The extraction of motion parameters from a moving image is very important in many aspect of the computer vision. A new structure of motion sensor is proposed. This sensor detects motion efficiently by a small hardware with high spatio-temporal resolutions. Several experimental results are shown. This sensor was found to be able to detect image motion with 10KHz temporal resolution and 4×4 pixels spatial resolution.

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This paper discribes a high precision optical profilometer. The apparatus is composed of a modified Linnik type interference microscope. In measurement, heights of a reference mirror and a sample are controlled to get maximum resolution with PZT actuators. With the control, the measured profiles are not affected by the roughness of the reference mirror.
Principles of the measurement are briefly described. And using various samples, the abilities of this apparatus are checked on reproducibility, internal vibration isolation function with optical differential method, and resolution.

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This paper is concerned with a theoretical possibility of a speedy 3D-pattern measurement based on reconstructing digital 3-dimensional images (3D-images). A theory of reconstructing from a few X-ray projection pictures into a smooth 3D density distribution which gratifies the condition of 3D sampling theorem and the Bernstein's approximation, is discussed. Fist, it is shown that we can reconstruct the original 3D distribution from a projection picture by reading at intervals of the 1/√n of one edge of a voxel. Then, a reconstruction algorithm for a smooth 3D density distribution is proposed, and the results of computer simulation with the algorithm are shown.

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In this paper, two remote sensors for noncontact surface hardness evaluation are proposed, which are based on acoustics of air in the space between sensor and object surface. In the first method the acoustic impedance of the air is measured with the sensor set in front of object surface. The impedance is sensitive to surface hardness near the resonant frequency in the room surrounded by the sensor and the object. In the second one, hardness is evaluated using the compound sound composed of a high tone and a low tone. The lower frequency tone is used for driving surface and the higher one for measuring the induced vibration of surface, from which hardness is evaluated. These two sensors have their own merits. Using them we can estimate dryness of wet painted surface, or state of components fixed on circuit board without any mechanical contact, and they do not suffer from any problems caused by mechanical contact which is, the inherent difficulty with any other tactile sensor.

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Development of a simple three-dimensional velocity vector measurement system is expected in order to analyze a highly complex flow field.
This paper presents a new measurement system of three-dimensional velocity vectors in a flow field based on color information. In this system, tracer particles are visualized by a color spectrum slit method which uses a prism spectrum. The color of a tracer particle can determine its position in one direction. In the other two directions, the tracer particle can be tracked on the two-dimensional tracer images by using both a correlation and a particle tracking methods. Consequently, a three-dimensional velocity vector is obtained. The system has been developed and actually applied to a uniform flow and a flow around a sphere in a water channel and the measurement uncertainties were evaluated. The results agree well with a typical pattern of the flows.
In comparison with a conventional stereoscopic measurement method, in which each tracer is tracked in a three-dimensional space, the largest advantage of this method is that a three-dimensional velocity vector distribution in a flow field is easily obtained by adding a color image processing to a simple technique of two-dimensional velocity vector measurement.

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A normal random signal is generated by weighted-addition of a binary random signal which is obtained easily. A weight vector of the adder is determined from a viewpoint of normality and whiteness of the generated random signal.
This paper proposes weight vectors based on an even-shift orthogonal sequence. This sequence is binary and uncorrelated for all even shifts except zero shift. And it is associated with another even-shift orthogonal sequence called “mate”. When a pair of the two even-shift orthogonal sequences is used as two weight vectors, two white and normal random signals can be generated very quickly. In order to investigate the statistical independency of the two random signals, high order cross-correlations of these signals are expressed by the function of two weight vectors. It is shown from the expression that the proposed weight vectors are effective to increase the independency of the two generated random signals.

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This paper studies the finite spectrum assignability of systems with non-commensurate time-delays.
A new notion of coprimeness over polynomial matrices of several variables is introduced. Using some results about the coprimeness, this paper presents a necessary and sufficient condition for the finite spectrum assignability of the systems represented by state equations. And furthermore, this paper compares it with the existing condition.

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In general, it seems to be considered that use of complex Kalman filter instead of real Kalman filter complicates a computation of the algorism and it is of no worth. In this paper, some advantages of complex Kalman filter to real Kalman filter for estimation of discrete frequency spectrum is discussed. The results, firstly, clarify the following advantages of complex Kalman filter in comparison with real Kalman filter using only real part of complex signal. (1) Procedure of computing frequency spectrum is simple, and components with negative frequency can be easily taken into account in estimation, (2) sampling interval is over twice as wide as that in real Kalman filter, (3) the estimate converges about two times as quickly as that in real Kalman filter, (4) amount of multiplication to obtain the desirable estimate is reduced to about 1/4, and etc. Secondary, in comparison of real Kalman filter using both real and imaginary parts of complex signal, (1) amount of multiplication to obtain the desirable estimate is reduced to about 1/2, and etc. In addition, from a view of probability density function and covariance matrix of estimate error, it is indicated that the complex Kalman filter is a reduced form of real Kalman filter.

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Many neural network studies have been performed in order to apply both flexibility and learning ability of the neural networks to robot controllers. Although, many researchers have been performed, the stability of a direct neural controller has not been studied, and it advantage has not been clarified in comparison with another control theory such as the adaptive control and learning control etc. Therefore, this paper aims at clarifying stability condition of the direct neural controller in the condition of the plant time lag effect.
As the first step of this research, we clarify the asymptotic local stability condition by taking quadratic parameter error as the Lyapunov function when the linear neural network is used as the controller. These theoretical results show that the adaptive control is tightly relate to the neural controller using two-layer perceptron type neural networks.
Next, numerical simulation is executed for the direct neural controller applied to the time lag system using the proposed stable learning algorithm. The simulation results indicates that nonlinear function of neuron can compensate the effect of nonlinear and parasitic term in plant. Experiment results using one freedom force control system show that the proposed learning algorithm is effective to the actual plant.

