Design, Development and Performance Evaluation of Eddy Current Displacement Sensor Based Pressure Sensor with Target Temperature Compensation

In Aerospace applications, pressure measurement plays a vital role as it serves as one of the input to onboard controller to aid decision- making on initiating or terminating some of the critical events. In this paper, the design aspects of pressure sensor using linear eddy current displacement sensor (ECDS) are presented along with its performance evaluation. The static calibration is carried out to select the best position of ECDS in the proposed pressure sensor. The effect of target temperature on sensor output is presented with test results to aid compensation. A compensation algorithm is developed to minimise the error due to target temperature. The developed compensation algorithm is validated using thermal calibration. The designed pressure sensor is calibrated using Arson dynamic pressure calibrator to evaluate its bandwidth. The calibration results are analysed to aid future sensor design towards improvement of accuracy, bandwidth and miniaturisation

Viable mass production method for cotton pink bollworm, Pectinophora gossypiella (Saunders)

AbstractCotton seed based artificial diet has been standardized for continuous rearing of pink bollworm Pectinophora gossypiella (Saunders) at the Central Institute for Cotton Research, Regional Station, Coimbatore. The ingredients of the diet are easily available and are cost effective. Basic ingredients of the diet are cotton seed flour (processed) and chick pea flour, Carbohydrate, Protein, Fat sources, multi vitamin, antimicrobial agents and agar as thickening agent are used as other ingredients. Micro centrifuge tubes with lid were used as rearing containers. Individual neonate larvae were released on each piece of the diet inside the micro centrifuge tube and the lids were closed. This prevented larval escape, retaining them inside the tubes and also prevented diet dehydration. The recovery of insect reared on diet was recorded as 95.56%. Egg hatchability and adult emergence were 100% while pupal malformation was nil. Eggs, larval and pupal periods were recorded as 4.8±0.632, 25.10±0.994 and 7.9±0.88days, respectively. Larval and pupal weights were recorded as 21.40mg±3.63, 18.00mg±2.73, respectively

Impact of Hybrid Intelligent Computing in Identifying Constructive Weather Parameters for Modeling Effective Rainfall Prediction

Uncertain atmosphere is a prevalent factor affecting the existing prediction approaches. Rough set and fuzzy set theories as proposed by Pawlak and Zadeh have become an effective tool for handling vagueness and fuzziness in the real world scenarios. This research work describes the impact of Hybrid Intelligent System (HIS) for strategic decision support in meteorology. In this research a novel exhaustive search based Rough set reduct Selection using Genetic Algorithm (RSGA) is introduced to identify the significant input feature subset. The proposed model could identify the most effective weather parameters efficiently than other existing input techniques. In the model evaluation phase two adaptive techniques were constructed and investigated. The proposed Artificial Neural Network based on Back Propagation learning (ANN-BP) and Adaptive Neuro Fuzzy Inference System (ANFIS) was compared with existing Fuzzy Unordered Rule Induction Algorithm (FURIA), Structural Learning Algorithm on Vague Environment (SLAVE) and Particle Swarm OPtimization (PSO). The proposed rainfall prediction models outperformed when trained with the input generated using RSGA. A meticulous comparison of the performance indicates ANN-BP model as a suitable HIS for effective rainfall prediction. The ANN-BP achieved 97.46% accuracy with a nominal misclassification rate of 0.0254 %

Rainfall Forecast Analysis using Rough Set Attribute Reduction and Data Mining Methods

Developments in information technology has enabled accumulation of large databases and most of the environmental, agricultural and medical databases consist of large quantity of real time observatory datasets of high dimension space. The curse to these high dimensional datasets is the spatial and computational requirements, which leads to ever growing necessity of attribute reduction techniques. Attribute reduction is a process of reducing the data space by removing the irrelevant, redundant attributes from large databases. The proposed model estimates the enhancement achieved in spatial reduction and classifier accuracy using Rough Set Attribute Reduction Technique (RSART) and data mining methods. The first module of this proposed model has identified an efficient attribute reduction approach based on rough sets for spatial reduction. The next module of the proposed model has trained and tested the performance of Naive Bayes (NB), Bayesian Logistic Regression (BLR), Multi Layer Perceptron (MLP), Classification and Regression Tree (CART) and J48 classifiers and evaluated the accuracy in terms of each classification models. The experimental results revealed that, the combination of RSART based on Genetic Algorithm approach and Bayesian Logistics Regression Classifier can be used for weather forecast analysis

AN EMPIRICAL MACHINE LEARNING APPROACH TO EXTRACT AND RANK MULTI-WORD PRODUCT NAMES USING iCIW APPROACH

Entity Extraction or product name extraction is a suitable premise for sentiment analysis. A sentiment discovers opinion of the customers on the product stated in the sentence. Extracting product names using the existing approaches from the customer’s reviews are not exact most of the time. Almost many existing approaches mainly lack in addressing the product name having multiple words or sequence of words (multi-word). When compared with single word named products, extracting opinion on multi-word named products are non-trivial task as customer use either full name or part of the product name (sub-word) in product reviews. Therefore, it is the foremost challenging task in sentiment analysis to recognize appropriate complete name of a specific product using the sub-word names. Secondly, the multi-word named products or sub-word named products may occur anywhere in the review sentences, the position of the product names cannot be predicted in advance. It may occur in the beginning, middle or at the end and also with any number of times. So, identifying the position of these product names is another key issue. Therefore, this research attempt to design a novel automated improved Context Information Weight (iCIW) approach to resolve the exiting issues. The iCIW assimilates the concept of lexicon and statistical approach. The proposed iCIW model is more suitable for document analysis related to medical reports, product details repository and political documents. The experimental result reveals that the proposed method performs very efficient than existing approaches in multi-word named product extraction