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Localized corrosion often occurs on steel members due to paint peeling led from insufficient durability of the coating material or human collision, which may significantly reduce the load carrying capacity and safety performance of the structures. The effects of the parameters such as localized corrosion penetration, circumferential corrosion ratio, longitudinal corrosion ratio, slenderness ratio and eccentricity of mechanical behaviors of locally corroded circular steel tubes under compression were studied experimentally and numerically. Based on the test results and the finite element analysis, a calculation method was proposed for predicting load carrying capacity of the corroded specimen under compression. It is found that localized corrosion did not change the failure modes of test specimens, and all of them failed in the limit point buckling mode. The factors affecting the degradation of load carrying capacity and stiffness of localized corrosion specimens from high to low were corrosion time, circumferential corrosion ratio and longitudinal corrosion ratio. The most unfavorable localized corrosion modes of load carrying capacity and stiffness were H3L3-270 and H3L1-270, respectively. The load carrying capacity degradation of members increases with the increase of the localized corrosion ratio, circumferential corrosion ratio and longitudinal corrosion ratio, while decreases with the increase of the eccentricity. The formula proposed can be used to predict the load carrying capacity of locally corroded circular steel tube members under compression.

In this paper, some new theorems for various cyclic contractions are established in Gb-metric spaces to discuss the existence and uniqueness of the solutions for a class of the operator equations, which generalize many known results in corresponding literatures. On the other hand, we introduce the notion of the cyclic α-ψφ-contractive mappings in Gb-metric spaces and establish a new fixed point theorem, which is applied to consider the existence of the solutions of the integral equations and ordinary differential equations. Moreover, some examples are given to support our main results.

Joint human activity localization and recognition has broad application prospects in human-computer interaction, virtual reality, smart healthcare system, security monitoring and robotics. Ultra-wideband (UWB) is an emerging technology adopted in real-time location system (RTLS) and has shown satisfactory performance in the task of human activity localization. However, few studies have been carried out to simultaneously recognize human activities based on UWB RTLS, which limits the use of UWB RTLS in many applications. In this study, we develop a RTLS based on UWB for the joint task of activity localization and recognition. A compressed sensing-based activity recognition approach is proposed for the task of activity recognition and several machine learning methods are designed to further improve the activity localization accuracy for the task of activity localization. The experimental results show that our UWB RTLS achieves good performance in this joint task.

High-precision indoor positioning has nowadays emerged as a critical function for many applications. However, many existing indoor positioning systems either fail to achieve a high positioning accuracy or are very easily affected by indoor environments composed of many obstacles, preventing them from satisfying many application requirements. Ultra wideband (UWB) has recently drawn extensive attention in the field of indoor positioning due to its great ability to achieve high ranging and localization accuracy while minimizing the effect of multipath interference. Meanwhile, advanced artificial intelligence and signal processing techniques have been explored to improve the precision and performance of indoor positioning system. In this paper, a high-precision UWB indoor positioning system integrating artificial intelligence and signal processing techniques is designed. And field tests are also conducted to validate the design of the system.

Indoor positioning system plays an important role in smart grid. Although GPS is the predominant outdoor positioning technology, it is unsuitable to be used in many fields of smart grid for three main reasons: first, signals sent from GPS could easily get blocked by solid materials such as metal or brick; second, the complex electromagnetic interference induced by electrical circuits greatly affects GPS signals; third, GPS can only achieve meter-level real time positioning accuracy, which is far from sufficient for many requirements of smart grid applications. Some other indoor positioning technologies, such as Bluetooth, Wi-Fi, ultrasound, infrared and RFID, fail in either the positioning accuracy, the positioning range, or the positioning speed required in many smart grid applications. Therefore, this paper proposes a real time indoor positioning system for smart gird based on a more promising technology, ultra-wideband (UWB). UWB is suitable for real-time localization in smart grid because UWB has short radio frequency pulse duration and wide bandwidth, which can minimize the effects of multipath interference and allow for high-resolution ranging and easier material penetration. In addition, since high-accuracy position information is required in many smart grid fields, a comprehensive framework integrating several artificial intelligence techniques, including outlier detection, line-of-sight/non-line-of-sight classification, filter design, range measurement correction and maximum likelihood localization estimation, is also proposed to further improve the positioning accuracy. At last, the performance of this system is verified through a series of experiments.

