The conventional photosensor are electric devices utilizing photoconductive, photovoltac, photoemissive and pyroelectric effect, while the pyromagnetic photosensor PPS is a detector based on pyro effect of a temperature dependant magnetic thin film. The PPS with 1.0μm-thickness is prepared by sputtering a temperature sensitive ferrite with low Curie temperature as a target. When the light is radiated to the PPS, the PPS absorbs light energy which causes the reluctance change to the PPS with temperature rise. The PPS shows pulse response to the step input of light and its peak value grows with an increase in light intensity. The reluctance change of a PPS can be read out as an output voltage by a magnetoresistance element. Therefore, the PPS acts as a photo-magnetic transducer. The PPS with a small heat capacity brings about the large reluctance change and a quick response due to the minute temperature change. It is confirmed that the PPS responds to visible rays and infrared rays, so the PPS is useful as a photosensor instead of the electric sensor. The techniques on the preparation of the PPS, the pyro-magnetic characteristics and the light signal read-out are discussed here.
The objective of this study is to perform mathematical model analysis mainly dealing with the anterior cruciate ligament's (ACL) strain elicited by the thigh muscles' contraction, attempting to get a picture of the ligament-muscle synergism in the knee. A 3-D mathematical model taking into account movements and forces of the patellofemoral and tibiofemoral joints was described. Simulation was performed to determine a direct relationship between the thigh muscles' forces and ACL strain as a function of knee angle. The simulation results demonstrated that quadriceps contraction can result in an anterior displacement of the tibia in the range of 0 to 75° of flexion and a posterior displacement over 75°, and hamstrings contraction always causes a posterior displacement of the tibia irrespective of any knee flexion angles. The results on the influence of thigh muscle contraction on ACL strain were also obtained and it was concluded that quadriceps contraction has direct impact on ACL strain, and relatively low and variable levels of hamstrings co-activation can effectively reduce ACL strain. Furthermore it was found that the antagonistic effect of the hamstrings co-contraction is small as long as the knee angle is small. These results suggested that a reflex are between ACL and hamstrings could exist.
In this paper, a new approach for the precise orbit determination technique, which is applicable to deep-space missions, is presented, instead of the conventional orbit determination system using RARR (Range And Range-Rate) data. In this approach, ΔVLBI (Delta Very Long Base-line Interferometry) data are employed together with supplemental range data. This technique is applied to a typical Marsian mission using simulation data and significant enhancement of precision in the orbit determination is demonstrated, compared to the conventional RARR method.
This study describes a system for analyzing drivers' visual direction. Rotational angular velocity sensors measured the head movements in the system we used in this study. A simple rotational machine using a pulse motor was produced to measure and verify the head movements. The eye movement was measured by an eye mark recorder. An ocular VOR characteristic was measured in order to verify the system. The relation between the head and eye movements was observed. We selected two conditions, which have significant effects on the visual direction in driving. These conditions were straight driving and lane change, both at legal speeds on an expressway. While driving, drivers' head movements and eye movements were recorded. The visual direction movements in straight driving were less as compared to lane changing. This result suggests that visual direction represents a specific characteristic of a driving situation. The measurement system for visual direction can be applied to evaluate safe driving.
In this paper, we propose a simultaneous design of a controller for multiple plants. First, we present a design of a reduced-order controller for a single plant. The design is based on the H∞ loop shaping method and the LMI optimization method. The robust stability problem used in the H∞ loop shaping method can be modified to a one block problem with a right inverse graph K of the controller and a parameter Q. Then controllers with fixed structure can be obtained by using LMI optimization. Second, we show that this design can be extended to a simultaneous design for multiple plants. This is possible because the controller structure is embedded in each problem in the same way and it is common for all LMI's (multiple LMI's can be regarded as a single large LMI). Thus, in our design, we can design a controller for multiple plants simultaneously where each plant is handled equally. Last, we demonstrate a design example of control for a flexible beam.
This paper is concerned with a neural stabilizing controller of general nonlinear systems. The stabilizing state feedback control law is approximated with a multi-layered neural network. Connection weights in the neural controller are determined by a min-max algorithm such that the Lyapunov stability theorem holds via a control Lyapunov function.
