Application of Artificial Neural Networks for Power Load Prediction in Critical Infrastructure: A Comparative Case Study

This article aims to assess the effectiveness of state-of-the-art artificial neural network (ANN) models in time series analysis, specifically focusing on their application in prediction tasks of critical infrastructures (CIs). To accomplish this, shallow models with nearly identical numbers of trainable parameters are constructed and examined. The dataset, which includes 120,884 hourly electricity consumption records, is divided into three subsets (25%, 50%, and the entire dataset) to examine the effect of increasing training data. Additionally, the same models are trained and evaluated for univariable and multivariable data to evaluate the impact of including more features. The case study specifically focuses on predicting electricity consumption using load information from Norway. The results of this study confirm that LSTM models emerge as the best-performed model, surpassing other models as data volume and feature increase. Notably, for training datasets ranging from 2000 to 22,000 instances, GRU exhibits superior accuracy, while in the 22,000 to 42,000 range, LSTM and BiLSTM are the best. When the training dataset is within 42,000 to 360,000, LSTM and ConvLSTM prove to be good choices in terms of accuracy. Convolutional-based models exhibit superior performance in terms of computational efficiency. The convolutional 1D univariable model emerges as a standout choice for scenarios where training time is critical, sacrificing only 0.000105 in accuracy while a threefold improvement in training time is gained. For training datasets lower than 22,000, feature inclusion does not enhance any of the ANN model’s performance. In datasets exceeding 22,000 instances, ANN models display no consistent pattern regarding feature inclusion, though LSTM, Conv1D, Conv2D, ConvLSTM, and FCN tend to benefit. BiLSTM, GRU, and Transformer do not benefit from feature inclusion, regardless of the training dataset size. Moreover, Transformers exhibit inefficiency in time series forecasting due to their permutation-invariant self-attention mechanism, neglecting the crucial role of sequence order, as evidenced by their poor performance across all three datasets in this study. These results provide valuable insights into the capabilities of ANN models and their effective usage in the context of CI prediction tasks.publishedVersio

Hazards identification and risk assessment for UAV-assisted bridge inspections

Unmanned Aerial Vehicles (UAV) technology has found its way into several civilian applications in the last 20 years, predominantly due to lower cost and tangible scientific improvements. In its application to structural bridge inspection, UAVs provide two main functions. The first, being the most common, detect damage through visual sensors. The 2 D image data can be used to quickly establish a basic knowledge of the structure’s condition and is usually the first port of call. The second reconstructs 3D models to provide a permanent record of geometry for each bridge asset, which could be used for navigation and control purposes. However, there are various types of hazards and risks associated with the use of UAVs for bridge inspection, in particular, in a cold operating environment. In this study, a systematic methodology, which is an integration of hazard identification, expert judgment, and risk assessment for preliminary hazard analysis (PHA) in the UAV-assisted bridge inspection system is proposed. The proposed methodology is developed and exemplified via UAV-assisted inspection of Grimsøy bridge, a 71.3 m concrete bridge, located in the Viken county in eastern Norway.publishedVersio

Drone-based bridge inspection in harsh operating environment: risks and safeguards

The inability to effectively and systematically identify and measure the damage in bridges will lead to an acceleration and dangerous deterioration of the health state of these structures. To repair and replace the aging and damaged bridge infrastructures, and prevent catastrophic bridge collapse, there is an urgent need to develop reliable, innovative, and efficient approaches to the performance assessment and inspection of bridges. Unmanned Aerial Vehicles (UAVs), also knowns as drone, technology has found its way into a number of civilian applications including inspection in the last 20 years, predominantly due to lower cost and tangible scientific improvements. The intent of this paper is to map the current state-of-the-art drone-enabled bridge inspection practices and investigated their associated hazards and risks. This paper will integrate scenario prediction and, assess hazards as well as the social and environmental loss in the case of drone-enabled bridge inspection. Further, this paper will follow rather closely a three-phase process: hazard identification, hazard analysis, and hazard evaluation, all executed with qualitative data and methods by experts of a variety of fields, methodologies for recognition of the impact of cold operating environment on the performance of drone and drone -pilots, creative interpretation of the hazard factors of identifiable problems. The proposed Preliminary Hazard Analysis (PHA) is exemplified via drone-enabled inspection of Håkenby bridge, which is located in the Viken county, in the eastern part of Norway.publishedVersio

