Fuzzy inference systems implemented on neural architectures formotor fault detection and diagnosis

Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection/diagnosis process and successful fault detection/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN/FZ) inference systems, namely, fuzzy adaptive learning control/decision network (FALCON) and adaptive network based fuzzy inference system (ANFIS), with applications to induction motor fault detection/diagnosis problems. The general specifications of the NN/FZ systems are discussed. In addition, the fault detection/diagnosis structures are analyzed and compared with regard to their learning algorithms, initial knowledge requirements, extracted knowledge types, domain partitioning, rule structuring and modifications. Simulated experimental results are presented in terms of motor fault detection accuracy and knowledge extraction feasibility. Results suggest new and promising research areas for using NN/FZ inference systems for incipient fault detection and diagnosis in induction motors     

Rapid and Digital Detection of Inflammatory Biomarkers Enabled by a Novel Portable Nanoplasmonic Imager

New point‐of‐care diagnostic devices are urgently needed for rapid and accurate diagnosis, particularly in the management of life‐threatening infections and sepsis, where immediate treatment is key. Sepsis is a critical condition caused by systemic response to infection, with chances of survival drastically decreasing every hour. A novel portable biosensor based on nanoparticle‐enhanced digital plasmonic imaging is reported for rapid and sensitive detection of two sepsis‐related inflammatory biomarkers, procalcitonin (PCT) and C‐reactive protein (CRP) directly from blood serum. The device achieves outstanding limit of detection of 21.3 pg mL^?1 for PCT and 36 pg mL^?1 for CRP, and dynamic range of at least three orders of magnitude. The portable device is deployed at Vall d'Hebron University Hospital in Spain and tested with a wide range of patient samples with sepsis, noninfectious systemic inflammatory response syndrome (SIRS), and healthy subjects. The results are validated against ultimate clinical diagnosis and currently used immunoassays, and show that the device provides accurate and robust performance equivalent to gold‐standard laboratory tests. Importantly, the plasmonic imager can enable identification of PCT levels typical of sepsis and SIRS patients in less than 15 min. The compact and low‐cost device is a promising solution for assisting rapid and accurate on‐site sepsis diagnosis.     

Investigation of carbon contamination in lost foam castings of low carbon steel

Lost foam casting(LFC) process is a special casting method in which polymeric foam patterns with refractory coatings are utilized as a mould component. In this work, four types of foam: expandable polyethylene(EPE), expandable polypropylene(EPP) and expandable polystyrene(EPS) foams with two different densities were employed as pattern materials. LFC and conventional green sand mould casting methods were used to cast a low carbon steel, A216 Grade WCB. Both casting processes were carried out at 1,580 °C. Chemical analysis results showed that the carbon contamination level was high and was influenced by pattern type. Metallographic investigations revealed a significant increase in the percentage of pearlite phase in all LFC samples. Densities of manufactured samples were calculated in order to evaluate porosity of the products. It was determined that the densities of the LFC samples were lower than the green sand mould cast reference sample(RS). Vickers hardness tests were also carried out and increments in hardness values with increased carbon content was observed.     

Market segmentation using service levels in data networks

The objective of this paper is to study a communication system based on aM ^(x)/ D/1 queueing system representing a cell-switch network like Asynchronous Transfer Mode (ATM) network. Network structure consists of a single link modeled as a batch arrival markovian queue with non-preemptive head of the line priority service. Network manager (NM) is assumed to be a decision maker at a Management Information System (MIS) department. This paper establishes the incentive compatible pricing which maximizes the net value of the overall corporation, while the delays have to satisfy the Quality of Service (QoS) guarantees. We obtain structural results for the two priority case in the short run. In equilibrium, we find that the network manager maximizes the price spread between the two priority class services. We prove that as the capacity level increases indefinitely, the market is equally divided among the priority classes. In the first part of the paper, we assume that the users do not respond to network manager’s prices. In the second part, we relax this assumption and look at a leader follower game. Users choose their willingness to pay by deciding on how much value they assign to timely transmission of messages after seeing the prices set by the network manager. Our results indicate that unless there is high enough capacity set upex ante, monopoly network provider cannot price discriminate by offering different quality of service via priority classes. This trade-off between ex ante capacity level choice and ex post price discrimination decision is eliminated if the capacity is set high. It is shown in the network literature that best effort services lead to lower quality of service, in general, for a single service. We show that this holds in multiple priority services as well. We prove that when the capacity is also considered as a decision variable, simultaneous capacity and price setting yields the same optimal level with sequential capacity and price choices.     

Investigation of surface roughness of aluminium solid mould investment castings

Solid investment moulds are used for precious or non-precious metals and alloys castings, the most common application areas are dental prosthesis and jewellery fabrications. Investment casting is known for high dimensional accuracy and smooth surface finish. Casting of ultra-thin sections and small complex parts require vacuum assisted or centrifugal filling. In this study, solid investment casting of aluminium has been tried and surface quality of the castings was investigated. Specially designed steppedwedge surface roughness specimens were produced with A413 aluminium alloy casting into gypsum bonded solid moulds. Casting operations were carried out in two ways; the first only with gravitational force and the second with vacuum assistance. Surface roughness of as cast specimens were measured, effects of section thickness, vacuum assistance and location of the pattern on the wax tree were determined.     

Rapid detection of fat adulteration in bakery products using Raman and near-infrared spectroscopies

Adulteration is frequently encountered in the food industry and can be identified using currently available techniques. Infrared and Raman spectroscopic procedures are the most attractive techniques regarding fats and oils. The objective of this study was to determine the adulteration of the fat source (margarine or butter) in bakery products using Raman and near-infrared (NIR) spectroscopies. Margarine and butter samples were purchased at local markets in Turkey and examined using Raman and NIR devices. A mixture (50 % margarine : 50 % butter) of fat samples was examined as well. The NIR and Raman spectral output data of all the fat samples were processed using principal component analysis (PCA). Good classification was obtained for margarine, butter and the 1:1 adulterated mixture. The chosen bakery product (cake) was produced using the same fat samples according to the method of the American Association of Cereal Chemists. Then, the fat fraction was extracted from the cakes with n-hexane. Extracted fat samples from the cakes were examined as before. PCA was applied to Raman and NIR spectral data to achieve the separation of fat sources in the cakes. PCA was also validated in each of the two stages. Significant decomposition was observed in the Raman study in contrast to the NIR study. A chemometric comparison was also applied to processed (baked) fat samples in cakes and purchased samples by PCA to assess the effects of heat treatment on sample spectra. Raman spectroscopy with multivariate analyses such as PCA can be used to detect the adulteration of the fat source in bakery products in a faster and more suitable way than the other methods.     

A new method for registration-based medical image interpolation

A new technique is presented for interpolating between grey-scale images in a medical data set. Registration between neighboring slices is achieved with a modified control grid interpolation algorithm that selectively accepts displacement field updates in a manner optimized for performance. A cubic interpolator is then applied to pixel intensities correlated by the displacement fields. Special considerations are made for efficiency, interpolation quality, and compression in the implementation of the algorithm. Experimental results show that the new method achieves good quality, while offering dramatic improvement in efficiency relative to the best competing method.