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Visual SLAM algorithms can obtain a large amount of texture information from the environment and usually perform very well in static scenes, but there are a large number of irregular dynamic points when running in dynamic scenes, which can lead to increased error in SLAM feature point matching and loss of tracking localization. To address this challenge this paper proposes a SLAM system (YVG‐SLAM) that adapts to dynamic scenes. YVG‐SLAM is an improvement on the ORB‐SLAM3 algorithm, integrating the view geometry algorithm and the current powerful YOLOv5 algorithm, while proposing a dynamic feature removal strategy without a priori assumptions to reduce the influence of dynamic targets. In this paper, we first lighten the YOLOv5 algorithm, then process the image frame to get the bounding box, then use the view geometry algorithm to determine the dynamic feature points on the image frame, and finally remove the moving objects in the image frame according to the dynamic feature recognition strategy proposed in this paper. The performance of the algorithm is tested on the TUM RGB‐D and BONN RGB‐D datasets. The experimental results show that the robustness of the proposed algorithm on most video sequences is better than the existing SLAM algorithms dealing with dynamic scenes such as Detect‐SLAM and DynaSLAM, and the RMSE index of the absolute trajectory error measured on high‐speed dynamic video sequences can be significantly reduced by more than 93% compared to ORB‐SLAM3. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

