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The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title & keywords combination outperforms other combinations with an F-measure score of 0.88.

Over the decades, the immense growth has been reported in research publications due to continuous developments in science. To date, various approaches have been proposed that find similarity between research papers by applying different similarity measures collectively or individually based on the content of research papers. However, the contemporary schemes are not conceptualized enough to find related research papers in a coherent manner. This paper is aimed at finding related research papers by proposing a comprehensive and conceptualized model via building ontology named COReS: Content-based Ontology for Research Paper Similarity. The ontology is built by finding the explicit relationships (i.e., super-type sub-type, disjointedness, and overlapping) between state-of-the-art similarity techniques. This paper presents the applications of the COReS model in the form of a case study followed by an experiment. The case study uses InText citation-based and vector space-based similarity measures and relationships between these measures as defined in COReS. The experiment focuses on the computation of comprehensive similarity and other content-based similarity measures and rankings of research papers according to these measures. The obtained Spearman correlation coefficient results between ranks of research papers for different similarity measures and user study-based measure, justify the application of COReS for the computation of document similarity. The COReS is in the process of evaluation for ontological errors. In the future, COReS will be enriched to provide more knowledge to improve the process of comprehensive research paper similarity computation.

Recently, it has been shown that channel feedback helps to enhance the physical layer security (PLS) of communication systems. The current use of feedback focuses on generating secret keys protecting the transmitted messages, and this feedback scheme is proved to be optimal for some physically degraded channel models. In this paper, we revisit the model of the state-dependent wiretap channel with the noncausal state at the transmitter, which plays an important role in the PLS of the wireless communication systems. We propose a new feedback scheme for this model and show that the proposed new scheme performs better than the already existing secret key-based feedback scheme. The results of this paper are further explained via a Gaussian example.

With the development of mature communication network technologies such as 5G, edge computing and slicing, business applications in areas such as Telematics and intelligent transportation also need the support of vehicle-road collaboration technologies. Due to the mobility of vehicles and the heterogeneity of communication interfaces, it brings many challenges for data transmission and processing of Telematics. Therefore, network resources, roadside unit devices and sunken edge nodes need to work together to guarantee real-time network communication. Thus, they can jointly help improve the quality of network services and user experience, and show the level of vehicle-road collaboration wisdom in terms of safety, efficiency and service. In view of the future 5G network-related technologies and C-V2X development trend, this paper provides a comprehensive technical solution framework for Telematics based on end-edge collaboration, combining network resource deployment optimization and edge caching technologies for video transmission tasks in Telematics application scenarios. By adding 5G base station type RSU and UAV mobile base station to work jointly, network resources are better allocated for scalable coded video, and a relatively better edge caching strategy is obtained based on user mobility analysis. Aiming to improve the network system performance, the vehicle-side, road-side and edge-side are jointly considered to improve the utilization efficiency of limited network resources. In this paper, we hope to provide a practical and effective service architecture for Telematics video application scenarios through a convergence scheme of network resource allocation and edge caching configuration.

This paper focuses on the problem that the current path planning algorithm is not mature enough to achieve the expected goal in a complex dynamic environment. In light of the ant colony optimization (ACO) with good robustness and strong search ability, and the dynamic window method (DWA) with better planning effect in local path planning problems, we propose a fusion algorithm named DACO that can quickly and safely reach the designated target area in a complex dynamic environment. This paper first improves the ant colony optimization, which greatly improves the convergence performance of the algorithm and shortens the global path length. On this basis, we propose a second-level safety distance determination rule to deal with the special problem of the research object encountering obstacles with unknown motion rules, in order to perfect the obstacle avoidance function of the fusion algorithm in complex environments. Finally, we carry out simulation experiments through MATLAB, and at the same time conduct three-dimensional simulation of algorithm functions again on the GAZEBO platform. It is verified that the algorithm proposed in this paper has good performance advantages in path planning and dynamic obstacle avoidance.

Machine reading comprehension is a very challenging task, which aims to determine the answer span based on the given context and question. The newly developed pre-training language model has achieved a series of successes in various natural language understanding tasks with its powerful contextual representation ability. However, these pre-training language models generally lack the downstream processing structure for specific tasks, which limits further performance improvement. In order to solve this problem and deepen the model’s understanding of the question and context, this paper proposes S&I Reader. On the basis of the pre-training model, skimming, intensive reading, and gated mechanism modules are added to simulate the behavior of humans reading text and filtering information. Based on the idea of granular computing, a multi-granularity module for computing context granularity and sequence granularity is added to the model to simulate the behavior of human beings to understand the text from words to sentences, from parts to the whole. Compared with the previous machine reading comprehension model, our model structure is novel. The skimming module and multi-granularity module proposed in this paper are used to solve the problem that the previous model ignores the key information of the text and cannot understand the text with multi granularity. Experiments show that the model proposed in this paper is effective for both Chinese and English datasets. It can better understand the question and context and give a more accurate answer. The performance has made new progress on the basis of the baseline model.

Indoor localization has become one of the fundamental services that is required in a diverse set of applications these days, such as patient monitoring and smart parking. Highly accurate localization techniques impose high latency and high energy consumption on the underlying application system. Thus, for such indoor location-based application, offloading the computation of the localization process to a remote server with high resource capability has been recently introduced as an avenue to address such a challenge. In this paper, a computation offloading problem is formulated to find the optimal decision with regard to the operation of the localization process. This decision includes: a) Where to compute the localization task, either locally on the end device or on the edge server or on the cloud server, b) Which localization technique should be used, and finally, c) Which transmission technology is recommended to be chosen in combination with the localization technique. All these decisions are constrained by the device, and the servers resource capabilities load. They are also constrained by the fact that the localization algorithm has to satisfy a certain application QoS requirement. Within such context, three algorithms are proposed for task offload decision making. First, the Indoor Localization Latency Optimal Offloading algorithm, which finds the optimal offloading decision that minimizes the total latency of the system and is considered a benchmark for the other algorithms. Second, Indoor Localization Latency Centralized Offloading algorithm that finds a sub optimal solution with lower complexity. Third, Indoor Localization Latency Game-Theoretic Offloading decentralized algorithm that converges after finite improvement steps and achieves Nash equilibrium. Altogether, the paper finds the optimum localization strategy for all users with the minimum latency under mobile edge computing environment.

