Influence of Body Mass Index on Comfort and Parametric Optimization Design of Seat

The influence of body mass index (BMI) on seat comfort was studied. 50 subjects were tested for body pressure distribution experiment about seat factors and BMI, and 118 subjects were tested for comfort survey experiment about the factors combination of seat height, backrest angle, and different body type on the perception of comfort. The experimental results revealed that there was positive correlation between the perception of comfort of foot and shin, foot and front of thigh, foot and back, foot and shoulder, foot and waist, with Pearson\u27s correlation coefficients of 0,608; 0,584: 0,672 and 0,620 (p < 0,05) and 0,853 (p < 0,01) respectively. Besides, there was negative correlation between body type and maximum pressure, body type and average pressure gradient, body type and maximum pressure gradient, with Pearson\u27s correlation coefficients of −0,673; −0,589 and −0,635 (p < 0,05) respectively. This study found that there was negative correlation between body type and shin, contact area and front of thigh, average pressure and front of thigh, average pressure and shoulder, with Pearson\u27s correlation coefficients of −0,769 (p < 0,01); −0,636; −0,682 and -0,605 (p < 0,05) respectively. In addition, this study also found positive correlations between maximum pressure and shin, average pressure gradient and front of thigh, maximum pressure gradient and front of thigh, average pressure gradient and shin, maximum pressure gradient and shin, with Pearson\u27s correlation coefficients of 0,681; 0,638; 0,694 (p < 0,05); 0,765 and 0,785 (p < 0,01) respectively. Moreover, when the seat height was set as knee height, and backrest angle was set as 120°, the subjective evaluation scores of three body types\u27 subjects were the highest. This study provided additional evidence that seat parameters may be a design approach for improving different body type user\u27s experience

On-site vibration test and dynamic response analysis of wind turbine of intertidal zone

In our study, the vibration signal of impulse response and attenuation response are extracted using the correlation function and power spectrum, and the natural frequency of wind turbine is determined. Compared with the rotation frequency of the blades of wind turbine which are 1p (one blade) and 3p (three blades), and wind vibration performance of the wind turbine is determined. The natural frequency of wind turbine is between the frequencies of one blade and three blades of wind turbine, which can avoid resonance phenomenon and meet the precision requirement for engineering application. The laws of acceleration and strain response along the wind turbine under ordinary wind load are obtained by installing acceleration sensor and strain gauge along the wind turbine. We found that the acceleration at the wind turbine top increases 10 times than that at the bottom. The acceleration influenced by tide is 1.14 times than that with no tide. The strain produced maximum value at the opening place of wind turbine and near the top, it should be paid attention in the engineering design

Effect of mechanical deformation on hydrogen storage properties of tife-based alloys

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RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation

Source-Free domain adaptation transits the source-trained model towards target domain without exposing the source data, trying to dispel these concerns about data privacy and security. However, this paradigm is still at risk of data leakage due to adversarial attacks on the source model. Hence, the Black-Box setting only allows to use the outputs of source model, but still suffers from overfitting on the source domain more severely due to source model's unseen weights. In this paper, we propose a novel approach named RAIN (RegulArization on Input and Network) for Black-Box domain adaptation from both input-level and network-level regularization. For the input-level, we design a new data augmentation technique as Phase MixUp, which highlights task-relevant objects in the interpolations, thus enhancing input-level regularization and class consistency for target models. For network-level, we develop a Subnetwork Distillation mechanism to transfer knowledge from the target subnetwork to the full target network via knowledge distillation, which thus alleviates overfitting on the source domain by learning diverse target representations. Extensive experiments show that our method achieves state-of-the-art performance on several cross-domain benchmarks under both single- and multi-source black-box domain adaptation.Comment: Accepted by IJCAI 202

Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur

Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of large-scale scenes from in-the-wild images with inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid neural rendering model that makes image-based representation and neural 3D representation join forces to render high-quality, view-consistent images. Besides, images captured in the wild inevitably contain artifacts, such as motion blur, which deteriorates the quality of rendered images. Accordingly, we propose strategies to simulate blur effects on the rendered images to mitigate the negative influence of blurriness images and reduce their importance during training based on precomputed quality-aware weights. Extensive experiments on real and synthetic data demonstrate our model surpasses state-of-the-art point-based methods for novel view synthesis. The code is available at https://daipengwa.github.io/Hybrid-Rendering-ProjectPage

Generative Type Inference for Python

Python is a popular dynamic programming language, evidenced by its ranking as the second most commonly used language on GitHub. However, its dynamic type system can lead to potential type errors, leading researchers to explore automatic type inference approaches for Python programs. The rule-based type inference approaches can ensure the accuracy of predicted variable types, but they suffer from low coverage problems. Supervised type inference approaches, while feature-agnostic, require large, high-quality annotated datasets and are limited to pre-defined types. As zero-shot approaches, the cloze-style approaches reformulate the type inference problem into a fill-in-the-blank problem. However, their performance is limited. This paper introduces TypeGen, a few-shot generative type inference approach that incorporates static domain knowledge from static analysis. TypeGen creates chain-of-thought (COT) prompts by translating the type inference steps of static analysis into prompts based on the type dependency graphs (TDGs), enabling language models to learn from how static analysis infers types. By combining COT prompts with code slices and type hints, TypeGen constructs example prompts from human annotations. TypeGen only requires very few annotated examples to teach language models to generate similar COT prompts via in-context learning. Moreover, TypeGen enhances the interpretability of results through the use of the input-explanation-output strategy. Experiments show that TypeGen outperforms the best baseline Type4Py by 10.0% for argument type prediction and 22.5% in return value type prediction in terms of top-1 Exact Match by using only five examples. Furthermore, TypeGen achieves substantial improvements of 27% to 84% compared to the zero-shot performance of large language models with parameter sizes ranging from 1.3B to 175B in terms of top-1 Exact Match.Comment: This paper has been accepted by ASE'2