Search

A faulty situation occurred in Service Oriented Architecture (SOA) based systems can affect its functionality. It is challenging to guarantee the reliability of service utilization in a distributed, concurrent and dynamic composition of web services. This paper aims to present a fault detection approach of SOA using dependency analysis. Data and control dependencies are identified and a fault diagnosis approach based on these dependencies is proposed in this paper. The presented approach extends discrete state space techniques to simulate the concurrent and distributed behavior through Colored Petri Nets (CPNs) for the SOA-based systems in case of the faulty situation. Concrete translation from SOA based system to a CPN model is presented. Model-based technique is used and some heuristics are presented for dependency analysis and fault detection purpose. The experimental performance analysis shows the feasibility of our proposed approach.

In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable.

A comprehensive review about machine learning application in power system especially in smart grid, renewable energy sector etc. is summarized in this paper. In the power sector, the power consumption is increased day by day very tremendously. So, it is very important that we have to generate the more power without disturbing the environment and whatever the generated power must be utilize effectively with minimum losses and higher efficiency. This will be possible with effective way of using the modern technology like machine learning (ML), artificial intelligence etc. This paper also describes the different types of machine learning techniques with diagram which will be very useful for many researchers who want to understand the basic fundamentals of machine learnings.

Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the performance and efficiency of smart grids (SGs). Integration of state-of-the-art machine learning algorithms and vision sensor networks approaches pave the way toward enhancing the wind farms performance. The generating power in a wind turbine farm is the most critical parameter that should be measured accurately. Produced power is highly related to weather patterns, and a new farm in a near area is also likely to have similar energy generation. Therefore, accurate and perpetual prediction models of the existing wind farms can be led to develop new stations with lower costs. The paper aims to estimate the angular velocity of turbine blades using vision sensors and signal processing. The high wind in the wind farm can cause the camera to vibrate in successive frames, and the noise in the input images can also strengthen the problem. Thanks to couples of solid computer vision algorithms, including FAST (Features from Accelerated Segment Test), SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), BF (Brute-Force), FLANN (Fast Library for Approximate Nearest Neighbors), AE (Autoencoder), and SVM (support vector machines), this paper accurately localizes the Hub and track the presence of the Blade in consecutive frames of a video stream. The simulation results show that determining the hub location and the blade presence in sequential frames results in an accurate estimation of wind turbine angular velocity with 95.36% accuracy. (C) 2021 The Authors. Published by Elsevier Ltd.

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: • A Bayesian model assessment which compares two sets of values using nested sampling. This approach is new in NF grouping. • A Arsinh-Ordered Weighted Average model (Arsinh-OWA) which applies the arsinh transformation to the distance between two NFs, and then rescales the result to the arsinh of a biologically relevant threshold before grouping using OWA based distance. This approach is new in NF grouping. • An x-fold comparison as used in the ECETOC NanoApp. • Euclidean distance, which is a highly established distance metric. The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.

Electricity load forecasting is an important part of power system dispatching. Accurately forecasting electricity load have great impact on a number of departments in power systems. Compared to electricity load simulation (white-box model), electricity load forecasting (black-box model) does not require expertise in building construction. The development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load simulation. Recent developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear characteristics. Based on the real load dataset, this paper evaluates and compares the two mainstream short-term load forecasting techniques. Before the experiment, this paper first enumerates the common methods of short-term load forecasting and explains the principles of Long Short-term Memory Networks (LSTMs) and Support Vector Machines (SVM) used in this paper. Secondly, based on the characteristics of the electricity load dataset, data pre-processing and feature selection takes place. This paper describes the results of a controlled experiment to study the importance of feature selection. The LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load forecasting. The predictive accuracy of these models are calculated based on the error between the actual and predicted loads, and the runtime of the model is recorded. The results show that the LSTMs model have a higher prediction accuracy when the load data is sufficient. However, the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized.

During the last decade, the Integrated Circuit industry has paid special attention to the security of products. Hardware-based vulnerabilities, in particular Hardware Trojans, are becoming a serious threat, pushing the research community to provide highly sophisticated techniques to detect them. Despite the considerable effort that has been invested in this area, the growing complexity of modern devices always calls for sharper detection methodologies. This paper illustrates a pre-silicon simulation-based technique to detect hardware trojans. The technique exploits well-established machine learning algorithms. The paper introduces all the background concepts and presents the methodology. The validity of the approach has been demonstrated on the AutoSoC CPU, an industrial-grade, safety-oriented, automotive benchmark suite. Experimental results demonstrate the applicability and effectiveness of the approach: the proposed technique is highly accurate in pinpointing suspicious code sections. None of the hardware trojans from the set has been left undetected.

There has been a huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationships with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding to different regions of interest (ROIs) and edges to connection strengths between ROIs. Due to the high-dimensionality and non-Euclidean nature of networks, it is challenging to depict their population distribution and relate them to human traits. Current approaches focus on summarizing the network using either pre-specified topological features or principal components analysis (PCA). In this paper, building on recent advances in deep learning, we develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer their relationships to human traits. We refer to our method as Graph AuTo-Encoding (GATE). We applied GATE to two large-scale brain imaging datasets, the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) for adults, to study the structural brain connectome and its relationship with cognition. Numerical results demonstrate huge advantages of GATE over competitors in terms of prediction accuracy, statistical inference, and computing efficiency. We found that the structural connectome has a stronger association with a wide range of human cognitive traits than was apparent using previous approaches. 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research [1U54MH091657]; McDonnell Center for Systems Neuroscience at Washington University; National Institutes of Health [U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, U01DA041025] Published version Data used in the preparation of this article were obtained from the Human Connectome Project (https://www.humanconnectome.org/) and Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org).The HCP WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) were funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The ABCD study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html.A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/scientists/workgroups/.ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This paper reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.; Dunson and Zhang want to acknowledge support from the National Institutes of Health (NIH) of the United States under award number MH118927. We also want to acknowledge the support from the NVIDIA Corporation with their donation of the Titan-V GPU.

