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With the increasing and diversified Internet of Things (IoT) devices, more IoT heterogeneous wireless networks have emerged, providing more network services for IoT devices, especially mobile phones and other roaming devices. However, there are also some malicious users who use different means to attack the security of the network, so that more users begin to pay attention to the identity authentication and privacy protection of the Internet of Things. This paper designs an IoT node roaming authentication model, which is used to enhance the security authentication capability of the Internet of Things to roaming devices. In order to effectively prevent malicious nodes from connecting to the network, this paper proposes a roaming authentication protocol based on heterogeneous fusion mechanism (HFM-IoT). The authentication protocol uses the remote authentication server in the local and remote areas to perform interactive authentication on the roaming device, which increases the difficulty of attacking or infecting multiple network areas by malicious nodes. According to the security analysis, the protocol can protect against multiple network attacks, and it can be seen from the experimental simulation results that the protocol has lower energy burden and authentication delay.

The UAV is emerging as one of the greatest technology developments for rapid network coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific area according to a desired quality of service requirement and to enable various entities in the network to have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard, by considering a multiple-UAV network where each UAV is associated to its own controlling operator, this paper addresses two major challenges: the selection of the UAV for the desired quality of network coverage and the development of a distributed and autonomous real-time monitoring framework for the enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based auction mechanism to model the interaction between the business agent who is interested in outsourcing the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of theoretical analysis and efficacy of the proposed model.

Rapidly evolving, the Internet of Things imposes new challenges for the developers of wireless networks. Various critical infrastructure monitoring scenarios require fast and reliable alert delivery. In such systems, multiple sensors are entrusted to react to the same emergency event. Thus, it is enough to receive an alert message from any of these sensors. However, such a message shall be reliably delivered as soon as possible. The recently published Wi-Fi HaLow standard defines the Restricted Access Window (RAW) mechanism that coordinates transmissions of numerous devices. Thus, it can improve reliability and reduce delays. The paper is the first to study the usage of RAW in a scenario of emergency alerts, where the alert shall be received from at least one sensor. The paper presents an easy-to-calculate mathematical model of alert delivery with RAW. The model allows dynamic online reconfiguration of RAW parameters to select such parameters that minimize consumed channel timeshare while providing satisfactory reliability and delivery delay for an alert. Intensive performance evaluation shows that the RAW is fruitful for mission-critical data delivery in the considered scenario.

The use of the digital twin has been quickly adopted in industry in recent years and continues to gain momentum. The recent redefinition of the digital twin from the digital replica of a physical asset to the replica of a living or nonliving entity has increased its potential. The digital twin not only disrupts industrial processes, but also expands the domain of health and well-being towards fostering smart healthcare services in smart cities. In this paper, we propose an ISO/IEEE 11073 standardized digital twin framework architecture for health and well-being. This framework encompasses the process of data collection from personal health devices, the analysis of this data, and conveying the feedback to the user in a loop cycle. The framework proposes a solution to include not only X73 compliant devices, but also noncompliant health devices, by interfacing them with an X73 wrapper module as we explain in this paper. Besides, we propose a configurable X73 mobile application, designed to work with any X73 compliant device. We designed and implemented the proposed framework, and the X73 mobile app, and conducted an experiment as a proof of concept of the digital twin in the domain of health and well-being in smart cities. The experiment shows promising results and the potential of benefiting from the proposed framework, by gaining insights on the health and well-being of individuals, and providing feedback to the individual and caregiver.

With advanced development of Internet communication and ubiquitous computing, Social Networks are providing an important information channel for smart city construction. Therefore, analyzing Location-based Social Network is a very valuable work in achieving reasonable urban zoning. In Social Networks, a main purpose of prestige assessment is to extract influential users who are regarded as the key nodes for community detection from Onine Social Networks (OSNs). However, social relationships of users are rarely used to evaluate the popularity of physical locations and zone physical locations. In order to achieve urban area function zoning by evaluating the prestige of geographic regions based on user relationships in Location based Social Networks (LBSNs), this paper proposes a Prestige Density-Based Spatial Clustering of Applications with Noise algorithm (P-DBSCAN) by improving the existing DBSCAN algorithm. Specifically, the algorithm first calculates the centrality of users in the social network, and then converts the centrality of users into the location-centrality through the users’ check-in data. After the centrality of each location is obtained, the discrete locations are clustered according to four constraints of the given radius. After clustering, the result of urban area function zoning can be achieved. Extensive experiments are conducted for demonstrating the effectiveness of our proposed algorithm in this paper. In addition, the visualization results reveal the correctness of our proposed approach.

Reasonable automatic guided vehicle path planning can shorten the transportation time of materials and improve the production efficiency of the intelligent assembly workshop. Ant colony algorithm is a widely used path planning method, however, it suffers from the shortcomings that being easy to fall into local optimum and low search efficiency. To overcome these shortcomings, first, this paper proposes a step optimization method to improve the search efficiency of the ant colony algorithm, and a path simplification method to avoid getting blindly tortuous paths; Second, to overcome the problem that the ant colony algorithm is easy to fall into the local optimum, this paper proposes an adaptive pheromone volatilization coefficient strategy, which uses different pheromone volatilization coefficients at different stages of the search path; third, for the path conflict problem of multiple automatic guided vehicles, this paper proposes a load balancing strategy to avoid it, which is based on the consideration that, path conflicts are caused by excessive concentration of multiple automatic guided vehicles paths. Extensive simulation results demonstrate the feasibility and efficiency of the proposed methods.

In pattern recognition, the elimination of unnecessary and/or redundant attributes is known as feature selection. Irreducible testors have been used to perform this task. An objective of the Minimum Description Length Principle (MDL) applied to feature selection in pattern recognition and data mining is to select the minimum number of attributes in a data set. Consequently, the MDL principle leads us to consider the subset of irreducible testors of minimum length. Some algorithms that find the whole set of irreducible testors have been reported in the literature. However, none of these algorithms was designed to generate only minimum-length irreducible testors. In this paper, we propose the first algorithm specifically designed to calculate all minimum-length irreducible testors from a training sample. The paper presents some experimental results obtained using synthetic and real data in which the performance of the proposed algorithm is contrasted with other state-of-the-art algorithms that were adapted to generate only irreducible testers of minimum length.

