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The rotational stiffness of the pocket-type connection used in system scaffolding to join horizontal members and vertical members was evaluated. To investigate the rotational stiffness of connections when subjected to bending moments in the downward, upward, and lateral rotation directions, load tests were performed on 30 specimens with cantilever specimens for each rotational direction. Results showed that failure shapes of pocket-type connections were different depending on the rotation direction. Owing to the differences in the failure phenomena, the maximum moment capacity of the upwardly rotated specimens was the largest, followed by that of the downwardly rotated specimens and the laterally rotated specimens in that order. Compared to the Eurocode 3 connection classification, the pocket-type connections correspond to semi-rigid connections when the rotation direction is downward or upward, and they can be considered nominal pin connections when the rotation direction is lateral. From the moment-rotation curves, it was concluded that the rotational stiffness of the pocket-type connections could be estimated as a bilinear model.

In this study, a modified two-time-scale model is proposed to overcome the limitations of the existing maneuvering analysis model. To this end, not only wave conditions but also all directions of ship operation velocities are considered in estimating wave drift force and moment. Subsequently, the increment of the drift force and moment induced by steady drift and yaw motion of a ship is imposed up to the first derivative of Taylor series expansion. By introducing this bilinear model, the burden of the drift force computation is reduced so that a more realistic and efficient seakeeping-maneuvering coupling analysis can be performed. A turning circle simulation in a regular short wave is carried out using the modified two-time-scale model. Then, the performance is validated by comparing its results with the direct coupling model. Moreover, quantitative improvement of the present numerical scheme and the influence of the operation velocities on ship maneuvering performance are discussed.

Robust control techniques for power converters are becoming more attractive because they can meet with most demanding control goals like uncertainties. In this sense, the Takagi-Sugeno (T-S) fuzzy controller based on linear matrix inequalities (LMI) is a linear control by intervals that has been relatively unexplored for the output-voltage regulation problem in switching converters. Through this technique it is possible to minimize the disturbance rejection level, satisfying constraints over the decay rate of state variables as well as the control effort. Therefore, it is possible to guarantee, a priori, the stability of the large-signal converters in a broad operation domain. This work presents the design of a fuzzy control synthesis based on a T-S fuzzy model for non-minimum phase dc-dc converters, such as boost and buck-boost. First, starting from the canonical bilinear converters expression, a Takagi-Sugeno (T-S) fuzzy model is obtained, allowing to define the fuzzy controller structure through the parallel distributed compensation technique (PDC). Finally, the fuzzy controller design based on LMIs is solved for the defined specification in close loop through MATLAB toolbox LMI. Simulations and experimental results of a 60 W prototype are presented to verify theoretical predictions.

The dynamics of maturation index, water and oil concentrations, and total phenols in olive fruit, together with fatty acids and total phenols content in olive oil, was studied as a function of time after flowering for super-high-density ‘Arbequina’, ‘Arbosana’ and ‘Koroneiki’ using data collected in three seasons. Oil concentration increased linearly from the middle of August until reaching a plateau by the end of November for all three cultivars, with ‘Koroneiki’ (45.7 g/100 g) and ‘Arbosana’ (45.4 g/100 g) showing higher maximum oil concentration than ‘Arbequina’ (41.8 g/100 g). While total phenols in the fruit decreased linearly during the harvest for all three cultivars, phenolics in oil peaked at 180 days after flowering (400 mg/kg) for ‘Arbequina’ and at 160 days after flowering (600 mg/kg) for ‘Koroneiki’. For ‘Arbosana’, total phenols in oil decreased linearly with a maximum of 400 mg/kg for early harvest oils. Fatty acid dynamics was largely affected by genotype, with ‘Arbequina’ displaying the largest variation in fatty acid concentrations along the season. ‘Koroneiki’ presented the highest values of oleic acid (73.0–77.1%), followed by ‘Arbosana’ (70.2–72.4%) and ‘Arbequina’ (64.4–74.0%). Results presented here are useful for growers to make planting and harvesting decisions, for producers to understand the differences in oil made from the most common super-high-density cultivars during the harvest seasons, and for regulators to consider legal standards for authentic olive oil.

Tensile performance of fiber-reinforced cementitious composite (FRCC) after first cracking is characterized by fiber-bridging stress–crack width relationships called bridging law. The bridging law can be calculated by an integral calculus of forces carried by individual fibers, considering the fiber orientation. The objective of this study was to propose a simplified model of bridging law for bundled aramid fiber, considering fiber orientation for the practical use. By using the pullout characteristic of bundled aramid fiber obtained in the previous study, the bridging laws were calculated for various cases of fiber orientation. The calculated results were expressed by a bilinear model, and each characteristic point is expressed by the function of fiber-orientation intensity. After that, uniaxial tension tests of steel reinforced aramid-FRCC prism specimens were conducted to obtain the crack-opening behavior and confirm the adaptability of the modeled bridging laws in crack-width evaluation. The experimental parameters are cross-sectional dimensions of specimens and volume fraction of fiber. The test results are compared with the theoretical curves calculated by using the modeled bridging law and show good agreements in each parameter.

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

Sign languages are developed around the world for hearing-impaired people to communicate with others who understand them. Different grammar and alphabets limit the usage of sign languages between different sign language users. Furthermore, training is required for hearing-intact people to communicate with them. Therefore, in this paper, a real-time motion recognition system based on an electromyography signal is proposed for recognizing actual American Sign Language (ASL) hand motions for helping hearing-impaired people communicate with others and training normal people to understand the sign languages. A bilinear model is applied to deal with the on electromyography (EMG) data for decreasing the individual difference among different people. A long short-term memory neural network is used in this paper as the classifier. Twenty sign language motions in the ASL library are selected for recognition in order to increase the practicability of the system. The results indicate that this system can recognize these twenty motions with high accuracy among twenty participants. Therefore, this system has the potential to be widely applied to help hearing-impaired people for daily communication and normal people to understand the sign languages.

Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.

Aiming at the problems of complex texture, variable interference factors and large sample acquisition in surface defect detection, a generic method of automated surface defect detection based on a bilinear model was proposed. To realize the automatic classification and localization of surface defects, a new Double-Visual Geometry Group16 (D-VGG16) is firstly designed as feature functions of the bilinear model. The global and local features fully extracted from the bilinear model by D-VGG16 are output to the soft-max function to realize the automatic classification of surface defects. Then the heat map of the original image is obtained by applying Gradient-weighted Class Activation Mapping (Grad-CAM) to the output features of D-VGG16. Finally, the defects in the original input image can be located automatically after processing the heat map with a threshold segmentation method. The training process of the proposed method is characterized by a small sample, end-to-end, and is weakly-supervised. Furthermore, experiments are performed on two public and two industrial datasets, which have different defective features in texture, shape and color. The results show that the proposed method can simultaneously realize the classification and localization of defects with different defective features. The average precision of the proposed method is above 99% on the four datasets, and is higher than the known latest algorithms.

The main purpose of fine-grained classification is to distinguish among many subcategories of a single basic category, such as birds or flowers. We propose a model based on a triple network and bilinear methods for fine-grained bird identification. Our proposed model can be trained in an end-to-end manner, which effectively increases the inter-class distance of the network extraction features and improves the accuracy of bird recognition. When experimentally tested on 1096 birds in a custom-built dataset and on Caltech-UCSD (a public bird dataset), the model achieved an accuracy of 88.91% and 85.58%, respectively. The experimental results confirm the high generalization ability of our model in fine-grained image classification. Moreover, our model requires no additional manual annotation information such as object-labeling frames and part-labeling points, which guarantees good versatility and robustness in fine-grained bird recognition.