The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux ...
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The paper presents project and its verification of a prototype integrated circuit containing an analog, programmable finite impulse response (FIR) filter, implemented in CMOS 350 nm technology. The structure of the fi...
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With increasing requirements for high quality, low bandwidth video transmission systems, comes a demand for more ad hoc video encoders. Unmanned Aerial Vehicles (UAVs), or just drones, have recently grown in popularit...
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Deep learning-based methods have enhanced the performance of many robot applications thanks to their superior ability to robustly extract rich high-dimensional features. However, it comes with a high computational cos...
In this paper, a real-time smooth motion planning method for a four mecanum wheeled omnidirectional mobile robot in dynamic environments that generates a smooth collision-free trajectory is proposed. The method employ...
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Machining of difficult-to-cut materials such as high-temperature metals is challenging due to their low machinability resulting in reduced productivity and high manufacturing cost. This investigation develops and opti...
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This report explores the experimental identification of magnetic flux density within the motor air gap, focusing on the development of a robot-based approach to automate the scanning procedure. Emphasis is placed on a...
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ISBN:
(数字)9798350362343
ISBN:
(纸本)9798350362350
This report explores the experimental identification of magnetic flux density within the motor air gap, focusing on the development of a robot-based approach to automate the scanning procedure. Emphasis is placed on analyzing the distribution of magnetic flux within the motor’s spatial air gap, as well as the amplification of harmonics resulting from changes in air gap orientation. Drawing upon experimental findings, a model is proposed to illustrate the three-dimensional distribution of magnetic flux within the gap.
The use of classification models to predict the probability of an employee leaving the job can greatly improve the ability of human resources to intervene in a timely manner and rectify the situation to prevent attrit...
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ISBN:
(纸本)9798350373875
The use of classification models to predict the probability of an employee leaving the job can greatly improve the ability of human resources to intervene in a timely manner and rectify the situation to prevent attrition. After the so-called "The Great Resignation", which took place after the pandemic period, studies on attrition have increased in order to regain the lost workforce and increase the loyalty of existing employees. In Turkey, however, studies in this field remain insufficient. The aim of this study is to predict the possible loss of employees, to take relevant measures and to reduce the financial losses of companies. The study was made available as SaaS (Software as a Service) based in order to facilitate accessibility to the target audience. For the analysis phase, the columns in the data set were analyzed with Pandas and Scikit-Learn library. Exploratory Data Analysis was performed by visualizing the analyzed data with Plotly and Seaborn libraries. Various insights were obtained with these inferences. The data was subjected to pre-processing stages such as cleaning, missing data completion, scaling, feature selection, data set balancing, dimension reduction, etc. to make it useful. Logistic Regression, KNN, SVM, Desicion Tree, Random Forest, ADABoost and Naive Bayes were used to train the model. In order to enrich the data set and increase the efficiency of the model, artificial intelligence-based synthetic data generation (Data Augmentation) was performed. In case of missing columns in the trained model, KNN-Data Imputation, one of the missing data completion methods, was used. In the optimization process of the model, hyperparameter optimizations were performed to achieve maximum efficiency with improvements. 5-fold cross-validation prevented the model from over-learning. Performance was analyzed on the basis of Accuracy, Recall, Precision and F1-score metrics and the success criterion was determined as F1-Score. The model was presented as a web se
In this study, we conducted a comparative analysis of three deep learning models - CNNs (90% accuracy), LSTMs (92% accuracy), and RNNs (95% accuracy) - for skeleton-based action recognition. The research focused on ev...
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In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based ...
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