Potential applications of phase change materials in concrete technology

In internal curing, pre-wetted lightweight aggregates (LWA) serve as internal reservoirs to supply the extra water needed by the cementitious and pozzolanic components of the concrete during their hydration processes. Due to their porous nature and reasonably high absorption capacity, the LWA can also be filled with other materials, such as phase change materials (PCMs). In this paper, three potential applications of PCM-filled LWA in concrete technology are presented. In addition to the previously explored application of increasing the energy storage capacity of concrete in residential and commercial construction by using a PCM with a transition temperature near room temperature, applications for higher and lower temperature PCMs also exist. In the former case, a PCM can be used to reduce the temperature rise (and subsequent rate of temperature decrease) of a large concrete section during (semi)adiabatic curing, to minimize thermal cracking, etc. In the latter case, a PCM can perhaps reduce the number or intensity of freeze/thaw cycles experienced by a bridge deck or other concrete exposed to a winter environment. In this paper, these latter two applications are preliminarily explored from both experimental and modeling viewpoints.     

Chloride ingress data from field and laboratory exposure – Influence of salinity and temperature

Marine exposure conditions provide an aggressive environment for reinforced concrete structures, mainly due to the occurrence of chloride-induced reinforcement corrosion. There are several influencing factors, but despite a lot of research little is known about the influence from variations in the exposure conditions. Therefore an exposure programme has been made, where concrete specimens, made from one single concrete composition, have been exposed at 12 marine locations around the world, along with a parallel study under laboratory conditions. The specimens have been constantly submerged during the exposure to get well-defined exposure conditions. The purpose was to get a quantitative measure of how different exposure conditions influence the durability of reinforced concrete, measured in terms of chloride ingress into the concrete. The results indicate that the exposure conditions (salinity and temperature of seawater), as expected, influenced the chloride ingress. This is further analysed and discussed in the paper.     

A steel surface defect inspection approach towards smart industrial monitoring

Journal of Intelligent Manufacturing - With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely...     

Collaborative representation with reduced residual for face recognition

Collaboration representation-based classification (CRC) was proposed as an alternative approach to the sparse representation method with similar efficiency. The CRC is essentially a competition scheme for the training samples to compete with each other in representing the test sample, and the training class with the minimum representation residual from the test sample wins the competition in the classification. However, the representation error is usually calculated based on the Euclidean distance between a test sample and the weighted sum of all the same-class samples. This paper exploits alternative methods of calculating the representation error in the CRC methods to reduce the representation residual in a more optimal way, so that the sample classes compete with each other in a closer range to represent the test sample. A large number of face recognition experiments on three face image databases show that the CRC methods with optimized presentation residual achieve better performance than the original CRC, and the maximum improvement in classification accuracy is up to 12 %.     

Full-scaled deep metric learning for pedestrian re-identification

Multimedia Tools and Applications - The pedestrian re-identification problem (i.e., re-id) is essential and pre-requisite in multi-camera video surveillance studies, provided the fact that...     

A fast updated algorithm to maintain the discovered high-utility itemsets for transaction modification

High-utility itemsets mining (HUIM) is a critical issue which concerns not only the occurrence frequencies of itemsets in association-rule mining (ARM), but also the factors of quantity and profit in real-life applications. Many algorithms have been developed to efficiently mine high-utility itemsets (HUIs) from a static database. Discovered HUIs may become invalid or new HUIs may arise when transactions are inserted, deleted or modified. Existing approaches are required to re-process the updated database and re-mine HUIs each time, as previously discovered HUIs are not maintained. Previously, a pre-large concept was proposed to efficiently maintain and update the discovered information in ARM, which cannot be directly applied into HUIM. In this paper, a maintenance (PRE-HUI-MOD) algorithm with transaction modification based on a new pre-large strategy is presented to efficiently maintain and update the discovered HUIs. When the transactions are consequentially modified from the original database, the discovered information is divided into three parts with nine cases. A specific procedure is then performed to maintain and update the discovered information for each case. Based on the designed PRE-HUI-MOD algorithm, it is unnecessary to rescan original database until the accumulative total utility of the modified transactions achieves the designed safety bound, which can greatly reduce the computations of multiple database scans when compared to the batch-mode approaches.     

Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function

Bartels–Stewart algorithm is an effective and widely used method with an O(n ³) time complexity for solving a static Sylvester equation. When applied to time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. Gradient-based recurrent neural network are able to solve the time-varying Sylvester equation in real time but there always exists an estimation error. In contrast, the recently proposed Zhang neural network has been proven to converge to the solution of the Sylvester equation ideally when time goes to infinity. However, this neural network with the suggested activation functions never converges to the desired value in finite time, which may limit its applications in realtime processing. To tackle this problem, a sign-bi-power activation function is proposed in this paper to accelerate Zhang neural network to finite-time convergence. The global convergence and finite-time convergence property are proven in theory. The upper bound of the convergence time is derived analytically. Simulations are performed to evaluate the performance of the neural network with the proposed activation function. In addition, the proposed strategy is applied to online calculating the pseudo-inverse of a matrix and nonlinear control of an inverted pendulum system. Both theoretical analysis and numerical simulations validate the effectiveness of proposed activation function.     

Point cloud completion via structured feature maps using a feedback network

Computational Visual Media - In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the...