Fuzzy Implications: Some Recently Solved Problems

In this chapter we discuss some open problems related to fuzzy implications, which have either been completely solved or those for which partial answers are known. In fact, this chapter also contains the answer for one of the open problems, which is hitherto unpublished. The recently solved problems are so chosen to reflect the importance of the problem or the significance of the solution. Finally, some other problems that still remain unsolved are stated for quick reference

Applications of deep learning in severity prediction of traffic accidents

This study investigates the power of deep learning in predicting the severity of injuries when accidents occur due to traffic on Malaysian highways. Three network architectures based on a simple feedforward Neural Networks (NN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) were proposed and optimized through a grid search optimization to fine tune the hyperparameters of the models that can best predict the outputs with less computational costs. The results showed that among the tested algorithms, the RNN model with an average accuracy of 73.76% outperformed the NN model (68.79%) and the CNN (70.30%) model based on a 10-fold cross-validation approach. On the other hand, the sensitivity analysis indicated that the best optimization algorithm is “Nadam” in all the three network architectures. In addition, the best batch size for the NN and RNN was determined to be 4 and 8 for CNN. The dropout with keep probability of 0.2 and 0.5 was found critical for the CNN and RNN models, respectively. This research has shown that deep learning models such as CNN and RNN provide additional information inherent in the raw data such as temporal and spatial correlations that outperform the traditional NN model in terms of both accuracy and stability

Telerobotic technologies in e-learning

Today, e-learning methods and techniques are commonly used. In the Internet age they mainly employ different standard forms of the transfer of text and audiovideo streams. However, there are disciplines where the education process cannot be realized by means of the standard e-learning technologies, e.g. physics, chemistry or other practical educational courses. The education process requires on-site presence, e.g. in specialist labs. The telerobotic technologies can allow e-learning for the courses including practical training. We have adapted a few types of the robotic manipulators to can use them for e-learning. Herein, we also present control systems and software developed by us for this idea. The presented works include the most sophisticated haptic equippment also

A teleoperation system to remote control robots

This paper describes a master-slave teleoperation system that is developed to evaluate the effectiveness of using computer networks to control robot manipulators. The system is designed to examine different hardware and control techniques to develop and improve intuitive user interfaces for the natural control of the manipulators. In the presented system, a Cartesian manipulator, very popular in various computer controlled devices (e.g. CNC machines, 3D printers, etc.), is used. The versatile system configuration allows us to use different devices as master controllers – from standard computers to mobile devices and haptic tools