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Background: Health-related patient-reported outcomes (HR-PROs) are crucial for assessing the quality of life among individuals experiencing low back pain. However, manual data entry from paper forms, while convenient for patients, imposes a considerable tallying burden on collectors. In this study, we developed a deep learning (DL) model capable of automatically reading these paper forms., Methods: We employed the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire, a globally recognized assessment tool for low back pain. The questionnaire comprised 25 low back pain-related multiple-choice questions and three pain-related visual analog scales (VASs). We collected 1305 forms from an academic medical center as the training set, and 483 forms from a community medical center as the test set. The performance of our DL model for multiple-choice questions was evaluated using accuracy as a categorical classification task. The performance for VASs was evaluated using the correlation coefficient and absolute error as regression tasks., Result: In external validation, the mean accuracy of the categorical questions was 0.997. When outputs for categorical questions with low probability (threshold: 0.9996) were excluded, the accuracy reached 1.000 for the remaining 65 % of questions. Regarding the VASs, the average of the correlation coefficients was 0.989, with the mean absolute error being 0.25., Conclusion: Our DL model demonstrated remarkable accuracy and correlation coefficients when automatic reading paper-based HR-PROs during external validation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Since deep low‐frequency tremors are considered to be associated with large earthquakes that occur adjacently on the same subducting plate interface, it is important to investigate tremors that occurred before the establishment of modern seismograph networks such as the High Sensitivity Seismograph Network (Hi‐net). We propose a deep‐learning solution to detect evidence of tremors in scanned images of paper seismogram records from over 50 years ago. In this study, we fine‐tuned a convolutional neural network (CNN) based on the Residual Network, which was pre‐trained using images of synthetic waveforms from our previous study, using a data set comprised of images generated from real seismic data recorded digitally by Hi‐net to facilitate a supervised analysis. The fine‐tuned CNN was able to predict the presence or absence of tremors in the Hi‐net images with an accuracy of 98.64%. Gradient‐weighted Class Activation Mapping heatmaps created to visualize model predictions indicated that the CNN's ability to detect tremors is not degraded by the presence of teleseisms. Once validated using the Hi‐net images, the CNN was applied to paper seismograms recorded from 1966 to 1977 at the Kumano observatory in southwest Japan, operated by Earthquake Research Institute, The University of Tokyo. The CNN showed potential for detecting tremors in scanned images of paper seismogram records from the past, facilitating downstream tasks such as the creation of new tremor catalogs. However, further training using an augmented data set to control for variables such as inconsistent plotting pen thickness is required to develop a universally applicable model. Plain Language Summary: In 2002, deep low‐frequency tremors, which periodically occur with much smaller amplitudes and longer durations than common earthquakes, were discovered owing to the establishment of dense seismic arrays in Japan. These tremors are considered to be associated with megathrust earthquakes because they occur in deeper regions of plate boundaries compared with common earthquakes. Investigating tremors that occurred before the establishment of dense seismic arrays is important since megathrust earthquakes recur on intervals of 100–200 years, and digital records from modern seismic arrays cover only a fraction of that time. In this study, we developed a convolutional neural network based on the Residual Network (ResNet) that extracts tremors from images of waveforms recorded more than 50 years ago, when seismometers drew waveforms directly on drum‐rolled paper using a pen. The proposed ResNet, which was trained on seismogram images generated from synthetic waveforms and real data recorded digitally by modern seismic arrays, was applied to scanned images of paper seismograms recorded from 1966 to 1977 at an observatory in southwest Japan. A list of tremors was successfully obtained; however, further training using data that accounts for variables such as inconsistent plotting pen thickness is required to develop a universally applicable model. Key Points: Convolutional neural network model for detection of deep low‐frequency tremors from seismogram images is proposedThe model trained with seismogram images converted from real seismic data successfully detects tremorsThe detection performances of the trained model for the paper records at the Kumano observatory in southwest Japan are discussed [ABSTRACT FROM AUTHOR]

Due to the advantages of coastal areas in the fields of agriculture, transport, and fishing, increasingly more people are moving to these areas. Sea level information is important for these people to survive after extreme sea level events. With the recent improvements in computing and storage capacities, image analysis as a new measuring method is being rapidly developed and widely applied. In this paper, a multi-camera-based sea level height measuring system was built along Japan's coast and a deep-learning-based stereo matching method has been proposed for this system to complete 3D measurements. In this system, cameras are set with long base distances to ensure the long-distance monitoring system's precision, which causes a huge difference between the fields of view of the left and right cameras. Since most common network structures complete stereo matching by depth-wise cross-correlation between left and right images, they rely too much on the high-quality rectification of two images and fail on our long-distance sea surface images. We established a feature detection and matching network to realize sea wave extraction and sparse stereo matching for the system. Based on our previous result using the traditional method, the initial disparity was computed to reduce the search range of stereo matching. A training set with 785 pairs of sea surface images and 10,172 pairs of well-matched sea wave images was constructed to supervise the network. The experimental results verified that the proposed method can realize sea wave extraction and mask generation. It can also realize sparse matching of sea surface images regardless of poor rectification. [ABSTRACT FROM AUTHOR]

In this paper, a scalable end-to-end tree boosting system called XGBoost has been applied for predicting the magnitude of an earthquake from the early part of earthquake waveform data. This model uses the features extracted from the early P wave phase of the records as an input. The model's effectiveness has been verified by using data on earthquakes occurring in the Eurasian plate of Japan Islands from 1996 to 2021. Feature engineering has given 29 new features identified from the early P wave phase of the record, which show a high correlation with the magnitude of an earthquake. The comparison of predicted and actual magnitude shows that a trained XGboost model, which uses a single input record for magnitude prediction, gives an average prediction error of 0.004 ± 0.57 for earthquakes in the test dataset. In contrast, the average prediction error of –1.1 ± 0.80 and –0.65 ± 0.69 has been obtained for the magnitude estimated from conventional τc and Pd methods using the same test dataset. It is further seen that the average predicted magnitude of a single earthquake of magnitude 4.5 and 6.1 (MJMA) obtained by using multiple nearfield records using XGBoost model is 4.58 ± 0.33 and 6.32 ± 0.29, which is close to the actual magnitude of the earthquake. The results presented in this paper clearly show that the structured data can be effectively used by complex machine learning or deep learning models to predict earthquake magnitude from single or multiple records. [ABSTRACT FROM AUTHOR]