YOLOv7 is one of the most effective algorithms for one‐stage detectors. However, when it is applied to pedestrian detection tasks in the industrial scene, it is still challenging for complex environments and multi‐scale changes of pedestrians. This paper proposes a new pedestrian detector for the industrial scene based on improved YOLOv7‐tiny and named as GP‐YOLO. First, the neck of YOLOv7‐tiny is replaced by RepGFPN structure, make full use of multi‐scale features to enhance the detection accuracy of objects with large‐scale changes. Second, a new gnconv branch is added to the feature fusion module, and the high‐order spatial interaction capability is introduced to further enhance the target detection accuracy. Finally, a lightweight method based on PModule is proposed, on this basis, a PConv bottleneck is designed to reduce the FLOPs and enhance the feature extraction. Experiments on a self‐made Industrial Pedestrian Data set show that before lightweight, the proposed algorithm achieves a 3.2% improvement in mAP@0.5:0.95 and a 3.7% improvement in Recall compared to the baseline YOLOv7‐tiny. After lightweight GP‐YOLO, compared to non‐lightweight, parameters and FLOPs are decreased by 26% and 23%, respectively, the mAP@0.5:0.95 is decreased by only 1.1% and the Recall is decreased by only 1.3%, which remains at a high level. Compared with baseline YOLOv7‐tiny, the lightweight GP‐YOLO has similar parameters and FLOPs, but the mAP@0.5:0.95 is increased by 2.1%, and the Recall is increased by 2.4%. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Research on global harmonic mitigation against distributed and high‐proportioned nonlinear loads in modern distribution networks has received extensive attention. In this paper, a distributed compensation method for active power filters (APFs) is proposed, which utilizes a small number of APFs with adjustable reference to alleviate the global voltage distortion caused by a large number of distributed nonlinear loads. The model predictive control (MPC) is employed to calculate the optimal current reference for compensators at each optimization moment by predicting the system's performance in a certain future time period. Furthermore, the Kalman filter (KF) algorithm is used to estimate the initial harmonic voltage for the MPC, reducing the unfavorable effects of the model uncertainty and measurement noise. The test results based on a seven‐bus distribution network in MATLAB are provided to verify the effectiveness of the proposed method. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, a new dual mode blind equalization method using neighborhood symbol‐based decision and decision‐directed (NSBD‐DD) algorithm is proposed to equalize the satellite communication signals. The proposed algorithm adopts the combined cost function to employ the advantages of symbol decision algorithm and the decision‐directed algorithm, which can further improve the universality of the proposed algorithm. Compared with the conventional blind equalization algorithm, the compensation ability of intersymbol interference and convergence speed of the proposed algorithm can be improved simultaneously. To verify the performance of the proposed algorithm, the computer simulation is performed, and the simulation results show that the residual intersymbol interference of the 64QAM signals can be decreased to −50 dB under the satellite channel, which has 30 dB lower than multi‐modulus algorithm (MMA), 26 dB lower than NSBD algorithm, and 10 dB lower than SBD algorithm. And compared with SBD algorithm, the number of iterations required for the NSBD‐DD algorithm can be decreased from 8000 iterations to 6000 iterations, which convergence speed is increased by 25%. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Objective. On the basis of inheriting the classical aesthetic orientation of Japanese literature, contemporary Japanese literature changes the creative methods of traditional Japanese writers, strives to transcend the national limitations of Japanese literature, and strives to capture literary materials with a "modern feel." Combined with modern western literary techniques to express the "modern self," and integrating popular literature with pure literature, Japanese writers created a "healing" literature that is popular all over the world. There is some value in exploring the correlation between Japanese literature and the influence of psychotherapy. Method. In this paper, an equation diagnosis algorithm based on iterative learning for a class of linear discrete systems is studied, and the existing discrete iterative learning diagnostics are improved. We exchange the order of time domain and iterative domain to achieve the purpose of point-by-point equation diagnosis. On this basis, a virtual influence initial value estimation algorithm with a sliding window is proposed. The impact that has not been diagnosed is estimated and used as the initial value of the impact, so as to reduce the number of iterations and improve the efficiency of equation diagnosis. This paper takes the public and readers as different perspectives, based on the use and satisfaction theory and prospect theory, and uses interviews and questionnaires to study the communication effect of Japanese literature from the three dimensions of cognition, emotion, and behavior. The cognitive part includes the public's understanding and readers' understanding and reading; the emotional part includes the public's and readers' impressions and emotional attitudes; the behavioral part includes the public's reading situation and readers' behavior changes. Results/Discussion. In the survey of loyal readers, although readers' understanding of Japanese literature has improved compared with the general public, it is not high, and their liking and influence on behavior are slightly lower than the preset level. This paper believes that the existing problems of Japanese literature dissemination are mainly concentrated in three aspects: the market is somewhat biased, which narrows the cognitive scope of users; as a cross-cultural literary work, Japanese literature still makes readers feel that it exists; there is some sense of estrangement, which will affect their enjoyment in reading; as a niche interest, readers lack timely communication and sharing objects after reading, so it is difficult to enhance their sense of identity. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a common sleep disorder and is diagnosed by polysomnography (PSG) as the gold standard. However, PSG is a time‐consuming and costly test, and patients have to endure long waits before receiving a PSG test in a hospital. In view of this, portable and wearable screening tools for OSA prediction have been developed recently as a low‐cost and easy‐to‐use method. In this paper, an OSA detection model, based on regression approach, using unsegmented electrocardiography (ECG) signals is developed to directly estimate the apnea‐hypopnea index (AHI) value, which is the first report in the literature. In this manner, this work can provide more details of OSA assessment to users and doctors. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of intermittently connected sensor networks where sensors are sleep most of time and become active randomly and rarely. In the networks, packet collision probability and power consumption can be reduced. Meanwhile, the delivery delay increases with the use of the low activation values. In this paper, we design the energy‐efficient intermittently connected sensor networks, considering this trade‐off of activation control. We first provide the algorithm that computes the optimal packet forwarding rule achieving the minimum delivery delay, which can be calculated using the activation rates. We then propose the activation rate control methods, which control the activation rates of the sensors based on the density of the sensors and/or the number of wake‐ups of sensors having relay packets. By doing this, we can improve the delay performance, while suppressing the increase in the packet collision probability and the power consumption of sensors. Through simulation experiments, we demonstrate the effectiveness of our activation control methods in the intermittently connected sensor networks. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has become a rising sophisticated technological application trend in the electrical industry in recent years. Such innovation provides optional methodologies for many existing applications, such as power and load profile forecasting, reliability evaluation, substation behavior detection and state observation of electrical equipment, and so on. This paper presents a review of various supervised and unsupervised ML techniques and applications for electrical power systems, including generation, transmission, distribution and micro‐grid. The algorithms and applications are mainly summarized from IEEE journals and the interest of this paper shows the roles and developments of most used algorithms and its corresponding extensions and performance in different applications. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Accurate assessment of lightning protection performance of extra high voltage transmission lines is important for transmission line design and different lightning protection. Based on the electro‐geometrical model, this paper introduces the angle influence factor, and presents an algorithm for shielding failure trip‐out rate of angle tower, and the existing algorithm is expanded. By establishing the three‐dimensional geometric model of the angle tower, the electrical geometric relations of conductors and ground wires on both sides are analyzed, respectively, and the projection area algorithm for shielding failure is modified. To further verify the application value of the calculation algorithm, the change of the shielding failure trip‐out rate of the typical angle tower with the bending angle of transmission line and the tower height in a 500 kV transmission line is analyzed. The analysis results show that the trip‐out rate of the outside corner is higher than that of the inside one, and with the increase of the angle and the height of the tower, the trip‐out rate shows an upward trend. When the tension tower height is 71.5 m and the angle increases from 0° to 80°, the outside shielding failure trip‐out rate increased by 10.9% and the inside decreased by 9.3%. Compared with the classical electro‐geometrical model calculation, the difference of trip‐out rate is 4.3% when the bending angle is 80°. When the tangent tower height is 70 m and the angle increases from 0° to 40°, the shielding failure trip‐out rate on the outside increased by 5.9% while the inside decreased by 6.2%. The research results provide a simple algorithm for solving bending transmission line and give some reference for lightning protection design of EHV transmission lines. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In order to construct an efficient and reliable global path for unmanned surface vehicles (USVs), this paper proposes a USV global path planning algorithm based on an optimized ant colony algorithm (OACA). The algorithm constructs a USV global path planning model by comprehensively considering energy consumption costs and turning control costs. In order to ensure the adaptability of the model to the task environment, the USV's motion characteristics are analyzed with full consideration of external interference, and on this basis, the characterization of energy consumption costs and turning control costs is completed. In order to obtain the global optimal solution of the model and accelerate the convergence speed of the model, in the initialization process of the ant colony algorithm, the initial pheromone is distributed unevenly by introducing the distance relationship among the intermediate node, the starting point, and ending point to improve the initial search efficiency. In the iterative process of searching for the optimal solution, enhancing the guidance effect of the current optimal solution on the offspring ants by introducing a weight factor to improve the update rule of pheromone, which accelerates the convergence speed of the algorithm. In the last two steps of the deadlock path, the number of lost ants is reduced by introducing a penalty factor to punish the pheromone which can guarantee the diversity of ants, and the algorithm's premature convergence is overcome. The simulation results prove the effectiveness of this algorithm. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The construction direction of Japanese teaching evaluation system based on hybrid teaching mode is to give full feedback to teaching process, diversify evaluation subjects and evaluation methods, and obtain comprehensive and objective evaluation results. Compared with most similar studies, more EEG data of teachers' emotional stress relief and healthy people in Japanese teaching process were collected, and a large number of features were extracted. An experiment of teachers' emotional stress relief recognition in Japanese teaching process based on EEG signal was designed. The feature selection algorithm was used to screen the EEG feature combinations of teachers' emotional stress relief and healthy subjects, and the classification experiment was carried out to verify the difference. Finally, this paper uses the feature selection algorithm of tree model and the random forest model classifier to establish the recognition model of teachers' emotional stress discharge in Japanese teaching process based on EEG signals and achieves the effect of more accurate recognition of teachers' emotional stress discharge in Japanese teaching process. First, the connotation, value, and influencing factors of teachers' emotion management ability are logically integrated and systematically expounded. Second, based on emotional psychology, emotional intelligence theory, psychotherapy theory, and management theory, teachers' emotional management ability is constructed according to the three-dimensional structure theory of intelligence and information processing theory. It is constructed from three dimensions: object dimension (teachers themselves, students, and students), content dimension (positive and negative emotions), and operational dimension (emotional perception, emotional understanding, emotional expression, and emotional regulation). [ABSTRACT FROM AUTHOR]