This paper tackles the problem of detecting incongruities between headlines and body text, where a news headline is irrelevant or even in opposition to the information in its body. Our model, called the graph-based hierarchical dual encoder (GHDE), utilizes a graph neural network to efficiently learn the content similarity between news headlines and long body paragraphs. This paper also releases a million-item-scale dataset of incongruity labels that can be used for training. The experimental results show that the proposed graph-based neural network model outperforms previous state-of-the-art models by a substantial margin (5.3%) on the area under the receiver operating characteristic (AUROC) curve. Real-world experiments on recent news articles confirm that the trained model successfully detects headline incongruities. We discuss the implications of these findings for combating infodemics and news fatigue.

A malicious attack may endanger human life or pollute environment on a cyber-physical system (CPS). However, successfully attacking a CPS needs not only the knowledge of information technology (IT) but also the domain knowledge of the system’s operation technology (OT). Therefore, it is critical to identify the vulnerabilities of a CPS. This paper proposes a systematic method for the security verification of a CPS, focusing on OT by using model checking with UPPAAL, so as to enhance cyber security. In our security analysis, we considered unsafe situations to be the result of a potentially effective security attack. Thus, we suggested a systematic method to generate security constraints based on the safety constraints (or safety checks) of the CPS and then enhance these security constraints by security verification using model checking with UPPAAL. UPPAAL’s simulation tool can perform a detailed search for each state in various possible model combinations and can explore human-computer interactions more deeply. The contributions of our method are as follows: First, a systematic method is proposed to generate security constraints based on the overall safety requirements at the OT level. Second, the security constraints thus generated can be used for run-time monitoring to identify the possible security attacks when they are violated. Third, this paper proposes to augment normal system modeling with a suggested Attack Module to simulate the potential OT attacks. Finally, the verification results may be used in the following twofold directions: to identify the vulnerabilities for possible design improvements and to suggest the further additions of security constraints.

This paper aims to provide an in-depth study of the detection of historical alarm subsequences, which are frequently used as an initial step for alarm flood analysis methods. Therefore, state-of-the-art approaches are comprehensively examined, evaluated, and compared. To overcome the limitations of these methods, a novel approach is presented, which uses outlier detection in time distances between alarm events (activation and return to normal) and an alarm coactivation constraint. The effectiveness and performance of the examined methods are illustrated by means of an openly accessible dataset, which is introduced in this paper. It is based on the “Tennessee-Eastman-Process”, a benchmark in process automation. The intent is to provide a suitable dataset for the development and evaluation of alarm management methods in complex industrial processes using both quantitative and qualitative information from different sources. It is shown that the integration of supplementary information is beneficial for the overall performance and robustness of the detection method proposed here. This method allows for a more accurate detection of coherent historical abnormal situations, including phases with active root-cause disturbances and the normalization phases that follow their termination. Furthermore, the proposed method has the advantage that the detection results are less influenced by the alarm count, the propagation velocity, the duration of the situation, and the time distance between two causally independent situations in comparison to state-of-the-art approaches.

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

The design concepts of the in-robot network (IRN) architectures of humanoid robots are proposed in this paper. First, this paper reveals the network requirements for humanoid robots to realize perception abilities and action execution abilities near those of human beings. Humanoid robots need to be equipped with many sensors to collect surrounding environmental information. It is also necessary to use many motion actuators to enhance the degree of freedom to achieve the smooth motion abilities of humans. To maintain reliable data transmission between a number of nodes, an efficient and reliable IRN architecture is needed. This paper first discusses the limitations of existing humanoid robots in the number of sensors and degrees of freedom and points out that one of the reasons for this limitation is the lack of reliable network architectures. In-vehicle networks (IVNs) and various network technologies are used. These include time-sensitive networks and control area networks (CANs) to name a few. Additionally, heterogeneous network protocols are used. IRNs also include networks of sensors and actuators with different performance parameters such as bandwidth, delay, and transmission speed. IRNs may adopt design ideas similar to those of IVNs to satisfy the network requirements for humanoids. To accomplish this, three feasible IRN architectures are proposed and analyzed. Finally, to compare and analyze the three proposed IRN architectures, this paper uses OMNeT++ simulation software.

In this paper, we show that adversarial training time attacks by a few pixel modifications can cause undesirable overfitting in neural networks for both discriminative and generative models. We propose an evolutionary algorithm to search for an optimal pixel attack using a novel cost function inspired by domain adaptation literature to design our training time attack. The proposed cost function explicitly maximizes the generalization gap and domain divergence between clean and corrupted images. Empirical evaluations demonstrate that our adversarial training attack can achieve significantly low testing accuracy (with high training accuracy) on multiple datasets by just perturbing a single pixel in the training images. Even under the use of popular regularization techniques, we identify a significant performance drop compared to clean data training. Our attack is more successful than previous pixel-based training time attacks on state-of-the-art Convolutional Neural Networks (CNNs) architectures, as evidenced by significantly lower testing accuracy. Interestingly, we find that the choice of optimization plays an essential role in robustness against our attack. We empirically observe that Stochastic Gradient Descent (SGD) is resilient to the proposed adversarial training attack, different from adaptive optimization techniques such as the popular Adam optimizer. We identify that such vulnerabilities are caused due to over-reliance on the cross-entropy (CE) loss on highly predictive features. Therefore, we propose a robust loss function that maximizes the mutual information between latent features and input images, in addition to optimizing the CE loss. Finally, we show that the discriminator in Generative Adversarial Networks (GANs) can also be attacked by our proposed training time attack resulting in poor generative performance. Our paper is one of the first works to design attacks for generative models.

Increasing human activities have caused serious disturbance to global forest resources, so how to accurately identify individual trees has become an important task of forest resources investigation. In order to get the accurate number of individual trees, this paper took coniferous forest and mixed coniferous and broad-leaved forest as experimental samples, as well as Digital Orthophoto Map and airborne LiDAR Point Cloud as research data. We propose a deep Learning individual tree segmentation method based on RetinaNet model and PCS algorithm by doing comparative analysis (classical Watershed Algorithm and Layer Stacking Algorithm) at the plots (with high, medium, and low densities). The experimental results show that the method proposed in this paper can solve the problem of individual tree segmentation in high density forest and improve its degree of automation. Compared with Watershed algorithm and Layer stacking algorithm, F-Measure is improved by 6%-29% and 7%-20%, respectively. In other words, the results of individual tree segmentation presented in this paper can not only improve the precision of individual tree segmentation, but also maintain a high detection rate, which can meet the accuracy and high efficiency of individual tree extraction, so as to meet the needs for modern forestry investigation.

