CFAR processing with switching exponential smoothers for nonhomogeneous environments

Conventional constant false alarm rate (CFAR) methods use a fixed number of cells to estimate the background variance. For homogeneous environments, it is desirable to increase the number of cells, at the cost of increased computation and memory requirements, in order to improve the estimation performance. For nonhomogeneous environments, it is desirable to use less number of cells in order to reduce the number of false alarms around the clutter edges. In this work, we present a solution with two exponential smoothers (first order IIR filters) having different time-constants to leverage the conflicting requirements of homogeneous and nonhomogeneous environments. The system is designed to use the filter having the large time-constant in homogeneous environments and to promptly switch to the filter having the small time constant once a clutter edge is encountered. The main advantages of proposed Switching IIR CFAR method are computational simplicity, small memory requirement (in comparison to windowing based methods) and its good performance in homogeneous environments (due to the large time-constant smoother) and rapid adaptation to clutter edges (due to the small time-constant smoother).     

Foam fractionation of spent sulphite liquor: Part I: Separation of surfactants

The surface activity of spent sulphite liquor (SSL) and the separation of surface active components by foam fractionation were studied. The maximum activity (lowest surface tension) of SSL was observed at a high pH. The surface tension was found to be 42 mN/m and 38 mN/m at acidic and basic pH values, respectively, and also a maximum foaminess (Σ) of 7 min was observed at pH 10. Foam fractionation reduced the surfactant concentration by 90% at an optimum pH of 10. The surfactants were concentrated in the collapsed foam as shown by the fact that the critical micelle concentrations of the unfractionated SSL and foam were 70% (v/v) and 10% (v/v), respectively. The removed surfactants were concentrated in 20% of the original liquid volume.     

Fuzzy boundary layer tuning for sliding mode systems as applied to the control of a direct drive robot

Chattering in the control signal is a significant problem in sliding mode control (SMC). The boundary layer approach is one of the many modifications proposed in the literature to avoid the chattering. In this approach, instead of the discontinuous SMC, a continuous feedback control law is employed in a boundary layer around the sliding surface. The thickness of the boundary layer is an important design parameter. This paper proposes a fuzzy online tuning method to adjust the boundary layer thickness for the best system performance without chattering. The method features the measurement of the chattering in the control signal. The paper validates the performance of the algorithm by experiments on a direct drive robot with a range of different payloads.     

A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information

Both academic and corporate interest in sustainable supply chains has increased in recent years. Supplier selection process is one of the key operational tasks for sustainable supply chain management. This paper examines the problem of identifying an effective model based on sustainability principles for supplier selection operations in supply chains. Due to its multi-criteria nature, the sustainable supplier evaluation process requires an appropriate multi-criteria analysis and solution approach. The approach should also consider that decision makers might face situations such as time pressure, lack of expertise in related issue, etc., during the evaluation process. The paper develops a novel approach based on fuzzy analytic network process within multi-person decision-making schema under incomplete preference relations. The method not only makes sufficient evaluations using the provided preference information, but also maintains the consistency level of the evaluations. Finally, the paper analyzes the sustainability of a number of suppliers in a real-life problem to demonstrate the validity of the proposed evaluation model.     

Time dependent processing in a parallel pipeline architecture

Pipeline architectures provide a versatile and efficient mechanism for constructing visualizations, and they have been implemented in numerous libraries and applications over the past two decades. In addition to allowing developers and users to freely combine algorithms, visualization pipelines have proven to work well when streaming data and scale well on parallel distributed-memory computers. However, current pipeline visualization frameworks have a critical flaw: they are unable to manage time varying data. As data flows through the pipeline, each algorithm has access to only a single snapshot in time of the data. This prevents the implementation of algorithms that do any temporal processing such as particle tracing; plotting over time; or interpolation, fitting, or smoothing of time series data. As data acquisition technology improves, as simulation time-integration techniques become more complex, and as simulations save less frequently and regularly, the ability to analyze the time-behavior of data becomes more important. This paper describes a modification to the traditional pipeline architecture that allows it to accommodate temporal algorithms. Furthermore, the architecture allows temporal algorithms to be used in conjunction with algorithms expecting a single time snapshot, thus simplifying software design and allowing adoption into existing pipeline frameworks. Our architecture also continues to work well in parallel distributed-memory environments. We demonstrate our architecture by modifying the popular VTK framework and exposing the functionality to the ParaView application. We use this framework to apply time-dependent algorithms on large data with a parallel cluster computer and thereby exercise a functionality that previously did not exist.     

A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers

It is well known that “green” principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan.     

Effects of annealing on the structural and magnetic properties of flame spray pyrolyzed MnFe2O4 nanoparticles

In this study, manganese ferrite (MnFe₂O₄) nanoparticles were produced through flame spray pyrolysis (FSP). To investigate the effects of heat treatment, the nanoparticles were annealed between 400 and 650°C for 4 h in air in a comparative manner. The structural, chemical, morphological, and magnetic properties of the nanoparticles were evaluated using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), dynamic light scattering (DLS), and vibrating sample magnetometry (VSM), respectively. The XRD results showed that the nanoparticles synthesized by the FSP method exhibited the MnFe₂O₄ spinel ferrite structure. The annealing process led to the decomposition of MnFe₂O₄ into various phases. According to the morphological analysis, the as-synthesized particles were hemispherical–cubic in shape and had an average particle size of less than 100 nm. In addition, the chemical bond structures of the nanoparticles were confirmed in detail by XPS elemental analysis. The highest saturation magnetization was recorded as 33.50 emu/g for the as-produced nanoparticles. The saturation magnetization of the nanoparticles decreased with increasing annealing temperature, while coercivity increased.     

Assessing support alternatives for longwall gateroads subject to changing stress

Longwall gateroad entries are subject to changing horizontal and vertical stress induced by redistribution of loads around the extracted panel. The stress changes can result in significant deformation of the entries that may include roof sag, rib dilation, and floor heave. Mine operators install different types of supports to control the ground response and maintain safe access and ventilation of the longwall face. This paper describes recent research aimed at quantifying the effect of longwall-induced stress changes on ground stability and using the information to assess support alternatives. The research included monitoring of ground and support interaction at several operating longwall mines in the U.S., analysis and calibration of numerical models that adequately represent the bedded rock mass, and observation of the support systems and their response to changes in stress. The models were then used to investigate the impact of geology and stress conditions on ground deformation and support response for various depths of cover and geologic scenarios. The research results were summarized in two regression equations that can be used to estimate the likely roof deformation and height of roof yield due to longwall-induced stress changes. This information is then used to assess the ability of support systems to maintain the stability of the roof. The application of the method is demonstrated with a retrospective analysis of the support performance at an operating longwall mine that experienced a headgate roof fall. The method is shown to produce realistic estimates of gateroad entry stability and support performance, allowing alternative support systems to be assessed during the design and planning stage of longwall operations.     

Biosorption of Acid Red P-2BX by lichens as low-cost biosorbents

The aim of this study is to examine the dye biosorption properties of lichen species called Cladonia convoluta and Evernia prunastri. Since lichens are extensively found in the environment, their suitability as a cheap adsorbent has been investigated in this study. The optimal parameters for textile dye biosorption were also determined. The dried lichen biomass showed better dye biosorption capacity than ash lichen biomass. C. convoluta had better dye biosorption capacity than E. prunastri. Dye biosorption rate was found as 71.41% at optimal conditions. This study concluded that C. convoluta was a successful and cheap biosorbent for treatment of water contaminated by Acid Red P-2BX dye.