This paper presents a deep learning‐based face recognition device. It aims to instantly remind users of the identities of those who they met before in parties or conferences. When greeted, one, who is correctly identified, may frequently feel more respected than another who is mistaken for someone else in the first place. The golden rule suggests one to treat others with respect, and then others will treat the one with respect in return. Empirically, making others feel respected can definitely facilitate communication among people for social and particularly business purposes. For this sake, this paper employed a pair of smart glasses as a hardware platform into which a deep learning‐based multi‐task cascaded convolutional networks and a FaceNet algorithm were integrated. This move provided a high face recognition accuracy and a strong robustness. In the end, this smart device as intended recognized captured images and displayed the identities of matched contacts in a face image database. Hopefully, this proposal can be applied to a wide diversity of fields. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In view of the poor performance of the traditional adaptive filtering algorithm in identifying sparse systems under impulse noise, an affine projection maximum correntropy criterion with compound inverse proportional function (APMCCCIPF) algorithm is proposed in this paper, and the compound inverse proportional function approaching l0 norm is introduced into the traditional affine projection maximum correntropy criterion (APMCC) algorithm. The fixed step‐size APMCCCIPF algorithm cannot obtain faster convergence speed and lower steady‐state error at the same time, and the most effective method to solve this contradiction is to change the fixed step‐size in the algorithm to a variable step‐size. Further, a variable step‐size factor is constructed in the APMCCCIPF algorithm, and a novel variable step‐size APMCCCIPF algorithm is proposed. A modified Gaussian function, which can resist impulse noise interference, is applied to adjust the step‐size change. Besides, the convergence and complexity of the proposed algorithms are analyzed. In the simulation of sparse system identification and echo cancellation under impulsive noise, the proposed algorithms not only achieve a lower and faster convergence rate, but also obtain a lower steady‐state error. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, a two-dimensional sliding block model --discretized in slices- is presented, in which the geometrical, mechanical and rheological variables of the landslide are considered in order to analyse the soil landslide kinematic behaviour. The movement of the sliding mass is presented as a system of adjacent blocks; the movement strength is given by the Coulombviscous rheological model, where the friction and viscous components are considered, represented by a parallel system of a slider and a damper, respectively. The movement equation of the model corresponds to a second order differential equation, which was solved by the finite difference method, obtaining an explicit equation to know the displacement, velocity and acceleration of each block. An algorithm was developed in MATLAB® software, where movement equations were programmed to obtain results of the sliding kinematics. With the algorithm developed it was possible to make a sensitivity analysis to the parameters of the model. This was implemented in the problem of the sliding block at infinity slope, where it was possible to know the influence of each of the parameters of the model. The model has also been applied to the Hiegaesi landslide occurred in Japan in 1998, where the kinematic behaviour has been analysed in the model, regarding the records reported. However, further research is needed in order to apply the published model for prediction of the kinematic behaviour of landslides due to different limitations discussed in this paper. [ABSTRACT FROM AUTHOR]

Missing data is a prevailing problem in predictive analytics. In this paper, a winner‐take‐all (WTA) autoencoder‐based piecewise linear model is developed to solve the nonlinear regression problem under the missing value scenario, which consists of two parts: an overcomplete WTA autoencoder and a gated linear network. The overcomplete WTA autoencoder is a stacked denoising autoencoder (SDAE) designed to play two roles: (1) to estimate the missing values; (2) to realize a sophisticated partitioning by generating a broad set of binary gate control sequences. Besides, an iterative algorithm with renewed teacher signals is developed to train the SDAE. On the other hand, the gated linear network with the generated binary gate control sequences implements a flexible piecewise linear model for nonlinear regression. By composing a quasi‐linear kernel based on the gate control sequences, the piecewise linear model is then identified in the same way as a support vector regression. Experimental results have shown that our proposed hybrid model has a better performance than traditional models. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper presents a parameter estimation based method to diagnose winding deformation and turn‐to‐turn fault of power transformers. First, an estimation model of transformer parameters is built, in which five equations are taken in account including voltage loop equation, active loss equation, input impedance equation, no‐load current equation and no‐load loss equation. Then, particle swarm optimization (PSO) is used to solve the model, and the characteristics of estimation data under winding faults are analyzed. At last, based on the estimation data, random forest (RF) algorithm is employed to classify transformer states and realize fault diagnostic. The simulation results show that the proposed parameter estimation method has high precision and are not affected by the factors including load power factors and the type and degree of winding deformation, and the impact of load rates can be avoided; the fault diagnostic scheme based on RF is quite sensitive and effective. The proposed method eliminates the need of transformer outage, and compared with other methods based on parameter estimation, it can distinguish winding deformation and turn‐to‐turn fault. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A new image dehazing algorithm based on adaptive sky region is proposed in this paper, which shows good fidelity in sky region and satisfying visual effect in non‐sky region. For robust sky segmentation, we propose a rough‐to‐fine method that can make a balance between efficiency and accuracy. Considering distribution of haze is inconsistent, we divide the input image into three parts and calculate their atmospheric lights respectively. To solve the problem of invalid dark channel prior, we make an improvement for the transmission estimation. Finally, image fusion is taken as a post processing that can solve the problem of partial darkness and ensure a visual pleasing result. The experimental results for both synthetic and natural hazy images demonstrate that our algorithm performs comparable or even better results than the state‐of‐the‐art methods in terms of various measurement indexes, such as the PSNR, SSIM, and so forth. Besides, the proposed algorithm can be also applied in FPGA platform due to the optimized performance. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed to identify the parameters of roll motion equation using the response data of ship in regular waves. The proposed method is based on the fact that if the estimated parameters are close to the true parameters, then, its response are also close to the true response. Therefore, the parameter identification is achieved via minimizing the mean square error between the true response and the estimated response. To effectively solving the parameter minimization problem, an improved imperialist competition algorithm (ICA), called opposition based learning Gaussian bare bone imperialist competition algorithm (OBL‐GBBICA) is proposed. The proposed OBL‐GBBICA integrates the opposition learning and Gaussian sampling technique into ICA to enhance its exploration ability and speed up its convergence. Experimental results show that the proposed method can accurately identify the parameters of roll motion equation. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Background: Sleep disorders have an enormous impact on occupational health and are counterproductive from an economic perspective. However, the processes of causing sleep disorders from psychosocial aspects have not yet been known. The purpose of this study was to describe how sleep disorders develop among workers with respect to different psychosocial conditions. Methods: A conventional qualitative content analysis was conducted with a semi-structured interview among twenty-seven workers (14 males and 13 females) who were diagnosed with sleep disorders or had a self-reported history of sleep difficulties. Study participants were recruited from a specialized clinic and communities using snowball sampling. This paper adhered to the Standards for Reporting Qualitative Research (SRQR) checklist. Results: The results showed that there were four steps involved in the sleep disorders development process. Firstly, participants with sleep disorders developed 'early warning signs' with 11 categories of triggers; secondly, 'aggravating factors' on top of these early warning signs; thirdly, workers tried to 'cope with' their sleep disorders in the ways they thought would be effective. Finally, when coping failed to improve the quality of sleep, it led to the onset of sleep disorders. Conclusion: The development of sleep disorders and triggers of psychosocial factors were revealed. An occupational health nurse can bring these findings in practice for preventing worker's sleep disorders. [ABSTRACT FROM AUTHOR]

