A Condition Monitoring System for Electric Vehicle Batteries Based on a Convolutional Neural Network Using Thermal Image

A new monitoring technique has been developed to evaluate the capacity and performance of Lithium-ion batteries batteries by utilizing two convolutional neural networks (CNNs) models, Deep convolutional neural network (DnCNN) and CNN with BFGS quasi-Newton optimization. The system utilizes thermal images of lithium-ion batteries as input for training and testing. DnCNN model is utilised to accurately calculate battery capacity and performance, and the performance is evaluated using mean squared error (MSE) and PSNR. The CNN-based training method employs the BFGS quasi-Newton algorithm to measure battery capacity accurately by evaluating the mean squared error (MSE) and regression results. The proposed condition monitoring system using thermal imaging and CNN models, specifically the CNN- BFGS quasi-Newton algorithm model, can accurately detect battery capacity with an accuracy rate of 98.5%, compared to the DnCNN model with an accuracy rate of 96.7%. The proposed system can address the critical issue of battery capacity and degradation in EVs, providing a more sustainable and efficient alternative for real-time applications

Anemia Detection using a Deep Learning Algorithm by Palm Images

Our aim is to detect anemia through a comparative analysis of three convolutional neural network (CNN) models, namely EfficientNet B3, DenseNet121, and CNN AllNet. A collection of 3,000 microscopic palm pictures, including 1,500 anaemic and 1,500 non-anemic samples, was used to train and test the algorithms. The dataset was preprocessed to balance the classes, augment the images, and normalize the pixel values. The models were trained using transfer learning on the ImageNet dataset and fine-tuned on the anemia dataset. The performance of the models was evaluated based on accuracy, precision, recall, and F1-score. The results showed that CNN ALLNET achieved the highest accuracy of 96.8%, followed by DenseNet121 with 94.4%, and EfficientNet B3 with 91.2%. The precision, recall, and F1-score also followed a similar trend. The study concludes that CNN ALLNET is the optimal model for anemia detection due to its high accuracy and overall better performance when compared with the different models. The findings of this research could provide a basis for further studies on anemia detection using CNN models, ultimately improving the accuracy and efficiency of anemia diagnosis and treatment

Magnetoresistance sensor-based rotor fault detection in induction motor using non-decimated wavelet and streaming data

In this paper, the giant magnetoresistance broken rotor (GBR) method is used to diagnose the induction motor (IM) rotor bar fault at an early stage from outward magnetic flux developed by IM.The outward magnetic field signal has anti-clockwise radiation due to broken rotor bar current.In this paper, the outward magnetic signal is acquired using a giant magnetoresistance (GMR) sensor. In the GBR method, IM rotor fault is analysed with a non-decimated wavelet transform (NDWT)-based outward magnetic signal. Experimental result shows the difference in statistical features and energy levels of sub-bands of NDWT for healthy and faulty IM. Least square-support vector machine(LS-SVM)-based classification results are verified by confusion matrix based on 150 outward magnetic signals from a healthy and damaged rotor (broken rotor). The proposed method identifies IM rotor faults with 95% sensitivity, 90% specificity and 92.5% classification accuracy. Furthermore, run-time IM condition monitoring is performed through the ThinkSpeak internet of things (IoT) platform for collecting outer magnetic signal data. ThinkSpeak streaming data of outward magnetic field help detect rotor fault at the initial stage and understand the growth of rotor fault in the motor. The proposed GBR method overcomes sensitivity, translation-invariance limitations of existing IM rotor fault diagnosis methods

Electron Beam Dynamics of SAMEER Linac

In the design of a linac such as the 4 MeV linac fabricated at SAMEER, simulation of electron beam dynamics plays an important role. We study electron beam dynamics to help in design of buncher cavity dimensions, linac length and effect of beam loading on electron energy and spectrum. We have written a program to calculate the electron trajectories for a given power input, with cavity dimensions, rf couplings and electron beam input voltage and current as parameters. By calculating the trajectories of electrons arriving at different rf phases, we get the average electron energy, percent of beam transmitted and electron energy spectrum. By running the program with different input parameters, we can choose the best combination for a required application such as radiography or cancer therapy

