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A multi-microgrid (MMG) consists of several individual microgrids (MGs) within a distribution system to improve the system’s stability and reliability. A MMG can operate in grid-connected or island mode and requires advanced control techniques and effective energy management. This paper proposes a novel energy management approach for a MMG at the tertiary level control (TLC) using an adaptive optimal control model. Operational costs of the MMG are minimized for short-term planning while satisfying operational constraints of the network; the influential indices, the energy not supplied (ENS) and fatigue life (FL), remain balanced. The information gap decision theory (IGDT) is used to consider uncertainties in power generation and consumptions. MATLAB and DigSilent are used simultaneously to model optimally connected individual MGs within a MMG. The Tunicate Swarm Algorithm (TSA) is used for TLC for cost calculation and forming optimal connection models of individual MGs. The proposed method is validated through several case studies, showing superior performance.

Numerous control strategies are used to optimize the performance of the three-phase rectifier. Sliding Mode Control (SMC) is chosen for its robustness, fast response, high-efficiency control, finite-time convergence, and other features. Several SMC methods utilized with three-phase rectifiers are outlined in this review study. This study is divided into two main parts; the first part discusses the technique used to optimize the performance of the rectifier, while the second part discusses the SMC algorithms used to enhance the performance of the rectifier. In addition to their theoretical components, this review paper investigates the practical implementation challenges of SMCs for three-phase rectifiers. This paper discusses the optimization parameters of SMC and stability and load fluctuation in the rectifier system. It introduces state-of-the-art algorithms that enhance the rectifier’s dynamic performance, including the adaptive and observation algorithms. This paper is vital for engineers, researchers, and experts focusing on the SMC of three-phase rectifiers. To our knowledge, no similar review of three-phase rectifiers is available for SMC. Finally, a research gap is identified –as far as we know, no predefined time (free-will arbitrary time) SMC algorithm is used to control the performance of any power electronic device, such as a three-phase rectifier. This active rectifier is used in our project to optimize the power system for the alternator used in our unmanned ground vehicle (UGV) as an oil palm autonomous grabber.

The accurate collection of operational data at airports is essential for ensuring the fair distribution of national funds. However, many U.S. airports lack control towers, which forces planners to rely on sound, radio, and transponder-based systems for detecting aircraft operations. While these methods are useful, they have limitations, such as low accuracy and an inability to identify specific aircraft. In our previous work, we developed a computer vision-based system capable of accurately counting and identifying aircraft. However, implementing this system requires powerful computing devices that are not typically found at non-towered airports. Additionally, cloud computing is not a viable option due to data transfer limitations and associated costs. To address these challenges, we propose an affordable solution utilizing edge computing. This paper describes the necessary software and hardware modules, including optimized machine learning methods and edge devices, for deploying the system at airports. We tested the system’s performance using two independent standalone setups, created with NVIDIA edge kits, at three non-towered airports. The first setup aimed to obtain an accurate count of operations without detailed aircraft information, while the second setup was designed to extract an operations count with comprehensive aircraft information, including aircraft type recognition and identification. The accurate retrieval of aircraft information in the edge computing system is achieved through the introduction of a tailored CNN-based recognition model and the execution of a 3-step tail number identification algorithm. The results demonstrate the practical value of the proposed system, indicating that an accurate count of operations with detailed aircraft information can be obtained at a reasonable cost.

To address the critical need for enhancing Quality of Service (QoS) monitoring in the logistics service delivery domain, this paper introduces a blockchain-based QoS monitoring framework that aims to automate service delivery and dispute resolution processes. Traditional QoS monitoring solutions rely heavily on human judgment and intervention, which may increase operational costs and reduce service reliability. Such behaviour highlights the demand for more efficient and transparent logistics services, which underlines the importance of utilizing blockchain technology’s immutable and decentralized nature. The proposed framework in this paper employs a graph-based approach to transform QoS requirements into Deterministic Finite Automata (DFA) format. This strategy simplifies delivery monitoring and the identification of service violations through efficient DFA traversal. By using the Ethereum network as the deployment environment, we demonstrate that traversing a DFA is computationally efficient and reduces operational costs. Extensive experiments were conducted to evaluate the cost-effectiveness of the framework, showing that monitoring a delivery using this framework costs approximately ${\$}$ 2.59. This finding underscores the framework’s advantages in operational cost optimization compared to traditional human-based methods. Moreover, the decentralized nature of our proposed framework allows customers and businesses to define and monitor QoS parameters jointly. Therefore, the business partners can establish a transparent and trust-based relationship.