Handwritten mathematical expressions are an essential part of many domains, including education, engineering, and science. The pervasive availability of computationally powerful touch-screen devices, similar to the recent emergence of deep neural networks as high-quality sequence recognition models, result in the widespread adoption of online recognition of handwritten mathematical expressions. Also, a deeper study and improvement of such technologies is necessary to address the current challenges posed by the extensive usage of distance learning, and remote work due to the world pandemic. This paper delineates the state-of-the-art recognition methods along with the user's experience in pen-centric applications for operating with handwritten mathematical expressions. Recognition methods have been categorized into classes, with a description of their merits and limitations. Particular attention is paid to end-to-end approaches based on encoder-decoder architecture and multi-modal input. Evaluation protocols and open benchmark datasets are considered as well as the comparison of the recognition performance, based on open competition results. The use of handwritten math recognition is illustrated by examples of applications for various fields and platforms. A distinctive part of the survey is that we also considered how UI design relies on the use of different recognition approaches, which is aimed at helping potential researchers improve the performance of the introduced approaches toward the best responses in practical applications. Finally, this paper presents the prospective survey of future research directions in handwritten mathematical expression recognition and their applications.

Network threats and hazards are evolving at a high-speed rate in recent years. Many mechanisms (such as firewalls, anti-virus, anti-malware, and spam filters) are being used as security tools to protect networks. An intrusion detection system (IDS) is also an effective and powerful network security system to detect unauthorized and abnormal network traffic flow. This article presents a review of the research trends in network-based intrusion detection systems (NIDS), their approaches, and the most common datasets used to evaluate IDS Models. The analysis presented in this paper is based on the number of citations acquired by an article published, the total count of articles published related to intrusion detection in a year, and most cited research articles related to the intrusion detection system in journals and conferences separately. Based on the published articles in the intrusion detection field for the last 15 years, this article also discusses the state-of-the-arts of NIDS, commonly used NIDS, citation-based analysis of benchmark datasets, and NIDS techniques used for intrusion detection. A citation and publication-based comparative analysis to quantify the popularity of various approaches are also presented in this paper. The study in this article may be helpful to the novices and researchers interested in evaluating research trends in NIDS and their related applications.

Dementia diseases are increasing rapidly, according to the World Health Organization (WHO), becoming an alarming problem for the health sector. The electroencephalogram (EEG) is a non-invasive test that records brain electrical activity and has a wide field of applications in the medical area, one of which is the detection of neurodegenerative diseases. The aim of this work is to present the results of a thorough review of the use of EEG systems for the detection of dementia diseases. Around 82 papers published between 2009 and 2020 were reviewed and compared obtaining data such as sampling time, number of electrodes, the most popular processing, classification, and validation techniques, as well as an analysis of the reported results. The relationship of the selected parameters with the efficiency obtained is shown. Some more common combinations in the reviewed papers that demonstrated to have reliability levels greater than 90%, and details to be considered at each stage of the process. An overview of the most commonly used classification tools and processing techniques is also described.

Blade tip timing (BTT) vibration measurement is a promising on-line blade monitoring method, but various uncertainties bring great challenge in engineering applications. Most existing works are based on the assumption of constant rotating speeds. However, rotating speed is hardly fixed under variable conditions. In this case, these uncertainties always become more serious. To deal with this problem, this paper proposes to investigate BTT measurement derivations in angular domain, instead of time domain. Firstly, this paper systematically analyzes the effects of variable rotating speed, static angle errors and translational blade motions on the accuracy of BTT vibration measurement. Then the corresponding calibration methods are presented by only using times of arrival (TOAs). In the end, Matlab/Simulink simulations are done to validate the proposed method under linear and quadratic variations of rotating speeds. The results demonstrate that BTT measurement deviation under low rotating speeds is more than those under high rotating speeds and BTT measurement deviation due to static angle errors is independent of rotating speeds. And the proposed TOAs-based calibration method can reduce BTT measurement deviation greatly under variable rotating speeds, compared with traditional methods. BTT measurement deviation due to static angle errors can be calibrated by using low rotating speeds and those due to translational blade motions are difficult to be calibrated under variable rotating speeds. Thus simulation results indicate great potential of the proposed method for practical applications of the BTT method.

Named Entity Recognition (NER) is one key step for constructing power domain knowledge graph which is increasingly urgent in building smart grid. This paper proposes a new NER model called Att-CNN-BiGRU-CRF which consists of the following five layers. The prefix Att means the model is based on attention mechanism. A joint feature embedding layer combines the character embedding and word embedding based on BERT to obtain more semantic information. A convolutional attention layer combines the local attention mechanism and CNN to capture the relationship of local context. A BiGRU layer extracts higher-level features of power metering text. A global multi-head attention layer optimizes the processing of sentence level information. A CRF layer obtains the output tag sequences. This paper also constructs a corresponding power metering corpus data set with a new entity classification method. The novelties of our work are the five layer model structure and the attention mechanism. Experimental results show that the proposed model has high recall rate 88.16% and precision rate 89.33% which is better than the state-of-the-art models.

Industry 4.0 is a revolution in manufacturing by introducing disruptive technologies such as Internet of Things (IoT) and cloud-computing into the heart of the factory. The resulting increased automation and the improved production synergy between stocks, supply chains and customer demands, come along with the threats and attacks from the Internet. Despite extensive literature on the cybersecurity topic, many actors in manufacturing factories are just realizing the impact of cybersecurity in the preservation of their business. This paper introduces step-by-step the concepts and practical aspects of an Industry 4.0 manufacturing factory that are related to cybersecurity. Based on a subdivision of a typical factory into several generic perimeters, we present the vulnerabilities and threats regarding the network and devices usually found in each perimeter. Therefore, it is more efficient to present the recent proposals of the literature regarding cybersecurity guidelines and solutions in Industry 4.0. Instead of spreading a lot of references regarding every aspect of cybersecurity, we focused on a limited number of papers among the recent references. However, for each paper, we provide the details about the purpose of the proposal, the methodology adopted, the technical solution developed and its evaluation by the authors. These solutions range from classical cybersecurity countermeasures to innovative ones, such as those based on honeypots and digital twins. In order to deliver a review also useful to non scientists, we present our guidelines along with those of some organizations involved in cybersecurity harmonization and standardization in the world.