This paper presents a novel distributed method for dynamic economic dispatch (DED) problems with cubic fuel cost functions based on the dual alternating direction method of multipliers (D‐ADMM) and interior point method (IPM). This scheme enables us to combine the excellent parallelism of ADMM with the superior convergence properties of the IPM. The idea adopted by the proposed algorithm is that the outer‐loop uses ADMM decoupling, and the inner‐loop uses IPM to solve the cubic polynomial optimization subproblems. When the unit set is divided into multiple partitions, D‐ADMM also has the nice central processing unit (CPU) runtime as the well as the number of iterations. In addition, we use a Decoupling‐Decomposition‐Backtracking and parallel improved multiple centrality corrections decoupling interior point method (DDB‐PIMCCD) to solve the aforementioned larger subproblems more efficiently. Finally, the numerical results, in both serial and parallel modes, on a set of 22 DED cases with the range of units from 8 to 1000 units show that the proposed method is very promising for large scale distributed DED problems. Because it can keep the unit information about each subset in secret to suit the market environment and can obtain high‐quality solutions with reasonable time. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The conventional artificial neural network (ANN) model is an effective way to detect harmonic signals. However, the solution of the model cannot converge when the initial value of the excitation function (IVEF) is not suitable, causing a large error in harmonic detection, especially in the case of power system noise. We improve the back propagation (BP) neural network (BNN) model by using an adjustable excitation function and the basic inertial algorithm in this paper. Based on this model and combined with the glowworm swarm optimization (GSO) algorithm, a harmonic/inter‐harmonic detection method is proposed. The network first uses an improved GSO algorithm with an adaptive step to optimize the IVEF of BNN. Then, BNN is trained at this initial value, so that amplitude, phase, and frequency of harmonics/inter‐harmonics can be obtained. The simulation data analyses show that that the method has good stability, convergence, strong anti‐noise ability, and ultrahigh detection accuracy. It can accurately separate the integer and non‐integer harmonics. Compared with the traditional ANN algorithm and Hanning‐FFT, the detection accuracy of this algorithm can be improved by 2–4 orders of magnitude. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In both the research and engineering fields, missing data is a serious problem that cannot be overlooked. Therefore, available datasets with missing data are a challenge to be modeled by conventional global prediction models. In this paper, we propose a hybrid model consisting of an autoencoder and a gated linear network for solving the regression problem under missing value scenario. A sophisticated modeling and identifying algorithm is developed. First, an extended affinity propagation (AP) clustering algorithm is applied to obtain a self‐organized competitive net dividing the datasets into several clusters. Second, a multiple imputation tool with top p% winner‐take‐all denoising autoencoders (DAE) is introduced to realize better predictions of missing values, in which rough estimates of missing values by using the mean imputation and similarity method within the clusters are used as teacher signals of DAE. Finally, a gated linear network is designed to construct a piecewise linear regression model with interpolations in the exact same way as a support vector regression with a quasilinear kernel composed using the cluster information obtained in the AP clustering step. Based on the experiments of five datasets, our proposed method demonstrates its effectiveness and robustness compared with other traditional kernels and state‐of‐the‐art methods, even on datasets with a large percentage of missing values. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis is an important way to diagnose transformer faults. Compared with the method of establishing a single classifier based on artificial intelligence for diagnosis, ensemble learning (EL) can combine multiple classifiers to achieve stronger generalization ability and better diagnostic performance. But the traditional EL belongs to homogenous ensemble in which the base learners are based on the same algorithm, so this kind of EL method lacks the differences among the base learners, as well as systematic combination strategy. For this problem, in the paper the Stacking ensemble strategy is applied to fault diagnosis. Multilayer perceptron, k‐nearest neighbor, decision tree and support vector machine are used as component learners, and random forest algorithm is used as a combination strategy to establish a Stacking diagnosis model. In addition, homogenous ensemble methods are applied to the above four algorithms. In the method, the content of five characteristic gases are taken as the input characteristic parameters. Primary diagnostic results can be obtained with each base classifier. Then the meta‐learner random forest model organizes the base classifiers, and uses the primary diagnostic output as the input of the meta‐learner for secondary diagnosis to get the final diagnosis. The experimental results show that the ensemble of multiple heterogeneous component learners can enhance the generalization ability of the model, and the diagnostic accuracy is better than single classifier and the homogenous ensemble classifier. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The maximum correntropy criterion (MCC) algorithm has popularly been used in suppressing impulsive noise. It mainly relies on the choice of step size and kernel width. With the study of step size, this paper proposes a novel algorithm that updates the step size based on constructing a variable step size function that uses the moving weighted average algorithm to keep performance stable and sigmoid function to speed the convergence rate. The proposed algorithm is robust against impulsive noises and generates a large step size to accelerate the convergence in iteration beginning, and system mutation makes the system identification performance more stable when the error is small. Based on the improved variable kernel width maximum entropy criterion (IVKW‐MCC) algorithm, which overcomes the shortcoming of how to choose a reliable kernel width in the MCC algorithm, simulation results are compared with other MCC algorithms under impulsive noise interference. Simulations in the system identification scenarios show that the proposed algorithm has better performance in improving the convergence speed over other algorithms. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

SUMMARY: In almost all Japanese railway companies, rolling stock management still relies on manual procedure. As density of trains and interconnectivity between lines are increased, a support system for the management gets to be important. This paper focuses on a scheduling task for vehicle units’ operation and a support system for rescheduling vehicle units’ operation in train service disruption. A schedule of vehicle units’ operation is modified by a dispatcher to keep consistency between train timetable and vehicle unit assignment. When there is no solution, the dispatcher has to ask a train service manager to change train timetable. To support this task, we developed an algorithm of rescheduling vehicle unit operation, which included relaxation of constraints. Our algorithm represents constraints as a network model, and, if necessary, relaxes constraints by modifying the network model during search of a solution. This paper indicates the network model, the algorithm of searching for a solution as relaxing constraints, and numerical experiments using a real middle‐size data. The results of the experiments showed that our algorithm could calculate various solutions with relaxed constraints. Analyzing the solutions at the viewpoints of train service and rolling stock operation, we showed the effectiveness of a system that proposes various solutions using our algorithm. [ABSTRACT FROM AUTHOR]

