In this paper, we consider a vibration suppression problem for a pneumatic isolation table with on-off valves using the feedforward control input profile. In this system, it is possible to enhance the vibration suppression performance by adjusting the flow rate of air into and from air springs. However, there is a significant problem that a steady state deviation, namely a offset, for displacement of the table occurs. Therefore, we propose an efficient search method of the optimal feedforward control input profile which provides high vibration suppression performance without a steady state deviation. A key idea is to update the control input profile to belong to a space where velocity of the table does not change.
This paper presents a new prediction method for time-varying parameter systems. This method is based on on-line estimation approximating continuous changes of system parameters by neural networks. However, since sufficient prediction accuracy can not be obtained with the previous method, an error function that considered a signal-to-noise ratio fluctuation and time-weight was used. The predictive values of time-varying parameters is sufficiently obtained by the learning ability and generalization ability of neural networks. According to the simulations, it has been confirmed that the proposed prediction method can obtain accurate prediction values compared with the conventional parameter prediction.
Stress measurements are required to prevent collapse accidents of structures. Surface SH-wave acoustoelasticity can measure principal stress difference non-destructively and easily. Currently, surface SH-wave acoustoelasticity can measure with high precision using a T-type surface SH-wave sensor in ideal environments such as in the laboratory. However, in actual environments, it can not be measured with high precision due to change in the temperature of the specimen. In this paper, temperature dependence of surface SH-wave acoustoelastic constants was verified to investigate the influence of specimen temperature on surface SH-wave acoustoelasticity. In addition, the measuring system of surface SH-wave acoustoelasticity using a cross T-type surface SH-wave sensor to reduce influence of specimen temperature was developed.
In this paper, we propose a method to recognize functional attributes of everyday objects for vision system of partner robots. On the related research, there is a method to optimize recognition result with dense (fully connected) CRF which use the estimation result of functional attribute for each pixels. However, since this method is optimized from RGB data, it isn't able to sufficiently consider the shape of object, which have a relationship with the function attribute. In the proposed method, the recognition accuracy of functional attributes is improved by considering the object shape with the dense CRF describing the three - dimensional positional relationship. As a result of the experiment, the recognition rate of the proposed method is 77.0 %, which is 3.8 % higher than the related method. In addition, we confirmed that the processing speed is high as a side effect by reducing processing cost by oversegmentation of input data and using high speed identification by Random Forests. The mean processing speed per an object was 109ms in the proposed method.
Based on electromyogram measurement, the influence of task contents on muscle activity during pseudo-haptics occurrence is basically examined. As a result of experiments with two kinds of tasks, it is considered that motivation and immersion to tasks improved by setting goals, which is effective for pseudo-haptics occurrence. It was suggested that the contents of the task and the setting goals influence the continuous occurrence of the pseudo-haptics.
Recently, Co-occurrence histograms of oriented gradients (CoHOG) describes image features to calculate the co-occurrence of pixels allocated at the local level and has attracted attention as an effective object detection method. However, this method has some problems. For feature descriptions that focus on individual pixels, calculation cost and the number of dimensions tend to increase exponentially with respect to the number of pixels. Multiresolution CoHOG (MRCoHOG) can suppress such exponential increases to linear increase without reducing the classification accuracy. This paper proposes a procedure in which a feature plane is divided using a Gaussian mixture model and a histogram is automatically divided to establish a less costly method for performing MRCoHOG. Experimental results demonstrate that the proposed procedure is more effective than conventional procedures.
Recently, deep learning technologies have been in the spotlight. Deep learning is one of a powerful technology to classify or recognize objects which captured by a camera. Such application has a high affinity with Internet-of-Things (IoT) devices. Therefore, it is considered that these technologies are used in embedded systems and IoT devices. In this paper, we verify deep learning applications like image classification can work well on a small computer such as Raspberry Pi. We develop three deep learning applications by using two types of deep learning frameworks (libraries). We prepare four types of small computers, and these applications are tested on the computers. In addition, we also investigate the relationship between the processing time, the memory consumption and the number of parameters of the deep neural network model. The verification experiments show that a program based on a deep learning library implemented by C++ language fast run and simple neural network models could work in real-time on small computers. Besides, the other experiment also clears that the more parameters increase the processing time and the consumption memory in proportion without depending on the deep learning libraries and small computers.
This paper proposes a new approach to improve and support Information Security Education based on a visual CPU simulator. Our visual CPU simulator can provide suitable demonstration for learners to comprehend how buffer overflow causes some remarkable effects for security matter. At the first stage of Information Security Education, a normal program including subroutine call and its return has been graphically and correctly executed by the visual CPU simulator, secondly demonstration of buffer overflow phenomena can be performed by the simulator through stack overwriting in order for learners to understand typical software vulnerability. Learners will be able to recognize how such an overflow phenomena easily occur some violence of stack replacement and exordinary program execution through utilization of the CPU simulator. This approach to visualize Information Security Education with our CPU simulator has been evaluated through questionnaire by learners in classroom lectures. It is confirmed that approach to visualize Information Security Education has been effective for learners to study buffer overflow phenomena by means of qualitative and quantitative analysis of the result of questionnaire.
