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Electric vehicle charging stations (EVCSs) are essential for solving the energy consumption and endurance anxiety problems of car owners. EVCSs also promote sustainable development in urban economies without relying on fossil fuels. This research proposes a hybrid approach that integrates the Bayesian network with best-worst method (BN-BWM) and a geographical information system (GIS) to address the site selection problem for electric vehicles (EVs). BN-BWM is employed to address the indicator system, which consists of nine criteria from three aspects. BN-BWM calculates the final distribution of the total preference of all decision-makers. Then, a GIS is utilized for spatial analysis and superposition analysis to determine appropriate sites for charging stations (CSs). The novelty of this study lies in the development of a new decision-making method based on the combination of BN-BWM and a GIS. This method is not only more innovative but also highly operational and convincing regarding the accuracy of the weight results. This research provides feasible and reliable ideas for the site selection and construction of CSs. It can also help EV companies and government personnel carry out strategic planning. The study verified the applicability and effectiveness of the developed hybrid method in sixteen administrative regions in Beijing. According to the results, 1) an indicator system consisting of nine criteria is established, and roads, charging stations, and slopes are identified as the most sensitive criteria for site selection; 2) Three alternative stations (ASs) are identified as the most suitable sites for the establishment of CSs.

In view of the fact that the existing network fault detection algorithms cannot meet the four requirements of determination of detection time, low detection overhead, multi-point fault detection ability and topology adaptability of industrial control network at the same time, a multi-point fault detection method of time sensitive network based on Boolean network mapping was proposed.The method was divided into offline preparation phase and online detection phase.In the offline preparation phase, the detection flow generation algorithm generated a set of detection flows based on the network topology.The detection flow set covered the edges of the network topology.In the online detection phase, the detection packet was sent periodically from the source node to the controller according to the predefined path.Then, the controller inferred the failed link according to the arrival state of each detection packet.The experimental results show that, compared with the existing methods, the proposed method can accurately identify multiple failed links in a certain time, and generate fewer detection path sets to meet the above four requirements.

In actual industrial application scenarios, noise pollution makes it difficult to extract fault features accurately via conventional methods. A novel method for rolling bearing fault diagnosis combining a convolutional neural network (CNN), an attention mechanism for a squeeze-and-excitation network (SENet) module and a broad learning system (BLS) is proposed (SECNN–BLS). The one-dimensional bearing vibration signal is processed by using multiple short-time Fourier transforms (STFT); the two-dimensional image in the time-frequency domain is used as the model input. In the CNN fault feature extraction process, SENet is introduced to replace the CNN convolution layer, and global information is obtained through the squeeze operation. Excitation operation captures the importance of image channels, assigns weights adaptively to improve the attention on important information and eliminates the interference of irrelevant features without increasing the spatial and temporal complexity. The weighted feature representation is then transferred to the BLS input. The BLS has the characteristics of a flat structure and ridge regression to quickly solve the weights; as a fault classifier, it can save more computing resources and improve the accuracy of the fault classification. The SECNN–BLS fault diagnosis has achieved more than 98% accuracy on the Society for Machinery Failure Prevention Technology (MFPT) dataset. We also demonstrate the excellent performance of SECNN–BLS in a noisy environment.

In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements.

Prognostics and health management (PHM) are gradually being applied to production management processes as industrial production is gradually undergoing a transformation, turning into intelligent production and leading to increased demands on the reliability of industrial equipment. Remaining useful life (RUL) prediction plays a pivotal role in this process. Accurate prediction results can effectively provide information about the condition of the equipment on which intelligent maintenance can be based, with many methods applied to this task. However, the current problems of inadequate feature extraction and poor correlation between prediction results and data still affect the prediction accuracy. To overcome these obstacles, we constructed a new fusion model that extracts data features based on a broad learning system (BLS) and embeds long short-term memory (LSTM) to process time-series information, named as the B-LSTM. First, the LSTM controls the transmission of information from the data to the gate mechanism, and the retained information generates the mapped features and forms the feature nodes. Then, the random feature nodes are supplemented by an activation function that generates enhancement nodes with greater expressive power, increasing the nonlinear factor in the network, and eventually the feature nodes and enhancement nodes are jointly connected to the output layer. The B-LSTM was experimentally used with the C-MAPSS dataset and the results of comparison with several mainstream methods showed that the new model achieved significant improvements.