In the actual power grid environment, there are severe class imbalances in the data sets of various monitoring systems. The existing deep learning methods are highly dependent on the training data sets, and it is difficult to mine the minority sample features in the imbalanced data sets, resulting in Classification recognition accuracy is severely limited. This paper takes the voltage sag as the research object, and proposes a dataset enhancement method based on an improved balanced generative adversarial network. This method combines the advantages of autoencoder and generative adversarial network, learns the public knowledge in the data set through the initialization training of autoencoder, and uses the majority class sample features to assist in generating minority class samples. In addition, an embedding layer is introduced into the self‐encoder architecture to add label information to the eigenvectors, which solves the problem of strong coupling between the distribution of voltage sag eigenvectors and improves the generation effect of samples. At the same time, the Wasserstein distance optimized by the gradient penalty term is used to guide the model adversarial training, which overcomes the problems of vanishing gradient and mode collapse of the original model. Through experimental analysis, the model can fully extract the features of minority samples under unbalanced data sets, ensure the distribution consistency between generated samples and original samples, and take into account the authenticity and diversity of generated samples, which can effectively improve the recognition effect of deep learning model on minority classes and improve the overall recognition accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model's robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel "policy-versus-policy" strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R 2 , MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF. [ABSTRACT FROM AUTHOR]

Power line insulator defect identification usually suffers from complex backgrounds, small defect target sizes, and inconspicuous defect features. Traditional identification methods based on image processing, image analysis, and pattern classification have many limitations in solving the aforementioned problems. In recent decades, deep learning classification methods have gradually replaced traditional identification methods in the task of power line insulator defect identification. To accurately identify the locations of insulator defects, this paper proposes an insulator defect detection algorithm using an improved lightweight YOLOv4‐tiny network (ILYTN). First, the CBL (Conv‐BN‐LeakyReLU) modules of the backbone network are replaced with MobileViT blocks to enhance the feature extraction capability of the backbone network. Second, coordinate attention (CA) is introduced in the feature fusion part to improve the network's ability to focus on the location of defects. Finally, an EIOU (efficient intersection over union) loss function, instead of the original CIOU loss function, is used so that the convergence speed of the network can be improved. To verify the effectiveness of the proposed algorithm in this paper, the mViT‐yolo algorithm is compared with the mainstream Faster‐RCNN algorithm, SSD algorithm, YOLOv3 algorithm, and YOLOv4‐tiny algorithm. The experimental results show that the algorithm proposed in this paper outperforms all the above algorithms in terms of detection accuracy. Compared with the traditional YOLOv4‐tiny algorithm, the proposed algorithm increases the mean average precision (mAP) by 1.64%, the average precision (AP) of missing insulator defects by 0.32%, and the average precision (AP) of broken insulator defects by 4.96%. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In Japan, the final tightening of bolts in the bolt-tightening operations is guaranteed to have been performed correctly by visually determining the change in markings during the temporary tightening operation performed by the technician. However, the engineer must confirm many bolts; further, the amount of time needed for the confirmation work and the inability to keep an objective record of the confirmation results present problems. To solve these problems, we developed a system for automating the final tightening of bolts using deep learning-based image-processing technology. The proposed system takes as input videos of bolt fastening points, extracts individual bolts, extracts markings on the extracted bolts, and makes fastening decisions based on the markings. In the judgment stage, the system processes information on each bolt where a marking is detected; thus, it is possible to leave this information as objective data. In this paper, we evaluated the accuracy of each automated step using an actual bridge video. We also compared the confirmation time with human confirmation. As a result of the confirmation, our proposed method reduces the confirmation time by about 33% in comparison to human confirmation. [ABSTRACT FROM AUTHOR]

Cesium-137, discharged by nuclear installations under normal operations and deposited in watersheds following atmospheric testing and accidents (i.e. Chernobyl, Fukushima …), has been studied for decades. Thus, modelling of 137 Cs concentration in rivers have been developed based on geochemical approaches and equilibrium assumptions (solid/liquid ratio) as this radionuclide has moved into rivers and oceans due to soil erosion. Recently a new approach is possible to model these concentrations with the popularization of data-driven models based on data acquired in the environment by monitoring networks. In this study, the concentrations of particulate cesium-137 measured near the mouth of the Rhône River (France), a highly nuclearized river, are simulated using two data-driven models, a Hierarchical Attention-Based Recurrent Highway Networks (HRHN) and a Random Forest Regressor (RF). The data-driven predictions were done using only hydrological data (water discharge and suspended solid fluxes) and industrial input of 137 Cs. Although the data-driven models provided a better prediction than a recent empirical model, the best prediction (R 2  = 0.71) was obtained with HRHN, a model that considers the temporal aspect of the monitoring data. The most important predictors were the hydrological data at the monitoring station and of the tributary that generate the most sediment flux (Durance River). In fact, the concentration of 137 Cs in the perimeter of this study was more related to hydrology than to nuclear release, as there were few events with high 137 Cs concentrations (concomitant nuclear release and low water discharge). However, the HRHN approach, which is more complex to implement than RF, can predict the concentrations of such events correctly despite their low representation of these events. The results of this study demonstrate the usefulness of data-driven models to assist monitoring programs by filling in gaps or helping to understand observed concentrations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Japan is one of the countries with the most frequent natural disasters in the world and is faced with various threats of natural disasters every year, which significantly impact Japan's social economy and people's lives. A great deal of information about disasters is preserved in Japanese literature. Interpreting and organizing this information help us to analyze the regularity of disasters and understand the preventive measures of ancient people. This paper aims to organize, analyze and save disaster data by collecting various information about disasters. Then a disaster spatiotemporal database system is constructed by using deep learning, image processing, and database technology. The system consists of two parts, namely, the disaster database and disaster website. The disaster database is the core of the whole system, which saves the disaster data after organizing and summarizing. The database collects disaster information from various sources, including key information such as disaster type, time, location, scale, and scope of impact. The Disaster website is the system's user interface, providing an interactive platform for users to access and use disaster data easily. The website has many functions, including search, visual display, disaster information query, etc. We also make a detailed analysis of the collected data, aiming to predict the causes and occurrence rules of disasters so as to achieve the target of disaster prediction. [ABSTRACT FROM AUTHOR]

