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A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation.

The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed.

An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines.

This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.

This study aims to focus on the main materials used in consolidation processes of illuminated paper manuscripts and leather binding.

For each material, chemical structure, chemical composition, molecular formula, solubility, advantages, disadvantages and its role in treatment process are presented.

This study concluded that carboxy methyl cellulose, hydroxy propyl cellulose, methyl cellulose, cellulose acetate, nanocrystalline cellulose, funori, sturgeon glue, poly vinyl alcohol, chitosan, chitosan nanoparticles (NPs), gelatin, aquazol, paraloid B72 and hydroxyapatite NPs were the most common and important materials used for the consolidation of illuminated paper manuscripts. For the leather bindings, hydroxy propyl cellulose, polyethylene glycol, oligomeric melamine-formaldehyde resin, acrylic wax SC6000, pliantex, paraloid B67 and B72, silicone oil and collagen NPs are the most consolidants used.

Illuminated paper manuscripts with leather binding are considered one of the most important objects in libraries, museums and storehouses. The uncontrolled conditions and other deterioration factors inside the libraries and storehouses lead to degradation of these artifacts. The brittleness, fragility and weakness are considered the most common deterioration aspects of illuminated paper manuscripts and leather binding. Therefore, the consolidation process became vital and important to solve this problem. This study presents the main materials used for consolidation process of illuminated paper manuscripts and leather bindings.

To enhance the fit, comfort and overall satisfaction of lower body attire for online shoppers, this study introduces a reclassification method of the lower body profiles of young females in complex environments, which is used in the framework of remote clothing mass customization.

Frontal and lateral photographs were collected from 170 females prior, marked as size M. Employing a salient object detection algorithm suitable for complex backgrounds, precise segmentation of body profiles was achieved while refining the performance through transfer learning techniques. Subsequently, a skeletal detection algorithm was employed to delineate distinct human regions, from which 21 pivotal dimensional metrics were derived. These metrics underwent clustering procedures, thus establishing a systematic framework for categorizing the lower body shapes of young females. Building upon this foundation, a methodology for the body type combination across different body parts was proposed. This approach incorporated a frequency-based filtering mechanism to regulate the enumeration of body type combinations. The automated identification of body types was executed through a support vector machine (SVM) model, achieving an average accuracy exceeding 95% for each defined type.

Young females prior to being marked as the same lower garment size can be further subdivided based on their lower body types. Participants' torso types were classified into barrel-shaped, hip-convex and fat-accumulation types. Leg profile shapes were categorized into slender-elongated and short-stocky types. The frontal straightness of participants’ legs was classified as X-shaped, I-shaped and O-shaped types, while the leg side straightness was categorized based on the knee hyperextended degree. The number of combinations can be controlled based on the frequency of occurrence of combinations of different body types.

This methodological advancement serves as a robust cornerstone for optimizing clothing sizing and enabling remote clothing mass customization in E-commerce, providing assistance for body type database and clothing size database management as well as strategies for establishing a comprehensive remote customization supply chain and on-demand production model.