A digital information model framework for uas-enabled bridge inspection

Unmanned aerial systems (UAS) provide two main functions with regards to bridge inspections: (1) high-quality digital imaging to detect element defects; (2) spatial point cloud data for the reconstruction of 3D asset models. With UAS being a relatively new inspection method, there is little in the way of existing framework for storing, processing and managing the resulting inspection data. This study has proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS). Previous efforts focusing on visualisation functionality have focused on BIM and GIS as separate entities, which has a number of problems associated with it. This methodology has a core focus on the integration of BIM and GIS, providing an effective and efficient information model, which provides vital visual context to inspectors and users of the BMS. Three-dimensional GIS visualisation allows the user to navigate through a fully interactive environment, where element level inspection information can be obtained through point-and-click operations on the 3D structural model. Two visualisation environments were created: a web-based GIS application and a desktop solution. Both environments develop a fully interactive, user-friendly model which have fulfilled the aims of coordinating and streamlining the BMS process.publishedVersio

Automatic Crack Segmentation for UAV-assisted Bridge Inspection

Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.publishedVersio

Six RNA Viruses and Forty-One Hosts: Viral Small RNAs and Modulation of Small RNA Repertoires in Vertebrate and Invertebrate Systems

We have used multiplexed high-throughput sequencing to characterize changes in small RNA populations that occur during viral infection in animal cells. Small RNA-based mechanisms such as RNA interference (RNAi) have been shown in plant and invertebrate systems to play a key role in host responses to viral infection. Although homologs of the key RNAi effector pathways are present in mammalian cells, and can launch an RNAi-mediated degradation of experimentally targeted mRNAs, any role for such responses in mammalian host-virus interactions remains to be characterized. Six different viruses were examined in 41 experimentally susceptible and resistant host systems. We identified virus-derived small RNAs (vsRNAs) from all six viruses, with total abundance varying from “vanishingly rare” (less than 0.1% of cellular small RNA) to highly abundant (comparable to abundant micro-RNAs “miRNAs”). In addition to the appearance of vsRNAs during infection, we saw a number of specific changes in host miRNA profiles. For several infection models investigated in more detail, the RNAi and Interferon pathways modulated the abundance of vsRNAs. We also found evidence for populations of vsRNAs that exist as duplexed siRNAs with zero to three nucleotide 3′ overhangs. Using populations of cells carrying a Hepatitis C replicon, we observed strand-selective loading of siRNAs onto Argonaute complexes. These experiments define vsRNAs as one possible component of the interplay between animal viruses and their hosts

