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The working life evaluation of metal oxide varistor (MOV) is always expected by MOV manufacturers and users. In Oct. 2019, the International Electrotechnical Commission (IEC) SC37B established a project team PT 61643–333 led by the author of this paper to draft a technical report about MOV life assessment. As an early researching achievement of PT 61643-333, this paper provides an evaluation method for the impulse life of MOV with the median rank regression (MRR) method based on the Weibull distribution, which provides an effective means for evaluating the impulse life of MOV. Firstly, the theory of Weibull distribution and impulse life characteristics of MOV are introduced. Then the evaluation method and procedure of MOV impulse life is derived from the combination of the MRR method and the impulse characteristics of MOV. Finally, an application example of the proposed evaluation method is given with the test results from real MOV samples. By establishing three basic impulse life characteristics equations, the life evaluation of MOV under arbitrary impulse current can be achieved. In addition, the fitting validity of six groups of samples is checked to prove the effectiveness of the proposed parameter model. And further sensitivity analysis of the variables has been done to examine the effect of changes in the values of the parameters on the propose model.

This paper proposes definitions of implementation and adoption delays, arising from firm and client behaviors, in the context of market share dynamics. Information delay refers to the existence of lags in the information used for the advertising policy, while adoption delay refers to the existence of a lag in the effect of the advertising policy. These are natural lags in the flow of information and have not been considered in several models proposed in the literature. In this paper, these delays are introduced into recently proposed extensions of the Vidale-Wolfe-Deal and Lanchester models of market share dynamics subjected to affine advertising control policies. Conditions for stability of the equilibrium market share are derived. In addition, it is shown that Hopf bifurcations leading to oscillatory behavior exist for certain parameter values, and corresponding conditions for these are given. The main results are: the equilibrium market shares of the extended Vidale-Wolfe-Deal and Lanchester models are both robust to implementation delays, but, in the case of adoption delays, for both models, numerical results show that there is a critical value such that if the sum of the adoption delays exceeds this value, there is an onset of oscillations of market shares, through a Hopf bifurcation.

Vision-based localization systems, namely visual odometry (VO) and visual inertial odometry (VIO), have attracted great attention recently. They are regarded as critical modules for building fully autonomous systems. The simplicity of visual and inertial state estimators, along with their applicability in resource-constrained platforms motivated robotic community to research and develop novel approaches that maximize their robustness and reliability. In this paper, we surveyed state-of-the-art VO and VIO approaches. In addition, studies related to localization in visually degraded environments are also reviewed. The reviewed VO techniques and related studies have been analyzed in terms of key design aspects including appearance, feature, and learning based approaches. On the other hand, research studies related to VIO have been categorized based on the degree and type of fusion process into loosely-coupled, semi-tightly coupled, or tightly-coupled approaches and filtering or optimization-based paradigms. This paper provides an overview of the main components of visual localization, key design aspects highlighting the pros and cons of each approach, and compares the latest research works in this field. Finally, a detailed discussion of the challenges associated with the reviewed approaches and future research considerations are formulated.

The AC / DC active distribution network (ADN) connected to a large number of renewable energy source (RES) and new loads such as EV will become the main form of the future ADN, which intensifies the volatility and uncertainty of the net load. To cope with this situation, considering various types of flexibility resources, this paper quantitatively evaluates the flexibility demand and supply in the system, and constructs a day-ahead flexible operation model of AC/DC ADN with a high proportion of RES. At first, the flexibility demand is quantified firstly by considering volatility and uncertainty comprehensively, and the conditional probability distribution of wind power volatility is obtained through the analysis of mutual information and other correlation coefficients. Then, a quantitative method to the time flexibility provided by orderly charging of electric vehicles (EVs) is proposed for the flexible cooperation with other resources. Besides, voltage source converter (VSC) with precise loss is considered as flexibility source to participate in dispatching. Furthermore, considering the preference of flexibility in different directions, a weighted node flexibility margin index is proposed. Considering the accuracy and computational efficiency, a second order cone relaxation method which is suitable for the elaborate VSC model is applied in this paper. In the end, Test results of an improved IEEE33-bus system with a three-terminal DC network show that the proposed model can improve the flexibility and reduce the operation cost of the ADN.

With the growing demand for information security, finger-vein recognition has become widespread. However, the robustness of the recognition process becomes a major problem. When identifying unseen categories with traditional finger-vein recognition systems, a few issues remain, such as recognition interference and low efficiency. This paper proposes a Deep Generalized Label Finger-Vein (DGLFV) model to extract feature maps and achieve high-accuracy recognition. The largest rectangular finger-vein region is extracted through image semantic segmentation and the advanced bidirectional traversing and center diffusion method for the known categories. Then we generalize all the unseen categories actively as Class $C+1$ to reduce interference from unregistered users. Furthermore, an adaptive threshold acquisition algorithm is proposed for Label Receiver Operating Characteristic (LROC), so that the procedures of classification, recognition, and verification are unified. Apart from Shandong University Homologous Multi-modal Traits (SDUMLA-HMT), we have conducted additional experiments on our self-built database, Finger Veins of Signal and Information Processing Laboratory (FV-SIPL). The recognition accuracy of the approach proposed in this paper has reached 99.25% and 99.08% testing on FV-SIPL and SDUMLA-HMT, with a low error rate at 1.481% and 2.228% and little time consumption of 0.157s for a single image, which is better than most state-of-the-art finger-vein recognition methods.

This paper presents a lightweight Multi-task Semantic Attention Network (MTSAN) to collectively deal with object detection as well as semantic segmentation aiding real-time applications of the Advanced Driver Assistance Systems (ADAS). This paper proposes a Semantic Attention Module (SAM) that introduces the semantic contextual clues from a segmentation subnet to guide a detection subnet. The SAM significantly boosts up the detection performance and computational cost by considerably decreasing the false alarm rate and it is completely independent of any other parameters. The experimental results show the effectiveness of each component of the network and demonstrate that the proposed MTSAN yields a better balance between accuracy and speed. Following the post-processing methods, the proposed module is tested and proved for its accuracy in the Lane Departure Warning System (LDWS) and Forward Collision Warning System (FCWS). In addition, the proposed lightweight network is deployable on low-power embedded devices to meet the requirements of the real-time applications yielding 10FPS @ 512 X 256 on NVIDIA Jetson Xavier and 15FPS @ 512 X 256 on Texas Instrument’s TDA2x.