Digital inspection twin (DIT) acting as a mirror between the physical and virtual measuring world. In this paper the measurement system with the CMMs ZEISS UMM 500 and DEA-IOTA-2203 was used as a physical twin, and a virtual machine, generated after modelling and configuring in PTC Creo software. Virtual measurement system is developed for inspection of the standard types of tolerances (concentricity, perpendicularity i.e.) and basic inspection features (IF) (plane, cylinder, truncated cone i.e.). The result of this paper is a new approach of the conceptual development of the digital inspection twin, based on CMMs - towards off-line DIT based on CMM by virtue of the control data list (CDL) and .ncl (DMIS) file.

Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. Since its introduction in 2003 for functional magnetic resonance imaging data, DCM has been extended to electrophysiological data, and several variants have been developed. Their biophysically motivated formulations make these models promising candidates for providing a mechanistic understanding of human brain dynamics, both in health and disease. However, due to their complexity and reliance on concepts from several fields, fully understanding the mathematical and conceptual basis behind certain variants of DCM can be challenging. At the same time, a solid theoretical knowledge of the models is crucial to avoid pitfalls in the application of these models and interpretation of their results. In this paper, we focus on one of the most advanced formulations of DCM, i.e. conductance-based DCM for cross-spectral densities, whose components are described across multiple technical papers. The aim of the present article is to provide an accessible exposition of the mathematical background, together with an illustration of the model's behavior. To this end, we include step-by-step derivations of the model equations, point to important aspects in the software implementation of those models, and use simulations to provide an intuitive understanding of the type of responses that can be generated and the role that specific parameters play in the model. Furthermore, all code utilized for our simulations is made publicly available alongside the manuscript to allow readers an easy hands-on experience with conductance-based DCM., NeuroImage, 245, ISSN:1053-8119, ISSN:1095-9572

A significant amount of previous research into feature selection has been aimed at developing methods that can derive variables that are relevant to an entire dataset. Although these approaches have revealed substantial improvements in classification accuracy, they have failed to address the problem of explainability of outputs. This paper seeks to address this problem of identifying explainable features using a class-specific feature selection method based on genetic algorithms and the one-vs-all strategy. Our proposed method finds relevant features for each class in the dataset and uses these features to enable more accurate classification, and also interpretation of the outputs. The results of our experiments demonstrate that the proposed method provides descriptive insights into prediction outputs, and also outperforms popular global feature selection techniques in the classifications of high dimensional and noisy datasets. Since there are no known challenging benchmark datasets for evaluating class-specific feature selection algorithms, this paper also recommends an approach for combining disparate datasets for this purpose.

It is becoming evident that, tomorrow's vehicles will be more electrified, automated, and connected respect to today's vehicles, which will make the future safer, cleaner, and more enjoyable. By now, several prototypes of self-driving cars have been built to demonstrate the concept of automated system and the usefulness of the system in situations like improving the traffic efficiency. However, tremendous issues still have to be addressed prior to achieve a mass production of high-level autonomous driving. One of those issues is the perception, which still dramatically expensive and hard to implement. More-over there are always the possibility that diverse components particularly sensing elements may fail. The paper presents the current state of the art and key progress made both in academia and industrial sector. A deep study carried out proposing and demonstrating the capability of low-cost sensor in covering needed area for collecting information for the safe control of the vehicle is elaborated. In addition, the paper proposed a fault detection approach based on the thresholding, whereas soft faults are projected as direct consequence. The model presented simplified the fault identification task by tracking whether the threshold is reached or not in critical area such as sensing components.

Citation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods.

The paper presents an application for virtual didactic laboratory dedicated for instrumentation classes using blockchain technology for saving students measurements data in order to ensure their uniqueness. The application is developed in Labview and it does both, the lab simulation and the data saving using blockchain technology in a local private wallet. The laboratory part is dedicated to a multimeter verification using an adjustable voltage source. The adjustable voltage source is based on a Feussner compensator and on a Weston standard. The blockchain part is connecting to the teacher private wallet and saves the data as a blockchain transaction every time when the student press the button. The application is delivered to the student as an executable program, so he does not know where and how the data is sent. Further, the lab part of the present application can be modified to use remote controlled instruments, except the comparator that can be replaced with a modern adjustable voltage source. The paper presents at the beginning the used blockchain technology, how the transaction are performed, how the traceability can be verified, how the data is organized within a transaction. Further, the lab part of the application is described. Each simulated instrument respects the construction error limits provided in their datasheets, while each individual run of the instrument will generate a uniquely random value within this interval. In the end some demo acquired data and discussions are provided.

To collect the handwritten format of separate Kurdish characters, each character has been printed on a grid of 14 × 9 of A4 paper. Each paper is filled with only one printed character so that the volunteers know what character should be written in each paper. Then each paper has been scanned, spliced, and cropped with a macro in photoshop to make sure the same process is applied for all characters. The grids of the characters have been filled mainly by volunteers of students from multiple universities in Erbil.

Research in Autonomous Driving is taking momentum due to the inherent advantages of autonomous driving systems. The main advantage being the disassociation of the driver from the vehicle reducing the human intervention. However, the Autonomous Driving System involves many subsystems which need to be integrated as a whole system. Some of the tasks include Motion Planning, Vehicle Localization, Pedestrian Detection, Traffic Sign Detection, Road-marking Detection, Automated Parking, Vehicle Cybersecurity, and System Fault Diagnosis. This paper aims to the overview of various Machine Learning and Deep Learning Algorithms used in Autonomous Driving Architectures for different tasks like Motion Planning, Vehicle Localization, Pedestrian Detection, Traffic Sign Detection, Road-marking Detection, Automated Parking, Vehicle Cybersecurity and Fault Diagnosis. This paper surveys the technical aspects of Machine Learning and Deep Learning Algorithms used for Autonomous Driving Systems. Comparison of these algorithms is done based on the metrics like mean Intersect in over Union (mIoU), Average Precision (AP)missed detection rate, miss rate False Positives Per Image (FPPI), and average number for false frame detection. This study contributes to picture a review of the Machine Learning and Deep Learning Algorithms used for Autonomous Driving Systems and is organized based on the different tasks of the system.

