A real time operating system based test-bed for autonomous vehicle navigation

Research and experiments on ... Autonomous Navigation Schemes and Algorithms need an efficient test-bed for objective performance analysis. These algorithms often require sensor inputs from the systems such as the speed and steering sensors to apply feedback control action. An efficient test-bed provides status of all sensors and records of all previous sensor values is very desirable. This work involves developing for such a test-bed to support research on Autonomous Navigation schemes and Algorithms involved in these applications. Different approaches are analyzed and an optimum approach to design test-bed is implemented --Abstract, page iii

TPM Review in a Sheet Metal Parts Manufacturing Company

In all manufacturing plants the machines and equipment’s are influenced by deterioration in performance due to its age and use, obsolescence due to improvement in technology and failure due to unplanned maintenance Improper maintenance leads to unavailability of machine Hence the effective maintenance becomes useful in improving equipment life, reducing manufacturing cost, improving quality and minimizing the many unforeseen losses which are responsible for reducing the potential of the manufacturing plant. This paper addresses the issue by taking a case study of a manufacturing company. Detailed analysis and calculations are carried out on data collected through discussion, interview and observations. The overall equipment effectiveness (OEE) calculation is used to find out the current situation of the production system of the case company. It calculates the availability of the production system which shows that maintenance system’s effectiveness. The quality rate calculations of the work stations show the conditions of the machines and the worker’s skill and the calculations of the performance efficiency of the work stations show the utilization of the machines. The result of analysis is presented here with recommendations to the company

A Comparative Analysis of EEG-based Stress Detection Utilizing Machine Learning and Deep Learning Classifiers with a Critical Literature Review

Background: Mental stress is considered to be a major contributor to different psychological and physical diseases. Different socio-economic issues, competition in the workplace and amongst the students, and a high level of expectations are the major causes of stress. This in turn transforms into several diseases and may extend to dangerous stages if not treated properly and timely, causing the situations such as depression, heart attack, and suicide. This stress is considered to be a very serious health abnormality. Stress is to be recognized and managed before it ruins the health of a person. This has motivated the researchers to explore the techniques for stress detection. Advanced machine learning and deep learning techniques are to be investigated for stress detection.  Methodology: A survey of different techniques used for stress detection is done here. Different stages of detection including pre-processing, feature extraction, and classification are explored and critically reviewed. Electroencephalogram (EEG) is the main parameter considered in this study for stress detection. After reviewing the state-of-the-art methods for stress detection, a typical methodology is implemented, where feature extraction is done by using principal component analysis (PCA), ICA, and discrete cosine transform. After the feature extraction, some state-of-art machine learning classifiers are employed for classification including support vector machine (SVM), K-nearest neighbor (KNN), NB, and CT. In addition to these classifiers, a typical deep-learning classifier is also utilized for detection purposes. The dataset used for the study is the Database for Emotion Analysis using Physiological Signals (DEAP) dataset. Results: Different performance measures are considered including precision, recall, F1-score, and accuracy. PCA with KNN, CT, SVM and NB have given accuracies of 65.7534%, 58.9041%, 61.6438%, and 57.5342% respectively. With ICA as feature extractor accuracies obtained are 58.9041%, 61.64384%, 57.5342%, and 54.79452% for the classifiers KNN, CT, SVM, and NB respectively. DCT is also considered a feature extractor with classical machine learning algorithms giving the accuracies of 56.16438%, 50.6849%, 54.7945%, and 45.2055% for the classifiers KNN, CT, SVM, and NB respectively. A conventional DCNN classification is performed given an accuracy of 76% and precision, recall, and F1-score of 0.66, 0.77, and 0.64 respectively. Conclusion: For EEG-based stress detection, different state-of-the-art machine learning and deep learning methods are used along with different feature extractors such as PCA, ICA, and DCT. Results show that the deep learning classifier gives an overall accuracy of 76%, which is a significant improvement over classical machine learning techniques with the accuracies as PCA+ KNN (65.75%), DCT+KNN (56.16%), and ICA+CT (61.64%)

A Validated HPTLC Method for the Estimation of Amitriptyline HCL in Bulk and Its Tablet Dosage Form

