Reliable Scheme Research Articles

RescQR: Enabling Reliable Data Recovery in Screen-Camera Communication System

With an increasing number of mobile devices equipped with screens and cameras, screen-camera communication (SCC) systems enable data exchange between devices conveniently and efficiently. By encoding data with spatial and temporal diversity on a screen, multiple users with a camera can receive data without setting up a wireless network. However, as the transmitter pushes the limits of increasing throughput with a high display rate, the receiver actually suffers from a low goodput caused by composite frames. Those frames cannot be decoded correctly with existing methods. To address this problem, we propose a reliable data recovery scheme named RescQR. In RescQR, a mixture separation scheme coupled with a dedicated frame border is proposed to separate composite frames. A Viterbi-based data recovery scheme is proposed to recover data from blurred regions in composite frames. Additionally, an auto-configuration method with the help of a front camera is proposed to adjust parameters automatically according to the estimated distance between the screen and the camera. Our prototype and experiments demonstrate that RescQR achieves a data goodput of 400+kbps even with standard QR codes, which significantly outperforms previous solutions.

Repeatability and reproducibility of hunter-harvest sampling for avian influenza virus surveillance in Great Britain

Emerging pathogens can threaten human and animal health, necessitating reliable surveillance schemes to enable preparedness. We evaluated the repeatability and reproducibility of a method developed previously during a single year at one study site. Hunter-harvested ducks and geese were sampled for avian influenza virus at three discrete locations in the UK. H5N1 highly pathogenic avian influenza (HPAIV) was detected in four species (mallard [Anas platyrhynchos], Eurasian teal [Anas crecca], Eurasian wigeon [Mareca penelope] and pink-footed goose [Anser brachyrhynchus]) across all three locations and two non-HPAIV H5N1, influenza A positive detections were made from a mallard and Eurasian wigeon at two locations. Virus was detected within 1-to-4 days of sampling at every location. Application of rapid diagnostic methods to samples collected from hunter-harvested waterfowl offers potential as an early warning system for the surveillance and monitoring of emerging and existing strains of avian influenza A viruses in key avian species.

Image fast reconstruction for sparse view computed tomography with reduced sampling integration time

Industrial computed tomography (CT) can detect the internal structure of objects nondestructively. Its scanning and image reconstruction are time-consuming, which limits its application in the fast detection field. In this work, we reduce the sampling integration time in sparse view CT to achieve ultra-fast scanning in only 5 s. However, this ultra-fast scanning method produces more noise and streak artifacts in the reconstructed CT images. The existing reconstruction methods cannot effectively reconstruct high quality CT images under this ultra-fast scanning. To solve this issue, a faster and higher quality CT image reconstruction method is proposed in this paper. We construct a novel objective function using multi-directional TV and adaptive non-local means (ANLM), and design a solution algorithm to solve the objective function. The multi-directional TV-norm takes into account the differences in horizontal, vertical, and 45° directional gradients to obtain more image detail information, while the ANLM-norm focuses on noise suppression. Simulation and practical experiments show that the proposed method can reconstruct higher quality CT images under faster CT scanning strategy, and the peak signal to noise ratio (PSNR) is 1.3 dB higher than the optimal baseline algorithm. On the other hand, our method converges faster and achieves a shorter CT image reconstruction time, which is 47 s shorter than the current popular iterative reconstruction methods. This reconstruction method provides a reliable scheme for faster CT scanning and image reconstruction.

Research on Intelligent Monitoring System of Intelligent Transmission Line Based on Computer Internet of Things Technology

The author proposes an intelligent early-warning system for preventing external damage of transmission lines. The system uses TI-TMS320DM6446 as the core, the peripheral circuit configuration and video monitoring; The moving objects captured by PTZ camera are detected and identified by using mixed Gaussian model, background difference and minimum tangent matrix, and alarm signals are provided. According to the particularity of the power supply of the equipment on the transmission line, the basic power management of the system is analyzed in detail. A reliable power management scheme is designed. The practical application proves that this method has good real-time performance and has obvious effect on preventing power cable damage by force.