In this paper, we explore Android applications’ file access characteristics, and find out that smartphone file accesses are different from traditional desktop applications in terms of the following aspects. 1) There exist a limited number of hot blocks, which are accessed consistently during the entire execution of an application. 2) Block accesses in Android are highly biased such that the top 20% blocks account for 80% of total accesses. 3) Hot blocks of the top 100 rankings are mostly involved in SQLite. 4) Unlike desktop applications, file accesses in Android applications are write-intensive. 5) In predicting future file accesses in Android applications, frequency is a better estimator than temporal locality. 6) The effect of traditional buffer cache is limited in Android as file I/O in Android has a lot of synchronous writes, which incurs immediate storage flushing. Based on these analyses, this paper presents the implication of buffer cache management in Android. Specifically, we add a small non-volatile write buffer and present how this write buffer can be managed efficiently. Experimental results show that the proposed scheme improves the storage write traffic by an average of 21.7% and a maximum of 48.1% compared to the conventional buffer cache system.

Due to the large overshoot and poor disturbance rejection impact the control accuracy of the permanent magnet synchronous motor (PMSM) in the proportional–integral–derivative (PID) control mode. In this paper, we propose an optimal double-loop pseudo-derivative feedback (PDF) control strategy. Firstly, to obtain high-precision feedback current and minimize the influence of sampling delay, a double-feedback PDF control structure is configured based on the $\Sigma $ – $\Delta $ sampling modulation and Sinc3 filter. Secondly, we adopt an instant-sampling and instant-update (ISIU) strategy based on a field-programmable gate array (FPGA), which can reduce the update delay of the pulse-width modulation (PWM), and design the hardware timing diagram, accordingly. Then, based on the influence of the closed-loop pole position on the dynamic performance of the system and the saturation characteristics of the power device, the PDF parameters tuning method of the current loop and speed loop is proposed. Finally, the system performance under different control strategies is compared and analyzed through simulations and experiments. The results show that the optimal double-loop PDF control strategy proposed in this paper can considerably expand the bandwidth of the current loop and has better disturbance rejection.

Taking the perspective of cybernetics theory, enterprise patent management can be described as a controllable information system which includes various activities and performances. This paper aims to analysis the relationship between enterprise patent activities and patent performance, and to help convert technological innovation achievements into patent rights more effectively and consolidate technology superiority. This paper uses System Dynamics method to construct a multiscale nonlinear enterprise patent management model. Validity of this model is tested, simulation is conducted in VENSIM, sensitivity analysis of this model shows that: Technology Disclosure Sufficiency does not have much influence on patent application quantity, but it has strongly positive effect on patent grant rate and patent portfolio diversity, it can also lower the risk of patent circumvention. Simulation shows that technology disclosure sufficiency is a key determinant to reduce information loss and improve output efficiency of enterprise patent management system. Collaboration of R&D personnel, patent engineers and patent agencies should be enhanced to improve TDS, so as to promote enterprise patent management efficiency. This SD model provides a foundation for future research in patent management optimization.

Context: In this paper, we study distributed and decentralized systems in which each part is modeled as an agent in a multi-agent system. Those systems provide more scalable and easier ways to control complex, distributed and interconnected systems of embedded components. We are particularly interested in methods to secure these systems. Objectives: This study aims to identify the main security properties studied, the parts of a multi-agent architecture that are considered most often in security studies and the technical solutions used to secure those systems. Methods: We conducted a systematic mapping study on research works addressing the security of multi-agent systems with embedded agents. We identified which security features were addressed, and their roles in global security architecture. Results: We identified 70 papers published in journals and conferences. We classified the extracted data reporting a tendency to focus on securing the availability of systems under attack by means of trust schemes, sometimes supported by cryptographic primitives. Conclusion: The use of cryptography appears to be limited in decentralized systems. However, solutions should be provided to overcome those limits as other solutions such as trust schemes do not protect the system from the same type of attacks.

In this paper, we introduce a subtitle synchronization and positioning system designed to increase the accessibility of deaf and hearing impaired people to multimedia documents. The main contributions of the paper concern: a novel synchronization algorithm able to robustly align, without any human intervention, the closed caption with the audio transcript and a timestamp refinement technique that adjusts the subtitle segments duration with respect to the audiovisual recommendations. Finally, we introduce a novel method that performs a high level understanding of the multimedia content, in order to determine the subtitle optimal positions, within the video frame, such that they do not overlap with other relevant textual information. The experimental evaluation performed on a large dataset of 30 videos taken from the French national television validates the approach with average accuracy scores superior to 90% regardless on the video genre. The subjective evaluation of the proposed subtitle synchronization and positioning system, performed with actual hearing impaired people, demonstrates the effectiveness of our approach.

This paper is concerned with the optimal state estimation problem for linear discrete-time systems with both multiplicative and cross-correlated noises. The measurement outputs for state estimation are collected from multiple sensors whose sampling rates are different that provide asynchronous data. In addition, the noises that affect the measurement information are correlated among different sensors and also coupled with the process noises as well. The aim of the addressed problem is to propose an optimal state estimation algorithm such that the estimation error is minimized in the mean-square sense with the existence of asynchronous data, possible sensor faults and correlated noises. In order to mitigate the impact of measurement missing, this paper utilizes neural networks to compensate the state estimation when measurement packets are dropping. Then, a fault detection mechanism that utilizes normalized innovation test is adopted to ensure that the abnormal data would be detected and removed. By resorting to the projection theorem and mathematical induction approach, sufficient conditions are derived for the existence of the desired optimal state estimator where the optimized estimation gains are formulated and can be computed iteratively at each time step. The proposed theoretical results are demonstrated via an illustrative numerical example.

Communication overhead is a key task for the large-scale wireless sensor networks (WSNs) in the cooperative localization problems. This paper is aimed to propose a low-overhead algorithm without sacrificing the performance of localization, which is more practical for energy-saving WSNs. In this paper, the cooperative localization problem is formulated as a variational reference problem on a factor graph (FG). Combining the high performance of particle-based algorithms and the overhead advantage of parametric ones, a parametric variational sum-product algorithm (PVSPA) is proposed for the cooperative localization of WSNs. In prediction operation, to ensure high accuracy, messages referenced inside sensor nodes are particle-based. In correction operation, Gaussian parametric representation is adopted to the observation message and the communication overhead is obviously decreased. Simulation results show that the localization accuracy of PVSPA equally matches the classic particle-based algorithms, while the communication overhead of it is far lower.

