Currently available techniques for surface reconstruction from multi-view images require a large number of images in order to assure high surface resolution and precise detail reconstruction. Obtaining such big number of images in some cases is impossible; moreover, it may impose high preparation cost. Therefore, to address this drawback, a novel method is presented. This method is based on the visual hull approach. It assumes to be able to reconstruct three dimensional manifolds without edge from images, the number of necessary images should be as small as possible, and the quality of reconstructed mesh should be good. Applying this method for four images from a dino dataset proved the proposed method works well.
A novel retarding type electron energy analyzer implementing an electrostatic lens system to achieve a high energy resolution sufficient to measure thermodynamic temperature has been investigated based on the electron trajectory analysis. The energy resolution of the analyzer depends linearly on the retarding energy with a modulus of 0.05 %. The voltages to be applied to the electrodes in the analyzer are found to be controllable linearly against the retarding voltage. The analyzer has threshold energies, whose effect can be excluded in practical measurements.
A special adaptation law which has the ability to constrain adjustable parameters of an adaptive controller to the specified convex space is often used in an adaptive control system (In the following, this is referred to as a projection adaptation law). In particular, the projection adaptation law which guarantees the existence of time derivative or high order time derivatives of adjustable parameters is referred to as a smooth projection adaptation law and it is used for an adaptive control system based on the adaptive back-stepping method or the dynamic certainty equivalent principle. In the conventional projection adaptation law, the convex set to be constrained is a hyper-rectangular or a hyper-sphere in order to accomplish a realization of an adaptive controller, an efficient estimation and robust stability of adaptation loop. This paper indicates new two smooth adaptation laws (high order tuner) to improve the transient response of a control system in addition to the ability of conventional restraint. Also, the authors propose design schemes of a model reference adaptive control system using them. They can constrain adjustable parameters to an internal space of hyper-parallelogram. Effectiveness of the proposed scheme is illustrated by the theoretical analysis and simple numerical simulation results.
Reinforcement learning is a method with which an agent learns an appropriate action policy for solving problems by the trial-and-error. The advantage is that reinforcement learning can be applied to unknown or uncertain problems. But instead, there is a drawback that this method needs a long time to solve the problem because of the trial-and-error. If there is prior information about the environment, some of trial-and-error can be spared and the learning can take a shorter time. The prior information can be provided in the form of options by a human designer. But the options can be wrong because of uncertainties in the problems. If the wrong options are used, there can be bad effects such as failure to get the optimal policy and slowing down of reinforcement learning. This paper proposes to control use of the options to suppress the bad effects. The agent forgets the given options gradually while it learns the better policy. The proposed method is applied to three testbed environments and two types of prior information. The method shows good results in terms of both the learning speed and the quality of obtained policies.
This paper mainly proposes a racket control method for returning a table tennis ball to a desired position with a desired rotational velocity. The method determines the racket's state, i.e., the racket's striking posture and translational velocity by using two physical models: the racket rebound model and the aerodynamics model. The algorithm of determining the racket's state is derived by solving nonlinear equations and solving a two-point boundary value problem of a differential equation. But this is not suitable for a real-time process because of large computing time. The paper proposed a modified algorithm which could be used for a real-time process by introducing a simple aerodynamics model. Numerical simulations and experimental results show effectiveness of the proposed methods.
The robust control design method has been studied in recent decades. A control system works well under the modeling errors and disturbances if controller design is based on the robust control method. However, it is well known that in control systems, generally, there exists a trade-off between control performance and robustness. To overcome the trade-off problem, this paper proposes an internal model type compensator structure that minimizes the modeling gap between the nominal model and actual plant dynamics. By using the proposed compensator, the dynamics of the compensated system closes to that of the nominal model. Then, a design method of the compensator parameters is also proposed for minimizing a set of plant dynamics. The proposed design method is reduced to the standard µ design control problem. If we use the proposed compensator for control systems instead of the plant itself, the output performance might be better despite plant uncertainty. Given that the proposed compensator can be used for the control of not only linear but also nonlinear plants, we can easily achieve robust control of nonlinear systems. The effectiveness of the proposed method is shown by numerical examples.
This paper proposes an objective skill level evaluation method for Taijiquan. The proposed skill level evaluation method utilizes both curve fitting to spiral motion and classification based on a logarithmic distribution diagram of curvature for a human body part. In order to demonstrate a possibility of the proposed method, the paper presents experimental results with a Kinect sensor for three subjects, i.e., a beginner, intermediate and expert.
This paper investigates cooperative energy network formation for distributed autonomous microgrids based on receding horizon control and game theoretic cooperative control. In particular, we focus on photovoltaics and aim at minimizing its temporal and spatial variability while reducing transmission losses over the whole network by forming an appropriate network of power transmissions. We first formulate a novel optimal network formation problem in the form of resource allocation games so that the welfare function reflects the above objectives. Then, the problem is reduced to a potential game through an existing utility design technique. The paper next presents a variation of a learning algorithm presented in one of the authors' previous works and newly provide a proof of convergence in probability to potential function maximizers. Moreover, we consider real time implementation of the presented framework based on receding horizon control, where it is shown that the information processing of the learning algorithm is almost distributed with the helps of a solar radiation forecasting/estimation system. Finally, this paper illustrates the effectiveness of the present approach through simulation using real data of a solar radiation estimation system.
Ground faults are major problems of power cable systems. Time-series data of voltage and current are available for diagnosis when a ground fault occurs. In the present work, a data-based fault diagnosis system of power cable systems was developed. In order to achieve the high fault diagnosis performance, new feature variables were generated by using wavelet analysis and cepstrum analysis. In addition, six classification techniques, i.e., k-nearest neighbor (k-NN), artificial neural network (ANN), boosted ANN (B-ANN), random forest (RF), classification and regression trees (CART), and boosted CART (B-CART), were compared. B-ANN and B-CART were combined with the naive Bayes classifier to cope with multiclass problems. The results of applying the proposed methods to real ground fault data show that B-ANN and B-CART with the naive Bayes classifier can achieve the best diagnosis performance, which satisfies the requirement for its industrial application.