Distributed Ledger Technologies (DLTs), like Blockchain, are characterized by features such as transparency, traceability, and security by design. These features make the adoption of Blockchain attractive to enhance information security, privacy, and trustworthiness in very different contexts. This paper provides a comprehensive survey and aims at analyzing and assessing the use of Blockchain in the context of Distributed Trust and Reputation Management Systems (DTRMS). The analysis includes academic research as well as initiatives undertaken in the business domain. The paper defines two taxonomies for both Blockchain and DTRMS and applies a Formal Concept Analysis. Such an approach allowed us to identify the most recurrent and stable features in the current scientific landscape and several important implications among the two taxonomies. The results of the analysis have revealed significant trends and emerging practices in the current implementations that have been distilled into recommendations to guide Blockchain's adoption in DTRMS systems.

Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. This paper addresses these factors and classifies them into three categories: data preparation based factors, feature representation based factors and the classification techniques based factors. The paper is a comprehensive literature-based survey that compares the performance of more than 100 DL-based SC approaches by using 21 public datasets of reviews given by customers within three specific application domains (products, movies and restaurants). These 21 datasets have different characteristics (balanced/imbalanced, size, etc.) to give a global vision for our study. The comparison explains how the proposed factors quantitatively affect the performance of the studied DL-based SC approaches. Xunta de Galicia Agencia Estatal de Investigación | Ref. TEC2017-84197-C4-2-R

This paper presents a hybrid method for the construction of cryptographically strong bijective substitution-boxes by utilizing the merits of chaotic map and algebraic groups. The hybrid method first generates the key-dependent dynamic S-boxes using chaotic heuristic search strategy and then the S-boxes are evolved with the help of potent proposed algebraic group structures. This paper proposes a new improved combination chaotic map to operate initial search strategy. To augment the strength of generated S-boxes, the algebraic group structures are discovered which have the power to improve their cryptographic strength. The performance assessments using standard criterions are rendered to quantify the strengths of proposed bijective S-boxes. The experimental results and comparisons with recent S-box research findings justify the effectiveness and competence of the proposed bijective S-boxes and anticipated hybrid generation method.

Understanding the peak time of popularity evolution can provide insights on recommendation systems and online advertising campaigns. Although popularity evolution has been largely studied, the problem of how to predict its peak time remains unexplored. Taking Twitter hashtags as case study, the goal of this study is to predict when popularity reaches the peak for Twitter hashtags, from the perspective of an online social network application, in the context of the Twitter social network. On the whole, this paper includes three research aspects. Firstly, this paper investigates how early popularity reaches its peaks. Then, it is found that popularity tends to peak in the early stage of its evolution. Secondly, this paper discusses when a peak time prediction should be triggered. Thirdly, this paper designs a multi-modal based deep learning method, where the state-of-art deep learning techniques, such as multi-modal embedding and attention mechanisms, are adopted. We find that in the early stage of popularity evolution, no matter which factor is used as the input, the prediction effect is poor. By contrast, the hashtag string factor has the weakest contribution to popularity prediction in the middle and late stages of popularity evolution. The overall performance of our proposed method is evaluated in terms of the minimum, quartiles, and maximum values of absolute errors. From the experimental results, the prediction method we designed is superior.

The Internet of Things is made of diverse networked things (i.e., smart, intelligent devices) that are consistently interconnected, producing meaningful data across the network without human interaction. Nowadays, the Healthcare system is widely interconnected with IoT environments to facilitate the best possible patient monitoring, efficient diagnosis, and timely operate with existing diseases towards the patients. Concerning the security and privacy of the patient's information. This paper is focused on Secure surveillance mechanism for a medical healthcare system with enabled internet of Things (sensors) by intelligently recorded video summary into the server and keyframes image encryption. This paper is twofold. Firstly, a well-organized keyframe extraction mechanism is called to extract meaningful image frames (detected normal/abnormal activities keyframe) by the visual sensor with an alert sent to the concerned authority in the healthcare system. Secondly, the final decision about the happened activity with extracted keyframes to keep highly secure from any adversary, and we propose an efficient probabilistic and lightweight encryption algorithm. Our proposed mechanism verifies effectiveness through producing results, robustness in nature, minimum execution time, and comparatively secure than other images (keyframes) encryption algorithms. Additionally, this mechanism can reduce storage, bandwidth, required transmission cost, and timely analysis of happened activity from any adversary with protecting the privacy of the patient's information in the IoT enabled healthcare system.

The supply chain management of blood products specifically deals with the aspect of efficient planning, implementing, and controlling of the in and out flow processes of blood unity in the blood bank system. Therefore, any improvement in the management of these chains would directly influence the manner in which blood and blood products are distributed to all the various sectors requiring such scarce and precious resources. Generally, the management of blood products is difficult due to the four ABO blood groups, which is further complicated by positive/negative rhesus factors. In this paper, a more simplified and robust dynamic mathematical model is presented for the efficient management of the blood bank. The corresponding sets of governing equations from an existing model are extended to cover the rhesus factors and the solution methods of the newly derived equations are subsequently investigated. In addition, a mathematical representation of the decision making process is presented as a function of the blood bank stockpile. Furthermore, in order to demonstrate the robustness of the developed model and to provide managerial insights, a new global hybrid symbiotic organisms search genetic particle swarm optimization algorithm is developed. Several numerical computations are performed using real-world datasets from the Enugu National Blood Transfusion Centre, in Nigeria, which fall within the monthly initial blood volume bounds of 300 over a period of eight years (2010 - 2018). Finally, the experimental results show that the mathematical model and metaheuristic optimization method proposed in this paper offer a better solution approach for blood allocation in dynamic environments. More so, the impact of some essential control parameters on the results are analysed to help the blood bank managers or decision makers to select accurately the desired parameters for optimal results yield.

Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. People collect the face images, and the recognition equipment automatically processes the images. The paper introduces the related researches of face recognition from different perspectives. The paper describes the development stages and the related technologies of face recognition. We introduce the research of face recognition for real conditions, and we introduce the general evaluation standards and the general databases of face recognition. We give a forward-looking view of face recognition. Face recognition has become the future development direction and has many potential application prospects.

Wire Arc Additive Manufacturing (WAAM) has developed rapidly in recent years and has been widely used in industry. Cold Metal Transfer (CMT), a kind of Gas Metal Arc Welding (GMAW), is widely used in the modeling of thin parts. In the monitoring of CMT process, it is necessary to monitor the weld reinforcement of the deposited layer. It can provide effective alerting for welding and help to improve welding quality. In this paper, the weld reinforcement and the position of molten pool image on the deposited layer are determined by the molten pool visual acquisition system. According to the data of the same direction additive manufacturing experiment, the future molten pool image is predicted by the improved prediction network (PredNet). Meanwhile through a regression network (SERes), the predicted results are regressed to the accurate weld reinforcement information of the deposited layer in advance. The experimental results show that the monitoring system designed in this paper can predict the change of molten pool shape 140ms in advance and the average precision of the weld reinforcement regression is better than 0.3mm. This paper provides the necessary basis for the shape online monitoring and controlling in WAAM process.

The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes' growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (°F), illumination (lx), and CO2 (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO2); when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system's overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maximum of 15-18wh.

In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis.

It is very difficult to predict the Maximum Power Demand (MPD) of customers in high performance because of various factors. In this paper, the problem of MPD prediction is studied by using fused machine learning algorithms. Firstly, an improved grey relation analysis method is adopted to analyze relevant influencing factors. Secondly, a modified prediction algorithm based on an adaptive cubature Kalman filter combined with Fbprophet is proposed according to the characteristics of customers' MPD. Finally, the proposed algorithm of this paper is applied to predict MPD and cost is evaluated. Experiment results show that the improved MPD prediction algorithm can comprehensively consider the relevant factors, and has good performance in time series prediction.

The classical belief rule-based (BRB) systems are usually constructed by arranging and combining referential values of antecedent attributes or by setting special fixed values, which can lead to overly large size of BRB systems in complex problems. This paper combines the decision tree classification method to analyze the information of data and extract the rules. Based on this, a new rule representation method with referential interval is proposed and the rule base is constructed according to the support degree and belief degree of the data. In the newly proposed method, the introduction of decision tree ensures that the size of the rule base is reasonable. Moreover, the rule parameters trained by the differential evolution (DE) algorithm are optimized and adjusted to further improve the system performance. The experiments are conducted on several commonly used public classification datasets. And the proposed algorithm can achieve better accuracy results compared with classical classification methods and the existing classification methods of BRB systems on average. The experimental results validate the reasonableness and effectiveness of the BRB construction method proposed in this paper.

A novel trial for estimating popularity of artists in music streaming services (MSS) is presented in this paper. The main contribution of this paper is to improve extensibility for using multi-modal features to accurately analyze latent relationships between artists. In the proposed method, a novel framework to construct a network is derived by collaboratively using social metadata and multi-modal features via canonical correlation analysis. Different from conventional methods that do not use multi-modal features, the proposed method can construct a network that can capture social metadata and multi-modal features, i.e., a context-aware network. For effectively analyzing the context-aware network, a novel framework to realize popularity estimation of artists is developed based on network analysis. The proposed method enables effective utilization of the network structure by extracting node features via a node embedding algorithm. By constructing an estimator that can distinguish differences between the node features, the proposed method can archive accurate popularity estimation of artists. Experimental results using multiple real-world datasets that contain artists in various genres in Spotify, one of the largest MSS, are presented. Quantitative and qualitative evaluations show that our method is effective for both classifying and regressing the popularity.

To facilitate accurate tracking in unknown/uncertain environments, this paper proposes a simple learning (SL) strategy for feedback linearization control (FLC) of aerial robots subject to uncertainties. The SL strategy minimizes a cost function defined based on the closed-loop error dynamics of the nominal system via the gradient descent technique to find the adaptation rules for feedback controller gains and disturbance estimate in the feedback control law. In addition to the derivation of the SL adaptation rules, the closed-loop stability for a second-order uncertain nonlinear system is proven in this paper. Moreover, it is shown that the SL strategy can find the global optimum point, while the controller gains and disturbance estimate converge to a finite value which implies a bounded control action in the steady-state. Furthermore, utilizing a simulation study, it is shown that the simple learning-based FLC (SL-FLC) framework can ensure desired closed-loop error dynamics in the presence of disturbances and modeling uncertainties. Finally, to validate the SL-FLC framework in real-time, the trajectory tracking problem of a tilt-rotor tricopter unmanned aerial vehicle under uncertain conditions is studied via three case scenarios, wherein the disturbances in the form of mass variation, ground effect, and wind gust, are induced. The real-time results illustrate that the SL-FLC framework facilitates a better tracking performance than that of the traditional FLC method while maintaining the nominal control performance in the absence of modeling uncertainties and external disturbances, and exhibiting robust control performance in the presence of modeling uncertainties and external disturbances. Published version

The blooming of cloud computing leads to the rapid expansion of data center networks (DCN). Conventional link state routing algorithms like OSPF are widely adopted in data center networks, however, those routing algorithms bring great control overheads and long convergence time. Recently, topology-aware routing methods are considered to be efficient especially in data center networks with regular topologies. Lots of topology-aware routing methods (e.g., Fat-Tree and BCube) have been proposed for specific data center network topologies. This paper first proposes a formalized method to describe regular topologies and a regular Topology Description Language (TPDL) based on this method. TPDL is well designed to accurately define regular network topologies in a clear way leveraging their regularities. Based on the Software-Defined Networking (SDN) technology, this paper also proposes a novel topology-aware routing scheme: cRetor (controller-side REgular TOpology Routing scheme). Different from other topology-aware routing methods, cRetor is a TPDL-based general routing method, which means it is expected to work on different kinds of regular topologies. In this scheme, TDPL files are used as a priori knowledge to build an initial topology in the SDN controllers, which eliminates the process of topology discovery via Link Layer Discovery Protocol (LLDP) and hence relieves the bandwidth and processing burdens on controllers. Besides, we also apply the A-star algorithm to SDN controllers to speed up the routing selection, where TPDL's distance formulas act as the heuristic function. The experimental results show that cRetor outperforms LLDP-based SDN, OSPF and DCell in routing calculation performance, convergence speed, routing overheads and fault tolerance.

