In 1995, Yates proposed an axiomatic framework of standard interference mappings and examined the iterative power control algorithm for a system with interference constraints. In the 2000s, Boche and Schubert built on a generalization of the theory of standard interference mappings and considered the feasibility of the constraints by the interference mapping in their sense. In this paper, we consider the signal to interference and noise ratio (SINR) region for any continuous and standard interference mapping, i.e., the set of all attainable values of SINR and make clear some properties of the SINR region. We also show the relations between the SINR regions for any continuous and standard interference mapping and its asymptotic mapping. In addition, we give a new and simple proof of the existence of positive eigenvalues and positive eigenvectors for any continuous and standard interference mapping making use of the properties of the SINR region and show there is an order relation among positive eigenvectors. Furthermore, we discuss an optimization problem with SINR constraints. Under the assumption of the feasibility of the problem, we prove that there exists a unique fixed point which is, at the same time, a unique optimal solution. We also provide a sufficient condition for the feasibility of the problem, based on a unique eigenvalue of the asymptotic mapping.
The performance of order picking in a warehouse is crucial in modern logistics. Automated guided vehicles (AGVs) have been developed recently and are in operation in many warehouses. Human pickers still play an important role in picking items that are different in shape and size and putting them on AGVs. Hence, co-operation among human pickers and AGVs is important. This co-operation should be effective in enabling every AGV and human to work efficiently. This paper proposes an algorithm that generates a sub-optimal co-operation schedule for both AGVs and human pickers. The effectiveness of the algorithm was evaluated through computer simulations involving an actual warehouse represented as a two-dimensional lattice-network model.
As distributed energy resources increase, distributed optimization for power system operation and demand response (DR) have been intensively investigated in recent years. This paper presents a distributed solution for a DC optimal power flow problem with DR, which can cope with communication delays and update delays of some subsystems by applying a distributed asynchronous primal-dual (DAPD) algorithm. Furthermore, we propose an accelerated DAPD algorithm that accelerates the DAPD algorithm based on the work of Nesterov. The effectiveness of the proposed algorithms is illustrated through simulation results.
This paper considers the open-loop system identification of a multi-input multi-output (MIMO) integrator model which is a multiplication of a constant matrix, called the action matrix, and the integrator. Our problem is to estimate the action matrix from the measured input and output data. The estimation algorithm is proposed by removing trends from the numerically integrated input and output so that the effects of input and output disturbances are eliminated. The proposed method is applicable to the basic scenario (constant input and output offsets), which is similar to previous studies, and the non-constant output offset; however, it is not applicable to the non-constant input offset. The discussion in this paper indicates the essential difficulties in the open-loop system identification with the integrator.
Recently in the railway field and the industry field, it becomes more necessary to use data effectively in order to store and succeed to fields' know-how by information technology and in order to plan for further increase in business efficiency. However, there is a large amount of various data from plural different railway business systems, so it is difficult to utilize these data transversely. In order to solve the above problems, we propose a data utilization platform, especially including platform architecture, data relation generating/visualizing functions with data relation network model, and analysis components that can be reused in plural applications, for the railway field as the first instance.
This paper proposes a design method of model predictive control (MPC) for multi-input multi-output (MIMO) plants with time-delay by using an interactor matrix and a sequential procedure to solve the matrix polynomial Diophantine equations required to be solved in the design. The equations are of matrix polynomials, and matrix calculations are not commutative; hence it is not easy to solve the equations, and it is necessary to obtain a sequential solving procedure. Also, the difficulty in the design of MPC of MIMO plants comes from the fact that a plant transfer function is a matrix, which is not commutative in multiplication. This paper avoids this difficulty by deriving a plant transfer function with a scalar polynomial denominator. And to handle the time-delay in MIMO plants, an interactor matrix is used to shift the outputs by time-delay steps. Then the design problem with time-delay is reduced to a problem without time-delay. There exist designs of MPC for time-delay plants by using a longer horizon than the time-delay steps. In this paper, it is shown by simulations that MPC having a long horizon is sensitive to disturbances and that the proposed MPC is less sensitive.
The objective of this paper is to propose a mechanomyogram (MMG)-based motion classification system comprised of a muscle-activity onset detector and a motion classifier. The detector identifies muscle-activity onset time using sampled time-series of MMG signals of biceps and triceps brachii of a human upper arm based on the Mahalanobis-Taguchi method. The classifier is based on the Recognition-Taguchi method and an AdaBoost ensemble learning technique, and distinguishes the flexion and extension of an elbow directly from time-series data of MMG signals of biceps and triceps brachii. We conducted an experimental comparison of the proposed classification system with our previous one based on discriminant analysis techniques to evaluate performance with research participants. Results verified the feasibility of the system and showed that the proposed system achieved higher classification performance than the previous one.
In this paper, we propose a method for automatically extracting buildings from scenery images. The method utilizes color segmentation and GrabCut on the basis of the fact that the background regions of scenery images tend to be found in the upper and lower parts of the images. We evaluated its extraction accuracy and computational time by using 106 high-quality scenery images (HQ dataset) and 89 low-quality ones (LQ dataset). Experiments showed that k-means clustering for color segmentation and HSV color space allow the proposed method to achieve higher extraction accuracy and faster computational time compared with the conventional method. The proposed method improved the extraction accuracy by 14% or more and reduced the computational time by 5% or more for both datasets compared with the conventional method. Comparing the extraction accuracy of the proposed method by using different color spaces, HSV color space improved the accuracy by more than 2.78% for the LQ dataset due to its noise robustness. The experiments, however, suggest that the proposed method has room for improvement in terms of the process of generating the initial seed used to initialize GrabCut.