An adaptive and reliable protection scheme for critical fault detection in IEC microgrid considering dissimilar AC faults and weather-based random scenarios

An adaptive and reliable protection scheme for critical fault detection in IEC microgrid considering dissimilar AC faults and weather-based random scenarios

Attitude oscillation suppression control of a XK-I spherical robot

PurposeIn complex environments, a spherical robot has great application value. When the pendulum spherical robot is stopped or disturbed, there will be a periodic oscillation. This situation will seriously affect the stability of the spherical robot. Therefore, this paper aims to propose a control method based on backstepping and disturbance observers for oscillation suppression.Design/methodology/approachThis paper analyzes the mechanism of oscillation. The oscillation model of the spherical robot is constructed and the relationship between the oscillation and the internal structure of the sphere is analyzed. Based on the oscillation model, the authors design the oscillation suppression control of the spherical robot using the backstepping method. At the same time, a disturbance observer is added to suppress the disturbance.FindingsIt is found that the control system based on backstepping and disturbance observer is simple and efficient for nonlinear models. Compared with the PID controller commonly used in engineering, this control method has a better control effect.Practical implicationsThe proposed method can provide a reliable and effective stability scheme for spherical robots. The problem of instability in real motion is solved.Originality/valueIn this paper, the oscillation model of a spherical robot is innovatively constructed. Second, a new backstepping control method combined with a disturbance observer for the spherical robot is proposed to suppress the oscillation.

Numerical Studies on the Classical Long Wave System Describing Shallow Water Waves With Dispersion

In this study, a reliable and efficient local discontinuous Galerkin scheme for numerically solving the classical long wave system is proposed. This scheme approximates the temporal derivatives using the explicit strong-stability-preserving higher-order Runge-Kutta method, while the space derivatives are approximated using the local discontinuous Galerkin method, yielding an ordinary differential equation system. Numerical examples for various test problems are presented to offer a comprehensive understanding of the accuracy and reliability of the proposed method. The results obtained, which confirm the sub-optimal order of accuracy, are displayed in multiple tables. Additionally, several two-dimensional and three-dimensional graphical depictions of the problem have been provided to illustrate the behavior of the solution.

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning.

The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches.

Fault classification and localization in microgrids: Leveraging discrete wavelet transform and multi-machine learning techniques considering single point measurements

Currently, microgrids are becoming more prevalent. Therefore, it is crucial to develop robust and reliable microgrid protection schemes. Researchers have recently explored various approaches to microgrid protection, including adaptive protection and AC microgrid protection. The study offers insights into fault detection and localization in microgrid protection, utilizing precise measurements from the point of common coupling (PCC). It distinguishes between fault occurrences and overload cases, addressing various challenges. Additionally, the analysis explores the role of Linear Discriminant Analysis (LDA) within the framework of multi-machine learning techniques, shedding light on its application in microgrid protection. Ultimately, the aim is to bolster microgrid resilience and reliability for end-users and stakeholders. The system model being tested has been created using Matlab/Simulink software and is based on a real system. The training and testing of the algorithms are developed and evaluated using MATLAB tools. The accuracy of the proposed method is demonstrated in the paper, indicating that it can be used to modernize the current protection apparatus in preparation for the eventual implementation of an advanced microgrid station protection system.

HCEL: Hybrid Clustering Approach for Extending WBAN Lifetime

Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network’s lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability.

Interference‐aware feedback based iterative Hungarian approach to allocate multiple channels to multiple D2D pairs to maximize underlay D2D network capacity

SummaryThe spectral efficiency of a cellular network can be increased significantly by allowing spatial reuse of its spectrum by an underlay device‐to‐device (D2D) network. In an underlay D2D network, devices in close vicinity are allowed to establish low‐power direct links with little to no involvement of the base station. In order to increase the spectral efficiency and the number of devices with channel access, multiple D2D pairs may transmit in each cellular channel. Additionally, each pair can be allowed to utilize multiple channels to transmit so as to maximize the D2D network capacity. This multiple‐pair multiple‐channel (MPMC) strategy is quite appealing but is limited by the resultant additional aggregate interference and the inherent complexity, hence necessitating the need for a fast and reliable channel allocation scheme. This work proposes a polynomial‐time iterative Hungarian assignment with feedback (IHAF) algorithm for multiple channel allocations amongst multiple D2D pairs that increases the D2D network capacity manifold while maintaining the desired minimum capacity for each cellular user.

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs).

Indirect Field Oriented Control of Induction Motor

The various control strategies for the control of the inverter-fed induction motor have provided good steady-state but poor dynamic response. In the last decades, many control techniques have been developed providing good performance. Field-oriented control is one of the most significant control methods for high performance AC electric machines and drives. In particular, for induction machines, indirect field oriented control is a simple and highly reliable scheme which has essentially become an industry standard. With the advent of the vector control methods, an induction motor can be operated like a separately excited dc motor for high performance applications. The speed control is observed in closed-loop control applications. The speed regulation is achieved with a PI controller which converts the command speed into the command torque and therefore the speed of the drive. In this paper PI controller base indirect field orientation control of 3-phase 415V, 50Hz Induction motor using cascaded multilevel inverter is proposed in MATLAB/SIMMLINK 7.10.0 (R2010a).

