Utpal Sarma

Prediction of Moisture Loss in Withering Process of Tea Manufacturing Using Artificial Neural Network

The first and foremost process in tea manufacturing, withering, is the foundation for producing good quality. Moisture plays an important role in the manufacturing process of tea to get the desired quality. In this paper, a novel in situ instrumentation technique is proposed and validated experimentally for prediction of moisture loss (ML) in the withering process. In the proposed technique, ML is predicted based on the inlet and the outlet relative humidity (RH) and temperature during the process of withering. Network capable smart sensor nodes are developed for the measurement of RH and temperature at the inlet and outlet of the withering trough. Architecture of the nodes and network is described. A scaled-down prototype of an enclosed trough is developed to perform withering of tea leaves. Based on the data measured by the system, ML is predicted by using artificial neural network. Nonlinear autoregressive model with exogenous inputs is used for predicting the ML. The predicted ML is compared with the actual amount of ML measured by weight loss. A total of nine experiments are conducted for nine batches of tea leaves. The data collection, their analysis and results are reported in this paper. The observed result shows a good agreement between the predicted and actual ML. The maximum mean error in prediction is −3.6%.

Modeling and Analysis the Effect of PZT Area on Square Shaped Substrate for Power Enhancement in MEMS Piezoelectric Energy Harvester

Modeling and analysis of a MEMS piezoelectric (PZT-Lead Zirconate Titanate) unimorph cantilever with different substrates are presented in this paper. Stainless steel and Silicon 〈110〉 are considered as substrate. The design is intended for energy harvesting from ambient vibrations. The cantilever model is based on Euler–Bernoulli beam theory. The generated voltage and power, the current density, resonance frequencies and tip displacement for different geometry (single layer and array structure) have been analyzed using finite element method. Variation of output power and resonant frequency for array structure with array elements connected in parallel have been studied. Strain distribution is studied for external vibrations with different frequencies. The geometry of the piezoelectric layer as well as the substrate has been optimized for maximum power output. The variation of generated power output with frequency and load has also been presented. Finally, several models are introduced and compared with traditional array MEMS energy harvester.

Time dependent indoor power line background noise: Analysis, simulation and effect on communication system: Study of indoor power line noise environment

Power lines are characterized by high, unpredictable and time variant noise. The latter are summation of background noise, impulsive noise and narrowband interferences. The background noise is usually taken to be constant in simulation of power line channels due to its slow change. In this paper, the time dependency of power line background noise in an indoor power line setting and its effect on channel capacity and efficiency of transceivers are studied. It is seen that the average channel capacity at night time is 22.4% and 2% more than that of day time for narrowband and broadband transmission respectively. The comparatively high noise in day time increases the Bit Error Rate of Orthogonal Frequency Division Multiplexing transceivers up-to 29% of the night value. This time dependency of background noise therefore cannot be neglected and has to be considered in implementation of successful power line communication systems. A background channel simulator following the statistics is proposed.

An Analysis of Indoor Power Line Network as a Communication Medium Using ABCD Matrices Effect of Loads on the Transfer Function of Power Line

Power line communication is a technique that uses the available power lines as a communication media. In this paper, the power line is considered as a two-wire transmission line and modeled using the transmission, chain, or ABCD matrices. The line is simulated for different conditions commonly found in practical networks and the salient features discussed in detail. It is found that channel shows a deterministic behavior if the complete network is known a priori. However, in practical cases, discrepancies occur due to unavailability of complete information of the channel. This leads to decreased correlation or/and variable attenuation between the theoretical and experimental readings. The effects of these shortcomings on the efficiencies of discrete multitone system commonly used for power line communications are discussed.

An ANN Model to Estimate the Impact of Tea Process Parameters on Tea Quality

Present study deals with the development of an artificial neural network (ANN)-based technique for tea quality quantification by monitoring fermentation and drying condition of the tea processing stages. An RS485 network-based instrumentation system has been developed and implemented for data collection for these two stages. Three calibrated sensor nodes are installed in the fermentation room due to its larger floor area to collect temperature and relative humidity (RH). Dryer inlet temperature is recorded using a calibrated thermocouple-based sensor node. From seven input parameters and target quality data obtained from tea taster, the ANN model has been developed to find the correlation between the process condition and the tea quality. From the correlation study, more than 90% classification rate is obtained from the model. The model is also validated with some independent data showing more than 60% correlation. Error in terms of root mean square error (RMSE) is about 0.17. This model will be helpful for improvement of tea quality.