In order to implement the measurement and the evaluation of analog signals in high accuracy, to detect not only amplitude level but also phase information is necessary. For example, measurement of very small phase difference is desired in fields such as electric power and organism-related. Particularly, in organism-related field, impedance measurement is important and it is used to measure the urinary bladder volume, the respiratory volume, and so on. For these purposes, portable and real-time measurement devices with small sized is suitable. In this paper, we propose the urinary bladder volume measurement circuit using a simplified very small phase difference measurement circuit. This circuit is able to infer the changes of urinary bladder volume by measuring bioimpedance and phase difference of urinary bladder noninvasively with four-terminal method.

In mobile ad hoc networks (MANETs), when information about the location of the destination node is known, efficient communication can be achieved by using location-based routing protocols. Even in location-based routing protocols, with omnidirectional antennas there is some redundant route discovery by blind flooding of route request packets outside the area between the source and destination. These redundant route discovery propagations can be reduced by using both a scheme of restricted flooding and directional antennas. In this paper, we propose a new location-based routing protocol using both restricted flooding and directional antennas, called Adaptive Location-Aware Routing with Directional Antennas (ALAR-DA). To investigate the performance of ALAR-DA, we compare it with existing location-aware routing protocols. Our simulation results show that ALAR-DA outperforms existing protocols from the standpoints of delay and route discovery traffic.

We are making a proposal of a watching system for aged single persons living alone by using smart meters. The 30-minute watt-hour data sent from the meters via “A” route are used for it. We are expecting that we can build the watching system inexpensively and effectively as one of the social infrastructures through cooperation between local governments and power utilities which operate the smart meters.

We hatched out “the fluctuation estimate method” as a watching algorism for the smart meters. We evaluated an influence of the watt-hour granularity of the meter on a watching certitude of the method. Then we clarified that 0.01kWh of the granularity was required for proper watching over. We also made it clear that “the fluctuation estimate method” was much better than the “average method” which we had developed before.

Mathematical simulation is utilized to visualize the invisible phenomena, such as distribution of temperature and/or stress in a concerning object, by using properly structuralized nodes on each grid point of the orthogonal coordinate system. Here, the close-packed nodes with same effective radius are set on the vertex of regular tetrahedrons. Thus, the mathematical simulation calculating the interactions among nodes can be appropriately performed on the newly proposed coordinate system, which consists of continuous connection of regular tetrahedrons with six basic lattices. Then, the regular tetrahedron lattice coordinate system is well confirmed for the description of definite nodes having interaction with their neighboring ones.

This paper deals with the development of a 5kW ultra-compact binary power generation plant using low temperature difference thermal energy and a control system design method with high control performance using low electrical power consumption actuator. It is necessary to save electrical power consumption of manipulated variables in the operation of the plant to realize the ultra-compact binary power generation. A three-input and two-output transfer function model that has one more manipulated variable of very low electrical power consumption than the number of controlled variable to improve control performance by using a non-squared (NS) decouple PID control system is proposed, and its strategy as a method of control system design is introduced. The model is presented with a linear transfer function that represents a dynamic characteristic at nearby operating point of the plant. The control results are compared with the other control systems through simulations to verify the validity of the proposed control system.

With an increase in the elderly population, traffic accidents caused by elderly drivers have become an important social issue in Japan. The primary factor of traffic accidents by elderly drivers is a reduction in their driving skill. Therefore, elderly drivers are required by law to conduct regularly a driving aptitude tests in Japan. If the driving skill of the driver is inadequacy, he is subject to punishment, such as driver license revoked. However, there is not appropriate evaluation index of driving skill.

