Exploration of Intelligent Manufacturing Methods for Complex Products Driven by Multisource Data

In order to improve the multisource data-driven fusion effect in the intelligent manufacturing process of complex products, based on the proposed adaptive fog computing architecture, this paper takes into account the efficient processing of complex product intelligent manufacturing services within the framework and the rational utilization of fog computing layer resources to establish a fog computing resource scheduling model. Moreover, this paper proposes a fog computing architecture for intelligent manufacturing services for complex products. The architecture adopts a three-layer fog computing framework, which can reasonably provide three types of services in the field of intelligent manufacturing. In addition, this study combines experimental research to verify the intelligent model of this article and counts the experimental results. From the analysis of experimental data, it can be seen that the complex product intelligent manufacturing system based on multisource data driven proposed in this paper meets the data fusion requirements of complex product intelligent manufacturing.

An Intelligent Mission Planning Model for the Air Strike Operations against Islands Based on Neural Network and Simulation

Mission planning of air strike operations is hard because it has to give instructions to a large number of units during a relatively long period of time in an uncertain environment. If some instruction parameters can be calculated by an intelligent agent, better strategies can be found more quickly. In a specific combat scenario of air strike operations against islands, an intelligent model is proposed to improve the performance and flexibility of mission planning. The proposed intelligent mission planning model is based on rule-based decision and uses a fully connected recurrent neural network to calculate some of the decision parameters. The proposed intelligent mission planning model shows better results as compared to rule-based decision making with randomized parameters, and it performs as good as experts in the test set of the specific combat scenario.

Implementation of an Intelligent Model Based on Machine Learning in the Application of Macro-Ergonomic Methods in a Human Resources Process Based on ISO 12207

The objective of this chapter is to implement an intelligent model based on machine learning in the application of macro-ergonomic methods in human resources processes based on the ISO 12207 standard. To achieve the objective, a method of constructing a Java language algorithm is applied to select the best prospect for a given position. Machine learning is done through decision trees and algorithm j48. Among the findings, it is shown that the model is useful in identifying the best profiles for a given position, optimizing the time in the selection process and human resources as well as the reduction of work stress.

Game Teaching Method in Preschool Education Based on Big Data Technology

The traditional preaching way of imparting knowledge can only stifle children’s imagination, creativity, and learning initiative a little bit, which is harmful to children’s healthy and happy growth. This paper combines big data technology to evaluate the effect of game teaching method in preschool education, analyzes the teaching effect of game teaching method in preschool education, and combines big data technology to find problematic teaching points. Based on the collaborative filtering algorithm of preschool children, this paper estimates the current preschool children’s score for the game by referring to the scores of neighbor preschool children on the predicted game and constructs an intelligent model. Finally, this paper combines experimental research to verify the model proposed in this paper. From the experimental research, it can be seen that the method proposed in this paper has a certain effect.

Data-driven model for ship encounter intention inference in intersection waters based on AIS data

Frequent collision accidents of ships in intersection waters have caused huge casualties and property losses. Unclear encounter intention, poor communication, or inaccurate judgment of the encounter intention are often the major causes of ships falling into dangerous and urgent situations, leading to collision accidents. There are few methods and models for automatically inferring ship encounter intention. In this study, an intelligent model driven by AIS data is proposed to infer the ship encounter intention in intersection waters. The Hidden Markov Model (HMM) is adopted to formulate the encounter process and perform intention inference. The encounter intentions, including crossing, overtaking and head-on, are modeled as unobservable states of the formulated HMM. The observable measures of HMM extracted from AIS data, include the relative distance, relative speed, and course difference between two ships. Subsequently, the Forward-Backward algorithm is employed to obtain the model parameters and the Viterbi algorithm is exploited to estimate the hidden state with the highest probability, resulting in the inferred intention. The main advantage of the proposed model is its ability to capture the spatial-temporal characteristics of the encounter process, that is, the spatial interaction between ships and the dynamic evolution of states of the encounter process. The AIS data collected from the Lantau Strait intersection waters are adopted to verify the effectiveness of the proposed model. The experimental results reveal that the model can achieve an inference accuracy of 95%, 91.33%, and 92.67% for crossing, overtaking, and head-on, respectively. Moreover, it has real-time performance that ensures the encounter intentions can be recognized at an early stage, which is very critical for the safe navigation of any ships encountered. Our results show that our model can infer the encounter intentions in a timely manner and with high accuracy.

A Hybrid Intelligent Model for Urban Seismic Risk Assessment from the Perspective of Possibility and Vulnerability Based on Particle Swarm Optimization

Assessing seismic risk is an essential element of urban risk management and urban spatial security work. In response to the issues posed by the complexity and openness of urban systems, the nonlinearity of driving factors, and sudden changes in geological processes that affect urban seismic research, this paper is based on a variety of intelligent algorithms to develop a hybrid intelligent model that integrates probability and vulnerability to evaluate and quantify the difference in the urban spatial units distribution of earthquake risk. We applied this model to Hefei, one of the few superlarge provincial capital cities on the “Tancheng-Lujiang” fault zone, one of the four major earthquake zones in China, which suffers frequent earthquakes. Our method combined the genetic algorithm (GA), particle swarm optimization (PSO), and backpropagation neural network methods (BP) to automatically calculate rules from inputted data on known seismic events and predict the probability of seismic events in unknown areas. Then, based on the analytic hierarchy process (AHP), spatial appraisal and valuation of environment and ecosystems method (SAVEE), and EMYCIN model, an urban seismic vulnerability was evaluated from the four perspectives of buildings, risk of secondary disasters, socioeconomic conditions, and urban emergency response capabilities. In the next step, the overall urban seismic risk was obtained by standardizing and superimposing seismic probability and vulnerability. Using the hybrid intelligent model, earthquake probability, seismic vulnerability, and overall seismic risk were obtained for Hefei, and the spatial characteristics of its overall seismic risk were examined. This study concludes that areas with very high, high, low, and very low earthquake risk in Hefei account for 8.10%, 31.90%, 40.94%, and 19.06% of its total area, respectively. Areas with very high earthquake risk are concentrated in the old city, the government affairs district, Science City, and Xinzhan District. This study concludes that government authorities of Hefei should target earthquake safety measures consisting of basic earthquake mitigation measures and pre- and postearthquake emergency measures. In the face of regional disasters such as earthquakes, coordinating and governing should be strengthened between cities and regions.