With the rapid development of autonomous driving technology, accurate perception of the surrounding environment has become increasingly critical, and 3D environment perception has emerged as a major research focus in this field. Traditional 3D perception systems rely heavily on expensive sensors such as LiDAR, which offer high accuracy but incur substantial costs and computational demands, limiting their scalability in large autonomous vehicle fleets. Although more recent 3D occupancy prediction methods rely solely on multicamera inputs, they typically require supervised learning with annotated 3D occupancy data, which is costly to obtain and consumes substantial memory. To address these challenges, this article proposes Image2Occupancy, an improved 3D Gaussian-splatting-based occupancy prediction method that uses only 2D surround-view camera images. The method enables effective semantic occupancy prediction of 3D scenes while reducing the need for annotated data and large memory capacity.

The Image2Occupancy framework consists of two components: (1) 2D-to-3D feature extraction and spatial mapping, and (2) self-supervised 3D occupancy representation learning. In the first component, BEVStereo and Swin Transformer modules extract 2D features from panoramic input images. These features are then interpolated and mapped to 3D space using the intrinsic and extrinsic parameters of the camera, yielding voxel-level feature representations. This process converts 2D image information into 3D semantic occupancy cues, providing accurate input for subsequent self-supervised learning. In the second component, an improved Gaussian splatting technique projects 3D voxel features back onto the 2D image plane while preserving semantic information. Gaussian points placed at each voxel center approximate scene occupancy, enabling rendering of semantic and depth maps by computing pixel-level depth and semantic information. A novel self-supervised learning framework generates pseudo-labels from the predicted depth and semantic maps of the model, eliminating the need for real 3D occupancy labels. A specialized loss function, combining cross-entropy and depth losses, minimizes discrepancies between rendered and ground-truth semantic and depth maps, optimizing prediction accuracy.

Experiments on the NuScenes dataset show that Image2Occupancy achieves an mIoU of 27.87, improving performance by 3.94 percentage points (a 16.5% increase) over existing 2D-input methods and performing comparable to, or better than, several 3D-input methods. Compared with NeRF-based approaches, GPU memory usage is reduced by 54.7% while maintaining the same number of Gaussian points. Ablation studies further validate the effectiveness of the core components of the method.

Image2Occupancy reduces hardware dependence and substantially decreases the need for large annotated datasets through self-supervised learning, offering a cost-effective and scalable 3D environment perception solution for autonomous driving systems with strong potential for practical deployment.

The heating, ventilation, and air conditioning (HVAC) system is a major energy consumer in buildings, with the chiller—the core component of the system—playing a vital role in meeting cooling demands by carrying heat. Therefore, flexible demand-based regulation of the chiller is essential to improve building thermal comfort and reduce energy consumption. As a nonlinear, highly coupled, and dynamic system, the chiller exhibits varying system characteristics under different operating points and environmental conditions. This necessitates a control strategy capable of adapting to diverse operating scenarios.

To address the challenge of multicondition chiller regulation in HVAC systems, an intelligent regulation method based on multiple model adaptive predictive control (MMAPC) was proposed. To analyze the dynamic characteristics of chiller under different operating conditions, its working mechanism was examined using thermodynamic theory, and a chiller control model suitable for real-time operations was established. Based on the influence of environmental factors, three representative operating conditions were identified and classified. For each condition, an incremental model predictive controller was designed using the mechanism-based model. These controllers were integrated through an adaptive weighted control variable fusion approach to form the overall MMAPC strategy. To evaluate the proposed approach, a real-time experimental platform was developed, comprising an intelligent chiller regulation unit, a supervisory computer with a large display screen, and several underlying control devices. The platform supports flexible communication configuration, high-volume data processing, and the deployment of various intelligent algorithms. Comparative experiments between single MPC control and the proposed MMAPC strategy were conducted on this platform.

Experimental results showed that the proposed MMAPC approach reduced the average tracking error by 70% compared with single MPC control. Additionally, it decreased the average overshoot between different operating conditions by approximately 75% and reduced the average standard deviation of the compressor valve opening by around 91%. These results demonstrated the feasibility and effectiveness of the proposed control strategy in achieving accurate chiller outlet temperature tracking while maintaining HVAC system stability. The developed experimental platform successfully enabled real-time data acquisition, strategy computation, and command issuance, while visually displaying system status on the large screen.

The intelligent experimental platform effectively supports real-time strategy verification and provides a practical foundation for teaching and research. The MMAPC strategy demonstrates excellent performance in multicondition chiller regulation and show strong potential for solving tracking control problems in dynamic, time-varying systems. This method lays the groundwork for deploying chiller operation optimization algorithms and contributes to energy-saving and emission-reduction goals under stable HVAC operation.

Due to the difficulties in experimental conditions and implementation, traditional first-order RC transition processes were only designed to verify theoretical knowledge. Many engineering elements that must be carefully considered in practical circuit design and debugging cannot be excavated and reflected. This may lead to a weak engineering background of experiment content, limited exploration space for students, and insufficient inspiration for students, which is not conducive to the improvement of the “two-property and one-degree” of the engineering course. To address these issues, this paper has made significant reforms in the experimental method, content, and teaching methods.