Analysis and Calculation of Two-Phase Flow in Industrial Pipelines Using Regime Classification

The analysis shows that using regime classification for individual phases, it is possible to obtain a more reliable design scheme for two phase flows. In contrast to the flow structure, the concept of mode is more accessible for calculating an industrial pipeline. In this case, transient processes can be determined and calculated. The analysis shows that in field conditions it is very difficult to determine transition boundaries for flow structures. However, taking into account the structural approach, it is very difficult to determine the boundaries of the flow, and based on the laws of hydrodynamics of a homogeneous liquid and gas, it is possible to determine the transition of one regime to another using the Reynolds number. This technique is based on the theory of the great Russian scientist Academician A.P. Krylov. Let us note that this theory has not yet lost its specialty in the fishing industry. Based on numerous data, a method for calculating the gas-liquid mixture in horizontal pipes is proposed. Based on the laws of homogeneous liquid and gas, an equation for the distribution of speed, flow and pressure loss in a pipeline is obtained. The analysis shows that, based on the laws of homogeneous liquid and gas, it is possible to propose a method for calculating the hydrodynamic parameters of a two-phase flow, such as liquid and gas in a round pipe. This technique is based on the theory of Academician A.P. Krylov, which characterizes the reliability of this technique.

Effect of coarse second-phase particles on mechanical properties of large-scale 2219 Al alloy rings

Large-scale 2219 Al alloy rings are widely used as important parts on launch vehicles and thus have high requirements for the mechanical properties of rings in different direction, which are associated with coarse second-phase particles. Therefore, the objective of this study is to provide a comprehensive and systematic quantitative analysis of the effect degree of the coarse second-phase particles characteristics on the mechanical properties of 2219 Al alloy rings in different direction, so as to guide the design and optimization of the global process based on the relationship between each process and the coarse second-phase particles. Firstly, according to the features of the coarse second-phase particles characteristics in the experimental results, a reliable research scheme with the particles size, percentage and distribution as the study variable was designed. Then, based on the experimental characterization results, a representation volume element modeling method incorporating coarse second-phase particles characteristics was developed. The representation volume element finite element models for different research schemes were established in the ABAQUS/EXPLICIT platform respectively. Next, the comparison of experimental and numerical simulation results verifies that the finite element model with coarse second-phase particles developed in this paper can accurately predict ultimate tensile strength and elongation of the tested specimen. Finally, based on the numerical simulation results of the different research schemes, the effects of different sizes, percentages and distributions of coarse second-phase particles on the mechanical properties of the ring are analyzed to determine the extent to which the mechanical properties can be improved with different coarse second-phase particles characteristics. By exploring the relationship between the manufacture process, coarse second-phase particles characteristics and mechanical properties, the design and optimization of the manufacture process for different mechanical properties requirements are guided. This study is valuable in achieving comprehensive regulation of the mechanical properties of large-scale 2219 Al alloy rings.

An Analysis of the Nonstandard Finite Difference and Galerkin Methods Applied to the Huxley Equation

The Huxley equation, which is a nonlinear partial differential equation, is used to describe the ionic mechanisms underlying the initiation and propagation of action potentials in the squid giant axon. This equation, just like many other nonlinear equations, is often very difficult to analyze because of the presence of the nonlinearity term, which is always very difficult to approximate. This paper aims to design a reliable scheme that consists of a combination of the nonstandard finite difference in time method, the Galerkin method and the compactness methods in space variables. This method is used to show that the solution of the problem exists uniquely. The a priori estimate from the existence process is applied to the scheme to show that the numerical solution from the scheme converges optimally in the L2 as well as the H1 norms. We proceed to show that the scheme preserves the decaying properties of the exact solution. Numerical experiments are introduced with a chosen example to validate the proposed theory.

Urban rail transit disruption management based on passenger guidance and extended bus bridging service considering uncertain bus running time