Electrical workers must wear insulating gloves during daily maintenance. Detecting the condition of these gloves is significant for power safety. Due to the limitations of existing detection algorithms in accuracy and lightweight on power construction sites, this paper proposes a Faster‐YOLOv8 algorithm to detect the wearing condition of insulating gloves. First, it replaces the Bottleneck in the C2f module with the Faster Block and introduces a new C2Faster module. This reduces model parameters while improving performance. Second, the feature pyramid is improved to enhance the combination of deep and shallow semantic information. This improvement aims to improve the network's focus on detecting small targets. Finally, the GSConv is introduced into the neck network to reduce the model parameters and calculation. Additionally, GSConv enhances feature information exchange through shuffle operation. The effectiveness of the Faster‐YOLOv8 algorithm is verified through comparisons with mainstream algorithms such as the Faster‐RCNN algorithm, SSD algorithm, YOLOv5 algorithm, and YOLOv7‐tiny algorithm. The results demonstrate that the Faster‐YOLOv8 algorithm outperforms the above algorithms in terms of detection accuracy. Compared with the original YOLOv8 network model, the mAP is improved by 3.8%, the mAP of the wrong glove increased by 9.8%, the model size is reduced by 33.3%, and the detection speed can reach 81 FPS. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Addressing the challenge of ensuring robustness in vision‐based target recognition algorithms under adverse weather conditions, such as rain, snow, and fog, is crucial. In this paper, we introduce a novel approach for road target detection that can effectively operate under various weather conditions. Our method is based on the cascade task framework of target detection, complemented by image restoration techniques. Specifically, we have developed a denoising network tailored to meet the demands of de‐raining and snow removal tasks. This network leverages prior knowledge about the mask, enhancing its effectiveness. In real‐world scenarios featuring fog, wet conditions, and snow‐covered roads, our proposed method demonstrates a significant improvement in both recall rate and accuracy compared to conventional single‐object detection algorithms. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Recently, the whole world was hit by COVID‐19 pandemic that led to health emergency everywhere. During the peak of the early waves of the pandemic, medical and healthcare departments were overwhelmed by the number of COVID‐19 cases that exceeds their capacity. Therefore, new rules and techniques are urgently required to help in receiving, filtering and diagnosing patients. One of the decisive steps in the fight against COVID‐19 is the ability to detect patients early enough and selectively put them under special care. Symptoms of this disease can be observed in chest X‐rays. However, it is sometimes difficult and tricky to differentiate "only" pneumonia patients from COVID‐19 patients. Machine‐learning can be very helpful in carrying out this task. In this paper, we tackle the problem of COVID‐19 diagnostics following a data‐centric approach. For this purpose, we construct a diversified dataset of chest X‐ray images from publicly available datasets and by applying data augmentation techniques. Then, we employ a transfer learning approach based on a pre‐trained convolutional neural network (DenseNet‐169) to detect COVID‐19 in chest X‐ray images. In addition to that, we employ Gradient‐weighted Class Activation Mapping (GradCAM) to provide visual inspection and explanation of the predictions made by our deep learning model. The results were evaluated against various metrics such as sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and the confusion matrix. The resulting models has achieved an average detection accuracy close to 98.82%. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Semantic segmentation has become one of the trending topics in the world of computer vision and deep learning. Recently, due to an increasing demand to solve a semantic segmentation task simultaneously with attribute recognition of objects, a new task named attribute‐aware semantic segmentation has been introduced. Since the task requires to handle pixel‐wise object class estimation with its attributes such as a pedestrian's body orientation, previous works had difficulties to handle ambiguous attributes such as body orientations in object‐level, especially when segmenting the pedestrians with their attributes correctly. This paper proposes the ColAtt‐Net that is an attribute‐aware semantic segmentation model augmented by a column‐wise mask branch to predict the pedestrians' orientations in the horizontal perspective of the input image. We firmly assume that the pedestrians captured by a car‐mounted camera are distributed horizontally so that for each column of the input image, the pedestrian pixels can be labeled with one orientation uniformly. In the proposed method, we split the output of the base semantic segmentation model into two branches; one branch for segmenting the object categories, while the other one, as the novel column‐wise attribute branch, is to map the recognition of pedestrian's orientations that are distributed horizontally. This method successfully enhances the performance of attribute‐aware semantic segmentation by reducing the ambiguity on segmenting the pedestrian's orientation. Improvements on the pedestrian orientation segmentation are confidently shown by the proposed method in the experimental results, both in quantitative and qualitative views. This paper also discusses how the improved performance becomes an advantage in the autonomous driving system. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, we explore the use of seismicity data on a single-station basis in site response characterization. We train a supervised deep-learning model, SeismAmp, to recognize and separate seismic site response with reference to seismological bedrock (VS  = 3.45 km s−1) in a broad frequency range (0.2–20 Hz) directly from single-station earthquake recordings (features) in Japan. Ground-truth data are homogeneously created using a classical multistation approach—generalized spectral inversion at a total number of 1725 sites. We demonstrate that site response can be reliably separated from single-station seismograms in an end-to-end approach. When SeismAmp is tested at new sites in both Japan (in-domain) and Europe (cross-domain), it achieves the lowest standard deviation among all tested single-station techniques. We also find that horizontal-to-vertical spectral ratio (HVSR) is not the optimal use of single-station recordings. The individual components of each record carry salient information on site response, especially at high frequencies. However, part of the information is lost in HVSR. SeismAmp could lead to improved site-specific earthquake hazard prediction in cases where recordings are available or can be collected at target sites. It is also a convenient tool to remove repeatable site effects from ground motions, which may benefit other applications, for example, improving the retrieval of seismic source parameters. Finally, SeismAmp is trained on data from Japan, future studies could explore transfer learning for practical applications in other regions. [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 recent years, the power quality problem has become more complicated in power grids because of the extensive usage of power electronics and multisource multitransformation features. The method, based on physical characteristics such as time domain, frequency domain and transform domain, is facing challenges in terms of adaptability, algorithm efficiency and accuracy for the recognition of complex disturbance recognition. The bidirectional long short‐term memory network is an algorithm in deep learning. It is based on data for characterization learning, which can effectively overcome the problem of information loss and generalization ability of physical methods. Moreover, it has the characteristics of memory, which can simultaneously consider historical information and future information and can better learn data features with time series characteristics. Aiming at the transient voltage sag time series data, this paper proposes a recognition method of the voltage sag causes based on the bidirectional long short‐term memory network's extraction eigenvalue, the full‐connection layer's high‐dimensional feature extraction and the Softmax network layer's classification. The experiment uses simulation data and measured data to prove that the model has good recognition ability and good antinoise performance in the recognition of voltage sag causes and can be reliably applied in practical engineering. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