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This paper proposes a method of estimating the rotor flux using a sliding observer together with a quantitative design method of robust observer based on disturbance rejection capability in time domain. The proposed design method is valid also for the reduced-order observer, hence yields a solution to the problem of designing a robust reduced-order observer which is not clearly understood at present. It is clarified that the designed sliding observer has several advantages over the reduced-order observer. The dynamic and steady-state characteristics of the estimation error are shown to be determined by H_∞-norm of the transfer function of the disturbances.

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Each joint of a conventional manipulator has one actuator. Dynamic coupling among joints used to be regarded as a disturbance for control of the manipulator. However, it is possible to control a manipulator with passive joints, which have no actuators, by utilizing the dynamic coupling. In this paper, a method of path tracking control of a manipulator with passive joints is proposed. A desired path is geometrically specified in an operational space. The position of the manipulator is controlled to follow the desired path. In this method, a path coordinate system based on the desired path is defined in the operational space. The path coordinates consist of a component parallel to the desired path, and components normal to the desired path. The equations of motion of the manipulator are described in terms of the path coordinates. The accelerations of the components normal to the desired path can be controlled by using the dynamic coupling among components. This in turn keeps the manipulator on the desired path. Results of path tracking experiments by a two-degree-of-freedom manipulator with a passive joint are presented.

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This paper studies on the control of robot to perform compliant manipulation on unknown dynamic environments. Compliant manipulation fundamentally requires robot to adapt for environment dynamics. For robot manipulators to be succeeded in performing such manipulation, it's already known that control of position or force alone is inadequate. Focused on the input-output responses of both position and force of the robot control system, we have proposed a model matching approach. In this approach, both for the selection of reference model and the design of force feedback compensator, the accurate information on environment dynamics was needed. However practically, in most of the robot's contact tasks, we never know the environment dynamics or only know the bounds of its parameters before exiting the task.
In this paper, by studying the most unfavourable environment dynamics for robot's compliance, the digital simulator for continuous linear dynamic environment, we try to give out an algorithm for robot manipulators to adapt for the unknown environment dynamics autonomously during the manipulation. A setup of experimental system of one degree of freedom robot and environment has also been made to study various of control approaches for robot's pushing manipulation on dynamic environment. Experiment results show the effectiveness of our design approach.

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An inverse kinematics problem of robot manipulators with redundancies has a bottleneck of computational time. In this paper, we proposed a method of solution to the above problem using a neural network with sufficient accuracy and short computational time. The learning algorithm proposed here automatically selects a set of joint angles, which maximizes a given cost function using redundant degrees of freedom, from a large number of solutions and forms the inverse kinematics model in the network. Application of the algorithm to the inverse kinematics problems of kinematic singularity and joint limit avoidance is shown as an example.

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In recent years, step motors have been widely used as digital actuators. The machine structures of step motors can be classified into variable reluctance type, permanent magnet type and hybrid type.
In this paper, the dynamic behaviors of closed-loop drive system using variable reluctance type step motor are presented. In these investigations, it is shown that the solutions of exciting modes of five phase step motor are derived from Kummer's equation. The measured angular velocity characteristics of the step motor can be approximated by the solutions of this equation. As a consequence, relations between excited switching angles and pulse frequencies of the step motor have been clarified from the solutions of this equation.

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This paper describes a load profile prediction algorithm that has recently been developed for use in an optimal operation of heat storage systems. It is highly desirable to devise an on-line prediction technique such that the difference between predicted load and actual load is as small as possible. To accomplish this, an ARIMA model is adopted. The modelling is first done for the past load data. Next, this model is used to predict the load profiles for the next day. The load profiles are updated every hour on the basis of the newly obtained load data.
When this on-line prediction algorithm is executed on a computer in field operation, some of the more important questions are left open:
1) What kind of evaluations should be considered for the model validity?
2) After air-conditioning system starts, how many data should be required to predict the load profile?
3) Half days and off days have different load profiles from ordinary days. How should the load profile on these days be predicted?
4) Does the forecasted highest ambient temperature for the next day become available to update the predicted load profile?
In this paper, the solutions to these questions are considered. Performance of the load profile prediction algorithm is assessed by recording cooling load and ambient temperature history (1987) at The Tokyo site. Results of this test show generally good agreement between actual and predicted loads. Errors are typically less than 10% on ordinary days; however, somewhat higher errors occur on half days and off days (errors being on the order of 20%).

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Eye interpolation of 1/10 division characteristics are experimentally studied. Subjects seemed to image the third scale line at the midpoint of the scale lines, and their readings deviate to the nearest scale line. The error was reduced by inverse filtering.

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The thick-film magnetic semiconductor sensor (TMS) is a sintered device prepared by ferrite, ruthenium compound and carbon black. The TMS responds to temperature, humidity and various fruit essences. The TMS can be used as an environmental and food monitor sensor.

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A Lyapunov design method and a sliding mode-control method based on the VSS theory are combined and developed to stablly control linear time-varying systems whose parameters may vary their values around the unknown but constant nominal values with known width of variation. The method is then applied to the model-following control problem.

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A selection method of noise covariances Q and σ² is developed to the estimation of time-varying parameters based on Kalman filter. Especially, the selected value of Q is obtained by solving some Riccati algebraic equation.

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In this paper, a manipulability of a truck crane with redundancy is derived from the normalized Jacobian matrix as a quantitative measure of load carrying ability. To develop the new anti-swing control algorithms for the crane, qualitative and quantitative properties of manipulability are investigated.

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