An Object Model and Interaction Method for a Simulated Experience of Pottery on a Potter’s Wheel

This paper introduces an object model and an interaction method for a simulated experience of pottery on a potter’s wheel. Firstly, we propose a layered cylinder model for a 3D object of the pottery on a potter’s wheel. Secondly, we set three kinds of deformation functions to form the object model from an initial state to a bowl shape: shaping the external surface, forming the inner shape (deepening the opening and widening the opening), and reducing the total height. Next, as for the interaction method between a user and the model, we prepare a simple but similar method for hand-finger operations on pottery on a potter’s wheel, in which the index finger movement takes care of the external surface and the total height, and the thumb movement makes the inner shape. Those are implemented in the three-dimensional aerial image interface (3DAII) developed in our laboratory to build a simulated experience system. We confirm the operation of the proposed object model (layered cylinder model) and the functions of the prepared interaction method (a simple but similar method to actual hand-finger operations) through a preliminary evaluation of participants. The participants were asked to make three kinds of bowl shapes (cylindrical, dome-shaped, and flat-type) and then they answered the survey (maneuverability, visibility, and satisfaction). All participants could make something like three kinds of bowl shapes in less than 30 min from their first touch

Methods of Generating Emotional Movements and Methods of Transmitting Behavioral Intentions: A Perspective on Human-Coexistence Robots

The purpose of this paper is to introduce and discuss the following two functions that are considered to be important in human-coexistence robots and human-symbiotic robots: the method of generating emotional movements, and the method of transmitting behavioral intentions. The generation of emotional movements is to design the bodily movements of robots so that humans can feel specific emotions. Specifically, the application of Laban movement analysis, the development from the circumplex model of affect, and the imitation of human movements are discussed. However, a general technique has not yet been established to modify any robot movement so that it contains a specific emotion. The transmission of behavioral intentions is about allowing the surrounding humans to understand the behavioral intentions of robots. Specifically, informative motions in arm manipulation and the transmission of the movement intentions of robots are discussed. In the former, the target position in the reaching motion, the physical characteristics in the handover motion, and the landing distance in the throwing motion are examined, but there are still few research cases. In the latter, no groundbreaking method has been proposed that is fundamentally different from earlier studies. Further research and development are expected in the near future

A Walking Training System with Customizable Trajectory Designing

This paper shows a novel walking training system for foot-eye coordination. To design customizable trajectories for different users conveniently in walking training, a new system which can track and record the actual walking trajectories by a tutor and can use these trajectories for the walking training by a trainee is developed. We set the four items as its human-robot interaction design concept: feedback, synchronization, ingenuity and adaptability. A foot model is proposed to define the position and direction of a foot. The errors in the detection method used in the system are less than 40 mm in position and 15 deg in direction. On this basis, three parts are structured to achieve the system functions: Trajectory Designer, Trajectory Viewer and Mobile Walking Trainer. According to the experimental results,we have confirmed the systemworks as intended and designed such that the steps recorded in Trajectory Designer could be used successfully as the footmarks projected in Mobile Walking Trainer and foot-eye coordination training would be conducted smoothly

A Transformer-Based Model for Super-Resolution of Anime Image

Image super-resolution (ISR) technology aims to enhance resolution and improve image quality. It is widely applied to various real-world applications related to image processing, especially in medical images, while relatively little appliedto anime image production. Furthermore, contemporary ISR tools are often based on convolutional neural networks (CNNs), while few methods attempt to use transformers that perform well in other advanced vision tasks. We propose a so-called anime image super-resolution (AISR) method based on the Swin Transformer in this work. The work was carried out in several stages. First, a shallow feature extraction approach was employed to facilitate the features map of the input image’s low-frequency information, which mainly approximates the distribution of detailed information in a spatial structure (shallow feature). Next, we applied deep feature extraction to extract the image semantic information (deep feature). Finally, the image reconstruction method combines shallow and deep features to upsample the feature size and performs sub-pixel convolution to obtain many feature map channels. The novelty of the proposal is the enhancement of the low-frequency information using a Gaussian filter and the introduction of different window sizes to replace the patch merging operations in the Swin Transformer. A high-quality anime dataset was constructed to curb the effects of the model robustness on the online regime. We trained our model on this dataset and tested the model quality. We implement anime image super-resolution tasks at different magnifications (2×, 4×, 8×). The results were compared numerically and graphically with those delivered by conventional convolutional neural network-based and transformer-based methods. We demonstrate the experiments numerically using standard peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. The series of experiments and ablation study showcase that our proposal outperforms others

