現在，第4次産業革命（インダストリー4.0）のもと発展した，IoT (Internet of Things)，ビッグデータ，人工知能の技術を統合的に活用した「Society5.0」と呼ばれる超スマート社会の実現を目指し，産学官を挙げて様々な分野でスマートシステムの研究開発が進められています。すなわち，

Data driven control system has been developed as IoT (Internet of Things) technology in recent years. On the other hand, optimization technologies on the local field devices such as PID temperature controller are necessary to make the IoT system more efficient. Therefore we developed an optimization technology for MV (Manipulated Value) of automatic PID parameter tuning.

In this paper, we propose a two-degree-of-freedom (TDOF) control system design method with an adaptive predictive control and an adaptive output feedback control for linear continuous-time multi-input/multi-output (MIMO) systems. In the proposed method we can obtain a predictive model of the system of simple structure by introducing PFC and can design a simple predictive control with higher control performance for uncertain systems with disturbances by designing adaptive predictive control to the uncertain systems method. It is shown that the stability of the obtained control system is maintained by the almost strictly positive real (ASPR) based adaptive feedback control strategy. Moreover, the effectiveness of the proposed method will be confirmed through numerical simulations for a simple two-input/two-output uncertain system.

The purpose of this study is to devise quantitative coaching method of batting motion. In order to quantify an abstractive coaching method, a relationship between a commonly used coaching method and kinematical feature values of batting motions of expert players is derived. In this paper, because the number of expert was 13 and could be considered a small sample, the 95 percentconfidence interval estimate with a t-distribution was performed to calculate the kinematical feature values of expert. The kinematical feature values of expert were applied to each Non-expert, and points of improvement in batting motion and coaching methods were proposed.

The shortage of care workers is an urgent problem in Japan. This problem is a one of cause for increasing opportunity that a care recipient has to practice some basic ADL (Activities of Daily Living) by oneself. In pervious study, our team developed a standing-sitting support system for care recipients who have to do sit on or stand up by oneself. And, it was proved the support system has a load reduction effect for standing motion of older-middle-aged and young peoples. This paper discusses a load reduction effect for sitting motion using the support system. Using analysis method based on a maximum value of waist and a knee joint torque in sitting motion, it is proved the support system also has load reduction effect for sitting motion. Significant load reduction effect on waist and knee joint torque was seen in both older-middle-aged and young peoples.

In recent years, with the development of information technology, the use of Web-Based Training (WBT) and other individualized learning programs are increasingly used and are gaining attention. In individual online learning, efficient learning is possible by providing individualized learning materials based on the degree of understanding and growth characteristics of each learner. However, actually, appropriate teaching materials for each individual learner is not be provided, which reduces his/her motivation to learn and makes it difficult for him/her to learn. The reason is that it is difficut to establish a desirable relationship between an educator model on the learning support system and each learner. This paper proposes a learner classification method based on learners' growth curves using the neural networks for the purpose of providing appropriate learning support to each learner. Since a huge amount of data is required for training of the neural networks, this paper construct an “educator-learner” model based on a control engineering approach representing the interaction between learning support systems and each learner, and virtual learner data is generated. The usefulness of the proposed method is shown by numerical experiments.

Dairy farming in Japan has low milk supply and reproductive rate per cattle, which is considered to be due to lack of proper breeding management. In order to solve this problem, there is a demand for a breeding management system that does not require human intervention, utilizing the ICT and robot technology. Studies on cow health and breeding management using various sensors have been reported, in this paper, we develop the system to collect the amount of activity and position of the grazing cattle in vast land. The developed system is (i) the “cattle sensor” composed of a GPS module and an acceleration sensor and (ii) the data transfer system composed of a wireless module and solar cells. The cow sensor can be used continuously for 2 months without battery replacement and is a system that can be used in vast land and mountain areas. And the data obtained from the sensor suggest the possibility of estrus detection.

Depending on the load requirement, active vibration isolation table may equip with redundant actuators. For such the table, it may be difficult to isolate the faulty actuator due to the redundancy. This paper presents a design of active fault diagnosis for the redundant driven active vibration isolation tables. The simulation results show the effectiveness of the proposed active fault diagnosis system by comparing to the conventional passive fault diagnosis method.