High‐fidelity segmentation of blood vessels plays a pivotal role in numerous biomedical applications, such as injection assistance, cancer detection, various surgeries, and vein authentication. Near‐infrared (NIR) transillumination imaging is an effective and safe method to visualize the subcutaneous blood vessel network. However, such images are severely blurred because of the light scattering in body tissues. Inspired by the Vision Transformer model, this paper proposes a novel deep learning network known as transformer connection (TRC)‐Unet to capture global blurred and local clear correlations while using multi‐layer attention. Our method mainly consists of two blocks, thereby aiming to remap skip connection information flow and fuse different domain features. Specifically, the TRC extracts global blurred information from multiple layers and suppresses scattering to increase the clarity of vessel features. Transformer feature fusion eliminates the domain gap between the highly semantic feature maps of the convolutional neural network backbone and the adaptive self‐attention maps of TRCs. Benefiting from the long‐range dependencies of transformers, we achieved competitive results in relation to various competing methods on different data sets, including retinal vessel segmentation, simulated blur image segmentation, and real NIR blood vessel image segmentation. Moreover, our method remarkably improved the segmentation results of simulated blur image data sets and a real NIR vessel image data set. The quantitative results of ablation studies and visualizations are also reported to demonstrate the superiority of the TRC‐Unet design. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Convolutional neural networks possess the capability of feature learning and nonlinear mapping, which has significant advantages in classifying and recognizing optical fiber vibration signals. In order to further enhance the recognition rate of vibration signals, this paper combines wavelet transform with convolutional neural networks and designs a convolutional layer based on parameterized wavelets. In this layer, the initial signal is convolved with parameterized Laplace wavelet dictionaries to complete the wavelet transform. Such customized filters make more sense than filters with randomly initialized parameters for traditional CNNs. Simultaneously, we introduce the channel attention mechanism to enhance the features of the filtered signals. Subsequently, standard CNNs are employed to extract and process signal features, ultimately utilizing a multi‐layer perceptron for recognition and classification. The experimental results show that the network model possesses better recognition refinement. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Millimeter wave MIMO wireless communication systems are deployed in 5G and next‐generation networks. The effectiveness of deep learning models for improving the performance of these systems has been proven in the literature. However, several deep learning models are vulnerable to security threats, such as adversarial attacks. Therefore, for the deployment of these systems, it is essential to make them resilient to such kinds of attacks for good quality secure communication. Adversarial training is a solution by which deep learning models are trained for adversarial attacks beforehand. Adversarial training for three types of adversarial attacks, that is, Fast Gradient Sign Method, Iterative Fast Gradient Sign Method, and Momentum Iterative Fast Gradient Sign Method is implemented in this paper. The simulation results depict a decrease in the error at the receiving end after adversarial training, even after an adversarial attack has been applied. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Early Japanese books, classical humanities resources in Japan, have great historical and cultural value. However, Kuzushi-ji, the old character in early Japanese books, is scratched, faded ink, and lost due to weathering and deterioration over the years. The restoration of deteriorated early Japanese books has tremendous significance in cultural revitalization. In this paper, we introduce augmented identity loss and propose enhanced CycleGAN for deteriorated character restoration, which combines domain discriminators and augmented identity loss. This enhanced CycleGAN makes it possible to restore multiple levels of deterioration in the early Japanese books. It obtains the high readability of the actual deteriorated characters, which is proved by higher structural similarity(SSIM) and accuracy of deep learning models than standard CycleGAN and traditional image processing. In particular, SSIM increases by 8.72%, and the accuracy of ResNet50 for damaged characters improves by 1.1% compared with the competitive CycleGAN. Moreover, we realize the automatic restoration of pages of early Japanese books written about 300 years ago. [ABSTRACT FROM AUTHOR]

Driven by deep learning, great breakthroughs had been made in the field of target detection. Small target detection algorithms were widely used in industry, agriculture and other fields. But the small target had few available features and the loss of small target detail information in feature extraction. So it led to the low accuracy of the small target detection algorithms. In this paper, we proposed DBF‐YOLO algorithm based on the classical YOLOV5. The classical YOLOV5 algorithm with high speed. The detection speed of the minimum model could reach 24 ms. However, the deep network structure led to the low detection accuracy of small targets. Our proposed DBF‐YOLO algorithm was an improvement on the problem of small target information being lost. The main contributions of this article were mainly: First, a shallow feature extraction network was introduced in P1 layer, more details of small targets could be well retained. Second, by adding the feature fusion network of shallow feature map and the detection output part in the FPN + PAN layers, the algorithm's accuracy and generalization ability were significantly enhanced. Compared to YOLOV5, the performance of the DBF‐YOLO algorithm was significantly improved. On the validation set, mAP@0.5 and mAP@0.5:0.95 were increased by 8.80 and 5.90%, respectively. Recall was increased from the initial 34.50–41.80%. Precision was increased from initial 44.20 to 50.70%. On the test set, mAP@0.5 and mAP@0.5:0.95 were increased by 6.40 and 3.90%, respectively. Recall was increased 5.10%. Precision was increased 6.60%. Experiments had shown that the improved algorithm achieved good results in accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Featured Application: Automatic Method for the detection of kanji characters within historical japanese "wasan" documents. Blob-detector-based kanji detection with deep learning-based kanji classification. "Wasan" is the collective name given to a set of mathematical texts written in Japan in the Edo period (1603–1867). These documents represent a unique type of mathematics and amalgamate the mathematical knowledge of a time and place where major advances where reached. Due to these facts, Wasan documents are considered to be of great historical and cultural significance. This paper presents a fully automatic algorithmic process to first detect the kanji characters in Wasan documents and subsequently classify them using deep learning networks. We pay special attention to the results concerning one particular kanji character, the "ima" kanji, as it is of special importance for the interpretation of Wasan documents. As our database is made up of manual scans of real historical documents, it presents scanning artifacts in the form of image noise and page misalignment. First, we use two preprocessing steps to ameliorate these artifacts. Then we use three different blob detector algorithms to determine what parts of each image belong to kanji Characters. Finally, we use five deep learning networks to classify the detected kanji. All the steps of the pipeline are thoroughly evaluated, and several options are compared for the kanji detection and classification steps. As ancient kanji database are rare and often include relatively few images, we explore the possibility of using modern kanji databases for kanji classification. Experiments are run on a dataset containing 100 Wasan book pages. We compare the performance of three blob detector algorithms for kanji detection obtaining 79.60% success rate with 7.88% false positive detections. Furthermore, we study the performance of five well-known deep learning networks and obtain 99.75% classification accuracy for modern kanji and 90.4% for classical kanji. Finally, our full pipeline obtains 95% correct detection and classification of the "ima" kanji with 3% False positives. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multi‐objective topology optimization with response surface approximation interpolating various topologies, which is derived by deep learning with training data in the actual design space. The response surface is constructed by means of autoencoder with the training data of geometrically diverse shapes of targeted devices, which can express the interpolations that combine the multiple characteristic structures. The global topology search is performed based on the gradient information of the response surface, by which we can obtain the initial shapes for level set optimization to efficiently obtain easily manufactured and excellent Pareto optimal solutions. The effectiveness of the proposed method is demonstrated by optimizing multi‐flux barriers in a synchronous reluctance motor for the objective functions of average torque and torque ripple. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