Multimedia sensors have recently become a significant data source in the Internet of Things (IoT), giving rise to the Internet of Media Things (IoMT). Since multimedia applications are usually latency-sensitive, data processing in the cloud is not always suitable. A strategy to minimize delay is processing multimedia streams closer to data sources, exploiting resources at the network edge. Moreover, virtualization is widely used to reduce complexity arising from heterogeneity in IoT environments. In this paper, we propose an edge-based architecture and platform to manage virtual multimedia sensors (VMS), enabling IoMT applications to be easily deployed. Our proposal encompasses V-PRISM, a software architecture tailored for IoMT, and ALFA, a distributed implementation of the architecture. V-PRISM components were designed to be deployed and executed in multiple edge nodes. VMSs are in charge of processing multimedia streams and provide an abstraction layer between IoMT applications and physical devices that produce those streams. This paper describes the proposal and the results of experiments showing that the proposed approach can successfully perform multimedia stream processing for applications that requires low-latency.

This paper presents a bio-inspired intelligent controller to enhance the performance of a nonlinear system. The controller is designed by capturing the emotional intelligence of mammalian brain mediated by the limbic system in which certain parts are responsible for generating emotions and these can be combined together as brain affective system inspired control architecture (BASIC). This controller has been used to cope with nonlinearities present in control applications. In this paper brain inspired control architecture is proposed while incorporating the sensory cortex explicitly in the architecture and the computational equations of various modules are suitably developed. The performance of the proposed controller is analyzed on a typical nonlinear system, namely, Permanent magnet synchronous motor for speed control, harmonics in stator phase currents and ripples produced in electromagnetic torque at different operating conditions. Its performance is compared with the existing control techniques in offline simulations as well as in real-time Hardware-in-loop environment. The results establish the computational efficiency, accuracy and robustness of the proposed controller.

Starting with a product quality problem, this paper presents the practical applications of Industry 4.0 technology to solve the problem in manufacturing technique and to establish manufacturing process control in new product introduction (NPI) with flexibility and reconfigurability, under the condition that this is a new plant with limited resources. With regard to solving the manufacturing technique problem, the Finite Element Method (FEM) has been used to analyze failure mode and to support design optimization, the regression analysis has been adopted to obtain the force-deformation relationship and to guide the improvement for manufacturing parameter adaptation. The analysis method that combined the FEM analysis and the regression analysis can be an effective approach for dealing with manufacturing technique problem. With regard to establishing the manufacturing process control, the Radio Frequency Identification (RFID) implementation functions as the medium for data transfer and for status confirmation, thus realizing the process control and the quality control in the form of cyber-physical system (CPS). Data strategy of the process control and the quality control can be applied to general manufacturing system. The Industry 4.0 implementations and methods mentioned in this paper can serve as a reference for the company that are planning to build a small or medium-sized manufacturing plant and to enhance its performance with Industry 4.0.

Vehicular Ad hoc Networks (VANETs) are a subset of the Internet of Things (IoT) that are used in smart traffic applications. Due to their high speed, mobility, and exposure to the environment, the security requirements for VANETs result in the conflicting design goals of protecting member privacy while also ensuring non-repudiation. Group signature schemes can fulfill these requirements, but often at the cost of expensive bilinear pairing operations. Furthermore, the cost of updating the group key information can be costly. Accordingly, this paper has two goals. First, it presents a group signature scheme that has been modified to remove pairing operations by caching computed values, while still preserving the critical requirement of conditional privacy. Second, this paper presents an argument for the abandonment of perfect forward and backward secrecy in VANET schemes in order to prevent the generation of keys that are never used, or used only once, and reduce the twin burdens of excessive key recalculation and key redistribution on the system.

Existing beamforming arrays suffer from the size and cost of the RF front-end and digital back-end components. In this paper, a novel hybrid beamforming configuration for wideband receivers is introduced. The design replaces phase shifters and local oscillators (LO) with cross mixing antenna elements to maximize diversity gain. In this paper, an analytical model of the cross mixing beamformer (CMB) is first presented. Simulations are carried out showing that a maximum diversity gain can be achieved with the CMB approach. Two prototypes were implemented using $2\times 1$ and $4\times 2$ element arrays and tested at 2.31 GHz. Measured results show that CMB achieves coherent signal combining and can preserve the phase delay information needed for hybrid beamforming setups.

Typical fault scenarios such as actuator stuck, wingtip damage, and sudden center of gravity change usually bring great challenge to fault-tolerant flight control laws. In this paper, adaptive sliding mode disturbance observer (SMDO) is studied under the background of designing a fault-tolerant flight controller. This paper proposes a switched adaptive SMDO for observation of system uncertainties considering fault accommodation. It consists of two adaptive SMDOs: baseline gain-adaptation SMDO and fault-oriented gain-adaptation SMDO. The former is designed for observing slow-changing uncertainties, and can guarantee that the observer gains are small when disturbances are small; the latter aims at achieving fast observing and accommodating of sudden and large faults. Thereafter, a switching logic is developed for the switch between these two adaptive SMDO modes to ensure that the fast gain-adaptation SMDO only works in the case that the uncertainties are large and fast-changing. Finally, the switched adaptive SMDO is integrated with an incremental terminal sliding mode controller, and are validated using simulation experiments based on the nonlinear F-16 model.

The attention-based SeriesNet (A-SeriesNet) combined augmented attention residual learning module-based convolutional neural network (augmented ARLM-CNN) subnetwork with hidden state attention module-based recurrent neural network (HSAM-RNN) subnetwork for conditional time series prediction with high accuracy. The augmented ARLM-CNN subnetwork has defects in extracting latent features of the multi-condition series. The forecasting accuracy will decrease when the feature dimension of the multi-condition series becomes high. The same problem also occurs in the HSAM-RNN subnetwork of A-SeriesNet. The dual-stage attention recurrent neural network (DA-RNN) proved that the attention-based encoder-decoder framework is an effective model for dealing with the above problem. This paper applies the DA-RNN to the HSAM-RNN subnetwork of A-SeriesNet and presents the triple-stage attention-based recurrent neural network (TA-RNN) subnetworks. Furthermore, this paper considers a CNN-based encoder-decoder structure named dual attention residual learning module-based convolutional neural network (DARLM-CNN) subnetwork to improve the augmented ARLM-CNN subnetwork of A-SeriesNet. Finally, this paper presents the triple-stage attention-based SeriesNet (TA-SeriesNet), which uses a new concatenation method instead of the element-wise multiplication of A-SeriesNet to parallel connect the proposed subnetworks and reduce the dependence of forecasting results on a certain subnetwork. The experimental results show our TA-SeriesNet is superior to other deep learning models in forecasting accuracy evaluation metrics for high feature dimensional time series datasets.