When facing the problem of modern logistics distribution under the large-scale network, the reasonable delivery task allocation has become an important part. For this problem, this paper uses the idea of decentralized control to establish a region division model in logistics distribution and transforms it into the multiple traveling salesmen problem with constraints of the road network and task allocation. The optimization goal of the model is to minimize the sum of the distances for logistics transport agents traveling all demand points in the system, with the constraints on the equilibrium of commodity requirements in each sub region. Then, a two-stage algorithm is proposed to solve the model. In the first stage, the $K$ -means clustering method is used to obtain a highly feasible initial solution; In the second stage, the initial solution is optimized by the algorithm which is combining swap algorithm and tabu search algorithm. In the two-stage solution evaluation, the TSP solution based on the Lin-Kernighan algorithm is applied to obtain the sets of demand points with ranking of the route and the corresponding shortest distances for each divided sub region. Finally, to verify the effectiveness of the proposed model and solving method, two cases are presented in this paper. The region division method in logistics distribution proposed in this paper not only helps to reduce the total distance, but also helps to balance the workload of the logistics transport agents, thereby making great potential to reduce logistics costs and improving the overall operational efficiency of logistics distribution.

Analysis and collection of time-series data as a major role of machine learning has been emphasized with an important key in cognitive science. Because the cognitive mechanisms such as human sensation and perception from cognitive science are fast responses ranging from a few milliseconds to hundreds of milliseconds, the method of pattern recognition and analysis of these brain signals must be done and it is necessary to derive some information. In this paper, we investigated time-series data of cognitive function of the brain obtained using a non-invasive technique on multiple channels via signal classification and analysis, using a cognitive science approach and experiments. The test dataset was collected in 19 channels using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) techniques with multiple rests and working conditions on eight subjects. From this perspective, the main contributions of this paper are that it completes the collection and analysis of cognitive-scientific time-series data and has scientific implications that extend to other integrated domains, energy, manufacturing, bioinformatics, and finance area. The use of Shapelet and DTW (Dynamic Time Warping) classification techniques on brain signal time-series shows the potential to identify neuro-biological phenomena that can proactively signal a disease or disorder. EEG bandwidth and frequency-specific data have also been categorized as machine learning algorithms and have shown accurate patterns and trends in measuring cognitive functions of scientific, biological and academic importance.

This paper deals with the stability, stabilization and ${L_{2}} - $ gain problem of periodic piecewise impulsive linear system. Firstly, by developing a new Lyapunov function called multiple piecewise time-varying Lyapunov function, a sufficient condition that guarantee the system $\mathop \lambda \nolimits ^{*} - $ exponentially stable is established. Moreover, a state-feedback controller is designed to stabilize the system. Then ${L_{2}} - $ gain analysis is investigated to obtain a result that guarantee the system $\mathop \lambda \nolimits ^{*} - $ exponentially stable and with ${L_{2}} - $ gain performance. Finally, numerical simulation examples are provided to verify the effectiveness of the results proposed in this paper.

The midsole is an important part of the shoe, but in its industrial production, the surface quality of the midsole currently relies on manual testing. It cost high and cannot meet the needs of industrial online real-time detection. To realize online surface defect detection, extracting the double edges of the midsole resulting from its special structure becomes an indispensable pre-work. This paper proposes a two-step Otsu method (TT-Otsu) to extract double edges of products. This method adopts the improved Otsu method to process the midsole image in two steps. It respectively combines with the Weighted Object Variance method (WOV) and the Neighborhood Valley-Emphasis method (NVE) to calculate the optimal threshold. Then the image is segmented to extract the edges and the misclassification error (ME) is 0.0007. To ensure the accuracy of the edge detection, the neighborhood gradient extreme value discrimination method is used to realize the local self-checking and make appropriate adjustments to the deviated edge. The false positive rate (FPR) and the false negative rate (FNR) of TT-Otsu are approximately equal to 5%. This method can effectively and clearly extract the double edges of the midsole. The precision rate is 95.61%, the average running time is 1.8s. The experiment demonstrates that the proposed method in this paper has good detection performance and good applicability.

Power electronic matrix converters are converters carrying out just one solitary transitional stage. AC-AC conversion automatism in matrix converters is attained through the exploitation of bidirectional controllable power switches associated with a structural matrix. The indirect matrix converter is an alternating preference to the direct matrix converter embodied by AC-DC converter and DC-AC inverter. In this paper, an assemblage of Pulse Width Modulation categories is used to control the dual stages of IMC. AC-DC converter is modulated through the well-known space vector algorithm, while DC-AC inverter is modulated through a novel algorithmic conception referred to as the digital scalar algorithm. Comparative analysis between the conventional DSVPWM modulation and the novel proposed modulation technique is escorted demonstrating that the latter is facilely comprehended and simply put into real-time practice. This paper comes forward with digital signal processor real-time enforcement, conceived for indirect matrix converter. Uniting both original approaches into one topological system entrained a significant decrease of 38% in real-time computation assesses. Throughout the paper, it is well proven that the proposed solution results in high-quality input/output waveforms, and averts from servicing supplementary devices that trigger silicon devices' gates. Numerous experimental enquires within the delivered research reveal advantageous aspects of the contemporary approach from overall simplicity to entire effectiveness.

