A Matching Degree Management Model of Human Body Shape and Fashion Design Based on Big Data Analysis

The existing clothing design model lacks the screening link of the human body part index, and the output clothing data are affected by the high correlation coefficient, resulting in large matching errors. Therefore, based on the analysis of human body shape, a management model of matching degree of human body shape and clothing design based on big data is constructed. After processing with big data methods, human body characteristic data used signals as the input layer of a neural network model and the matching degree management model of human body shape and fashion design. The simulation results show that the built-up model has a matching error of less than 5%, which can effectively improve the matching of human body shape and clothing design.

Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network

Advances in video capturing devices enable adaptive posture estimation (APE) and event classification of multiple human-based videos for smart systems. Accurate event classification and adaptive posture estimation are still challenging domains, although researchers work hard to find solutions. In this research article, we propose a novel method to classify stochastic remote sensing events and to perform adaptive posture estimation. We performed human silhouette extraction using the Gaussian Mixture Model (GMM) and saliency map. After that, we performed human body part detection and used a unified pseudo-2D stick model for adaptive posture estimation. Multifused data that include energy, 3D Cartesian view, angular geometric, skeleton zigzag and moveable body parts were applied. Using a charged system search, we optimized our feature vector and deep belief network. We classified complex events, which were performed over sports videos in the wild (SVW), Olympic sports, UCF aerial action dataset and UT-interaction datasets. The mean accuracy of human body part detection was 83.57% over the UT-interaction, 83.00% for the Olympic sports and 83.78% for the SVW dataset. The mean event classification accuracy was 91.67% over the UT-interaction, 92.50% for Olympic sports and 89.47% for SVW dataset. These results are superior compared to existing state-of-the-art methods.

The Law of Self-Eating—Milk, Placenta, and Feces Consumption

Humans have consumed nearly every human body part. Today, the consumption of milk, placenta, and feces, in particular, is on the rise. Milk, placenta, and feces circulate directly among people given that no medical expertise is required to consume them in unprocessed form, but they are also distributed by institutionalized medical entities (e.g., biobanks, hospitals, pharmaceutical, biotechnology, or cosmetic companies). They are considered simultaneously valuable (as they are typically donated gratuitously and primarily used for nutritional, health, and research purposes) and dangerous (as they can transmit viruses, bacteria, parasites, and pollutants). This article has two main goals. First, in examining the social meanings of milk, placenta, and feces consumption, it considers whether and how the circulation of these bio-commodities shapes the limits of human bodies and communities. Second, it asks whether there is something different or specific about the way in which self-consumption (i.e., the consumption of human body materials by humans) is regulated compared to that of foods, drugs, and supplements derived from animal bodies, plants, or other non-human sources.

Conceptual metaphor of “thread” in Vietnamese idioms and folk songs

From the in-depth study of the concept of “thread” in Vietnamese idioms and folk songs, the article has established metaphorical structures of the concept of human\body part as “thread”; The predestined object is the “thread”; love\affection means “thread”; human activities\perceptions are activities with “threads”; human mood is considered as “thread”; talent\quality is a “thread”; The situation is the “thread” and thereby, this paper may contribute to further conceptual metaphor of cognitive linguistics illustration; and show a unique part of Vietnamese language - thinking - culture.

Construction and application of an ergonomic simulation optimization method driven by a posture load regulatory network

The optimization of man–machine systems is a critical component in the research and development of products but it has been a struggle to improve the optimization accuracy. This study presents an ergonomic optimization method driven by a posture load regulatory network (PLRN). Considering that the differences in work-related musculoskeletal disorders are caused by different occupations, human body part data collection is completed using the deconstructions of man–machine task sequences, which applies in partial theory of the complex network to build the PLRN model. Then the approach connects the human body part data with the regulatory network to calculate the cumulative load tendency and the performance of the load group; results of the analysis provide support for the ranking of human body part loads. In addition, we derive the mapping relationship between the man–machine workload and product engineering modules based on the quality function deployment theory, which can reflect the man–machine system problems of products and assist designers in optimizing and making decisions in man–machine systems. To this end, this paper provides a case study research for evaluating the feasibility of the PLRN simulation optimization method. Results show that our method is capable of explaining the change and the predicting the tendency of human load during the man–machine operation. Compared with the traditional subjective analytic hierarchy process, the PLRN simulation optimization method provides more accurate and objective evaluation on product ergonomics, and new research opportunities on ergonomic optimization.