The rapid development of tools for communication such as social networks, tweeting and Whatsapp has generated a large mass of important textual data. Also, the COVID-19 pandemic has inflamed social networks, hence the automatic analysis of opinions has become paramount. The purpose of this paper is to analyze Arabic tweets in terms of positivity, negativity, or neutrality.In analyzing the opinions of the Arabic language, a real challenge is encountered, which lies in the use of different dialects (Egyptian, Saudian, Maghrebian, Gulfian, Levantine, Syrian $\ldots $ ). In this paper, we introduce two major components: The first employs six machine learning (ML) methods, including Decision Trees (DT), Logistic Regression (LR), k Nearest Neighbors (K-NN), Random Forests (RF), Support Vector Machines (SVM), and Nave Bayes (NB), with the TF-IDF method acting as the feature extraction.While, the second part consists of testing three variants of Deep Learning (DL) based on multiplicative Long Short Term Memory (mLSTM), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) by applying word embedding as the input vector. The experimental study was validated using three Arabic language corpora (TEAD, ATSAD, and ASTD) and two learning modes (Hold out and 10-folds cross validation). The obtained results in terms of Accuracy (ACC), Precesion (PREC), Recall (REC), and F1-score (F1) show a clear performance for DL techniques based on a 10-folds strategy compared to the state-of-the-art. The experiments shown in the paper reveal that the proposed DL models accomplished the best results.

In response to national and international carbon reduction goals, renewable energy resources like photovoltaics (PV) and wind, and energy storage technologies like fuel-cells are being extensively integrated in electric grids. All these energy resources require power electronic converters (PECs) to interconnect to the electric grid. These PECs have different response characteristics to dynamic stability issues compared to conventional synchronous generators. As a result, the demand for validated models to study and control these stability issues of PECs has increased drastically. This paper provides a review of the existing PEC model types and their applicable uses. The paper provides a description of the suitable model types based on the relevant dynamic stability issues. Challenges and benefits of using the appropriate PEC model type for studying each type of stability issue are also presented.

State-of-health (SOH) estimation is a critical factor in ensuring the efficiency, reliability, and safety of lithium-ion batteries (LIBs) in electric vehicles (EVs). However, due to the complexity of electrochemical processes in batteries and the dynamics of working conditions, it is challenging to estimate SOH accurately, especially in real-world EV application scenarios. Thus, various data-driven methods with robust and adaptive features for SOH estimation have been widely proposed in the current literature. However, there is a lack of a comprehensive investigation and performance comparison of those methods, which makes them hard to be adopted in practice. Hence, in this paper, we have studied current major data-driven methods with real-world EV battery data to evaluate the performance. Besides, we summarize each method’s advantages and limitations with the consideration of the critical features required to achieve accurate SOH estimation in real-world applications. Hopefully, this paper provides a practical insight into the related fields.

Technology forecasting not only helps business managers to make the right decisions but also helps researchers to grasp the direction of technology development. Technology forecasting, which facilitates the identification of the development technologies with high potential, can be an effective tool to support the management and plan for the future research activities. For this purpose, this paper firstly constructs Multi-modal input based on deep learning (MIDL) text classification model to extract relevant SCI papers from Web of Science database from 1996 to 2019 for topic classification, and then apply the Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM) neural networks to build an EEMD-LSTM technology forecasting model to predict the future development of each research field. Besides, we verify the validity of the method by taking robotics as an example in this paper. The results show that the accuracy, recall and F1 of MIDL text classification model are 0.826, 0.822 and 0.824 respectively. As compared with the optimal results of other classification models, the accuracy, recall and F1 are improved by 4.1%, 3.5%, and 3.8% respectively. The mean MAPE of the EEMD-LSTM model is 7%, which is 11% lower than ARIMA, 8% lower than LSTM, and 10% lower than 2-layer-LSTM.

The exploitation of aerial base stations (A-BSs) in conjunction with terrestrial base stations (T-BSs) is envisioned as a promising solution to provide connectivity to devices and user-equipment (UE) in crowded situations (viz. in the sports event) and emergency situations (viz. in the disaster management). However, the use of A-BSs with existing terrestrial networks intensifies the inter-cell interference (ICI) to the devices and UEs, therefore leading to a degraded signal-to-interference-ratio (SIR). This paper addresses this issue by exploiting different radio access technology (RAT) (mmWave/microwave) for aerial and terrestrial networks. Indeed, the network connectivity is always a top priority for all applications. However, there are also some applications such as remote patient monitoring, and remote working, which requires both coverage and high data-rates. But, most of the existing research claims the trade-off between the coverage and the data-rate performance. Whereas this paper aims to improve coverage and rate simultaneously in an aerial-terrestrial networks by employing an optimal combination of mmWave and microwave RAT based on the proposed association strategy. The essential analysis of such an integrated network involves the evaluation of parameters based on the analytic model. Hence, this paper analytically obtains the coverage probability (CP) and average rate expressions for the proposed integrated aerial-terrestrial networks. The analysis is supported by probabilistic models-based simulations that agree closely with analytical results. The results claim that the proposed model leads to improved performance in terms of both CP and average rate. Also, the paper provides parametric analysis for CP and rate with A-BSs height and A-BSs density to enable its practical implementation in 5G/6G technologies

Blockchain has recently attracted significant academic attention in research fields beyond the financial industry. In the Internet of Things (IoT), blockchain can be used to create a decentralized, reliable, and secure environment. The use of blockchain in IoT applications is still in its early stages, particularly at the low end of the computing spectrum. As a result, the future roadmap is hazy, and several challenges and questions must be addressed. Several articles combining blockchain technology with IoT have recently been released, but they are limited to shallow technological potential discussions, with very few providing an in- depth examination of the complexities of implementing blockchain technology for IoT. Therefore, this paper aims to coherently and comprehensively provide current cutting-edge efforts in this direction. It provides a literature review of IoT and blockchain integration by examining current research issues and trends in the applications of blockchain-related approaches and technologies within the IoT security context. We have surveyed published articles from 2017 to 2021 on blockchain-based solutions for IoT security, taking into consideration different security areas and then, we have organized the available articles according to these areas. The surveyed articles have been chronologically organized in tables for better clarity. In this paper, we try to investigate the vital issues and challenges to the integration of IoT and blockchain, and then investigate the research efforts that have been conducted so far to overcome these challenges.