Compressive sensing related methods have achieved success in power system fault classification. However, those methods, either only focus on sparsity without considering the correlation within power system fault signals, or emphasize too much on the correlation but neglect the sparsity property of power system fault signals, resulting in the decrease of classification precision and the increase of the number of training samples needed. In addition, those methods do not explore other machine learning techniques at the same time to further improve the classification accuracy and speed. To overcome the drawbacks and limitations of those methods, this paper proposes an adaptive compressive sensing and machine learning classification framework to quickly and accurately classify power system faults considering both the correlation and the sparsity properties inherited in power system fault signals. First, random projection is applied to reduce the dimensionality of the fault signal. Second, a k-nearest neighbor based algorithm is designed to reduce the number of fault signals needed in the training dataset. Finally, the classification of power system faults is formulated as solving an adaptive L1-norm and L2-norm combined objective function using an alternating direction optimization method. Simulation results and analyses show that the proposed framework has better performance than the existing compressive sensing based power system faulty classification methods in both accuracy and speed.

Canonical correlation analysis (CCA) is a popular and powerful technique for two-view dimension reduction and feature extraction. But, CCA is not able to directly handle more than two view data and has a rigorous assumption that all the samples from two different views are paired. However, practical multiple view data are often semi-paired. To address this problem, we in this paper propose a novel semi-paired multiview dimension reduction approach, which takes cross-view neighborhood relationship among semi-paired data and within-view global structure information into consideration. The proposed approach can not only deal with multiview (more than two) data, but also take sufficient advantage of unpaired multiview data and then mitigate overfitting effectively caused by the limited paired data. Experimental results on two benchmark data sets demonstrate the effectiveness of our proposed method.

A key aspect of the mimetic defense system is the scheduling of heterogeneous executions to ensure that the system is dynamic. The existing scheduling strategy only performs random scheduling to satisfy heterogeneity and service quality. Combined with the historical information of the arbiter in the mimicry defense system, this paper proposed a random seed scheduling algorithm based on historical confidence for maximum heterogeneity and quality of service. First, the seed performer is randomly selected in the historical confidence executor that satisfies the threshold, and then the scheduling scheme is determined according to the maximum heterogeneity type and the quality of service indicator. The simulation results showed that the scheduling period of the algorithm is lower than the random scheduling algorithm. However, the scheduling effect is higher than the random scheduling algorithm, which achieved a good dynamic balance between dynamics, security, and quality of service.

Anti-washout underwater concrete (AWC) is widely used in nondrainage strengthening, however, there still exist some problems with it, such as high strength loss and poor interfacial bond in practical engineering application. Based on the study of self-stressed concrete (SSC), a research on the mix ratio for the C30 self-stressed anti-washout underwater concrete (SSAWC) was carried out in this paper in hope of solving the above problems, specifically, by adding an expansive agent to the AWC. The parameters, such as strength, fluidity, anti-dispersity, and expansibility, were picked as target indices in determination of the mix ratio. The orthogonal test design and range analysis were used to determine the reasonable mix ratio and study the influence of various parameters on the performance of SSAWC. The experimental program conducted includes a series of strength, fluidity, anti-dispersity, and expansibility tests on 18 groups of specimens. The results show that C30 SSAWC has an excellent performance using the optimal mix ratio. Compared with AWC, the expansibility and self-stress of the SSAWC can be easily observed, and the compressive strength ratio of the SSAWC casted in water to that casted in air is much bigger. This implies that SSAWC is applicable to the nondrainage strengthening.

We investigate facial expression recognition using state-of-the-art classification models. Recently, CNNs have been extensively used for face recognition. However, CNNs have not been thoroughly evaluated for facial expression recognition. In this paper, we train and test a CNN model for facial expression recognition. The performance of the CNN model is used as benchmark for evaluating other pre-trained deep CNN models. We evaluate the performance of Inception and VGG which are pre-trained for object recognition, and compare these with VGG-Face which is pre-trained for face recognition. All experiments are performed on publicly available face databases, namely, CK+, JAFFE and FACES.

The initial rotational stiffness of connections is important for the design of semirigid frames. However, most current studies on steel beam to concrete-filled steel tube column connections...

