In order to implement the measurement and evaluation of analog signals in high accuracy, to detect not only amplitude level but also phase information is necessary. Particularly, measurement of very small phase difference is desired in fields such as electric power and organism-related. Therefore, we have proposed the simplified very small phase difference measurement circuit with automatic measuring system using microcontroller (Arduino) and automatic amplitude adjustment program. In this paper, we examine a new automatic measuring system in order to improve the measurement accuracy.
Polarization of an optical light transmitted through an OPGW is changed by influence of electric current flows in the twisted metal line at the surface of the OPGW. High speed polarization change is caused by serge current such as lightning stroke current or power line fault current. It might induce a trouble of an optical communication system using an OPGW. In order to evaluate the speed of the polarization change at the moment of flowing surge current, polarization state of the optical signal through an OPGW in the field is observed from the summer to the winter. As a result, examples of polarized change according to the falling of a thunderbolt to an OPGW or the other causes are obtained.
In the rehabilitation, experts assesses the recovery reaction while feeling the reaction force from the patient directly. This is due to important to grasp recovery state of muscle of the patient during the rehabilitation. This paper proposes the evaluation methodology of recovery state of muscle, by performing quantitative evaluation of muscular power from relationship between EMG and muscle load, and quantitative evaluation of muscle fatigue from use ratio of muscular fiber type. In addition, the decision method of appropriate muscle load and number of times in rehabilitation is proposed based on the evaluation of recovery state of muscle. Verification and effectiveness of the proposed method are assumed based on a constructed rehabilitation system by simulation work.
In this report, we compare the hemodynamic change at frontal lobe by ingestion of glucose. The brain activity is evaluated by the form of oxidized hemoglobin concentration, measured by wearable optical topography system. The data are measured during Paced Auditory Serial Addition Test (PASAT) which is known for its usefulness in the area of attentional dysfunction in cognitive rehabilitation. Properties of blood flow change are estimated by transition of time-series data or principal components analysis as well as the relation between blood sugar level and activation in frontal lobe.
This paper proposes a data-driven control parameter tuning method based on generalized minimum variance (GMV) evaluation in regulatory control. The proposed method can perform with no need for plant characteristics nor disturbance ones. For GMV control, this paper introduces a data-driven variance criterion which consists of the input and output data generated by stochastic disturbance. The control parameters are derived by using system parameters as optimization variables. Advantages of the method include applicability to routine operating data, which brings that additive experiments are not required. The efficiency of the method is demonstrated through numerical examples.
In many fields including control systems society, sparse estimations are attracting the most attention. Especially, L1 regularized linear regression is applied to many applications because it is easy to deal with. However, in calculations using all measurement data at once, the more number of measurement becomes, the lager computational costs become. In this paper, we propose a recursive algorithm for the L1 regularized linear regression. In order to derive the proposed recursive algorithm, we introduce upper and lower bounds of a criterion of the L1 regularized linear regression. Moreover, we show that we can solve a minimization problem of the both bounds analytically and recursively, and we use the analytic solutions as an approximate solution of the L1 regularized linear regression. We demonstrate the effectiveness of the proposed method by numerical simulations, in which we use random systems to evaluate the proposed method.
With regard to the system structure of the train control, conventional systems have had the horizontal independent structure because an individual piece of equipment, such as an ATC (Automatic Train Control) equipment, an electronic interlocking equipment and a facility monitoring equipment, has been separately developed and introduced into systems. In contrast, the concept of our unified train control system is vertical and hierarchically structured. It consists of a “logic layer”, a “network layer” and a “terminal layer”. This vertical hierarchical structure is intended to make systems lean and simple by bringing corresponding segments of the pieces together into each layer and to allow the evolution of systems by accommodating train operator's needs more flexibly. The logic layer consolidates functions of individual pieces of conventional equipment and manages a whole system centrally. The network layer provides standard network interfaces by means of the IP networking without making users conscious of the existence of a network. The terminal layer facilitates reducing, adding and removing local facilities in an effort to downsize them.
In this paper, we describe the patch learning-based super-resolution using simple pattern figure images (ex. circle) rather than natural images to learn image patches. In the learning-based super-resolution, if we use natural images for learning, we have to consider whether the image characteristics are suitable and how many images are needed to learn. However, by using simple pattern figure image learning we overcome these difficulties and enables good restoration in result images.
Recently, small sized area type fingerprint sensors have been installed into popular mobile devices for fingerprint verification. Because of high-density integration and convenience, small area-type sensors have been chosen for biometric authentication on many mobile devices. However small area-type sensors capture small area fingerprint images and cause lack of minutia points on minutia verification. We have developed a new matching algorithm available to small area type fingerprint sensor. In the proposed method, polar coordinates sampling is used in extracting feature and DP matching is used for matching features. The results of the experiments show that proposed method performed high accuracy verification using a small area-type sensor.
This paper proposes a method for automatic labeling for incremental learning. In our method, an ESOINN (Enhanced Self-organizing Incremental Neural Network) and a counter propagation neural network are used. ESOINN is a neural network that copes with incremental learning. However, since the training of an ESOINN uses unsupervised learning, users have to label the input data based on the output of the ESOINN by hand. In our proposed method, output values of the ESOINN are used as input to the counter propagation neural network. The counter propagation neural network is trained by supervised learning. The desired output values of the counter propagation neural network are the label of the data that are corresponding to input to ESOINN. By using these neural networks, our method is able to label input data automatically. The proposed method was applied to two clustering problems: handwritten digit recognition and natural image recognition. In these applications, our method showed better performance for clustering and incremental learning than did an ESOINN alone.
In recent years a study of evolvable hardware (EHW) which can adapt to new and unknown environments attracts much attention among hardware designers. EHW is reconfigurable hardware and can be implemented combining reconfigurable devices such as FPGA (Field Programmable Gate Array) and evolutionary computation such as Genetic Algorithms (GAs). As such research of EHW, Block-Based Neural Networks (BBNNs) have been proposed. BBNNs have simplified network structures and their weights and network structure can be optimized at the same time using GAs. The learning of BBNNs without constraint of network structures is, however, not efficient because the degree of difficulty of learning depends on network structures. In this paper, we propose a new evaluation index of network structures for BBNNs based on the least number of routes which are from inputs to outputs, and apply it to the structure search. The learning of BBNNs is efficiently executed with structure constraint condition based on the proposed index because the network structures which are difficult to learn are excluded. In order to evaluate the proposed method, we apply it to XOR, 3 bit-parity, square function approximation, contact lenses fitting, Fisher's iris classification and Wine classification. Results of computational experiments indicate the validity of the proposed method.
Based on the Proximate Optimality Principle (POP) and a big valley structure in combinatorial optimization problems, an estimation mechanism for quantitatively estimating structural characteristics (landscape) of combinatorial optimization problems is developed in this paper. Using the results of a numerical evaluation of landscape for several types of combinatorial optimization problems including a traveling salesman problem, a 0-1 knapsack problem, a flow-shop scheduling problem and a quadratic assignment problem, a new multi-point combinatorial optimization method having the landscape estimation mechanism is also proposed. The proposed combinatorial optimization method uses the estimated landscape information of a given combinatorial optimization problem to control diversification and intensification during a search. The search capabilities of the proposed combinatorial optimization method are examined based on the results of numerical experiments using typical benchmark problems.
Many new college or university students use the housing conditional search system for a leasehold house on Web recently. There is a recommendation system for a leasehold house for students with conditional search and decision rule in rough set. This system performs conditional search using housing information, and evaluate desirable houses by decision rule in rough set. However, the users have a heavy load of judgement about many specimen houses which is prepared in advance, in order to grasps the target user preference. This paper presents the recommendation system in rough set for a leasehold house for students based on two kinds of target user's preference. This system grasps the target user preference based on two kinds of rules which correspond to two kinds of judgement “good” and “bad”. These judgments for selected sample about houses can make the target user's load small. Through the experiment, it is confirm that the system has the same degree of satisfaction as the usual system, and the load of the user in the system is less than the one in usual system.