In our previous papers, we have reported that automated arrangement of color image by Histogram Matching (HM) based on Gaussian Distribution (HMGD) gives good feeling impression if the brightness histogram of the original image has single peak. However, if there are multi-peaks in the histogram, the arrangement by HMGD processing does not always bring good results. In this paper, we propose an HM method using moving averaged brightness histogram of original image as the reference histogram instead of using Gaussian distribution. Also we present the experimental results by applying the both processing to the same image.
Ultrasonic diagnosis enables us to evaluate lifestyle diseases. However, operating probe precisely for several ultrasonic diagnoses is difficult for general people in clinic or house. In this study, we developed a reassembled ultrasonic diagnosis robot with parallel link mechanism which has six degrees of freedom by three trays. The robot was designed by genetic algorithm to be small and storable. In addition, we proposed a calibration method for assembly errors of directions among trays by driving each tray and evaluated the precision of the proposed method.
The autonomic nervous system (ANS) activity has been considered as an effective measure of the mental load increasing in modern information society. The heart rate variability (HRV) are commonly used to evaluate the balance between the sympathetic and parasympathetic activities. However, HRV can also be influenced by non-autonomic contributions such as blood flow. Moreover, the nonlinear interaction between the sympathetic and parasympathetic activities affect the HRV and thus they might not be clearly distinguished solely by the heart rate measurements. Therefore, more accurate evaluation requires investigation on co-variation across the indices of the ANS activities not only the HRV but also the ones which are not influenced by the change in blood flow and which are innervated by the sympathetic and parasympathetic branches in distinct ways. In this study, under the stimulation of postural change, three indices of the ANS activities; cardiovascular activity, sweat, and pupil diameter are measured simultaneously and evaluated. This experiment reveals high correlations between posture angle and each indices, and there is significant difference between correlations of RRI (R-R interval) and pupil diameter and between changes in RRI and sweat in supine position. These difference suggests difference between effects of sympathetic and parasympathetic activities.
In automated driving at Level 3, drivers constantly need to allocate attention to the driving environment to react immediately to a take-over request. However, the amount of attention of drivers to the driving environment has not been quantified. The objective of this study was a quantitative evaluation of the amount of attention to the driving environment, and the psychophysiological state of the driver, during automated driving at Level 3. Attention and driver state at Level 3 were evaluated using ERP and psychophysiological indices respectively with compared to Level 2. The dual task method was used, in which twelve subjects performed driving tasks (Level 2 and Level 3) on a driving simulation system while in parallel performing an auditory oddball task. The data showed that the amount of attention to the driving environment at Level 3 decreased in comparison to Level 2, as indirectly measured by a 19.2% increase in attention to the auditory oddball task. Sympathetic nervous system activity during Level 3 automated driving decreased as compared to Level 2. In the psychological state, comfortable feeling and arousal level decreased at Level 3.
As railway rolling stock is expensive, it is necessary to utilize them as efficiently as possible. Making rolling stock schedule (RSS) is one of processes in the planning phase. RSS is a sequence of daily schedules. A daily schedule is a sequence of train services for a specific train-set. RSS is to make daily schedules and a sequence of them so as to satisfy constraints for daily and monthly inspections which are performed within a certain period of time or a certain mileage, respectively. Each train-set is equally used regarding on cumulative mileage by following the RSS. However, it is often necessary to assign train-set(s) to daily schedules for some reasons irrespective of RSS. Rolling stock assignment (RSA) is to find an assignment of train-sets to daily schedules provided in RSS. In this paper, we represent a problem of rolling stock assignment by a network in which sequences of daily schedule for train-sets correspond to paths and formulate it into a set covering type problem. Furthermore, we propose a solution method based on column generation and rolling schedule.
When change over time of hydrogen gas concentration in a certain point is measured, a gas sensor is fixed at the measurement point, or a syringe is utilized to sample the gas, which is analyzed by gas chromatography. However, the gas sensor and the syringe have an influence on gas flow and its measurement. Non-contact measurement technology with high temporal resolution and high spatial resolution is necessary. Therefore, in this study, for the purpose of non-contact measurement of the hydrogen gas concentration in the small area under the gas flow, a specialized experimental device was developed. A laser beam (wavelength 355nm, pulse energy 120µJ, PRF 1kHz) with the beam diameter 1mmϕ at the measurement spot was irradiated to the hydrogen gas. Raman scattering light at the measurement point of 750mm ahead was focused by Fresnel lens, which is arranged at the orthogonal direction against the transmitting laser beam. As a result, it was proved that Raman signal intensity had good linear correlation with hydrogen density of more than 100ppm. The gas concentration in a small area could be quantitatively estimated without interference of the gas flow.
This paper proposes an optimal control law to suppress ripples in inter-sample of transient state of multi-rate sampled-control systems. The law is obtained by deriving equations to calculate output ripples as solutions of Lyapunov equations. So far, to reduce the ripples in transient-state, there exists only trial and error method and does not exist a systematic method. In this paper, first a reference model is defined which generates a reference output with no ripples and has the same outputs with multi-rate control systems at sampled points. Then the ripples in transient state is measured by the integrals of squared output errors. And equations to calculate the integrals are given by solving of Lyapunov equations. Finally the optimal control law is derived by differentiating the equations of several sampling intervals by the control inputs. The sampling intervals are called by horizon.