In this paper, we present the problem formulation and methodology framework of Super Resolution Perception (SRP) on smart meter data. With the widespread use of smart meters, a massive amount of ...electricity consumption data can be obtained. Smart meter data is the basis of automated billing and pricing, appliance identification, demand response, etc. However, the provision of high-quality data may be expensive in many cases. In this paper, we propose a novel problem - the SRP problem as reconstructing high-quality data from unsatisfactory data in smart grids. Advanced generative models are then proposed to solve the problem. This technology makes it possible for empowering existing facilities without upgrading existing meters or deploying additional meters. We first mathematically formulate the SRP problem under the Maximum a Posteriori (MAP) estimation framework. The dataset namely Super Resolution Perception Dataset (SRPD) is designed for this problem and released. A case study is then presented, which performs SRP on smart meter data. A network namely Super Resolution Perception Convolutional Neural Network (SRPCNN) is proposed to generate high-frequency load data from low-frequency data. Experiments demonstrate that our SRP models can reconstruct high-frequency data effectively. Moreover, the reconstructed high-frequency data can lead to better appliance identification results.
In Geological Carbon Sequestration (GCS), mineralization is a secure carbon dioxide (CO2) trapping mechanism to prevent possible leakage at a later stage of the GCS project. Modeling the ...mineralization mechanism during GCS relies on numerical reservoir simulation, but the computational cost is prohibitively high due to the complex physical processes. Therefore, deep learning (DL) models can be used as a computationally cheaper and more reliable at the same time, alternative to conventional numerical simulations. In this work, we have developed a DL approach to effectively predict the dissolution and precipitation of various essential minerals, including Anorthite, Kaolinite, and Calcite, during CO2 injection into deep saline aquifers. We have established a reservoir model to simulate the geological CO2 storage process. Seven hundred twenty-two numerical realizations were performed to generate a comprehensive dataset for training DL models. Two convolution neural networks (CNN), Fourier Neural Operator (FNO), and U-Net were trained. The trained models used reservoir and well properties along with time information as input and predicted the precipitation and dissolution of minerals in space and time scales. During the training process, root-mean-squared-error (RMSE) was used as a loss function. To gauge prediction performance, we have applied the trained model to predict the concentrations of different minerals on the test dataset, which is 15% of the entire dataset, and two metrics, including the average absolute percentage error (AAPE) and the coefficient of determination (R2), were adopted. The FNO model resulted in the R2 of 0.95 for the Calcite model, 0.94 for the Kaolinite model, and 0.93 for the Anorthite model. The U-Net model resulted in the R2 of 0.88 for the Calcite model, 0.89 for the Kaolinite model, and 0.912 for the Anorthite model. The model’s prediction CPU time (0.2 s/case) was much lower than that of the physics-based reservoir simulator (3600 s/case). Therefore, the proposed method offers predictions as accurate as our physics-based reservoir simulations while providing a substantial computational time acceleration.
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•A robust deep learning (DL) workflow is presented.•DL workflow can efficiently predict the spatial and temporal mineralization process.•DL workflow showed substantial acceleration compared to full numerical reservoir simulation.•Multi model approach enhanced the prediction performance of CO2 mineralization process.
This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the ...smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.
COVID-19 outbreak revealed fundamental weaknesses of current diagnostic systems, particularly in prediction and subsequently prevention of pandemic infectious diseases (PIDs). Among PIDs detection ...methods, wastewater-based epidemiology (WBE) has been demonstrated to be a favorable mean for estimation of community-wide health. Besides, by going beyond purely sensing usages of WBE, it can be efficiently exploited in Healthcare 4.0/5.0 for surveillance, monitoring, control, and above all prediction and prevention, thereby, resulting in smart sensing and management of potential outbreaks/epidemics/pandemics. Herein, an overview of WBE sensors for PIDs is presented. The philosophy behind the smart diagnosis of PIDs using WBE with the help of digital technologies is then discussed, as well as their characteristics to be met. Analytical techniques that are pushing the frontiers of smart sensing and have a high potential to be used in the smart diagnosis of PIDs via WBE are surveyed. In this context, we underscore key challenges ahead and provide recommendations for implementing and moving faster toward smart diagnostics.
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•The necessity of an inevitable transition from pure diagnostics towards smart diagnostics is discussed.•The impact of wastewater-based epidemiology assisted by digital technologies is highlighted.•Characteristics that should be met by analytical technologies to be exploited for the smart diagnosis of pandemic infectious diseases are underscored.•WBE biosensors that are pushing the frontiers of smart sensing with a high potential to be used in smart diagnosis are critically discussed.•Challenges ahead for moving faster toward smart diagnostics of pandemic infectious diseases are pointed out.