Paraphrase Generation is one of the most important and challenging tasks in the field of Natural Language Generation. The paraphrasing techniques help to identify or to extract/generate phrases/sentences conveying the similar meaning. The paraphrasing task can be bifurcated into two sub-tasks namely, Paraphrase Identification (PI) and Paraphrase Generation (PG). Most of the existing proposed state-of-the-art systems have the potential to solve only one problem at a time. This paper proposes a light-weight unified model that can simultaneously classify whether given pair of sentences are paraphrases of each other and the model can also generate multiple paraphrases given an input sentence. Paraphrase Generation module aims to generate fluent and semantically similar paraphrases and the Paraphrase Identification system aims to classify whether sentences pair are paraphrases of each other or not. The proposed approach uses an amalgamation of data sampling or data variety with a granular fine-tuned Text-To-Text Transfer Transformer (T5) model. This paper proposes a unified approach which aims to solve the problems of Paraphrase Identification and generation by using carefully selected data-points and a fine-tuned T5 model. The highlight of this study is that the same light-weight model trained by keeping the objective of Paraphrase Generation can also be used for solving the Paraphrase Identification task. Hence, the proposed system is light-weight in terms of the model’s size along with the data used to train the model which facilitates the quick learning of the model without having to compromise with the results. The proposed system is then evaluated against the popular evaluation metrics like BLEU (BiLingual Evaluation Understudy):, ROUGE (Recall-Oriented Understudy for Gisting Evaluation), METEOR, WER (Word Error Rate), and GLEU (Google-BLEU) for Paraphrase Generation and classification metrics like accuracy, precision, recall and F1-score for Paraphrase Identification system. The proposed model achieves state-of-the-art results on both the tasks of Paraphrase Identification and paraphrase Generation.

In modern times, obesity has become a significant threat all over the world. Obesity means an unnatural or excessive amount of fat that is present in our bodies. People are constantly moving towards an unhealthy lifestyle, eating excessive junk food, late-night sleep, spend a long time sitting down. Especially, adolescents are being affected because of their unconscious attitudes. It is a medical problem known as a very complex disease. It promotes the spread of complex illnesses, stroke, heart disease, liver cancer. Consequently, as an aware multitude of Bangladesh, we have to move forward to prevent this risk of obesity. The purpose of this paper is to move towards a machine-learning-based pathway for predicting the risk of obesity using machine-learning algorithms. The great thing about this paper is that people will know the risk of obesity and the reasons behind their obesity. We collect more than 1100 data from many varieties of people of different ages and collect information from both are suffering obesity and non-obesity. For this research, we apply nine prominent machine learning algorithms. We used the algorithm of k-nearest neighbor (k-NN), random forest, logistic regression, multilayer perceptron (MLP), support vector machine (SVM), naive Bayes, adaptive boosting (ADA boosting), decision tree, and gradient boosting classifier, and we have measured the performance of each of these classifications in terms of some prominent performance metrics. From the experimental results, we determine the obesity of high, medium, and low. The Logistic Regression Algorithm achieves the highest accuracy of 97.09% as compared to the other classifiers. In addition, the gradient boosting algorithm gave the poorest accuracy of 64.08% as well as the lowest metric values.

A primary challenge in goods distribution using Electric Commercial Vehicles (ECVs) pertains to tackling their limited driving range. This paper proposes a multi-faceted approach towards increasing the driving range of ECVs by coordinating the options of: (i) intra-route recharging at an intermediate Recharging Station (RS), with (ii) synchronised en-route battery swapping services performed by Battery Swapping Vans (BSVs) at a pre-planned rendezvous time and space. We introduce and solve a variant corresponding to an Electric Vehicle Routing Problem with Time Windows, RSs and Synchronised Mobile Battery Swapping (EVRPTW-RS-SMBS). In the proposed model, route planning is carried out synchronously for two interdependent fleets, i.e., ECVs and BSVs, which work in tandem to complete the delivery tasks. To address methodological complications arising from the simultaneous consideration of intra-route recharging at RSs and the synchronised battery swapping on-the-fly, the paper develops a pre-optimisation procedure based on a Non-Dominated Path Identification (NDPI) algorithm that is used in deriving a significantly strengthened path-based formulation of the problem, and an efficient dynamic programming based heuristic algorithm. To gain practical insights on the economic and environmental added value and viability of the proposed logistics model, we compare different scenarios for goods distribution using ECVs in urban and regional levels in London and Southeast England, respectively. A set of numerical experiments are further performed to demonstrate the efficiency of the proposed algorithms. Our results indicate significant cost and emissions savings and an opportunity for going beyond last mile local deliveries using ECVs with the proposed logistics model.

The rapid development of technology is leading to the emergence of smart factories where the Artificial Intelligence paradigm of deep learning plays a significant role in processing data streams from machines. This paper presents the application of Augmented Attention Blocks embedded in a deep convolutional neural network for the purposes of estimating the state of remote machines using remotely collected acoustic data. An Android application was developed for the purposes of transferring audio data from a remote machine to a base station. At the base station, we propose and developed a deep convolutional neural network called MAABL (MobileNetv2 with Augmented Attention Block). The structure of the neural network is constructed by combining an inverted residual block of MobileNetv2 with an augmented attention mechanism block. Attention Mechanism is an attempt to selectively concentrate on a few relevant things, while ignoring others in deep neural networks. Due to the presence of audio frames containing silent features not relevant to the task at hand, an Attention Mechanism is particularly important when processing audio data. The MAABL network proposed in this paper obtains the state of the art results on the accuracy and parameters of three different acoustic data sets. On a relatively large-scale acoustic dataset regarding machine faults, the method proposed in this paper achieves 98% accuracy on the test set. Moreover, after using transfer learning, the model achieved the state of the art accuracy with less training time and fewer training samples.

