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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]

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]

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]

Benefiting from rich agricultural land, easy transport and fishing, etc., more and more people are moving to live in coastal areas, with more than 200 million people now living in coastal areas that are vulnerable to extreme sea level events. Sea level information is useful for coastal societies. Image measurement is rapidly developing as a new type of measurement tool. A multicamera-based sea level monitoring system along Japan's coast near the Pacific is proposed in this paper, and a long-distance sea wave matching method for this system is described. The whole system employs multiple binocular vision systems to take sea surface images and obtain the sea level height based on the disparity between the field of views of the left and right cameras, forming a local measurement and overall analysis monitoring system. Sea level monitoring requires a high processing accuracy and speed to realize a timely response to extreme events. Thus, the paper extracts sea waves and integrates a sea wave's appearance features as feature points and descriptors and pioneers the idea of searching deterministic features for fast image processing. The average stereo matching precision of the proposed method is up to 89.9% with a running time smaller than 40 ms for most pairs of images. Experiments on various real sea surface image pairs are conducted to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Decision Intelligence is a methodology that integrates complex systems, machine learning, and decision analysis. It is increasingly interested in finding optimal solutions to the uncertainties noted in machine learning in today's complex AI systems. Furthermore, Decision Intelligence is a new engineering discipline that augments data science with theories from various sciences. Since decisions are made in all kinds of situations, they will become even more critical in a wide range of academic fields in the future. This paper introduces the ideas of proponents of Decision Intelligence, the promotion of digital decisioning toward automation, and trends in Western companies and Japan. Then, using risk management procedures, our examination of methods to ensure safety in the case of automated driving will be described. In addition, I will describe the challenges of each technology that promotes Decision Intelligence. I will also introduce a new synchronous AI that I am currently working on with the inventor of the challenge. This algorithm generation method is based on a metaphysical view of the synchronous nature of life activity. [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]

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]

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]

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]

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]

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]

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]

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]

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]

To reduce the short‐term load forecasting (STLF) error of off‐line forecasting model, a VMD‐IWOA‐LSTM (VIL) method for STLF is proposed. Firstly, variational mode decomposition (VMD) is used to decompose the historical power load signals. Then, the decomposed signals are reconstructed according to the similarity of Pearson correlation coefficient (PCC), and meteorological data are chosen for each reconstructed component based on the set PCC threshold. The long short‐term memory (LSTM) models are used to predict the corresponding components, and improved whale optimization algorithm (IWOA) is used to optimize the parameters in LSTM. Finally, the forecast results of each component are added together to get the final forecast result. The experimental results of power load data in a certain area show that the proposed method has the advantages of strong anti‐interference performance and high prediction accuracy compared with other methods, and has strong practicability. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In recent years, intelligent structural damage diagnosis algorithms using machine learning have achieved much success. However, because of the fact that in real bridge applications, the working environment (load, temperature, and noise) is changing all the time, degradation of the performance of intelligent structural damage diagnosis methods is very serious. To address these problems, a novel bridge diagnosis algorithm based on deep learning is proposed. Our contributions include: First, we proposed an improved denoising auto‐encoder‐based deep neural networks, which is optimized by the gray relational analysis. It is able to automatically extract high‐level features from raw signals via a multi‐layer extraction to satisfy any damage diagnosis objective and thus does not need any time consuming denoising prepossessing. The model can achieve high accuracy under noisy environment. Second, the algorithm does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working environment is changed. Numerical simulations and experimental investigations on real bridges conducted to present the accuracy and efficiency of the proposed algorithm, comparing with other commonly machine learning‐based algorithms. The result shows it is deemed as an ideal and effective method for damage diagnosis of bridge structures. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The Unazuki Reservoir is located on the Kurobe River, which is influenced by a catchment with one of the highest sediment yields in Japan. Due to a sufficiently available discharge during flood events, annual sediment flushing with full water-level drawdown (i.e., free-flow sediment flushing) is conducted to preserve the effective storage capacity of the reservoir. Nevertheless, the upstream half of the reservoir (i.e., study segment) suffers from the excessive deposition of coarser sediments. Remobilization of these coarser materials and their transportation further downstream of the reservoir is a priority of reservoir owners for sustainable reservoir functions, such as flood-risk management and hydroelectric energy generation. In this paper, an already conducted sediment-flushing operation in the Unazuki Reservoir is simulated, and its effects on sediment scouring from the study segment of the reservoir together with changes in bed morphodynamics are presented. A fully 3D numerical model using the finite volume approach in combination with a wetting/drying algorithm was utilized to reproduce the hydrodynamics and bed changes using the available onsite data. Afterwards, the effects of discharge adjustment on the morphological bed changes and flushing efficiency were analysed in the study segment using an additional single-discharge pulse supplied from upstream reservoirs. Simulation results showed that an approximately 75% increase in the average discharge during the free-flow stage changed the dominant morphological process from deposition into an erosive mode in the study segment. If the increase in discharge reaches up to 100%, the flushed volume of sediments from the target segment can increase 2.9 times compared with the initiation of the erosive mode. [ABSTRACT FROM AUTHOR]

Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network. [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]

Impedance cardiography (ICG) and peripheral bioimpedance may be useful for monitoring several cardiovascular parameters such as cardiac output, stroke volume, blood pressure and heart rate. It is a noninvasive and low‐cost method and could be useful for diagnosis and treatment in different clinical situations. This study involved 100 volunteers and their age ranging from 18 to 26 years. The acquired bioimpedance signal was pre‐processed and used to estimate the heart rate. Thus, heart rates of each subject were calculated and then compared with actual measurement from FUZZY model MP3 digital blood pressure monitor. The results showed high precision with a 1.38 mean error a high sensitivity of 99.81% and a positive predictivity value of 99.63%, respectively. Compared with six other methods selected from the literature review and which are initially dedicated to ECG signals, the proposed method has been found to be more efficient and more precise with an error in determining the percentile HR = 1.78%. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Due to the unavailability of accurate data and the limitations of the existing methods, reliable input–output tables (IOTs) may not be available for all the regions in a country. This study proposes a novel approach to estimate the regional input coefficients. It harnesses the capabilities of machine-learning (ML) algorithms to estimate the regional input coefficients of one region based on the IOTs of multiple other regions for which reliable data are available. The application of three ML algorithms is investigated using data from Japan. The results highlight the superior performance of the ML models compared with location quotient models. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the prevalence of hoarseness and its association with the severity of dysphagia in patients with sarcopenic dysphagia. Design: Cross-sectional study using the Japanese sarcopenic dysphagia database. Setting: 19 hospitals including 9 acute care hospitals, 8 rehabilitation hospitals, 2 long-term care hospitals, and 1 home visit rehabilitation team. Participants: 287 patients with sarcopenic dysphagia, aged 20 years and older. Measurements: Sarcopenic dysphagia was diagnosed using a reliable and validated diagnostic algorithm for the condition. The presence and characteristics of hoarseness classified as breathy, rough, asthenic, and strained were assessed. The prevalence of hoarseness and the relationship between hoarseness and Food Intake LEVEL Scale (FILS) were examined. Order logistic regression analysis adjusted for age, sex, naso-gastric tube, and handgrip strength was used to examine the relationship between hoarseness and FILS at baseline and at follow-up. Results: The mean age was 83 ± 10 years. Seventy-four (26%) patients had hoarseness, while 32 (11%), 20 (7%), 22 (8%), and 0 (0%) patients had breathy, rough, asthenic, and strained hoarseness, respectively. Median FILS at the initial evaluation was 7 (interquartile range, 5–8). Hoarseness (β=0.747, 95% confidence intervals= 0.229, 1.265, p=0.005), age, sex, naso-gastric tube, and handgrip strength were associated independently with baseline FILS, while hoarseness (β=0.213, 95% confidence intervals= −0.324, 0.750, p=0.438) was not associated independently with the FILS at follow-up. Conclusions: Hoarseness was associated with the severity of dysphagia at baseline, however not a prognostic factor for sarcopenic dysphagia. Resistance training of swallowing and respiratory muscles and voice training as part of rehabilitation nutrition might be useful for treating sarcopenic dysphagia. [ABSTRACT FROM AUTHOR]