Introduction The present study conducted a secondary data analysis of a comprehensive survey from Child Guidance Centers in Japan to identify factors that are associated with child abuse severity in ...infancy (0–3 years, 1,868 cases) and preschool age (4–6 years, 1,529 cases). A predictive model for abuse severity was developed. Methods The data originated from a nationwide survey that was conducted in April 2013, consisting of details of abuse cases, including child characteristics, abuser attributes, and family situation. Abuse severity was assessed on a five-level scale (suspected, mild, moderate, severe, and life-threatening) that was converted into a binary outcome. Logistic regression analysis was used to create a predictive model using two-thirds of the data, which was validated with the remaining third of the data. Results and discussion As a result, in infancy, risks of severity increased with younger age of the abused child, physical abuse, neglect, witnessing domestic violence, and the involvement of Child Guidance Centers or hospitals in detection. The abuser's mental problems and cumulative child damage contributed to severity. For preschool age, similar factors applied, with additional risks that included abuse overlap and guardian separation. Cumulative abuser issues and child physical damage impacted severity. Validation yielded moderate prediction accuracy (areas under the curve: 0.703 and 0.714).
Abstract This paper introduces a method for analysing motion patterns that can be utilised to optimise data-driven systems. The aim is to use surveillance cameras and artificial intelligence to track ...multiple objects in a reliable manner, thereby preserving the authenticity of movement patterns for numerous and similar objects. In a case study, this method is applied to optimize lighting conditions in animal husbandry. Furthermore, this approach can be utilized not only in animal husbandry but also in other domains.
This paper discusses the concepts of aesthetic and nonaesthetic in streetscapes. The nonaesthetic refers to physical elements and spatial compositions that make the aesthetic emerge differently ...depending on the observer who sees them. Using Ashihara’s (1979) methodology for evaluating aesthetic townscapes and Sibley’s (1959) concept of aesthetic properties, this paper proposes a quantification of the nonaesthetic of streetscapes, focusing on streets in Ginza, Tokyo, and examining the spatial clustering of their nonaesthetic. For this, we used deep learning to quantify the distribution of physical elements and their spatial composition and a spatial clustering analysis to uncover hidden patterns in the streetscapes. These methodologies enabled us to assess streets across neighborhoods and districts and produce a comparative microscale analysis that covers a wider area. Thus, our study combines the fields of aesthetics, architecture, urban planning, and community design.
•Novel artificial intelligence identification tool for rare diseases registry.•Patient identification tool using big data analysis has 92.3% sensitivity.•The tool enables a centralized rare disease ...patient registry to be established.
Patient registries are crucial for rare disease management. However, manual registry construction is labor-intensive and often not user-friendly. Our goal is to establish Hong Kong’s first computer-assisted patient identification tool for rare diseases, starting with inborn errors of metabolism (IEM).
Patient data from 2010 to 2019 was retrieved from electronic databases. Through big data analytics, patient data were filtered based on specific IEM-related biochemical and genetic tests. Clinical notes were analyzed using a rule-based natural language processing technique called regular expression. The algorithm classified each extracted paragraph as “IEM-related” or “not IEM-related.” Pathologists reviewed the paragraphs for curation, and the algorithm’s performance was evaluated.
Out of 46,419 patients with IEM-related tests, the algorithm identified 100 as “IEM-related.” After pathologists’ validation, 96 cases were confirmed as true IEM, with 1 uncertain case and 3 false positives. A secondary ascertainment yielded a sensitivity of 92.3% compared to our previously published IEM cohort.
Our artificial intelligence approach provides a novel method to identify IEM patients, facilitating the creation of a centralized, computer-assisted rare disease patient registry at the local and national levels. This data can potentially be accessed by multiple stakeholders for collaborative research and to enhance healthcare management for rare diseases.
Plot sizes and farm-plot distances affect the economic performance of agricultural production. Their economic effects likely differ between conventional and organic farming systems due to major ...differences in crop production programs. Our paper quantifies these effects based on big data on resource requirements of field operations, summarized by regression models. Combined with detailed case study information obtained through interviews, we assess plot size and farm-plot distance effects for three case study farms which recently converted to organic farming. Our results show for both farming systems, as expected, that larger plot sizes reduce labor requirements and costs associated with crop production while larger farm-plot distances increase them. At same plot sizes and farm-plot distances organic farms face lower costs in crop production and, at given market prices, higher profits. Cost savings from larger plot sizes are, however, higher in conventional farming systems as are cost increases from growing farm-plot distances. This implies that economic benefits of conversion are higher for farms managing smaller plots farther away from the farm. Land fragmentation might hence favor switching to organic production and motivate regionally differentiated subsidy rates.
•Large-scale sensitivity analysis on effects of plot sizes and farm-plot distances•Application of the analysis results to conventional and organic case study farms.•Conventional farms benefit to a greater extent from large plots and small distances.•Economic benefits of conversion are greater in high fragmented land scape settings.•Organic farms face lower costs in crop production and higher labor requirements.
In this paper, we review some advances made recently in the study of
mobile phone datasets
. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized ...datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the
social networks
that can be constructed with such data, the study of
personal mobility
,
geographical partitioning
,
urban planning
, and
help towards development
as well as
security and privacy issues
.
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