On the interaction of Class C fly ash with Portland cement–calcium sulfoaluminate cement binder

Shrinkage cracking in concrete is a widespread problem, especially in concrete structures with high surface-to-volume ratio such as bridge decks. Expansive cements based on calcium sulfoaluminate phase were developed to mitigate the shrinkage cracking of concrete. The compressive stress induced due to restrained expansion of concrete has been shown to counteract the tensile stress generated during drying shrinkage. This research attempts to address the differential behavior of fly ash type (i.e., Class C vs. Class F) on early-age expansion and hydration characteristics of ordinary Portland cement (OPC)–calcium sulfoaluminate (CSA) cement blend. It was observed earlier that the presence of Class C fly ash (CFA), unlike Class F fly ash, shortened the expansion duration of OPC–CSA cement blend, which was hypothesized to be correlated to early depletion of gypsum. This paper presents a detailed verification of the hypothesis. Addition of external gypsum to OPC–CSA–CFA blend led to simultaneous increase in expansion and disappearance of a shoulder peak in the calorimetric curve. Thermodynamic calculations using a geochemical modeling program (GEMS-PSI) revealed higher saturation levels of ettringite in presence of external gypsum, which led to higher crystallization stress, and thereby increased expansion.     

Modelling the Pareto-optimal set using B-spline basis functions for continuous multi-objective optimization problems

In the past few years, multi-objective optimization algorithms have been extensively applied in several fields including engineering design problems. A major reason is the advancement of evolutionary multi-objective optimization (EMO) algorithms that are able to find a set of non-dominated points spread on the respective Pareto-optimal front in a single simulation. Besides just finding a set of Pareto-optimal solutions, one is often interested in capturing knowledge about the variation of variable values over the Pareto-optimal front. Recent innovization approaches for knowledge discovery from Pareto-optimal solutions remain as a major activity in this direction. In this article, a different data-fitting approach for continuous parameterization of the Pareto-optimal front is presented. Cubic B-spline basis functions are used for fitting the data returned by an EMO procedure in a continuous variable space. No prior knowledge about the order in the data is assumed. An automatic procedure for detecting gaps in the Pareto-optimal front is also implemented. The algorithm takes points returned by the EMO as input and returns the control points of the B-spline manifold representing the Pareto-optimal set. Results for several standard and engineering, bi-objective and tri-objective optimization problems demonstrate the usefulness of the proposed procedure.     

High-Speed Friction Stir Welding of AA7075-T6 Sheet: Microstructure,Mechanical Properties,Micro-texture,and Thermal History

Friction stir welding (FSW) is a cost-effective and high-quality joining process for aluminum alloys (especially heat-treatable alloys) that is historically operated at lower joining speeds (up to hundreds of millimeters per minute). In this study, we present a microstructural analysis of friction stir welded AA7075-T6 blanks with high welding speeds up to 3 M/min. Textures, microstructures, mechanical properties, and weld quality are analyzed using TEM, EBSD, metallographic imaging, and Vickers hardness. The higher welding speed results in narrower, stronger heat-affected zones (HAZs) and also higher hardness in the nugget zones. The material flow direction in the nugget zone is found to be leaning towards the welding direction as the welding speed increases. Results are coupled with welding parameters and thermal history to aid in the understanding of the complex material flow and texture gradients within the welds in an effort to optimize welding parameters for high-speed processing.     

A flexible probabilistic framework for large-margin mixture of experts

Mixture-of-Experts (MoE) enable learning highly nonlinear models by combining simple expert models. Each expert handles a small region of the data space, as dictated by the gating network which generates the (soft) assignment of input to the corresponding experts. Despite their flexibility and renewed interest lately, existing MoE constructions pose several difficulties during model training. Crucially, neither of the two popular gating networks used in MoE, namely the softmax gating network and hierarchical gating network (the latter used in the hierarchical mixture of experts), have efficient inference algorithms. The problem is further exacerbated if the experts do not have conjugate likelihood and lack a naturally probabilistic formulation (e.g., logistic regression or large-margin classifiers such as SVM). To address these issues, we develop novel inference algorithms with closed-form parameter updates, leveraging some of the recent advances in data augmentation techniques. We also present a novel probabilistic framework for MoE, consisting of a range of gating networks with efficient inference made possible through our proposed algorithms. We exploit this framework by using Bayesian linear SVMs as experts on various classification problems (which has a non-conjugate likelihood otherwise generally), providing our final model with attractive large-margin properties. We show that our models are significantly more efficient than other training algorithms for MoE while outperforming other traditional non-linear models like Kernel SVMs and Gaussian Processes on several benchmark datasets.