Summary: Areal BMD (aBMD) from DXA is not a sufficiently accurate predictor of fracture. Novel volumetric BMD derived from 3D modeling of the hip from DXA images significantly improved the predictive ability for hip fracture relative to aBMD at the femoral neck, but not aBMD at the total hip. Introduction: To clarify whether volumetric and geometric indices derived from novel three-dimensional (3D) modeling of the hip using dual-energy X-ray absorptiometric (DXA) images improve hip fracture prediction relative to areal bone mineral density (aBMD). Methods: We examined 1331 women who had completed the baseline survey and at least one follow-up survey over 20 years (age 40–79 years at baseline). Each survey included aBMD measurement at the hip by DXA. Volumetric and geometric indices of the hip at baseline and the 10-year follow-up were estimated from DXA images using a 3D modeling algorithm. Incident hip fractures during the 20-year follow-up period were identified through self-report. Cox proportional hazards regression models allowing for repeated measurements of predictors and outcomes were constructed, and their predictive ability for hip fracture was evaluated using areas under receiver operating characteristic curves (AUCs) and net reclassification improvement (NRI) over aBMD at the femoral neck (FN) and total hip (TH) as references. Results: During a median follow-up of 19.8 years, 68 incident hip fractures were identified (2.22/1000 person-years). A significantly larger AUC of trabecular volumetric BMD (vBMD) at the total hip (AUC = 0.741), femoral neck (AUC = 0.748), and intertrochanter (AUC = 0.738) and significant NRI (0.177, 0.149, and 0.195, respectively) were observed compared with FN-aBMD (AUC = 0.701), but not TH-aBMD. Conclusions: vBMD obtained from 3D modeling using routinely obtained hip DXA images significantly improved hip fracture risk prediction over conventional FN-aBMD, but not TH-aBMD. Trial registration: The Japanese Population-Based Osteoporosis (JPOS) Cohort Study was retrospectively registered as UMIN000032869 in the UMIN Clinical Trials Registry on July 1, 2018. [ABSTRACT FROM AUTHOR]

A simple and fast diagnostic method for multiple open‐circuit faults in voltage‐source inverters based on the current vector phase is proposed. The characteristics of the current vector phase in both healthy and faulty conditions are analyzed in detail, and simple fault symptom variables are defined to extract the features for fault isolation. The proposed method can diagnose 21 types of open‐circuit faults only by using current vector phase information, and it is fast, robust and easy to implement in existing systems without additional sensors. Several experimental results verify that the diagnostic algorithm has no false alarms under speed and load transient conditions, and the fault can be isolated within 15% of the current fundamental cycle in the case of the fastest diagnostic process and no more than 80% of the current cycle in the worst cases. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Reliability information of satellite precipitation products is required for various applications. This study describes and evaluates a reliability flag of the Global Satellite Mapping of Precipitation Near-Real-Time precipitation product (GSMaP_NRT). This flag was developed to characterize the reliability of GSMaP_NRT data simply and qualitatively by considering its algorithm characteristics. The reliability at each pixel is represented by any one of ten levels (10 being the best and 1 the worst) by considering three major factors: 1) "surface type reliability"--which takes into account that estimation of rainfall using passive microwave sensors is better over the oceans than over land and coastal areas; 2) "low-temperature reliability"--which takes into account the lower reliability due to surface snow cover in low-temperature conditions; and 3) "Moving Vector with Kalman Filter (MVK) propagation reliability"--which means that the reliability gets worse with the increase in time since the last overpass of the passive microwave sensor. To evaluate the utility of the reliability flag, statistical indices are calculated for each reliability level using gauge-calibrated ground radar data around Japan. It is found that the reliability flag represents the differences in GSMaP accuracy: the accuracy worsens as the reliability decreases. The GSMaP errors exhibit seasonal changes that are well represented by the ten levels of the reliability flag, indicating that the reliability flag can be used to catch seasonal variations in GSMaP accuracy due to changes in environmental factors. This study also raises the possibility of improving the reliability flag by using information related to heavy orographic rainfall. It is shown how the error features of heavy orographic rainfall differ from those of the total rainfall, and it is suggested that heavy orographic rainfall information can be utilized to further improve the reliability flag. [ABSTRACT FROM AUTHOR]

