Acoustic Based Induction Motor Fault Detection System Using Adaptive Filtering Algorithm and Fusion Based Feature Extraction Method

The proposed machine fault diagnostic system utilizes acoustic signal processing and machine learning for early fault detection and localization in induction motors. The growth of the fault in an induction motor tends to be quick and can result in a significant failure that can lead to economic loss and huge maintenance expenses. Therefore, developing accurate and sensitive induction motor fault diagnostic procedures for the maintenance system is crucial. The main purpose of this paper was to propose an optimized noise reduction technique for an induction motor fault diagnosis system and two novel acoustic feature vectors that can be used in machine learning algorithms. The contribution of this paper is to implement the effectiveness of the fusion features of acoustic signals by concatenating them from different domains. The acoustic dataset for an induction motor is collected in a motor workshop, and the NLMS algorithm is used for background noise cancellation due to its quick adaptation, stability, and efficient error minimization. Data are segmented and normalized during pre-processing, and the induction motor fault diagnosis system is implemented using MATLAB. Zero Crossing Rate (ZCR), Spectral Entropy (SE), and Energy Entropy (EE) feature vectors are combined, and the F1 feature vector is built. Correlation calculations are employed to assess the motor's condition status, and if a fault is detected, the system proceeds with feature extraction for fault localization. In the feature extraction stage for induction motor (IM) fault localization, Gammatone Cepstral Coefficients (GTCC) and Mel Frequency Cepstral Coefficient (MFCC) features are combined to construct the second feature vector (F2). This feature vector is used as training feature data in machine learning algorithms. If the input test signal is strongly correlated with the faulty signals, the type of faults is classified using a Support Vector Machine (SVM) classifier. According to the experimental results, the proposed system achieved an average accuracy of 99% in fault detection, 97.5% in fault localization, and an error rate of 2.5%

Classification of Publication Papers by Using K-nearest Neighbor Algorithm

Classification is a data mining or machine learningtechnique used to predict group membership for datainstances. Several major kinds of classificationmethod including decision tree induction method,Bayesian networks method, k-nearest neighborclassification method, case-based reasoning, geneticalgorithm and fuzzy logic techniques. Classificationis the task of deciding whether a paper belongs to aset of pre-specified classes of papers. Automaticclassification schemes can greatly facilitate theprocess of categorization. Categorization ofdocuments is challenging, as the number ofdiscriminating words can be very large. In this paper,we presented categorization of publication papersby applying k-nearest neighbor classification usingthe Euclidean Distance measure.K-nearest neighbormethod is the simplest and most straightforwardmethod among all classification methods. Hence, knearestneighbor method is used to classify differentnumber of nearest neighbors for different categories,rather than a fixed number across all categories inthis system.This system is intended to classifydifferent categories from different papers in data setsand to save time for searching papers

Analysis on Malware Detection with Multi Classifiers on M0Droid and DroidScreening Datasets

The number of applications for smart mobiledevices is steadily growing with the continuousincrease in the utilization of these devices. theInstallation of malicious applications on smartdevices often arises the security vulnerabilities suchas seizure of personal information or the use of smartdevices in accordance with different purposes bycyber criminals. Therefore, the number of studies inorder to identify malware for mobile platforms hasincreased in recent years. In this study, permissionbasedmodel is used to detect the maliciousapplications on Android which is one of the mostwidely used mobile operating system. M0Droid andDroidScreening data sets have been analyzed usingthe Android application package files andpermission-based features extracted from these files.In our work, permission-based model which appliedpreviously across different data sets investigated toM0Droid and DroidScreening datasets and theexperimental results has been expanded. Whileobtaining results, feature set analyzed using differentclassification techniques. The results show thatpermission-based model is successful on M0Droidand DroidScreening data sets and Random Forestsoutperforms another method. When compared toM0Droid system model, it is obtained much bet terconclusions depend on success rate. Our approachprovides a method for automated static code analysisand malware detection with high accuracy andreduces smartphone malware analysis time

Crowding Effect on the Survival Rate of Ornamental Fish (Swordtail)

Survival rate and growth play an important role in rearing of fish. The experiment was conducted from July to December of 2010. The present study was carried out to find the efficiency of different stocking density in Xiphophorus helleri, Swordtail provided with the same live food (Tubifex). A total of 90 healthy fingerlings were selected and used. They were divided into three groups of 5, 10, 15 per group and bred in glass aquaria of 50 liter capacity. Three replicate tanks were made for each stocking density. During experiment the total weight of each group was taken on monthly. Mortality and survival rate were checked in each tank every day. Stocking density had a significant effect on growth and survival. But in present study there is no mortality rate in three different stocking densities. The optimum stocking density for good growth of Xiphophorus helleri fingerlings is 5 fingerling/50 liters feeding at the rate of 5% of total body weight

