Industrial Technology
http://hdl.handle.net/10211.3/7506
2024-03-28T12:12:02Z
-
Integrated model of assisted parking system and performance evaluation with entropy weight extended analytic hierarchy process and two-tuple linguistic information
http://hdl.handle.net/10211.3/198055
Integrated model of assisted parking system and performance evaluation with entropy weight extended analytic hierarchy process and two-tuple linguistic information
Zhang, Daming; Hua, Yiding; Jiang, Haobin
Evaluating comprehensive performance of assisted parking system has been a very important issue for car companies for years, because the overall performance of assisted parking system directly influences car intellectualization and customers' degree of satisfaction. Therefore, this article proposes two-tuple linguistic analytic hierarchy process to evaluate assisted parking system so as to avoid information loss during the processes of evaluation integration. The performance evaluation attributes for assisted parking system are established initially. Subsequently, the information entropy theory is proposed to improve the evaluation attribute weight determined by analytic hierarchy process for the influencing factors of the randomness in parking test process. Furthermore, the evaluation attribute measure values of comprehensive performance are calculated and the assisted parking system evaluation results are obtained with ordered weighted averaging operator. Finally, numerical examples of vehicle types equipped with eight different assisted parking systems and computational results are presented.
Freua ADVANCES IN MECHANICAL ENGINEERING (ISSN: 1687-8140). Vol.8(7), pp. 1-14.
2016-07-01T00:00:00Z
-
Energy Balance of a Typical U.S. Diet
http://hdl.handle.net/10211.3/193777
Energy Balance of a Typical U.S. Diet
Alexandrou, Athanasios; Tenbergen, Klaus; Adhikari, Diganta
Today’s agriculture provides an ever increasing population with sufficient quantities of food. During food production, processing, handling and transportation, an amount of energy is invested into the various products. An energy analysis of a typical American diet provides policy makers, farmers and the public with the necessary information to evaluate and make informed decisions as to how to improve the efficient use of energy. At the same time, an informed consumer may become energy conscious and be able to make dietary choices based on food energy balance. This paper studies the energy sequestered in a typical American diet as defined in Food and Agriculture Organization of the United Nations, Statistics Division (FAOSTAT). The amount of energy incorporated in this diet of 3628 kcal (15.18 MJ) per person and day to produce, transport, handle and process the foods is calculated and found to have approximately 39.92 GJ (9.54 Gcal) sequestered per person and year. It is shown that a diet in line with the United States Department of Agriculture (USDA) recommendation of around 2100 kcal (8.79 MJ) per day person will result in a reduction of energy inputs by 42% on an annual basis. This reduction for the whole population of the United States of America (USA), corresponds to approximately 879 million barrels of oil equivalent (boe) savings. Energy efficiency for the food categories studied varies from 3.4% to 56.5% with an average of 21.7%. Food energy efficiency can be further improved in some food categories through either a reduction of energy inputs or yield increase.
From Foods 2013, 2(2), 132-142.
0003-01-01T00:00:00Z
-
A simulation study on reinforcement learning for navigation application
http://hdl.handle.net/10211.3/177227
A simulation study on reinforcement learning for navigation application
Mahalik, Nitaigour; Bal, Jaspreet
In this paper we have contributed work on implementation of Q-learning, a reinforcement, nonparameter
based learning and decision making method. have formulated and demonstrated
the Q-learning algorithm via simulation. The work includes formulation of a pseudo-code and
development of algorithm taking into account of an application. The application of reinforcement
learning is simulated through a conceptual agriculture field, where a robot is commanded to
reach at trees and finally delivers the fruits to the storage point (goal). We have studied the
effectiveness of g and a. The results show that the learning parameter (g) and the learning
rate (a) are the two important parameters to be considered while developing Q-learning based
reinforcement algorithm for specific application. We have also established the optimal values of
g and a of an application through a simulation study.
From Artificial Intelligence and Applications, Volume 1, Number 2, pp.43-53, 2014.
2014-01-01T00:00:00Z
-
Fault detection in a centrifugal pump using vibration and motor current signature analysis
http://hdl.handle.net/10211.3/177225
Fault detection in a centrifugal pump using vibration and motor current signature analysis
Mahalik, Nitaigour; Dastidar, Sabyasachi G.; Mohanty, Amiya Ranjan; Pradhan, Prasanta Kumar
Due to growth of mechanisation and automation, today’s industrial systems are becoming more complex. A small breakdown of any non-redundant machine component affects the operation of the entire system. To increase the availability and reliability, automated health monitoring and self-diagnostic capability (SDC) becoming essential to many industrial machineries like pumps, motors, etc. Condition monitoring does not prevent the failure, but it can predict the possibility of future failure by measuring certain machine parameters. Though there are various condition monitoring techniques, vibration analysis and motor current signature analysis (MCSA) are most suitable for detection of faults and abnormalities in machine systems. This work attempts to develop an SDC framework and diagnose the impeller condition of a centrifugal pump using MCSA. Time and frequency domain analyses are done for different impeller conditions of the pump, such as normal impeller and defective impellers. Significant differences are observed and a fault prediction strategy is recommended.
From International Journal of Automation and Control Vol. 6(3-4), pp. 261-276, available online: http://dx.doi.org/10.1504/IJAAC.2012.051884. Copyright © 2012 by Inderscience.
2012-01-01T00:00:00Z