Disruptions occurring at an urban rail transit (URT) system can severely affect its normal operations, and an effective bus bridging service (BBS) is able to help to reduce the negative effects. Transit operators usually arrange BBS to depart from the disrupted stations to evacuate the stranded passengers. However, the overload of passengers at the disrupted stations, especially at turnover and transfer stations, may incur the secondary operation disruptions such as stampede accidents. To mitigate the negative effects of disruptions and reduce the number of passengers stranded at the disrupted stations, the strategy based on the passenger guidance and extended BBS (E-BBS) is introduced in this paper. Different from the widely applied standard BBS running within the disrupted links, E-BBS runs among the normal operating stations or between the normal operating stations and the disrupted stations. A mixed integer linear programming model is developed in this paper to guide the stranded passengers and design an optimal E-BBS solution to transport them. Considering the bi-directional running trains along the disrupted links, a dynamic decision framework is developed to manage the disruptions. Given the impacts of the high uncertainty on bus running time which could affect the performance of E-BBS, a robust model is proposed to obtain more reliable travel guidance and E-BBS schemes. Numerical experiments based on Chengdu subway system in China are conducted. The results indicate that the proposed model can obtain optimal travel guidance and E-BBS solutions in a timely manner. When the uncertainty on bus running time is ignored, the total travel cost for affected passengers is reduced 14.20 % on average with the aid of the optimal travel guidance and E-BBS solutions. Moreover, the number of passengers gathering at the disrupted stations decreases by 36.80 %. The robust model can obtain more reliable travel guidance and E-BBS schemes in consideration of uncertain bus running time. The proposed model shows great potential to effectively mitigate the negative effects of disruptions and help to enhance the capability of a URT system to respond to disruptions.

Kulla‐RIV: A composing model with integrity verification for efficient and reliable data processing services

AbstractThis article presents the design and implementation of a reliable computing virtual container‐based model with integrity verification for data processing strategies named the reliability and integrity verification (RIV) scheme. It has been integrated into a system construction model as well as existing workflow engines (e.g., Kulla and Makeflow) for composing in‐memory systems. In the RIV scheme, the reliability (R) component is in charge of providing an implicit fault tolerance mechanism for the processes of data acquisition and storage that take place in a data processing system. The integrity verification (IV) component is in charge of ensuring that data transmitted/received between two processing stages are correct and are not modified during the transmission process. To show the feasibility of using the RIV scheme, real‐world applications were created by using different distributed and parallel systems to solve use cases of satellite and medical imagery processing. This evaluation revealed encouraging results as some solutions that assumed the cost (overhead) of using the RIV scheme, for example, Kulla (the Kulla‐RIV solution), achieve better response times than others without the RIV scheme (e.g., Makeflow) that remain exposed to the risks caused by to the lack of RIV strategies.

Measurement of high quality development of manufacturing industry empowered by big data based on intelligent sensor systems

Manufacturing industry is an important field of national economic development, but it is difficult to realize intelligent management and optimize production. Therefore, this study aims to explore the role of big data authorization based on intelligent sensor systems for the high-quality development of manufacturing. Through reviewing the relevant literature and field research, the technical characteristics of intelligent sensor system and its advantages and application scenarios in the manufacturing industry are sorted out. Based on the deep research of sensor technology and big data analysis, the paper analyzes sensor selection and configuration, data acquisition and processing, data storage and transmission, data analysis and application. The feasibility and effectiveness of the proposed measurement method are verified through the verification and evaluation of actual cases. This paper summarizes the importance of measurement of intelligent sensor system, and points out the direction and challenge of further research, which provides a reliable measurement scheme for manufacturing industry and provides important data support for high-quality development.

Quantum image encryption algorithm based on four-dimensional chaos

Background: Quantum image processing is rapidly developing in the field of quantum computing, and it can be successfully implemented on the Noisy Intermediate-Scale Quantum (NISQ) device. Quantum image encryption holds a pivotal position in this domain. However, the encryption process often encounters security vulnerabilities and entails complex computational complexities, thereby consuming substantial quantum resources. To address this, the present study proposes a quantum image encryption algorithm based on four-dimensional chaos.Methods: The classical image is first encoded into quantum information using the Generalized Quantum Image Representation (GQIR) method. Subsequently, the trajectory of the four-dimensional chaotic system is randomized, and multi-dimensional chaotic keys are generated to initially encrypt the pixel values of the image. Then, the Arnold transformation is applied to randomly encrypt the pixel positions, resulting in the encrypted image. During the decryption process, the inverse process of encryption is employed to restore the original image.Results: We simulated this process in the Python environment, and the information entropy analysis experiment showed that the information entropy of the three encrypted images reached above 7.999, so the system has good encryption. At the same time, the correlation of the pixel distribution after the encryption algorithm is weak, which proves that the control parameters of the chaotic system can effectively reduce the correlation between pixels in the image. In the final key space analysis, the key space issued by our encryption can reach $10140\gg 2128$.Conclusion: Our method is resistant to destructive attacks and can produce scrambled images with higher encryption and usability. This algorithm solves the problems of general encryption algorithms such as periodicity, small key space, and vulnerability to statistical analysis, and proposes a reliable and effective encryption scheme. By making full use of the characteristics of Arnold transformation permutation, ergodicity and the randomness of the four-dimensional chaotic system, the encryption algorithm uses the larger key space provided by the four-dimensional Lorenz system.