid electricity consumption are calculated. • The location of the system is an important factor for increasing performance.

This paper investigates the mitigation of DDoS attacks in TCP/IP networks, which pose a significant threat to modern information systems. An analysis of the primary types of DDoS attacks and existing detection and neutralization approaches is conducted. The study justifies the implementation of the Proof-of-Work (PoW) mechanism as an effective tool for rate-limiting requests by establishing a computational barrier for clients. In TCP/IP environments, the PoW mechanism leverages computational cost asymmetry, where a client performs resource-intensive computations before transmitting a request, while server-side verification remains rapid and low-overhead. An approach is proposed to integrate PoW into the TCP connection establishment process, specifically during the three-way handshake phase, to reduce the volume of half-open connections and enhance resilience against SYN flood attacks. System performance was modeled with varying numbers of concurrent clients, and the results confirm the effectiveness of the proposed approach in reducing server load and increasing the attack cost for adversaries. The findings demonstrate the viability of utilizing PoW as a core component of comprehensive cyber defense systems and outline perspectives for its further development. Keywords: information security, cryptographic protection, cyber threat, network attack, user behavior analytics, server, TCP/IP, TLS, PoW, Zero Trust, DDoS.

This study proposes a novel double-slope chirp symbol, termed double–slope start zero stop minimum (DSSZ–SM), for efficient data communication. Unlike conventional chirp coding, which often involves complex generation and synchronization, the DSSZ–SM provides a simpler structure with inherent clock synchronization using a PWM-based generator. System performance is evaluated through analysis and simulations over additive white Gaussian noise (AWGN) and Rayleigh fading channels. Two asynchronous decoding methods, with and without an integrator, are compared. Results show that the non-integrator approach achieves lower error rates under both channel conditions. The proposed DSSZ–SM offers a simplified and robust alternative for efficient data communication. Keywords: Double-slope chirp, chirp encoding, asynchronous decoding, data communication

Plastisphere enhanced resistance genes propagation in sulfur autotrophic/heterotrophic denitrification system under mixed quaternary ammonium compounds pressure.

Prediction and optimization of partial denitrification and anammox performance by machine learning and key bioindicators identification.

Progressive state transfer for BFT with larger-than-memory state

Multimode control of a variable-speed wind energy system coupled to standalone DC microgrid

Thermodynamic analysis of power generation and waste heat recovery in CO2-plume geothermal systems in aquifers of varying heterogeneity

Iron-based anodes in CW-MFCs: mechanisms and performance enhancement for contaminant removal and sulfamethoxazole elimination.

Transverse flux PM linear generator (TF-PMLG) enables a higher power density under low-speed wave characteristics. However, thrust pulsation and magnetic leakage are major issues in these generators. To address the foregoing problems, a hybrid excitation TF-PMLG is proposed. However, it presents significant control challenges due to large phase inductance and non-negligible internal resistance, particularly under conditions of insufficient bus voltage. Additionally, the necessity for electromagnetic thrust to adapt in real time to wave variations further intensifies the demands on the dynamic performance of the control system. To this end, this paper proposes a model predictive thrust control (MPTC) strategy based on optimal flux linkage vector selection. It can effectively mitigate thrust fluctuations of the generator in response to wave action. Furthermore, a dynamic flux-enhancing strategy is developed, which can reduce the dependence on bus voltage magnitude, thus increasing the peak thrust of the generator. Finally, simulations and experimental results are provided to validate the effectiveness of the proposed MPTC methods in direct-drive wave energy converter (DDWEC) system.

Vapor bypass technology to enhance the performance of an air source heat pump system with 5 mm diameter finned tube heat exchanger

A multi-function shared hybrid battery energy storage system for commercial applications: Energy arbitrage, university building and electrical vehicle charging station