The bolted T-stub connection joining beam with column is being widely applied. To enhance the energy dissipation capacity of conventional T-stub connections, two rectangular plates are proposed to be inserted between the T-stub and column, so that the T-stub flange can yield both under tensile and compressive loads. This study put forward a mechanical model of a new T-stub connection with inserted plates and investigated important factors that could affect its mechanical behavior through experimental tests. Thirty specimens were designed with different configurations that differed according to the existence or absence of inserted plates, the fabrication method and the width of inserted plates. These configurations were tested under axial and cyclic loading conditions, and results showed that the proposal aiming to improve the energy dissipation capacity was feasible. The mechanical model presented coincided with the test observation and data. The advent of two inserted plates elevated the load bearing capacity, stiffness and ductility of connections under compression, whereas in tension the properties were not substantially enhanced. The welded T-stub connections outperformed those cut from standard section steel. The energy dissipated by connections with inserted plates was about 150% of that by traditional connections without inserted plates. Only within a reasonable range can the increment of plate width promote the energy dissipation capacity of T-stub connections. The optimum width of plates in terms of energy consumption accounted for around 31% of the overall width of connections.

Circular steel tube members with the absence of anticorrosive protection or coating failure are prone to uniform corrosion, which threatens the reliability and safety of members in the atmospheric environment. To fully study the mechanical behavior of uniformly corroded circular steel tubes, compression test and theoretical analysis were conducted, and two methods considering section reduction and material degradation, respectively, were adopted for the calculation of ultimate load carrying capacities of specimens. The results indicate that uniform corrosion did not change the failure modes of specimens, and all of them belonged to global buckling failure. The load carrying capacities and stiffness of specimens decreased with the increase of corrosion ratio, and the degree of reduction was greater than that of material degradation, showing a linear relationship with the corrosion rate. Under the same corrosion ratio, the specimens with larger eccentricity represented more obvious load carrying capacity and stiffness degradation. The load carrying capacities predicted by both methods were in good agreement with the test results and had a certain safety margin. The conservative degree of calculation results from three specifications followed a descending order of ANSI/AISC 360-16, GB 50017-2017, and EN 1993-1-1. Under the same corrosion ratio, the load carrying capacity variation of specimens between one-sided corrosion and two-sided corrosion was less than 3%.

We investigate facial expression recognition based on geometric features, and image appearance, using a range of classifier models. First, we evaluate expression recognition of face images based on geometric features, namely, facial landmarks based on the Active Appearance Model, and Action Units based on the Facial Action Coding System. A generalized linear model and a neural network are used to classify face images based on these geometric features. Second, the classification achieved by facial landmarks and Action Units is compared with the classification achieved by convolutional neural network (CNN) which extracts image features from raw pixels. Finally, we use transfer learning technique to evaluate classification using a pre-trained model. All experiments are performed on the state-of-the-art CK+ face database.

This paper proposes a new structural novelty detection method in the case of vast measurement data having uncertainties. Considering the effects of measurement accuracy and environmental variations on measurement variance, precise analytical methods of adaptive confidence distance and measurement variance are presented on the basis of statistical theory, and thus an adaptive consensus data fusion algorithm has been firstly developed to deal with the large volume of data involving considerable uncertainties. The proposed adaptive fusion algorithm can adaptively choose sensors whose data will be subsequently fused. The algorithm is then incorporated with wavelet analysis for the purpose of structural novelty detection. Two numerical examples are carried out to validate the efficiency and adaptability of the proposed method. The obtained results have been compared with those from other existing methods, which demonstrate the high efficiency of the proposed method in data processing considering uncertainties and unsatisfied performance of some sensors, as well as its accuracy in structural novelty detection. The proposed method also shows some robustness to noise.

We investigate facial expression recognition (FER) based on image appearance. FER is performed using state-of-the-art classification approaches. Different approaches to preprocess face images are investigated. First, region-of-interest (ROI) images are obtained by extracting the facial ROI from raw images. FER of ROI images is used as the benchmark and compared with the FER of difference images. Difference images are obtained by computing the difference between the ROI images of neutral and peak facial expressions. FER is also evaluated for images which are obtained by applying the Local binary pattern (LBP) operator to ROI images. Further, we investigate different contrast enhancement operators to preprocess images, namely, histogram equalization (HE) approach and a brightness preserving approach for histogram equalization. The classification experiments are performed for a convolutional neural network (CNN) and a pre-trained deep learning model. All experiments are performed on three public face databases, namely, Cohn–Kanade (CK[Formula: see text]), JAFFE and FACES.