Search

In the past few decades, although people have conducted in-depth research on community detection in one-mode networks, community detection in bipartite networks has not been extensively researched. In this paper, we propose an improved artificial bee colony algorithm named IABC-BN, which is used to detect the communities in two-mode graphs (i.e. bipartite graphs) with two kinds of vertices in the cluster (i.e. community). Firstly, this paper proposed a novel population initialization process of artificial bee colony (ABC) method for two-mode graph cluster identification. This initialization method can improve the diversity of initial population of ABC and speed up its convergence rate. Secondly, in the employed bee search step of the algorithm, a new combinatorial search equation is proposed. This equation is guided by the global optimal solution and the better neighbour solution of the current solution. By using this combination equation and the increased parameter perturbation frequency, the exploitation ability of the algorithm is further enhanced. Thirdly, in the onlooker bees step, another new combination search equation is also proposed. This equation improves the exploitation level of the algorithm, and an opposition based studying method is employed to promote the exploitation ability of the algorithm. Lastly, in scout bee stage, a probability threshold $\beta $ is introduced to enhance the exploration ability of the algorithm and improve the population diversity of the algorithm. To our knowledge, the IABC-BN method presented in this paper is the first ABC method used to cluster identification in two-mode graphs with two kinds of vertices in the cluster. For verifying the accuracy of the results of the proposed method, a large number of experiments are carried out making use of synthetic bipartite graphs and real bipartite graphs. The test outcomes show that this algorithm is an excellent algorithm for cluster discovery in two-mode graph.

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

We address the problem of depth estimation from a single monocular image in the paper. Depth estimation from a single image is an ill-posed and inherently ambiguous problem. In the paper, we propose an encoder-decoder structure with the feature pyramid to predict the depth map from a single RGB image. More specifically, the feature pyramid is used to detect objects of different scales in the image. The encoder structure aims to extract the most representative information from the original image through a series of convolution operations and to reduce the resolution of the input image. We adopt Res2-50 as the encoder to extract important features. The decoder section uses a novel upsampling structure to improve the output resolution. Then, we also propose a novel loss function that adds gradient loss and surface normal loss to the depth loss, which can predict not only the global depth but also the depth of fuzzy edges and small objects. Additionally, we use Adam as our optimization function to optimize our network and speed up convergence. Our extensive experimental evaluation proves the efficiency and effectiveness of the method, which is competitive with previous methods on the Make3D dataset and outperforms state-of-the-art methods on the NYU Depth v2 dataset.

Effective shared autonomy requires a clear understanding of driver's behavior, which is governed by multiple psychophysiological and environmental variables. Disentangling this intricate web of interactions requires understanding the driver's state and behaviors in different real-world scenarios, longitudinally. Naturalistic Driving Studies (NDS) have shown to be an effective approach to understanding the driver's state and behavior in real-world scenarios. However, due to the lack of technological and computing capabilities, former NDS only focused on vision-based approaches, ignoring important psychophysiological factors such as cognition and emotion. The main objective of this paper is to introduce HARMONY, a human-centered multimodal naturalistic driving study, where driver's behaviors and states are monitored through (1) in-cabin and outside video streams (2) physiological signals including driver's heart rate and hand acceleration (IMU data), (3) ambient noise, light, and the vehicle's GPS location, and (4) music logs, including song features such as tempo. HARMONY is the first study that collects long-term naturalistic facial, physiological, and environmental data simultaneously. This paper summarizes HARMONY's goals, framework design, data collection and analysis, and the on-going and future research efforts. Through a presented case study, we first demonstrate the importance of longitudinal driver state sensing through using Kernel Density Estimation Methods. Second, we leverage the application of Bayesian Change Point detection methods to demonstrate how we can identify driver behaviors and responses to the environmental conditions by fusing psychophysiological information with features extracted from video streams.

Timing synchronization has a vital role in swarm drones’ network (SDN) or a swarm of unmanned aerial vehicle (UAV) network. Current timing synchronization methods focus on enhancing single-hop skews which remarkably improve timing synchronization precision at this level. The improper clock of the drone system can cause interference, affect spectrum precision and interrupt the operation of the transceiver. In the drones’ network, master drones’ (MD) neighbor drone’s timing synchronization approaches like Reference Broadcast System (RBS) realize a good performance. However, the requirement of one super drone with a large number of broadcasts for RBS makes it unrealistic to use in some situations like SDN network situation. Appropriate study and adjustments are needed to have real timing synchronization by eliminating the clocks drift and enhancing the timing synchronization precision. Therefore, a new self-timing synchronization approach is proposed in this paper where several MD drones can autonomously generate swarm clusters. The cluster head (CH) instigates a timing synchronization procedure starting with intra-Swarm cluster timing synchronization. The intermediate drones (ID) are elected between two swarm clusters to synchronize all drones in line with the inter-swarm cluster timing synchronization approach. The proposed approach is distributed and flexible to achieve high timing synchronization precision. The paper proposes a novel self-timing synchronization approach for in large scale semi-flat SND network architecture. Self-timing synchronization is swarm cluster-based and applicable for a huge number of master drones in SDN. One is the intra-Swarm cluster where the timing synchronization procedure starts with the CH to synchronize all CM. Secondly, in the inter-swarm cluster timing synchronization, two clusters are synchronized via intermediate drone (ID). However, the simulations demonstrated that in many cases all CHs are synchronized by the synchronized CHs from intra-swarm cluster timing synchronizations; this increased the system throughput and synchronization delay to about 75% compared to what we planned to achieve. Moreover, the simulation results also proved that the achieved synchronization precision can be used for position estimation and prediction with high accuracy.