[EN] Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified., This work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0) ' (RTI2018-101344-B-I00) , the European Union H2020 research and innovation programme with grant agreement No. 825631 "Zero Defect Manufacturing Platform (ZDMP) " and the European Union H2020 research and innovation programme with agreement No. 958205 "In-dustrial Data Services for Quality Control in Smart Manufacturing (i4Q) ".

Fuchsia is the project name for a “modular, capability-based” operating system currently being developed by Google. There is speculation that Fuchsia may be a successor to the Android OS or a replacement for several other operating systems currently supported by the organisation. This paper examines the file systems found in this operating system and provides a breakdown of the content and structure of the unique volume manager and other partitions found on system. The findings outlined in this paper should allow digital investigators to expedite their understanding of the underlying data found on the platform. This paper also highlights how the zxcrypt encryption subsystem may inhibit the ability of practitioners to carry out an investigation of the MinFS partition. As Fuchsia is still in development, these findings are reliant on there not being significant changes made to structure of partitions examined. There remain unanswered questions regarding the content of the BootFS disk image found in the ZIRCON partition and the structure of entries within the Slice Allocation Table in the FVM.

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings, (2) the detection of anomalies from data reported by sensors, and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

As a disruptive technology, the blockchain is continuously finding novel application contexts, bringing new opportunities and radical changes. In this paper, we use blockchain as a communication infrastructure to support multi-party business processes. In particular, through smart contracts specifically generated by the mentioned business process, it is possible to derive a trustable infrastructure enabling the interaction among parties. Moreover, the emergence of different blockchain technologies, satisfying different characteristics, gives the possibility to support the same business process dealing with different non-functional needs. In this paper, we propose a novel engineering methodology supported by a practical framework called Multi-Chain. It permits to derive, using a model-driven strategy, a blockchain-based infrastructure, that can be deployed over a specific blockchain technology (e.g., Ethereum or Hyperledger Fabric). The objective is to permit the single definition and multiple deployments of the business process, to deliver the same functionalities, but satisfying different non-functional needs. In such a way, organisations willing to cooperate can select the multi-party business process and the blockchain technology they would like to use to satisfy their needs. Using Multi-Chain, they will be able to automatically derive from a Business Process Modelling Notation (BPMN) choreography diagram a blockchain infrastructure ready to be used. This overcomes the need to get acquainted with many details of the specific technology.

In view of most current studies on text sentiment classification focus on the deep learning model to obtain the sentimental characteristics of English text. Chinese text sentiment analysis is rarely involved, and only the context information of the statement is considered, but the syntax information of the statement is rarely considered. In this paper, a novel sentiment classification model is proposed (Dependency Tree Graph Convolutional Network, DTGCN) combined Chinese syntactically dependent tree with graph convolution. Firstly, the Bi-GRU (Bi-directional Gated Recurrent Unit) model is used to learn the contextual feature representation of a given text. Secondly, the syntax-dependent tree structure of a given text is constructed, then obtain its adjacency matrix according to the syntax-dependent tree, with the initial features extracted from the bidirectional gate control network, input into the graph convolutional neural network (GCN) to extract the sentimental features of the text; the obtained sentimental characteristics are then input into the classifier SoftMax for text sentimental polarity classification. Finally, the data set is compared with the mainstream neural network model. The experimental results show that the accuracy of the proposed DTGCN model proposed on the data set is 90.51% and the recall rate is 90.34%. Compared with the benchmark models (LSTM, CNN, TextCNN and Bi-GRU), the proposed DTGCN model shows a 4.45% advantage in accuracy. It shows that the proposed DTGCN model can effectively use the grammatical information of Chinese text to mine the hidden relationship in statements, it can improve the accuracy of Chinese text sentiment classification. In addition, the proposed DTGCN model not only improves the performance of sentiment classification in the essay, it also provides a new research method for social network public opinion identification.

This paper reviews the safety monitoring systems, bridge safety assessment methods, and hesitant fuzzy set theories at home and abroad, and presents a novel bridge safety monitoring and evaluation method based on hesitant fuzzy set, which has greatly significance to scientific research. Through multi-scale estimation and Butter Worth (BWorth) filtering, the structural response signals were identified and extracted under car load and crowd load. Then, a mathematical model was established for bridge safety monitoring and evaluation based on hesitant fuzzy set. Drawing on hesitant fuzzy similarity theory, the authors designed damage recognition algorithms based on single index, dual-index linkage, and multi-index linkage. Finally, example analysis was conducted to confirm the feasibility of multi-index linkage, coupled with multi-period dynamic recognition, in the evaluation of bridge structural damages.

Visualization of the knowledge extraction process is a front line to reveal the detail process and data structure, which is an advanced technique for the presentation of data modeling. However, the mechanisms for healthcare are challenging and dynamic processes to gain a clear insight or understanding of patient care. In this paper, we proposed a new approach of architecture and optimization of data mining modeling for visualization of knowledge extraction by analyzing clinical data sets to define the determinant attributes through modeling techniques. Therefore, architecture for the visualization of the knowledge extraction process is a systematic approach to support users to the best of their knowledge of the issues over the challenge of visualizing techniques. The proposed approach is capable and dynamic to handle and analyze large-scale data in its dimension and context. Such a variable is defined using various techniques to characterize them towards the detection of determinant variables as its influential circumstance. We focused on modeling based visualization as model representation, factor's interaction and integration. The detection process experimented in a different approach and justification as discussed in section five. The finding showed a deep understandability for an advanced and dynamic data mining modeling techniques to integrate applications with domain contexts for the optimal and understandable decision process. The strength of this approach is the depth for visualization towards the knowledge extraction process and its understandability for users as per their background and circumstances. It is also essential to inference for architecture based modeling and visualization for large scale data. Researchers, physicians, experts, and other users are the potentials to refer to these novel ideas and findings.

