Study of corrugations in motor bearings

Corrugation formation is investigated in bearing components in squirrelcage induction motors. The study, conducted on site, measured shaft voltage and analysed motor bearing vibrations from 48 motors on nine sites. The on-site frequency data was compared with the measured natural frequency of the motors. Detailed profilometric, optical and SEM studies were carried out on the surface of failed bearings to aid discussion on the formation of corrugations in bearings used in squirrelcage induction motors.     

Toward an interactive poster using digital watermarking and a mobile phone camera

A method for embedding a watermark in print media, posters or other paper printouts and reading the watermark information blindly with a camera phone is proposed. A subtractive-additive embedding method is applied in which the message is coded with a directed periodic pattern. The message is detected and read by searching regularities in the autocorrelation function of a periodic signal. The robustness to disturbance occurring during printing process due to air interface and camera phone properties is ensured using noise reduction, modified JND model, enhanced peak detection with filtering and shaping and two-level coding of the message. The validity of the approach is proven with tests, and an application example of an interactive poster is examined.     

Use of Anionic Micelles in Photogalvanic Cells for Solar Energy Conversion and Storage Dioctylsulfosuccinate-mannitol-safranine System

Abstract

The photogalvanic cell containing dioctylsulfosuccinate micellar species, mannitol as an electron donar and safranine as photosensitizer has been used for solar energy conversion and storage. The electrical output of the cell was 870.0 mV as photopotential 150.0 μA as photocurrent and 130.5 μW as power at power point of the cell. The current voltage characteristics of the cell have been studied, and observed conversion efficiency was 0.7603%; determine fill factor was 0.50. The performance of the cell in dark was 40.0 minutes.     

Fractional IWSOA-LB: Fractional Improved Whale Social Optimization Based VM Migration Strategy for Load Balancing in Cloud Computing

International Journal of Wireless Information Networks - Data centres have seen significant growth recently as a result of the phenomenal rise of cloud computing. These data centres typically use...     

Load Based Key Generation for MANETs: A Comparative Study with DSR and AODV

Wireless Personal Communications - Automatic key establishment schemes are the root of secure communication in Mobile adhoc networks(MANETs). These schemes are not universal, their performance...     

Influence of Speed on the Tribochemical Reaction Products and the Associated Transitions for the Dry Sliding of Silicon Nitride against Steel

Dry sliding of Si₃N₄ against steel has been studied using a pin-on-disk machine as a function of speed at a pressure of 15.5 MPa. Four types of friction and wear behaviors can be observed with associated transition in their values, called regions R1, R2, R3, and R4. R1 (0.1 to 1.25 ms^-1) is associated with high friction as well as the wear rate of the pin and the negligible wear rate of the disk. In R2 (1.25 to 3.5 ms^-1) friction is low and the wear rate of the pin is negligible but the wear rate of the disk is high. The friction and the wear rate of the pin as well as that of the disk are high in R3 (3.5 to 6.5 ms^-1). In R4 (6.5 to 12.0 ms^-1) the friction is low. The wear rate of the pin is similar to that in R3, while that of the disk is very low. SEM, EDXA, XRD, and IR reveal that each of these changes is associated with certain tribochemical interaction. R1 is associated with reaction between Si₃N₄ and water vapor to form hydrated silica (SiO₂·ⁿH₂O), R2 is controlled by softening of SiO₂·ⁿH₂O, R3 is characterized by microfracture and limited formation of Y₂SiO₅, and R4 is associated with increased formation of Y₂SiO₅ and SiO₂.     

Automated skin lesion detection and classification using fused deep convolutional neural network on dermoscopic images

Skin cancer becomes a deadly disease that affect people of all ages globally. The availability of various types of benign and malignant melanoma makes the skin lesion diagnostic process difficult. Since the visual inspection of skin cancer is costlier and lengthy process, it is needed to design automatic diagnosis model to classify skin lesions accurately and promptly. Computer-aided diagnosis models can be employed to identify the presence of skin lesions using dermoscopic images. The automatic identification of skin lesions can assist the doctors and enable the detection process at an efficient and faster rate. With this motivation, this article presents an automated skin lesion detection and classification using fused deep convolutional neural network (ASDC-FDCNN) on dermoscopic images. The ASDC-FDCNN technique aims to identify the existence of skin lesions from dermoscopic images. The ASDC-FDCNN model involves the design of two deep learning models namely VGG19 and ResNet152 models. Besides, the fusion based feature extraction process is performed to derive feature vectors. In addition, the DCNN technique was employed as classifier for identifying the presence or absence of skin lesions. The performance validation of the ASDC-FDCNN technique takes place utilizing benchmark skin lesion dataset. A comparative results analysis reported the better performance of the ASDC-FDCNN model over the recent technique with respect to various measures.     

An intelligent IoT with cloud centric medical decision support system for chronic kidney disease prediction

At present days, Internet of Things (IoT) and cloud platforms become widely used in various healthcare applications. The enormous quantity of data produced by the IoT devices in the healthcare sector can be examined on the cloud platform instead of dependent on restricted storage and computation resources exist in the mobile gadgets. For offering effective medicinal services, in this article, an online medical decision support system (OMDSS) is introduced for chronic kidney disease (CKD) prediction. The presented model involves a set of stages namely data gathering, preprocessing, and classification of medical data for the prediction of CKD. For classification, logistic regression (LR) model is applied for classifying the data instances into CKD and non-CKD. In addition, for tuning the parameters of LR, Adaptive Moment Estimation (Adam), and adaptive learning rate optimization algorithm is applied. The performance of the introduced model is examined using a benchmark CKD dataset. The experimental outcome observed the superior characteristics of the presented model on the applied dataset.