A simple index for assessing fuel moisture content

Assessing fuel moisture content to within a reasonable degree of accuracy is an important part of wildland fire management. In this paper we introduce a fuel moisture index that provides a simple and intuitive method for assessing fuel moisture content. The method can be quickly and easily applied in a field setting to provide a dimensionless measure of fuel moisture content. We compare the index with predictions from several models for fuel moisture content and conclude that it provides an equivalent measure of fuel moisture content for a number of fuel types. We go on to briefly discuss how the index could be used to construct a simple and intuitive fire danger index.     

A strategic classification support system for brownfield redevelopment

Brownfield redevelopment (BR) is an ongoing issue for governments, communities, and consultants around the world. It is also an increasingly popular research topic in several academic fields. Strategic decision support that is now available for BR is surveyed and assessed. Then a dominance-based rough-set approach is developed and used to classify cities facing BR issues according to the level of two characteristics, BR effectiveness and BR future needs. The data for the classification are based on the widely available results of a survey of US cities. The unique features of the method are its reduced requirement for preference information, its ability to handle missing information effectively, and the easily understood linguistic decision rules that it generates, based on a training classification provided by experts. The resulting classification should be a valuable aid to cities and governments as they plan their BR projects and budgets.     

A software tool for translating deterministic model results into probabilistic assessments of water quality standard compliance

Most assessments of whether a water body will comply with pollutant standards after modification of land use, loading, or climate change are based on the results of deterministic simulation models. These models, including those used to support the United States Environmental Protection Agency (USEPA) total maximum daily load (TMDL) program, typically do not account for common sources of assessment uncertainty. Instead, model results are typically represented by a time series of predicted pollutant concentration values or the parameters of a frequency-based distribution of these values over a specified time period. The rate of exceedance of relevant pollutant limits is then assessed directly from this time series or distribution to determine standard compliance. In this way, sampling and analysis-based variability and model uncertainty are typically ignored, although they may substantially influence the probability of non-compliance. To help address this problem, we introduce ProVAsT (Probabilistic Water Quality Standard Violation Assessment Tool), a software tool encoded in the graphical model-based package Analytica^®. Here, we present a version of ProVAsT which translates model-predicted in situ fecal indicator bacteria (FIB) pollutant concentrations into the expected frequency of water quality standard violations and provides a Bayesian measure of the degree of confidence in this assessment. We call this version ProVAsT-FIB. Along with inputting their own simulation model results, users can specify the particular water quality analysis methods employed (e.g. the analytic procedure used and the number and volume of sample aliquots) as well as the numeric limits pertaining to local water quality standards. It is our hope that ProVAsT will encourage the rational consideration of uncertainty and variability in water quality assessments by reducing the burden of complex statistical calculations.     

Practical and scalable evolution of digital circuits

This paper addresses the scalability problem prevalent in the evolutionary design of digital circuits and shows that Evolvable Hardware (EHW) can indeed be considered as a viable alternative design methodology for large and complex circuits. Despite the effort by the EHW community to overcome the scalability problems using both direct mapped techniques and developmental approaches, so far only small circuits have been evolved. This paper shows that, by partitioning a digital circuit and making use of a modular developmental approach, namely, the Modular Developmental Cartesian Genetic Programming (MDCGP) technique, it is indeed possible to evolve large circuits. As a proof of concept, a 5 × 5 multiplier is evolved for partition sizes of 32 and 64. It is shown that compared to the direct evolution technique, the MDCGP technique provides five times reduction in terms of evolution times, 6–56% reduction in area and improved fault tolerance. The technique is readily scalable and can be applied to even larger partition sizes, and also to sequential circuits, thus providing a promising path to evolve large and complex circuits.     