Robot simulation is an important way to verify the correctness of model and feasibility of design. Taking the Delta parallel mechanism as the effector of a weeding robot, this paper designs the workspace for the weeding mechanism in the field of corn seedlings, when each corn seedling has three to five leaves, and then carries out a simulation analysis on the workspace of the weeding mechanism. To realize variable rate precision spraying, the authors planned a three-segment gate-shaped trajectory for the end effector, detailed the trajectory under the joint coordinate system, and analyzed the motion law of S-curve motion profile. Then, the law of each kinematics parameter was solved on MATLAB, and plotted into graphs. Based on the simulation results, a prototype was developed for the proposed weeding mechanism, and subject to weeding experiments to obtain the end effector trajectory. Compared with the simulated results, the maximum deviation in the end effector trajectory during actual operation was 11.58%; the prototype could complete a weeding action every 2.8 s, while advancing at the speed of 0.4 m/s. The results fully demonstrate the feasibility of applying Delta parallel mechanism in weeding, and the reasonability of our workspace design and trajectory planning.

Change detection is the amount of changes that can guide to more concrete understandings into essential method concerning land cover, land usage and ecological variations. This paper deals with an intelligent methodology to optimize the solution out of the solution space that improves the efficiency of the change detection process. To support the theme, an integrated semi-supervised method is designed with a focus on image fusion, semi supervised clustering and binary swarm based optimization. A new approach of handling fusion using sparse coding is referred to expand the amount of information. Using the extracted information, change detection process is carried out by a constrained clustering technique to provide a solution reflecting the level of changes occurred in the region of investigation. To improve the accuracy by proposing a global optimum solution, still the result is refined through binary swarm based optimization process and hence the results are accelerated towards the increased level of accuracy followed by which the change map is reconstructed to show case changes prominently. To determine the accurateness of the proposed methodology quantitative and qualitative analysis has been done with different datasets. The proposed method has been evaluated with existing techniques such as k-means, AKM, FCM, ECKM and ASCC to show the efficiency and proved to be the preeminent change detection methodology compared to the state-of-art methods.

The ever increasing active and reactive power demands, along with limited sources of generation and delays in transmission expansion projects, have led many power systems to operate near their voltage stability limits. In this context, voltage stability monitoring methodologies have become an important topic in power systems research. This paper presents a novel methodology for long-term voltage stability monitoring in power systems that exploits the feasibility of phasor-type information in order to estimate the long-term voltage stability status. The information regarding the current system condition is acquired through synchronized phasor measurements and the power system is divided in sub-areas for improving its supervision; then, an artificial intelligence approach based on kernel extreme learning machine is used for long-term voltage stability assessment. The proposed scheme allows foreseeing the voltage instability caused by limitations in reactive power transmission, and it also permits alerting when a system area experiences a deficit of reactive power from supply sources. The validation of the proposed method is performed on the 39-bus test system, obtaining feasible results. The tests confirmed that the proposed method works properly under different scenarios and system conditions, always ensuring proper voltage stability status results independently of its cause.

Over the last decade, virtualization technologies have seen unprecedented growth in the system-level domain and promptly have moved to the embedded system domain. While there are numerous applications of virtualization for off-the-shelf hardware such as security, sandboxing, testing tools, etc. However, there are few open-source virtualization solutions available for embedded systems. For real-time virtualization, good runtime performance is a deterministic factor for its practical use in the embedded domain. In this paper, the internal architecture of HTTM (an open-source type-2 hypervisor for MIPS64 architecture) was thoroughly analyzed. ISA virtualization unit and execution cycle control were two primary units that were profiled and explored for optimization. Dynamic Binary Translation (DBT) unit was modified to generate highly efficient code. While the reduction in switching between the translated code and management layer improved the overall execution cycle. The implementation of these strategies resulted in a 72–91% improvement in bandwidth benchmarks. Similarly, latency benchmarks show a 2–92% improvement from its vanilla version. Collectively producing an overall 1–5 times the improvement in execution time. The performance of optimized HTTM is also compared with Quick Emulator (QEMU). HTTM performs 44–80% better than QEMU in bandwidth benchmarks while QEMU performs better in latency operations.

Multimedia event classification has been one of the major endeavors in video event analysis. For event identification, feature extraction plays a critical role, merely distinguishing the right features becomes a challenging job. So, in this paper, feature extraction and selection for video event detection are proposed. For video event detection or classification, identification of object structure and its motion are a basic needs. So, for object detection, curvelet features are considered, due to its high directional selectivity and high anisotropic properties. Next, an algorithm for Shot Boundary Detection (SBD) called Motion based SBD (MSBD) is proposed to identify the shot boundaries. Also, to make the users to access the queried event in less time, an algorithm for representing few representative frames containing whole video content is proposed. To make an event search efficient, object-based features from dominant features are extracted and to enhance the features much more efficient and dominant, feature selection is performed using the ranking method. Lastly, an SVM classifier with RBF kernel is used for event classification. The proposed work is experimented using Columbia Consumer Video (CCV) dataset and evaluated using mean Average Precision (mAP) and find that, it outperforms various other existing methods.

The excessive growth of high dimensional big data has resulted in a greater challenge for data scientists to efficiently obtain valuable knowledge from these data. Traditional data mining techniques are not fit to process big data. Predictive analytics has grown in prominence alongside the emergence of big data. In this paper, an efficient predictive analytics system for high dimensional big data is proposed by enhancing scalable random forest (SRF) algorithm on the Apache Spark platform. SRF is enhanced by optimizing the hyperparameters and prediction performance is improved by reducing the dimensions. The effectiveness of the proposed system is examined on five real-world datasets. Experimental results demonstrated that the proposed system achieves the highly competitive performance compared with RF algorithm implemented by Spark MLlib.