The most important point to learn network and system management is to provide a proper and practical training environment to try learning tasks for system administrator to all learners. Although several popular software for virtualization with the emulation technologies are developed and released in nowadays and anyone is able to use the software to learn system management, it is difficult to learn with the Unix type OS for non-specialist learners. The OS (platform) in general does not have ideal functions to advise to the users and also to lead users to the goal. We are constructing a training environment with an advisory function for system administrator on Linux server. In this article, we propose a learning model for server administrator and implementation method as a browser application program, and also we discuss the result of the experiment for system evaluation as example of Web server construction training.
In the study reported in this paper, we investigated the relationship between a schema and applied programming skills in computer programming education. A schema is a cognitive structure that is gained from experience, and it is assumed to affect an applied skill. The experiment in the present study comprised eight writing tasks and 45 reading tasks, and was designed to investigate three issues: (1) the details of a schema (i.e., knowledge related to program writing skills), (2) the relationship between a schema and applied skills, and (3) the differences between a schema and fragile knowledge. The results show that the group with high writing scores showed advanced applied skills, and there was certain common knowledge in the group with high writing scores that may reflect a programming schema. These results suggest that a teaching method designed to increase the experience of various programming codes through a large number of examples may be effective, with those codes slightly different from each other in order to create a programming schema.
In this paper, we introduce a new conceptional circuit element for complex signal processing. The element, provisionally named “conjugate imaginary resistor,” is derived from mismatch error in an imaginary resistor and its current is proportional to complex conjugate of voltage across it. Complex power calculation using complex phasor suggests that the conjugate imaginary resistor will affect magnitude response of a doubly terminated passive complex filter.
In this paper, a series-parallel asymmetrical half-bridge converters with high efficiency has been proposed. The proposed circuit has series connection of asymmetrical half-bridge converters (ASY-HBs) in the primary side, and parallel connection of ASY-HBs in the secondary side. Because of the series connection in the primary side, low-voltage MOSFETs with low on-resistances can be used. Also, the conduction loss in the secondary side can be decreased by parallel connection of ASY-HBs. Therefore, it has been shown that the efficiency of the proposed circuit can be improved when the number of ASY-HBs are increased.
Direct sampling mixer is a kind of filters composed of a lot of capacitors and switches. The circuit realizes decimation or mixing as well as filtering by use of multi-rate clocks. Each capacitor performs roles of holding electric charge and transferring delayed voltage signal. After those roles it does not contribute to signal processing until the electric charge is reset and newly supplied charge in the next period. In other words, there are some intervals when capacitors do not work at all. Accordingly, capacitor utilization is increased by removing some capacitors and designing clock signals to keep characteristics unchanged. It achieves reduction of the number of the capacitors to allocate each capacitor plural roles.
We have proposed a body temperature measurement circuit for a wearable wireless medical device. The proposed circuit can realize a low power and a high-capability which is required in a continuous long-term body temperature monitoring system. In this paper, our proposed circuit is validated by performance comparison with a circuit commonly used.
We have proposed a neck-band clinical thermometer, which can realize a continuous long-term body temperature monitoring with non-restrictive. This paper describes a verification of effectiveness of our proposed neck-band clinical thermometer. Our neck-band clinical thermometer is validated with comparison standard body temperature measured methods. Verification results proved the effectiveness of our neck-band clinical thermometer for a health care and an emergency medical device.