In recent years, the Digital Transformation (DX) has been promoted by the Japanese government. New value is expected to be created by utilizing new digital technologies and data through DX. In particular, the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) is developing a data integration platform that allows easy access to data and models in the cloud. On the other hand, Internal Model Control (IMC) based on the memory-based modeling (MBM) using data and model has been studied. It can be able to adjust control and internal model parameters adaptively using real-time data of the control system. However, the control performance might be deteriorated by this modeling method, because it can not calculate appropriate parameters when new system input and output data is not involved in the database. This paper presents a new design manner of an IMC system based on the database-driven modeling. The effectiveness of the proposed method is verified by the simulation that is assumed work of the hydraulic excavators.

In this paper, we expand ERIT to non-minimum phase systems. The key idea is to embed a model of the non-minimum phase part of a plant into both of the model implemented in the feedfoward controller and the desired tracking transfer function. To do this, we use inner-outer factorization as a model of the non-minimum phase part if the structure of the nonminimum phase part known. Otherwise, we approximate the non-minimum phase part of a plant by using Laggurre expansion or time delay. Embedding these model of nonminimum phase part to ERIT, the proposed method yields a desired tracking controller by using only the initial output data and other available information except the model of the plant. Numerical examples are given to show the validly of proposed method.

Tracing control is used in various precision processing systems for industrial products. It is important for a tracing control system to be maintained so as to achieve the accuracy of positioning of the gap between the workpiece and the machining tool during the operation. For this requirement, the control gain is implemented as a look-up table corresponding to the gap. This controller is also used to attenuate the effect of an unevenness of the workpiece which is regarded as a measurable disturbance in the output of the tracing control system. In this paper, we present a data-driven update method of table-typed controller gain in tracing control system for disturbance attenuation.

In an electric power system with modern smart technologies on the demand side such as smart meters and home energy management systems, electric power is utilized efficiently by a price-based demand response to selling prices provided by electricity retailers. For realizing the smart power system, competition among the retailers under a deregulated electricity market plays an important role. In this paper, the equilibria reached by selfish electricity retailers and consumers with the price-based demand response in a competitive electricity retail market are studied based on non-cooperative game theory. The rational behavior of each retailer considering the best response of each consumer is modeled by a mathematical program with equilibrium constraints (MPEC). An equilibrium problem with equilibrium constraints (EPEC) is formulated by using the MPECs of all the retailers. The equilibria are efficiently identified by solving a mixed-integer linear programming problem, that is converted from the non-convex EPEC including complementarity conditions. Among the alternative equilibria in simple case studies, we analyze meaningful equilibria that provide us with knowledge on an oligopolistic electricity retail market with the demand responses.

In PID control, it is important to properly adjust the control parameters, and one of the tuning methods is FRIT (Fictitious Reference Iterative Tuning) (Soma et al., 2004). FRIT is used in actual control because it can appropriately adjust the control parameters based on the results of one control experiment. Adjusting control parameter problems are multidimensional nonlinear optimization problem, and the genetic algorithms (GA) are used to determine the contorol parameters in FRIT. Azuma and Watanabe (2014) and Murakami et al. (2018) have improved particle swarm optimization (PSO) to improve solution search performance for FRIT, and they have showed that higher control performance can be obtained than the parameter determination method using GA. This paper tries to improve PSO to improve the performance of control parameter adjustment in FRIT using PSO.

A measurement system for motorcycle rider's driving operation was developed to evaluate the rider's driving skill. The system consists of a nine axis acceleration sensor for detecting the bank angle of the motorcycle, linear stroke sensors for measuring the contraction of suspensions, a rotary encoder for detecting the angle of the steering, a speed sensor, and a throttle operation sensor and four one-board computers. The operation of throttle and steering during driving on the eight-shaped turn course by six riders showed that the high frequency spectrum intensity of throttle and steering operations were complementary.

In this paper, we propose a new method of the cylindricity control in the honing process. We apply Fictitious Reference Iterative Tuning (FRIT) to achieve that each honing machine automatically adjusts a feedback gain to each environment. As a result, our proposed method enables us to minimize defective items without adjusting processing conditions.

It is important for the security operating of the machine to carry out the diagnosis of conditions. Many diagnosis methods based on the operating sound have been proposed because abnormal conditions are often suggested by abnormal sound of the working machine. This paper explains to classify method from operating sound of the gearbox. And, this classify method is utilized in skill evaluation for technology education. First, modeling error based on autoregressive models from a sound data are used as the classification of gearboxes. Next, this paper adopts this classification method as the achievement metrics for the assembling of gearboxes. Finally, the evaluation of this approach is verified in the experimental working by undergraduate students.

