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%)

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

Avoid duplicate key overheads for same data in storage

De-duplication is a technique used to weaken the amount of storage needed by service providers. Now a day the most originating challenge is to perform secure de-duplication in cloud storage. Although convergent encryption has been extensively adopted for secure de-duplication, a demanding issue of making convergent encryption practical is to efficiently and reliably managea massive number of convergent keys. We first introduce a baseline approach in which each user holds an autonomous master key forencrypting the convergent keys and outsourcing them to the server. As a proof of concept, encompass the implementation framework of proposed authorized duplicate check scheme and conduct experiments using these prototype. In proposed system involve authorized duplicate checkscheme sustain minimal overhead compared to normal operations.De-duplication is one of important data compression techniques for eliminating duplicate copies of repeating data. For that purpose Authorized duplication check system is used. This paper addresses problem of privacy preserving de-duplication in cloud computing and introduce a new de-duplication system supporting for Differential Authorization, Authorized Duplicate Check, Unfeasiblity of file token/duplicate-check token, In distinguishability of file token/duplicate-check token, Data affinity.In this project we are presenting the certified data de-duplication toprotect the data security by counting differential privileges of users in the duplicate check.Different new de-duplication constructions presented for supporting authorized duplicate check

Effect of pretreatment and temperature on drying characteristics and quality of green banana peel

In banana cultivation, a considerable amount of the production is wasted every year because of various constraints present in the post-harvest management chain. Converting green banana pulp and peels into flour could help to reduce losses and enable the food sector to keep the product for an entire year or more. In order to use green banana fruit and peel flour in the food industry as a raw ingredient such as in bakery and confectionery items—namely biscuits, cookies, noodles, nutritious powder, etc.—it is essential to standardize the process for the production of the flour. As a result, the purpose of this study was to investigate the influence of pretreatment and temperature on the drying capabilities and quality of dried green banana peel. The green banana peel pieces were pretreated with 0.5 and 1.0% KMS (potassium metabisulfite), and untreated samples were taken as control, and dried at 40°, 50°, and 60 °C in a tray dryer. To reduce the initial moisture content of 90–91.58% (wb) to 6.25–9.73% (wb), a drying time of 510–360 min was required in all treatments. The moisture diffusivity (Deff) increased with temperature, i.e., Deff increased from 5.069–6.659 × 10−8, 6.013–7.653 × 10−8, and 4.969–6.510 × 10−8 m2/s for the control sample, 0.5% KMS, and 1.0% KMS, respectively. The Page model was determined to be the best suited for the drying data with the greatest R2 and the least χ2 and RSME values in comparison with the other two models. When 0.5% KMS-pretreated materials were dried at 60 °C, the water activity and drying time were minimal. Hue angle, chroma, and rehydration ratio were satisfactory and within the acceptable limits for 0.5% KMS-pretreated dried banana peel at 60 °C

Calculating a Severity Score of an Adverse Drug Event Using Machine Learning on the FAERS Database

An Adverse Drug Event (ADE) is a medical injury that can result from a prescription or over the counter drug that causes an allergic reaction, overdose, reaction with other drugs or is the result of a medication error. Vulnerable populations such as children and the elderly are most susceptible to ADEs. This lack of standardized data has kept FAERS from fulfilling its full potential as a pharmacovigilance tool and its limitations have been the subject of numerous studies. Our motivation is to improve drug safety by creating a new type of pharmacovigilence system that 1. Performs data cleaning and standardization of FAERS data, 2. Computes a drug reaction severity score for each ADE based on the reported indications and coded using a modified Hartwig Severity scale, 3. Models the data to A) empirically identify drug-interaction events and their relative strength of event in specific symptom-related incidents and to B) identify drug-disease event severity for specific indications such as hypertension, stroke and cardiac failure, 4. Computes a predicted severity score for the models using machine learning algorithms 5. Evaluates the accuracy of the predicted severity score versus actual severity on a holdout dataset, and 6. Builds a predictive clinical tool for physicians that can interact with a patient’s EHR and identify adverse reaction potential at the point of prescription. We propose a global data-driven approach with the TylerADE System. This system uses advanced machine-learning techniques to sift through data and uncover potentially unknown drug events. This research has the potential to 1) improve the efficiency of pharmacological research by identifying potentially unknown n-drug events that merit further study; 2) create a risk score of potential medication events that physicians can use in a clinical setting; and 3) improve patient safety

Simultaneous Determination of Amlodipine and Valsartan

A spectrophotometric method was developed for simultaneous determination of amlodipine (Aml) and valsartan (Val) without previous separation. In this method amlodipine in methanolic solution was determined using zero order UV spectrophotometry by measuring its absorbency at 360.5 nm without any interference from valsartan