Evolutionary algorithm (EA) is a generic term for optimization algorithms inspired by biological optimization processes in the natural world. Although EAs are widely applied to complex real‐world problems because they do not require mathematical expression of target problems, theoretical design methods of EAs have not been established. To solve this issue, this paper proposes an approach of designing EAs within an algorithm framework in which mathematical characteristics are derived, and it also presents a concrete framework for that purpose. The presented framework, Regulated Evolution Strategies (RES) provides a stability analysis result that contributes to designing algorithms with expected behavior. The RES framework has a high degree of freedom in designing algorithms, so that it is possible to incorporate various contrivances such as local improvement of samples and reduction of constraint violations in RES‐based algorithms while maintaining the stability analysis result. Numerical experiments prove that the RES framework has a capability for designing high‐performance EAs. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Recently, multiperson tracking across non‐overlapping camera views is gaining increasing attention in the video surveillance field due to its importance for public security. Most existing methods find a correspondence of person images individually across each pair of adjacent two cameras. However, in such a naive approach, redundant matching where one of the person images is selected for the matching pair for several times, often occurs. It leads to tracking failure. To overcome this problem, in this paper, by considering the matching problem as an instance of the "marriage problem" that is well known in Economics, we propose a simultaneous image matching method for multiple persons based on the stable marriage algorithm. We confirmed that the proposed method outperforms some of the existing state‐of‐the‐art methods on several well‐known public datasets. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the properties of the superior solution set search (SSSS) problem and point out the structural similarities between it and the multiobjective optimization problem. Based on the analyzed properties, we propose a new Firefly algorithm (FA) based on superior relations, which can search for the superior solution set by using the user's quantitative desire level as a search strategy. By introducing the distance of the problem space into the moving mechanism, FA makes it possible to search for multiple local optimal solutions by dividing the solution set into multiple groups. By analyzing this property of FA, we clarify the affinity between FA and the SSSS problem. We subsequently analyze the SSSS problem and FA, and discuss their properties together in a similar problem setting. We propose an FA based on superior relations as a new optimization technique for the superior solution set problem based on these analyses. Numerical experiments are then conducted using the SSSS problem and demonstrate the usefulness of the proposed method by comparing the performance of the proposed method with the conventional FA. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Differential evolution algorithms have become one of the most competitive evolutionary algorithms, among which a chaotic differential evolution (CJADE) is a recently proposed state‐of‐the‐art variant. But CJADE still suffers from the premature convergence problem. This paper further improves CJADE by innovatively incorporating a success‐intensity‐based roulette wheel selection method into it. The resultant algorithm called SCJADE shows its superiority over its peer in terms of solution quality and convergence speed on IEEE CEC2017 optimization test suit. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of causes that lie behind poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. The common acronym for investigating the causes of bad and good performance of trading is transaction cost analysis Rosenthal [Performance Metrics for Algorithmic Traders, 2009]). Automated algorithms take care of most of the traded flows on electronic markets (more than 70% in the US, 45% in Europe and 35% in Japan in 2012). Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance (like in Almgren and Chriss [J. Risk, 2000,3(2), 5–39]), a stochastic control (e.g. Guéantet al.[Math. Financ. Econ., 2013,7(4), 477–507]), an impulse control (see Bouchardet al.[SIAM J. Financ. Math., 2011,2(1), 404–438]) or a statistical learning (as used in Laruelleet al. [Math. Financ. Econ., 2013,7(3), 359–403]) viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions thatinfluencedits efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, calledanomaly detectors, can be created for each market order (following for instance Cristianini and Shawe-Taylor [An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 2000]). We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts (i.e. haveinfluence, in the sense of predictive power or information defined in Basseville and Nikiforov [Detection of Abrupt Changes: Theory and Application, 1993], Shannon [Bell Syst. Tech. J., 1948,27, 379–423] and Alkoot and Kittler [Pattern Recogn. Lett., 1999,20(11), 1361–1369]) which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we callinfluence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method can be taken advantage of to automatically adjust their trading action in the post trade analysis of algorithms. [ABSTRACT FROM PUBLISHER]

Abstract: Evidence supporting the practice of skin‐to‐skin contact and breastfeeding soon after birth points to physiologic, social, and psychological benefits for both mother and baby. The 2009 revision of Step 4 of the WHO/UNICEF “Ten Steps to Successful Breastfeeding” elaborated on the practice of skin‐to‐skin contact between the mother and her newly born baby indicating that the practice should be “immediate” and “without separation” unless documented medically justifiable reasons for delayed contact or interruption exist. While in immediate, continuous, uninterrupted skin‐to‐skin contact with mother in the first hour after birth, babies progress through 9 instinctive, complex, distinct, and observable stages including self‐attachment and suckling. However, the most recent Cochrane review of early skin‐to‐skin contact cites inconsistencies in the practice; the authors found “inadequate evidence with respect to details … such as timing of initiation and dose.” This paper introduces a novel algorithm to analyse the practice of skin to skin in the first hour using two data sets and suggests opportunities for practice improvement. The algorithm considers the mother's Robson criteria, skin‐to‐skin experience, and Widström's 9 Stages. Using data from vaginal births in Japan and caesarean births in Australia, the algorithm utilizes data in a new way to highlight challenges to best practice. The use of a tool to analyse the implementation of skin‐to‐skin care in the first hour after birth illuminates the successes, barriers, and opportunities for improvement to achieving the standard of care for babies. Future application should involve more diverse facilities and Robson's classifications. [ABSTRACT FROM AUTHOR]