Deep Learning Based One-Class Detection System for Fake Faces Generated by GAN Network

Recently, the dangers associated with face generation technology have been attracting much attention in image processing and forensic science. The current face anti-spoofing methods based on Generative Adversarial Networks (GANs) suffer from defects such as overfitting and generalization problems. This paper proposes a new generation method using a one-class classification model to judge the authenticity of facial images for the purpose of realizing a method to generate a model that is as compatible as possible with other datasets and new data, rather than strongly depending on the dataset used for training. The method proposed in this paper has the following features: (a) we adopted various filter enhancement methods as basic pseudo-image generation methods for data enhancement; (b) an improved Multi-Channel Convolutional Neural Network (MCCNN) was adopted as the main network, making it possible to accept multiple preprocessed data individually, obtain feature maps, and extract attention maps; (c) as a first ingenuity in training the main network, we augmented the data using weakly supervised learning methods to add attention cropping and dropping to the data; (d) as a second ingenuity in training the main network, we trained it in two steps. In the first step, we used a binary classification loss function to ensure that known fake facial features generated by known GAN networks were filtered out. In the second step, we used a one-class classification loss function to deal with the various types of GAN networks or unknown fake face generation methods. We compared our proposed method with four recent methods. Our experiments demonstrate that the proposed method improves cross-domain detection efficiency while maintaining source-domain accuracy. These studies show one possible direction for improving the correct answer rate in judging facial image authenticity, thereby making a great contribution both academically and practically

National trends in the outcomes of subarachnoid haemorrhage and the prognostic influence of stroke centre capability in Japan: retrospective cohort study

Objectives To examine the national, 6-year trends in in-hospital clinical outcomes of patients with subarachnoid haemorrhage (SAH) who underwent clipping or coiling and the prognostic influence of temporal trends in the Comprehensive Stroke Center (CSC) capabilities on patient outcomes in Japan.Design Retrospective study.Setting Six hundred and thirty-one primary care institutions in Japan.Participants Forty-five thousand and eleven patients with SAH who were urgently hospitalised, identified using the J-ASPECT Diagnosis Procedure Combination database.Primary and secondary outcome measures Annual number of patients with SAH who remained untreated, or who received clipping or coiling, in-hospital mortality and poor functional outcomes (modified Rankin Scale: 3–6) at discharge. Each CSC was assessed using a validated scoring system (CSC score: 1–25 points).Results In the overall cohort, in-hospital mortality decreased (year for trend, OR (95% CI): 0.97 (0.96 to 0.99)), while the proportion of poor functional outcomes remained unchanged (1.00 (0.98 to 1.02)). The proportion of patients who underwent clipping gradually decreased from 46.6% to 38.5%, while that of those who received coiling and those left untreated gradually increased from 16.9% to 22.6% and 35.4% to 38%, respectively. In-hospital mortality of coiled (0.94 (0.89 to 0.98)) and untreated (0.93 (0.90 to 0.96)) patients decreased, whereas that of clipped patients remained stable. CSC score improvement was associated with increased use of coiling (per 1-point increase, 1.14 (1.08 to 1.20)) but not with short-term patient outcomes regardless of treatment modality.Conclusions The 6-year trends indicated lower in-hospital mortality for patients with SAH (attributable to better outcomes), increased use of coiling and multidisciplinary care for untreated patients. Further increasing CSC capabilities may improve overall outcomes, mainly by increasing the use of coiling. Additional studies are necessary to determine the effect of confounders such as aneurysm complexity on outcomes of clipped patients in the modern endovascular era