With the maturity of technologies such as Internet of Things (IoT) and Cloud Computing, more and more medical institutions share patient's medical data in the IoT-connected healthcare system, whereas it also brings hidden dangers to data security and privacy protection. The application of Hierarchical Attribute-Based Encryption (HABE) in IoT-connected healthcare system helps to solve the problem of data sharing with a large number of users. Delegation is a function of HABE, which can realize the transfer of hierarchical user access rights, effectively reducing the workload of the trusted authority. However, when “delegation” is used to generate decryption keys for users who do not comply with the access structure, the “key-delegation abuse” problem arises, which can seriously damage the privacy of patient's data. Nevertheless, it has not attracted much attention in previous research. This paper proposes the problem of key-delegation abuse in Ciphertext-Policy Hierarchical Attribute-Based Encryption (CP-HABE). After the cryptanalysis of typical schemes, we conclude two reasons for key-delegation abuse in CP-HABE, which are randomizing the original key elements or keeping parts of them in a new decryption key. This paper proposes a new mechanism specifically by using directed graph and construct a CP-HABE scheme against key-delegation abuse (CP-HABE-AKDA). Aiming at the scenario of key leaking, we further present the traceable CP-HABE-AKDA solution for the IoT-connected healthcare system, which has the additional function of tracking and verifying the identity of key leaker.

The goal of video super-resolution technique is to address the problem of effectively restoring high-resolution (HR) videos from low-resolution (LR) ones. Previous methods commonly used optical flow to perform frame alignment and designed a framework from the perspective of space and time. However, inaccurate optical flow estimation may occur easily which leads to inferior restoration effects. In addition, how to effectively fuse the features of various video frames remains a challenging problem. In this paper, we propose a Local-Global Fusion Network (LGFN) to solve the above issues from a novel viewpoint. As an alternative to optical flow, deformable convolutions (DCs) with decreased multi-dilation convolution units (DMDCUs) are applied for efficient implicit alignment. Moreover, a structure with two branches, consisting of a Local Fusion Module (LFM) and a Global Fusion Module (GFM), is proposed to combine information from two different aspects. Specifically, LFM focuses on the relationship between adjacent frames and maintains the temporal consistency while GFM attempts to take advantage of all related features globally with a video shuffle strategy. Benefiting from our advanced network, experimental results on several datasets demonstrate that our LGFN can not only achieve comparative performance with state-of-the-art methods but also possess reliable ability on restoring a variety of video frames. The results on benchmark datasets of our LGFN are presented on https://github.com/BIOINSu/LGFN and the source code will be released as soon as the paper is accepted.

How much information can be transported, i.e., the transmission rate, is a subject of great interest in hybrid wireless networks. The main focus of this paper is the effect of channel fading in hybrid wireless network, in which a wired network of base stations is deployed to support long-range communications between wireless nodes. Two types of transmission mode in hybrid wireless network, i.e., intra-cell mode and infrastructure mode, are considered. To effectively overcome fading impairment, optimal multiple access technique is applied, allowing opportunistic sources to transmit concurrently with the scheduled source. Those different sources, much like in wideband CDMA system, share the entire bandwidth. A successive interference cancellation (SIC) strategy is then introduced at receiver side to limit the intra-cell interference and achieve the maximum capacity. Meanwhile, frequency reuse scheme is employed to minimize the inter-cell interference. Since the outage capacity over different fading channels will exhibit different asymptotic behaviors, in this paper we examine the Rayleigh, Rician and Nakagami-m models, which are the most commonly used fading models. Close-form solutions for outage throughput capacity at high signal-to-noise-plus-interference ratio (SNIR) are derived. It is showed that, with opportunistic sources, the intra-cell mode effectively combats fading as wireless nodes increases; however, the infrastructure mode is bottlenecked by the downlink transmission since base station is the only transmitter in the cell during the downlink phase. The theoretical bounds obtained and proofs are instrumental to the future network modeling and design.

The rapid development of information technology has led to the development of medical informatization in the direction of intelligence. Medical health big data provides a basic data resource guarantee for medical service intelligence and smart healthcare. The classification of medical health big data is of great significance for the intelligentization of medical information. Due to the simplicity of KNN (K-Nearest Neighbor) classification algorithm, it has been widely used in many fields. However, when the sample size is large and the feature attributes are large, the efficiency of the KNN algorithm classification will be greatly reduced. This paper proposes an improved KNN algorithm and compares it with the traditional KNN algorithm. The classification is performed in the query instance neighborhood of the conventional KNN classifier, and weights are assigned to each class. The algorithm considers the class distribution around the query instance to ensure that the assigned weight does not adversely affect the outliers. Aiming at the shortcomings of traditional KNN algorithm in processing large data sets, this paper proposes an improved KNN algorithm based on cluster denoising and density cropping. The algorithm performs denoising processing by clustering, and improves the classification efficiency of KNN algorithm by speeding up the search speed of K-nearest neighbors, while maintaining the classification accuracy of KNN algorithm. The experimental results show that the proposed algorithm can effectively improve the classification efficiency of KNN algorithm in processing large data sets, and maintain the classification accuracy of KNN algorithm well, and has good classification performance.

The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc. have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird’s eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.

Most graph-based semi-supervised classification methods do not perform well in hyperspectral image classification tasks due to their high complexity and other limitations. This paper proposes a label propagation semi-supervised classification algorithm which uses a few selected important paths to considerably reduce computational costs. It establishes that the most important paths for label propagation are minimum cost paths. It proves that minimum cost paths exist in minimum cost trees (MCT), and proposes a method based on a variant minimum spanning tree (MST) combined with priority queue to construct MCTs. The algorithm propagates labels from unlabeled nodes to labeled ones, a unique way different from any other studies where propagation is in the opposite direction, which brings about several clear advantages. These include that only one propagation path is required for each unlabeled node, improving both timing and memory performance. It also helps to solve a problem posed by sparse graphs where some image pixels cannot be classified, a situation which is especially problematic in large-scale image classification. The proposed method has the advantages of linear computational complexity, is independent of data dimension, has fewer parameters and is insensitive to the values of parameters. Moreover, it does not need large numbers of labelled pixels nor complex training processes. Experiments on hyperspectral images have shown that, compared with several existing algorithms, the proposed method achieves better performance in less time. The paper addresses some fundamental issues regarding propagating labels in graph based semi-supervised classifications. Due to the simplicity and the fast speed of the algorithm, it is also suitable to be integrated into both state-of-the-art and future hyperspectral image classification frameworks which have a label propagation stage.