Progressive learning addresses the problem of incrementally learning new tasks without compromising the prediction accuracy of previously learned tasks. In the context of artificial neural networks, several algorithms exist for achieving the progressive learning goal of learning without forgetting. However, these algorithms have traditionally been tested on the well-known and widely available datasets from the domain of image understanding and computer vision. Very little has been done on exploring the suitability of progressive learning algorithms in the important area of network threat detection. On a more fundamental level, progressive learning algorithms are still faced with the challenge of predicting the ultimate ability of a given neural network architecture to add more tasks to its repertoire without undergoing catastrophic forgetting. The goal of this paper is to address such a challenge in the context of cyber security threat detection. It does so by providing a unified conceptual and computational framework where progressive learning algorithms can be analyzed, compared, and contrasted in terms of their learning capacity and prediction accuracy for specific datasets from the cloud cyber security domain. In particular, this paper provides rigorous metrics for predicting the onset of catastrophic forgetting in the cyber security domain and contrasts them with their usage in the imaging domain. Our extensive numerical results show that progressive learning, along with the proposed criteria for catastrophic forgetting, provides a very structured framework for automating network threat detection as new threats emerge throughout network operation.

The Internet of Things is based on a communication network to automatically receive various information from the natural world. It uses intelligent objects with perception, communication and computers to automatically receive various information from nature. All independently managed physical objects are connected to each other to achieve integrated perception, reliable transmission and intelligent processing, and to establish intelligent information between people and objects, and objects and object service systems. This article mainly introduces the impact of industrial agglomeration on the regional economy in a simulated intelligent environment based on machine learning. This paper proposes a method to detect industrial agglomeration index to analyze industrial agglomeration. Through the establishment of the industrial agglomeration index system, the level of integration of the manufacturing industry in our city was objectively analyzed, and the impact of the integration of manufacturing industry on our city was tested empirically. Finally, the relationship between industrial integration and regional economic development was tested. The experimental results in this paper show that industrial integration in a smart environment based on simulation learning has a significant positive impact on regional economic development. Among them, the level of cooperation between the manufacturing service industry and the manufacturing industry increased by 1%, and the level of regional economic development increased by 0.025%.

The selection of the system-architecture is critical step in software-engineering lifecycle. Unfortunately, the selecting a suitable architecture is a hard mission. The difficulty of the adopting architecture increases in inconsistence case as in SOA and MSA. The paper answers “when architecture transform”, “to which architecture migrate” and “how”. The paper addresses the lack of evaluation ERPs in post-implementation phase. The paper provides static and dynamic analysis for monolith, SOA and MSA by numerical-comparison and Benchmark. This paper proposes a roadmap for migration-architecture. The roadmap consists of flowchart of migration and migration-decision model that is supported with criteria of evaluation form for assessing system’s baseline and making a consistence decision by Neutrosophic-ANP. Because of the dependency between factors of information system evolution, ANP is used. Furthermore, Neutrosophic set is selected because its accuracy in inconsistence. Benchmark assesses OpenBravoERP, Odoo12 and Metasfresh5.144 to represent monolith, SOA and MSA respectively. Benchmark assesses performance, time to the market, stability, future proof, network issue and cost. They are tested based on scenario tests which grantee that transactions are executed within more than one module of ERPS, simulates real business’s procedures, huge number of records and concurrent load by virtual users. The proposed model and benchmark help “Hitac’ enterprise in two decisions for its expansion-plan in COVID-19 crisis. The numerical steps of this case-study are provided. Most published papers and literatures about Microservices are not empirical. The main paper’s result is filling this gap by a one of the empirical aspects with an academic study support.

To fully mine the relationship between temporal features in load data, improve the accuracy and efficiency of short-term load forecasting and overcome the difficulties caused by load nonlinearity and volatility in accurate load forecasting. In this paper, a hybrid neural network short-term load forecasting model based on temporal convolutional network (TCN) and gated recurrent unit (GRU) is proposed. Firstly, the correlation between meteorological features and load is measured with the distance correlation coefficient, and the fixed-length sliding time window method is used to reconstruct the features. Next, temporal convolutional network is adopted to extract the hidden historical information and time relationship including meteorological features, electricity price, etc., and a better-performing gated recurrent unit is utilized for perdition. Furthermore, the state-of-the-art AdaBelief optimizer and Attention mechanism are utilized to enhance the prediction accuracy and efficiency. The effectiveness and superiority of the proposed model are verified by load and weather data from Spain and PJM power system data. Short-term load forecasting results in different periods and comprehensive comparisons with the performance of different models show that the proposed model can provide accurate load forecasting results rather quickly. The highlights of this paper are that temporal convolutional network and gated recurrent unit are combined for load forecasting for the first time, and the forecasting performance is improved by the novel optimizer AdaBelief and feature selection based on distance correlation coefficient.

Autonomous driving (AD) provides a reliable solution for safe driving by replacing human drivers responsible for the majority of accidents. The emergence of Machine Learning, specifically Deep Reinforcement Learning (DRL), and its ability to solve complex games proved its potential to address AD challenges. However, model-free methods still suffer from safety-related issues that can be resolved using safe-DRL approaches. The addition of model-based safety filters to the learning-based algorithms provides safety bounds on their performance and constraint satisfaction. In this paper, we investigate the addition of a safety filter based on Model Predictive Control and show an increase in mean testing episode reward by 110% from −75 mean episode reward during testing for 50 episodes for Deep Deterministic Policy Gradient ( $DDPG$ ) to 7.758. We study the impacts of safety filters (7.758 mean reward), heuristic rules, bounded additive noises (0.49% performance increase comparing to noise-free case), and exploration (3.425 mean reward) on the learning algorithm. We compare the effects of filters in the context of simulated exploration and bounded exploration and prove that bounded exploration results in 9.86% increase in mean reward and 12.95% decrease in std comparing to the other method. Additionally, inspired by Deep Internal Learning and biological mechanisms like brain plasticity, we investigate the idea of using each sample for training only once instead of utilizing stochastic batches which increases the mean testing accumulated reward by 1.87% and leads to the best performance (7.942 mean reward and 0.048 std). Finally, the results demonstrate better automotive results for our proposed method than DDPG. Our proposed method, DDPG with safety filter in bounded exploration and adaptive learning under noisy input conditions, has a success rate of 100% under different traffic densities for the simulation environment used in this paper and our assumptions. The proposed method’s automotive results are shown for a braking scenario to avoid collision with other road users.

