Multi-robot task allocation (MRTA) and route planning are crucial for a large-scale multi-robot system. In this paper, the problem is formulated to minimize the total energy consumption and overall task completion time simultaneously, with some constraints taken into consideration. To represent a solution, a novel one-chromosome representation technique is proposed, which eases the consequent genetic operations and the construction of the cost matrix. Lin–Kernighan–Helsgaun (LKH), a highly efficient sub-tour planner, is employed to generate prophet generation beforehand as well as guide the evolutionary direction during the proceeding of multi-objective evolutionary algorithms, aiming to promote convergence of the Pareto front. Numerical experiments on the benchmark show the LKH guidance mechanism is effective for two famous multi-objective evolutionary algorithms, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm (NSGA), of which LKH-guided NSGA exhibits the best performance on three predefined indicators, namely C-metric, HV, and Spacing, respectively. The generalization experiment on a multiple depots MRTA problem with constraints further demonstrates the effectiveness of the proposed approach for practical decision making.

Music genre classification (MGC) is the basis for the efficient organization, retrieval, and recommendation of music resources, so it has important research value. Convolutional neural networks (CNNs) have been widely used in MGC and achieved excellent results. However, CNNs cannot model global features well due to the influence of the local receptive field; these global features are crucial for classifying music signals with temporal properties. Transformers can capture long-range dependencies within an image thanks to adopting the self-attention mechanism. Nevertheless, there are still performance and computational cost gaps between Transformers and existing CNNs. In this paper, we propose a hybrid architecture (CNN-TE) based on CNN and Transformer encoder for MGC. Specifically, we convert the audio signals into mel spectrograms and feed them into a hybrid model for training. Our model employs a CNN to initially capture low-level and localized features from the spectrogram. Subsequently, these features are processed by a Transformer encoder, which models them globally to extract high-level and abstract semantic information. This refined information is then classified using a multi-layer perceptron. Our experiments demonstrate that this approach surpasses many existing CNN architectures when tested on the GTZAN and FMA datasets. Notably, it achieves these results with fewer parameters and a faster inference speed.

Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for large-scale multi-robot task allocation problems. This paper proposes a novel graph deep-reinforcement-learning-based approach, which solves the problem through learning. The framework leverages the graph sample and aggregate concept as the encoder to extract the node features in the context of the graph, followed by a cross-attention decoder to output the probability that each task is allocated to each robot. A graph normalization technique is also proposed prior to the input, enabling an easy adaption to real-world applications, and a deterministic solution can be guaranteed. The most important advantage of this architecture is the scalability and quick feed-forward character; regardless of whether cases have a varying number of robots or tasks, single depots, multiple depots, or even mixed single and multiple depots, solutions can be output with little computational effort. The high efficiency and robustness of the proposed method are confirmed by extensive experiments in this paper, and various multi-robot task allocation scenarios demonstrate its advantage.

Energy is the most limited resource for sustainable systems, especially battery-based systems, such as Wireless Sensor Network (WSN) or Cyber-Physical System (CPS) nodes. Harvesting energy from a deployed environment is proved to be a feasible method to enable a sustainable operation. Meanwhile, field-programmable gate arrays (FPGAs), which are a reconfigurable fabric, are widely used in sustainable systems. Various applications can be configured into FPGA as required. The available energy varies during the harvesting process. Therefore, configuring FPGA to match the processing requirement within the varied energy is an important issue. As opposed to the traditional scheduling methods that solely focus on reducing computational energy, we adapted a weather forecast method in studying the history data to predict the future harvested data in this study. The predicted future harvested energy was then used in energy-based scheduling. Experiment results indicate that reconfigurable hardware can save up to 50% energy compared to software-based execution. Using the weather forecast based scheduling method can execute 35% more task compared to the past-predict-future method.

Intelligent empty bottle inspection system is an important inspection equipment of empty bottle before filling beer, and it is a blend of machine vision, precision machine and real-time control. They need to cooperate perfectly to achieve the desired effect. In the design of the empty bottle inspection system, one of the key technologies is the bottle bottom detection which affects the speed and accuracy of the system. It includes positioning and defect recognition of bottle bottom. For the problems such as the slow detection speed and low detection precision of bottle bottom detection, some new methods are proposed in this paper. The positioning algorithm of the bottle bottom in images is studied after preprocessing the obtained images, and the accurate positioning is achieved by improving the Randomized Hough transform. In the defect recognition of bottle bottom, a method of calculating optimum radius in Fourier spectrum is used to solve the problem of the detection accuracy being influenced by the antiskid veins of bottle bottom. It can improve the recognition accuracy effectively. Experiments show the methods proposed in this paper can effectively improve the precision and speed of the bottle bottom detection.