Md. Shirajum Munir 교수

The increased non-linear loads in today's typical home are a growing concern for utility companies. This situation might be worsened by the harmonic resonance introduced by the installation of capacitor banks in the distribution network. To mitigate the harmonic distortions, passive or active filters are typically used. However, with the increasing implementation of distributed generation (DG) in residential areas, using DG systems to improve the power quality is becoming a promising idea, parti

The computational tasks at multiaccess edge computing (MEC) are unpredictable in nature, which raises uneven energy demand for MEC networks. Thus, to handle this problem, microgrid has the potentiality to provides seamless energy supply from its energy sources (i.e., renewable, nonrenewable, and storage). However, supplying energy from the microgrid faces challenges due to the high uncertainty and irregularity of the renewable energy generation over the time horizon. Therefore, in this paper, we

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. E

Increased non-linear residential loads in today's distribution system is a concern due to the harmonics related power quality issues. The situation gets worsened by the harmonic resonance introduced by the installation of power factor correction (PFC) capacitor banks in the distribution network. At the same time, more and more renewable energy-based distributed generation (DG) units are being installed in the residential area. These DG systems can be used as an effective way to mitigate the harm

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. Thus, a reliable XAI twin system becomes essential to discretizing the physical behavior of the Internet of Everything (IoE) and identifying the reasons behind that behavior for enabling ZSM. To address the challenges of extensible, modular, and stateless management functions in ZSM, a novel neuro-symbolic XAI twin fr

The stringent requirements of mobile edge computing (MEC) applications and functions fathom the high capacity and dense deployment of MEC hosts to the upcoming wireless networks. However, operating such high capacity MEC hosts can significantly increase energy consumption. Thus, a base station (BS) unit can act as a self-powered BS. In this article, an effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. First, a two-stage linear sto

Smart city is the prospect of current intellectual technology toward the ecological growth of urban technology and commercial expansion. In addition, the Internet of Things (IoT) based smart city services ensure the eminence of life and well-being to the smart citizens. In order to ensure quality services, each of city service gathers multidimensional data from abundant IoT nodes. Therefore, the centralized traffic management has become critically challenging for a large volume of multidimension

The ongoing development of edge computing in fifth-generation (5G) networks promises to provide an artificial intelligence-as-a-service (AIaaS) for meeting the stringent requirements of everything as a service (XaaS) in the edge of the networks. Therefore, the concept of edge-artificial intelligence (edge-AI) is not only evolving but also emergent enabler toward AI service fulfillment. In this paper, we investigate an AI-based service aggregation problem for a mobile agent in AIaaS-enabled edge

The nature of multi-access edge computing (MEC) is to deal with heterogeneous computational tasks near to the end users, which induces the volatile energy consumption for the MEC network. As an energy supplier, a microgrid is able to enable seamless energy flow from renewable and non- renewable sources. In particular, the risk of energy demand and supply is increased due to nondeterministic nature of both energy consumption and generation. In this paper, we impose a risk- sensitive energy profil

Voltage source inverters (VSIs) with output LCL filters are the key interfaces for today's distributed energy resource. There are mainly two groups of current control methods of a VSI: direct error tracking control with PWM, and closed-loop feedback control. Direct current error control, such as predictive control and hysteresis control, has some drawbacks like system parameter sensitivity, variable switching frequency, etc. On the other hand, the closed-loop feedback control could eliminate man

In this work, we propose a risk-aware physical distancing system to assure a private safety distance from others for reducing the chance of being affected by the COVID-19 or such kind of pandemic. In particular, we have formulated a physical distancing problem by capturing Conditional Value-at-Risk (CVaR) of a Bluetooth-enabled personal area network (PAN). To solve the formulated risk-aware physical distancing problem, we propose two stages solution approach by imposing control flow, linear mode

Urban air mobility (UAM) has become a potential candidate for civilization for serving smart citizens, such as through delivery, surveillance, and air taxis. However, safety concerns have grown since commercial UAM uses a publicly available communication infrastructure that enhances the risk of jamming and spoofing attacks to steal or crash crafts in UAM. To protect commercial UAM from cyberattacks and theft, this work proposes an artificial intelligence (AI)-enabled exploratory cyber-physical s

Understanding the potential of generative AI (GenAI)-based attacks on the power grid is a fundamental challenge that must be addressed in order to protect the power grid by realizing and validating risk in new attack vectors. In this paper, a novel zero trust framework for a power grid supply chain (PGSC) is proposed. This framework facilitates early detection of potential GenAI-driven attack vectors (e.g., replay and protocol-type attacks), assessment of tail risk-based stability measures, and

As renewable energy based distributed generation (DG) units are being increasingly connected throughout today's distribution system, they can be used to mitigate harmonics caused by the wide adoption of nonlinear residential loads. To make the best use of all these DGs' ratings, it is important to develop a method to coordinate DGs' participation efforts in harmonic compensation according to their ratings and locations. Due to the low droop slope for the harmonic controller in DGs, traditional h

In this article, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an electrical vehicle (EV) charging facility for human-driven connected vehicles (CVs) and AVs. The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its ac