Choosing optimal Deep Learning (DL) architecture and hyperparameters for a particular problem is still not a trivial task among researchers. The most common approach relies on popular architectures proven to work on specific problem domains led on the same experiment environment and setup. However, this limits the opportunity to choose or invent novel DL networks that could lead to better results. This paper proposes a novel approach for providing general recommendations of an appropriate DL architecture and its hyperparameters based on different configurations presented in thousands of published research papers that examine various problem domains. This architecture can further serve as a starting point of investigating DL architecture for a concrete data set. Natural language processing (NLP) methods are used to create structured data from unstructured scientific papers upon which intelligent models are learned to propose optimal DL architecture, layer type, and activation functions. The advantage of the proposed methodology is multifold. The first is the ability to eventually use the knowledge and experience from thousands of DL papers published through the years. The second is the contribution to the forthcoming novel researches by aiding the process of choosing optimal DL setup based on the particular problem to be analyzed. The third advantage is the scalability and flexibility of the model, meaning that it can be easily retrained as new papers are published in the future, and therefore to be constantly improved.

Accurate electricity consumption forecasting in the power grids ensures efficient generation and distribution of electricity. Keeping this in mind, the paper introduces a novel deep learning model, termed Gated-FCN, for short-term load forecasting. The key idea is to introduce an automated feature selection and deep learning model for forecasting. The model includes an eight-layered Fully Convolutional Network (FCN-8) in which the hand-crafted feature selection that requires expert domain knowledge is avoided. Furthermore, the model also reduces noise as it learns internal dependencies and the correlation of the time series. Enhanced Bidirectional Gated Recurrent Unit (EBiGRU) is used in combination with FCN-8 to learn long-term temporal dependencies of the time series. Moreover, a weighted averaging mechanism of multiple snapshot models is adopted in the proposed model to assign optimized weights to BiGRU. At the end of FCN-8 and BiGRU, a fully connected dense layer is used that gives final prediction results. Gated-FCN is an end-to-end forecasting model that does not require any other model for enhancing its forecasting efficiency. Different activation functions are initially analyzed to determine how the proposed model learns complex patterns from the time series data. Later, the activation function having the best accuracy is used for forecasting. The proposed model extracts both spatial and temporal features from the data. Furthermore, this paper also provides predictive and exploratory data analyses to assist policymakers in making optimal decisions regarding power production and dispatch. In order to demonstrate the applicability of the proposed technique, the simulations are performed using nine years’ load consumption data taken from Independent System Operators New England (ISO-NE). The comparison with five state-of-the-art techniques is also provided to prove the fact that Gated-FCN gives the best forecasting accuracy as compared to other benchmark techniques in terms of two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).

The condition of the transformer can be accurately assessed based on dissolved gas online monitoring and analysis technology. With the widespread use of Dissolved Gas Analysis (DGA) technology, a large amount of dissolved gas time series data is obtained and forms DGA big data, which brings challenges to the data selection process of traditional condition assessment methods. Inaccurate results might be generated by selecting excessive or insufficient data from a long-span dissolved gas time series. The shorter dissolved gas time series fails to fully characterize the operation law of transformers, and the longer dissolved gas time series contains redundant information that results in inaccurate analysis models and excessive calculation consumption. This paper attempts to mine the optimal data selection rule of long-span dissolved gas time series for more accurate and efficient analysis. In this paper, C-C method was employed to calculate the phase space parameters of the time series. By analyzing the convergence of the correlation integral, the boundary conditions that maintain the time series phase space stability were revealed, and so was the relationship between the optimal length and the embedding dimension. The real cases show that the optimal data set provides the same accuracy and significantly improves the computation speed by 113.54 times. The optimal data selection rule eliminates redundant information in the long-span dissolved gas time series and retains the “optimal data”, which not only preserves the characteristics of the original data, but also improves the computation speed of transformer condition assessment.

This paper proposes a novel approach for investigating how energy storage devices (ESD) can affect a profit-maximizing distribution network aggregator’s bidding strategy in day-ahead (DA) electricity markets in the presence of uncertainties. The impacts of random renewable generation and demand on the aggregator’s real-time (RT) energy transactions and on network security parameters (e.g., line loadings and nodal voltages) are modelled using distributionally robust chance constraints (DRCC), which guarantee their validity with high probability as long as the true (but unknown) probability distribution of random variables belongs to an ambiguity set. This paper adopts a data-driven approach for defining the ambiguity set using the first and second order moments of random variables extracted from historical data. The aggregator’s overall profit maximization problem (incorporating ESDs and DRCCs) is reformulated into a convex, deterministic equivalent and solved using the MATLAB-based Disciplined Convex Programming software CVX. Results from simulations performed on an actual 11-kV distribution feeder show that the proposed bidding strategy enables the aggregator to achieve an efficient trade-off between profitability in the DA market and risk exposure in RT. Additionally, the results demonstrate the ability of ESDs to further increase the aggregator’s DA profits (through energy arbitrage) without exposing it to additional risks in RT. The impact of incorporating ESDs on distribution network security parameters is also investigated under different settings of the aggregator’s risk tolerance. Finally, some considerations on computational burden and optimisation model accuracy are presented.

This paper presents a complete procedure on prototyping using the FPGA of the STEMlab board and is intended to serve as a guide for developers, students and researchers interested in speeding up their projects and experiments. Due to the reconfigurability of its internal circuitry, being as simple as a code modification (using hardware description language), FPGA technology allows testing of several controller topologies and/or parameters without the need of any physical change at hardware. This feature allows a much faster development cycle of either commercial products or academic experiments. Besides the reconfigurability of the FPGA, the STEMlab board also offers the advantage of several peripheral already available, which includes, among others, high speed analog-to-digital and digital-to-analog converters, Ethernet communication and a dual-core ARM processor capable of running a Linux operating system. In this paper, a didactic method of use of this board is presented, from getting started to a complete academic and industrial application: detection of early damage on an induction motor using frequency response analysis.

This paper proposed a novel fuzzy best-worst multi-criteria group decision-making method to solve the group decision-making (GDM) problem with multi-granular linguistic approach, which is an effective and promising technique to tackle this issue. In the proposed method, the selectable multi-granularity linguistic term sets (LTS) are firstly provided for experts to expressed their individual assessment information. Then, the improved fuzzy BWM is employed to calculate the weights of criteria with the form of fuzzy numbers. In current several studies using the BWM for group decision-making, only two unified best and worst criteria are given, which cannot reflect the evaluation of the best and worst criteria by different experts, resulting in the omission of information. Moreover, the difference between the best and worst criteria initially given and the experts’ ideas will cause the experts to be inaccurate in the comparison of each criterion. Therefore, in this paper, in order not to omit too much information, each expert will determine the best and the worst criteria. The evaluation information of each expert is integrated into two comparison vectors according to the transformation formula proposed in this paper. What’s more, an improved input-based consistency measurement is proposed, which can provide the DMs with a clear guideline on the revision of the inconsistent judgement(s). Finally, two examples are given to illustrate the effectiveness and applicability of the proposed method.

Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.

With the recent growth of multimedia traffic over the Internet and emerging multimedia streaming service providers, improving Quality of Experience (QoE) for HTTP Adaptive Streaming (HAS) becomes more important. Alongside other factors, such as the media quality, HAS relies on the performance of the media player’s Adaptive Bitrate (ABR) algorithm to optimize QoE in multimedia streaming sessions. QoE in HAS suffers from weak or unstable internet connections and suboptimal ABR decisions. As a result of imperfect adaptiveness to the characteristics and conditions of the internet connection, stall events and quality level switches could occur and with different durations that negatively affect the QoE. In this paper, we address various identified open issues related to the QoE for HAS, notably (i) the minimum noticeable duration for stall events in HAS; (ii) the correlation between the media quality and the impact of stall events on QoE; (iii) the end-user preference regarding multiple shorter stall events versus a single longer stall event; and (iv) the end-user preference of media quality switches over stall events. Therefore, we have studied these open issues from both objective and subjective evaluation perspectives and presented the correlation between the two types of evaluations. The findings documented in this paper can be used as a baseline for improving ABR algorithms and policies in HAS.

Dyslexia is a neurological disorder that is characterized by imprecise comprehension of words and generally poor reading performance. It affects a significant population of school-age children, with more occurrences in males, thus, putting them at risk of poor academic performance and low self-esteem for a lifetime. The long-term hope is to have a dyslexia diagnostic method that is informed by neural-biomarkers. In this regard, large numbers of machine learning methods and, more recently, deep learning methods have been implemented across various types of dataset with the above-chance classification accuracy. However, attainment of clinical acceptability of these state-of-the-art methods is bedeviled by certain challenges including lack of biologically-interpretable biomarkers, privacy of dataset and classifiers, hyper-parameter selection/optimization, and overfitting problem among others. This review paper critically analyzes recent machine learning methods for detecting dyslexia and its biomarkers and discusses challenges that require proper attentions from the users of deep learning methods in order to enable them to attain clinically relevance and acceptable level. The review is conducted within the premise of implementation and experimental outcomes for each of the 22 selected articles using the Preferred Reporting Items for Systematic review and Meta-Analyses (PRISMA) protocol, with a view to outlining some critical challenges for achieving high accuracy and reliability of the state-of-the-art machine learning methods. As an evidence-based protocol for reporting in systematic reviews and meta-analyses, PRISMA helps to ensure clarity and transparency of this paper by showing a four-phase flow diagram of the selection process for articles used in this review. It is therefore, envisaged that higher classification performance of clinical relevance can be achieved using deep learning models for dyslexia and its biomarkers by addressing identified potential challenges.

Medical files can help people prevent diseases, increase cure rates, promote medical development and help solve major public health crises. However, medical files are strongly private. It is an urgent problem needed to be solved that how to share medical files with privacy and data security. The existing models based on the centralized certificate authority is a feasible method, but it is possible to experience a single point failure. Besides there is a mismatch between the models and real-life scenario since they are only suitable for single patient. Therefore, this paper proposes a practical medical file sharing scheme based on blockchain and decentralized attribute-based encryption. The blockchain is used to record application and grant of authorizations. Smart contracts provide an interactive platform for all users in the system. By utilizing decentralized attribute-based encryption, fine-grained access control of medical files is carried out to ensure privacy and security as well as avoiding single point failure. Attribute-based algorithm that support multi-person democratic decision making and dynamic personnel changes are designed to make the model much closer to the real scene. Finally, through security, performance and comparing analysis with other solutions, the scheme in this paper can meet the needs of real-life scenarios in terms of security and practicability, and provides a new practical model for medical file sharing.

Air pollution has become the fourth leading cause of premature death on Earth. Air pollution causes poor health and death; about one case out of every ten deaths worldwide is caused by air pollution, which is six times more than malaria. Human activities are the main cause of air pollution, such as chemical industries, road traffic, and fossil fuel power plants. Over the span of several years, monitoring air quality has become an exigent and essential task. In order to limit the health impact of air pollution and to ensure safe operation of chemical processes, it is necessary to quickly detect and locate instrumentation defects. There are several process monitoring techniques in the literature. Among these techniques is the one selected for this work for the detection and location of sensor faults: the kernel principal component analysis (KPCA) method, which was selected for its primary advantages of easy employment and less necessity for prior knowledge. Using the KPCA method for monitoring nonlinear systems, the calculation cost and the memory size are related to the number of initial data. This is currently a major limitation of the KPCA method, especially in industrial environments. In order to remedy this limitation, in this paper we propose a new method of detection and localization. The key idea of this approach is to extend the method of localization based on the principle of reconstruction-based contributions (RBC) by downsizing the kernel matrix in the characteristic space. The proposed technique is named reconstruction-based contribution reduced rank kernel principal component analysis(RBC-RRKPCA). The approach is demonstrated using real air quality monitoring network data and simulated data from the Tennessee Eastman process (TEP) as a challenging benchmark problem. We also present a comparative study of the performances of the conventional diagnostic technique RBC-KPCA and the proposed technique RBC-RRKPCA. The results in this paper reveal that the proposed technique achieves the highest detection and localization accuracy.

DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes. Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems. Nonetheless, few methods have been proposed to address the intersection of multi-class imbalanced and high-dimensional problems concurrently due to their intricate interactions. This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization algorithm (GOA). The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm. The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA. The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.

Diabetes is one of the most rapidly growing chronic diseases, which has affected millions of people around the globe. Its diagnosis, prediction, proper cure, and management are crucial. Data mining based forecasting techniques for data analysis of diabetes can help in the early detection and prediction of the disease and the related critical events such as hypo/hyperglycemia. Numerous techniques have been developed in this domain for diabetes detection, prediction, and classification. In this paper, we present a comprehensive review of the state-of-the-art in the area of diabetes diagnosis and prediction using data mining. The aim of this paper is twofold; firstly, we explore and investigate the data mining based diagnosis and prediction solutions in the field of glycemic control for diabetes. Secondly, in the light of this investigation, we provide a comprehensive classification and comparison of the techniques that have been frequently used for diagnosis and prediction of diabetes based on important key metrics. Moreover, we highlight the challenges and future research directions in this area that can be considered in order to develop optimized solutions for diabetes detection and prediction.

