Characterization of Properties in Friction Welded Stainless Steel and Copper Materials

The aim of this study is to investigate the metallurgical and mechanical properties of friction welded stainless steel-copper joints. One of the manufacturing methods used to produce parts made from different materials is the friction welding method. Application of classical welding techniques to such materials is difficult because of they have different thermal properties. Stainless steel-copper joints are inevitable for certain applications due to unique performances such as higher electric conductivity, heat conductivity, corrosion resistance, and mechanical properties. In the present study, austenitic stainless steel and copper parts were joined by friction welding. Tensile, fatigue, and notch-impact tests were applied to friction welded specimens, and the results were compared with those for the original materials. Microstructure, energy dispersive x-ray, and x-ray diffraction (XRD) analysis and hardness variations were conducted on the joints. Results showed that various intermetallic phases such as FeCu₄ and Cu₂NiZn occurred at the interface. It was found from the microstructure and XRD analysis that intermetallic phases formed in the interface which further caused a decrease in the strength of the joints. However, hardness of the copper increased slightly, whereas the hardness of steel decreases slightly on the horizontal distance from the center.     

Determination of radiation attenuation coefficients of heavyweight- and normal-weight concretes containing colemanite and barite for 0.663 MeV γ-rays

Accurate measurements have been made to determine radiation transmission of concretes produced with barite, colemanite and normal aggregate by using beam transmission method for 0.663 MeV γ-rays energy of ¹³⁷Cs radioactive isotopes by using NaI(Tl) scintillation detector. Linear and mass attenuation coefficients of thirteen heavy- and four normal-weight concretes were calculated. It was determined that the linear attenuation coefficient (μ, cm⁻¹) decreased with colemanite concentration and increased with barite concentration in both type of the concretes. Mass attenuation coefficient values of our concretes were compared with the values proposed by the United States National Institute of Standards and Technology (NIST).     

Closed-loop stimulation of hypoglossal nerve in a dog model of upper airway obstruction

Electrical stimulation of upper airway (UAW) muscles has been under investigation as a treatment method for obstructive sleep apnea (OSA). Particular attention has been given to the electrical activation of the genioglossal muscle, either directly or via the stimulation of the hypoglossal nerve (HG), since the genioglossus is the main tongue protrusor muscle. Regardless of the stimulation site or method, an implantable electrical stimulation device for OSA patients will require a reliable method for detection of obstructive breaths to apply the stimulation when needed. In this paper, we test the hypothesis that the activity of the HG nerve can be used as a feedback signal for closed-loop stimulation of the HG nerve in an animal model of UAW obstruction where a force is applied on the submental region to physically narrow the airways. As an advantage, the method uses a single electrode for both recording and stimulation of the HG nerve. Simple linear filtering techniques were found to be adequate for producing the trigger signal for the electrical stimulation from the HG recordings. Esophageal pressure, which was used to estimate the size of the UAW passage, returned to the preloading values during closed-loop stimulation of the HG nerve. The data demonstrate the feasibility of the closed-loop stimulation of the HG nerve using its activity as the feedback signal.     

Glyceride studies. Part IV. The component glycerides of ten seed oils containing linoleic acid

The component glycerides of ten seed oils (safflower, tobacco, sunflower,Argemone mexicana, maize, cotton, groundnut,Macadamia ternifolia, Gmelina asiatica, andMadhuca latifolia) have been estimated by chromatographic procedures. The results agree with those obtained by lipolysis or calculated directly from the component acids on the basis of the theory of positional distribution.     

Header Detection to Improve Multimedia Quality Over Wireless Networks

Wireless multimedia studies have revealed that forward error correction (FEC) on corrupted packets yields better bandwidth utilization and lower delay than retransmissions. To facilitate FEC-based recovery, corrupted packets should not be dropped so that maximum number of packets is relayed to a wireless receiver's FEC decoder. Previous studies proposed to mitigate wireless packet drops by a partial checksum that ignored payload errors. Such schemes require modifications to both transmitters and receivers, and incur packet-losses due to header errors. In this paper, we introduce a receiver-based scheme which uses the history of active multimedia sessions to detect transmitted values of corrupted packet headers, thereby improving wireless multimedia throughput. Header detection is posed as the decision-theoretic problem of multihypothesis detection of known parameters in noise. Performance of the proposed scheme is evaluated using trace-driven video simulations on an 802.11b local area network. We show that header detection with application layer FEC provides significant throughput and video quality improvements over the conventional UDP/IP/802.11 protocol stack     

New classification techniques for electroencephalogram (EEG) signals and a real-time EEG control of a robot

This paper studies the state-of-the-art classification techniques for electroencephalogram (EEG) signals. Fuzzy Functions Support Vector Classifier, Improved Fuzzy Functions Support Vector Classifier and a novel technique that has been designed by utilizing Particle Swarm Optimization and Radial Basis Function Networks (PSO-RBFN) have been studied. The classification performances of the techniques are compared on standard EEG datasets that are publicly available and used by brain–computer interface (BCI) researchers. In addition to the standard EEG datasets, the proposed classifier is also tested on non-EEG datasets for thorough comparison. Within the scope of this study, several data clustering algorithms such as Fuzzy C-means, K-means and PSO clustering algorithms are studied and their clustering performances on the same datasets are compared. The results show that PSO-RBFN might reach the classification performance of state-of-the art classifiers and might be a better alternative technique in the classification of EEG signals for real-time application. This has been demonstrated by implementing the proposed classifier in a real-time BCI application for a mobile robot control.     

A cost-sensitive decision tree approach for fraud detection

With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems.     

Comparison of tool life between ceramic and cubic boron nitride (CBN) cutting tools when machining hardened steels

This paper describes a comparison of tool life between ceramics and cubic boron nitride (CBN) cutting tools when machining hardened bearing steels using the Taguchi method. An orthogonal design, signal-to-noise ratio (S/N) and analysis of variance (ANOVA) were employed to determine the effective cutting parameters on the tool life. First order linear and exponential models were carried out to find out the correlation between cutting time and independent variables. Second order regression model was also extended from the first order model when considering the effect of cutting speed (V), feed rate (f), hardness of cutting tool (TH) and two-way of interactions amongst V, f, TH variables. The results indicated that the V was found to be a dominant factor on the tool life, followed by the TH, lastly the f. The CBN cutting tool showed the best performance than that of ceramic based cutting tool. In addition, optimal testing parameter for cutting times was determined. The confirmation of experiment was conducted to verify the optimal testing parameter. Furthermore, the second order regression model and exponential model supported the first order model regarding the prediction capability. Improvements of the S/N ratio from initial testing parameters to optimal cutting parameters or prediction capability depended on the S/N ratio and ANOVA results. Moreover, the ANOVA indicated that the cutting speed was a higher significant but other parameters were also significant effects on the tool lives at 90% confidence level. The percentage contributions of the cutting speed, tool’s hardness, and feed rate were about 41.63, 32.68, and 25.22 on the tool life, respectively.