We have proposed a modeling method of driving behavior to support one. Proposed driving behavior model is generated based on the actual driving data of driver, and the generated model has some kind of habit and pattern of the driver. Therefore, it is considered that it is possible to evaluate driving skill of driver by analyzing the generated model. According this reason, we aim to propose a modeling method of driving behavior to evaluate driving skill. Here, a coincident timing skill of driver is focused as a driving skill. In the case of driving behavior, the execution timing of driving operations is decided based on the prediction of subsequent change of the outside environment information. The accuracy of this decided execution timing of operation depend on the above prediction is called as the coincident timing skill. In the proposed model, the causality between the situation around a person and operation of driver is modeled by Hidden Markov Model, and timing probability distribution is added to Hidden Markov Model in order to evaluate this coincident timing skill. The timing probability distribution expresses the execution timing of next operation of driver. Therefore, it is possible to detect the information of the coincident timing skill of driver from the timing probability distribution. In the experiment, the controller operation of a radio controlled vehicle is modeled by the proposed method, and the usefulness of the proposed model is examined through some experimental results.

By achieving low sensitivity, it is possible to suppress influence of perturbation in a plant and disturbances. In model-based controller syntheses, the sensitivity function is minimized using a mathematical model of a plant while internal stability of the closed-loop system is maintained. However, there are some difficult cases where a precise mathematical model of the plant cannot be obtained. Moreover, if there is a large error between a plant model and an actual plant, the designed controller might not show desired performance, at the worst case, the system would be destabilized. This paper focuses on data-driven controller tuning methods because they does not need system identification and design controller by using only input-output data. This paper proposes a design method to achieve sensitivity minimization by using only input-output data.

In this paper, we propose Interactive Tutor Robot (ITR) to enhance collaborative e-Learning system for Science and Technology education field. We implemented the robot with powerful communication specification to e-Learning contents. It makes learner to get more efficient environment and education contents by a communication with tutor robot interactively. Based on this idea, we defined a simple prototype system to realize an experimental emotion on the target education contents, and tried to confirm a possibility to extend the function for collaborative e-Learning system according to noble “Qualia” and “Awareness”. We propose one of methods to realize the two above concepts.

Critical dimension scanning electron microscope (CD-SEM) is widely used as an essential tool for measuring semiconductor patterns. It is necessary to set imaging sequence including corrections of imaging position and focusing of electron beam in evaluation point (EP) for the reliable measurement. Previously, addressing point (AP) and autofocus point (AF) suitable for these processing are selected by the hand and this is a drop factor of SEM operation rates. In our previous work, we developed a multifactor layout analysis (MLA) method, which automatically generate imaging sequence for each EP from design data of the pattern layout. In this paper, we propose an enhanced MLA method, which optimizes the imaging sequence to share AP and AF between multiple EPs. For 203 EPs, the imaging time of the proposed method is twice as fast as that of conventional MLA method. Without the hard improvement of the SEM apparatus, the proposed method can realize significant throughput improvement for numerous EPs

We built classification trees that provide sorting rules to reproduce the stock groups identified by hierarchical network clustering of Japanese stocks listed on the Tokyo Stock Exchange. The clustering method was based on a modularity maximization algorithm, which is frequently used for community detection in a network. We applied the clustering method to a correlation network in our previous research work. When building the classification trees, we tested various types of non-price data and selected effective variables with relative importance scores. The selection of variables proved to be consistent with a standard stock price model. Specifically, market capitalization and price book-value ratio were included as significantly important variables. Some other factors were also included as variables that clarify the properties of the Japanese stock market. The classification tree method can be applied to categorize stocks without a full set of continuous time series data into some groups, when our network clustering is difficult to be implemented. It can eventually contribute to improving risk measurement and management of stock portfolios.

The bullwhip effect is a phenomenon wherein demand fluctuations increase upstream in a supply chain. It can be to reduced through information sharing and demand forecasting. In research on the bullwhip effect for multi-stage supply chain simulations, an approach recommending the use of demand forecast models has been proposed. Since forecast models used in previous studies have been batch learning, they are useful only in situations where sufficient data has already been accumulated. Therefore, it is difficult to apply the batch learning model to a supply chain for which adequate past transaction data is unavailable. In this study, we apply an online learning model to the demand forecaster for a multi-stage supply chain simulation model. We have adopted adaptive regularization of the weight vector as the estimation algorithm for the demand forecaster. Since the proposed model is more powerful than a general online learning algorithm, from the point of view of generalization performance and convergence speed, the proposed method is promising in supply chain simulations. The effectiveness of our approach is confirmed, through computer experiments using the multi-stage supply chain model.

