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2. Controlling a House’s Air-Conditioning Using Nonlinear Model Predictive Control.

Author

Oleiwi, Bashra Kadhim and Sabry, Ahmad H.

Abstract

As per literature, there is a potential for energy savings of (5% to 20%) using building embedded control systems. This letter aims to control a house air-conditioning system using model predictive control (MPC) with a neural state space prediction (NSSP) Model to maintain interior temperature set-point and reduce energy consumption. Here, we present a high-fidelity model that is validated by an experimental prototype of a house air-conditioning system that is controlled using a nonlinear MPC. The house air-conditioning system models the air-conditioner and the thermal dynamics of the house. The outdoor temperature is modeled by simulated signals and real measurements. The controller problem is to maintain a house temperature within 20 °C to 22 °C and to minimize energy costs. Compared with the generic nonlinear MPC controller, multistage nonlinear MPC provides a more flexible and efficient way to implement MPC with staged costs and constraints. [ABSTRACT FROM AUTHOR]

Published
2024
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18. An Industrial Cloud-Based IoT System for Real-Time Monitoring and Controlling of Wastewater

Author

Ranya M. M. Salem, M. Sabry Saraya, and Amr M. T. Ali-Eldin

Subjects
General Computer Science, industrial wastewater, cloud-based IoT, General Engineering, General Materials Science, Industrial Internet of Things (IIoT), Electrical engineering. Electronics. Nuclear engineering, sensors, Internet of Things (IoT), TK1-9971
Abstract

Wastewater treatment is considered the most important process for reducing pollutants in wastewater to levels that nature can cope with. At many sewages treatment plants, industrial wastes cause more difficulties in the treatment process than any other single problem where the plant operators have to deal with. These plants may not be designed to handle these types of wastes and the accelerated deterioration of sewage treatment plant structures. In this paper, we propose a new industrial IoT cloud-based model for real-time wastewater monitoring and controlling. The proposed system monitors the power of hydrogen (pH) and temperature parameters from the wastewater inlet that will be treated in the wastewater treatment plant, thereby avoiding impermissible industrial wastewater that the plant cannot handle. The system collects and uploads real-time sensor readings to the cloud via an IIoT Wi-Fi Module. Additionally, it reports observed or identified unexpected industrial wastewater inlets via SMS notifications and alarms and controls the valves of the gates. This is needed to change the path of the water to the industrial wastewater treatment plant that can treat this type of wastes. Experimental work shows the effectiveness of the proposed system compared to related work.

Published
2022

46. Sub-10nm Ultra-thin ZnO Channel FET with Record-High 561 µA/µm ION at VDS 1V, High µ-84 cm2/V-s and1T-1RRAM Memory Cell Demonstration Memory Implications for Energy-Efficient Deep-Learning Computing

Author

Chand, Umesh, primary, Sabry Aly, Mohamed M., additional, Lal, Manohar, additional, Chun-Kuei, Chen, additional, Hooda, Sonu, additional, Tsai, Shih-Hao, additional, Fang, Zihang, additional, Veluri, Hasita, additional, and Voon-Yew Thean, Aaron, additional

Published
2022
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49. Single-Phase Grid-Tied Transformerless Inverter of Zero Leakage Current for PV System

Author

N. H. Nik Ali, Farah Hani Nordin, Ali Saadon Al-Ogaili, Ahmed Haroun Sabry, and Zeina Mueen Mohammed

Subjects
General Computer Science, Computer science, Context (language use), Topology (electrical circuits), 02 engineering and technology, solar PV, law.invention, law, 0202 electrical engineering, electronic engineering, information engineering, Hardware_INTEGRATEDCIRCUITS, General Materials Science, IGBT inverters, Leakage (electronics), Total harmonic distortion, business.industry, 020208 electrical & electronic engineering, Photovoltaic system, General Engineering, Electrical engineering, 021001 nanoscience & nanotechnology, Power (physics), Capacitor, Capacitor voltage, Harmonic, Inverter, lcsh:Electrical engineering. Electronics. Nuclear engineering, 0210 nano-technology, business, lcsh:TK1-9971, Voltage, transformerless grid-connected photovoltaic inverter
Abstract

Multi-level transformerless inverters are widely used in grid-tied PV systems since they characterized by higher efficiency and lower cost. In this context, new topologies, modulation, and control schemes were presented to solve problems of a common-mode voltage and leakage current. This work proposes a transformerless five-level inverter with zero leakage current and ability to reduce the harmonic output content for a grid-tied single-phase PV system. The neutral of the grid links to a common on which the negative and the positive terminals of the DC-link are connected via parasitic capacitors that can eliminate the leakage current. The proposed topology, with its inherent circuit structure, leads to boost the overall efficiency. Simulation and experimental results show almost zero leakage current and a high-quality output when maintaining balanced capacitor voltages on the DC-link input. The experimental results show 1.07% THD and 96.3 % maximum efficiency when injecting a power of 1.1 kW that verify the performance of the proposed inverter with PV sources.

Published
2020

50. Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities

Author

Farida Sabry, Wadha Labda, Aiman Erbad, and Qutaibah Malluhi

Subjects
Artificial intelligence, machine learning, fraud detection, volatility prediction, deanonymization, lcsh:Electrical engineering. Electronics. Nuclear engineering, price prediction, lcsh:TK1-9971
Abstract

Decentralized cryptocurrencies have gained a lot of attention over the last decade. Bitcoin was introduced as the first cryptocurrency to allow direct online payments without relying on centralized financial entities. The use of Bitcoin has vastly grown as a financial asset rather than just a tool for online payments. A lot of cryptocurrencies have been created since 2011 with Bitcoin dominating the cryptocurrencies' market. With plenty of cryptocurrencies being used as financial assets and with millions of trades being executed through different exchange services, cryptocurrencies are susceptible to trading problems and challenges similar to those traditionally encountered in the financial domain. Price and trend prediction, volatility prediction, portfolio construction and fraud detection are some examples related to trading. In addition, there are other challenges that are specific to the domain of cryptocurrencies such as mining, cybersecurity, anonymity and privacy. In this paper, we survey the application of artificial intelligence techniques to address these challenges for cryptocurrencies with their vast amount of daily transactions, trades and news that are beyond human capabilities to analyze and learn from. This paper discusses the recent research work done in this emerging area and compares them in terms of used techniques and datasets. It also highlights possible research gaps and some potential areas for improvement.

Published
2020

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