Capture of free-flying payloads with flexible space manipulators

A recently developed control system for capturing free-flying payloads with flexible manipulators is discussed. Three essential points in this control system are, calculating optimal path, using a vision sensor for an external sensor, and controlling active vibration. Experimental results are shown using a planar flexible manipulator

Magnetic properties and magnetic domain observation of ndfeb sintered magnets treated by grain boundary diffusion process with dyal co-sorption

Effective grain boundary diffusion (GBD) process with DyAl co-sorption is applied to enhance the coercivity of NdFeB sintered magnets. The coercivity of the magnet (HcJ = 1789 kA m⁻¹) subjected to the present GBD treatment was observed to be superior to that of the untreated magnet (HcJ = 1003 kA m⁻¹) and the conventional GBD magnet (HcJ = 1661 kA m⁻¹) treated with DyAl alloy. In the present GBD magnet, the DyAl co-sorption process facilitated Dy diffusion into the center region of the magnet (thickness: 3.5 mm), resulting in high coercivity. Further, magnetic domain observations were made using magnetic force microscopy (MFM) to observe the thermal demagnetization behavior of the present GBD magnet. The present GBD magnet suppressed the continuous domain reversal of adjacent grains; thus, the partially persistent single-domain structure remained, even at 453 K.M.Uenohara, H.Nishio, K.Toyoki, et al. Magnetic properties and magnetic domain observation of ndfeb sintered magnets treated by grain boundary diffusion process with dyal co-sorption. Materials Transactions 62, 1216 (2021); https://doi.org/10.2320/matertrans.MT-M2020389

Reconstructing bifurcation diagrams only from time-series data generated by electronic circuits in discrete-time dynamical systems

Bifurcation-diagram reconstruction estimates various attractors of a system without observing all of them but only from observing several attractors with different parameter values. Therefore, the bifurcation-diagram reconstruction can be used to investigate how attractors change with the parameter values, especially for real-world engineering and physical systems for which only a limited number of attractors can be observed. Although bifurcation diagrams of various systems have been reconstructed from time-series data generated in numerical experiments, the systems that have been targeted for reconstructing bifurcation diagrams from time series measured from physical phenomena so far have only been continuous-time dynamical systems. In this paper, we reconstruct bifurcation diagrams only from time-series data generated by electronic circuits in discrete-time dynamical systems with different parameter values. The generated time-series datasets are perturbed by dynamical noise and contaminated by observational noise. To reconstruct the bifurcation diagrams only from the time-series datasets, we use an extreme learning machine as a time-series predictor because it has a good generalization property. Hereby, we expect that the bifurcation-diagram reconstruction with the extreme learning machine is robust against dynamical noise and observational noise. For quantitatively verifying the robustness, the Lyapunov exponents of the reconstructed bifurcation diagrams are compared with those of the bifurcation diagrams generated in numerical experiments and by the electronic circuits

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p.64, p.80, p.88, p.94, p.103, p.10

A probabilistic definition of salient regions for image matching

A probabilistic definition of saliency is given in a form suitable for applications to image matching. In order to make this definition, the values of the pixels in pairs of matching regions are modeled using an elliptically symmetric distribution (ESD). The values of the pixels in background pairs of regions are also modeled using an ESD. If a region is given in one image, then the conditional probability density function for the pixel values in a matching region can be calculated. The saliency of the given region is defined to be the Kullback-Leibler divergence between this conditional pdf and a background conditional pdf. Experiments carried out using images in the Middlebury stereo database show that if the salience of a given image region is high, then there are relatively few background regions that have a better match to the given region than the true matching region

Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017

The Computers in Biology and Medicine (CBM) journal promotes the use of com-puting machinery in the fields of bioscience and medicine. Since the first volume in 1970, the importance of computers in these fields has grown dramatically, this is evident in the diversification of topics and an increase in the publication rate. In this study, we quantify both change and diversification of topics covered in CBM. This is done by analysing the author supplied keywords, since they were electronically captured in 1990. The analysis starts by selecting 40 keywords, related to Medical (M) (7), Data (D)(10), Feature (F) (17) and Artificial Intelligence (AI) (6) methods. Automated keyword clustering shows the statistical connection between the selected keywords. We found that the three most popular topics in CBM are: Support Vector Machine (SVM), Elec-troencephalography (EEG) and IMAGE PROCESSING. In a separate analysis step, we bagged the selected keywords into sequential one year time slices and calculated the normalized appearance. The results were visualised with graphs that indicate the CBM topic changes. These graphs show that there was a transition from Artificial Neural Network (ANN) to SVM. In 2006 SVM replaced ANN as the most important AI algo-rithm. Our investigation helps the editorial board to manage and embrace topic change. Furthermore, our analysis is interesting for the general reader, as the results can help them to adjust their research directions