Edge computing is a promising alternative to cloud computing for offloading computationally heavy tasks from resource-constrained mobile user devices. Placed at the edge of the network, edge computing is particularly advantageous to delay-limited applications for having a short distance to end-users. However, when a mobile user moves away from the service coverage of the associated edge server, the advantage gradually vanishes, increasing response time. Although service migration has been studied to address this problem focusing on minimizing the service downtime, both zero-downtime and the amount of traffic generated as a result of migration need further study. In this paper, an optimal live migration for containerized edge computing service is studied. This paper presents three zero-downtime migration techniques based on state duplication and state reproduction techniques, and then, proposes an optimal migration technique selection algorithm that jointly minimizes the response time and network traffic during migration. For validation and performance comparison, the proposed migration techniques are implemented on off-the-shelf hardware with Linux operating system. The evaluation results showed that compared with a naive migration, the optimal approach reduced the response time and network load by at least 74.75% and 94.79%, respectively, under considered scenarios.

For the on-orbit service spacecraft attitude tracking task with external disturbances, considering the limited capacity of its actuators and high control performance required, an improved performance constraint control algorithm based on command filters is proposed in this paper. For the proposed control algorithm, before the theoretical control signal is transmitted to the actuator, it needs to be filtered by the command filter. This filter can constrain the rate, magnitude, and bandwidth of the signals, smooth the command signals and avoid peak interference. In terms of performance constraints, inspired by the performance constraint curve, this paper uses the determined points to re-design a new performance constraint function, which shows the maximum settling time parameters, allowing designers to pre-defined this limited time of the closed-loop system according to needs. Therefore, the maximum settling time of the actual closed-loop system will be less than the pre-defined maximum settling time. The controller based on the command filter is designed, and on this basis, the corresponding performance-constrained control algorithm with pre-defined maximum settling time guaranteed is proposed. Through the proof and simulation, the rationality and effectiveness of the proposed controller are further demonstrated.

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

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

This paper discusses a novel fault location approach using single ended measurement. The natural dissipation of the circuit parameters are considered for fault location. A relationship between the damped natural frequency of oscillation of the transmission line current and fault location is established in this paper. The hybrid dc circuit breaker (dcCB) interrupts the fault current and the line current attenuates under the absence of any driving voltage source. The line capacitance discharges into the fault at a specific frequency of oscillation and rate of attenuation. Utilizing this information, the fault location in a multi-terminal direct current (MTdc) network can be predicted. A three terminal radial model of a MTdc is used for performance evaluation of the proposed method using Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc)., 8 pages, 12 Figures, 6 Tables

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

The accurate prediction of fertilizer strength can reduce the crushing rate in the process of transportation and utilization, ensure the efficient utilization of fertilizer, so as to realize the sustainable and clean production of crops. To achieve this goal, for the first time, a fertilizer strength prediction model based on the shape characteristics is proposed to this paper by the support vector machine and the improved differential evolution algorithm. Firstly, the fertilizer shape characteristics and strength are measured by the independently developed agricultural material shape analyzer and the digital strength tester. Secondly, a fertilizer strength prediction model based on a support vector machine is constructed, in which the optimal combined kernel function is proposed according to the Pearson correlation coefficient and the normal distribution law. Finally, a differential evolution algorithm is improved to optimize the internal parameters of the fertilizer strength prediction model. The experiment results show that the maximum error rate of the fertilizer strength prediction model is between −5% and 5%. The proposed method of this paper can lay a solid foundation for fertilizer production and quality inspection, which will help reduce fertilizer crushing, improve fertilizer utilization to realize the sustainable and clean production of crops.

Process capability index PCIs have long been used to assess process efficiency in the manufacturing. While PCIs assess the process stability and efficiency, real measurements of product quality depended on estimate the standard deviation for a short-term variance was calculated as the base approach in several previous studies for measuring PCIs. Studies in recent years seek to estimate shift of the process mean from target. In today’s smart manufacturing production climate, manufacturers keep track of production performance. Since the deviance of process mean comparative to the target is different. This allows a more robust norm to be implemented by estimate the relative shift to describe the variance of process in Six Sigma ( SS ). For that purpose, this paper suggests an evaluation method by comparative analysis to four different ways of calculating yield indexes $S_{pk}$ based on six sigma and control information, of $\bar {X}_{S}$ chart. Moreover, this paper extends the univariate process index yield $SSS_{pk}$ to a multi- generalized yield index is called $SSMS_{pk}$ . An established an updated approach to assess univariate and multi-characteristic, based on understanding variation which is critical to quality (CTQ) and focusing on the position and actions of natural process tolerance between defined tolerances limits. To demonstrate the trueness verification proficiency of this approach, this paper includes an industrial case study to assess the process efficiency in Aden’s oil refinery, Yemen. The findings of this study indicated that the indexes which are calculating based on six sigma outperformed existing indices and that this study has important implications for industrial practitioners, researchers and quality control experts interested in the evaluation of process performance.

