With the rapid development of new media, it has also promoted the transformation of animation expression. One is the change of publishing media, which makes the dissemination of online media work faster and more convenient; the other is the order of magnitude change and the diversity of publishing channels and communication. The choice has contributed to a geometric increase in the number of works published. According to the flow of the new media animation production process, design and establish the classroom situation of animation studios, introduce work into the classroom, integrate occupations into studies, highlight occupations in studies, and strengthen professional concepts in the courses, in the form of animation studios, combined with animation companies. The production process and assessment standards effectively organize students to actively master animation theory knowledge and production technology; these experiences can accumulate effective and valuable experience for students career development. This article mainly introduces the research on the innovative and entrepreneurial practice education model of animation digital media major based on intelligent information collection and intends to provide some ideas and directions for the innovation and entrepreneurship practice education model reform of animation digital media major. This article proposes a studio-based animation digital media professional innovation and entrepreneurship practice education model research methods, used for studio-based animation research and experiment on innovation and entrepreneurship practice education mode of digital media major. The experimental results of this paper show that the Cronbachs α coefficients of the questionnaire are all greater than 0.8, indicating that the survey content of the questionnaire is more authentic and highly reliable.

Safety during physical human–robot interaction is the most basic requirement for robots. Collision detection without additional sensors is an economically feasible way to ensure it. In contrast, current collision detection approaches have an unavoidable trade-off between sensitivity to collisions, signal smoothness, and immunity to measurement noise. In this paper, we present a novel sliding mode momentum observer (NSOMO) for detecting collisions between robots and humans, including dynamic and quasistatic collisions. The collision detection method starts with a dynamic model of the robot and derives a generalized momentum-based state equation. Then a new reaching law is devised, based on which NSOMO is constructed by fusing momentum, achieving higher bandwidth and noise immunity of observation. Finally, a time-varying dynamic threshold (TVDT) model is designed to distinguish between collision signals and the estimated lumped disturbance. Its coefficients are obtained through offline data recognition. The TVDT with NSOMO enables fast and reliable collision detection and allows collision position assessment. Simulation experiments and hardware tests of the 7-DOF collaborative robot are implemented to illustrate this proposed method’s effectiveness.

The attitude and heading reference system (AHRS), which consists of tri-axial magnetometer, accelerometer, and gyroscope, has been widely adopted for three-dimensional attitude determination in recent years. It provides an economical means of passive navigation that only relies on gravity and geomagnetic fields. However, despite the advantages of small size, low cost, and low power, the magnetometer and accelerometer are susceptible to external disturbances, such as the magnetic interference from nearby ferromagnetic objects and current-carrying conductors, as well as the motional acceleration of the carrier. To eliminate such disturbances, a vector-based parallel structure is introduced for the attitude filter design, which can avoid the mutual interference between gravity and geomagnetic vectors. Meanwhile, an approach to estimate and compensate the external disturbances in real time for magnetometer and accelerometer is also presented. Compared with existing designs, the proposed filter architecture and external disturbance rejection algorithm can feasibly and effectively cooperate with mainstream data fusion techniques, including complementary filter and Kalman filter. According to experiment results, in the case that large and persistent external disturbances exist, the proposed method can improve the accuracy and robustness of attitude estimation, and it outperforms the existing methods such as switching filter and adaptive filter. Furthermore, through the experiments, the critical role of fading factor in handling the external disturbance is revealed.

The current popular two-stream two-stage image fusion framework extracts features of infrared and visible images separately and then performs feature fusion. The extracted features lack interaction between the source images and have limited cross-modal complementary capability. To address these issues, we propose a novel one-stream infrared and visible image fusion (OSFusion) framework that connects a source image pair to achieve bidirectional information flow. In this way, the fused features with cross-modal complementary information can be dynamically extracted by mutual guidance. To further improve the inference efficiency and obtain high-quality fused images, a feature extraction and fusion module (FEFM) is proposed based on Transformer structure. The combination of feature extraction and feature fusion is realized by using it. Since there is no need for an extra feature interaction module and the implementation is highly parallel, the speed of image fusion is extremely fast. Benefiting from the one-stream structure and FEFM, OSFusion achieves promising infrared and visible image fusion performance on MSRS, M3FD, and RoadScene datasets. Besides, our method achieves a good balance in the trade-off between performance and complexity, and also shows a faster convergence trend.

Considering the nonlinearity, large inertia and large time delay of superheated steam temperature system of the supercritical boiler unit, the nonlinear dynamic neural network inverse models of superheated steam temperature water-spray desuperheating system were established, and the historical operation data of the unit were used to train and verify the models. Based on the trained models, neural network inverse controllers with PID compensation links were constructed, and a real-time control program was programmed in MATLAB. With the full-scope simulation system of the 600MW supercritical unit, the superheated steam temperature control simulation tests were carried out under setpoint value disturbance and continuous variable load disturbances. The results show that the neural network inverse control scheme with PID compensation can effectively reduce the control deviation of superheated steam temperature and greatly shorten the process stabilization time.

This paper introduces magnetic integrated high-order trap–trap–inductor (TTL) and inductor–trap–trap (LTT) filters featuring two LC-traps designed for grid-tied inverters, aimed at reducing the size of output-power multi-trap filters. The proposed filters exhibit excellent harmonic absorption capabilities alongside a compact design. Building on the conventional integrated inductor–capacitor–inductor (LCL) filter, the approach involves connecting a small capacitor in parallel with either the inverter-side or grid-side inductors to create an LC trap. Additionally, a second LC trap can be achieved by integrating the filter capacitor in series with the equivalent trap inductance, established by the magnetic coupling between the grid-side inductor and inverter-side one. This paper thoroughly analyzes the characteristics of the proposed filters. Moreover, a design method is presented to further minimize the size of the output filter components. Finally, validation through simulations and hardware-in-the-loop (HIL) experiments confirms the proposed approach’s effectiveness and feasibility. The integrated designs achieve a size reduction of 35.4% in comparison with the discrete windings. Moreover, these designed filters comply with IEEE standards, maintaining a grid-side current total harmonic distortion (THD) of less than 0.9%, with all current harmonics below 0.3% of the fundamental current.