Induction motor’s rotor slot variation measurement using logistic regression

Rotor slots in induction motor expand due to thermal imbalance and create magnetic stress. Magnetic stress is a force that develops on the laminated surface of the rotor due to the curving or stretching magnetic flux. Traditional motor fault detection methods never measure magnetic stress on the rotor; a significant problem frequently arises in the motor. Magnetic stress is proportional to slot size variations in the rotor. High slot size variations on the laminated surface of the rotor lead to more curving and stretching magnetic flux and damage the rotor and stator, reducing their efficiency and induce harmonics. In this paper, the Average rotor Slot Size Variation (ASSV) in the rotor is predicted during the running condition of the motor through logistic regression. Logistic regression predicts ASSV by multimodal sensor signal sub-band energy values and measures rotor slot sizes from microscope images. Multimodal sensor signal is obtained from various sensors, such as vibration, temperature, current and Giant Magneto Resistance (GMR). Signal sub-band energy is obtained from Over complete Rational-Dilation Wavelet Transform (ORaDWT). From experimental results, ASSV is more than 75% from standard size, damaging the rotor and stator. The accuracy of ASSV prediction is about 92%

Driven Morse Oscillator: Model for Multi-photon Dissociation of Nitrogen Oxide

Within a one-dimensional semi-classical model with a Morse potential the possibility of infrared multi-photon dissociation of vibrationally excited nitrogen oxide was studied. The dissociation thresholds of typical driving forces and couplings were found to be similar, which indicates that the results were robust to variations of the potential and of the definition of dissociation rate. PACS: 42.50.Hz, 33.80.WzComment: old paper, 8 pages 6 eps file

The Neutrino Magnetic Moment Induced by Leptoquarks

Allowing leptoquarks to interact with both right-handed and left-handed neutrinos (i.e., ``non-chiral'' leptoquarks), we show that a non-zero neutrino magnetic moment can arise naturally. Although the mass of the non-chiral vector leptoquark that couples to the first generation fermions is constrained severely by universality of the $\pi^+$ leptonic decays and is found to be greater than 50 TeV, the masses of the second and third generation non-chiral vector leptoquarks may evade such constraint and may in general be in the range of $1\sim 100$ TeV. With reasonable input mass and coupling values, we find that the neutrino magnetic moment due to the second generation leptoquarks is of the order of $10^{-12}\sim 10^{-16} \mu_{\rm B}$ while that caused by the third generation leptoquarks, being enhanced significantly by the large top quark mass, is in the range of $10^{-10}\sim 10^{-14} \mu_{\rm B}$.Comment: 11 pages, 3 eps figures, uses revte

Herbage Mass and Chemical Composition of the Heterogeneous Grasslands Affected by Harvesting TIME in Subtropical Terrain Nepal

A study was carried out to evaluate the productivity and chemical composition of heterogeneous grasslands at Agriculture and Forestry University (AFU) livestock farm. The four grassland ecotypes were chosen as upland north, upland south, lowland south and lowland north. The dominating herbage species and cover abundance by the botanical groups were studied on day before the harvesting. Later, the herbage dry matter productivity was estimated by quadrat cutting during May and June, 2017. Chemical analysis was done by using the proximate method for dry matter (DM), crude protein (CP), crude fiber (CF) and ether extract (EE) content. Research results showed that the AFU grassland dominated by perennial grasses and sedges followed by the forbs. The mean coverage of grasses and sedges was about 55%, whilst that of forbs was about 29% and the least was for legumes (about 4%). The cumulative herbage mass was about 1.53 t/ ha on the DM basis, whilst the highest DM was found in the upland-south (1.74 t/ha) and the least was in the upland-north (1.334 t/ha). The proximate analysis further revealed that the site had no effect on CF content, whilst the CP was significant only at the second harvest for the lowland north (8.34%).  Data revealed that the herbage composition might depend upon the soil moisture availability and geographical aspect. The dominance of perennial grasses at AFU grasslands revealed the yield stability, but needs the improvement through inoculation with leguminous forages for improved feed quality

Fabrication and Electro-optic Properties of MWCNT Driven Novel Electroluminescent Lamp

We present a novel, cost-effective and facile technique, wherein multi-walled carbon nano-tubes (CNTs) were used to transform a photoluminescent material to exhibit stable and efficient electroluminescence (EL) at low-voltages. As a case study, a commercially available ZnS:Cu phosphor (P-22G) was combined with a very low concentration of CNTs dispersed in ethanol and its alternating current driven electroluminescence (AC-EL) is demonstrated. The role of CNTs has been understood as a local electric field enhancer and facilitator in the hot carrier injection inside the ZnS crystal to produce EL in the hybrid material. The mechanism of EL is discussed using an internal field emission model, intra-CNT impact excitation and the recombination of electrons and holes through the impurity states.Comment: 9 Figure