As the final stage of power system restoration, the critical task of load restoration is to restore the remaining load as quickly as possible. With the continuous increase of the temperature-controlled load and the proportion of electric vehicle load in the urban power grid, the complexity of the load side in the restoration process gradually increases. Therefore, based on the existing grid environment, this paper considers the sudden increase in load recovery caused by cold load pick-up and the auxiliary effect of electric vehicle discharge on load recovery during the load recovery process. From the perspective of economy, safety, and speed, this paper establishes a multi-objective function that includes the amount of load, improved weighted power flow entropy, and the number of recovered lines. The multi-objective evolutionary algorithm based on decomposition is used to optimize the constructed multi-objective load recovery model. Through the IEEE30 node system, it is verified that the method proposed in this paper can effectively establish a fast and safe load recovery plan that meets the actual grid environment.

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

This paper proposes a novel approximate adder that exploits an error-reduced carry prediction and constant truncation with error reduction schemes. The proposed adder design techniques significantly improve overall computation accuracy while providing excellent hardware efficiency. Particularly, the proposed carry prediction technique can reduce a prediction error rate by up to 75% compared to existing approximate adders considered in this paper. Furthermore, the error reduction technique also enhances the overall computation accuracy by decreasing the error distance (ED). Our experimental results show that the proposed adder improves the normalized mean ED (NMED) and mean relative ED (MRED) by up to 91.4% and 98.9%, respectively, compared to the other approximate adders. Importantly, an excellent design tradeoff allows the proposed adder to be the most competitive of the adders under consideration. Specifically, the proposed adder achieves up to 95.7%, 91.1%, and 93.2% reductions of the power-NMED, energy-NMED, and area-delay product (ADP)-NMED products, respectively, compared to the other adders. Our adder enhances the power-, energy-, and ADP-MRED products by up to 99.4% compared to the others. In particular, the figure of merit (FoM) considering both hardware and accuracy of the proposed adder is up to 93.05% smaller than that of the other approximate adders considered herein. Furthermore, we confirm that the approximation errors caused by the proposed adder have very little impact on output quality when adopted in practical applications, such as digital image processing and machine learning.

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

In this paper, a modified COMSOL Multiphysics model with artificial stability is adopted, and the boundary condition developed in the previous paper are applied to the calculation of the photoionization rate. The main processes involved in air discharge were simulated by 39 plasma chemical reactions composed of 12 kinds of particles. The characteristics of low-temperature sub-atmospheric pressure air discharge under unipolar square wave pulse voltage with 13kV applied amplitude, 25 kHz-50 kHz frequency and a duty cycle of 50%-75% are discussed. The results show that: When the duty cycle increases, the average density of electron, N2+, N4+, O2+, O4+, and N2O2+ show an increasing trend, and the average electron temperature changes little; When the frequency increases, the average electron density, the average density of N4+ and O4+ show a downward trend, the average density of N2+, O2+, N2O2+ change little; The average electron temperature shows an increasing trend, but the duration of high electron temperature becomes shorter. When discussing a single period, it is found that the potential difference between the electrode and the plasma determines the direction of the electric field in the space. For the photoionization reaction, even if its influence on the negative streamer is not important, it still needs to be considered in the simulation. The effect of temperature on discharge is also taken as the research emphasis, it includes the following aspects: Firstly, at low temperature, the collision reaction and attachment reaction rates are increased, while the recombination reaction rate is almost unaffected; Secondly, low temperature results in the decrease of secondary electron emission coefficient, which indirectly increases the electric field gradient and the peak value of electric field; Finally, at low temperature, the mobility of carriers, including electrons, positive and negative ions, is increased, but the streamer velocity is decreased.

In order to overcome the short battery life and poor long-distance maneuverability of Unmanned Aerial Vehicle (UAV) in power inspection operations, some provincial and municipal power companies promote a new mode of “UAV and operating vehicle (OV)” collaborative power inspection. This paper investigates into the actual problems in the cooperative inspection operations of the OV and multi-UAVs, and proposes a model of task allocation for the cooperative inspections of the OV and multi-UAVs, which includes four sub-problems: the division of a single operation circle, the location of parking spots, multi-UAVs task allocation, and the path planning of OV. The proposed model is solved by using improved K-means algorithm and genetic algorithm, and case analysis is carried out. The calculation results show that the model of task allocation for cooperative inspections proposed in this paper is practical and feasible, which can provide theoretical guidance for actual power inspections.

