首页期刊导航|International Journal of Intelligent Systems Technologies and Applications
期刊信息/Journal information
International Journal of Intelligent Systems Technologies and Applications
Inderscience Enterprises Ltd.
Inderscience Enterprises Ltd.
1740-8865
International Journal of Intelligent Systems Technologies and Applications/Journal International Journal of Intelligent Systems Technologies and ApplicationsEI
查看更多>>摘要：This paper investigates two real-time vision-based control algorithms for delta robots. The first one aims to enable the robot to track different objects based on their colours and shapes. This algorithm does not need any initial calibration. Instead, it depends on the least squares algorithm (LSA) to generate the required transformation matrixes. Also, it is implemented on a standalone controller with no additional time complexity added to the main controller. The second one is a self-calibrating human hand gesture tracking algorithm, which can perform automatic calibration and generates transformation matrixes automatically based on the initial measurements of the user's body. The algorithms are designed, implemented, and scheduled in a real-time manner. The results show that these algorithms can track fast-moving objects effectively regardless of the initial configuration of the robot. They provide important solutions for common problems related to visual servoing such as field of view and calibration.
查看更多>>摘要：The electric power grid is the world's largest engineering system, and its secure and reliable operation is vital to human activities. The introduction of intelligence in the electrical power grid through smart grids imposes challenges that require new techniques and approaches to provide cyber-physical security. In this article, we discuss the use of blockchain to provide security and reliability to smart grids. Blockchain allows untrusted nodes to correctly and verifiably interact with each other in a distributed peer-to-peer network, without any reliable intermediary. We explore smart contracts, codes resident in blockchain that automate multi-step processes, as a way to automatically trade electric energy. We also discuss initiatives, challenges, and research opportunities of blockchain technologies in the electrical sector.
查看更多>>摘要：The stock market prediction problems have received increased attention from researchers due to the high stakes involved and the need for better prediction accuracy. We have developed an architecture by combining a deep autoencoder and long short-term memory to give a novel deep learning framework to forecast the stock price. In stock price forecasting, applying a deep autoencoder that extracts deep features is a new concept. The autoencoder denoise the stock data, and the LSTM model stores past information to predict the future stock price. The deep learning framework that we have used comprises multiple stages. The data is fed into the deep autoencoder to generate a noise-free dataset of the stock price. In the next stage, the deep autoencoder's output is provided as input into the LSTM model to predict the price after n days. Our proposed model could overcome the limitations of traditional machine learning models used in financial prediction. We have validated the model 's effectiveness using multiple datasets and compared the performance with existing models in the literature. The results show that the proposed DAE-LSTM model outperforms the current models.
Ganesh P. PrajapatSanjay Kumar BansalPratyasa BhuiD.K. Yadav...
15页
查看更多>>摘要：The output aerodynamic power from a wind turbine is estimated through a classical c _(1)- c _(6)formulae in most of the research works especially when it is considered for the generation of electrical power. This approach sometimes may not be useful where the actual aerodynamic power with better accuracy is required. This paper investigates the blade element momentum (BEM) method in-depth with the impact of wind speed, turbine speed and air-foil geometry. An artificial intelligence model (AIM) of BEM for its use in simulation has also been proposed in this paper. AIM helps to reduce the computational time significantly since the BEM when run in whole takes a lot of time during simulation. A neural network has been made and trained with the data obtained from the BEM method. Further, the turbine power resulted from the BEM approach through AIM has been used for the generation of the electrical power with its maximum power tracking. The simulation has been performed on NREL's 5-MW test wind turbine.
查看更多>>摘要：Computer-assisted process planning systems help human planners to create better process plans. Feature-based modelling is the current trend in recognising part features. SolidWorks 2018 software is used for CAD modelling and storage of the part manufacturing details in STEP242 file format. This file type stores details such as material, size, stock, dimensional tolerance, and surface finish and interfaces with neural networks to figure out the required machining operation and cutting tool. Throughout this work, different prismatic features, such as a hole, slot, pocket, boss, chamfer, fillet, and face, were considered. A sample prismatic component with nine features was analysed and found to be highly effective. This research concentrates on neural networks-based manufacturing operation selection and cutting tool selection. Levenberg-Marquardt and scaled conjugate gradient neural networks have proven to be more effective in machining operation selection and cutting tool selection respectively. The process parameter selection is done using SolidCAM software followed by NC code generation.
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