Digital transformation of manufacturing is a hot topic among strategic managers of manufacturing companies. The crux of digital transformation lies in the digitalization of manufacturing supply chain (MSC). However, the digital transformation of the MSC is highly uncertain, owing to the dynamic and complex changes of its nodes and structure in response to growing customer demand and fierce market competition. To propel the MSC digital transformation, it is crucial to effectively identify and predict the risk factors in the course of digital transformation. Therefore, this paper attempts to help manufacturing companies in China to successfully switch to a digital MSC. Firstly, the risk sources of the MSC digitization were identified, and complied into an evaluation index system for the digital transformation of the MSC. Next, the principal component analysis (PCA) was performed to reduce the dimension of the original data by revealing the three key principal components, and then the characteristic parameters of risk prediction are selected, so as to simplify the structure of neural network and improve the speed and efficiency of network training. On this basis, a backpropagation neural network (BPNN) was constructed for predicting the risks in MSC digitization. The results of training the model based on some data show that the proposed BPNN model has a good predictive effect. Furthermore, our model was compared with the traditional artificial neural network (ANN) model on a test set. The comparison demonstrates that our model achieved better effect than the traditional model in risk prediction. The results also show that the selected three principal components are reasonable, and the evaluation index system is valuable. The research results provide new insights to the smooth digital transformation of the MSC.

Sentiment analysis SA is concerned with determining users’ opinions from text reviews. However, mining these opinions from massive amounts of unstructured and lengthy documents is a challenging task. In this paper, we propose methods that extract valuable opinions and values from online movie reviews to improve SA in Arabic. First, we propose a method that explores the role of n-gram and skip-n-gram models in opinion classification. Second, we study a method that exploits subjective words such as adjectives and nouns by applying Part-Of Speech tagging. Both of the methods are combined with a feature reduction technique to enhance SA results. Third, we present a method that seeks to extract relevant opinions such as review summaries and conclusion opinions. Then, a combined approach is proposed to augment opinion classification results. Forth, we introduce a method for analyzing customers’ opinions by determining factors impacting their attitudes based on the costumer value model. Experimental results conducted on two datasets prove that our proposed methods are effective and provide better scores than baseline sentiment classifiers. The best obtained classification results reached 96% in F-Measure. These results indicate also that the aesthetic factor is the most influent factor in Arabic movie reviews.

Intrusion detection acts as a vital function in providing information security, and additionally the key technology is to precisely classify diverse attacks. Intrusion detection system (IDS) is identified as an important security issue within the cloud network environment. In this paper, IDS is given based on an innovative optimized custom RC-NN (Recurrent Convolutional Neural Network) which is proposed for intrusion detection along with the Ant Lion optimization algorithm. By this method, CNN (Convolutional Neural Network) is made hybrid with LSTM (Long Short Term Memory). Thus, all the attacks identified with the network layer of cloud are classified efficiently. The experimental results shown below describe the presentation of the IDS classification model with high accuracy, thus improving the detection rate or error rate. The optimized custom RC-NN-IDS model thus achieved an improved classification accuracy of 94% and also a decreased error rate of 0.0012. Additionally true positive rate, true negative rate and precision are considered as performance metrics. The proposed approach is evaluated using the DARPA IDS evaluation Data Sets and CSE-CIC-IDS2018 dataset and is compared with some existing approaches.

High Blood Pressure (BP) is one of the most dangerous and widespread diseases due to which uncontrolled BP increases the risk of health problems that affects many body organs. Unfortunately, accurate BP measurements require several medical devices and a specialist person who has experience in BP measurement and measured in separate times. Thus, this paper proposes a simple calibration-free method to estimate BP by training BP and Photoplethysmography (PPG) data signals on a machine learning (ML) regression model. The proposed method overcomes drawbacks of BP measurement accuracy and provides enough capability for reliable and calibration-free BP estimation. The obtained results clarify that the error standard deviation (STD) is about 5.3 and 6.4 mmHg of systolic pressure (SP) and diastolic pressure (DP), respectively. In addition, the mean absolute error (MAE) is about 4.2 and 4.5 mmHg of SP and DP, respectively. These results achieve grade “A” for both SP and DP based on the Britain Hypertension Society (BHS) standard. Finally, the results of BP estimation regression models meet the International Organization for Standardization (ISO) requirements for non-invasive BP devices and consequently they can be utilized later in life experiments.

The consensus problem of network-based heterogeneous multi-agent systems considering communication network induced time delays is evaluated in this paper. In this regard, two efficient adaptive event-triggered based approaches are developed for multi-agent systems featuring communication network delays. Moreover, an event-triggered interval is considered as a specific delay and considered into the unified round trip time (RTT) delay. Subsequently, an efficient optimal predictive-based coordination control scheme is proposed to compensate the non-ideal behaviour of communication channels with induced delays and realize the coordination control among the agents. Furthermore, some sufficient and necessary conditions are derived for the system stability as well as the state consensus. To evaluate and verify the feasibility and capability of the suggested approach it is reflected on a network-based i multi-agent system with coupled subsystems. The obtained simulation results confirm the useability and efficiency of the developed approach to enhance the performance of the network-based multi-agent systems featuring non-ideal communication channels.

This paper describes the development of an integrated approach for water pipe network calibration and lea quantification. The proposed approach provides a practical procedure that merges field measurements and mathematical modeling for leak quantification and network calibration. The use of Evolutionary Algorithms (EA) using both Genetic Algorithms (GA) and Shuffled Complex Evolution – University of Arizona (SCE-UA) to calculate the pinpointed leak outflows well as to identify any faulty meters. Also, by treating pinpointed physical leaks as junction points, the model, via EPAnet connection, calibrates the network and calculates their demands (which is equivalent to physical losses) and estimating pipes roughness factor (CHW) for better network calibration. Besides, throughout the process, the developed model uses GA and SCE-UA but departs from traditional use in using floating-point representations and introducing a new self-adaptive constraint handling function, which yields optimal results in fewer generations (model runs). Two pilot networks are selected, a well-known hypothetical network (Hanoi network) to present the general mathematical model, while a real network (Faisal city network) is used to test the integrated approach. The model integrates EPAnet for the required hydraulic modeling during the simulation and GIS for input data integration and output representation during the whole procedure. The results prove the approach's accuracy and efficiency.