The mobile demands and future business context are anticipated to be resolved by the fifth-generation (5G) of mobile communication systems. It is expected to provide an utterly mobile device, connected society, and support the demanding services of various use cases (UCs). This is intended to meet the demand requirement by providing services at tens of Gbps in terms of data rates, higher mobility range, lower latencies, and massive connectivity density devices per square kilometer. A comprehensive and up-to-date survey of the different developed and proposed use cases is presented in this paper. The first part of the paper presents the overview of the new 5G Architecture by introducing new features such as the new radio interface (New Radio), an overview of the 5G Core Network, minimum requirements, and the Radio Access Network, 5G spectrum requirements and other fundamentals of the network. Secondly, a detailed review of the developed and proposed use cases for 5G communications by the standards development organizations (SDO) and other key players in mobile communication is provided. Thirdly, we went ahead to propose spectrum bands for the deployment of the various use cases based on the low-, mid-, and high-band spectrum and further classified the use cases with respect to their relevance and family, identifying the IMT-2020 test environments and the usage scenarios derived by the 3GPP, fourthly, the channel capacity and the bandwidth of the spectrum was studied, simulated and compared to ascertain the spectrum proposed in this paper for each UC family. Hence, this paper serves as a guideline for understanding the future 5G deployment scenarios in various environments. This would allow system developers to design and implement 5G channel characterization models specific to the usage scenarios to meet the system requirements.

This paper proposes a linear compensator algorithm to improve the performance of an absolute multipolar magnetic encoder (AMPME). An AMPME is an absolute magnetic rotary encoder that uses a multipolar magnet (MPM) to increase the resolution. The resolution can be dramatically increased in proportion to the number of poles in the MPM. However, various hardware problems that occur during the encoder manufacturing process degrade the AMPME performance. Also, harmonic components occur in the raw data due to various problems, such as the resistance error of the analog circuit, magnetic field overlap between the magnets, position error between the sensor and magnet, and pole-pitch difference. In particular, during the magnetization process of the MPM, the pole-pitch difference becomes a problem when the sizes of each pole are not uniform. This problem causes harmonic components that reduce the absolute position accuracy. To solve these problems, this paper proposes a linear compensation method. The proposed linear compensator consists of two parts. The first part is the enhanced ratiometric linearization for phase calculation and calibration. The second is the phase compensator for removing the phase difference via the pole-pitch difference of the MPM. The linear compensator improves various parameters by precomputing the offset, amplitude, and phase corrections. After compensating for sinusoidal signals, the linear compensator applies appropriate parameters at the appropriate times. This method is faster, easier to set up, and more accurate than the conventional method. Furthermore, this method is experimentally verified against the existing harmonic rejection method. Experimental results are provided to verify the effectiveness of the proposed method.

This paper introduces the application of a newly developed heuristic nature-inspired optimization technique, viz, tuned Marine Predator Algorithm (MPA), to solve the optimal power flow (OPF) problem of multi-regional systems. The paper proposes MPA parameters' tuning to enhance the algorithm performance. The paper takes into account the variability of different types of renewable energy resources (RERs) and loads. Two modeling approaches are presented: holistic (multi-regions are modeled as one large network) and inter-bounded (modeling the regional interfaces). The MPA is applied to the IEEE-48 bus connected system, and the results are compared with another well-established heuristic algorithm, namely the Genetic Algorithm (GA). The results demonstrate the validation, applicability and effectiveness of using the MPA for solving multi-region OPF problem considering renewable energy sources and load variability.

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

This paper is concerned with the finite-time annular domain bounded control of Ito-type stochastic systems with Wiener and Poisson random disturbance. First, utilizing different quadratic function methods, some sufficient conditions for finite-time annular domain bounded-ness (FTADB) of the system are achieved. Second, two finite-time annular domain bounded controllers are skillfully developed to ensure the FTADB of the closed-loop system, of which one is state feedback controller and the other is dynamic output feedback controller. Furthermore, an algorithm is provided to deal with the obtained matrix inequalities. Finally, two examples are used to demonstrate the effectiveness of the theorems in this paper.

Information systems security is considered one of the key issues concerning organizations’ management. Despite the massive investment that organizations make to safeguard their systems, there are still many internal security breaches. The increase in insider threats to information systems can be related to the employees’ compliance toward information security policy. Several review papers were conducted to explore information security policy compliance behavior research. However, the literature lacks insight into the positive and negative (direct or indirect) influence of human and organizational theories and their factors influencing information security policy compliance behavior. Therefore, this paper provides a systematic literature review synthesizing the psychological theories, organizational theories, and other internal and external factors on information security policy compliance researches. The results analysis of 87 studies showed that the general deterrence theory, theory of planned behavior, and protection motivation theory are the most frequently used. The influencing factors of theories are mostly similar in the results. Furthermore, information security education, training and awareness, trust, and leadership, among many other internal and external factors, are highly used. This study is one of the first researches that explores the relationship types among the influencing factors; emphasizing the direct and indirect effect, and information security policy compliance behavior. This paper also identifies some gaps in information security policy compliance behavior research and proposes future works. In addition, it provides a theoretical contribution and practical insight in the context of information security policy compliance.

The individual-machine-equal-area-criterion method already shows potential in the transient stability assessment of multimachine systems. This two-paper series systematically analyzes the transient characteristics of the system trajectory from a genuine individual-machine transient energy perspective. In the first paper, the stability property that characterizes a critical machine is investigated by defining the residual kinetic energy, which only occurs at the maximum individual-machine potential energy point. We indicate that the transient energy conversion inside a critical machine can delineate the trajectory stability of the machine. In this way, the mapping between individual-machine kinetic energy and individual-machine trajectory can hence be established. Simulation results show that the individual-machine transient energy describes the system trajectory more precisely than the superimposed global transient energy in terms of transient stability analysis, and we also demonstrate that the conjecture in early individual-machine studies might fail in special simulation cases.

