This study provides an extension of the Student's t-process regression (TPR) on the space of probability density functions as a method of system identification for the data set consist of noisy inputs and deterministic outputs with additive noises. With introducing the distance metrics of the probability density functions, the TPR can be naturally extended to the space of the probability density functions and thus prediction and hyper parameter estimation can be implemented by the same fashion of the ordinary model. In addition, with a numerical example of the proposed model, we introduce the Markov Chain Monte Carlo method for hyper parameter estimation.

Knowing the exact location of a spacecraft is one of the most important tasks in the operation of a satellite. However, it is difficult to accurately determine the position and velocity of a satellite far from the ground. In order to meet such requirements, various estimation methods have been developed. In this study, we propose a t-process dynamic estimation model, which is based on the t-distribution, and it is suggested that it is more robust than the Gaussian model. In the present study, we have successfully developed a particle filter-based t-process dynamic estimation model.

In this paper, we describe a method of applying CLAS (Centimeter Level Augmentation Service) correction data distributed from QZSS (Quasi-zenith Satellite System) to the algorithm based on our GR (GNSS Regression) models for double frequency PPP of GNSS (Global Navigation Satellite System). Finally, we show the positioning results by applying our algorithms for using actual mesurement data.

In this paper, we propose a new low-rank Kalman-Bucy filter. We approximate the Riccati equation associated with the Kalman-Bucy filter by low-rank matrices and discuss its convergence property. Furthermore, a condition under which the proposed low rank Kalman-Bucy filter becomes stable is derived. The performance between the proposed and previous approximations are shown numerically.

We propose to use Gaussian-sum predicted state probability density functions (PDFs) in the algorithm of the ensemble Kalman lter (EnKF) to enhance its l tering accuracy. We analyze the EnKF in terms of the moment-matched linearization for the nonlinear observation model and show that the ltering accuracy of the EnKF can be improved by using the Gaussian-sum predicted state PDFs. We numerically con rm the effectiveness of the new lters through simulations using benchmark ltering problems of the vector nonlinear growth model and the satellite reentry.

The stability of the stochastic infectious models under subclinical infection is considered in this paper. As we all know, the corona virus (COVID-19) has a great impact on economy and society since last December. Many people have still been suffering from COVID 19 infection. The control of COVID-19 infection is an emergent issue in epidemiology. One of characteristics of COVID-19 infection is the existence of subclinical infection. Hence, we analyze the stability of the infectious disease under subclinical infection in this paper. In the realistic spread of the infectious disease, environmental change and individual difference cause some kinds of random fluctuations in the model parameters. So, we introduce the random fluctuation into consideration in the model construction and we propose the stochastic infectious models with subclinical infection. Since the stability analysis of the infectious model is effective in the control of the spread of the infectious disease, we consider the stability analysis of the stochastic infectious model with subclinical infection. We show the efficacy of the stability theorems derived in this paper by numerical simulations.

In this paper, we tackle the problem of the existence and uniqueness of the solution to double barrier backward doubly stochastic differential equations, by means of the penalization method.

We present a new impulse control problem of degenerate parabolic Fokker-Planck equations that govern stochastic growth dynamics of biological resource from the viewpoint of probability density functions of the body weight. The problem we focus on is impulsively transporting a resource population from a habitat to other habitat(s), which stems from a recent engineering problem in a river in Japan. A formula of the optimal control is theoretically derived with the help of an adjoint equation method. A remarkable point is simplicity of the problem such that finding the optimal control can be achieved by only solving an adjoint equation. We present a demonstrative computational example of Ayu sweetfish Plecoglossus altivelis altivelis utilizing a very recent Weighted Essentially Non-Oscillatory method.

In this paper, a new approach to solve data-driven Chance Constrained Problems (CCPs) is proposed. First of all, a large data set is used to formulate CCP because such a large data set is available nowadays due to advanced information technologies. However, since the size of the data set is too large, a Support Vector Machine (SVM) is used to estimate the probability of meeting all constraints of CCP for the large data set. In order to generate a training data set for the SVM, a sampling technique called Space Stratified Sampling (SSS) is proposed in this paper. According to the first principal component obtained by Principal Component Analysis (PCA), SSS divides the large data into several strata and selects some data from each stratum. The SVM trained by SSS is called S SVM. In order to solve CCPs based on large data sets efficiently, a new optimization method called Adaptive Differential Evolution with Pruning technique (ADEP) is also proposed.