A simple, rapid, reliable and accurate HPTLC method has been developed for the quantitative determination of Amitriptyline HCL in bulk and tablets. Various aliquots of the sample solution were spotted automatically by means of camag ATS 4 applicator on precoated silica gel 60 F254 on aluminium sheet as stationary phase pre washed with methanol using Toluene: Methanol: Acetone: Ammonia (5:3:2:0.2)v/v/v/v as mobile phase. The spots were scanned at 254 nm. The Rf value of AMITRIPTYLINE HCL was 0.66 ± 0.02. Calibration curves were linear in the range of 67.5 - 472.5 ng/band. The limit of detection and limit of quantification were found to be 9.37 ng/band and 2.80 ng/band respectively. The suitability of this method for the quantitative determination of compound was proved by validation in accordance with requirements of pharmaceutical regulatory standards. Objectives: This study aimed to enhance the stability indicating chromatographic method for determining Amitriptyline hydrochloride in the presence of degradation products and impurities, ensuring purity and stability of the bulk drug using HPTLC, in compliance with ICH recommendations. Methods: The study used a pure gift sample of Amitriptyline HCL from Unichem Laboratories Ltd, GOA, India, and prepared a mobile phase with methanol, toluene, acetone, and ammonia. The substance\u27s melting point was determined, and solubility was tested in various solvents. The ideal solvent was methanol, as it was completely soluble, stable, and economical. A standard stock solution of 0.1 mg/ml of Amitriptyline HCL was prepared by dissolving 10 mg in methanol. A sample was prepared by powdering 20 tablets, adding 50mL of methanol, sonicating, and filtering the solution.Chromatography was conducted on aluminium packed silica gel 60 F254 HPTLC plates, which were washed and dried before use. Samples were applied as 6 mm bands, and ascending development was performed at 25°C with a mobile phase of Toluene, Methanol, Acetone, and Ammonia. Densitometric scanning was performed using a Camag TLC scanner 4 with Wincats software. The following validation parameters are typically monitored for HPTLC method : Linearity, Sensitivity, Specificity, Precision, Data of repeatability, Interday Precision Data and Recovery. The mean weight of 20 tablets was determined, and a finely powdered powder equivalent to 1 tablet of Amitriptyline HCL was prepared, with a drug content of 99.53. Results: The HPTLC method was validated according to ICH guidelines and was found to be linear, accurate, and precise for Amitriptyline HCL. Its sensitivity was found to be low, with a sensitivity coefficient of 0.999 and a coefficient of variation of 0.72%. Conclusions: The developed HPTLC technique is precise, specific and accurate. The advantages lie in the simplicity of sample preparation and the low cost of reagents used. Statistical analysis proves that the method is suitable for the analysis of Amitriptyline HCL as bulk drug and in Pharmaceutical formulation without any interference from the excipient. Hence this HPTLC method can be used for routine drug analysis

On forbidden configuration of pseudomodular lattices

summary:We characterize the pseudomodular lattices by means of a forbidden configuration

Structural behavior of beam column joint retrofitted using carbon fiber reinforced polymer

Beam column joints are one of the most critical components of reinforced concrete (RC) structures since it is subjected to large forces during severe ground shaking. The present study comprises four exterior beam-column joint specimens having different reinforcement arrangements detailed as per IS 13920: 1993, tested under reversed cyclic loading up_to failure. The test was force-controlled and the specimen was loaded by increasing the load level during each cycle. The load was applied forward cyclic and reverse cyclic and deflection, were measured from every 5kN by using a linear variable digital transducer (LVDT) with the digital arrangement. The deflection was measured at the loading point and at the centre of the beam.  Damaged specimens were repaired and retrofitted with carbon fibre reinforced polymer (CFRP) to prevent shear damage and strength deterioration and to achieve a more ductile response. Retrofitted specimens were subjected to similar cyclic loading. Results for displacement were obtained. Hysteresis behaviour of non-retrofitted and retrofitted specimens were studied with respect to ultimate load, maximum displacement, energy dissipation capacity, stiffness degradation and general failure pattern. The comparisons showed that CFRP sheets improved the shear resistance of the joint and increased its energy dissipation capacity.  Retrofitting makes the joint so strong that failure is directed towards the beams as it helps the structure in energy dissipation through plastic hinge formation in the beam