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

PnP problem is well researched in many fields, such as computer vision. It is considered the fundamental method to solve the key problems of robot SLAM. However, in pedestrian visual localization, uncalibrated PnP (UPnP), specifically PnP with unknown focal length (PnPf) is more suitable for solving the problem. Recently, a few researchers proposed some methods to alleviate this problem. However, the localization accuracy of the existing methods is not satisfied when image pixel noise is larger. In other words, RANSAC should be running before solving the PnPf problem to get less noisy input, which means the localization delay increases inevitably. In this paper, we propose a more robust method for solving the PnPf problem without the help of RANSAC based on Gröbner basis and convex optimization. We build a Gröbner basis solver on the offline stage with one instance in the prime field. Then, we substitute the coefficients with real value and find multiple solutions on the online stage. Finally, we construct a convex optimization program to seek the final robust solution to PnPf problem. The other purpose of this paper is to provide a second-level localization experience for the end-user. The simulation result shows that our method can give a localization solution, which is more reliable than benchmark methods by both synthetic and real data verified.

This paper presents new and efficient modulation techniques applied on the recently introduced compact nine-level switched-capacitor inverter (C9LSCI). The paper also discusses the performance of the inverter under selective harmonic elimination (SHE) and mitigation (SHM) based on the heuristic Bat-Algorithm (BA) technique. Two new modulation techniques employing CD-type carrier waves and M-type carrier waves are proposed, showing a rise in efficiency, an increase in RMS voltage, reduction in capacitor voltage ripple, and a reduction in harmonic distortion (THD). Four recently introduced modulating signals, namely the third harmonic injection, thirty-degree bus clamped PWM (THTDBCPWM), third harmonic injection sixty-degree bus clamped PWM (THSDBCPWM), one pole clamped PWM (OPCPWM). Loss balancing two-pole clamped PWM (LBTPCPWM) are applied to the inverter with the proposed carriers, and a comparative analysis is carried out on different performance parameters. The proposed carriers’ efficacy in Alternate Phase Opposition Disposition (APOD), Phase Opposition Disposition (POD), and Phase Disposition (PD) is validated. Further, Bat algorithm (BA) is applied as a heuristic approach to determine the optimum angles for the application of SHE and SHM. The efficacy of all the above techniques on the C9LSCI is verified in MATLAB/Simulink environment and further validated on an experimental setup.

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

The precise matching of the optical system and the detector has attracted more and more attention to satisfy requirements of future infrared (IR) imaging systems, both for miniaturization and long-distance detection. Considering the trade-off between the spatial resolution and the sensitivity, this paper proposes a general optimization method, which can optimize parameters of the IR imaging system automatically by modifying $\lambda \text{F}$ /d. Firstly, on the basis of the trade-off between the spatial transfer characteristics and the spatial sampling characteristics, we establish a more accurate minimum resolvable temperature difference (MRTD) model, and analyze the coupling relationship between the optical system and the detector under the optimum MRTD. Secondly, we analyze the relationship between parameters of the IR imaging system and MRTD model, and propose a trade-off optimization approach of the IR imaging system. Finally, this paper verifies the rationality of the optimization method by optimizing CA-295 system. The trade-off optimization method proposed in this paper can not only satisfy the design requirement, but also reduce the size, weight, power consumption and cost (SWaP$) of IR imaging systems.

In this paper, the secrecy performance of the physical layer security (PLS) of the hybrid free-space optical/radio frequency (FSO/RF) communication system is analyzed. The transmission protocol of the considered system is performed under the eavesdropper's attempt to overhear the RF link between the transmitter and legitimate receiver of the hybrid system. The FSO link is characterized by Málaga-M distribution while the RF links are modeled by Nakagami-m distribution. The two practical eavesdropping modes considered in this paper include: colluding and non-colluding. Exact closed-form expressions for the system's secrecy outage probability (SOP), the asymptotic of the SOP (SOP∞), the probability of strictly positive secrecy capacity (SPSC), the average secrecy capacity (ASC), and the asymptotic of the ASC (ASC∞), are specifically derived under the influence of both eavesdropper modes. Our derived analytical expressions present an efficient tool to investigate the impact of some channel parameters on the secrecy performance, namely the fading severity of the RF links, atmospheric turbulence severity, pointing error of the FSO link, number of eavesdroppers, and the power of the eavesdropper links. The results show that the increase of the eavesdroppers' number under both modes profoundly degrades the considered system secrecy performance. The accuracy of the numerical results obtained is validated by Monte-Carlo simulations.

In recent years, for many offshore projects, including offshore oil and gas exploration and offshore wind farm construction, it is necessary to control the position of elevating of Jack-up Rig (JuR) in stable and balanced control. Designing the elevating control systems of JuR is an upcoming technology, which is extensively used in offshore drilling and other marine structures. To maintain the position of platforms from displacement, the jacking system is used automatically to control and stabilize the position of the JuR in sea state disturbances. In recent years, the theory of modern control has been studied and developed in terms of robustness and stability to achieve greater accuracy and reduce the movement of the platform caused by environmental disturbance and other affecting factors. This paper presents a state-of-the-art review of approaches for JuR control strategies and architectures. In addition, it recommends possible control principles and makes a matching between the advantages and disadvantages of existing literature. Some details for future research on control challenges are discussed in this paper.