The communication network of wide‐area protection may be interrupted because of the severe environment or artificial damage. The delay of communication will increase so much that the wide‐area protection cannot work normally in some severe cases mentioned above. That is one of the factors which affect the application of wide‐area protection. A reconstruction algorithm including different circuitous modes for different situations is proposed to maintain the transmission of the data on the interrupted channel. On the one hand, a single‐path circuitous mode is proposed when the data in the shut path is small. In this situation, the best circuitous path can be found out by Floyd considering the several reference factors affection. On the other hand, a multi‐paths circuitous model is proposed to solve the congestion which is caused by detouring under single‐path circuitous mode. The available margin of the bandwidth of the weakest channel called the short‐board channel in each circuitous path is the focus of the algorithm. In this situation, the optimal shunt coefficients are determined according to the bandwidth margin ratio of the short‐board channel. Then the data on the interrupted channel is divided into several streams based on the shunt coefficients. Therefore, the balance of the communication network can be ensured after the interrupted channel is divided. Finally, with OPNET communication simulation software, the IEEE14 nodes network is built. Delay and balance of system based on this reconstruction algorithm are tested. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In Japan, the aging population is expected to increase the number of elderly people in the future. The purpose of this study was to develop a hip lift transfer assistive system to improve the QOL of elderly living and to prevent back pain for caregivers. We extracted the impediment factor and the necessary scene for the assistance, decided on the transfer process from the wheelchair in the toilet, and considered the reduction method of the burden based on the quantitative evaluation of the caregiver's lumbar burden and developed the device. Then we proposed the algorithm of the system by grasping the behavior and lumbar burden characteristics at the time of using the hip lift transfer assistive system by the developed device in which the proposed support algorithm of standing seating assistance operation is implemented in the actual use environment. Through the assistive movement evaluation experiment and the actual operation in the toilet, we have verified that the use of this device can reduce the caregivers' lumbar burden below the standard value (3400 N) and have proved the effectiveness of the proposed transfer assistive system. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement mission is a major U.S.–Japan joint mission to understand the physics of the Earth's global precipitation as a key component of its weather, climate, and hydrological systems. The core satellite carries a dual-precipitation radar and an advanced microwave imager which provide measurements to retrieve the drop size distribution (DSD) and rain rates using a Combined Radar-Radiometer Algorithm (CORRA). Our objective is to validate key assumptions and parameterizations in CORRA and enable improved estimation of precipitation products, especially in the middle-to-higher latitudes in both hemispheres. The DSD parameters and statistical relationships between DSD parameters and radar measurements are a central part of the rainfall retrieval algorithm, which is complicated by regimes where DSD measurements are abysmally sparse (over the open ocean). In view of this, we have assembled optical disdrometer datasets gathered by research vessels, ground stations, and aircrafts to simulate radar observables and validate the scattering lookup tables used in CORRA. The joint use of all DSD datasets spans a large range of drop concentrations and characteristic drop diameters. The scaling normalization of DSDs defines an intercept parameter NW, which normalizes the concentrations, and a scaling diameter Dm, which compresses or stretches the diameter coordinate axis. A major finding of this study is that a single relationship between NW and Dm, on average, unifies all datasets included, from stratocumulus to heavier rainfall regimes. A comparison with the NW–Dm relation used as a constraint in versions 6 and 7 of CORRA highlights the scope for improvement of rainfall retrievals for small drops (Dm < 1 mm) and large drops (Dm > 2 mm). The normalized specific attenuation–reflectivity relationships used in the combined algorithm are also found to match well the equivalent relationships derived using DSDs from the three datasets, suggesting that the currently assumed lookup tables are not a major source of uncertainty in the combined algorithm rainfall estimates. [ABSTRACT FROM AUTHOR]