This study considers a service-deployment problem of C4ISR(Command, Control, Communication, Computer, Intelligence, Surveillance and Reconnaissance) system under service-oriented architecture. In order to make C4ISR sufficiently agile to meet the challenges brought by diverse combat missions and highly adversarial combat processes, this paper adopts a distributed deployment method to deploy the service processes that constitute C4ISR in multiple data centers. Considering the service information requirements and the location of specific data, a deployment constraint for a service is proposed. Further, in order to improve resource utilization and reduce communication overhead, this paper establishes a distributed service deployment model facing the characteristics of C4ISR. For the large search space and complex constraints of the optimal problem, a multi-objective genetic simulated annealing algorithm is proposed by designing the neighboring individual generation rules and combining the simulated annealing mechanism and the genetic algorithm to enhance the ability of both local search and global search. The simulation results show that this method can produce a high-quality C4ISR service-deployment scheme effectively.

Nature-inspired optimization is a modern technique in the past decades. Researchers report their successful applications in various fields such as manufacturing, biomedical, and environmental engineering, while other researchers doubt its applicability. In this paper, we collect newly emerging nature-inspired optimization algorithms proposed after 2008, present them in a unified way, implement them, and evaluate them on benchmark functions. Moreover, we optimize the behavioural parameters for these algorithms. Since it is impossible to cover all interesting topics regarding nature-inspired optimization, this paper only focuses on the continuous encoding algorithms for single objective global problems, which is fundamental for other related topics.

This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can be viewed as a learning constraint geared towards cases where the input variables are known or assumed to be dependent. It addresses the problem of learning multiple disjoint subgraphs that do not enable full propagation of evidence. This problem is highly prevalent in cases where the sample size of the input data is low with respect to the dimensionality of the model, which is often the case when working with real data. The paper presents a novel hybrid structure learning algorithm, called SaiyanH, that addresses this issue. The results show that this constraint helps the algorithm to estimate the number of true edges with higher accuracy compared to the state-of-the-art. Out of the 13 algorithms investigated, the results rank SaiyanH 4th in reconstructing the true DAG, with accuracy scores lower by 8.1% (F1), 10.2% (BSF), and 19.5% (SHD) compared to the top ranked algorithm, and higher by 75.5% (F1), 118% (BSF), and 4.3% (SHD) compared to the bottom ranked algorithm. Overall, the results suggest that the proposed algorithm discovers satisfactorily accurate connected DAGs in cases where other algorithms produce multiple disjoint subgraphs that often underfit the true graph.

This paper investigates Kalman Filter-based Heuristic Ensemble (KFHE), which is a new perspective on multi-class ensemble classification with performance significantly better or at least as good as the state-of-the-art algorithms. We prove that the sample weight tuning method used in KFHE is a version of adaptive boosting, and the weight distribution does not change anymore and leads to redundant classifiers when the algorithm iterates enough times. This motivates us to select a sub-ensemble to alleviate the redundancy and improve the performance of the ensemble. An Ordering-based Kalman Filter Selective Ensemble (OKFSE) is proposed in this paper to select a sub-ensemble using the margin distance minimization approach. We demonstrate the effectiveness and robustness of OKFSE through extensive experiments on 20 real-world UCI datasets, and the statistical test shows that OKFSE significantly outperforms the state-of-the-art KFHE and clustering-based pruning methods on these datasets with 5% and 10% class label noise.

The development and improvement of agricultural financial information service system is of great significance to the development of rural modernization, the improvement of rural comprehensive competitiveness and the construction of new socialist countryside. The construction of rural financial information service platform is directly related to the quality of rural financial information service, and directly affects the construction of new socialist countryside. In order to solve the problems of information collection, processing and integration of rural financial information service platform in China, the diversification, personalization, timeliness and accuracy of information demand are difficult to be met, and the organization and operation mode of the platform are not perfect. In this paper, based on the intelligent sensor, the whole digital transformation is realized through the reference of big data. Based on this, this paper establishes the research model of rural financial information service platform under the smart financial environment. From the current situation of the construction and application of rural financial information service platform in China, it studies the basic situation of the construction of rural financial information service platform in China from three aspects of functional scope, service mode and operation mode, and draws the improvement conclusion. The results show that the efficiency of rural financial information service is increased by 20% after using the improved method in this paper, which has certain use value.

One of the difficulties faced when using XML as the data storage structure is query inefficiency. Therefore, various indexing methods have been proposed. When designing indexing methods, the first step is to choose the labeling method. Some labeling methods can work well; however, if they cannot effectively support update operations, their use is subject to considerable limitations. Most of the update-friendly labeling methods proposed in the literature assign a unique label to each node in XML and provide an expandable mechanism for future insertion. However, they encounter some difficulties, such as increasing the index space, more difficulty in evaluating the relationships between nodes, and increasing the complexity of labels. In this paper, we introduce a novel update-friendly labeling scheme called branch map, which records the correspondence between parent and child nodes instead of assigning a label to each node. The space required for the index is reduced considerably. More importantly, the branch map can maintain the profile as if it was encoded initially, even after being frequently updated. This paper also proposes a compact indexing scheme called UCIS-X. Experimental results indicate that UCIS-X performs well in terms of index size, query, and update efficiency.