This paper gives a necessary and sufficient condition for the well-posedness by using inclusion condition of uncertainty, and also gives a set of all the systems corresponding to the uncertainty with which a closed loop system is well-posed. Furthermore, we analyze the topology of systems based on the well-posedness. We give the number of the connected components of the set of uncertainty in the cases of complex matrix uncertainty and 2×2 real matrix uncertainty.
The H∞ control problem with block-diagonal constant scaling is considered. The problem is not convex in general, and hence it is difficult to find a global solution. The purpose of this paper is to provide an algorithm to find a sub-optimal solution with any specified small tolerance from the globally optimal solution for the optimization problem. We analyze the computational complexity of the algorithm and show that its worst case order is polynomial in the inverse of the tolerance, with fixed size of scaling matrix.
By switching control laws, a VSS (variable structure system) control method realizes a new performance that can not be achieved by an individual control law. Chattering caused by lag-time on the switching is a serious problem, if the VSS control method is applied to actual plants. Chattering factors are switching speed, needless high-gain and lag-time of dynamics in inputs and outputs. This paper considers the needless high-gain and the lag-time in the factors. To reduce the needless high-gain, an adaptive gain VSS control method has been developed. In this method, a disturbance is estimated and a controller gain is modified by the estimated disturbance. Therefore this method can always supply suitable gain for the disturbance. But the chattering becomes larger as the disturbance grows larger. To realize more reduction of the chattering, a hybrid control system has been constructed. As the adaptive gain VSS controller only reduces the needless high-gain, the hybrid control method is more effective. The hybrid control system combines the adaptive gain VSS controller with a disturbance observer which considers the dynamics in inputs and outputs. In the hybrid control method, the disturbance is quickly compensated by the adaptive gain VSS controller. The gain and the chattering decrease as an estimation error of the observer becomes smaller. Experimental results evidence that the chattering is reduced by our methods and that the disturbance is compensated quickly.
The trajectory control of the manipulator based on the learning control can not generalize its target trajectory. Because the input data for a certain trajectory which has been acquired through the learning operaion can not be applied to the other trajectory. In this paper, we propose new learning control system with a Neural network. The Neural network can acquire and memorize the dynamics of the system based on the data obtained from the conventional learning processes. With the system, we can obtain well-approximated input data for any trajectory at first trial.
PID control schemes have been widely used in most of process control systems represented by chemical processes for a long time. However, it is still a very important problem how to determine or tune the PID parameters, because these parameters have a great influence on the stability and the performance of the control system. On the other hand, in the last twenty years, a genetic algorithm (GA) is attracted as one method which gives us optimal answers for search, optimization and machine learning problems. In this paper, we propose a new genetic tuning algorithm of PID parameters, in which the search area of PID parameters is reduced sharply by considering an effective parameters' area from the viewpoint of the control engineering. First, we explain the tuning method of PID parameters briefly. Next, by using the GA, we present a scheme to find optimum values of user-specified parameters included in this method. Finally, we illustrate a simulation example to show the effectiveness of the proposed scheme and consider the statistical properties.
In this paper, first of all, three kinds of concepts are defined to select essential elements in a human evaluation model by fuzzy measures and integrals. Increment Degree implies the increment degree from fuzzy measures of composed elements to the fuzzy measure of a combined element. Average of Increment Degree of an element means the relative possibility of superadditivity of the fuzzy measure of each combined element. Necessity Degree means the selection degree of each combined element as a result of the human evaluation. Next, task experiment, which consists of a static work and two dynamic works, is performed by the use of some human interfaces in order to apply the evaluation model to the design of human interfaces. In the experiment, (1) a warning sound which gives an attention to subjects, (2) a color vision which can be distinguished easily or not, (3) the size of working area and (4) a response of confirmation that is given by an interface, are considered as human interface elements. Subjects answer the questionnaire after the experiment. From the data of the questionnaire, fuzzy measures are identified and are applied to the proposed model. The guiding principle of the design of human interfaces is obtained by the comparison of human interface elements extracted from the proposed model and those from the questionnaire. On the basis of the obtained guideline, another human interface is designed and another experiment is performed with the designed interface. Effectiveness of this method is confirmed by the second task experiment.