The study of the collaboration, coordination and negotiation among different agents in a multi-agent system (MAS) has always been the most challenging yet popular in the research of distributed artificial intelligence. In this paper, we will suggest for RoboCup simulation, a typical MAS, a general decision-making model, rather than define a different algorithm for each tactic (e.g. ball handling, pass, shoot and interception, etc.) in soccer games as most RoboCup simulation teams did. The general decision-making model is based on two critical factors in soccer games: the vertical distance to the goal line and the visual angle for the goalpost. We have used these two parameters to formalize the defensive and offensive decisions in RoboCup simulation and the results mentioned above had been applied in NOVAURO?, original name is UJDB, a RoboCup simulation team of Jiangsu University, whose decisionmaking model, compared with that of Tsinghua University, the world champion team in 2001, is a universal model and easier to be implemented.

Complex and customized manufacturing requires a high level of collaboration between production and logistics in a flexible production system. With the widespread use of Internet of Things technology in manufacturing, a great amount of real-time and multi-source manufacturing data and logistics data is created, that can be used to perform production-logistics collaboration. To solve the aforementioned problems, this paper proposes a timed colored Petri net simulation-based self-adaptive collaboration method for Internet of Things-enabled production-logistics systems. The method combines the schedule of token sequences in the timed colored Petri net with real-time status of key production and logistics equipment. The key equipment is made ‘smart’ to actively publish or request logistics tasks. An integrated framework based on a cloud service platform is introduced to provide the basis for self-adaptive collaboration of production-logistics systems. A simulation experiment is conducted by using colored Petri nets (CPN) Tools to validate the performance and applicability of the proposed method. Computational experiments demonstrate that the proposed method outperforms the event-driven method in terms of reductions of waiting time, makespan, and electricity consumption. This proposed method is also applicable to other manufacturing systems to implement production-logistics collaboration.

To deal with the problem of resource integration and optimal scheduling in cloud manufacturing, based on the analyzation of the existing literatures, multitask oriented virtual resource integration and optimal scheduling problem is presented from the perspective of global optimization based on the consideration of sharing and correlation among virtual resources. The correlation models of virtual resources in a task and among tasks are established. According to the correlation model and characteristics of resource sharing, the formulation in which resource time-sharing scheduling strategy is employed is put forward, and then the formulation is simplified to solve the problem easily. The genetic algorithm based on the real number matrix encoding is proposed. And crossover and mutation operation rules are designed for the real number matrix. Meanwhile, the evaluation function with the punishment mechanism and the selection strategy with pressure factor are adopted so as to approach the optimal solution more quickly. The experimental results show that the proposed model and method are feasible and effective both in situation of enough resources and limited resources in case of a large number of tasks.

The paper presents a formal proof of a machine closed theorem of TLA+ in the theorem proving system Coq. A shallow embedding scheme is employed for the proof which is independent of concrete syntax. Fundamental concepts need to state that the machine closed theorems are addressed in the proof platform. A useful proof pattern of constructing a trace with desired properties is devised. A number of Coq reusable libraries are established.

Mobile ad hoc network (MANET) is a dynamic wireless communication network. Because of the dynamic and infrastructureless characteristics, MANET is vulnerable in reliability. This paper presents a novel reliability analysis for MANET. The node mobility effect and the node reliability based on a real MANET platform are modeled and analyzed. An effective Monte Carlo method for reliability analysis is proposed. A detailed evaluation is performed in terms of the experiment results.

As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.

The study of the collaboration, coordination and negotiation among different agents in a multi-agent system (MAS) has always been the most challenging yet popular in the research of distributed artificial intelligence. In this paper, we will suggest for RoboCup simulation, a typical MAS, a general decision-making model, rather than define a different algorithm for each tactic (e.g. ball handling, pass, shoot and interception, etc.) in soccer games as most RoboCup simulation teams did. The general decision-making model is based on two critical factors in soccer games: the vertical distance to the goal line and the visual angle for the goalpost. We have used these two parameters to formalize the defensive and offensive decisions in RoboCup simulation and the results mentioned above had been applied in NOVAURO®, original name is UJDB, a RoboCup simulation team of Jiangsu University, whose decision-making model, compared with that of Tsinghua University, the world champion team in 2001, is a universal model and easier to be implemented.