Balancing of Production Line in a Bearing Industry to improve Productivity

Line balancing addresses the issues of balancing production or assembly line and it generally minimizes the idle time for all the events and the combinations of workstations. Factors such as lack of materials, design changes in the product, labor position may also be needed for line balancing. We approached the industry and proposed a heuristic solution solving in two different platforms, by rearranging the existing tasks over the work stations, so that the idle time of the machines are reduced to minimum and the other is by using Timer Pro software where grouping of similar kind of activities are to find out the optimized productivity . The experiment was conducted in a bearing company where analysis about line balancing were made and latter comparisons are made with that of Timer Pro Professional software to analyze the productivity. The results were much more higher compared to that of existing productivity. We address the solutions by experimenting, analyzing either ways through practical as well as software and propose how optimization can enhance the productivity in an industry

Reliability Level List Based Iterative SISO Decoding Algorithm for Block Turbo Codes

An iterative Reliability Level List (RLL) based soft-input soft-output (SISO) decoding algorithm has been proposed for Block Turbo Codes (BTCs). The algorithm ingeniously adapts the RLL based decoding algorithm for the constituent block codes, which is a soft-input hard-output algorithm. The extrinsic information is calculated using the reliability of these hard-output decisions and is passed as soft-input to the iterative turbo decoding process. RLL based decoding of constituent codes estimate the optimal transmitted codeword through a directed minimal search. The proposed RLL based decoder for the constituent code replaces the Chase-2 based constituent decoder in the conventional SISO scheme. Simulation results show that the proposed algorithm has a clear advantage of performance improvement over conventional Chase-2 based SISO decoding scheme with reduced decoding latency at lower noise levels

Development of material models to predict the crashworthiness of tubes

Metallic tubes have been extensively studied for their crashworthiness as they closely resemble automotive crash rails. Recently, the demand to produce light weight yet safer vehicles has led to the need to understand the behaviour of novel materials such as composites, metallic foams and sandwich structures durign a crash. This paper presents a method to predict the crashworthiness of structural components using material modes. The material factors that most affect the crushing response are determined and quantified by developing and validating the crushing of a square tube model in Abaqus. The inputs from the model are used to construct a simple, physically realistic constitutive model and new test methods for predicting the material behaviour at high strain rates using low test speeds. These material models enable a designer to predict the crash behaviour of a structure without the need to perform extensive physical tests, thus reducing the time and cost of development

Evaluation of Prognostic Scoring System in Preparation Peiotonits: Comparative study between APACHE II and Manheim’s Peritonitis Index Systems

CONCLUSION AND SUMMARY: • APACHE II score is the current gold standard for assessing the severity of acute perforation peritonitis. • The mortality rate in our study of 50 patients was found to be 18 %. • An APACHE II score of 15 and above predicted mortality in our study population with a positive predictive value of 100%. • The overall accuracy of this score was found to be 100%. • APACHE II score is more physiological in emergency settings compared to Manheim’s score. • Compared to the MPI score, APACHE II score could be used serially to monitor the patient in the immediate post operative period. • Patient treatment can be optimized by appropriate intensive supportive care when it is determined to be needed. • APACHE II score can triage the patients with the treatment directed to the most effective patient. • Scoring patients into groups based on risk could help future clinical research by comparing therapeutic interventions in similar patients. Of the two scoring systems evaluated, the APACHE II seems to be better suited to achieve these goals

A fully integrated violence detection system using CNN and LSTM

Recently, the number of violence-related cases in places such as remote roads, pathways, shopping malls, elevators, sports stadiums, and liquor shops, has increased drastically which are unfortunately discovered only after it’s too late. The aim is to create a complete system that can perform real-time video analysis which will help recognize the presence of any violent activities and notify the same to the concerned authority, such as the police department of the corresponding area. Using the deep learning networks CNN and LSTM along with a well-defined system architecture, we have achieved an efficient solution that can be used for real-time analysis of video footage so that the concerned authority can monitor the situation through a mobile application that can notify about an occurrence of a violent event immediately