Plastometric study of hot formability of hypereutectoid C – Mn – Cr – V steel

Formability of hypereutectoid C-Mn-Cr-V steel in hot condition was investigated with use of plastometric methods. A wide range of deformation temperatures 1 300 - 640 °C for hot tensile tests was proposed with use of nil-strength temperature (NST), determined by special plastometric method, and as well as with use of the calculated temperatures of phase transformations during heating of the investigated steel. Ultimate tensile strength of the investigated steel was increasing exponentially with the decreasing deformation temperature. Ductility of the investigated steel in hot condition increased with the increasing deformation temperature up to the temperatures ranging from 1 150 to 1 250 °C, after which a sharp decline of formability took place in investigated material

Real-Time Hand Shape Classification

The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom. Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time. In this paper we discuss our parallel framework for real-time hand shape classification applicable in real-time applications. We show how the number of gallery images influences the classification accuracy and execution time of the parallel algorithm. We present the speedup and efficiency analyses that prove the efficacy of the parallel implementation. Noteworthy, different methods can be used at each step of our parallel framework. Here, we combine the shape contexts with the appearance-based techniques to enhance the robustness of the algorithm and to increase the classification score. An extensive experimental study proves the superiority of the proposed approach over existing state-of-the-art methods.Comment: 11 page

Transformation kinetics of selected steel grades after plastic deformation

The aim of this article was to assess the impact of previous plastic deformation on the kinetics of transformations of four selected steels. The research was conducted with use of the universal plastometer GLEEBLE 3800, when Continuous Cooling Transformation (CCT) and Deformation Continuous Cooling Transformation (DCCT) diagrams of selected steels were constructed on the basis of dilatometric tests. The research confirmed that the strain accelerates the particularly the transformations controlled by diffusion. Bainitic transformation was accelerated in three of the four steels. In the case of martensitic transformation the effect of the previous deformation was relatively small, but with clearly discernible trend

Credibility of various plastometric methods in simulation of hot rolling of the steel round bar

Rolling of round bar made of steel 32CrB4 was simulated physically. The interrupted torsion test led to the final microstructure with coarser grains, however, thanks to comparatively large dimensions of the sample this type of experiment was not so sensitive to the cooling rate of the final cooling as compression test. Too high temperature of the end of controlled cooling of the samples after compression test led to partial self-quenching of the investigated material. Decelerated cooling below the temperature of the start of martensitic transformation led in both applied types of compression tests to the parameters of the resulting microstructure and hardness closest to the parameters obtained after laboratory rolling

Damage detection study for a pedestrian cable-stayed bridge using ANSYS

The sponsorship from grant GACR 21-32122J of the Czech Science Foundation and of the joint research project 109WFD0410468 of the Taiwan´s Ministry of Science and Technology are very much appreciated

Towards Detecting High-Uptake Lesions from Lung CT Scans Using Deep Learning

Automatic detection of lung lesions from computed tomography (CT) and positron emission tomography (PET) is an important task in lung cancer diagnosis. While CT scans make it possible to retrieve structural information, PET images reveal the functional aspects of the tissue, hence combined PET/CT imagery allows for detecting metabolically active lesions. In this paper, we explore how to exploit deep convolutional neural networks to identify the active tumour tissue exclusively from CT scans, which, to the best of our knowledge, has not been attempted yet. Our experimental results are very encouraging and they clearly indicate the possibility of detecting lesions with high glucose uptake, which could increase the utility of CT in lung cancer diagnosis

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications

Damage detection study for a pedestrian cable-stayed bridge using ANSYS

The sponsorship from grant GACR 21-32122J of the Czech Science Foundation and of the joint research project 109WFD0410468 of the Taiwan´s Ministry of Science and Technology are very much appreciated