Structural behavior of beam column joint retrofitted using carbon fiber reinforced polymer

Beam column joints are one of the most critical components of reinforced concrete (RC) structures since it is subjected to large forces during severe ground shaking. The present study comprises four exterior beam-column joint specimens having different reinforcement arrangements detailed as per IS 13920: 1993, tested under reversed cyclic loading up_to failure. The test was force-controlled and the specimen was loaded by increasing the load level during each cycle. The load was applied forward cyclic and reverse cyclic and deflection, were measured from every 5kN by using a linear variable digital transducer (LVDT) with the digital arrangement. The deflection was measured at the loading point and at the centre of the beam.  Damaged specimens were repaired and retrofitted with carbon fibre reinforced polymer (CFRP) to prevent shear damage and strength deterioration and to achieve a more ductile response. Retrofitted specimens were subjected to similar cyclic loading. Results for displacement were obtained. Hysteresis behaviour of non-retrofitted and retrofitted specimens were studied with respect to ultimate load, maximum displacement, energy dissipation capacity, stiffness degradation and general failure pattern. The comparisons showed that CFRP sheets improved the shear resistance of the joint and increased its energy dissipation capacity.  Retrofitting makes the joint so strong that failure is directed towards the beams as it helps the structure in energy dissipation through plastic hinge formation in the beam

DeepPlace: Learning to Place Applications in Multi-Tenant Clusters

Large multi-tenant production clusters often have to handle a variety of jobs and applications with a variety of complex resource usage characteristics. It is non-trivial and non-optimal to manually create placement rules for scheduling that would decide which applications should co-locate. In this paper, we present DeepPlace, a scheduler that learns to exploits various temporal resource usage patterns of applications using Deep Reinforcement Learning (Deep RL) to reduce resource competition across jobs running in the same machine while at the same time optimizing for overall cluster utilization.Comment: APSys 201

Climatological Trends and Effects of Aerosols and Clouds on Large Solar Parks: Application Examples in Benban (Egypt) and Al Dhafrah (UAE)

Solar energy production is vastly affected by climatological factors. This study examines the impact of two primary climatological factors, aerosols and clouds, on solar energy production at two of the world’s largest solar parks, Benban and Al Dhafrah Solar Parks, by using Earth observation data. Cloud microphysics were obtained from EUMETSAT, and aerosol data were obtained from the CAMS and assimilated with MODIS data for higher accuracy. The impact of both factors was analysed by computing their trends over the past 20 years. These climatological trends indicated the variations in the change in each of the factors and their resulting impact over the years. The trends were quantified into the actualised drop in energy production (Wh/m2/year) in order to obtain the impact of each factor. Aerosols displayed a falling trend of −17.78 Wh/m2/year for Benban and −44.88 Wh/m2/year for Al Dhafrah. Similarly, clouds also portrayed a largely falling trend for both stations, −36.29 Wh/m2/year (Benban) and −70.27 Wh/m2/year (Al Dhafrah). The aerosol and cloud trends were also observed on a monthly basis to study their seasonal variation. The trends were further translated into net increases/decreases in the energy produced and the resulting emissions released. The analysis was extended to quantify the economic impact of the trends. Owing to the falling aerosol and cloud trends, the annual production was foreseen to increase by nearly 1 GWh/year (Benban) and 1.65 GWh/year (Al Dhafrah). These increases in annual production estimated reductions in emission released of 705.2 tonne/year (Benban) and 1153.7 tonne/year (Al Dhafrah). Following these estimations, the projected revenue was foreseen to increase by 62,000 USD/year (Benban) and 100,000 USD/year (Al Dhafrah). Considering the geographical location of both stations, aerosols evidently imparted a larger impact compared with clouds. Severe dust storm events were also analysed at both stations to examine the worst-case scenario of aerosol impact. The results show that the realized losses during these events amounted to 2.86 GWh for Benban and 5.91 GWh for Al Dhafrah. Thus, this study showcases the benefits of Earth observation technology and offers key insights into climatological trends for solar energy planning purposes