Planetscope Nanosatellites Image Classification Using Machine Learning

To adopt sustainable crop practices in changing climate, understanding the climatic parameters and water requirements with vegetation is crucial on a spatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps are one of the highest resolution data that can transform agricultural practices and management on a large scale. High-resolution PS nanosatellite data was utilized in the current study to monitor agriculture’s spatiotemporal assessment for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVI was utilized to assess the vegetation pattern change in the study area. The current study area has sparse vegetation, and exposed soil exhibits brightness due to low soil moisture, constraining NDVI. Therefore, a machine learning (ML) based Random Forest (RF) classification model was used to compare the vegetation extent and computational cost of NDVI. The RF model has been compared with NDVI in the current investigation. It is one of the most precise classification methods because it can model the complexity of input variables, handle outliers, treat noise effectively, and avoid overfitting. Multinomial Logistic Regression (MLR) was implemented to compare the performance of both NDVI and RF-based classification. RF model provided good accuracy (98%) for all vegetation classes based on user accuracy, producer accuracy, and kappa coefficient.     

Recent advancement in VM task allocation system for cloud computing: review from 2015 to2021

Cloud computing is new technology that has considerably changed human life at different aspect over the last decade. Especially after the COVID-19 pandemic, almost all life activity shifted into cloud base. Cloud computing is a utility where different hardware and software resources are accessed on pay per user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization.VM used in data center for distribution of resource and application according to benefactor demand. Cloud data center faces different issue in respect of performance and efficiency for improvement of these issues different approaches are used. Virtual machine play important role for improvement of data center performance therefore different approach are used for improvement of virtual machine efficiency (i-e) load balancing of resource and task. For the improvement of this section different parameter of VM improve like makespan, quality of service, energy, data accuracy and network utilization. Improvement of different parameter in VM directly improve the performance of cloud computing. Therefore, we conducting this review paper that we can discuss about various improvements that took place in VM from 2015 to 20,201. This review paper also contain information about various parameter of cloud computing and final section of paper present the role of machine learning algorithm in VM as well load balancing approach along with the future direction of VM in cloud data center.

     

Optimized Gated Recurrent Unit for Mid-Term Electricity Price Forecasting

Electricity price forecasting (EPF) is important for energy system operations and management which include strategic bidding, generation scheduling, optimum storage reserves scheduling and systems analysis. Moreover, accurate EPF is crucial for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Nevertheless, accurate time-series prediction of electricity price is very challenging due to complex nonlinearity in the trend of electricity price. This work proposes a mid-term forecasting model based on the demand and price data, renewable and non-renewable energy supplies, the seasonality and peak and off-peak hours of working and non-working days. An optimized Gated Recurrent Unit (GRU) which incorporates Bagged Regression Tree (BTE) is developed in the Recurrent Neural Network (RNN) architecture for the mid-term EPF. Tanh layer is employed to optimize the hyperparameters of the heterogeneous GRU with the aim to improve the model’s performance, error reduction and predict the spikes. In this work, the proposed framework is assessed using electricity market data of five major economical states in Australia by using electricity market data from August 2020 to May 2021. The results showed significant improvement when adopting the proposed prediction framework compared to previous works in forecasting the electricity price.     

Time forward observer based adaptive controller for a teleoperation system

This paper presents a design of a teleoperation system using time forward observer-based adaptive controller. The controller is robust to the time-variant delays and the environmental uncertainties while assuring the stability and the transparent performance. A novel theoretical framework and algorithms for this teleoperation system have been built up with neural network-based multiple model control and time forward state observer. Conditions for stability and transparency performance are also investigated.     

Tracking setpoint robust model predictive control for input saturated and softened state constraints

This paper starts with a brief review of robust model predictive control (RMPC) schemes for uncertain systems using linear matrix inequalities (LMIs) subject to input saturated and softened state constraints. However when RMPC has both input and state constraints, difficulties will arise due to the inability to satisfy the state constraints. In this paper, we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. A brief comparative simulation of the two new RMPC schemes is implemented via examples to demonstrate the ability of the new RMPC schemes.     

Propagation of non-linear waves in a non-ideal relaxing gas

We have derived an evolution equation governing the far-field behaviour of small amplitude waves in a non-ideal relaxing gas for planar and converging flow. Asymptotic expansions of the flow variables for small amplitude waves have been used to derive the evolution equation. This equation turns out to be a generalized Burger's equation. The numerical solution of this equation is obtained by using the homotopy analysis method (HAM) proposed by Liao with two different initial conditions. Using the HAM, we have studied the effect of relaxation and nonlinearity. The convergence control parameter enables us to find a good approximate solution for such a complex flow problem. This method also confirms the capabilities and usefulness of convergence control parameter and HAM for complex and highly non-linear problems.     

A text representation model using Sequential Pattern-Growth method

Text representation is an essential task in transforming the input from text into features that can be later used for further Text Mining and Information Retrieval tasks. The commonly used text representation model is Bags-of-Words (BOW) and the N-gram model. Nevertheless, some known issues of these models, which are inaccurate semantic representation of text and high dimensionality of word size combination, should be investigated. A pattern-based model named Frequent Adjacent Sequential Pattern (FASP) is introduced to represent the text using a set of sequence adjacent words that are frequently used across the document collection. The purpose of this study is to discover the similarity of textual pattern between documents that can be later converted to a set of rules to describe the main news event. The FASP is based on the Pattern-Growth’s divide-and-conquer strategy where the main difference between FASP and the prior technique is in the Pattern Generation phase. This approach is tested against the BOW and N-gram text representation model using Malay and English language news dataset with different term weightings in the Vector Space Model (VSM). The findings demonstrate that the FASP model has a promising performance in finding similarities between documents with the average vector size reduction of 34% against the BOW and 77% against the N-gram model using the Malay dataset. Results using the English dataset is also consistent, indicating that the FASP approach is also language independent.     

Lakes of Malaysia: Water quality,eutrophication and management

The present study was undertaken to evaluate and verify the water quality status and trophic state of 15 major lakes and reservoirs in Malaysia. The lake water quality assessments were based on the National Water Quality Index (NWQI), while the trophic state assessments were based on Carlson's Trophic State Index (TSI). The findings of this water quality assessment, based on data collected between September and October 2012, indicated that a majority of the lakes were classified as Class II (Clean) waters suitable for recreational use. The results of the trophic state assessments, however, indicated that all of the lakes were eutrophic, meaning they were nutrient‐rich, they could experience algae blooms or macrophyte problems, and they were likely to exhibit poor water quality. Sustainable management measures and strategies are suggested to address the eutrophication problems of Malaysian lakes and reservoirs, with the national responses on lake and reservoir management also being discussed.