To ensure the speed, accuracy, and fairness of the building damage survey, a deep learning diagnostic support technique for crack damage that occurs around openings such as windows on the exterior finish is studied. Firstly, using training images based on fake crack images generated by Deep Convolutional Generative Adversarial Network (DCGAN), a deep learning model that can detect with high accuracy was constructed. Generally, in order to clarify the relationship between exterior finish damage and inter‐story drift ratio, it is necessary to analyze a wide range of intervening parameters. Secondly, this paper begins by focusing on "the relationship between exterior finish surface damage and the deformation of the exterior finish itself", and this relationship is investigated by an optical measurement method applied for the in‐plane shear force tests. Finally, to analyze the relationship between the deep learning model and the image acquisition conditions, outdoor camera shooting test of damaged specimens was conducted. [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]

With increasing amounts of wind power generation installed, the steep fluctuation of wind power generation output, called ramp events, causes serious problems for power system operation. Controlling fluctuations is an important issue for increasing the amount of wind power generation as a wind farm (WF) in the future. The authors reported the scheduled operation method of WF using a battery energy storage system (BESS) and forecast data of wind power generation output. In this paper, the authors propose a new scheduled operation method of WF. In particular, we propose the application of deep reinforcement learning to decide the output schedule of WF. Moreover, we compare the conventional method, the reinforcement learning method, and the deep reinforcement learning method in terms of the number of ramp events. In addition, we calculate the reducing effect of the storage capacity of BESS. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Accurate traffic volume prediction plays a crucial role in urban traffic control by relieving congestion through improved regulation of traffic volume. Network‐level traffic volume prediction and detector failure have rarely been considered in the literature. This paper proposes a framework based on long short‐term memory and the multilayer perceptron that can predict network‐level traffic volumes even with detector failure. A profile model learns the profile of the detector's signature (traffic pattern). Detectors with similar profiles are considered to have similar traffic patterns and are grouped into a cluster. Failed detectors can obtain reference information from similar detectors in the same cluster without additional information. A predictive model is developed for each cluster. The proposed method is validated using Japan Road Traffic Information Center data for three cities. The computational results indicate that the proposed method performs well both on typical days and atypical days (the COVID‐19 lockdown period and the 2021 Tokyo Olympics). Further, it considers detector reliability: the increase in mean absolute error is less than 1 veh/5 min when the probability of detector failure increases to 20%. [ABSTRACT FROM AUTHOR]

