This study considers an extension model of the Two-Echelon Location Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD), namely the Two-Echelon Location Routing Problem with Simultaneous Pickup and Delivery and Parcel Locker (2E-LRPSPD-PL). 2E-LRPSPD-PL deals with the accumulation of routing plan and facilities' locations decisions, i.e., satellite and parcel lockers. The first echelon of 2E-LRPSPD-PL consists of one depot, opened satellites, and opened parcel lockers. While the second echelon is described by opened satellites which create routes to fulfill demands of home service customers and by opened parcel lockers which accommodate the demands of parcel locker customers. We propose two-indexed Mixed Integer Linear Programming (MILP) formulation to describe the problem. CPLEX solver is employed to solve small to medium test instances of the 2E-LRPSPD-PL. The computational result shows that CPLEX effectively results in an optimum solution for small instances of 2E-LRPSPD-PL. Furthermore, in the execution of small to medium test instances, some scenarios regarding the number of parcel locker customers are considered to analyze their impact toward the model.
An air cargo terminal is an essential gateway to move goods for import shipping or to load goods for export shipping. The air cargo terminal provides high-quality physical cargo handling services for import, export, and transshipment cargo, with extensive space and standard operating procedures. Terminals must increase their import throughput to use available space and infrastructure efficiently. This study presents an approach merging ordinal optimization and golden jackal optimization to solve the optimal allocation of container freight stations in air cargo. Golden jackal optimization is a metaheuristic method replicating the golden jackals’ natural hunting behaviors. The developed approach is adopted to decide the optimal allocation of shipping/receiving dock in an air cargo terminal such that the long-term average waiting time of trucks is minimized. Simulation results show that the presented approach could improve the convergence speed and local and global search capabilities.
In the modern era, research on Brain-Computer Interfaces (BCIs) is thriving, with increasing focus on the application of Motor Imagery (MI). This study has developed a game aimed at collecting MI signals from subjects imagining movements of their left hand, right hand, and being in a rest state, capturing the Electroencephalography (EEG) signals 1 second before each imagined movement. To achieve real-time interpretation of these signals, this research employed the Transformer architecture for processing EEG signals. Thanks to its self-attention mechanism's strong adaptability to sequential data, the Transformer not only effectively eliminated noise but also successfully extracted key features. Subsequently, these Transformer-processed signals were inputted into EEGNet for a three-class classification. In tests conducted with eight participants, our model achieved an average accuracy of 71.32%, marking a 9.17% improvement over methods that only used EEGNet. This outcome not only demonstrates the potential of combining Transformer and EEGNet in BCI applications but also offers a new direction for future research.
This study introduces a coverage control algorithm designed to optimize the monitoring and tracking of targets utilizing camera-equipped drones. The algorithm tackles the challenge posed by the limited sensing range of cameras by harnessing the flexibility of drones to achieve comprehensive target coverage effectively. In addition to enhancing coverage, the algorithm places emphasis on augmenting estimation accuracy to bolster system stability and facilitate future applications. It specifically targets estimation errors that are typically overlooked in conventional coverage control methods, with the aim of refining accuracy. A crucial criterion in this endeavor is the determination of the state covariance lower limit, which can be assessed through observability evaluation. In consideration of the probability of target occurrence, the primary objective is to ensure continuous coverage of areas with high information density within the field of view of drone-equipped cameras. Observability plays a pivotal role in promoting efficient collaboration through the exchange of information and positional data. To minimize errors, factors such as target locations, estimation accuracy, and camera characteristics on each drone are taken into account. In scenarios involving communication between drones, the exchange of observational data enables the estimation of target groups. The algorithm leverages observability as a decision-making indicator along with a Control Barrier Function to ensure that drones steer clear of losing track of targets. The effectiveness of the developed controller is corroborated through simulations.
In this paper we focus on Bluetooth RSSI positioning and develop a new Bluetooth positioning framework to reduce the problem of wasting resources caused by the lack of flexibility in the number of Bluetooth beacons required for large-scale positioning using the current framework. We break the traditional concept of the one being located, so that it can also function as a beacon after obtaining the coordinate position, providing more information to assist calculations, thus expanding more locatable areas and devices. This greatly reduces the cost and difficulty of densely deploying beacons. Utilizing the characteristics of low-power Bluetooth and its scanning and broadcast functions, we made a network structure similar to Bluetooth MESH, allowing information to flow layer by layer between devices, and ultimately allowing the central computing unit to locate the locations of all devices within the framework. Finally, this paper increased the number of devices in the experiment, and found that the increase in the number of devices rises the possibility of being located, and shows a logarithmic growth curve.