In the new guidelines to be implemented from 2020, the programming education is introduced from the elementary school stage. But, on implementation of the programming education, it is said that there are various problems. The purpose of this research is to develop a robot that draws an arithmetic [5th grader] regular polygon in elementary school without using visual programming. Specifically, by inserting three blocks into the top of the robot, some regular polygons can be written. These three blocks set the size of the interior angles of some regular polygons, the number of repetitions, and the length of the sides. In order to evaluate the robot, we showed a video of the robot in action and several questionnaires were conducted to the elementary school teachers.

Corresponding points search is an indispensable process of stereo measurement. In this research, we suggest a method to reduce corresponding point error using deep learning. We found that upscaling was effective for binary markers the size of which was from 20 to 180 pixels. Although the amount of error reduction changed depending on the shape, rotation angle and size of markers, the results showed that the error was always reduced. The effectiveness was confirmed in both computer simulation and practical measurement.

Recently, there has been an increasing need of a face alignment or facial feature point tracking for applying face recognition, facial expression estimation, face attributes prediction, and clinical face observation etc. This study proposes a robust facial feature tracking method against camera shake and face orientation changes. The method applies a cascaded composed learning (CCL) based facial feature point tracking method by incorporating optical flow for improving tracking accuracy and robustness. Experiments are conducted to show that efficient tracking is achieved by performing CCL combined with initial shape estimation via the optical flow.

There is a great demand for high-quality indoor thermal environments while achieving comfort and saving the energy for heating, ventilation, and air conditioning (HVAC) devices offer a tradeoff. The room shapes, partitioning, and the location of diffusers cause uneven comfort and degrade thermal satisfaction. Therefore, an HVAC control method that provides uniform thermal comfort while saving energy is required. In such cases, providing uniform comfort in a room can be a low-cost and energy-efficient solution. We propose a deep reinforcement learning (DRL)-based HVAC control method to uniformize comfort in a room. The proposed method considers the use of sensors, which are already deployed in existing HVAC devices and is adaptive to rooms with different conditions. The proposed method controls the HVAC device based on the prediction of the room's thermal response using wall heatmaps of the target room. The heatmaps are obtained by using a simple measuring system of an infrared thermopile array sensor. For evaluating the proposed method's adaptivity to rooms with different conditions, a computer flud dynamics (CFD)-based simulation was designed. The coldest period throughout a year was chosen as the evaluation period; uniformizing comfort is the most difficult when the temperature difference between outside and inside a room and the validity of the proposed method for the other periods is implied. The usefulness of the proposed method for the chosen period was shown for the selected period; the proposed method achieved an improvement of 33.9% in thermal comfort performance while on average increasing energy consumption by 6.3%, against that of comparable approaches. Moreover, the comfort performance during learning is confirmed to be similar to those of the existing method.

Japanese electric power system has been liberalized in multiple stages and feed-in-tariff (FIT) of the renewable energy system such as solar power generation (PV) has been adopted for the limited period. Now many PV systems have finished the FIT period. So, expectations for many types of electricity transaction between individuals such as use of home surplus electricity for EV charging at workplace are emerged. From this point of view, this paper reports our trial implementation and results of experiment about an oneself-to-oneself electricity transaction scheme using a system with block-chain technology.

In recent years, 3D printers can be purchased at relatively low prices, are used in various fields, and have been introduced in elementary and junior high schools. However, the 3D printers introduced at school sites are often of the Fused Deposition Modeling(FDM) type, which is made by melting resin. In addition, many expensive stereolithography type 3D printers for industrial use are equipped with sensors on the motors that move each table to perform feedback control. Furthermore, since FDM is targeted at individuals, position control is performed by predicting feedforward control without attaching sensors to reduce costs. However, if the origin position moves due to mechanical trouble, etc., there is a problem that the correction cannot be made and an error occurs. Such cases are not desirable at school sites, so improvement of accuracy with current equipment. In this letter, it aims at verification of modeling accuracy.

In data-driven controller tuning methods, we sometimes suffer from unexpected behavior of a designed closed-loop system since the response of the designed closed-loop system cannot be evaluated in advance. This paper proposes a method to estimate the response of the closed-loop system by using input and output data of the plant. Finally, the effectiveness of the proposed method is confirmed by the simulation and the experiment.

This paper studies an extension of generalized minimum variance control. So far, the control law has been extended by directly adding a certain signal to it or by defining new generalized output. This paper considers an extended method of generalized minimum variance control systems using noise term in the state space approach.