As an increase in gastroesophageal reflux disease (GERD) has been reported in Japan, and public interest in GERD has been increasing, the Japanese Society of Gastroenterology published the Evidence-based Clinical Practice Guidelines for GERD (1st edition) in 2009. Six years have passed since its publication, and there have been a large number of reports in Japan concerning the epidemiology, pathophysiology, treatment, and Barrett's esophagus during this period. By incorporating the contents of these reports, the guidelines were completely revised, and a new edition was published in October 2015. The revised edition consists of eight items: epidemiology, pathophysiology, diagnosis, internal treatment, surgical treatment, esophagitis after surgery of the upper gastrointestinal tract, extraesophageal symptoms, and Barrett's esophagus. This paper summarizes these guidelines, particularly the parts related to the treatment for GERD. In the present revision, aggressive proton pump inhibitor (PPI) maintenance therapy is recommended for severe erosive GERD, and on-demand therapy or continuous maintenance therapy is recommended for mild erosive GERD or PPI-responsive non-erosive GERD. Moreover, PPI-resistant GERD (insufficient symptomatic improvement and/or esophageal mucosal break persisting despite the administration of PPI at a standard dose for 8 weeks) is defined, and a standard-dose PPI twice a day, change in PPI, change in the PPI timing of dosing, addition of a prokinetic drug, addition of rikkunshito (traditional Japanese herbal medicine), and addition of histamine H2-receptor antagonist are recommended for its treatment. If no improvement is observed even after these treatments, pathophysiological evaluation with esophageal impedance-pH monitoring or esophageal manometry at an expert facility for diseases of the esophagus is recommended. [ABSTRACT FROM AUTHOR]

Background: An innovative measurement system using a computerized adaptive testing technique based on the item response theory (CAT) has been expanding to measure mental health status. However, little is known about details in its measurement properties based on the empirical data. Moreover, the response time (RT) data, which are not available by a paper-and-pencil measurement but available by a computerized measurement, would be worth investigating for exploring the response behavior. Purpose: We aimed at constructing the CAT to measure depressive symptomatology in a community population and exploring its measurement properties. Also, we examined the relationships between RTs, individual item responses, and depressive levels. Method: For constructing the CAT system, responses of 2061 workers and university students to 24 depression scale plus four negatively revised positive affect items were subjected to a polytomous IRT analysis. The stopping rule was set for standard error of estimation < 0.30 or the maximum 15 items displayed. The CAT and non-adaptive computer-based test (CBT) were administered to 209 undergraduates, and 168 of them administered again after 1 week. Results: On average, the CAT was converged by 10.4 items. The θ values estimated by CAT and CBT were highly correlated ( r = 0.94 and 0.95 for the 1st and 2nd measurements) and with the traditional scoring procedures ( r's > 0.90). The test-retest reliability was at a satisfactory level ( r = 0.86). RTs to some items significantly correlated with the θ estimates. The mean RT varied by the item contents and wording, i.e., the RT to positive affect items required additional 2 s or longer than the other subscale items. Conclusion: The CAT would be a reliable and practical measurement tool for various purposes including stress check at workplace. [ABSTRACT FROM AUTHOR]

A novel multimodal and bio-inspired approach to biomedical signal processing and classification is presented in the paper. This approach allows for an automatic semantic labeling (interpretation) of sleep apnea events based the proposed data-driven biomedical signal processing and classification. The presented signal processing and classification methods have been already successfully applied to real-time unimodal brainwaves (EEG only) decoding in brain-computer interfaces developed by the author. In the current project the very encouraging results are obtained using multimodal biomedical (brainwaves and peripheral physiological) signals in a unified processing approach allowing for the automatic semantic data description. The results thus support a hypothesis of the data-driven and bio-inspired signal processing approach validity for medical data semantic interpretation based on the sleep apnea events machine-learning-related classification. [ABSTRACT FROM AUTHOR]

In this paper, we propose a location-dependent earthquake prediction based on recurrent neural network algorithms. The location-dependent prediction consists of clustering the seismic dataset based on its geographical parameters (longitude and latitude) using the K-Means algorithm. In addition, we divide each cluster into two specific subsets. The first set consists of events with an average magnitude between 2 and 5, and the second set contains seismic events with magnitudes >5. Clustering allows our models to focus on each region independently. Associated with splitting methods, it helps them to learn the specific trends of each cluster with accurate performance. Moreover, in our work, the large earthquakes with few events are trained independently and remain unaffected by the other dominant events. We implement our models using the powerful recurrent neural network algorithms: Long Short Memory (LSTM), Gated Recurrent Network (GRU), and their hybrid model (LSTM-GRU). They are tested using data related to three different regions: Morocco, Japan, and Turkey. We evaluate the performance according to three metrics: mean absolute error, mean squared error, and root-mean-square error. Compared to other works in the literature, our models generally perform well, especially in predicting large earthquakes. - A location-dependent prediction using a geographic clustering by K-means algorithm. - Earthquake prediction using recurrent neural network algorithms: LSTM and GRU. - Three different seismic regions are studied: Japan, Turkey, and Morocco. - Discussion and comparison of results. [ABSTRACT FROM AUTHOR]

At present, for adults with chronic hepatitis B virus (HBV) infection, two new analogues, entecavir (ETV) and tenofovir, are recommended as the first-line therapy by the EASL (European Association for the Study of the Liver), AASLD (American Association for the Study of Liver Diseases), and APASL (Asian Pacific Association for the Study of the Liver) guidelines. The use of pegylated interferon-α (PEG IFN-α) is recommended as the first-line therapy instead of standard IFN-α according to the above 3 guidelines. In this paper, the aim was to assess: (1) the long-term efficacy and safety as well as the resistance to ETV and tenofovir disoproxil fumarate (TDF); (2) the efficacy of PEG IFN-α; (3) the role of combination therapy with IFN plus two analogues, such as lamivudine and ETV; (4) the efficacy and safety of two analogues with cirrhosis, and (5) suppression of hepatocellular carcinoma (HCC) by ETV and IFN treatment. The results are as follows: (1) both ETV and TDF showed long-term efficacy and safety; (2) PEG IFN-α resulted in a greater decline in HBV DNA levels and a higher rate of HBeAg seroconversion; (3) combination therapy with IFN plus two analogues did not elevate the rate of sustained responses; (4) both ETV and TDF showed efficacy and safety with cirrhosis (ETV especially displayed efficacy and safety with decompensated cirrhosis), and (5) suppression of HCC was observed by ETV and IFN. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

This paper proposes an application method of the cooperative distributed multiagent model in multipath allocation algorithm and confirms its valid calculation results by realizing this model in a distributed computer environment. The multiagent model in this paper is an environment where each agent has the same function of asynchronous and parallel calculation and no globally controlling agents exist. To realize this model, this paper proposes an appropriate dividing method of the multipath allocation problem for asynchronous and parallel calculation, a unifying method of each agent's answers end interagent communication protocols. It also compares the required calculation time and the number of allocated paths of a single agent with that of the multiagents end shows the practical calculation ability of the multiagent model. [ABSTRACT FROM AUTHOR]

