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Adapted 8Ds methodology in manufacturing industries for securing customer’s need

This paper explores the contribution that can be given by 8Ds Methodology to secure customer concerns over quality issues and miss delivery of a product which created by production problem and at the end keeping the sustainability of organizations. Specifically analyzing the application of 8Ds Methodology in manufacturing industries located in Surabaya-Indonesia. The research method carried out is through field observations, following the problem analysis, setting up the action recommended and monitoring the progress of communication and result. Empirical evidence adds to the existing literature on this problem by showing the structured approach of 8Ds Methodology and using cause and effect matrix that significantly influences organizational performance in problem-solving of fail production startup and prevents customer complaints due to late product delivery. By following the 8Ds Methodology, strong teamwork, and employee learning behavior of problem-solving, the problem of production failure can finally be resolved and the customer's interests can be saved.

Published by: Tri Wahjoedi

Author: Tri Wahjoedi

Paper ID: V6I2-1305

Paper Status: published

Published: March 27, 2020

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Research Paper

Defect detection in Mango fruit using Image Processing

Image processing technology has been widely used in the agricultural field. Most of it is applied to the robot that can be utilized for picking fruit and for inspection vehicles. Defect detection is a major challenge for computer vision to achieve near-human levels of recognition. The fruits and vegetable defect detection are useful in the supermarkets and can be utilized in computer vision for the automatic sorting of fruits from a set, consisting of different kinds of fruits. The objective of this work is to develop an automated tool, which can be capable of identifying and classifying mango fruits based on shape, size and color features by digital image analysis. However, defect detection by a human is labor-intensive and time-consuming. The proposed methodology is useful in supermarkets for the automatic sorting of fruits from a set of different kinds of fruits. This system minimizes error and also speeds up the time of processing. The objective of this work is to present a novel method to detect surface defects of fruit using RGB images. The proposed method uses pre-processing, segmentation, edge-detection and feature extraction to classify the fruit as defected or fresh. MATLAB has been used as the programming tool for the identification and classification of fruits using the Image Processing toolbox. The proposed method can be used to detect the visible defects, stems, size and shape of mangoes.

Published by: Shruti Padmanaban, Shobhana M., L. N. Sneha Narasimhan

Author: Shruti Padmanaban

Paper ID: V6I2-1272

Paper Status: published

Published: March 27, 2020

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Research Paper

Performance analysis of low power 1-Bit CMOS full adder

One-bit full-adder efficiency and interpretation are anatomized into smaller parts. Such modules are thoroughly tested. Multiple designs are created, simulated and evaluated for each full adder. Twenty separate 1-bit full-adder modules are designed by integrating the various designs of these modules. Each of these full adder exhibits different power consumption, speed, area, and driving capabilities. Two realistic circuit structures that include full adders are used for simulation. The main aim of our project is to use CMOS technology to construct an optically reconfigurable low power full adder circuit. Analysis of time parameters in all existing full adders and comparison with the time study of our proposed circuit.

Published by: Nafies, Jenith K., Gowtham I. R., Arun Samuel T. S.

Author: Nafies

Paper ID: V6I2-1251

Paper Status: published

Published: March 27, 2020

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Others

Image de-noising and segmentation based on Fuzzy C-means clustering using Gaussian Noise

Image sweetening technology is the most efficient essential technologies in the image process field. The aim of image sweetening is to boost the interpretability or perception of knowledge in pictures for human viewers or to supply `better' Input for alternative machine-controlled image process techniques. Image Segmentation is one in every of the very important steps in the Image process for gathering data from the photographs. To check the effectiveness of noise in pictures, a noise like Gaussian noise measure added to the first image. The separate wave remodels (DWT) and Thomas Bayes Shrink soft thresholding is then applied for the removal of clamorous pixels and to smoothen the image. The planed technique is additional economical than the abstraction domain-based technique, is found to supply higher sweetening compared to alternative compressed domain-based mostly approaches. Within the end, the fuzzy-based mostly changed FCM bunch is performed on the de-noised pictures to provide clusters of segmented results.

Published by: D. Deepan, N. Vishal, Devanshu Gedam, G. Hari Aditya, V. Reji

Author: D. Deepan

Paper ID: V6I2-1303

Paper Status: published

Published: March 23, 2020

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Review Paper

Auto maintenance of poultry farm

The main idea of our project is to find the poultry farm parameters like Moisture, Temperature, water and feeding the chicks using temperature and humidity sensor, level sensor and timer. Moreover, we just update these values to the farmer using GSM, so that the users can get to know about the parameters of their farm from any distinct points away from their farm. The user can also manipulate and control the device which will be placed on the farm like turning OFF and ON through the mobile itself. Thus, the user can get details about their farm and also control the whole hardware device being in any part of the world away from their farm. Thus, our solution turns out to be a cost-effective one and also promotes healthy and efficient monitoring of poultry farms.

Published by: Oviya M., Susan Mano Derry V., Pavithira B., Nesavi S.

Author: Oviya M.

Paper ID: V6I2-1291

Paper Status: published

Published: March 23, 2020

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Research Paper

Artificial Intelligence in Software Defined Networking

This paper focuses on the challenges and opportunities of Software Defined Networking (SDN), as well as how to select the best possible SDN controller, which will help reduce the complexity of a network, price of implementation, and the maintenance of a network in any big organization. This is followed by a basic introduction to Artificial Intelligence and some of its important domains and their use in SDN.

Published by: Arhaan Aggarwal

Author: Arhaan Aggarwal

Paper ID: V6I2-1287

Paper Status: published

Published: March 23, 2020

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