Rear‐lamp tracking at night is a research topic in night vision that is essential for Advanced Driver Assistance Systems (ADAS). Most current computer vision‐based methods address this problem using color features because rear lamps are lit during night driving. However, such methods are sufficiently robust in complex environments owing to the lack of feature diversity. On the other hand, as there is no off‐the‐shelf dataset, the application of deep learning to nighttime rear‐lamp tracking has been sparsely explored. To tackle the above issues, in this paper, we propose hierarchical rear‐lamp tracking at nighttime (H‐RTN) and create a novel dataset for rear‐lamp tracking. Specifically, the H‐RTN consists of a rough detection hierarchy (R‐hierarchy), an accurate detection hierarchy (A‐hierarchy), and an optimization hierarchy (O‐hierarchy). The R‐hierarchy determines the region of interest (ROI) containing the target rear lamps, the A‐hierarchy samples the rear‐lamp candidates in the ROI, and the O‐hierarchy selects the best pair of candidates as the final location of the target rear lamps. The experimental results show that H‐RTN outperforms the alternative existing methods. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper presents a deep neural network (DNN)‐based model to recognize fishing vessels. In Taiwan, the vast majority of small fishing vessels are not equipped with an automatic identification system (AIS). As a consequence, the staff in a fishing port administration become heavily loaded when monitoring and managing the fishing vessels accessing a port. The workload is expected to be eased using this work. For the first time in the literature, a captured fishing vessel image was converted to a 128‐dimensional embedding for recognition purposes. The presented model gave a false positive rate (FPR) as low as 1.13% and an accuracy up to 99.47% at threshold = 0.772379. Finally, all the performance metrics, namely, the true positive rate (TPR), the FPR, precision and accuracy, are actually functions of the threshold which can be specified by users to meet specific requirements. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The successful application of deep learning in bridge damage diagnosis relies on the assumption that the training and test data sets obey the same distribution. However, it is difficult to obtain labeled data of damage status for a bridge in using. Otherwise, it is difficult to apply a model trained with bridge A (source domain) to diagnose bridge B (target domain) because of the distribution discrepancy of data from different working environments or bridges. In response to these problems, motivated by transfer learning, a new bridge damage diagnosis method, namely, the multichannel domain adaptation deep transfer learning based method (MDADTL), is proposed in this paper. First, a CNN based multichannel multi‐scale feature extractor is introduced to extract features. Second, a multichannel domain adaptation module based on maximum mean discrepancy (MMD) is proposed for transfer learning, so that the learned features are domain‐invariant. Through the above process, MDADTL trained with labeled data obtained in the laboratory or the testing bridge is expected to diagnose other bridges with unlabeled data. Experiments prove the effectiveness and advancement of the proposed method. This exploration will promote the practical application of deep learning in bridge damage diagnosis. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper investigates a deep learning‐based wind‐forecasting model to establish an accurate forecasting model which can support the increasing growth of wind power generation. The wind forecasting means wind speed and direction forecasting at the same time. Proposed forecasting model consists of three‐dimensional convolutional neural network and deep convolutional long short‐term memory (3DCNN‐DConvLSTM), and forecasts the wind vector which expressed as time‐sequential images. DConvLSTM model learns spatiotemporal features from time‐series image data that represent a spatial and temporal change of wind speed and direction. The forecasting model combined of 3DCNN and DConvLSTM is effective to decrease training time, and forecasting error in comparison to the DConvLSTM model. Input of the forecasting model is wind speed and direction that is expressed as an image on 2D coordinate and uses the measured data by the Automated Meteorological Data Acquisition System (AMeDAS), Japan. Forecasting accuracy with one‐hour ahead and its usefulness of the proposed forecasting model is evaluated with simulation results for four seasons that is typical of Japan climate, and demonstrated by comparison with fully connected‐LSTM (FC‐LSTM), encoder‐decoder based 3DCNN (ED‐3DCNN), DConvLSTM, and persistent models. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Spotlight synthetic aperture radar (SAR) achieves a high azimuth resolution with long integration times. Meanwhile, the long integration times also cause defocused and smeared images of moving objects such as cruising ships This is a typical imaging mechanism for moving objects in Spotlight SAR images. Conversely, ships can be classified as stationary or moving from the amount of smearing, and this classification method is, in general, based on manual observation. This paper proposes an automatic method for detecting cruising ships using deep learning known as the "You Only Look Once (YOLO) v5 model", which is one of the frameworks of the YOLO family. In this study, ALOS-2/PALSAR-2 L-band Spotlight SAR images over the waters around the Miura Peninsula, Japan, were analyzed using the YOLO v5 model with a total of 53 ships' images and compared with Automatic Identification System (AIS) data. The results showed a precision of approximately 0.85 and a recall rate of approximately 0.89 with an F-measure of 0.87. Thus, sufficiently high values were achieved in the automatic detection of moving ships using the deep learning method with the YOLO v5 model. As for false detections, images of breakwaters were classified as ships cruising in the azimuth direction. Further, range moving ships were found to be difficult to detect. From the present preliminary study, it was found that the YOLO v5 model is limited to ships cruising predominantly in the azimuth direction. [ABSTRACT FROM AUTHOR]

The quality of training sample is an important factor for wind speed prediction using data‐driven approaches, such as deep learning. This paper proposes a novel local predictor based on dynamic time wrapper (DTW) as training sample adaptation for wind speed prediction. After analyzing the similarity of wind speed time series using dynamic time wrapper, a local predictor is applied to improve the quality of training sample. An evaluation index is firstly proposed for estimating the quality of selected training sample. To verify the effectiveness of the proposed method, a random forest model with local predictor based on dynamic time warping (RF‐DTWLP) is applied to predict the wind speed. The simulation results demonstrate the prediction of wind speed by local predictor based on DTW has higher prediction accuracy. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposes a novel integrated solution for real‐time power quality (PQ) disturbance analysis. Traditionally, the recognitions are based on the feature extraction and are implemented offline or on the advanced reduced instruction set computer machine/digital signal processor platforms. In this paper, the optimized deep belief network (DBN) analyzes the PQ signals by learning knowledge from raw data of signals directly, which maximizes the features of original signals, and runs on an embedded parallel computing platform (EPCP). In simulation studies, eight types of common PQ disturbances are divided into 17 classes of data frame models according to the PQ disturbances that may occur during a fixed period of time. These samples of data frame models are utilized to train and optimize the DBNs on a central server. Compared with the existing classifiers, the simulation results demonstrate that the proposed approach has higher accuracy and stronger robustness. Then, the optimized DBN is sent to EPCP, and the previous DBN model is updated. The EPCPs are used to recognize the signals of PQ disturbances from a real time digital system (RTDS). The proposed integrated solution has excellent performance regarding accuracy but is time consuming. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Pedestrians are essential component in traffic, and with the advent of intelligent transportation systems, conflicts over right‐of‐way between pedestrians and other traffic participants have become prevalent. Predicting pedestrian intentions and motions is crucial for autonomous driving and intelligent transportation. However, pedestrian prediction faces challenges such as the variability of pedestrian intentions and the flexibility of their motions, as well as the need for highly generalization capability and lightweight modeling on autonomous vehicles with limited computing power. To face these challenge, this paper proposes a Social Artificial Potential Field (Social‐APF) method for pedestrian trajectory prediction. The method combines target intention feature and surrounding obstacles features. We test the performance of Social‐APF on several public datasets. The results demonstrate that the Social‐APF method outperforms state‐of‐the‐art approaches on the public datasets in both lightweight and model accuracy. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

• This paper aims to explain the role of GEE in seismic anomalies detection. • Two recent powerful earthquakes in Japan (13 February and 20 March 2021) were discussed. • Time series of AOT, Chlorophyll and Ozone precursors deduced from GEE platform. • They were investigated using Median and LSTM methods. • Our satisfactory results show that we will see a significant leap forward in studies of earthquake precursors. Nowadays, satellite data is an appropriate and undeniable source for studying earthquake precursors due to their diversity, wide coverage, being up to date and low cost. Time series analysis of satellite data plays an important role in the process of detecting seismic anomalies in earthquake warning systems. But in order to reduce uncertainty during the seismic anomalies detection, it is necessary the use a variety of satellite data, although it leads to increase of data size and processing time. This paper aims to explain the role of Google Earth Engine (GEE) cloud platform in considerable progress of seismo-Lithospheric Atmospheric Ionospheric (LAI) anomalies detection in earthquake early warning systems. Among the different studied earthquakes, for example two recent powerful earthquakes in Japan (13 February and 20 March 2021) have been discussed. Deduced time series of three precursors (i.e. Aerosol Optical Thickness (AOT), Chlorophyll and Ozone) from GEE platform were investigated using Median method and a Long Short-Term Memory (LSTM) neural network to detect potentially seismo-LAI anomalies. Our satisfactory results show that with the addition of other various satellite data and also known predictors intelligent algorithms such as deep learning to GEE platform, we will see a significant leap forward in studies of earthquake precursors. [ABSTRACT FROM AUTHOR]

