A STUDY ON THE REDUCTION OF NON VALUE ADDED ACTIVITY OF FEEDERS IN A SEMI AUTOMATION INDUSTRY

M.M.Devarajan ¹, Dr.R.Muruganandham ^2
1Assistant Professor, Department of MECH, Vickram College of Engineering, Madurai, Tamilnadu, India.
² Assistant Professor, Department of MECH, Thiagarajar College of Engineering, Madurai, Tamilnadu, India.

     This article deals with the research carried out at a SEMI AUTOMATION INDUSTRY where most of their work process was done manually by their feeders. Feeders carry out manual loading and unloading of parts in the industry. Due to lack of plan order and feeder’s improper work, missing parts, rework, the production line stops. This line stoppage leads to less material handling which causes less production than the fixed level of production. By evolving the data, feeders contribute 60% in the value added activity. Hence to reduce the remaining non value added activity of the feeders, the major non value added activity is identified by analysing the data obtained during time study. Critical path method is used to create the exact or absolute path for the process of material handling. ARENA is implemented to study the newly created process is acceptable or not. This study involves in identifying the non value added activity and reducing the critical activity by changing the material handling process using arena to improve the production rate.

View full abstract...

A MODIFIED UNIDIRECTIONAL 3-PHASE MULTILEVEL HYBRID PWM RECTIFIER FOR POWER CONVERSION

A.J.Bhuvaneshwari¹, K.Saravanan^2
1PG Scholar, Department of ECE, Raja College of Engineering & Technology,Madurai, India,
²Assistant Professor,Department of EIE, Sethu Institute of Technology, Virdhunagar,India,

     A Pulse width modulation (PWM) rectifier consists of three switching legs and remains one of the popular Power Factor Control rectifiers. This paper proposes a new circuit topology for a unidirectional multilevel PWM rectifier for AC-DC conversion. A PWM rectifier can reduce the harmonic current dramatically because, the grid current is able to control. The proposed method forms a new circuit which is the combination a diode clamp-type topology and a flying capacitor-type topology. In this topology, the number of switches and clamping capacitor and diodes are reduced. Further, the proposed scheme can obtain good performance, as same as a conventional multilevel circuit. This paper describes about the features of the proposed topology; the Space vector modulation control strategy. The proposed converter achieved a total harmonic distortion of 9.6% for the input current and efficiency of 84.4% are simulated with Mat lab / Simulink model.

View full abstract...

CLASSIFICATION OF EEG SIGNALS FOR LIMB MOVEMENTS AND IMAGINARY TASKS USING SOFT COMPUTING TECHNIQUES

R.Kottaimalai ¹, J.Goldwyn Sudhakar ², T.ElizabethRani^3
1 Assistant Professor. Dept of ECE ,Sree Sowdambika College of Engineering, Aruppukottai, India
² Assistant. Professor. Dept of EIE, Sree Sowdambika College of Engineering, Aruppukottai, India.
³ Asstistant Professor. Dept of EIE, Sree Sowdambika College of Engineering, Aruppukottai, India.

     The people who have lost movement or language function due to traffic accidents or neuromuscular disease, necessitating large numbers of care assistants to support severely disable patients who have almost no voluntary control of body movements. Brain-Computer interface (BCI) is a direct communication pathway between a human brain and an external device. Such systems permit people to communicate through direct measurements of brain activity, without requiring any movement. The task of the BCI is to identify and predict behaviorally induced changes or cognitive states in a user‘s brain signals. Brain signals are recorded from electrodes placed on the scalp. In this report the Soft Computing technique like ANFIS was applied on the EEG signals which are taken during six limb movement tasks and also during six imaginary tasks, and the data are taken for classification. From the test results it is observed that the probability of correct classification has been increased for the limb movements by using ANFIS and for feature extraction we use wavelet transform.

View full abstract...

PERFORMANCE OF LOW CALCIUM FLY ASH BASED GEOPOLYMER CONCRETE STRUCTURAL ELEMENTS WITH M-SAND, METAKOLIN & POLYPROPYLENE FIBRE

R.Don isaac¹, Dr.C. Selvamony², A. Maria rajesh³,M.Shaju pragash^4
1 Research Scholar,Anna University-Chennai, Tamilnadu, India
² Professor, Department of Civil Engineering, Sun College of Engineering & Technology Tamilnadu,
³ Professor, Department of Civil Engineering, Arunachala College of Engineering, Tamilnadu,
⁴ Assistant Professor, Ponjesly Engineering College, Nagercoil,Tamilnadu

     Concrete is the fundamental material in civil engineering industry. This project deals with the effect of geopolymer concrete by replacing cement. Cement is the important ingredient in the conventional concrete is the Portland cement. The production of cement in the factory emits enormous amount of carbon-dioxide which pollutes the environment, to prevent this low calcium fly ash based geopolymer concrete is used. Here geopolymers cement is used which is obtained from the reaction of low calcium fly ash with the alkaline solution (i.e.) sodium hydroxide and sodium silicate. It does not pollute the environment and so it is eco-friendly. Metakolin is obtained by Thermal treatment of China clay. It increases the long term strength, durability and resistance to attack in peaty/acidic environments. Fiber reinforced concrete (FRC) may be defined as a composite materials made with Portland cement, aggregate, and incorporating discrete discontinuous fibers. Polypropylene fibers increases the durability of geopolymer concrete. To enhance the curing Metakolin is added. It is proposed to determine and compare the differences in properties of geopolymer concrete with Metakolin and Polypropylene fiber. The investigation are to be carried out using several tests which include workability test, impact value test, sieve analysis, specific gravity test, compression test, split tensional strength and flexural strength.

View full abstract...

CONTENT BASED IMAGE RETRIEVAL FOR MRI BRAIN IMAGES USING SVM CLASSIFIER

E. Sandhiya¹, Mr. G. Raghuraman^2
1Master of Computer Science and Engineering Department of Computer Science and Engineering SSN College of Engineering,Chennai, India.
²Assistant Professor Department of Computer Science and Engineering SSN College Engineering,Chennai, India.

     Content-based image retrieval might help the radiologists throughout medical diagnosis involving human brain tumor by simply searching and retrieving the similar images through a medical image repository. It makes use of image features, such as color, shape and texture, to index images with minimal human intervention. Among many retrieval features associated with CBIR, texture retrieval is one of the most powerful. As a way to tackle this concern , proposed a new method for medical image retrieval using a supervised classifier which concentrates on extracted features. We have obtained the texture based features such as GLCM (Gray Level Co-occurrence Matrix) of MRI images that contains information about the position of pixels having similar gray level values. SVM classifier is performed to classify the affected images into two categories such as normal and abnormal. The query image is classified by the classifier to a particular class and the relevant images are retrieved from the database. This will help the physician or radiologist to perform the diagnosis in a faster and non invasive way and help to increase the response time and also gives the accuracy of retrieval results.

View full abstract...