In this research we apply Markov decision processes to cultivation management with sensors. We propose a new cultivation management method which maximizes profit with reference to a Bayes criterion. The effectiveness of the proposed method is shown by a computational example.

Retinal rod photoreceptors in the eyes play a role in the first steps of the visual information processing and convert light into neural signals. In recent years, mathematical models of phototransduction mechanism in the outer segment(OS) of the rod have been developed and applied to analyze the biophysical processes. Although the membrane potential is responsible for sending messages to second order neurons generated in the inner segment(IS), the membrane electrical properties of IS have not been well modeled yet.

In this study, in order to clarify the function of the rod, we integrate the multiscale properties into a model. The model is based on the previous studies, i.e., the model of the OS (Dell'Orco et al.) and the model of the IS (Kamiyama et al., Publio et al.). To evaluate the model, the photocurrent in the OS and photovoltage in the IS were analyzed under a variety of conditions, including effects by channel blockers and the mutaion of retinal pigment epithelium (RPE). As a result, the model reproduced the photoresponses observed in the previous studies.

This paper considers a control system that has saturation and quantization of its control input. A system with saturation may be suffered from windup. Then, quantization generates quantization errors, which may deteriorate control performance. In this paper, the windup is mitigated by an anti-windup controller, whereas the effect of the quantization errors is reduced by a ΔΣ modulator. To keep the stability obtained by the anti-windup controller, the ΔΣ modulator is tuned off when the control signal is saturated. The ΔΣ modulator is designed to minimize the H₂ norm of the transfer function from the quantization error to the system output. Numerical examples are provided to demonstrate the performance of our proposed system.

In this paper, we propose a control algorithm with information reliability and energy management for persistent visual coverage ensuring field of views’ overlap. We first introduce a visual coverage control with the control barrier function which ensures no coverage holes in-between sensing areas. In order to persistify this visual monitoring, we utilize a control barrier function for energy management of robots, and propose a persistification method using information reliability over monitoring area with an integrated persistent visual coverage algorithm. Finally, we demonstrate the validity of the proposed algorithm through a simulation and experiment.

In previous work, the design method of IIR filters using PSO (Particle Swarm Optimization) has been proposed. However, it is known that an objective function of the design problem has a region like a saddle point and a lot of local minimums under the min-max criterion. Those characteristics cause one of the factors of the stagnation. In this paper, a design method IIR filters using PSO with penalty and CSO (Cat Swarm Optimization) is proposed for enumerating a lot of local minimums and a succesive search in such the region. Design examples are shown to present the effectiveness of the proposed method.

Drones, whose applications are rapidly expanding in recent years, have a wide range of uses, such as material transportation, pesticide spraying, structure inspection, and even the recently-flying cars, but it is difficult to fly in bad weather such as gusts. On the other hand, a CMG (Control Moment Gyro) is known which has the property of precessing when it is rotated by a force applied to the rotary shaft from the outside. Therefore, we wondered if the self-sustained control of the motorcycle using the CMG developed by the authors could be applied to the attitude control of the drone.

In the paper, based on these findings, we modeled CMG and CMG-equipped drone based on the law of conservation of momentum, and the control law is obtained by using the optimal regulator method for the linearized model of the nonlinear system. The control effect has been confirmed by simulations. Based on the feedback gain confirmed by the simulation, the experiments have shown that CMG onboard drone are effective in suppressing wind disturbances equivalent to 22 m/sec and small vibrations of 0.7 to 4 Hz.

The optimal memoryless regulator is a class of linear quadratic regulators of systems with time-delay in the states. It is constructed via a memoryless feedback, whose gain matrix is calculated with a solution of some finite dimensional Riccati equation. In this paper, a method to construct an observer is considered. It is based on the optimal memoryless regulator technique. The observer gain is obtained from a solution of a finite dimensional Riccati equation, which has a weighting parameter that tends to infinity. It is shown that the loop transfer function of the overall system of the optimal memoryless regulator and the proposed observer asymptotically approaches to that of the regulator based on the state feedback, so that the robustness which the linear quadratic regulator based on the state feedback has, of the overall system is recovered. A numerical design example is given to illustrate how the loop transfer function is recovered asymptotically.