First, the experiment method has been reformed using a DC stabilized power supply as a circuit excitation instead of square waves, similar to traditional experiments. The circuit is controlled by a mechanical switch to start the charging and discharging process of the capacitor. The instantaneous charging and discharging waveforms of the capacitor are captured through the single trigger function of the digital storage oscilloscope, exposing and displaying multiple “abnormal” phenomena that seem to be inconsistent with theoretical analysis, which facilitates students' active thinking and improves the innovation of the experiment. Second, the experiment content has been upgraded using heuristic teaching methods to guide students to deeply explore the “abnormal” phenomena occurring during the experimental process. We then study the engineering characteristics widely existing in actual circuits, including stray capacitance, switch jitter, and instrument input impedance. Theoretical knowledge will be used to analyze and explain these actual phenomena and characteristics, and improvement and avoidance plans will be proposed to promote students' intuitive understanding of actual circuits and improve the high order of the experiment. Finally, the teaching methods have been updated, and a hybrid intelligent learning loop consisting of online SPOC learning and offline intelligent experiments has been constructed. The “Circuit Experimental Technology” SPOC course has been developed on the MOOC website of Chinese universities, with over 30 course preview and operation explanation videos uploaded. The students preview the course before class through the SPOC course, laying a strong foundation for their knowledge. Afterward, they come to the laboratory and conduct self-directed learning of first-order RC circuit experiments using the intelligent teaching system developed by our team, following the new experimental process of “six steps of self-directed learning,” which includes “automatic preview test” “self-directed video learning” “intelligent experiment operation” “intelligent assisted troubleshooting” “whole process accompanying evaluation” and “electronic report submission”. After completing the experimental operations, students will continue to review knowledge and take unit tests on the SPOC platform after class to complete this experimental learning. Based on the aforementioned measures, students have been promoted to independently learn the assessment of the experiment process, and the challenges associated with the experiment have increased.

This experiment project has been normally used in the “Circuit Experimental Technology” course of our school for five years, with a total of 130 classes and more than 4000 students benefiting from this course. Based on a series of educational reform measures with this project, the “Circuit Experimental Technology” was awarded the title of “High Quality Undergraduate Course in Beijing Universities” in 2022, and the teaching staff was also awarded the title of “Excellent Professional Course Lecturer in Beijing Universities”.

Recent practice has shown that the reformed first-order RC circuit experiment helps deepen students' understanding of the essence and connotation of the dynamic circuit response process, improves their ability to analyze problems and debug circuits, exercises their thinking, and enhances engineering accomplishments. The outcomes indicate the high application potential of the experiment.

Compound attitude control based on aerodynamic, thrust vector, direct force, and other manipulation structures is a hot research topic in modern aeronautics and astronautics. Additionally, improving the tracking ability of the system under high altitude and low aerodynamic efficiency is essential. To simplify the design, direct force is generally regarded as a continuous control variable in the study of compound attitude control for aircraft, which is different from the actual discrete engineering characteristics. In view of the large gap between direct force continuous processing and actual engineering characteristics, this study proposes control system construction and control parameter optimization methods based on the discrete characteristics of direct force.

First, based on the operational characteristics of each manipulation structure, a pulse modulator, which embodies the discrete characteristics of direct force, is used to construct the framework of the attitude control system. The Simulink simulation model of the compound aircraft attitude control system is constructed using MATLAB software according to the designed control system structure and the mathematical model of each link. Second, given the complex characteristics of nonlinear, strong coupling, and multiconstraints for the compound control system, the Particle Swarm Optimization (PSO) algorithm is adopted to optimize the control law and control assignment parameters based on the algorithm framework of intelligent control and intelligent distribution laws. Thus, an intelligent control system is constructed. Third, the research method for intelligent control problems is explained through digital simulation from three aspects: control algorithm, PSO algorithm parameters, and aircraft system parameters.

The simulation results show that the proposed method greatly improves the control performance of the attitude-tracking problem, including steady-state error, rise time, adjustment time, and overshoot. It is obviously superior to the traditional PID control method in terms of tracking precision, speed, and security. Additionally, it is beneficial to overcome the difficulty of adjusting the control parameters caused by the change in the flight parameters. The proposed system can adapt to real-time changes in the flight environment and state, effectively exert the control efficiency of each executing agency, and improve the adaptive control ability of the system. Finally, based on the above research achievements, the experimental system for intelligent aircraft attitude control is designed and developed. This system is applied to the practical teaching of intelligent control theory and process control systems. It can also be used to strengthen students’ understanding of the application and innovation of intelligent control theory in actual engineering problems from the aspects of attitude control principle, control structure design, mathematical model building, intelligent control algorithm design and implementation, and algorithm research.

This experimental system combines the engineering problems of aircraft attitude control with experimental teaching. By guiding students to project-based learning independently, it stimulates their interest in learning, effectively improves their ability to combine theory with practice, helps them to master scientific research methods, and cultivates and promotes students’ practical and innovative abilities. In this way, an independent and open experimental research platform for the practical teaching of intelligent control courses and the cultivation of innovative talents is provided.