In this paper, we propose a multi-branch neural network architecture named Coupled Modular Neural Network (CMNN). A CMNN is a network consisting of $\beta $ closely coupled sub-networks, where $\beta $ is termed as the branching factor in this paper. We call the whole network a super-graph and each sub-network a sub-graph. Each sub-graph is a stand-alone neural network and shares a common block with other sub-graphs. To effectively leverage the super-graph we propose a simple but easy-to-implement Round-Robin-based learning algorithm. Each training iteration contains two phases. In the first phase, we choose a sub-graph in a Round-Robin fashion and train it using knowledge of the super-graph (distillation). In the second phase, we fine-tune the super-graph based on the updated sub-graphs. This algorithm produces a different copy of the super-graph at each iteration which acts as an improved teacher network for the sub-graph; and a different copy of one of the sub-graphs which functions as a new building block for the super-graph. To validate and test CMNN and the proposed algorithm, we conduct experiments on CIFAR-10, CIFAR-100, Tiny ImageNet and a private On-Road-Risk (ORR) datasets. Empirical results on all these four datasets indicate that we not only obtain a strong sub-graph network, the learning framework can also produce strong ensemble performance which substantiates the diversity introduced throughout the learning framework.

Data parallel applications in data centers generate, process, and store huge volumes of data. Coflow Completion time (CCT) is one of the major performance metrics to capture application-level semantics. This paper is the first one to study the joint consideration of task placement, coflow bandwidth scheduling, and path choice to minimize the average CCT in intra-data center. This paper proposes a joint online scheduling framework, which first develops a 2-approximation algorithm to reduce the CCT of a single coflow, and then follows the Shortest Remaining Time First (SRTF) principle to schedule multiple coflows. Extensive simulations based on practical trace demonstrate that the proposed framework has better performance than the state-of-the-art works.

Most academic courses in information and communication technology (ICT) or engineering disciplines are designed to improve practical skills; however, practical skills and theoretical knowledge are equally important to achieve high academic performance. This research aims to explore how practical skills are influential in improving students’ academic performance by collecting real-world data from a computer programming course in the ICT discipline. Today, computer programming has become an indispensable skill for its wide range of applications and significance across the world. In this paper, a novel framework to extract hidden features and related association rules using a real-world dataset is proposed. An unsupervised k-means clustering algorithm is applied for data clustering, and then the frequent pattern-growth algorithm is used for association rule mining. We leverage students’ programming logs and academic scores as an experimental dataset. The programming logs are collected from an online judge (OJ) system, as OJs play a key role in conducting programming practices, competitions, assignments, and tests. To explore the correlation between practical (e.g., programming, logical implementations, etc.) skills and overall academic performance, the statistical features of students are analyzed and the related results are presented. A number of useful recommendations are provided for students in each cluster based on the identified hidden features. In addition, the analytical results of this paper can help teachers prepare effective lesson plans, evaluate programs with special arrangements, and identify the academic weaknesses of students. Moreover, a prototype of the proposed approach and data-driven analytical results can be applied to other practical courses in ICT or engineering disciplines.

This paper considers the economy and reliability of the microgrid cluster system, and proposes a bi-level optimized operation strategy for the microgrid cluster, which aims to improve the economic benefits of the microgrid cluster and reduce the operating risk of microgrid. The upper layer takes the microgrid cluster as the research object, with the goal of minimizing the total operating cost of the system, and establishes the optimal model of the microgrid cluster. The lower layer takes the sub-microgrid as the research object, and establishes the microgrid optimization model with the goal of optimal operating cost and operating risk index. The method of combining mixed integer linear programming and IABC algorithm is used to solve the multi-objective bi-level optimization model, and to provide decision makers with two microgrid dispatching schemes with different focuses. Finally, an example is used to verify the effectiveness of the bi-level optimization strategy and improved algorithm proposed in this paper, as well as the practicability of the two dispatching schemes.

Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumours. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: a lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted a great deal of attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic literature review of the applications of GANs in anomaly detection, covering 128 papers on the subject. The goal of this review paper is to analyze and summarize: (1) which anomaly detection techniques can benefit from certain types of GANs, and how, (2) in which application domains GAN-assisted anomaly detection techniques have been applied, and (3) which datasets and performance metrics have been used to evaluate these techniques. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area., Comment: 34 pages,5 figures

In this paper, stabilization control of a class of delayed inertial neural networks (INN) is investigated. Employing matrix measure method and two Halanay-type inequalities, some succinct stabilization criteria in terms of algebraic inequalities are derived for the INN with time-varying delays under periodically intermittent control (PIMC) strategy. Moreover, more precise results are obtained to stabilize the INN with time-invariant delays by using comparison principle. Specifically, the criteria of matrix measure form proposed in this paper can be converted into LMI-type condition for the case of 2-norm, which provides a bridge between the matrix-measure method and the Lyapunov function method. Finally, two numerical examples validate the efficacy of the derived results. The comparative research shows that the proposed methods generalize and develop some known results.

This paper presents the application of different approaches of intelligent controllers based on LAMDA (Learning Algorithm for Multivariate Data Analysis) to perform the trajectory tracking task of a mobile robot Pioneer 3-DX simulated in the Virtual Robot Experimentation Platform V-REP environment. The paper proposes the design of a cascade control strategy composed of two controllers, the external controller based on a feedback linearization for the robot kinematics and the internal controller applied to the robot dynamics where the LAMDA approaches are applied. In this work, it is considered an uncertain dynamic model. Thus, its identification and control are based on a reduced-order model for designing purposes. Finally, the dynamic controller is designed using different controllers based on LAMDA, such as LAMDA-PID, LAMDA-Sliding Mode Control, and Adaptive LAMDA. To perform a comparative analysis between them that includes a conventional PID, SMC, and Fuzzy-PID, different trajectories are examined to evaluate the methods. The obtained results are compared qualitatively and quantitatively to determine which proposal performs the most accurate control and presents the best results in terms of performance.

As a non-contact measurement technology with high data acquisition efficiency, photogrammetry is an ideal choice for collecting the data needed in the safety evaluation of port hoisting machinery. However, the radius fitting result accuracy cannot meet the requirements of safety assessment due to the limitation of the port crane itself and the working environment characteristics, when the existing photogrammetry method is used to measure the rotary body structure represented by the portal crane slewing mechanism. In order to solve this problem, an iterative optimization algorithm for weighted radius prediction for the photogrammetry of the slewing mechanism of port hoisting machinery is proposed in this paper. First, the algorithm uses the generalized multi-line rendezvous model to transform the radius fitting problem into the multi-line intersection point prediction problem, which lays a theoretical basis for the subsequent algorithm implementation. Second, by introducing a weighting algorithm based on the camera optical distortion model, the algorithm optimizes the accuracy of radius fitting results. In addition, through the quantitative evaluation method of fitting accuracy based on weighted algorithm, the algorithm also establishes a set of iterative rules to balance the accuracy of measurement results and the execution efficiency of the algorithm. Finally, this paper designs theoretical verification tests and simulation engineering tests based on the characteristics of the algorithm and the engineering practice of port hoisting machinery photogrammetry. The experimental results demonstrate that the algorithm described in this paper can significantly improve the accuracy of radius fitting results when the data quantity is small and the data quality is poor compared with the traditional algorithm.

This paper introduces an energy management strategy design method for a plug-in hybrid electric vehicle based on the data-driven approach and online signal analysis. It includes two parts, mode division strategy design, and power distribution strategy design. Using the random forest in data mining technology to analyze optimization results of dynamic programming can quickly extract key information and establish optimal and understandable mode division strategy with high precision and stability directly. Besides, integrating the classic “Engine optimal operating curve control” strategy with wavelet transform and Markov prediction, which not only enhances the adaptability of the strategy to different driving conditions but also improves the fuel economy by reducing the impact of transient power on the engine operation. At the same time, to improve the prediction accuracy of the algorithm without increasing the computational complexity, this paper adds a prediction result correction function to the first-order Markov prediction model to reduce the impact of slow update of the probability matrix on prediction accuracy. The simulation results show the average prediction error of the improved Markov prediction model is reduced by 5.3% and the new energy management strategy designed reduces fuel consumption by 8.28% at the cost of a small increase in electricity consumption.

With the commercialization of 5G, in order to recognize QAM signals, one of the main modulation modes in 5G communication systems, this paper put forward the BP-BSA network model based on bird swarm algorithm (BSA) and BP neural network. Firstly, the instantaneous features and high-order cumulants were selected as the appropriate feature statistics by analyzing the characteristics of MQAM signals. After that, the structure of the BP neural network model was determined, and the initial parameters of the BP neural network were optimized using BSA to accelerate the convergence speed of the network. Finally, the features processed with signal to noise ratio (SNR) disorder were used to train and test the BP neural network model. The BP-BSA network model proposed in this paper applies the bird swarm algorithm to the field of modulation recognition for the first time. And the simulation results show that the recognition accuracies of 16, 32, 64, 128, 256QAM signals in the SNR range of −5 dB to 20 dB all reach above 98%. Compared with the same type algorithms, the algorithm proposed in this paper has good recognition performance.

Prostate cancer is a malignant tumor that occurs in the male prostate. Prostate cancer lesions have the characteristics of small size and blurry outline, which is a challenge to design a robust prostate cancer detection method. At present, clinical diagnosis of prostate cancer is mainly based on magnetic resonance (MR) imaging. However, it is difficult to obtain prostate cancer data, and the data with true values is also very limited, which further increases the difficulty of prostate cancer detection methods based on MR images. To solve these problems, this paper designs a new method of prostate cancer detection based on MR images, which is recorded as ProCDet. The method consists of three modules: registration of prostate MR images, segmentation of prostate, and segmentation of prostate cancer lesions. First, the registration between different sequences of MR images is performed to find the spatial relationship between the different sequences. Then, the designed prostate segmentation network based on the attention mechanism is used to segment the prostate to remove the interference of background information. Finally, a 3D prostate cancer lesion segmentation network based on Focal Tversky Loss is applied to determine the specific location of prostate cancer. Moreover, in order to take full advantage of unlabeled prostate data, this paper designs a self-supervised learning method to improve the accuracy of prostate cancer detection. The proposed ProCDet has been experimentally verified on the ProstateX dataset. When the average number of false-positive lesions per patient is 0.6275, the true-positive rate is 91.82%. Experimental results show that the ProCDet can obtain competitive detection performance.

For the manufacturing project of large and complex equipment, due to the large number of activities, and the complex structure of the project, there are often cases where multiple projects are going on at the same time, which might make the project’s network planning extremely difficult. Based on the analysis of the BOM and classification characteristics of complex manufacturing equipment, this paper proposed an innovative method, which can decompose and transform the component units based on the BOM, so that the BOM of the complex project can be hierarchically optimized and simplified. Then it can be transformed into project network plan by Petri net unit transformation method, so as to realize the decomposition and arrangement of multiple project plans. On this basis, aiming at the problem of resource leveling in multi-project planning management, this paper proposes a genetic algorithm, which provides a transformation method for resource optimization. In addition, this paper presents a new genetic coding method and improves the crossover operation. Case study shows that the algorithm can solve the resource balance problem of multi-project planning successfully. Finally, in order to verify the effectiveness of the algorithm, the proposed algorithm is compared with the other two algorithms intuitively and quantitatively.

The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.

As is the case in many big data mining scenarios with a large scale of samples, the heavy computation cost hinders the application of machine learning, which has to iteratively compute by passing over the whole dataset without considering the roles of different samples in training computation. However, we argue that most of the samples dominating computation resources contribute little to the gradient-based model update, particularly when the model is close to convergence. We define this observation as the Sample Contribution Pattern (SCP) in machine learning. This paper proposes two approaches to exploit SCP by detecting gradient characteristics and triggering the reuse of outdated gradients. In particular, this paper reports research results in (1) the definition and description of SCP to reveal an intrinsic gradient contribution pattern of different samples; (2) a novel SCP-based optimizing algorithm (SCPOA) that outperforms alternative tested algorithms in terms of computation overhead; (3) a variant of SCPOA that incorporates discarding-recovering mechanisms to carefully tradeoff between model accuracy and computation cost; (4) the implementation and evaluation of two algorithms based on popular distributed big data mining platforms running typical sample-sets; (5) intuitive convergence proof of the algorithms. Our experimental results illustrate that the proposed approaches can significantly reduce the computation cost with competitive accuracy.

The widespread use of Unmanned Aerial Vehicles (UAV) has made the security and computing resource application efficiency of UAV a hot topic in the security field of the Internet of Things. In this paper, an optimized lightweight identity security authentication protocol, Optimized Identity Authentication Protocol (ODIAP) is proposed for Internet of Drones (IoD) networks. The protocol is targeted to the security risks faced by IoD networks, and proposes the security authentication mechanism consisting of 3 phases and 7 authentication processes, which enables the protocol has both forward and backward security, and can resist mainstream network attacks. Meanwhile, this paper fully considers the computational load and proposes the identity information generation and verification method based on the Chinese residual theorem, which reduces the computational load of resource-constrained nodes and shifts the complex computational process to server nodes with abundant computational resources. Moreover, after security protocol analysis and tool verification based on the automated security verification tool Proverif, the protocol in this paper has complete security. At the same time, the performance analysis and comparison with other mainstream protocols shows that this protocol effectively optimizes the use of computing resources without compromising security.

In recent years, a variety of supervised manifold learning techniques have been proposed to outperform their unsupervised alternative versions in terms of classification accuracy and data structure capturing. Some dissimilarity measures have been used in these techniques to guide the dimensionality reduction process. Their good performance was empirically demonstrated; however, the relevant analysis is still missing. This paper contributes to a theoretical analysis on a) how dissimilarity measures affect maintaining manifold neighbourhood structure and b) how supervised manifold learning techniques could contribute to the reduction of classification error. This paper also provides a cross-comparison between supervised and unsupervised manifold learning approaches in terms of structure capturing using Kendall’s Tau coefficients and co-ranking matrices. Four different metrics (including three dissimilarity measures and Euclidean distance) have been considered along with manifold learning methods such as Isomap, ${t}$ -Stochastic Neighbour Embedding ( ${t}$ -SNE), and Laplacian Eigenmaps (LE), in two datasets: Breast Cancer and Swiss-Roll. This paper concludes that although the dissimilarity measures used in the manifold learning techniques can reduce classification error, they do not learn well or preserve the structure of the hidden manifold in the high dimensional space, but instead, they destroy the structure of the data. Based on the findings of this paper, it is advisable to use supervised manifold learning techniques as a pre-processing step in classification. In addition, it is not advisable to apply supervised manifold learning for visualization purposes since the two-dimensional representation using supervised manifold learning does not improve the preservation of data structure.

Controlled drugs are open to abuse, misuse, and diversion. Therefore, they are regulated and tracked across the healthcare sector to protect the health of the general public which is a highly prioritized rule in the health professional’s code of ethics. Healthcare centers that provide controlled medication to patients are still using manual papers to record controlled drugs production, delivery, prescription, administration, and disposal which causes delays in the system. Moreover, instances of controlled drugs misuse, abuse, and diversion still exist, which shows how the currently used system is inefficient in detecting such activities. Therefore, to ensure that the public health is safe and secure, an end-to-end system that tracks the whole healthcare supply chain is necessary. In this paper, we introduce a private Ethereum blockchain-based solution for the management of controlled medication. We ensure transparency, accountability, security, and data provenance by developing smart contracts that record all actions on an immutable ledger. We utilize off-chain storage, which is represented in the IPFS to store content that is large in size such as images. We present algorithms of the different phases in the proposed solution to illustrate how each phase will be carried out. We showcase the functionality of the proposed solution by performing tests and validating the smart contracts. We assess the performance of the proposed solution by conducting privacy, security, and confidentiality analysis. Performance evaluation shows that our solution is secure against common attacks and vulnerabilities and preserves the privacy and confidentiality of the patients. The smart contracts code is made publicly available along with the testing scripts.

Intra prediction is the key technology to reduce spatial redundancy in the modern video coding standard. Recently, deep learning based methods that directly generate the intra prediction by neural network achieve superior performance than traditional directional based intra prediction. However, these methods lack the ability to handle complex blocks which contain mixed directional textures or recurrent patterns since they only use the neighboring reference samples of the current one. The other intermediate information denoted as reference priors in this paper generated during the coding process is not exploited. In this paper, a Current Frame Priors assisted Neural Network (CFPNN) is presented to improve the intra prediction efficiency. Specifically, we utilize the local contextual information provided by the neighboring multiple references as the primary inference source. In addition to the neighboring references, we additionally use the other two reference priors within the current frame – the predictor searched by intra block copy (IntraBC) and the corresponding residual component. The IntraBC predictor provides useful nonlocal information to help generate more accurate prediction for complex blocks together with neighboring local information. While the residual component contains unique information that reflects the characteristics of the block to some extent is utilized to reduce the noise contained in the reconstructed reference samples. Moreover, we investigate the best way to integrate the proposed method into the codec. Experimental results demonstrate that compared to HEVC, our proposed CFPNN achieves an average of 4.1% BD-rate reduction for the luma component under the All Intra configuration.

Subjective paper evaluation is a tricky and tiresome task to do by manual labor. Insufficient understanding and acceptance of data are crucial challenges while analyzing subjective papers using Artificial Intelligence (AI). Several attempts have been made to score students’ answers using computer science. However, most of the work uses traditional counts or specific words to achieve this task. Furthermore, there is a lack of curated data sets as well. This paper proposes a novel approach that utilizes various machine learning, natural language processing techniques, and tools such as Wordnet, Word2vec, word mover’s distance (WMD), cosine similarity, multinomial naive bayes (MNB), and term frequency-inverse document frequency (TF-IDF) to evaluate descriptive answers automatically. Solution statements and keywords are used to evaluate answers, and a machine learning model is trained to predict the grades of answers. Results show that WMD performs better than cosine similarity overall. With enough training, the machine learning model could be used as a standalone as well. Experimentation produces an accuracy of 88% without the MNB model. The error rate is further reduced by 1.3% using MNB.