The continuity and the constantness of both the curvature and the velocity in a trajectory are essential for the vehicle trajectory design from the viewpoint of the followability. This paper presents a new method of generating a vehicle trajectory whose curvature and velocity continuously change while both are constant in large part. The generated trajectory is called a circular-clothoid trajectory defined as the trajectory connecting circular curves with clothoid curves. In the method, the trajectory generation problem is formulated as an L1/L2-optimal control problem for front-steering vehicles. The desired trajectory is obtained numerically by solving a two-point boundary value problem that is relevant to the optimal control problem. The effectiveness of the proposed method is confirmed through some examples.
Artificial muscle actuators (AMAs) driven by the pressure of tap water are flexible and lightweight and are therefore safe for humans and suitable for wearable power-assist devices. This paper proposes a modeling approach for a water-hydraulic AMA based on least squares support vector machines (LS-SVMs). Modeling tests are carried out on this LS-SVM approach with experimentally acquired data and show that the proposed model can capture the hysteretic characteristics of the water-hydraulic AMA. In addition, we constructed a state-space model of the water-hydraulic AMA into which the proposed LS-SVM-based model was incorporated and propose a model predictive control system based on the state-space model. An experimental comparison of the proposed control system and a control system with an inverse model of the water-hydraulic AMA, used in our previous study, demonstrated that the proposed one exceeds the previous one in control performance.
The accuracy of a motion capture (MoCap) system based on inertial measurement units (IMUs) depends on the IMU-to-segment (I2S) calibration, in which the IMU alignment relative to a body segment is calculated using a reference pose such as a standing T-pose. This study proposes a novel I2S calibration system for a reliable MoCap system. In this system, the reference pose for calibration is constrained by physical objects, and this pose is generated by an optimization-based method which incorporates the IMU measurements with the physical constraints between the body and the object. To demonstrate the system, we estimated the chair-sitting and half-squat motions based on the calibration using a chair and estimated the center-of-mass movements of a rider when riding a motorcycle based on the calibration using a motorcycle. The experiments confirmed the improvements of the motion estimation accuracy by the proposed system, in both chair-sitting and half-squat motion and center-of-mass tracking of a rider. Furthermore, the proposed system enables the I2S calibration using product-use poses.
To maximize the growth of plants, controlling VPD is applied by fog cooling in a plant factory with solar light. The spatial data of environmental information cannot be obtained without interpolation among the detected values by the allocated sensors. In this paper, we evaluated the errors between the interpolated values with Kriging and the detected values in the spring, autumn, and winter, respectively, in the plant factory with solar light. The experimental results show that the maximum error of the interpolated values using Kriging are within the acceptable error range, ±0.1kPa. Therefore, Kriging is useful for interpolation in the plant factory with solar light.
Robot mapping and exploration tasks are crucial for many robotic applications and allow mobile robots to autonomously navigate in unknown environments. An accurate model of the environment is, therefore, essential for the robots to localize and perform navigation. In this paper, we present a line segment based mapping of indoor environments using range sensors for solving the simultaneous localization and mapping problem. The proposed method uses a modified Hough transform algorithm for line segment detection from laser range sensor data. The line extraction algorithm incorporates a noise model from the range sensor along with robot pose uncertainties. The proposed method is integrated with the extended Kalman filter. The extracted lines are merged to represent different structure in the environment correctly, and we show the results of our mapping method on simulated and real data sets. The experimental results demonstrate that the proposed method is capable of building an accurate line segment map of the environment for robot navigation.
Nowadays, with the development of automotive driving technologies, more and more functions and devices with control systems based on tactile, optical, and acoustic sensors are assembled into cars. However, these systems are faced with environmental limitations such as environmental noise and illumination conditions. Moreover, operations of these systems will cause lack of concentration on driving, which is a major cause of car accidents. In order to overcome these limitations, in this paper, an infrared array sensor is applied to construct a hand posture recognition system for in-vehicle device control. In the system, 10 kinds of target hand postures and posture movements toward four directions are combined to achieve the aim of the device selection and operations. The input images are separated into images with objects and without objects. Then, images in which object appears in boundary areas as well as blurred images are removed to improve the accuracy of the system. A convolutional neural network is applied as a classifier in order to realize the recognition of the 10 target hand postures and non-target postures for the in-vehicle device selection. After that, a detection method of the posture movement directions is applied for the device operations. Both indoor and in-vehicle experiments are conducted to verify the robustness of this system, and the results show that the proposed system can overcome the disadvantages of other systems and has a wide application with high accuracy.
This paper presents a numerical approach to solve the Hamilton-Jacobi-Bellman (HJB) equation, which arises in nonlinear optimal control. In this approach, we first use the successive approximation to reduce the HJB equation, a nonlinear partial differential equation (PDE), to a sequence of linear PDEs called a generalized-Hamilton-Jacobi-Bellman (GHJB) equation. Secondly, the solution of the GHJB equation is decomposed by basis functions whose coefficients are obtained by the collocation method. This step is conducted by solving quadratic programming under the constraints which reflect the conditions that the value function must satisfy. This approach enables us to obtain a stabilizing solution of problems with strong nonlinearity. The application to swing up and stabilization control of an inverted pendulum illustrates the effectiveness of the proposed approach.