Bioplastics from Fruit Waste

The present research work with an emphasis on the synthesis of bioplastic material by using fruit waste mainly banana peels. Bioplastic can be defined as plastic made of biomass such as corn, banana peels and sugarcane. Making bioplastics from banana peels instead of traditional petroleum-based plastic is believed to be a successful solution to increase the efficiency of the plastic industry. The polymer produced using the banana peel blended with the glycerol could help in the formation of plastic having the characteristic features of pliability, other tests like solubility and swelling studies were conducted to ensure commercial properties of these bioplastic materials, characterization of the synthesized product was carried out by FTIR, the confirms the polymer is bioplastic. One of the most significant results obtained during the research is degradation tractability of the developed product. This paper deals with the method to generate bioplastic from banana peels and help to reduce pollution

Omnious T-wave inversions: Wellens’ syndrome revisited

Wellens’ syndrome is characterized by T-wave changes in electrocardiogram (EKG) during pain-free period in a patient with intermittent angina chest pain. It carries significant diagnostic and prognostic value because this syndrome represents a pre-infarction stage of coronary artery disease involving proximal left anterior descending (LAD) artery, which can subsequently lead to extensive anterior myocardial infarctions (MIs) and even death without coronary angioplasty. Therefore, it is crucial for every physician to recognize EKG features of Wellens’ syndrome in order to take appropriate immediate intervention to reduce mortality and morbidity for MI. Here, we report a case of an overweight man with 35 pack-year of smoking history who presented to Easton Hospital with intermittent pressing chest pain of 5/6 times within 10 day-period and was found to have type A Wellens’ sign, which was biphasic T-waves in precordial leads V2 and V3 during pain-free period with no cardiac enzymes elevation. He was given therapeutic lovenox and subsequently underwent coronary angioplasty and had 95–99% occlusion in proximal LAD artery. The unique feature of our case was that Wellens’ type B EKG changes were seen after reduction of stenosis with LAD artery stent, which was likely explained by the reperfusion of the ischemic myocardium. Therefore, it is important for physicians to recognize EKG features of Wellens’ syndrome in order to take appropriate therapy to reducing mortality and morbidity form impending MI

Omnious T-wave inversions: Wellens\u27 syndrome revisited.

Wellens\u27 syndrome is characterized by T-wave changes in electrocardiogram (EKG) during pain-free period in a patient with intermittent angina chest pain. It carries significant diagnostic and prognostic value because this syndrome represents a pre-infarction stage of coronary artery disease involving proximal left anterior descending (LAD) artery, which can subsequently lead to extensive anterior myocardial infarctions (MIs) and even death without coronary angioplasty. Therefore, it is crucial for every physician to recognize EKG features of Wellens\u27 syndrome in order to take appropriate immediate intervention to reduce mortality and morbidity for MI. Here, we report a case of an overweight man with 35 pack-year of smoking history who presented to Easton Hospital with intermittent pressing chest pain of 5/6 times within 10 day-period and was found to have type A Wellens\u27 sign, which was biphasic T-waves in precordial leads V2 and V3 during pain-free period with no cardiac enzymes elevation. He was given therapeutic lovenox and subsequently underwent coronary angioplasty and had 95-99% occlusion in proximal LAD artery. The unique feature of our case was that Wellens\u27 type B EKG changes were seen after reduction of stenosis with LAD artery stent, which was likely explained by the reperfusion of the ischemic myocardium. Therefore, it is important for physicians to recognize EKG features of Wellens\u27 syndrome in order to take appropriate therapy to reducing mortality and morbidity form impending MI

Melioidosis in Myanmar

Sporadic cases of melioidosis have been diagnosed in Myanmar since the disease was first described in Yangon in 1911. Published and unpublished cases are summarized here, along with results from environmental and serosurveys. A total of 298 cases have been reported from seven states or regions between 1911 and 2018, with the majority of these occurring before 1949. Findings from soil surveys confirm the presence of Burkholderia pseudomallei in the environment in all three regions examined. The true epidemiology of the disease in Myanmar is unknown. Important factors contributing to the current gaps in knowledge are lack of awareness among clinicians and insufficient laboratory diagnostic capacity in many parts of the country. This is likely to have led to substantial under-reporting