As-built Building Information Models (BIM) are increasingly used to facilitate the management of all aspects of built infrastructure’s life cycle. Existing studies mainly focus on automating as-built BIM development for surface elements but often ignore embedded elements such as rebar due to the inaccessibility with typical sensing devices, such as image-based or time-of-flight-based methods. To tackle the issue, this research utilizes Ground Penetrating Radar (GPR) together with the photogrammetry method to generate BIMs for in- service buildings considering both surface elements (e.g., column, slab, wall, etc.) and rebar. As the first step, as-built BIM for surface elements is generated and then existing rebar is identified by using GPR. A calibration label is designed and attached to elements which are scanned by GPR device, and a series of images are captured from those elements and then used with other images to generate point clouds. Faster RCNN is then utilized to recognize labels among all images. Next, an inverse photogrammetry approach is deployed to identify the scanned elements in BIM. By matching the recorded timestamps of GPR data and labeled images, links between the rebar in GPR data and elements in BIMs are successfully established. Finally, IFC (Industry Foundation Classes) is developed to generate as-built BIM models. Six case studies demonstrate that the system is capable of automatically developing as-built BIM, while embedded rebar could be efficiently localized and projected into corresponding elements in BIM.

Fresh Fruit Bunch (FFB) is the main ingredient in palm oil production. Harvesting FFB from oil palm trees at its peak ripeness stage is crucial to maximise the oil extraction rate (OER) and quality. In current harvesting practices, misclassification of FFB ripeness can occur due to human error, resulting in OER loss. Therefore, a vision-based ripe FFB detection system is proposed as the first step in a robotic FFB harvesting system. In this work, live camera input is fed into a Convolutional Neural Network (CNN) model known as YOLOv4 to detect the presence of ripe FFBs on the oil palm trees in real-time. Once a ripe FFB is detected on the tree, a signal is transmitted via ROS to the robotic harvesting mechanism. To train the YOLOv4 model, a large number of ripe FFB images were collected using an Intel Realsense Camera D435 with a resolution of $1920\times 1080$ . During data acquisition, a subject matter expert assisted in classifying the FFBs in terms of ripe or unripe. During the testing phase, the result of the mean Average Precision (mAP) and recall are 87.9 % and 82 % as the detection fulfilled the Intersect over Union (IoU) with more than 0.5 after 2000 iterations and the system operated at the real-time speed of roughly 21 Frame Per Second (FPS).

Providing the desired security for constrained devices in the edge of Internet of Things (IoT) systems is a challenging task. Given that those devices are in shortage of the area and energy, many lightweight and ultra-lightweight protocols have been proposed so far in the literature. On the other hand, while we see many new proposals in the literature to secure communications on IoT systems, security analysis of those schemes has not received enough attention. Hence, in this paper, we analyse the security of three recently protocols for constrained environments and show their security loopholes. The analysed schemes include two protocols which have been published by IEEE Access and a recently proposed protocol entitled Extremely Good Privacy (EGP). The designers of all those protocols claimed optimal security against active adversaries. However, in this paper, we propose an efficient secret disclosure attack against EGP that recovers the whole secret parameters of the protocol after eavesdropping/blocking several sessions of the protocol and doing some off-line computations. The probability of the adversary to recover whole $2l$ secret parameters of the tag after eavesdropping/blocking 68 sessions of the protocol is 0.99, targeting a 128-bit security level by $l=128$ . In addition, we show that an adversary can efficiently desynchronize the target tag from the reader/server in polynomial time. In the case of the other protocols, we also present efficient attacks that contradict the designers’ security claims.

Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations’ data and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.

In this paper, a Switched Reluctance Motor (SRM) drive system is proposed to make a trade-off between torque profile and vibration amplitude. In the proposed drive system, the power factor correction and fast demagnetization are achieved using a single-phase Vienna rectifier as a front-end converter for SRMs with a nominal voltage equal to half DC-link voltage. The three-level Vienna rectifier provides doubled negative voltage in demagnetization, resulting in a shorter current tail and faster demagnetization. But, applying higher voltage in demagnetization has a negative impact on the vibration amplitude at the point of demagnetization. Therefore, a control algorithm is designed to apply a negative modulated voltage to the motor phase, with a minimum change rate at the beginning of demagnetization and a linear average during the demagnetization interval. Accordingly, the mitigation of the vibration occurring at the commutation point and prevention of negative torque are simultaneously achieved over a wide speed range. The simulation and experimental results demonstrate the proposed drive system’s positive effect on the vibration amplitude, grid side current quality, and SRM performance in a wide range of speed variations.

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.