This paper considers an unknown nonlinear static system with an adjustable parameter (input parameter). The problem is to find the least-square approximation input parameter. A recursive algorithm is proposed that estimates this least-square approximation parameter without an identification of the system. An output of the system is observed under a noisy environment. Suppose that the dimension of the output is equal to that of the input. In this case, the system characteristic can be regarded as a regression function, hence a recursive algorithm that can be constructed that minimizes tile difference between the expectation of the output and a desired value of the output by using the stochastic approximation method or the adaptive Robbins-Monro procedure. On the other hand, if the dimension of the output is greater than that of the input, the regression equation has no solution. Therefore, the usual stochastic approximation method or the adaptive Robbins-Monro procedure cannot be used. The algorithm proposed in this paper estimates the least-square approximation input parameter that minimizes the squared error of the output. Especially, this algorithm agrees with the usual adaptive Rohbins-Monro procedure if the dimension of the output is equal to that of the input. Some numerical simulations ale shown and the availability of the algorithm presented herein is confirmed. [ABSTRACT FROM AUTHOR]

Background: The Japanese Society of Emergency Pediatrics has formulated evidence-based guidelines for the management of intussusception in children in order to diagnose intussusceptions promptly, to initiate appropriate treatment as early as possible, and to protect intussuscepted children from death. Methods: Literature was collected systematically via the Internet using the key words 'intussusception' and 'children.' The evidence level of each paper was rated in accordance with the levels of evidence of the Oxford Center for Evidence-based Medicine. The guidelines consisted of 50 clinical questions and the answers. Grades of recommendation were added to the procedures recommended on the basis of the strength of evidence levels. Results: Three criteria of 'diagnostic criteria,''severity assessment criteria,' and 'criteria for patient transfer' were proposed aiming at an early diagnosis, selection of appropriate treatment, and patient transfer for referral to a tertiary hospital in severe cases. Barium is no longer recommended for enema reduction (recommendation D) because the patient becomes severely ill once perforation occurs. Use of other contrast media, such as water-soluble iodinated contrast, normal saline, or air, is recommended under either fluoroscopic or sonographic guidance. Delayed repeat enema improves reduction success rate, and is recommended if the initial enema partially reduced the intussusception and if the patient condition is stable. Conclusions: The guidelines offer standards of management, but it is not necessarily the purpose of the guidelines to regulate clinical practices. One should judge each individual clinical situation in accordance with experiences, available devices, and the patient's condition. [ABSTRACT FROM AUTHOR]

A short-baseline weather radar network consisting of Ku-band broadband radars (BBR) for meteorological application is under development. The BBR is a short-range and high-resolution Doppler radar for meteorological application, whose range and temporal resolution are several meters and 1 min per volume scan, respectively and coverage is from 50 m to 15 or 20 km. A radar network consisting of BBRs is useful for rapidly and accurately detecting and analyzing small-scale weather phenomena such as localized scattered thunderstorms, tornadoes, and downbursts in locations with a complex topography such as urban or mountainous area. The BBR network project plans to deploy three BBRs in the north Osaka, Japan, area and to observe and analyzing small-scale weather phenomena in detail. At present, two functioning BBRs have been deployed in the Osaka area. This paper presents a description of the BBR network project and its preliminary assessment. Observations of the two-BBR test bed derive expected properties of the three-BBR network, which efficiently covers troposphere in the overlapped area with a minimum observable altitude of tens of meters and a sensitivity of 10.2 dBZ (spatial average at a 5000-km altitude). Integrated reflectivity and preliminary algorithm for multiradar products in the two-BBR test bed, presenting a promising fine structure of precipitation cells, is also shown. [ABSTRACT FROM AUTHOR]

Abstract: Traffic optimization of railroad networks was considered using an algorithm that was biologically inspired by an amoeba-like organism, plasmodium of the true slime mold, Physarum polycephalum. The organism developed a transportation network consisting of a tubular structure to transport protoplasm. It was reported that plasmodium can find the shortest path interconnecting multiple food sites during an adaptation process (Nakagaki et al., 2001. Biophys. Chem. 92, 47–52). By mimicking the adaptation process a path finding algorithm was developed by . In this paper, the algorithm is newly modified for applications of traffic distribution optimization in transportation networks of infrastructure such as railroads under the constraint that the network topology is given. Application of the algorithm to a railroad in metropolitan Tokyo, Japan is demonstrated. The results are evaluated using three performance functions related to cost, traveling efficiency, and network weakness. The traffic distribution suggests that the modified Physarum algorithm balances the performances under a certain parameter range, indicating a biological process. [Copyright &y& Elsevier]

We developed a 3D archive system for Japanese traditional performing arts. The system generates sequences of 3D actor models of the performances from multi-view video by using a graph-cuts algorithm and stores them with CG background models and related information. The system can show a scene from any viewpoint as follows; the 3D actor model is integrated with the background model and the integrated model is projected to a viewpoint that the user indicates with a viewpoint controller. A challenge of generating the actor models is how to reconstruct thin or slender parts. Japanese traditional costumes for performances include slender parts such as long sleeves, fans and strings that may be manipulated during the performance. The graph-cuts algorithm is a powerful 3D reconstruction tool but it tends to cut off those parts because it uses an energy-minimization process. Hence, the search for a way to reconstruct such parts is important for us to preserve these arts for future generations. We therefore devised an adaptive erosion method that works on the visual hull and applied it to the graph-cuts algorithm to extract interior nodes in the thin parts and to prevent the thin parts from being cut off. Another tendency of the reconstruction method using the graph-cuts algorithm is over-shrinkage of the reconstructed models. This arises because the energy can also be reduced by cutting inside the true surface. To avoid this tendency, we applied a silhouette-rim constraint defined by the number of the silhouette-rims passing through each node. By applying the adaptive erosion process and the silhouette-rim constraint, we succeeded in constructing a virtual performance with costumes including thin parts. This paper presents the results of the 3D reconstruction using the proposed method and some outputs of the 3D archive system. [ABSTRACT FROM AUTHOR]