The efficient segmentation of customers of an enterprise is categorized into groups of similar behavior based on the RFM (Recency, Frequency and Monetary) values of the customers. The transactional data of a company over is analyzed over a specific period. Segmentation gives a good understanding of the need of the customers and helps in identifying the potential customers of the company. Dividing the customers into segments also increases the revenue of the company. It is believed that retaining the customers is more important than finding new customers. For instance, the company can deploy marketing strategies that are specific to an individual segment to retain the customers. This study initially performs an RFM analysis on the transactional data and then extends to cluster the same using traditional K-means and Fuzzy C- Means algorithms. In this paper, a novel idea for choosing the initial centroids in K- Means is proposed. The results obtained from the methodologies are compared with one another by their iterations, cluster compactness and execution time.

This paper investigates the impact of integrating large-scale Doubly-Fed Induction Generator (DFIG)-based wind energy conversion system (WECS) on the voltage stability of the 52-bus, 330 kV Nigerian power grid. Indices derived from Active Power–Voltage (PV) and Reactive Power–Voltage (QV) analyses have been utilized to determine the voltage stability limits in terms of the maximum active power margin (APM) and minimum reactive power margin (RPM) of the system with the associated critical voltage-reactive power ratio (CVQR) of the system buses. Simulations have been done in DIgSILENT PowerFactory and the results analysed using MATLAB. This work also demonstrates the effectiveness of DFIG-based WECS in mitigating the overvoltage issues in the Northern region by ensuring that all bus voltages are within the acceptable limits of 1.0 ± 0.05 p.u. The results show that the optimal DFIG-based WECS penetration level (PL) that satisfies a bus voltage criterion of 1.0 ± 0.05p.u with the APM and CVQR not falling below their respective base case values and the loading of all critical power system equipment not exceeding 80% is 35%. Therefore, this work has demonstrated the possibilities of large-scale DFIG-based WECS as a viable solution for voltage stability improvement of a weak National grid while meeting the increasing energy demand.

Rapid growth and development around the world will lead to a gradual increase in electricity consumption. At present, colleges and universities have become the primary unit of daily electricity consumption. Therefore, accurately predicting the power consumption of colleges and universities is of great significance to the energy conservation and emission reduction of colleges and universities. Taking the actual power consumption of colleges and universities as an example, this article first analyzes its power consumption data characteristics. Based on the analysis and “decomposition and integration” concept, this paper proposes a hybrid network based on the improved complete ensemble empirical mode decomposition with adaptive noise (iCEEMDAN) and long short-term memory (LSTM) to achieve accurate colleges and universities Short-term load forecasting. First, the original power consumption data is decomposed into a series of patterns with noticeable differences by iCEEMDAN. Then use Bayesian-optimized LSTM to predict each mode individually. Finally, the prediction results of each mode are superimposed and reconstructed to form an overall prediction result. In each training, the Bayesian optimization algorithm is used to select the most suitable LSTM hyperparameter values to match the data characteristics of each model. At the same time, the structure of the LSTM prediction large data set is discussed. The results show that, compared with the prediction errors of other models, the proposed hybrid model can accurately predict university power consumption and provide the highest prediction ability among all survey models.

The level of protection of high voltage power substations depends on the considerations of the electricity company, the level of threats and the value of their assets. On the other hand, the level of countermeasures or defenses for an acceptable level of risk depends on the number of defense equipment at the substation level. Therefore, the number of these countermeasures should be estimated according to the status of each substation and the rate of investment and its importance. In addition, appropriate defense methods depending on their type and condition, as well as how the design and position of the substation can identify system security vulnerabilities. The aim of this paper is to investigate and determine the importance of protecting high voltage power substations against cyber-attacks. For this purpose, power substations are examined in four different groups: geostrategic, industrial strategy, automation and control systems, and vulnerable. According to the decision-making method of Fuzzy Analytical Hierarchy Process (FAHP), the defense cost function is presented in each substation, which includes hardware and software equipment and optimal routing of data transmission cables and cable shield. In addition, decisions are made simultaneously between the defender and the attacker. It was found that, from the proposed defense methods, the optimal path for communication cables and hardware equipment used in the power substations was more important than other cases. In addition, by shifting the weights of the criteria of substations 2 and 4, the strategy 3 ranks first in the need for defense budgets among other strategies, accounting for about 49.5% of the total defense budget, and it needs 11.2% more defense budget than the strategy S4. Furthermore, sensitivity analysis is provided to examine the impact of various factors as well as to confirm the accuracy of the results.

A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. To demonstrate the validity of this methodology, the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain, is implemented and tested. The geographical aggregation level considered is the postal code since it is the highest data resolution available through the open data sources used in the research work. The development and the experimental tests are supported by a web application environment formed by interactive user interfaces specifically developed for this purpose. The paper’s novelty relies on the application of statistical data methods able to infer the main energy performance characteristics of a large number of urban districts without prior knowledge of their building characteristics and with the use of solely measured data coming from smart meters, cadastre databases and weather forecasting services. A data-driven technique disaggregates electricity consumption in multiple uses (space heating, cooling, holidays and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from this disaggregated energy uses to obtain the energy characterisation of the buildings within a specific area. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sector. This work emanated from research conducted with the fi-nancial support of the European Commission through the H2020project BIGG , grant agreement 957047, and the JRC Expert Con-tractCT-EX2017D306558-102.D.ChemisanathanksICREAfortheICREA Acadèmia. Dr J. Cipriano also thanks the Ministerio deCiencia e Innovación of the Spanish Government for the Juan dela Cierva Incorporación grant

The increasing electrical power generation from renewable energy resources poses new challenges to the electrical grid. These challenges are mainly related to the inconsistent nature of renewable energies and the burden on transmission lines due to the increasing generated power. In this paper, the correlation between renewable energy generation and the transmission capability of electrical power lines is studied. In order to facilitate the accommodation of renewable energies the actual dynamic line rating (DLR) is estimated based on actual weather conditions. To achieve this purpose a new DLR estimation method using machine learning is presented. Line rating depends on various meteorological factors (wind speed, wind direction, ambient temperature, and solar radiation). A machine-learning model was trained using Random Forest (RF) regressor to estimate the rating of a transmission line based on these meteorological factors. The data used for model training is actual measurements of weather conditions, conductor temperature, and conductor current. Results, where the trained model is used to estimate the rating of an ACSR Lynx conductor with a thermal limit of 80°C, are presented.