The rapid development and implementation of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) in the engineering and manufacturing field have embraced a virtual identity to ensure nearly real-time adjustment. Warehouses are challenged to reassess its order fulfillment operations while simultaneously being provided with the opportunity to develop its own cloud-based CPS with the aid of IoT devices. Robotic Mobile Fulfillment System (RMFS) is a system controlling mobile robots, mobile storage rack, putaway and picking workstations, charging stations, and wireless communication infrastructure in the context of robotic-assisted warehouse. This paper addresses the value creation utilizing cloud-based CPS in RMFS. By providing an analysis of cloud services and IoT enhancement, theoretical concepts from the literatures are consolidated to solve the research que-stions on how RMFS offering better order fulfillment can gain benefits in terms of operational efficiency and system reliability. The paper also proposes a cloud-based CPS architecture, providing a comprehensive understanding on conflict avoidance strategy in the multi-layers multi-deeps warehouse layout. This research presents six conflict classifications in RMFS and provides a case study in the real-life context. Dock grid conflict is a new type of conflict appearing in multi-deeps RMFS. A scenario analysis with real customer orders is applied to present the collision detection and solution. Nanyang Technological University National Research Foundation (NRF) Published version This work was supported in part by the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University,Hong Kong under Grant RK2F and Grant K-ZM31, in part by the School of Mechanical and Aerospace Engineering, NanyangTechnological University, Singapore, in part by the Research Committee and the Department of Industrial and Systems Engineering, TheHong Kong Polytechnic University, Hong Kong and The Innovation and Technology Commission, The Government of the Hong KongSAR, Hong Kong under Project PRP/002/19FX/K.ZM31, and in part by the Delta Electronics Inc., and the National Research Foundation (NRF) Singapore through the Corporate Laboratory @ University Scheme, Nanyang Technological University, Singapore under Grant RCA-16/434 and Grant SCO-RP1.

The paper describes the design and implementation of a semantic web service that retrieves theses and extends the keyword based-search of a DSpace repository taking into account the roles of advisors and steering committee members formally represented into a custom-made ontology. The service uses SPARQL queries and the serialization module of RDF DSpace, this links the item submission process and the ontology, thus the more theses are added into a repository, the more instances are inserted into the ontology. The paper provides empirical insights about how to reuse theses metadata and includes the results of an exploratory and self-management survey of usability heuristic evaluation of a web site that enables to access the proposed service. Heuristics were estimated with a purposive sample of students, teachers, and managers, the results indicated a high satisfaction level and showed that the service increased theses accessibility in the web environment. The service also generates semantically enriched datasets that coexist with the repository, they are of utility and value to educational organizations as they give institutional visibility.

Classification and identification of plants are helpful for people to effectively understand and protect plants. The leaves of plants are the most important recognition organs. With the development of artificial intelligence and machine vision technology, plant leaf recognition technology based on image analysis is used to improve the knowledge of plant classification and protection. Deep learning is the abbreviation of deep neural network learning method and belongs to neural network structure. It can automatically learn features from big data and use artificial neural network based on back propagation algorithm to train and classify plant leaf samples. The main content of this paper is to extract plant leaf features and identify plant species based on image analysis. Firstly, plant leaf images are segmented by various methods, and then feature extraction algorithm is used to extract leaf shape and texture features from leaf sample images. Then the comprehensive characteristic information of plant leaves is formed according to the comprehensive characteristic information. In this paper, 50 plant leaf databases are tested and compared with KNN-based neighborhood classification, Kohonen network based on self-organizing feature mapping algorithm and SVM-based support vector machine. At the same time, the leaves of 7 different plants were compared and it was found that ginkgo leaves were easier to identify. For leaf images under complex background, good recognition effect has been achieved. Image samples of the test set are input into the learning model to obtain reconstruction errors. The class label of the test set can be obtained by reconstructing the deep learning model with the smallest error set. The results show that this method has the shortest recognition time and the highest correct recognition rate.

Validation is one of the most important aspects of clustering, particularly when the user is designing a trustworthy or explainable system. However, most clustering validation approaches require batch calculation. This is an important gap because of the value of clustering in real-time data streaming and other online learning applications. Therefore, interest has grown in providing online alternatives for validation. This paper extends the incremental cluster validity index (iCVI) family by presenting incremental versions of Calinski-Harabasz (iCH), Pakhira-Bandyopadhyay-Maulik (iPBM), WB index (iWB), Silhouette (iSIL), Negentropy Increment (iNI), Representative Cross Information Potential (irCIP), Representative Cross Entropy (irH), and Conn_Index (iConn_Index). This paper also provides a thorough comparative study of correct, under- and over-partitioning on the behavior of these iCVIs, the Partition Separation (PS) index as well as four recently introduced iCVIs: incremental Xie-Beni (iXB), incremental Davies-Bouldin (iDB), and incremental generalized Dunn’s indices 43 and 53 (iGD43 and iGD53). Experiments were carried out using a framework that was designed to be as agnostic as possible to the clustering algorithms. The results on synthetic benchmark data sets showed that while evidence of most under-partitioning cases could be inferred from the behaviors of the majority of these iCVIs, over-partitioning was found to be a more challenging problem, detected by fewer of them. Interestingly, over-partitioning, rather then under-partitioning, was more prominently detected on the real-world data experiments within this study. The expansion of iCVIs provides significant novel opportunities for assessing and interpreting the results of unsupervised lifelong learning in real-time, wherein samples cannot be reprocessed due to memory and/or application constraints.

Bus arrival prediction has important implications for public travel, urban dispatch, and mitigation of traffic congestion. The factors affecting urban traffic conditions are complex and changeable. As the predicted distance increases, the difficulty of traffic prediction becomes more difficult. Forecast based on historical data responds quite slowly for changes under the short-term conditions, and vehicle prediction based on real-time speed is not sufficient to predict under long-term conditions. Therefore, an arrival prediction method based on temporal vector and another arrival prediction method based on spatial vector is proposed to solve the problems of remote dependence of bus arrival and road incidents, respectively. In this paper, combining the advantages of the two prediction models, this paper proposes a long short-term memory (LSTM) and Artificial neural networks (ANN) comprehensive prediction model based on spatial-temporal features vectors. The long-distance arrival-to-station prediction is realized from the dimension of time feature, and the short-distance arrival-to-station prediction is realized from the dimension of spatial feature, thereby realizing the bus-to-station prediction. Besides, experiments were conducted and tested based on the entity dataset, and the result shows that the proposed method has high accuracy among bus arrival prediction problems.