An asynchronous gossip-based Lagrangian heuristic algorithm is proposed for Generalized Mutual Assignment Problem (GMAP) which is a combinatorial maximization problem in distributed environments. Lagrangian decomposition based formulation is introduced for GMAP in order to apply the asynchronous gossip algorithm. This enables us to obtain a feasible solution with quality bounds.

To compensate for severe shortage of scrub nurses who support surgeons during surgery, Miyawaki et al. have developed a scrub nurse robot (SNR) system [1,2,3,4,5]. One of its current challenges is how to make the SNR recognize surgical procedures which compose a surgical operation and understand/predict surgeons' intentions. Therefore, in this paper, we propose a visual recognition system for surgeons' actions based on convolutional neural network (CNN). We developed a temporal pose feature (TPF) CNN, which is a method to recognize surgical procedures based on the body movements of a surgeon's stand-in during a simulated surgical operation. We used OpenPose to extract the pose feature vectors from every frame of the short videos filmed our simulated surgery. Besides, we used a matrix of which the pose vectors were chronologically ordered as the input of CNN by considering it as the pseudo-grayscale image. We show that the TPF CNN was more accurate in the objects of this study than the conventional LSTM, which is used to recognize time series data. The TPF CNN shows higher recognition accuracy with fewer training than LSTM. Our results suggest that surgeons’ body movements may contain much information to be required for recognizing subtle differences in several types of surgical procedures.

The image-to-image translation networks, such as U-net [1] or Pix2pix [2], are known to be able to convert input images into different images where the image quality is improved or desired semantic information hidden in the input images are extracted. Several types of research based on such image translation networks have been carried out to realize noise removal systems that convert low-quality images taken with a low-performance microscope into high-quality images taken with a high-performance microscope [3,4,5]. In this paper, we develop denoising and deblurring methods to improve the image quality taken by the conventional scanning electron microscope (SEM) as the level of the image quality taken by the field emission (FE) SEM. In order to realize such methods, we utilize Pix2pix and U-net as the image denoiser. We compare the results of each image denoiser qualitatively and quantitatively. We show that the images generated by the conventional U-net [6] are apt to be slightly but entirely blurred, and the generative adversarial networks (GAN) [7] comprising a part of Pix2pix has a risk to inappropriately modify image details. Hence, we propose and evaluate U-net using Structural similarity (SSIM) loss function. We show that SSIM U-net can avoid a slight blur caused by the conventional U-net with fewer falsification than Pix2pix.

In this study, we propose a new three degree-of-freedom (DOF) fluctuation model that accurately reproduces the probability density functions (PDFs) of human-bicycle balance motions as simply as possible. First, we measure the PDFs of the roll angular displacement, wheel’s lateral displacement, steering angular displacement, and each velocity. Next, using these PDFs as training data, we identify the model parameters by means of particle swarm optimization (PSO); in particular, we minimize the squared residuals between the experimental PDFs from the participants and our simulated PDFs. The resulting PDF fitnesses were over 97% for all participants, indicating that our simulated PDFs reproduced the human PDFs.

This paper proposes a sampled-data state estimator which is suboptimal with respect to the error variance, over lossy networks. When signal loss is detected, the estimator switches gains and continues to update the estimate based upon recently received measurement. The gains are designed by means of a common solution of linear matrix inequalities.

Cliques have interesting properties that make them an ideal subject for social network analysis. Their members are close-knit and share common interests, which makes cliques a potent force for spreading influence. Since social networks often contain multiple cliques, it is important to understand how they can influence one another. In this study, we propose a model that describes the opinion dynamics of interconnected cliques. The model has two versions, one with randomized dynamics and the other with deterministic dynamics. We perform some analysis on their convergence properties and demonstrate their behaviors via simulations.