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

Wind turbine fault diagnosis and early warning are important to reduce wind farm operation and maintenance costs and improve power generation efficiency. In this paper, we take the Supervisory Control and Data Acquisition (SCADA) data as the research object and research wind turbine health data purification, fault diagnosis model building, and unit operation status monitoring from a completely data-driven perspective. Firstly, for the problem that Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm cannot identify high-density anomalous data. An anomaly data processing scheme combining a density clustering algorithm and normal power interval estimation is proposed. The accuracy of extracting health data from wind turbines is improved. Secondly, to address the problem that the eXtreme Gradient Boosting (XGBoost) algorithm has more hyperparameters, we propose an optimization scheme based on the Bayesian Optimization Algorithm (BOA) and tree model for feature weight measurement, which improves the efficiency and accuracy of intuitive mapping from SCADA system monitoring data to fault features. Finally, a wind turbine condition monitoring scheme based on the information fusion of multi-characteristic monitoring parameters is designed. The wind turbine condition monitoring scheme proposed in this paper can warn generator system failure 3.67 hours, gearbox system failure 5.17 hours in advance, and hydraulic system failure 2.33 hours in advance.

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

Reliability enhancement of AlGaN / GaN HEMT is a significant thrust area due to rapidly improving material and processing technology. In this paper, a detailed analysis of gate–shaped AlGaN / GaN HEMT with field plate is presented. Although AlGaN / GaN HEMT with field–plate is well known, its blending with gate–shaping leading to a more robust and reliable behaviour is described in this paper. It is observed that the threshold voltage and transconductance invariably remain constant for various combinations of gate–shaped and field plate placements. The threshold voltage for all the devices are found to be – 5.8 V. The peak transconductance for the devices without field plate and with field plate is $\sim ~0.16$ S/mm and $\sim ~0.15$ S/mm, respectively. Apart from leakage current, the electric field also gets mitigated for both gate–shaped and field–plated devices by $\sim ~45$ % and $\sim ~68$ %, respectively. The moderation in electric field further assists in the reduction of electron temperature for gate–shaped and field–plated structures by $\sim ~12$ % and $\sim ~85$ %, respectively. Additionally, breakdown voltage increases for the gate–shaped devices to 133 V as compared to 120 V of conventional devices without field plate. Significant reduction in leakage current, electric field, and electron temperature is accompanied by a minor increment in capacitance for the field–plated structure, hence, the proposed structure is expected to enhance reliability of the device. Thus, it is anticipated that the proposed devices with enhanced reliability would be a step ahead of conventional devices and would find major applications in high power domain.

This paper initially proposes an optimization problem and after presents its optimal solution. Then, this result is applied to obtain relaxed conditions to design controllers for nonlinear plants described by Takagi-Sugeno (TS) models, based on fuzzy Lyapunov function (FLF) and Linear Matrix Inequalities (LMI). The FLF is given by $V(x(t)) = x(t)^{T}P(\alpha (x(t)))x(t)$ , where $x(t)$ is the plant state vector, $P(\alpha (x(t))) = \alpha _{1}(x(t))P_{1} + \alpha _{2}(x(t))P_{2} + \cdots + \alpha _{r}(x(t))P_{r}$ , $P_{i}=P_{i}^{T} > 0$ and $\alpha _{i}(x(t))$ is the weight related to the local model $i$ in the representation of the plant by TS fuzzy models, for $i=1,2,\cdots,r$ . When one calculates the time derivative of this $V(x(t))$ , it appears the term $x(t)^{T}\dot {P}(\alpha (x(t)))x(t)$ , that is usually handled using conservative upper bounds, supposing that the bounds of the time derivative of $\alpha _{i}(x(t))$ , $i=1,2,\cdots,r$ , are available. The main result of this paper is a procedure to obtain optimal upper bounds for the term $x(t)^{T}\dot {P}(\alpha (x(t)))x(t)$ , such that they contemplate the maximum value and are always smaller than or equal to the maximum value. It is a relevant result on this subject, because these optimal upper bounds do not add any constraint. With these optimal upper bounds, a relaxed design method for stabilization of TS fuzzy models is proposed. Two numerical examples illustrate the effectiveness of this procedure.

This paper devises an algorithmic process mining framework characterized by the mathematical process model of structured information control nets (SICN) and the concept of mass-driven $\rho $ -function as a decision-making criterion of structural process patterns. In order to prove the functional correctness of the proposed algorithmic framework, this paper also implements all the related algorithms as a process mining system and carries out an operational experiment on a typical synthetic dataset of process enactment event logs prepared and released in the 4TU Centre for Research Data. The core contribution of the paper is just the algorithmic framework development named as the $\rho $ -Algorithm, which ought to be a novel approach not only for mining all the primitive process patterns, such as linear (sequential), disjunctive (selective-OR), conjunctive (parallel-AND), and repetitive (iterative-LOOP) process patterns, with perfectly keeping the structural properties of matched pairing and proper nesting, but also for reasonably discovering structured (even unstructured) information control nets from such IEEE XES-formatted datasets of process enactment event logs. The mining functionality of the $\rho $ -Algorithm is made up of three stepwise algorithms: STEP-1, STEP-2 and STEP-3 algorithms, and these algorithms are formally described as an algorithmic framework supported by the conceptual process mining architecture with a series of theoretical concepts with the temporal work-case model and the temporal loop-case model. Finally, we validate the functional correctness as well as the discovery perfectness of the proposed algorithmic framework named as $\rho $ -Algorithm by deploying the implemented $\rho $ -Algorithm on a synthetic, non-noise and IEEE XES-formatted dataset of process enactment event logs recorded from the 10,000 work-cases with 113 activities of the Petrinet-oriented process model named as the Large Bank Transaction Process Model.