Uncertainty measurement of the basic probability assignment function has always been a hot issue in Dempster-Shafer evidence. Many existing studies mainly consider the influence of the mass function itself and the size of the frame of discernment, so that the correlation between the subsets is ignored in the power set of the frame of discernment. Without making full use of the information contained in the evidence, the existing methods are less effective in some cases given in the paper. In this paper, inspired by Shannon entropy and Deng entropy, we propose an improved entropy that not only inherits the many advantages of Shannon entropy and Deng entropy, but also fully considers the relationship between subsets, which makes the improved entropy overcome the shortcomings of existing methods and have greater advantages in uncertainty measurement. Many numerical examples are used to demonstrate the validity and superiority of our proposed entropy in this paper.

This paper deals with the principal aspects emerging from the application of trust techniques to the IoT domains with respect to the particular viewpoint of an IoT environment. We consider intelligent agents technology to add social behavior to the community of the smart objects, and we analyze the fundamental role of the concepts of trust and reputation in this perspective. Also, we analyze the existing architectures for IoT and explain how to integrate IoT with fog/edge computing. Besides discussing the main proposals present in the literature, the key contribution of this paper consists of providing a comparative study of the main current architectures for modeling trust in IoT environments. In this setting, we propose a comparison based on the important characteristics of the IoT layer and the architecture type. Such an analysis allows us to highlight both advantages and limitations of each approach, and to discuss the emerging research challenges.

In the practice of cloud manufacturing, there still exist some major challenges, including: 1) cloud based big data analytics and decision-making cannot meet the requirements of many latency-sensitive applications on shop floors; 2) existing manufacturing systems lack enough reconfigurability, openness and evolvability to deal with shop-floor disturbances and market changes; and 3) big data from shop-floors and the Internet has not been effectively utilized to guide the optimization and upgrade of manufacturing systems. This paper proposes an open evolutionary architecture of the intelligent cloud manufacturing system with collaborative edge and cloud processing. Hierarchical gateways connecting and managing shop-floor things at the “edge” side are introduced to support latency-sensitive applications for real-time responses. Big data processed both at the gateways and in the cloud will be used to guide continuous improvement and evolution of edge-cloud systems for better performance. As software tools are becoming dominant as the “brain” of manufacturing control and decision-making, this paper also proposes a new mode - “AI-Mfg-Ops” (AI enabled Manufacturing Operations) with a supporting software defined framework, which can promote fast operation and upgrading of cloud manufacturing systems with smart monitoring-analysis-planning-execution in a closed loop. This research can contribute to the rapid response and efficient operation of cloud manufacturing systems.

Contribution: Recently, real-time data warehousing (DWH) and big data streaming have become ubiquitous due to the fact that a number of business organizations are gearing up to gain competitive advantage. The capability of organizing big data in efficient manner to reach a business decision empowers data warehousing in terms of real-time stream processing. A systematic literature review for real-time stream processing systems is presented in this paper which rigorously look at the recent developments and challenges of real-time stream processing systems and can serve as a guide for the implementation of real-time stream processing framework for all shapes of data streams. Background: Published surveys and reviews either cover papers focusing on stream analysis in applications other than real-time DWH or focusing on extraction, transformation, loading (ETL) challenges for traditional DWH. This systematic review attempts to answer four specific research questions. Research Questions: 1)Which are the relevant publication channels for real-time stream processing research? 2) Which challenges have been faced during implementation of real-time stream processing? 3) Which approaches/tools have been reported to address challenges introduced at ETL stage while processing real-time stream for real-time DWH? 4) What evidence have been reported while addressing different challenges for processing real-time stream? Methodology: A systematic literature was conducted to compile studies related to publication channels targeting real-time stream processing/joins challenges and developments. Following a formal protocol, semi-automatic and manual searches were performed for work from 2011 to 2020 excluding research in traditional data warehousing. Of 679,547 papers selected for data extraction, 74 were retained after quality assessment. Findings: This systematic literature highlights implementation challenges along with developed approaches for real-time DWH and big data stream processing systems and provides their comparisons. This study found that there exists various algorithms for implementing real-time join processing at ETL stage for structured data whereas less work for un-structured data is found in this subject matter.

Recently, radar high-resolution range profile (HRRP) recognition based on convolutional neural networks (CNNs) has received considerable attention due to its robustness to translation and amplitude changes. Most of the existing methods require that sufficient labeled data with complete aspect angles be used as training data, which is a difficult task in practice. In addition, HRRP signals have a high sensitivity to the aspect angle. Therefore, the representative and discriminative powers of the features extracted from typical CNN models are reduced due to incomplete aspect angles in the training data, which significantly limit the recognition performance. This paper first considers the problem of HRRP recognition with incomplete aspect angle training data and addresses the problem by a deep transfer learning framework. Specifically, the two proposed methods enhance the recognition performance by exploring the discriminative power and the intraclass consistency with auxiliary data, which have HRRP signals with complete aspect angles. This paper generates a simulated HRRP dataset from public data to validate the proposed work. The comparisons of the recognition results demonstrate that the proposed framework outperforms the latest CNN-based models.

In recent years, with the rapid development of wind power generation, some problems are gradually highlighted. At present, one of the essential methods to solve these problems is to predict wind speed. In this paper, a hybrid BRR-EEMD method is proposed for short-term wind speed prediction based on the Bayesian ridge regression prediction method and ensemble empirical mode decomposition. We use ensemble empirical mode decomposition of the hybrid method to decompose complex time series of wind speed into several relatively milder, more regular, and stable subsequences. Then each subsequence is carried out by using the Bayesian ridge regression method. The value of each subsequence is predicted by it. Finally, the value of multiple subsequences is fused to form the prediction results of the original complex time series of wind speed. In order to verify the proposed method comprehensively, this paper selects two data to test. According to the results, predicted values have shown higher accuracy compared with the various prediction methods. Therefore, the hybrid BRR-EEMD method is accurate and effective in predicting wind speed, which has practical significance and potential value.