In this study, a breath monitoring system is being developed using a contact type microphone attached the neck and measuring the sound at the time of respiration (breath sound). In this paper, I made s sound measuring amplifier with adjustable frequency bandwidth amplification degree, so it fits each subject properly as well as improving the sound measuring circuit. Then I demonstrated that normal breathing, apnea and hypopnea could be determined analysing the relation between nasal airflow and the amplitude of the sound produced at neck level doing respiration. In addition, I verified the efficacy of the breath monitoring system by comparing it to an equipment that can, in a simple manner, examine the sleep apnea.
In the development of the aging society, it is important for patients who has physical disabilities to use the adaptive welfare equipment. However, it is difficult to support them suitably by using general welfare equipment because there are a lot of individual disabilities. Therefore, the adaptive welfare equipment is needed in near future. In this study, 1-parameter tuning which is one of the self-tuning control scheme in the process industries is applied to a welfare equipment. Simulation results are provided to demonstrate the effectiveness of the proposed control scheme.
Autonomous driving needs to detect the road area. There are several methods for road detection. We have selected a method of using the image of a car-mounted camera. As our method, we cut out blocks from one image of a car-mounted camera and calculate color feature statistics from each of the block. We distinguish between the road and other block by using all of the statistics. In this paper, we select block size and the number of regions and evaluate our road detection method.
In recent years, the study of time-series processing using Neural Networks has been attracted attention. Pulsed Neural Networks (PNNs) are suitable for time-series processing because they have integrator elements in themselves. Recurrent Neural Networks (RNNs) are also suitable for time-series processing because they have feedback loop in the networks. In this letter, we propose new Recurrent Pulsed Neural Networks (RPNNs) combining PNNs and RNNs in order to enhance time-series processing ability. We apply the proposed RPNNs to the controller of an autonomous mobile robot. Computer experiments indicate the efficacy of the proposed RPNNs
Since the installation of renewable energy and electricity liberalization, Voltage and Reactive Power Control (VQC) for maintaining the voltage stability is expected to become more important processes. VQC is achieved by the power factor adjustment of the generator, the interconnection and parallel off of the phase modifying equipment (for example, power capacitor and shunt reactor) at the transformer substation and the change of the transformer tap. In this paper, we discuss the determining method of optimal control quantity of the phase modifying equipment using Branch and Bound Method. In addition, the continuous relaxation problem at each sub problem of Branch and Bound Method are solved using the active set method. The effectiveness of the proposed method is evaluated through computational simulation.
Demand for inexpensive and wide band microphone for machine noise in factory and power station is increasing. However, frequency response is limited by diaphragm in conventional microphone. In this paper, a laser microphone based on self-coupling effect of the semiconductor laser is proposed. The laser microphone has a wide and flat frequency response. The features of the laser microphone do not need optical adjustment with simple optical system and not need post-processing by digital signal processing. The laser microphone can detect a sound wave and a human voice. This signal voltage is proportional to sound pressure and flat in frequency response in audible sound. This microphone has a omni-directivity in vertical direction.
An adaptive equalizer is adopted in a high speed digital power line carrier system. A surge noise is produced in a digital power line carrier system by opening and closing operations of an electrical equipment. This surge noise causes a serious problem, which is the divergence of tap coefficient of adaptive equalizer. In this paper, we propose a simple tap coefficient divergence prevention scheme and confirm its effectiveness by an experiment.
Mobile traffic from many smartphone devices to base stations is rapidly increasing. In a case many users communicate in moving vehicles such as a bullet train, it is desirable to multiplex their traffic and relay it to a base station located along the railroad line for improving spectral efficiency. However, high speed channel variation occurs on the entrance circuit of a bullet train. A sequentially switched antenna array receiver is known as a promising technology for compensating such channel variations. This technology slows down the channel variation by switching the antenna element in the opposite direction of the train travel in order to practically fix the receiving point. When designing such a receiver, spatial intervals between the antenna elements and temporal switching intervals need to be considered. In this paper, we propose a multiple antenna combining scheme to satisfy the requirements for the sequentially switching, and show its effectiveness via computer simulations.