National Defence University, Pakistan

About: National Defence University, Pakistan is a education organization based out in Islamabad, Pakistan. It is known for research contribution in the topics: Context (language use) & Decision support system. The organization has 802 authors who have published 816 publications receiving 3701 citations. The organization is also known as: National Defence University of Pakistan & National Defence University Islamabad.

Advanced studies of selection schemes for dual virtual-metrology outputs

Abstract: Advanced Studies of selection schemes between neural-network (NN) and multiple-regression (MR) outputs of a virtual metrology system (VMS) are presented in this paper. Both NN and MR are applicable algorithms for implementing VM conjecture models. But a MR algorithm may achieve better accuracy only with a stable process, whereas a NN algorithm may has superior accuracy when equipment property drift or shift occurs. To take advantage of the merits of both MR and NN algorithms, the simple-selection scheme (SS-scheme) was proposed in CASE 2008 to enhance virtual-metrology (VM) conjecture accuracy. This SS-scheme simply selects either NN or MR output. Recently, with advanced studies, a weighted-selection scheme (WS-scheme), which computes the VM output with a weighted sum of NN and MR results, has been developed. Besides the example with the CVD process of fifth generation TFT-LCD used in the CASE 2008 paper, a new example with the photo process is also adopted in this paper to test and compare the conjecture accuracy among solo NN, solo MR, SS-scheme, and WS-scheme. One-hidden-layered back-propagation neural network (BPNN-I) is adopted for establishing the NN conjecture model. Test results show that the conjecture accuracy of the WS-scheme is the best among those of solo NN, solo MR, SS-scheme, and WS-scheme algorithms.

Implementation of innovative projects using renewable energy sources in the fields of “future economy”

Abstract: The aim of the article is to study the implementation of innovative projects for the use of renewable energy sources in the “economy of the future”. According to the concept of “green" energy transition, the following areas of "economy of the future" are identified: energy efficient industry, buildings, heat energy; electric transport; circulating economy, waste reduction; support for research and innovation on electricity storage, production and storage of green hydrogen; digitalization and technological changes; renewable energy sources – wind, solar, bioenergy. It is proved that renewable energy sources can meet 80% growth in electricity demand over the next 10 years. By 2025, renewable energy sources will displace coal as the main means of electricity generation. If states adopt more aggressive policies, the role of renewable energy will be even more active in the next five years. It is proved that it is important to create an appropriate market environment to attract large-scale private investment in innovative renewable energy projects. After all, without sufficient investment, networks will be a weak link in the transformation of the electricity sector, which will affect the reliability and security of electricity supply. The transition to renewable energy sources in the general energy supply, including transport and heating, is most active in large cities. To transition the city to the “green” energy, the issues of attracting investment, changing consumer behavior, integration of electricity with heat supply and transport, the state of existing energy infrastructure (electricity, gas, heating networks), distribution of energy consumption between sectors (buildings, mobility) and players in supply (large energy companies, enterprises, cooperatives). The research of the basic tendencies of realization of projects of use of renewable energy sources in Ukraine is carried out. To ensure competitive conditions for the production of electricity from alternative energy sources, the introduction of incentive mechanisms and the installation of capacities for the accumulation of electricity at power plants is envisaged. Financial support for renewable energy at the state level is provided in two areas: tax benefits and credit support. Among the tax benefits and mechanisms in world practice are the following: investment tax credit; production tax credit; mechanism of partial or full compensation of interest for the use of loans by industrial companies and individual farms for the installation of energy storage systems; mechanism for exemption from taxation of imported equipment for energy storage systems, etc. However, Ukraine has not yet taken sufficient legislative and diplomatic steps to do so. Important are the problems of balancing the network, defaults and debts to market participants, the restructuring of the “green” tariff.

An Investigation on the Effect of La3+ Alteration on Structural Properties of Perovskite PbTiO3: Total Energy Calculation

Abstract: In this study, first principles calculations based on density functional theory were used to evaluate optimized sructures and the total energy of the La doped PbTiO3 tetragonal (P4mm phase group). The calculations were conducted using local density approximation (LDA) functional as implemented in Cambridge Serial Total Energy Package (CASTEP) computer code. The different composition of Lanthanum (x) were doped on PbTiO3 resulting Pb1-xLaxTiO3 and its effect on the structural of Pb1-xLaxTiO3 were investigated. The different composition of La changed the lattice parameter and the volume of Pb1-xLaxTiO3. The total energy also were calculated and x= 0.2 is suitable composition of dapant to doped with PbTiO3 which is more stable compared with the other composition. The results are compared with experimental and other theoretical data.

Predicting the Stock Market using Machine Learning: Long short-term Memory

Abstract: The stock market is notorious for its intense uncertainty and instability, and researchers and investors alike often try a detailed and useful way to direct their stock trading. Long short-term memory (LSTM) neural networks are a subtype of Recurrent neural networks (RNNs) having significant practical utility in a wide variety of applications. Moreover, due to its unique ability to ‘remember,’ LSTMs do not depend on the long-term and can, therefore help forecast financial time series such as the stock market. In this study, we use sci-kit learn’s min-max scaler to transform the data, extract features, and establish our model for prediction. To make our analysis holistic, we use daily price data for two entities listed on two different stock exchanges. All stages of the study have been conducted using various libraries of the Python programming language using the iPython Notebook. Our results suggest that LSTMs may be more effective than traditional linear techniques such as ARIMA since the latter can not capture the non-linear factors of a problem. Furthermore, even though LSTM is better for the issue at hand, they may perform worse for others unless tuned accordingly.