Search

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]

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]

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]

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]

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]

• 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]

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]

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]

Automated fault detection and diagnosis (AFDD) plays a crucial role in enhancing the energy efficiency of air conditioning systems. Deep learning has emerged as a promising tool for image classification, and its application in the context of AFDD of HVAC systems is gaining traction due to its exceptional performance. However, the deployment cost of deep models in practical scenarios is increased due to the large number of parameters and the lack of interpretability. This paper focuses on improving the potential of deep learning models for AFDD in real HVAC systems. We use pruning to reduce the number of parameters in the model and use layer-wise relevance propagation (LRP) to improve the interpretability of the model. The case study builds a simulation model and 31 kinds of fault data sets based on the actual HVAC in Japan. Based on the findings, Without loss of accuracy, the pruning method can reduce the model size by more than 99 % and maintain 90% classification accuracy. The LRP score allows model users to find out the input data that most affects the results at each diagnosis, improving interpretability. • The study presents a solution for cutting costs of deep learning deployment in HVAC system AFDD. • The method combines pruning and LRP, reducing model size by over 99 • LRP score improves model interpretability by showing impactful input data on diagnosis results. • The study shows the potential of deep learning in HVAC AFDD, enhancing their practicality and usefulness. [ABSTRACT FROM AUTHOR]