Phase Equilibrium studies in the system “FeO”–SiO2–Al2O3–CaO–MgO at 1200 °C and Po2 10-8atm

Iron oxides and silica are the major components of copper smelting slag. The oxides of aluminum, calcium and magnesium are also present in the slag that is introduced through copper concentrate, flux and refractories. Liquidus temperatures of the copper smelting slags are usually controlled by Fe/SiO₂. The concentrations of Al₂O₃, CaO and MgO, and FeO/Fe₂O₃ in the slag can also affect the liquidus temperatures where FeO/Fe₂O₃ is a function of oxygen partial pressure. High temperature equilibration under controlled oxygen partial pressure followed by quenching and electron probe microanalysis were used to determine the compositions of the liquid and solid phases at 1200 °C and Po₂ 10^-8 atm. The experimental results are presented in the forms of pseudo-ternary sections “FeO”-CaO-SiO₂ at fixed 2, 4 and 6 wt pct MgO, and 2 + 2, 4 + 4 and 6 + 6 wt pct MgO + Al₂O₃. Spinel and tridymite are the major primary phases in the composition range investigated. In addition, CaSiO₃, pyroxene, olivine, and melilite are also present. The isotherms in the spinel and tridymite primary phase fields move towards higher SiO₂ concentration directions with increasing CaO, Al₂O₃, and MgO concentrations. The experimentally determined results are compared with the FactSage calculations.     

Phase equilibria and microstructure development in Mg-rich Mg-Gd-Sr alloys: Experiments and CALPHAD assessment

Mg-Sr alloys are promising to fabricate orthopedic implants. The alloying of rare earth elements such as Gd may improve the comprehensive mechanical properties of Mg-Sr alloys. The information on the phase diagram and the microstructure development are required to design chemical composition and microstructure of Gd alloyed Mg-Sr alloys. The phase equilibria and the microstructure development in Mg-rich Mg-Gd-Sr alloys (Gd, Sr < 30 at. %) are experimentally investigated via phase identification, chemical analysis, and microstructure observation with respect to the annealed ternary alloys. The onset temperatures of liquid formation are measured by differential scanning calorimetry. A thermodynamic database of the Mg-rich Mg–Gd–Sr ternary system is developed for the first time via CALPHAD (CALculation of PHAse Diagram) approach assisted by First-Principles calculations. The thermodynamic calculations with the developed database enable a well reproduction of the experimental findings and the physical-metallurgical understanding of the microstructure formation in solidification and annealing.     

An effective MBSE approach for constructing industrial robot digital twin system

Recently, the rapid development of digital twin (DT) technology has been regarded significant in Cyber-physical systems (CPS) promotion. Scholars are focusing on the theoretical architecture and implementing applications, in order to establish a high-fidelity, dynamic, and full-lifecycle DT model and achieve a deep fusion of real and virtual. As a typical complex system with multi-disciplines, multi-physics, and multi-domain characteristics, industrial robot (IR) involves various processes and elements from the two other levels of the system: components and production lines. Their complex relationships lead to a huge challenge to build a comprehensive DT model. Current researchers usually concentrates on single-layer services because of limited construction methodology, which results in enormous isolated models, and leads to low reusable system blocks, finite scalability, and high costs of design, adjustment, upgrade, and maintenance. To address these issues, a standardized methodology and a hierarchical, modular, and generic architecture are proposed to depict comprehensive and variable industrial robot digital twin (IRDT). Firstly, the ontology information model is presented by analyzing variable factors systematically. Then, model-based system engineering (MBSE) based methodology is introduced, including construction process and variants management. After modeling process of three levels (problem domain, solution main, and implementation domain) and four viewpoints (requirement, structure, behavior, and parameter), a generic architecture of IRDT is constructed and a feature-based variants management method is described. Besides, a six-axis IRDTS is implemented to illustrate the mapping of logical architecture and physical system as a multi-level elements and processes representation example. And the steps of numerical evaluations consist of system delay and derivation. Finally, results show the effectiveness and the potential of the proposed theoretical methodology for constructing IRDTS and other industrial applications.     

Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises

Anomaly detection of machine tools plays a vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods.