This study optimizes vehicle suspension dynamics by introducing a controllable degree of nonlinearity, characterized by a parameter ε, into the spring element of Inerter-Spring-Damper (ISD) systems. Quarter-vehicle models for parallel and series ISD configurations are established, and a multi-objective genetic algorithm optimizes the parameters under random road excitation to minimize body acceleration (BA), suspension working space (SWS), and dynamic tire load (DTL). Results demonstrate that optimizing ε brings advantages: compared to a conventional passive suspension, the optimized parallel ISD suspension reduces BA, SWS, and DTL by 7.98%, 8.57%, and 1.69%, respectively, with the BA reduction notably improving from 5.94% (achieved by the linear ISD with ε = 0) to 7.98%. Similarly, the optimized series ISD achieves reductions of 2.53%, 7.62%, and 6.42% in BA, SWS, and DTL, showing a more balanced enhancement over its linear counterpart. The analysis reveals how ε distinctly influences the performance trade-offs, validating that strategically tuning the spring nonlinearity degree, in synergy with the inerter and damper, provides an effective method for superior suspension performance customization.

Digital image processing technology is widely used in the further application of computer graphics. Based on analysis of the characteristics of digital image processing course, instructional objectives, the project based learning theory and method was applied to the instructional reform and practice in experimental of digital image processing. Based on the necessity of digital image processing in teaching reform, a practical teaching method based on engineering application, which is including four parts such as designing project components, organizing teaching continents, practicing after class and evaluating students was proposed. On the basis of the target of teaching reform, set of teaching content, arrangement of the class hour and teaching method, the research of teaching practice has been done, and good effects in teaching of engineering application have been achieved. Educating contents are organized by considering project components and teaching syllabus. Different degree of difficulty objects can be chosen by students to satisfy with different students.

This paper proposes a novel method, the Convergent Radiation Algorithm (CRA), aimed at multi-attribute group decision-making (MAGDM) in circular intuitionistic fuzzy settings. The approach is aimed at reaching geometric consensus among experts, with uncertainties and hesitancies expressed via circular intuitionistic fuzzy numbers (CIFNs). First, the qualitative judgment in professionals is converted into a geometric space where experts’ assessments are represented as spatial points that reflect the differences between the opinions. All these points are gradually combined with the help of a radiation–reflection–convergence mechanism, which iteratively finds the Optimal Consensus Point (OCP) to minimize the overall weighted divergence over the evaluations. After that, a projection-based scoring method is used to locate good and bad optimal solutions, and the alternatives are ranked based on a comparison of their projection distance. It presents a numerical example with data supplied by the Hubei agro-ecological zone to demonstrate that the offered method helps to capture collective agreement and convergence behavior that is consistent, and makes the decision results readable and reliable.