Uav-based bridge inspection via transfer learning

As bridge inspection becomes more advanced and more ubiquitous, artificial intelligence (AI) techniques, such as machine and deep learning, could offer suitable solutions to the nation’s problems of overdue bridge inspections. AI coupling with various data that can be captured by unmanned aerial vehicles (UAVs) enables fully automated bridge inspections. The key to the success of automated bridge inspection is a model capable of detecting failures from UAV data like images and films. In this context, this paper investigates the performances of state-of-the-art convolutional neural networks (CNNs) through transfer learning for crack detection in UAV-based bridge inspection. The performance of different CNN models is evaluated via UAV-based inspection of Skodsberg Bridge, located in eastern Norway. The low-level features are extracted in the last layers of the CNN models and these layers are trained using 19,023 crack and non-crack images. There is always a trade-off between the number of trainable parameters that CNN models need to learn for each specific task and the number of non-trainable parameters that come from transfer learning. Therefore, selecting the optimized amount of transfer learning is a challenging task and, as there is not enough research in this area, it will be studied in this paper. Moreover, UAV-based bridge inception images require specific attention to establish a suitable dataset as the input of CNN models that are trained on homogenous images. However, in the real implementation of CNN models in UAV-based bridge inspection images, there are always heterogeneities and noises, such as natural and artificial effects like different luminosities, spatial positions, and colors of the elements in an image. In this study, the effects of such heterogeneities on the performance of CNN models via transfer learning are examined. The results demonstrate that with a simplified image cropping technique and with minimum effort to preprocess images, CNN models can identify crack elements from non-crack elements with 81% accuracy. Moreover, the results show that heterogeneities inherent in UAV-based bridge inspection data significantly affect the performance of CNN models with an average 32.6% decrease of accuracy of the CNN models. It is also found that deeper CNN models do not provide higher accuracy compared to the shallower CNN models when the number of images for adoption to a specific task, in this case crack detection, is not large enough; in this study, 19,023 images and shallower models outperform the deeper models.publishedVersio

Evaluation of Tourism Effects on Rural Areas of Tourist village of the Central District of Firoozabad County Using Factor Analysis Model

This survey research was conducted with the aim of evaluating the effects of tourism development on rural tourism destination areas. The statistical population was composed of 437 households resided in central District of Firoozabad county from which, 155 households were selected by using Bartlett Table and simple random sampling method. The research instrument was a custom-made questionnaire. Factor analysis findings showed that in terms of the economic effects of the combination of 21 variables, four factors (economic opportunities, living expenses, employment and economic gap) explained 63.30 % of variances. In the field of social effects, factor loadings of the 33 items were produced seven factors (community consolidation, social harm and promotion of culture, cultural exchanges, acculturation, assimilation and changing lifestyle) and overall accounted for 65.646 % of variances. A combination of 27 physical- environmental items yielded four factors, health and sanitation problems, infrastructural effects, landscape degradation and natural environment change

Uav-based bridge inspection via transfer learning

As bridge inspection becomes more advanced and more ubiquitous, artificial intelligence (AI) techniques, such as machine and deep learning, could offer suitable solutions to the nation’s problems of overdue bridge inspections. AI coupling with various data that can be captured by unmanned aerial vehicles (UAVs) enables fully automated bridge inspections. The key to the success of automated bridge inspection is a model capable of detecting failures from UAV data like images and films. In this context, this paper investigates the performances of state-of-the-art convolutional neural networks (CNNs) through transfer learning for crack detection in UAV-based bridge inspection. The performance of different CNN models is evaluated via UAV-based inspection of Skodsberg Bridge, located in eastern Norway. The low-level features are extracted in the last layers of the CNN models and these layers are trained using 19,023 crack and non-crack images. There is always a trade-off between the number of trainable parameters that CNN models need to learn for each specific task and the number of non-trainable parameters that come from transfer learning. Therefore, selecting the optimized amount of transfer learning is a challenging task and, as there is not enough research in this area, it will be studied in this paper. Moreover, UAV-based bridge inception images require specific attention to establish a suitable dataset as the input of CNN models that are trained on homogenous images. However, in the real implementation of CNN models in UAV-based bridge inspection images, there are always heterogeneities and noises, such as natural and artificial effects like different luminosities, spatial positions, and colors of the elements in an image. In this study, the effects of such heterogeneities on the performance of CNN models via transfer learning are examined. The results demonstrate that with a simplified image cropping technique and with minimum effort to preprocess images, CNN models can identify crack elements from non-crack elements with 81% accuracy. Moreover, the results show that heterogeneities inherent in UAV-based bridge inspection data significantly affect the performance of CNN models with an average 32.6% decrease of accuracy of the CNN models. It is also found that deeper CNN models do not provide higher accuracy compared to the shallower CNN models when the number of images for adoption to a specific task, in this case crack detection, is not large enough; in this study, 19,023 images and shallower models outperform the deeper models