This paper presents an intelligent photovoltaic (PV) fault detection system using Adaptive Neuro-Fuzzy Inference System (ANFIS) methodology. To accomplish this objective, it is necessary to train the ANFIS model for an effective PV fault detection and classification system by deploying Grid Partition (GP) and Subtractive Clustering (SC) strategies using some research data. Moreover, the ANFIS SC approach’s performance was better and more accurate than the ANFIS GP approach to predict and classify various PV systems’ faults. Additionally, the trained model with the ANFIS SC approach demonstrated excellent performance when it was compared with the unseen empirical data points, which were not included in the training process. The values obtained from statistical analysis such as coefficient correlation R, root mean squared error (RMSE), and coefficient of determination R2 were 0.9989, 0.0383, and 0.9978. These obtained results show that the ANFIS SC framework with cluster radius 0.6 can remarkably diagnose the PV system faults with high accuracy.

Internet of Things and Blockchain are considered two major technologies. Lower latency and a higher linked system number provide greater flexibility for remote execution of Internet of Things (IoT) applications. It is no secret that IoT devices often have insufficient computing capacity (both in terms of processing power and storage requirements) to support robust protection and encryption algorithms. The Internet of Things is facing many challenges such as poor interoperability, security vulnerabilities, privacy, and lack of industry standards. Cyber-attacks on IoT devices can have an impact on energy trading privacy and security. This paper suggests a method for introducing a basic interface to an IoT device’s security gateway architecture along with Blockchain to provide decentralization and authentication. It adds much-needed anonymity and versatility to IoT infrastructure, which is currently lacking. The solution enhances the reliability of data sent to remote services by applying compatible cryptographic algorithms to it before sending it. The solution’s benefits include compatibility with all IoT products and the ability to run any cryptographic algorithm on data that can be used for microgrid trading and can be initialized and securely transported over 5G or 6G network infrastructures. As a part of this work, a security procedure has been created that supports every cryptographic algorithm for all IoT devices in the network. In addition, the interface is guarded by the Blockchain technology which eliminates single control authority, records historical transactions performed by the IoT devices and provides a trust between devices.

This paper presents the modeling, design, and experimental analysis of an Automatic Generation Control (AGC) for a hydropower plant using Adaptive-Neuro-Fuzzy Inference system (ANFIS). This was aimed at reducing the frequency deviations which occur during power generation. The Adaptive-Neuro-Fuzzy Inference system (ANFIS) was used to intelligently control the selection of parameters for the effective control of power in the hydropower plant. The proposed ANFIS was trained with input–output data of the fuzzy logic controller (FLC). The ANFIS model is used as a hybrid learning model which includes the Least Square Estimate (LSE) and back propagation algorithm (BPA). The conventional PID, FLC, and ANFIS controllers were investigated using MATLAB. In order to determine the best controller, the controllers were experimented and compared to determine the controller with the best performance. The results show that frequency deviations occur as a result of a continuous variation of loads, which make the deviations difficult to control when a governor is not applied. Furthermore, the response of the AGC of the hydropower plant (in the single and double area) with step load changes was studied. The simulation results show that the ANFIS controller performs better compared to the PID as well as the FLC. Further results indicate that the proposed ANFIS controller helps to speed up the performance of the AGC of the hydropower plant. The ANFIS controller not only improved the performance but also made the fuzzy inference system (FIS) less dependent on the expert system.

Building information modelling (BIM) is trying to overcome the issue of interoperability by promoting the collaboration between different professions, which enables Building Performance Assessments (BPA) to take place earlier in the design process. Nevertheless, the interoperability is rarely perfect and information is lost during translation from BIM authoring tool to simulation software or the interoperability is limited to the translation of the geometry. The discrepancies in data translations from BIM authoring tool to specific Calculation Virtual Environments (CVE) for energy and acoustic evaluation are considered. For the energy performance evaluation two software tools are tested in the present research paper, namely the Design Builder and IES VE , using IFC and gbXML as data exchange formats. Furthermore, a solution is proposed for overcoming the interoperability issue about acoustic evaluation by adapting programming language in Dynamo Graphical Interface for calculation of acoustic parameters, namely the Reverberation time and Schroeder’s frequency, which allow room quality evaluation to take place earlier in the design process as they are crucial for educational and other facility usage buildings.

Virtual machine migration (VMM) is a crucial way to ensure load balancing and save energy consumption of cloud hosts. In most previous studies, they apply the same virtual machine migration strategy for overloaded hosts, which ignore the possible communication between the migrated VM, and other factors. Thus, they are prone to incur excessive network overhead. This paper proposes a virtual machine migration strategy based on the three-way decision (VMM-3WD) to save cloud hosts’ energy consumption while considering the network correlation between virtual machines. The strategy first is to classify hosts into overloaded hosts, regular load hosts, and under-loaded hosts based on their load situation. Then, different migration strategies are targeted developed for these three types of cloud hosts. Specifically, the approach migrates the VMs in under-loaded hosts to regular load hosts. And then, the approach further develops two thresholds to divides overloaded hosts into massively overloaded hosts, moderately overloaded, and lightly overloaded hosts. The migration decision of VMs in all stages pursuing the goal of reducing the energy consumption of the network during the migration process. The experimental results show that the proposed algorithm can reduce the energy consumption generated by virtual machine migration and data center (DC) energy consumption while meeting the SLA.