In this paper, a color image encryption algorithm based on the fractional-order multi-scroll Chen chaotic system and DNA mutation principle is proposed. The dynamical performances of the fractional-order multi-scroll Chen chaotic system are analyzed by phase diagram, Lyapunov exponential spectrum and bifurcation diagram. Meanwhile, we realized the hardware circuit simulation of the chaotic system on the DSP platform. Based on comprehensive analysis, an image encryption algorithm is designed. In particular, a DNA algorithm with mutation effect has been introduced into the encryption algorithm. Finally, the performances of the designed encryption scheme are analyzed by key space, correlation coefficients, information entropy, histogram, differential attacks and robustness analysis. The experimental results prove that the algorithm has strong encryption capabilities and can withstand multiple decryption methods. The new color image encryption scheme proposed in this paper can realize the secure communication of digital images.

This paper presents the development of an easy-to-deploy and smart monitoring IoT system that utilizes vibration measurement devices to assess real-time condition of bulldozers, power shovels and backhoes, in non-stationary operations in the mining industry. According to operating experience data and the type of mining machine, total loss failure rates per machine fleet can reach up to 30%. Vibration analysis techniques are commonly used for condition monitoring and early detection of unforeseen failures to generate predictive maintenance plans for heavy machinery. However, this maintenance strategy is intensively used only for stationary machines and/or mobile machinery in stationary operations. Today, there is a lack of proper solutions to detect and prevent critical failures for non-stationary machinery. This paper shows a cost-effective solution proposal for implementing a vibration sensor network with wireless communication and machine learning data-driven capabilities for condition monitoring of non-stationary heavy machinery in mining operations. During the machine operation, 3-axis accelerations were measured using two sensors deployed across the machine. The machine accelerations (amplitudes and frequencies) are measured in two different frequency spectrums to improve each sensing location's time resolution. Multiple machine learning algorithms use this machine data to assess conditions according to manufacturer recommendations and operational benchmarks Proposed data-driven machine learning models classify the machine condition in states according to the ISO 2372 standards for vibration severity: Good, Acceptable, Unsatisfactory, or Unacceptable. After performing field tests with bulldozers and backhoes from different manufacturers, the machine learning algorithms are able to classify machine health status with an accuracy between 85% - 95%. Moreover, the system allows early detection of “Unacceptable” states between 120 to 170 hours prior to critical failure. These results demonstrate that the proposed system will collect relevant data to generate predictive maintenance plans and avoid unplanned downtimes.

Recent years have led the path to the evolution of automotive technology and with these new developments, modern vehicles are getting increasingly astute and offering growing quantities of innovative applications that cover various functionalities. These functionalities are controlled by hundreds of Electronic Control Units (ECUs) which are connected to each other via the Control Area Network (CAN) bus. Although ECUs are designed to offer various amenities that are associated with modern vehicles including comfort, such features expose new attack surfaces that can be harnessed by attackers. This trend is exacerbated by the fact that many of these ECUs rely on wireless communication for interacting with the outside world. Therefore, making them vulnerable to common threats such as malware injection that can compromise the overall security of modern vehicles. In this paper, we provide a detailed description of the architecture associated with intelligent vehicles, and identify various security issues and vulnerabilities that impact such systems. We provide an overview of different malware types and the vectors of attacks they leverage for infecting modern vehicles. This work also presents a detailed survey of available defenses against such attacks including: signature, behavior, heuristic, cloud, and machine learning-based detection measures. Furthermore, this paper intends to assist researchers in becoming familiar with the available defenses and how they can be applied to secure intelligent vehicles against emerging malware threats that can compromise the security of today’s vehicles. It also provides future directions for researchers who are interested in developing new defenses that can safeguard intelligent vehicles systems against malware attacks.

It is expected that peer to peer energy trading will constitute a significant share of research in upcoming generation power systems due to the rising demand of energy in smart microgrids. However, the on-demand use of energy is considered a big challenge to achieve the optimal cost for households. This paper proposes a blockchain-based predictive energy trading platform to provide real-time support, day-ahead controlling, and generation scheduling of distributed energy resources. The proposed blockchain-based platform consists of two modules; blockchain-based energy trading and smart contract enabled predictive analytics modules. The blockchain module allows peers with real-time energy consumption monitoring, easy energy trading control, reward model, and unchangeable energy trading transaction logs. The smart contract enabled predictive analytics module aims to build a prediction model based on historical energy consumption data to predict short-term energy consumption. This paper uses real energy consumption data acquired from the Jeju province energy department, the Republic of Korea. This study aims to achieve optimal power flow and energy crowdsourcing, supporting energy trading among the consumer and prosumer. Energy trading is based on day-ahead, real-time control, and scheduling of distributed energy resources to meet the smart grid’s load demand. Moreover, we use data mining techniques to perform time-series analysis to extract and analyze underlying patterns from the historical energy consumption data. The time-series analysis supports energy management to devise better future decisions to plan and manage energy resources effectively. To evaluate the proposed predictive model’s performance, we have used several statistical measures, such as mean square error and root mean square error on various machine learning models, namely recurrent neural networks and alike. Moreover, we also evaluate the blockchain platform’s effectiveness through hyperledger calliper in terms of latency, throughput, and resource utilization. Based on the experimental results, the proposed model is effectively used for energy crowdsourcing between the prosumer and consumer to attain service quality.

Due to the lack of inertial support of the physical rotor of reliable power, the problem of weak inertia of the system is prominent when large-scale grid connection. Aiming at the weak inertia problem of new energy power systems, this paper takes the photovoltaic power generation system with generalized droop control as the research object. Firstly, the paper introduces the structure of the photovoltaic power generation system and the mathematical principle of generalized droop control At the same time, the electrical torque model of the system is established in order to reveal the inertia characteristics of the grid-tied inverter under the generalized droop control, moreover, the influencing factors of the system’s inertia characteristics and the influence rules of parameters are analyzed and studied. Finally, simulation analysis verifies the correctness of the analysis. The research results show that the photovoltaic inverter system under the generalized droop control has obvious inertia characteristics, the change of inertia size is affected by the dominant zero pole of the controller, and the equivalent inertia coefficient has frequency domain characteristics.