Energy router is an emerging device based on power electronic technology, which can realize the flexible distribution of electric energy in the power system. Therefore, the use of energy routers to improve the operating conditions of the power system not only has broad development prospects, but also brings economic benefits. In this regard, this paper proposes a steady-state model of a single energy router and considers the loss of the DC/DC transformer, establishes a power flow equation with multiple energy routers, and proposes a distribution network power flow alternate iteration that takes into account multiple energy routers Calculation method. A case study of an improved IEEE-33 system with multiple energy routers under different working conditions is carried out. The calculation results show that the alternate iterative power flow calculation method used in this paper is effective and feasible, and it reasonably reflects that the network analysis model with multiple energy routers can achieve stable operation, and it can improve the node voltage and reduce the active power loss of the grid. It makes the operation of the distribution network more flexible.

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

Many studies showed the feasibility of detecting Freezing of Gait (FOG) of Parkinson’s patients by using several numbers of inertial sensors worn on the body and in back-end computing power. This work uses machine learning approaches for analyzing the data of one single body-worn inertial sensor system to classify and detect FOG. Long-Short-Term-Memory (LSTM) is employed as the FOG detection algorithm and the Daphnet (FOG and normal gait) dataset provides the data for model training and testing in this paper. The model considers raw data from three channels of the acceleration sensor mounted on the patient’s shank and ignores all other data from other sensors. The model is patient dependent and uses sensitivity and specificity metrics to evaluate the model’s performance. In this paper, we propose a novel padding method that is applied to the windows of FOG and non-FOG with zero overlaps on the training set and adapts the padding to the individual regions. This method produces windows of only one type of data and label. The proposed padding method reduces the padding amount by two orders of magnitude compared to bigger batch sizes in the sequence splitting method offered by MATLAB 2019a. The padding amount is independent of the batch size. Raw data is fed to the model in the testing mode without any pre-processing or data transformation. The standard rolling window generates fixed-size windows for the test set without overlap and the higher amount of FOG or Normal walking data which defines the label of the individual window. The model for one-second long windows applied in this work outperformed the literature results with a sensitivity of 92.57% and a specificity of 95.62% compared to 82% and 94% reported by Masiala et al.

This paper presents a new method for detecting power quality disturbances, such as faults. The method is based on the dynamic mode decomposition (DMD) – a data-driven method to estimate linear dynamics whose eigenvalues and eigenvectors approximate those of the Koopman operator. The proposed method uses the real part of the main eigenvalue estimated by the DMD as the key indicator that a power quality event has occurred. The paper shows how the proposed method can be used to detect events using current and voltage signals to distinguish different faults. Because the proposed method is window-based, the effect that the window size has on the performance of the approach is analyzed. In addition, a study on the effect that noise has on the proposed approach is presented.

Sparrow search algorithm (SSA) is easy to fall into local convergence and convergence stagnation. In order to solve these problems, this paper introduced Circle chaos map into the original SSA to improve its global search ability at the beginning of iteration. Meanwhile, it introduced T-distribution variation to affect the sparrow population position update rules in different iteration periods. Finally, we constructed the “similarity function” to measure the “dispersion” of the sparrow population, and formulated the search rules of the sparrow population under different “dispersion”. In order to test the specific optimization performance of the proposed algorithm, the test results of 54 test functions are compared with those of 9 other algorithms which are widely used, and then the test results are analyzed using non-parametric tests in statistics. At the same time, this paper introduces this algorithm into three concrete engineering test problems for testing. The results of these tests all prove that the proposed algorithm has stronger global optimization ability and higher convergence precision compared with other algorithms.

This paper discusses impact of recovering missing Electroencephalography (EEG) data on classification accuracy of hand movements using tensor-based methods. Improvement in classification accuracy is important for efficient performance of prosthesis. Classification accuracy relies on quality of the observed data. In practice, observed data is usually incomplete because of disconnection of electrodes and other artefacts, which negatively affect the classification accuracy. In this paper, we employ tensor-based imputation methods (canonical/polyadiac decomposition (CPD), weighted optimization version of CPD (CP-WOPT and Nonnegative Matrix Factorization (NMF)) to recover missing data in EEG signals and apply various classifiers to classify hand movements on recovered data. In particular, structured missing data was considered because that is how data gets missed in real-life data acquisition. Percentage of missing data was changed from 10% to 50%. Classifiers are applied on complete, missing and recovered data explicitly to test the performance of our framework. Results show that mean classification accuracy on complete data, missing data and recovered data was 71%, 53% and 64% respectively which shows applicability of our framework.