In this paper, efficient and accurate linear and nonlinear models are proposed for indicating comprehensive health requirements of the transformer using health index (HI) concept. The models are established with 336 experimental datasets including oil characteristics and dissolved gas analysis (DGA) of various types of transformers placed in different areas. The significance of DGA parameters in transformer health condition is considered with the inclusive DGA factor (DGAF) parameter, which considers the weighting importance of seven dissolved gases. Nonlinear models used in this paper are artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), which represent the behavior of transformer insulation parameters. The nonlinear models are compared with multiple linear regression (MLR) which is a linear statistical model. The models are established with 80 percent of the experimental dataset. The other 20 percent of data are utilized for the efficiency assessment of the models. The results demonstrate that the models provide an assessment of the health condition of the transformers comparable to existing models with high accuracy. The contributions of this paper are: 1) Evaluating the overall HI of the transformer employing a complete set of 15 input parameters of transformer oil-paper insulation system. 2) Adding DGAF, %WaterPaper, IFT parameters and showing the importance of these parameters. 3) Regarding the condition of solid insulation of the transformer particularly. 4) Applying a diverse and large practical dataset composed of 336 different transformers located in different country areas. 5) Using the MLR method for three purposes. 6) Providing linear (MLR) and nonlinear (ANN, ANFIS) models for HI calculation of the dataset, simultaneously. 7) Verifying the applicability and efficiency of the ANFIS model for simulating HI value.

With the increased number and reduced cost of smart devices, Internet of Things (IoT) applications such as smart home (SHome) are increasingly popular. Owing to the characteristics of IoT environments such as resource constrained devices, existing authentication solutions may not be suitable to secure these environments. As a result, a number of authentication solutions specifically designed for IoT environments have been proposed. This paper provides a critical analysis of existing authentication solutions. The major contributions of the paper are as follows. First, it presents a generic model derived from an SHome use-case scenario. Secondly, based on the model, it performs a threat analysis to identify possible means of attacks. The analysis leads to the specification of a set of desirable security requirements for the design of authentication solutions for SHome. Thirdly, based on the requirements, existing authentication solutions are analysed and some ideas for achieving effective and efficient authentication in IoT environments are proposed.

In a smart urban environment, providing accurate information about human activities is an important task. It is a trend to implement human activity recognition (HAR) algorithms and applications on smart phones, including health monitoring, self-management system, health tracking and so on. However, human activity recognition(HAR) is very complex, and it is important to use the best technology and machine learning to understand human activities. One of the main problems of the existing HAR strategies is that the classification accuracy is relatively low, and in order to improve the accuracy, it needs high computational overhead. The purpose of this paper is to use an exploratory data analysis method to deal with HAR, after the implementation of different data mining techniques, the results of dimensionality reduction and visualization are obtained. The HAR method based on smart phone and EDA proposed in this paper is a high precision method. Compared with other classifiers, its accuracy is 96.56%. This article will discuss computational prediction activities and the computational limitations of using exploratory data to analyze (EDA) on 564 feature data frames. The experimental results show that HAR-based exploratory data analysis is a common sensory signal processing technology. The GridSearchCV and LinearSVC algorithm can provide accurate automatic human activity recognition (HAR), for elderly and disabled patients in need of continuous care, and it is a decision-making tool to support sports coaches to make plans.

Considering smartphone-based indoor localization and tracking, combination of Wi-Fi fingerprinting and pedestrian dead reckoning (PDR) becomes prevalent due to wide deployment of indoor Wi-Fi access points and easily access of inertial measurement units (IMUs) on smartphones. Since Wi-Fi fingerprinting depends on the similarities between online and offline received signal strengths, it suffers from the fluctuation of radio signal measurements. Meanwhile, the PDR relies on the IMUs, which undergo accumulated errors in long walking distance. In this paper, we aim to improve the indoor tracking accuracy, while filling up the gap between two popular Android and iOS platforms. In particular, we propose a hybrid tracking algorithm that integrates the PDR with Wi-Fi/iBeacon signal features. There are three key points in our paper. First, we process Wi-Fi/iBeacon signals to build conversion functions to convert signals to related proximity distances. Besides, in order to support mobile tracking, we introduce a placement methodology for iBeacon installation for a specific region of interest. Second, a mobile smartphone uses an improved PDR to localize itself, while incorporating Wi-Fi/iBeacon data for estimating a starting point and correcting its location along the walking path. Finally, we build an iOS app to implement the proposed scheme and display visual tracking results. Experiment results show that our proposed scheme is more robust and accurate compared to the conventional schemes.

The Power system is getting larger and more complicated due to development of multiple energy supplies. Solving large-scale power flow equations efficiently plays an essential role in analysis of power system and optimizing their performance during normal or contingencies operation. The traditional Newton-Raphson (NR) algorithm used for power flow calculations is computationally expensive due to updating Jacobian matrix in each iteration. As alternative to update the Jacobian matrix repeatedly, this paper presents a GPU-based sparse modified Newton’s method by the introduction of a fixed Jacobian matrix, which integrates vectorization and parallelization technique to accelerate power flow calculations. Moreover, this research in the paper also investigates the performance of the corresponding CPU versions and a MATLAB-based library package, MATPOWER. The comparison of the results on several power system and power distribution systems demonstrate that the GPU variant is more reliable and faster for power flow calculation in large-scale power systems.

This paper investigates a real-time neural speech synthesis system on CPUs that can synthesize high-fidelity 48 kHz speech waveforms to cover the entire frequency range audible by human beings. Although most previous studies on 48 kHz speech synthesis have used traditional source-filter vocoders or a WaveNet vocoder for waveform generation, they have some drawbacks regarding synthesis quality or inference speed. LPCNet was proposed as a real-time neural vocoder with a mobile CPU but its sampling frequency is still only 16 kHz. In this paper, we propose a Full-band LPCNet to synthesize high-fidelity 48 kHz speech waveforms with a CPU by introducing some simple but effective modifications to the conventional LPCNet. We then evaluate the synthesis quality using both normal speech and a singing voice. The results of these experiments demonstrate that the proposed Full-band LPCNet is the only neural vocoder that can synthesize high-quality 48 kHz speech waveforms while maintaining real-time capability with a CPU.