This paper describes a method that can realize high resolution range-Doppler imaging for the front and lateral direction of an on-vehicle radar. A Doppler shift compensation method for millimeter wave radar using stepped multiple frequency has been proposed. This method can compensate the spread of Doppler frequency due to multiple frequency transmission, range walk and Doppler walk caused by movement of the radar in a measuring period. A high focusing accuracy in range-Doppler image is achieved even when the measuring period is long using this method. The proposed method is verified by computer simulation and by applying it to a field experiment result using a corner reflector and a vehicle for targets of the radar. And this paper also describes the fast processing by using FFT for Doppler frequency spreading correction and range walk compensation with large processing load, and shows the results of Doppler imaging by this method.

A neural network based automatic adjustment system of PID gains is proposed. By this online learning method, the 4-rotor flying robot can deal with model errors and disturbances. Meanwhile, the sensor system makes the robot able to track objects and avoid obstacles automatically. The effectiveness of the proposed system is proven by experiments.

Recently, cashless payment services have been rapidly introduced, increasing the demand for recommender systems for real stores. Recommender systems have been well-studied and successful in many Internet services. While factors such as the diversity and coverage of products should be considered, the prediction accuracy of user purchasing behaviors is the most crucial factor in a recommender system. This research proposes a purchasing behavior prediction model for real stores by using purchase history data collected from a supermarket in Saitama City, Japan, as a part of a smart city project. Time-series patterns in purchasing behavior indicate the sales characteristics of supermarkets, where daily products are predominately purchased. For example, some users repeatedly purchase specific products in a certain period or purchase different products from their previous visit. Many previous studies have only considered the compatibility between the features of users and items; however, this work also models the time-series patterns of purchasing behavior. The proposed prediction model adopts ensemble learning, in which weak learners learn the two factors (the compatibility between users and items) and time-series patterns of purchasing behavior separately. Moreover, product names in supermarkets often contain meta-information of the product alongside the item name. For instance, the product name “three tomatoes from Gunma Prefecture” includes information about the quantity and manufacturer as well as the item name “tomato”. However, products that are the same item but have different product names are likely to be purchased together in the context of purchasing activity, thus deteriorating the prediction accuracy. Therefore, a product classification method is proposed to categorize the products for each item. The experimental results show 3.2% and 10.6% improvements in the precision-recall area under the curve (PR-AUC) for the proposed method compared to models that consider only the compatibility between users and items and the time-series patterns of purchasing behavior, respectively.

In this paper, the parallel data restoration is evaluated with assumption of the storage node restart trend under large-scale disaster. In our previous study, we evaluated a proposed method for the data replication and data restoration for large-scale disasters. The proposed data replication copies data among nearby storage nodes each other to ensure reachability. Additionally, to broaden throughput, the proposed data restoration gets the backup data in parallel. The erasure coding is applied to avoid data lost. In this way, the data of the broken storage nodes are restored as soon as possible. However, the evaluations of previous study do not cover the transitional situation of recovery. After large-scale disaster, it is supposed that a part of storage nodes destructs and some storage nodes widely stop due to blackout. After a while, the storage nodes that temporally stop restart gradually. Thus, the number of the working storage nodes gradually increases. Therefore, a challenge of the parallel data restoration is to examine the relation between the suppression of the load and the prolongation of the restoration execution time under the situation and reveal the appropriate configurations. In this study, the model and method for the evaluation of the parallel data restoration are created. Additionally, the load and the restoration execution time are evaluated. From the results, the configuration of two data chunks and 30 parity chunks is suitable when the throughput is high. Meanwhile, the configuration of eight data chunks and 24 parity chunks is suitable when the throughput is low and the gradient of the storage node restart trend is steep. Moreover, if the gradient of the storage node restart trend is gradual, then for each throughput, the PDR configuration with the smallest number of data chunks of the configurations that can restore backup data is suitable.

We fabricated a cruciform PMMA beam (width: 2.5 mm, thickness: 0.5 mm) in a route (sectional size: about 19×7 mm) in which air passes in and out through in a snorkel. We then fixed a very small acceleration sensor (2×2×1 mm) onto the beam. By using the snorkel with the sensor in an experimental water tank, we were able to observe the frequency power spectra corresponding to the voice and motion of a subject. Our findings indicate that an acceleration sensor mounted on a breathing equipment can be used to monitor a diver's safety.

In this paper, a pole assignment method by means of the static output feedback for linear time-invariant single-input l-output systems is proposed. The basic idea is to compute the coefficients of the residual characteristic polynomial which are parameterized by the assigned poles. Usefulness of the proposed method is illustrated by a numerical example with the judgement of the stabilization of the control system.