Addressing the challenges inherent in passive unmanned search and rescue missions at sea,including difficulties in target identification,broad search areas,and slow route planning,a strategic process was introduced for maritime search and rescue area planning and a route planning model specifically designed for passive unmanned missions.By thoroughly understanding the emergency response operations at sea and the specific needs for route planning,an optimal routing model have been developed considering factors such as the efficiency of search and rescue area coverage and the cost of rescue routes.The objective function is constructed within these constraints and solved using the whale optimization algorithm.The validity of the model is confirmed through designated scenario experiments,indicating that our proposed model for maritime passive unmanned search and rescue route planning is capable of swiftly identifying the search and rescue area and efficiently discovering a route with reduced costs.

In response to the significant impact of urban waterlogging on residents, the economy, and urban infrastructure in recent years, this study introduces an innovative wargame-based evaluation approach for emergency rescue plans. The primary goal of this research is to improve emergency rescue capabilities while minimizing costs and identifying gaps in existing emergency rescue plans. To effectively evaluate these capabilities, we extract specific content related to OODA (Observe, Orient, Decide, Act) dynamics in rescue actions. Furthermore, a comprehensive index system is developed to evaluate emergency rescue capabilities in the context of urban waterlogging scenarios. To address the challenges associated with intelligent optimization and evaluation of such systems, we employ a radial basis function neural network and conduct wargame experiments to obtain data and measure capability indices. The evaluation model is trained using data samples to ensure robust performance. In addition to the proposed model evaluation and analysis framework, we also present an evaluation and analysis method for RBF (Radical Basis Function) neural networks and compare the prediction results with those obtained from GRNN (Generalized Regression Neural Network), PNN (Product-based Neural Network), and BP (Back Propagation) neural network algorithms. This model efficiently processes and fits data by simulating expert experience for evaluation purposes. Such an approach takes advantage of machine learning's sensitivity to data characteristics, effectively avoiding the influence of human factors while stably reflecting the mapping relationship between indicators and performance outcomes. This research presents a novel solution with significant implications for the development of urban emergency rescue systems that address the challenges posed by urban waterlogging incidents.

Quick and efficient mission planning is essential in maritime search and rescue (SAR). This includes defining the search area and developing an effective strategy. The task is fraught with challenges due to the difficulty of determining location information and the impact of complex meteorological environments. The primary objective of SAR mission planning is the rapid deployment of unmanned surface vehicles (USVs) to the incident area. While many planning algorithms prioritize the shortest route, there’s a lack of mission planning measures that maximize SAR effectiveness. In addition, the joint deployment of USVs increases the success rate compared to individual operations. Therefore, this paper presents a task assignment framework for USVs in SAR missions that considers the probability of success and time constraints. USVs are used to search for lost targets, and the framework consists of the following three modules: (1) a module for predicting the location of the overboard target to be rescued; (2) a module for modeling the probability of mission success; (3) a module for assigning search tasks to USVs. The framework first analyzes the search area. Then, it predicts the target location with a stochastic particle method, which incorporates marine environment forecast data to update the mission target location. To improve the scientific nature of USV search and rescue mission plans, an evaluation model is developed to assess mission capability. Simulation experiments and task scheme analysis validate its effectiveness.

Air defense operation is important in modern warfare. We study the weapon target assignment(WTA) problem and corresponding heuristic algorithm. Three-stage WTA model was proposed and novel objective function was constructed to emphasize the different benefits of shooting down missiles at different stages. The original sparrow search algorithm(SSA) was improved and the experiments we performed in this paper showed that the modified algorithm is more effective in solving the three-stage WTA problem.

Radio spectrum prediction is of great significance for dynamic spectrum management and alleviating spectrum congestion. Based on the real spectrum dataset, this paper constructs a multi-channel temporal-frequency fusion network (MTF 2 N) for spectrum prediction. The network consists of two parts: first, it uses CNN to extract the latent features of the occupancy state of multi-channel and multi-slot; then, it uses the latent features of the spectrum occupancy state to predict occupancy state through the memory property of LSTM. Experiments show that the network model designed in this paper can achieve comparable performance to the LSTM network in short-term spectrum prediction. In the long-term spectrum prediction, MTF 2 N is more accurate than LSTM, Seq2seq and GRU networks since MTF 2 N integrates more channel and time slot correlation features. The prediction accuracy of 200 prediction time slots for 40 channels in GSM1800DOWN service band reaches 92.26% and more robust performance is obtained.

Aiming at the production mode of combining order production and inventory production of manufacturing companies at present, the production scheduling system under MTS / MTO mixed mode is designed and developed. This paper deeply discusses how to deal with order priority and batch processing under MTS / MTO mixed mode, and puts forward specific processing methods, so as to quickly form an optimized production scheduling scheme under limited resources, maximize the delivery time, and improve enterprise efficiency.

The emergency response system of a higher education institution is very important in protecting the safety, health and property of faculty, administrators, staff and students. The types and nature of emergencies that may occur on college campuses are summarized and analyzed in this paper. Effective and efficient emergency management mechanisms are studied. The systemic construction of emergency response plans and their evaluation are discussed. A case study on a public health crisis in an institution is presented. Some future research directions are also pointed out at the end of the paper.

The arrival of the high-speed rail era has profoundly affected China’s tourism demand, making it towards uncertainty, and traditional tourism demand forecasting methods need to be innovated. Using Matlab (2014a) to construct a BP neural network model based on genetic algorithm (GA) optimization, taking the number of tourists and tourism income of Sanjiang Dong Autonomous County in Guangxi as sample data, the model is trained repeatedly, and the number of tourists and tourism income are predicted and analyzed, and the model is applied to the forecast of tourism demand in the era of high-speed railway. The example simulation results show that the GA-optimized BP neural network model in the high-speed rail era has better adaptability and prediction accuracy in tourism demand.

In order to realize transit of goods among different floors inside a building, this study developed a new type of in-building transport robot to be capable of moving in the corridors, and an intelligent manipulator is equipped to autonomously press button and take elevator for lifting up-down to different floors. Firstly, a mechanical structure of the robot body is designed according to the functional requirements, and then a visual recognition method is adopted for manipulator recognizing and pressing elevator button independently. Next, an integrated positioning and navigation method is proposed by application of a dead reckoning, distance measurement using ultrasonic sensor, and visual localization according to the doorplate number in the corridors. Finally, a prototype of transport robot was developed to perform a validation experiments, and the experimental results indicates that the developed transport robot can realize accurate positioning in the corridors, and autonomous usage of elevator to go up and down different floors. As a conclusion, this study is therefore a useful attempt for achievement of an in-building transport robot in the near further.

Aiming at the problem of signal timing at road intersections, this paper uses the artificial bee colony algorithm to optimize the road model of single point multi phase intersection. In this paper, the cost function is the weighted sum of the average delay time, the average number and the capacity. Using the artificial bee colony algorithm to optimize the signal timing of a typical crossroad, and using MATLAB experimental platform to simulate, it shows that the artificial bee algorithm can enhance the road traffic efficiency.