Xianghua Chu

This study presents an improved deep-learning model, termed Enhanced Time Series Mixer (E-TSMixer), for the prediction of particulate matter. By analyzing the temporal evolution of PM_2.5 concentrations from multivariate monitoring data, the model demonstrates significant prediction capabilities while maintaining consistency with observed pollutant transport characteristics in the urban boundary layer. In E-TSMixer, a fully connected output layer is proposed to enhance the predictive capability for complex spatiotemporal dependencies. The relevant data on air quality and traffic flow are fused to achieve high-precision predictions of PM_2.5 concentrations through a multivariate time-series forecasting model. An asymmetric penalty mechanism is added to dynamically optimize the loss function. Experimental results indicate that the proposed E-TSMixer model achieves higher accuracy for the prediction of PM_2.5, which significantly outperforms the traditional models. Additionally, an intelligent dual regulation of fixed and dynamic threshold model is introduced and combined with E-TSMixer for the decision-making model of the real-time adjustments of the frequency, routes, and timing of water truck operation in practice.

Medical care is a guarantee of people's daily life. Improving healthcare contributes to people's well-being. However, healthcare resources are characterized by uneven distribution. Financially well-off areas will have higher quality health care resources. Most of the medical resources are concentrated in public general hospitals, however, primary care institutions can hardly meet the growing needs of people. To solve this problem, Medical Union achieves efficient deployment of resources by integrating various medical institutions in the same area. In the process of logistics integration of the medical union, the scale of logistics distribution expands accordingly. Transportation vehicles have high operating costs and produce greenhouse gases in the process of distribution. The optimization of the driving path of logistics distribution vehicles can reduce the operating cost, fuel consumption and carbon emission. To solve this kind of decentralized and complex vehicle routing problem, this paper proposes a pollution routing problem model considering electrical vehicle usage, customer's soft time window expectation, open path and carbon cost. A modified Differential Search Algorithm with the comprehensive learning strategy and dynamic Cauchy variation strategy is advanced to solve the problem. Results show that the improved algorithm has good solving performance, and verifies the rationality of the proposed model, which will help to reduce carbon emissions and save the logistics and operating costs of medical devices.

Smart manufacturing system is very complex and there are a lot of different types of data to deal with, which lead to the difficulty of usage. Frequently manually tuning hyperparameters and modifying the architecture of the network have become a major problem for participants, and it has seriously affected the application and promotion of Deep learning (DL) in industry. In order to solve this problem, a novel self-evolving deep CNN method: two-stage coevolution method (TSC) is proposed in this paper to automatically optimize the hyperparameters and effectively evolve the most suitable network by summarizing the characteristics of the excellent artificial architectures. The first stage is mainly to optimize the hyperparameters with Orthogonal experimental algorithm. The second stage is to produce the best deep CNN with the optimized hyperparameters through self-evolving computation. In the second stage, three well-known deep-CNN architectures are used as the initialization seeds and each seed is presented by a particle and a gene to coevolve the necessary factors for a deep CNN driven by particle swarm optimization (PSO) and genetic algorithm (GA). At last, a case study for smart manufacturing systems was carried out to demonstrate the effectiveness and convenience of the proposed method. And the TSC method was also compared with other two self-evolving methods. The experiment results show that TSC method is superior over other well-known algorithms.