Deep learning models have been shown to perform well in terms of the radar-based precipitation nowcasting problem when compared with advection-based models. Yet, most of the existing literature has used locally trained models, and relatively little is known about how to construct deep learning–based nowcasting models applicable in wider domains. We conduct experiments for precipitation nowcasting using deep learning models spanning the region around Japan with the Japan Meteorological Agency radar precipitation dataset with 1-km spatial resolution and 5-min temporal resolution. We trained the model with radar data sampled from all over Japan, then applied transfer learning with regional data. Through this experiment, it is found that combining data from all over Japan is effective in improving the forecast performance of heavy precipitation with deep learning models. Significance Statement: This paper proposes a methodological improvement for the problem of short-term rainfall forecasting (nowcasting) using deep learning. We trained deep learning models with data sampled from all over Japan, then applied transfer learning with regional data. We compared the performance metrics of several modeling approaches and demonstrated that learning from heavy precipitation examples collected from wider domains improves the performance significantly. This finding suggests that there are a lot of commonalities in the time evolution of rainfall patterns at different geographical locations, which could be exploited to improve the model's performance. A future research topic could be to learn from a larger dataset, including precipitation datasets from other countries. [ABSTRACT FROM AUTHOR]

With the increasing availability of large datasets and improvements in prediction algorithms, machine-learning-based techniques, particularly deep learning algorithms, are becoming increasingly popular. However, deep-learning algorithms have not been widely applied to predict container freight rates. In this paper, we compare a long short-term memory (LSTM) method and a seasonal autoregressive integrated moving average (SARIMA) method for forecasting the comprehensive and route-based Shanghai Containerized Freight Index (SCFI). The research findings indicate that the LSTM deep learning models outperformed SARIMA models in most of the datasets. For South America and the east coast of the U.S. routes, LSTM could reduce forecasting errors by as much as 85% compared to SARIMA. The SARIMA models performed better than LSTM in predicting freight movements on the west and east Japan routes. The study contributes to the literature in four ways. First, it presents insights for improving forecasting accuracy. Second, it helps relevant parties understand the trends of container freight markets for wiser decision-making. Third, it helps relevant stakeholders understand overall container shipping market trends. Lastly, it can help hedge against the volatility of freight rates. [ABSTRACT FROM AUTHOR]

Since the appearance and evolution of earthquake ionospheric precursors are expected to show a nonlinear and complex behaviour, the use of nonlinear predictor models seems more appropriate. This paper proposes a new approach based on deep learning as a powerful tool for extracting the nonlinear patterns from a time series of ionospheric precursors. A Long Short-Term Memory (LSTM) network as a type of Recurrent Neural Network (RNN) was used to investigate 52 six-month time series, deduced from the three Swarm satellite (Alpha (A), Bravo (B) and Charlie (C)) measurements, including electron density (Ne), electron temperature (Te), magnetic scalar and vector (X, Y, Z) components, Slant and Vertical Total Electron Content (STEC and VTEC), for day and night periods around the time and location of a seismic event. This new approach was tested on a strong Mw = 7.1 earthquake in Japan on 13 February 2021, at 14:07:50 UTC by comparing the results with two implemented methods, i.e., Median and LSTM methods. Furthermore, clear anomalies are seen by a voting classification method 1, 6, 8, 13, 31 and 32 days before the earthquake. A comparison with atmospheric data investigation is further provided, supporting the lithosphere–atmosphere–ionosphere coupling (LAIC) mechanism as a suitable theory to explain the alteration of upper geolayers in the earthquake preparation phase. In other words, using multi-method and multi-precursor analysis applied to 52 time series and also to the orbit-by-orbit investigation, the observed anomalies on the previous day and up to 32 days before the event in normal solar and quiet geomagnetic conditions could be considered as a striking hint of the forthcoming Japan earthquake. [ABSTRACT FROM AUTHOR]

In order to provide guidance for demand‐side management and improve energy efficiency, the accuracy of residential electricity demand forecasting plays a significant role. Data‐driven methods and deep learning network methods have been proved an effective methods for time series forecasting. In the context, the current research work proposes a novel neural network model based on convolutional neural network (CNN)‐attention‐bidirectional long‐short term memory (BiLSTM) to predict residential energy consumption. The proposed approach combines CNN, attention mechanism, and BiLSTM, initially implementing CNN to extract the effective features of the original data. Subsequently, the extracted segments are employed as the input of attention‐BiLSTM to predict energy consumption, where the attention mechanism comprehensively considers the output of BiLSTM neurons and assigns weights to neurons at each timestamp. In this article, we evaluate the proposed method using household electricity consumption data. This paper selects different input timestamp lengths (10/60/120 min, respectively) to validate the model's performance. We compare evaluation indicators (root mean square error, mean absolute error, mean absolute percentage error [MAPE]) between the proposed method and the state‐of‐the‐art prediction methods. The experimental result shows that the proposed method achieves higher energy consumption forecasting accuracy and has the lowest average MAPE (3.7%). © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