M2M (Machine to Machine) / IoT (Internet of Things) systems are expected to be used in various application areas such as social infrastructure, industrial automation, and health and medical care. Prototyping of M2M/IoT systems is an effective way to embody the idea on the systems and to examine their feasibility. This paper describes a method for prototyping of M2M/IoT systems, which consists of how to embody the idea of them, how to select their components and how to build them using a framework, so that people can easily construct M2M/IoT systems to solve their problems. This paper also describes three different applications of this method, which proves its effectiveness.

Cloud storage becomes popular, but low data-access performance through WANs (wide area networks) is becoming an issue. We proposed a cloud-storage cache, called “cloud on-ramp” (CoR), located at remote offices and connected to the cloud storage at a data center. Users access the CoR, which stores frequently accessed data as a cache, through a LAN (local area network). In this way, the CoR can improve access performance in case of cache hits; however, delays due to cache misses are a challenge because the data need to be transferred through a WAN. To ensure that applications have reliable access to data even in the case of a cache miss, we also proposed a recall method called “partial recall”. After the CoR receives the predefined size of partial data, it returns the data to the client. In this paper, we propose an implementation design of the partial recall. The method is formulated by utilizing a stochastic Petri-net model, and a proper size of partial data is determined through Petri-net simulation. Also, its data access performance improvement is evaluated in comparison with a conventional method, and a limit performance of partial recall method is measured.

After the Great East Japan Earthquake Tsunami, new Tsunami disaster-prevention and mitigation methods for the next millennium Tsunami are actively being discussed. Consequently, the methods have highlighted the importance of both hard disaster-prevention method, which means direct prevention using infrastructure such as breakwater, and soft disaster-prevention method, including disaster-prevention education. In this paper, a visualization and polygonization system that can be effectively utilized for Tsunami disaster-prevention is proposed for large-scale particle simulation. The system includes two important features, such as engineering-purposed visualization and polygonization for hard and soft tsunami disaster-prevention methods, respectively. Moreover, the system can handle the large-scale particle-simulation data that is computed by a high-performance computer via an offline on-demand tool without data transfer.

An adiabatic circuit was designed for charging and discharging a supercapacitor stepwise. The duty ratio of the switching transistors is changed in a stepwise fashion and controlled by a microprocessor. The charging efficiency, that is the ratio between the work done by the power supply and electrostatic energy of the capacitor, is calculated from experimental results. The efficiency is 95% when the number of stepwise voltage is 32, which is consistent with the theoretical value.

A new cluster-structured Firefly Algorithm for a superior solution set search problem for single-objective optimizations is proposed in this letter. After a swarm of Firefly is divided into some sub-swarms (clusters), interactions between sub-swarms are added so as to improve the search ability of Firefly Algorithm. The advantage of the proposed cluster-structured Firefly Algorithm is demonstrated through some numerical simulations.

The purpose of this research is to consider a new method to generate a perceptual hash using the sign of the DCT coefficient of the image. DCT is performed by reducing the input image to 32×32 pixels, and the DCT coefficient of the low frequency component 8×8 pixels is extracted by raster scan. A 64-bit hash sequence is generated by setting 1 if the DCT coefficient is 0 or more and 0 if it is negative. The Hamming distance of the hash sequence of different images is 20 or more. On the other hand, it was found that the Hamming distance between images that people feel similar is 10 or less. From many experimental results, it was found that perceptual hash can be generated using the sign of the DCT coefficient of the low frequency component of the image.