A distributed scheduling system is proposed for processes consisting of a sequence of production stages. In the proposed system, an individual sub-scheduling system is installed for each stage. A whole schedule is generated by repeating the schedule generation at each sub-system and by exchanging data among sub-systems using message passing. The simulation results show that the schedule generated by the proposed system is as good as the one obtained by the centralized scheduling method. Furthermore, by using multiple PCs concurrently, the scheduling time can be reduced drastically compared with the case where a single PC is used.
We describe the acoustic characteristic of leakage sound and the possibility to detect the gas leakage sound under noisy environment. The purpose of this study is to establish the acoustic diagnosis technique for the leakage sound. In order to realize the purpose, we examined the possibility of the adoption of neural networks to the acoustic diagnosis for actual sound involved the background noise. We applied Fast Fourier Transform (FFT) as the pre-processing method and examine features of power spectrum for the gas leakage sound. The feature is that the power spectrum for the gas leakage sound are more than those for the normal sound within the range from about 5kHz to 20kHz. We, furthermore, applied the neural network as the discrimination method. We got the result that the discrimination accuracy is about 96%.
The progressive evolution method is a promising way to accelerate learning by making the problem size small in genetic learning. This method leads the population to the final goal by giving step-by-step learning targets, called subgoals. Giving subgoals divides a large search space into a small one, thereby accelerating the evolution. Previous implementation of progressive learning is, however, not practical because subgoals must be given by hand for each specific problem. This paper proposes a progressive evolution method that allows the population to autonomously get subgoals. The primary feature of our proposal is to concurrently perform both global search for the final goal and local search for the subgoal to enable the population to get subgoals. The global search moves the subgoal to the final goal. On the other hand, the local search leads the population to the current subgoal, and also moves the subgoal close to the final goal more quickly. For the subgoal to move faster to the final goal, we use the following fitness function. The function defines the neighborhood of the current subgoal, and gives a higher fitness value to individuals within the neighborhood as they exist further from the subgoal. This allows individuals that are closer to the final goal than the subgoal to survive. Moreover, the neighborhood is determined by the degree of achievement in the search. Specifically, we let the neighborhood be smaller as the subgoal approaches more closely to the final goal. This improves the efficiency of the search. We confirm that the population can more quickly evolve with our method by an experiment that generates the control circuit of the artificial ant than with conventional methods.
This paper demonstrates that a recognition mechanism based on a biological one can be useful for recognizing “unknown” patterns, and also useful for self-learning of them. An essential point of our proposed mechanism is a dynamical recognition using chaotic dynamics of recurrent neural networks. Harnessing the complex dynamics, the networks can recognize the “known” patterns and their neighbors as the conventional recognition methods are possible. We present some simulation results illustrating that our networks are able to decide whether input patterns are “known” or “unknown” by observing temporal stability of output patterns. In addition, it is shown that recognition of “unknown” patterns makes it possible the networks to learn the new patterns automatically.
This paper deals with the constitution of a meteorological station using buoy on a lake. This instrument is able to measure the direction and velocity of wind and the temperature of air and water. These data are then trasfered to the base station by means of the Multichannel Access (MCA) radio system.
This paper proposes a controller design method which guarantees prescribed gain and phase margins, by imposing constraints on the Nyquist plots of open-loop transfer functions. It is shown that the controllers can be obtained by solving equivalent H∞-control problems.
The conventional sliding mode control (SMC) theory assumes the discontinuous control law, therefore the dynamical system describing the closed loop doesn't satisfy the Lipschitz condition. In this note, we consider the SMC system assuming the continuous control law, and analyze the stability and the robustness from new viewpoints.
This paper proposes an algorithm to obtain the solution of optimization problem. This method describes the solution by B-spline curves and optimizes the values of control points of B-spline by the Complex Method. The optimality of the algorithm is numerically confirmed by applying to the problems of differential equation and optimal control.