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    Volume 1, Issue 2 - Table of Contents
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    Cover Story: Unauthorized drone intrusions pose significant threats to sensitive indoor environments like warehouses and laboratories. Featured as a cover paper, a novel simulation framework using self-governing drone swarms is introduced to detect and contain these unauthorized drones. The system leverages the Received Signal Strength Indicator (RSSI) to identify alien drones with differing signal characteristics from approved swarms. A key feature of this technology is its real-time threat detection capability. Upon identifying an intruder, the drone swarm promptly organizes to encircle and immobilize the target within 10 seconds, effectively neutralizing the threat. This RSSI-based solution offers a fast, effective, and reliable approach to indoor drone security.
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    Free Access | Research Article | 29 September 2024
    Investigation on the Mechanism of Nebulized Droplet Particle Size Impact in Precision Plant Protection
    by
    Hui Ma
    ,
    Jiajia Tang
    ,
    Huimin Lv
    ,
    Wenyan Chu
    ,
    Shengdi Sun
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 91-100, 2024 | DOI:10.62762/TIS.2024.307219
    Abstract
    Precision plant protection, a crucial facet of precision agriculture, assumes a paramount role throughout diverse stages of agricultural pesticide utilization. It not only furnishes indispensable reference parameters for agricultural production but also minimizes the employment of pesticides and their environmental footprint. This investigation employs a laser particle size analyzer to gauge the particle size information of the atomization field under assorted conditions, commencing with ground plant protection. The findings reveal that particle size escalates with the ascent of spray pressure and spray angle while diminishing with their augmentation. It proposes that pressure adjustments ca... More >
    Graphical Abstract
    Investigation on the Mechanism of Nebulized Droplet Particle Size Impact in Precision Plant Protection
    Free Access | Research Article | 29 September 2024
    On-line Configuration Identification and Control of Modular Reconfigurable Flight Array
    by
    Bin Ren
    ,
    Jianhui Liu
    ,
    Simin Zhang
    ,
    Chunxi Yang
    ,
    Jing Na
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 80-90, 2024 | DOI:10.62762/TIS.2024.681878
    Abstract
    With the increasing complexity of the working environment and the diversification of mission requirements of UAVs, traditional UAVs have a fixed structure and single function. It is difficult to be applied in occasions with complex environments and changing load demands. The modular reconfigurable flight array (MRFA) is composed of no less than four isomorphic unit modules that are freely spliced together. By adding or removing flight unit modules and adjusting the arrangement of flight unit modules, the configuration of the MRFA can be changed, so that it can adapt to complex environments and then complete different flight missions. In the process of MRFA research and development, online co... More >
    Graphical Abstract
    On-line Configuration Identification and Control of Modular Reconfigurable Flight Array
    Free Access | Research Article | 27 September 2024
    Long-term Traffic Flow Prediction using Stochastic Configuration Networks for Smart Cities
    by
    Yuqi Lin
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 68-79, 2024 | DOI:10.62762/TIS.2024.952592
    Abstract
    Accurate predictions of traffic flow are very meaningful to city managers. With such information, traffic systems can better coordinate traffic signals and reduce congestion. By understanding traffic patterns, navigation systems can provide real-time routing suggestions that avoid traffic jams, save time, and reduce fuel consumption. However, traffic flow will be interfered with by multiple factors such as collection time and place. In this paper, we propose to use stochastic configuration networks (SCNs) to predict the traffic flow. The network is trained through stepwise construction, and the network parameters are effectively optimized based on the approximation theorem and convergence an... More >
    Graphical Abstract
    Long-term Traffic Flow Prediction using Stochastic Configuration Networks for Smart Cities
    Free Access | Research Article | 23 September 2024
    Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations
    by
    Ghulam E Mustafa Abro
    ,
    Zain Anwar Ali
    ,
    Ayman M Abdallah
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 58-67, 2024 | DOI:10.62762/TIS.2024.807714
    Abstract
    A Novel simulation framework using self-governing drones is used to locate and reduce unauthorized drones in interior environments. The recommended method uses Received Signal Strength Indicator (RSSI) to identify an alien agent drone, which has different signal characteristics than the approved swarm of UAVs. Real-time threat detection is possible with this technology. After detecting the drone, the swarm organizes itself to encircle and besiege it for 10 seconds, making it inert before returning to their original positions. This unique solution uses RSSI to quickly identify and mitigate enclosed area concerns. It provides a reliable and effective indoor drone security solution. The simulat... More >
    Graphical Abstract
    Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations
    Free Access | Review Article | 23 September 2024
    Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application
    by
    Wei Zhang
    ,
    Qian Hong
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 58-68, 2024 | DOI:10.62762/TIS.2024.680959
    Abstract
    The integration of graph neural networks (GNNs) with brain functional network analysis is an emerging field that combines neuroscience and machine learning to enhance our understanding of complex brain dynamics. We first briefly introduce the fundamentals of brain functional networks, followed by an overview of Graph Neural Network principles and architectures. The review then focuses on the applications of these networks and address current challenges in the field, such as the need for interpretable models and effective integration of multi-modal neuroimaging data. We also highlight the potential of GNNs in clinical perimenopausal areas such as perimenopausal depression research, demonstrat... More >
    Graphical Abstract
    Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application
    Free Access | Research Article | 20 September 2024
    A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City
    by
    Syed Haider Ali
    ,
    Inam Ullah
    ,
    Syed Ashraf Ali
    ,
    M Ihtisham UL Haq
    ,
    Niamat Ullah
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 49-57, 2024 | DOI:10.62762/TIS.2024.329126
    Abstract
    This paper proposes designing and structuring a Cyber-Physical System (CPS) with a specific focus on vehicles equipped with on-board diagnosis (OBD-II). The purpose of the CPS is to collect and assess data pertaining to the vehicle's Electronic Control Unit (ECU), such as engine RPM, speed, and other relevant parameters. The OBD-II scanner utilizes the obtained data on mass airflow (MAF) and vehicle speed to compute CO2 gas emissions and fuel consumption. The data is wirelessly communicated using a GSM module to a Semantic Web. The CPS also uses GPS tracking to ascertain the vehicle's whereabouts. A Semantic Web is utilized to construct a database management system that stores and manages se... More >
    Graphical Abstract
    A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City