In order to simplify the permanent magnet synchronous linear motor (PMSLM) system structure, optimize the moving performance of the control system, and further improve the position tracking accuracy of the PMSLM, a PMSLM position controller based on the model prediction algorithm is proposed (PMPC). This paper designs two position model predictive controller schemes: the first scheme is to directly use the linear displacement and q-axis current of the PMSLM to establish a second-order mathematical model, and combine the model predictive algorithm to design the PMPC. The second scheme is to first combine the linear motor motion equation and thrust equation with the model prediction algorithm to design a velocity model predictive controller (VMPC), then on the basis of the VMPC, use the relationship between the PMSLM displacement and the running speed to comprehensively design the PMPC is used as the outer loop controller of the control system. The two PMSLM control methods designed in this paper only need position loop and current loop to achieve precise trajectory tracking. Then, the analysis of the two control schemes shows that the second scheme is superior to the first scheme in terms of control performance. Finally, the PMPC model of the second scheme is built by simulation software to simulate and test. By analyzing the experimental data, it shows that the control method not only simplifies the control system in terms of structure, but also improves the accuracy of PMSLM tracking the direction change of the motion trajectory, and optimizes the control ability of the control system.

Vehicular ad hoc networks (VANETs) have become increasingly common in the past decades and provides essential and efficient communication for vehicles within intelligent transportation systems. Securing the VANETs wireless communication channel is one of the principal challenges in VANETs since existing security schemes are still vulnerable to security and privacy issues and have substantial computational and communicational overheads. To overcome these issues, this paper focuses on enhancing an authentication scheme based on conditional privacy-preserving and improving its performance efficiency. This paper reviews the security vulnerabilities of the existing schemes. It also proposes enhancements to the identity-based conditional privacy-preserving authentication scheme to secure and improve the efficiency of VANETs communications. The proposed scheme not only satisfies the security and privacy requirements but also has been proven secure under the random oracle model. Finally, the performance evaluation shows that the proposed scheme is more efficient computationally and communicational than the existing schemes in signing and verifying VANETs messages.

With the advent of smart grid, load forecasting is emerging as an essential technology to implement optimal planning and control of grid assets. Ergo in recent years, a significant thrust can be witnessed for the research towards the improvement of the prediction of the energy demand. However thus far there has not been any one technique in the literature that is shown to give best forecasts for a variety of sites; almost all the papers published on load forecasting, report their best results on just one of the dataset. This problem accentuates further when the training data does not have enough data points to learn patterns over all the seasons. Hence to devise a load forecasting technique that can yield the best estimates on diverse datasets, especially when the training data is limited, is a big challenge, which is addressed in this paper. The paper presents a novel combination of deep learning with feature engineering for short-term load forecasting. The proposed architecture, named as Deep Derived Feature Fusion (DeepDeFF), is based on the sequential model in conjunction with the hand-crafted derived features in order to aid the model for better learning and predictions. The raw data and the hand-crafted features are trained at separate levels, then their respective outputs are combined to make the final prediction. The efficacy and robustness of the proposed methodology is evaluated on diverse datasets from five countries with completely different patterns. The extensive experiments and results demonstrate that the proposed technique is superior to the existing state of the art.

This paper presents a hybrid ontology-XLNet sentiment analysis classification approach for sentence-level aspects. The main objective of the proposed approach allows discovering user social data considering the extracted in-depth inference about sentiment depending on the context. Thus, in this paper, we investigate the contribution of utilizing the lexicalized ontology to improve the aspect-based sentiment analysis performance through extracting the indirect relationships in user social data. The XLNet model is utilized for extracting the neighboring contextual meaning and concatenating it with each embeddings word to produce a more comprehensive context and enhance feature extraction. In the proposed approach, Bidirectional Long Short Term Memory (Bi-LSTM) networks are used for classifying the aspects in online user reviews. Various experiments considering Adverse Drug Reactions (ADRs) discovery are conducted on six drug-related social data real-world datasets to evaluate the performance of the proposed approach using several measures. Obtained experimental results show that the proposed approach outperformed other tested state-of-the-art related approaches through improving feature extraction of unstructured social media text and accordingly improving the overall accuracy of sentiment classification. A significant accuracy of 98% and F-measure of 96.4% are achieved by the proposed ADRs aspect-based sentiment analysis approach.

By caching multiple copies of the sensed data and suppressing repeated content deliveries from the sensor nodes, the in-network caching exhibits strong potentials to reduce energy consumption and avoid service loss in the Internet of Things (IoT). In this paper, we review the fundamentals of the in-network caching in IoT, especially the deployment principles of the cache-enabled brokers. Since in-network caching is not the optimal choice in some cases, we propose the dynamic in-network caching scheme to decide whether the sensed data should be cached in brokers or not, where the location and delay requirement of the clients, traffic load of the brokers as well as the energy level of the sensor nodes are encapsulated in the proposed scheme. Next, we give a practical case study to demonstrate the effectiveness of the proposed in-network caching scheme in the IoT scenario. It shows that the proposed caching scheme is superior to the traditional sensor-to-client scheme in terms of energy saving. Finally, we conclude this paper, and summarize the future research directions to achieve the in-network caching for implementing the green IoT.

Prescription dispensing accuracy is of paramount importance for all hospitals. However, human errors are inevitable due to multiple reasons, such as fatigue, stress, heavy workload, lack of effective verification measures, mismanagement. Such human errors pose serious safety and health concerns on the part of patients and may as well lead to a series of medical disputes. Based on induced deep learning, this paper proposes a real-time Blister Package Identification System (BPIS) to assist pharmacists’ drug verification and dispensing. Under the guidance of the induction strategy, image preprocessing is introduced to form a standardized image containing the front and back side of the blister package, which is subsequently sent to CNN-based object identification network for feature extraction and identification. This preprocessing method allows the identification system to promote the deep learning system to focus on feature learning to obtain more information about the appearance of the package ruling out confounding factors such as background noise, size, shape or positioning. In addition, this article collects and establishes an image dataset of adult lozenges. Under this dataset, this paper verifies the enhancement of Induced Deep Learning (IDL) on YOLO v2, ResNet, and SENet. By optimizing the deep learning identification network with the help of the embedded technology and a two-side extraction mechanism, a real-time BPIS is built. Long-term tests in hospitals prove the effectiveness of the proposed system.