The repletion rate of guide dogs for visually handicapped persons is roughly 10% nationwide. The reasons for this low rate are the long training period and the expense for obtaining a guide dog in Japan. Motivated by these two reasons, we are developing guide-dog robots. The major objective is to develop an intelligent human-robot interface. This paper describes two novel interface algorithms and strategy to guide visually handicapped person. We developed new leading edge searching method, which uses a single laser range finder (LRF) developed to find the center of the corridor in an indoor environment. We also developed a new twin cluster trace method that can recognize the led-person's walking conditions measured by the LRF. The algorithm allows the guide-dog robot to accurately estimate and anticipate the led-person's next move. We experimentally verified these algorithms. The results show that the algorithms are reliable enough to enable the guide-dog robot and the led-person to maneuver in a complex corridor environment. [ABSTRACT FROM AUTHOR]

Precise positioning of moving vehicles is necessary for many applications but the accuracy of global positioning system (GPS) is seriously affected in dense urban areas. This paper presents a general framework for matching such vehicle GPS traces with urban road networks based on a pipeline approach, where the matching is divided into several sequential stages. The GPS position points with inadequate satellite geometry are first filtered by common filters of fix and horizontal dilution of precision (HDOP). Then three advanced filters are developed to filter the deviant position points that have good satellite geometry but are not consistent with the vehicle motion model, i.e. velocity, angular velocity and heading changes. Finally, two curve-to-curve distance measurement algorithms - Hausdorff and Frechet are implemented to match these filtered trajectories with urban road networks. The examination of this approach is carried out using the vehicle track logging data in Sugimotocho station area of Osaka city, Japan. The results show that the pipelining of several filter algorithms can remove error position points on trajectories significantly. It also suggests that the Frechet algorithm outperforms Hausdorff by including the topology of road networks. [ABSTRACT FROM AUTHOR]

Abstract: PHITS (Particle and Heavy-Ion Transport code System) is a general-purpose three-dimensional Monte Carlo code, developed and maintained by RIST, JAEA and KEK in Japan together with Sihver et al. at Chalmers in Sweden. PHITS can deal with the transports of all varieties of hadrons and heavy ions with energies up to around 100GeV/nucleon, and in this paper the current status of PHITS is presented. We introduce a relativistically covariant version of JQMD, called R-JQMD, that features an improved ground state initialization algorithm, and we will present the introduction of electron and photon transport in PHITS using EGS5, which have increased the energy region for the photon and energy transport from up to around 3GeV to up to several hundred GeV depending on the atomic number of the target. We show how the accuracy in dose and fluence calculations can be improved by using tabulated cross sections. Benchmarking of shielding and irradiation effects of high energy protons in different materials relevant for shielding of accelerator facilities is also presented. In particular, we show that PHITS can be used for estimating the dose received by aircrews and personnel in space. In recent years, many countries have issued regulations or recommendations to set annual dose limitations for aircrews. Since estimation of cosmic-ray spectra in the atmosphere is an essential issue for the evaluation of aviation doses, we have calculated these spectra using PHITS. The accuracy of the atmospheric propagation simulation of cosmic-ray performed by PHITS has been well verified by experimental cosmic-ray spectra taken under various conditions. Based on a comprehensive analysis of the simulation results, an analytical model called “PARMA” has been proposed for instantaneously estimating the atmospheric cosmic-ray spectra below the altitude of 20km. We have also performed preliminary simulations of long-term dose distribution measurements at the ISS performed with the joint ESA-FSA experiment MATROSHKA-R (MTR-R) led by the Russian Federation Institute of Biomedical Problems (IMBP) and the ESA supported experiment MATROSHKA (MTR), led by the German Aerospace Center (DLR). For the purpose of examining the applicability of PHITS to the shielding design in space, the absorbed doses in a tissue equivalent water phantom inside an imaginary space vessel has been estimated for different shielding materials of different thicknesses. The results confirm previous results which indicate that PHITS is a suitable tool when performing shielding design studies of spacecrafts. [Copyright &y& Elsevier]

Abstract: A ladder lottery, known as “Amidakuji” in Japan, is a common way to choose a permutation randomly. A ladder lottery corresponding to a given permutation is optimal if has the minimum number of horizontal lines among the ladder lotteries corresponding to . In this paper we show that for any two optimal ladder lotteries and of a permutation, there exists a sequence of local modifications which transforms into . We also give an algorithm to enumerate all optimal ladder lotteries of a given permutation. By setting , the algorithm enumerates all arrangements of pseudolines efficiently. By implementing the algorithm we compute the number of arrangements of pseudolines for each . [Copyright &y& Elsevier]

Abstract: This paper introduces a recursive particle filtering algorithm designed to filter high dimensional systems with complicated non-linear and non-Gaussian effects. The method incorporates a parallel marginalization (PMMC) step in conjunction with the hybrid Monte Carlo (HMC) scheme to improve samples generated by standard particle filters. Parallel marginalization is an efficient Markov chain Monte Carlo (MCMC) strategy that uses lower dimensional approximate marginal distributions of the target distribution to accelerate equilibration. As a validation the algorithm is tested on a 2516 dimensional, bimodal, stochastic model motivated by the Kuroshio current that runs along the Japanese coast. The results of this test indicate that the method is an attractive alternative for problems that require the generality of a particle filter but have been inaccessible due to the limitations of standard particle filtering strategies. [Copyright &y& Elsevier]

This paper addresses a robust decentralized proportional-integral (P1) control design for power system load- frequency regulation with communication delays. In the proposed methodology, the P1-based load-frequency control (LFC) problem is reduced to a static output feedback control synthesis for a multiple-delay system. The proposed control method gives a suboptimal solution using a developed iterative linear matrix inequalities algorithm via the mixed H2 /H∞ control technique. The control strategy is suitable for LFC applications that usually employ the PI control. To demonstrate the efficiency of the proposed control strategy, an experimental study has been performed at the Research Laboratory, Kyushu Electric Power Company, Japan. [ABSTRACT FROM AUTHOR]

In this paper, we describe an intelligent location-aware city guide system, which adapts to each user’s preferences, and uses an intuitive “metal detector” interface for navigation. Our system analyzes each user’s past location data history to estimate individual preferences, and allows users to find shops that match their tastes in the same way a metal detector would be used to detect metal objects. The procedure with which the system picks out shops that match each user’s preferences includes a newly developed place learning algorithm, which can efficiently find frequented places, complete with their proper names (e.g. “The Ueno Royal Museum”). We have conducted a series of evaluation tests at a popular shopping district inside Tokyo, and the results validate the effectiveness of our overall approach. [ABSTRACT FROM AUTHOR]