There are various structures in the underground space, and the structures are managed as 3D data in underground geospatial information map system (UGIMS). However, there is a problem that takes a long time to load the data into a mobile app from UGIMS because the data are very large. In order to reduce the loading time of 3D data, the number of data vertices should be reduced within a range that can keep the shape of 3D object in a mobile app. In this paper, we try to reduce the number of vertices using a 3D Mesh Simplification method to reduce the loading time of data in a mobile app. The proposed method applies simplification by extracting refined vertex feature information using a deep learning encoder‐decoder based model and performing vertex clustering by grouping similar vertices together with the feature information using K‐means clustering. We were able to reduce the number of vertices by 30% compared with the previous data. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Deep residual network (ResNet), one of the mainstream deep learning models, has achieved groundbreaking results in various fields. However, all neurons used in ResNet are based on the McCulloch‐Pitts model which has long been criticized for its oversimplified structure. Accordingly, this paper for the first time proposes a novel dendritic residual network by considering the powerful information processing capacity of dendrites in neurons. Experimental results based on the challenging COVID‐19 prediction problem show the superiority of the proposed method in comparison with other state‐of‐the‐art ones. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper presents a deep neural network‐based technique to recognize pets' body movements. The motivation to develop the technique arises from the fact that there are an increasing number of animal lovers, say dog or cat lovers, who spend a tremendous amount of effort to take care of their beloved pets, and even to capture the pets' images in daily life and at particular memorable moments for sharing among friends. For illustrative purposes, the recognition model was trained and then tested using a number of cats' images. As it turned out, the model well recognized three body movements: eating, tail raising and yawn, with a recognition accuracy up to 99.45%. Using this recognition technique, pets' images can be captured automatically once specific movements are detected, and job as a pet photographer can be made easy accordingly. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In the detection of protein complexes, the completeness of a protein–protein interaction (PPI) network is crucial. Complete PPI networks, however, are not available for most species because experimentally identified PPIs are usually very limited. This paper proposes a deep learning based PPI predictor to construct a complete PPI network, from which protein complexes are detected using a spectral clustering method. For this purpose, the unknown PPIs are estimated by using a deep PPI predictor consisting of a semi‐supervised SVM classifier and a deep feature extractor of the convolutional neural network (CNN). Meanwhile, the similarities of gene ontology (GO) annotations contribute to protein interactions, and the differences of subcellular localizations contribute to negative interactions. Considering that, we pretrain the deep CNN feature extractor in a class of deep GO annotation and subcellular localization predictors using datasets from the type species, then transfer it to the PPI prediction model for fine‐tuning. In this way, we have a deep PPI detector enhanced with transfer learning of GO annotation and subcellular localization prediction. Experimental results on benchmark datasets show that the proposed method outperforms the state‐of‐the‐art methods. © 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 polysomnography (PSG) is the gold standard for diagnosing OSA. However, patients often have to wait long before receiving the costly PSG test in a hospital. Hence, low‐cost and easy‐to‐use portable screening tools were developed for predicting OSA. Based on our recent study, this paper presents a feature extraction method with multiple receptive fields applied to the input electrocardiography (ECG) signals of a model to improve its performance in OSA severity classification. This work also employs unsegmented ECG signals as input to keep all the advantages from our original approach. The proposed model achieves an overall accuracy of 59.49% for four‐level OSA severity classification, giving an improvement of approximately 2% compared to our original work. The effectiveness of the proposed method is demonstrated. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Since the large‐scale protein sequence data is available, applying deep neural networks to mine better features from the sequences becomes possible. Eukaryotic protein subcellular localization prediction which makes a contribution in many biology process, has used protein sequences in many automatic predicting methods. Moreover, gene ontology (GO) annotation has been shown to be helpful in improving the prediction accuracy of subcellular localization. However, experimentally annotated proteins are not always available. On the other hand, experimentally annotated proteins are available for certain species such as human, mouse, Arabidopsis thaliana, etc. It is highly motivated to perform deep learning of GO annotations on the available experimentally annotated proteins and to transfer it to subcellular localization prediction on other species. In this paper, we propose a deep protein subcellular localization predictor, consisting of a linear classifier and a deep feature extractor of convolution neural network (CNN). The deep CNN feature extractor is first shared and pre‐trained in a deep GO annotation predictor, and then is transferred to the subcellular localization predictor with fine‐tuning using protein localization samples. In this way, we have a deep protein subcellular localization predictor enhanced with transfer learning of GO annotation. The proposed method has good performances on the Swiss‐Prot datasets, when transfer learning using the protein samples both within and out species. Moreover, it outperforms the state‐of‐the‐art traditional methods on benchmark datasets. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Traditional modeling and optimization methods for high voltage equipment, specially, circuit breaker components and nozzles, require manual testing and improvement of a large number of parameters, and the efficiency is relatively low. The strong processing power of artificial intelligence technology in the identification and prediction of complex systems is an effective solution in such case. This paper presents the optimization approach of image recognition combined with deep learning for circuit breaker nozzle. The nozzle model was conducted using the high Mach flow model in a commercial software (COMSOL Multiphysics 5.4) to study the gas flow state and behavior during cold flow, and obtains Shock wave image recognition model of the nozzle chamber based on the convolutional neural network (CNN) method of multiscale layered features. On the basis of effective image recognition, combined with deep learning Convolutional Recurrent Neural Network (CRNN), the image sequence under different parameters is sent to the convolutional layer for feature extraction, and then the feature map is input into the loop. The prediction sequence is obtained through the layer, and finally the relationship between the kinematic parameters of the nozzle and the internal gas flow state is predicted through the prediction layer. Results indicated, according to the prediction of CRNN, the range of the throat length should be between 7 and 13 mm and the angle should be between 8 ∼ 15°. The presented method could be also used for similar materials and components, with certain universality. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Multifocus image fusion is a technique that can integrate the focus information of different source images into a single composite image. At present, most fusion methods still suffer from problems such as block artifacts, artificial edges, halo effects, ringing effects, and contrast reduction. To address these problems, a novel multifocus image fusion method based on a convolutional deep belief network is proposed in this paper. The convolutional operator can effectively extract the focus information of source images, and the focused features extracted from source images can effectively distinguish focused windows from defocused windows. After multiple rounds of training, the convolutional deep belief network model can establish an effective mapping between source images and a score map, which is essential to generate an accurate focus map. Then, the focus map is further modified using binary segmentation and small region filtering, and the final decision map for fusion is obtained. Finally, according to the weights provided by the final decision map, a final fusion image will be formed by fusing multiple source images. The experimental results show that the proposed method is superior to other existing fusion methods in terms of subjective visual effects and objective quantitative evaluation. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN), as one of the mainstream deep learning models, has achieved great success in image recognition. All neurons used in CNN are based on the McCulloch‐Pitts model, which is over‐simplified. To further improve CNN's learning capacity, this paper proposes a novel dendritic CNN (DCNN), which considers the nonlinear information processing functions of dendrites in a single neuron. The superiority of DCNN is confirmed based on four widely used image recognition tasks. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]