In the strapdown inertial navigation system, the accuracy of the solving attitude and position is closely related to the quaternion update algorithm. For this reason, what method is used to update the quaternion has become an important project. At present, the primary method is to update the quaternion by solving the equivalent rotation vector. A new quaternion update method is proposed in this paper. By using the third-order and fifth-order Taylor series to approximate the solution of the quaternion differential equation, two attitude quaternion update equations are derived. For single sample and two samples two cases, the derived formula is completely consistent with the common formula derived based on the equivalent rotation vector method. For the three samples in the coning motion environment, the fifth-order formula is derived in this paper, the error analysis method is applied to assess the accuracy of the formula, the two coefficients of the formula correction is optimized, and the drift rate error and drift rate are calculated, which are compared with the drift rate errors of the optimized three-sample equivalent rotation vector algorithm. The expressions for calculating the drift rate errors of the two are the same, and the calculation results are the same. In order to simplify the calculation, the established fifth-order formula is simplified to fourth-order one in this paper, but the drift rate error and drift rate remain unchanged. Finally, simulations and experiments demonstrate the correctness of the attitude quaternion update method proposed in this paper. The attitude quaternion update method proposed in this paper has certain theoretical research and application value for the attitude and position calculation of strapdown inertial navigation application, as well as the further studies of integrated navigation algorithm, and enriches the theoretical system of inertial navigation.

Continuous cuff-less blood pressure (BP) monitoring has become a research hotspot in recent years. Researches have studied the impact of pulse transit time (PTT) and ballistocardiogram (BCG) signals on BP. However, the accuracy of these methods are not high enough to put them into practice on a large scale. In this paper, we propose a new BP estimation model which combines features extracted from electrocardiogram (ECG), BCG and photoplethysmogram (PPG). We calculate several features containing amplitude, time and energy from these three signals and use stepwise regression to select key ones for this combination model. The combination model was examined in 20 young healthy subjects and it presents good results: a correlation coefficient (R) of 0.84 (systolic blood pressure, SBP) and 0.7 (diastolic blood pressure, DBP), a root-mean-squared error (RMSE) of 8.16 mmHg (SBP) and 6.63 mmHg (DBP), and a mean absolute error (MAE) of 6.84 mmHg (SBP) and 5.46 mmHg (SBP). Besides, The PTT-based BP estimation model and BCG-based estimation model are also established in this paper. The comparison of these three models shows that the PPG-ECG-BCG-based model has better performance.

I point out critical errors in the paper "Bell's Theorem Versus Local Realism in a Quaternionic Model of Physical Space" by J. Christian, published in IEEE Access. Christian's paper in fact contains several conflicting models. None of them form counterexamples to Bell's theorem. Most of Christian's paper is devoted to a model based on the detection loophole due to Pearle (1970)., 6 pages. This is version 4 - the paper is yet again revised in response to a third round of refereeing by an unusually large number of referees many of whom disagree with one another

Laparoscopic surgery is a surgical procedure performed by inserting narrow tubes into the abdomen without making large incisions in the skin. It is done with the aid of a video camera. Laparoscopic videos are affected by various distortions during surgery which lead to loss of visual quality. Identification of these distortions is the primary requisite in automated video enhancement systems used to classify the distortions correctly and accordingly select the proper algorithm to enhance video quality. In addition to high accuracy, the speed of distortion classification should be high, and the system must consider real-time conditions. This paper aims to address the issues faced by similar methods by developing a fast and accurate deep learning model for distortion classification. The dataset proposed by the ICIP2020 conference challenge was used for training and evaluation of the proposed method. This challenging dataset contains videos that have five types of distortions such as noise, smoke, uneven illumination, defocus blur, and motion blur with four levels of intensity. This paper discusses the proposed solution which received the first prize in the ICIP2020 challenge. The solution utilized a transfer learning approach to transfer representation from the domain of natural images to the domain of laparoscopic videos. We used a pre-trained ResNet50 convolutional neural network (CNN) to extract informative features that were mapped by support vector machine (SVM) classifiers to various distortion categories. In this work, the problem of multiple distortions in the same video was formulated as a multi-label distortion classification problem. The approach of transfer learning with decision fusion was applied and was found to outperform other solutions in terms of accuracy (83%), F1 score of a single distortion (94.7%), and F1 score of single and multiple distortions (94.9%). In addition, the proposed solution can run in real time with an inference speed of 20 frames per second (FPS).

Small cells (SCs) are expected to be ultra-densely deployed in or close to the traffic hot-spots in the fifth generation (5G) mobile networks to provide wireless capacity cost-effectively. Traffic hot-spots change over time, which means SCs cannot be deployed in a one-off manner as macrocells normally do, rather they should be constructed in a staged process. Hence, mathematical models that capture the time-varying staged-construction process, are urgently needed for operators to effectively predict the construction period, but are currently lacking. In this paper, inspired by the foam bursting process-a natural phenomenon that can be observed in daily life such as hand-washing, we first propose a novel model that can predict the time-varying expectation and logarithmic variance of SC coverage areas. Then, we verify the model by real network deployment cases. Additionally, in order to extract parameters from historical base station deployment data, a parameter estimation algorithm is designed and verified. The findings of the paper reveal that mobile operators should construct ultra-dense SC networks in a staged manner like how larger foams split into smaller ones.