Figure 1: A simulated centipede walking autonomously over a crystal skull. AbstractUnlike two, four, six, and eight legged animals, Myriapoda-i.e., centipedes, millipedes, etc.-have been largely overlooked in the computer graphics literature. We present an artificial life framework for modeling these arthropods and animating their locomotive behavior over regular or irregular surfaces in real time with compelling physical and biological realism. Our hybrid approach combines kinematic and dynamic simulation, as well as a decentralized, distributed leg control system whose emergent behavior is suitable for animating simulated myriapoda of different morphologies with the characteristically vivid leg wave patterns of their biological counterparts. The simulated creature's antennae sense its virtual environment and the sensory information guides its adaptive behaviors, including obstacle avoidance and foraging. Figure 2: Different simulated myriapoda forming the letters SCA.
Fuzzy clustering theory is widely used in data mining of full-face tunnel boring machine. However, the traditional fuzzy clustering algorithm based on objective function is di cult to e ectively cluster functional data. We propose a new Fuzzy clustering algorithm, namely FCM-ANN algorithm. The algorithm replaces the clustering prototype of the FCM algorithm with the predicted value of the arti cial neural network. This makes the algorithm not only satisfy the clustering based on the traditional similarity criterion, but also can e ectively cluster the functional data. In this paper, we rst use the t-test as an evaluation index and apply the FCM-ANN algorithm to the synthetic datasets for validity testing. Then the algorithm is applied to TBM operation data and combined with the crossvalidation method to predict the tunneling speed. The predicted results are evaluated by RMSE and R 2 . According to the experimental results on the synthetic datasets, we obtain the relationship among the membership threshold, the number of samples, the number of attributes and the noise. Accordingly, the datasets can be e ectively adjusted. Applying the FCM-ANN algorithm to the TBM operation data can accurately predict the tunneling speed. The FCM-ANN algorithm has improved the traditional fuzzy clustering algorithm, which can be used not only for the prediction of tunneling speed of TBM but also for clustering or prediction of other functional data.
People are increasingly enthusiastic about pursuing spiritual life as economic and social development continues. Consequently, public cultural content has emerged as a pivotal instrument for promoting international soft power across diverse nations and regions. In today’s era of advanced artificial intelligence, cultural sign design optimization has become achievable through its deployment. This article establishes an automatic layout optimization framework, specifically tailored to meet the visual communication requirements of public cultural signage. Our framework employs Faster-R-CNN for detecting and extracting key elements of the poster, yielding an impressive average detection accuracy of 94.6%. Subsequently, we use the three-division method in design to optimize the layout, ensuring that cultural logo design conforms to visual communication principles. Our framework produced an average cultural logo satisfaction rating exceeding 70% in actual tests, providing novel insights for cultural sign design within the artificial intelligence context and significantly enhancing the efficacy of visual communication conveyed through such signage.
In the age of big data, visual communication has emerged as a critical means of engaging with customers. Among multiple modes of visual communication, digital animation advertising is an exceptionally potent tool. Advertisers can create lively and compelling ads by harnessing the power of digital animation technology. This article proposes a multimodal visual communication system (MVCS) model based on multimodal video emotion analysis. This model automatically adjusts video content and playback mode according to users’ emotions and interests, achieving more personalized video communication. The MVCS model analyses videos from multiple dimensions, such as vision, sound, and text, by training on a large-scale video dataset. We employ convolutional neural networks to extract the visual features of videos, while the audio and text features are extracted and analyzed for emotions using recurrent neural networks. By integrating feature information, the MVCS model can dynamically adjust the video’s playback mode based on users’ emotions and interaction behaviours, which increases its playback volume. We conducted a satisfaction survey on 106 digitally corrected ads created using the MVCS method to evaluate our approach’s effectiveness. Results showed that 92.6% of users expressed satisfaction with the adjusted ads, indicating the MVCS model’s efficacy in enhancing digital ad design effectiveness.
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