     

SYMCON—A Hybrid Symbolic/Connectionist System for Word Sense Disambiguation

Connectionist methods and knowledge-based techniques are two largely complementary approaches to natural language processing (NLP). However, they both have some potential problems which preclude their being a general purpose processing method. Research reveals that a hybrid processing approach that combines connectionist with symbolic techniques may be able to use the strengths of one processing paradigm to address the weakness of the other one. Hence, a system that effectively combines the two different approaches can be superior to either one in isolation. This paper describes a hybrid system—SYMCON (SYMbolic and CONnectionist) which integrates symbolic and connectionist techniques in an attempt to solve the problem of word sense disambiguation (WSD), which is arguably one of the most fundamental and difficult issues in NLP. It consists of three sub-systems: first, a distributed simple recurrent network (SRN) is trained by using the standard back-propagation algorithm to learn the semantic relationships among concepts, thereby generating categorical constraints that are supplied to the other two sub-systems as the initial results of pre-processing. The second sub-system of SYMCON is a knowledge-based symbolic component consisting of a knowledge base containing general inferencing rules in a certain application domain. Third, a localist network is used to select the best interpretation among multiple alternatives and potentially ambiguous inference paths by spreading activation throughout the network. The structure, initial states, and connection weights of the network are determined by the processing outcome in the other two sub-systems. This localist network can be viewed as a medium between the distributed network and the symbolic sub-system. Such a hybrid symbolic/connectionist system combines information from all three sources to select the most plausible interpretation for ambiguous words.     

An image steganographic algorithm based on spatial desynchronization

Calibration based attack is one of the most important steganalytic attacks in recent past specifically for JPEG domain steganography. In calibration attack, the attacker generally predicts the cover image statistics from the stego image. Preventing attackers from such prediction is used to resist these attacks. Domain separation (or randomization) is such a technique which is used for hiding the embedding domain from the attacker. It is observed that existing domain randomization techniques cannot provide enough randomization such that they are easily be detected by recent steganalysis techniques. In this paper, we have extended our previous work based on spatial desynchronization using statistical analysis. It is also experimentally shown that proposed algorithm is less detectable against the calibration based blind as well as targeted steganalytic attacks than the existing JPEG domain steganographic schemes.     

Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals

We develop an autonomous system to detect and evaluate physical therapy exercises using wearable motion sensors. We propose the multi-template multi-match dynamic time warping (MTMM-DTW) algorithm as a natural extension of DTW to detect multiple occurrences of more than one exercise type in the recording of a physical therapy session. While allowing some distortion (warping) in time, the algorithm provides a quantitative measure of similarity between an exercise execution and previously recorded templates, based on DTW distance. It can detect and classify the exercise types, and count and evaluate the exercises as correctly/incorrectly performed, identifying the error type, if any. To evaluate the algorithm's performance, we record a data set consisting of one reference template and 10 test executions of three execution types of eight exercises performed by five subjects. We thus record a total of 120 and 1200 exercise executions in the reference and test sets, respectively. The test sequences also contain idle time intervals. The accuracy of the proposed algorithm is 93.46% for exercise classification only and 88.65% for simultaneous exercise and execution type classification. The algorithm misses 8.58% of the exercise executions and demonstrates a false alarm rate of 4.91%, caused by some idle time intervals being incorrectly recognized as exercise executions. To test the robustness of the system to unknown exercises, we employ leave-one-exercise-out cross validation. This results in a false alarm rate lower than 1%, demonstrating the robustness of the system to unknown movements. The proposed system can be used for assessing the effectiveness of a physical therapy session and for providing feedback to the patient.     

An Architecture-Independent Graphical Tool for Automatic Contention-Free Process-to-Processor Mapping

Mapping of parallel programs onto parallel computers for efficient execution is a fundamental problem of great significance in parallel processing. This paper presents an architecture-independent software tool for contention-free mapping of arbitrary parallel programs onto parallel computers with arbitrary configurations. This mapping tool is based on an efficient heuristic algorithm that runs in time O(n ³+m ⁴) in the worst case for mapping n tasks onto m processors, where m n in most practical cases. It is fully implemented and incorporated into a graph editing system to produce a graphical mapping tool which enables its user to monitor and control the mapping process. The user can assist the mapping process or employ the algorithm to map automatically. Our mapping tool has been tested and its performance evaluated extensively. Experimental results show that our tool combines user intuition and mapping heuristics effectively to make it a powerful mapping tool which is practical to use. Our mapping tool can be easily extended for use in the more general case when the link contention-degree is bounded to a fixed system-specified value without increasing its complexity.