Reliable Scheme Research Articles

A CUSUM control chart for the Gamma distribution with cautious parameter learning

In scenarios involving cycle times and processes governed by single specification limits, the Gamma distribution is a model that often characterizes the behavior of the data. While CUSUM control charts can achieve optimal performance in detecting shifts when in-control parameters are known, in practice, estimating Gamma parameters from limited Phase I samples can lead to an undesirable Phase II performance, as measured by the average run length (ARL). Approaches guaranteeing performance solve this issue at the expense of power. To address this challenge, our study introduces a CUSUM control chart for the Gamma distribution with cautious parameter learning. Using Monte Carlo simulations, we assess the ARL of a CUSUM scheme designed to monitor scale changes with estimated parameters. We demonstrate the efficacy of ARL performance with cautious learning, comparing performance under in-control and out-of-control conditions and emphasizing the guaranteed conditional in-control performance of our cautious learning approach. Finally, we demonstrate the practical applicability of this methodology in a packaging process using multi-head weighing machines. This research provides professionals interested in reliable monitoring schemes with implementation guidelines, including algorithms and reference tables.

Research on Enhanced Belief Propagation List Decoding Algorithm for Polar Codes in UAV Communications for 6G

The introduction of sixth-generation mobile communication technology (6G) poses new requirements for the capacity, rate, latency, and reliability of communication systems. As a vital component of 6G technology, unmanned aerial vehicle (UAV) communications also face various challenges, such as noise interference and limited hardware resources. To meet the high demands of 6G, advanced channel coding techniques need to be adopted. Polar codes, due to their theoretically achievable Shannon limit performance, have potential applications in UAV communication systems. Constructing reliable polar decoding schemes is currently a research hotspot in the field of communications. The Belief Propagation List (BPL) decoding algorithm for polar codes can effectively enhance the accuracy of polar code BP decoding. However, existing BPL decoding algorithms for polar codes face issues such as high hardware resource consumption and unsatisfactory decoding accuracy. Addressing the aforementioned issues, this paper proposes a BPL decoding algorithm for polar codes based on information geometry. An information geometry framework is constructed, where the soft information output by the BP decoder is treated as points on a statistical manifold, and their geometric properties are calculated. By introducing the concept of the soft information centroid and a path selection criterion based on the soft information centroid, combined with geometric distance as a weight, the decoding performance is improved, and hardware overhead is reduced. Simulation results show that under the conditions of a maximum of 60 iterations and 5 decoders, the proposed algorithm reduces the bit error rate by 16.2–74.9% compared to the classic BPL algorithm, providing strong technical support for the application of polar codes in scenarios such as UAV communications.

Risk-averse transactions optimization strategy for building users participating in incentive-based demand response programs

By participating in demand response programs, building users can provide energy flexibility for the power grid while earning economic benefits for themselves. However, the win-win situation described above is often challenged by the presence of ineffective demand response, over-target demand response and under-target demand response, which pose risks to the economic returns of building users and the stable operation of the grid. To this end, this study proposes a risk-averse transaction optimization strategy to offer bidding schemes and operational strategies that effectively balance the effectiveness of demand response with operational economic benefits. First, a novel model for calculating incentive subsidy is developed, which takes into account the power grid's requirements for demand response effectiveness, as well as differentiated subsidy and penalty mechanisms. Then, a risk-averse optimal dispatch model for building integrated energy systems is established. By collaboratively optimizing the declared response quantity and operation strategies, this method reduces the risk of incentive subsidy loss faced by building users and enhances demand response effectiveness. Lastly, the proposed models are applied in the power market of Shenzhen, China, and the effectiveness of the method is assessed based on three key performance indicators: incentive subsidy loss rate, operating cost reduction rate, the actual load response rate. The results indicate that the total incentive subsidy loss rate is reduced from 37.16 % to 17.75 % through the application of the risk-averse optimal dispatch model proposed in this paper, with operating cost savings ranging from 3 % to 8.09 %. More importantly, the effectiveness of demand response is significantly improved, with the proportion of effective response duration increasing from 18.60 % to 65.93 %. The proposed method is further validated by adjusting the penalty coefficient. Findings show that the risk-averse method provides more robust results and provides more reliable bidding schemes for declared response quantity.

Reliable federated learning based on delayed gradient aggregation for intelligent connected vehicles

As an organic combination of the Internet of Vehicles and intelligent vehicles, Intelligent Connected Vehicles (ICVs) have very high research and application value. Traditional data application methods require the local aggregation of sensitive user data, which poses a threat to user data privacy. Federated learning (FL) is a promising machine learning method that leverages distributed, personalized datasets to enhance performance while preserving user privacy. However, in mobile environments, unreliable client data can degrade the global model, reducing accuracy. Additionally, the mobility of ICVs can destabilize the training process, prolonging model updates and diminishing aggregation accuracy. To address these challenges, this paper proposes a dynamic asynchronous aggregation method that improves both reliability and training efficiency in FL for mobile networks. Therefore, it becomes crucial to find reliable aggregation of mobile device participation in FL tasks. To this end, we propose a reliable FL scheme, which only selects reliable mobile devices to participate in model aggregation to improve the generalization ability of the model. In addition, we design a dynamic asynchronous aggregation method based on reputation scores without affecting the model. Reduce model training time without compromising performance. Through experimental analysis, it is proved that this method can improve the reliability and effectiveness of FL tasks in mobile networks.

Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty

Microgrids (MGs) can enhance the consumers’ reliability. Nevertheless, besides significant outcomes, some challenges arise. Regarding the intermittent nature of Renewable Energy Resources (RESs), MGs are not operated radially. Accordingly, the reliable protection of MGs considering uncertainty in RESs is crucial for planners and operators. This paper uses data analysis to extract knowledge from locally available measurements using RMS values of symmetrical components. The learning-based characteristic of the suggested technique with a low computational burden exempts the need for an available communication infrastructure in the MG. The Support Vector Machine (SVM) technique is applied to train the Intelligent Electronic Devices to have a reliable MG protection scheme. The proposed method, which performs fault detection and classification together, just requires local information and functions effectively to discriminate faulty from normal conditions considering different uncertainty of resistance faults. Digital simulations on an MV test network were conducted to construct an appropriate database to consider some aspects of uncertainty in the network. The various faults considering their uncertainty, the different modes of operation, the uncertainty of RESs generation, and the load levels are combined to produce myriad scenarios. The simulation results confirm the effectiveness of the proposed adaptive protection approach in accurately distinguishing different system modes and consistently protecting the MG, achieving an accuracy rate of 99.75%. Furthermore, it offers the MG an optimal protection scheme that is not limited by selectivity constraints across diverse conditions.

Derivative analysis and evaluation of roll-slip fretting wear mechanism of ultra-thin-walled bearings under high service

UTWB (Ultra-thin-walled bearings) are mainly used in high-precision robots and as power transmission components. In this study, the raceway wear mechanism of UTWB after high service (continuous 7000 h wear test) was analyzed. From the macroscopic observation, it is observed that the surface layer of the raceway has roll-slip wear. The wear mechanism was derivative analyzed and evaluated from the experimental conclusions. Another form of surface damage, associated with oscillatory movements tangential to the surface, was fretting wear. Compared with the residual stress of the non-high service raceway, the tangential and axial residual stresses of the outer and inner raceways with high service wear increased. Grain delamination appeared on the subsurface layer of the high serviced outer and inner ring raceway. The rolling wear and sliding wear of the inner ring raceway were more serious than those of the outer ring raceway. The formation of nanocrystalline layer increases the surface hardness of the raceway. Meanwhile, with the transfer from the surface layer of the raceway to the subsurface layer, the degree of grain refinement gradually decreases and the interplanar crystal spacing gradually increases. Through the above data analysis, the roll-slip wear mechanism of UTWB raceway was evaluated in multiple dimensions, and the reliability scheme to improve the actual working conditions of UTWB raceway processing-wear-service was put forward, which provides scheme and data support for relevant manufacturing enterprises and scholars.

A reliable protection scheme for high resistance fault detection in wind generator-integrated HVDC transmission system using ensemble of kNN

A reliable protection scheme for high resistance fault detection in wind generator-integrated HVDC transmission system using ensemble of kNN

A Cropland Disturbance Monitoring Method Based on Probabilistic Trajectories

Acquiring the spatiotemporal patterns of cropland disturbance is of great significance for regional sustainable agricultural development and environmental protection. However, effective monitoring of cropland disturbances remains a challenge owing to the complexity of the terrain landscape and the reliability of the training samples. This study integrated automatic training sample generation, random forest classification, and the LandTrendr time-series segmentation algorithm to propose an efficient and reliable medium-resolution cropland disturbance monitoring scheme. Taking the Amur state of Russia in the Amur river basin, a transboundary region between Russia and China in east Asia with rich agriculture resources as research area, this approach was conducted on the Google Earth Engine cloud-computing platform using extensive remote-sensing image data. A high-confidence sample dataset was then created and a random forest classification algorithm was applied to generate the cropland classification probabilities. LandTrendr time-series segmentation was performed on the interannual cropland classification probabilities. Finally, the identification, spatial mapping, and analysis of cropland disturbances in Amur state were completed. Further cross-validation comparisons of the accuracy assessment and spatiotemporal distribution details demonstrated the high accuracy of the dataset, and the results indicated the applicability of the method. The study revealed that 2815.52 km2 of cropland was disturbed between 1990 and 2021, primarily focusing on the southern edge of the Amur state. The most significant disturbance occurred in 1991, affecting 1431.48 km2 and accounting for 50.84% of the total disturbed area. On average, 87.98 km2 of croplands are disturbed annually. Additionally, 2495.4 km2 of cropland was identified as having been disturbed at least once during the past 32 years, representing 83% of the total disturbed area. This study introduced a novel approach for identifying cropland disturbance information from long time-series probabilistic images. This methodology can also be extended to monitor the spatial and temporal dynamics of other land disturbances caused by natural and human activities.

Environmental Assessment and Restoration of the Hunjiang River Basin Based on the DPSIR Framework

The Hunjiang River, a vital water system in northeastern China, has suffered severe ecological damage due to overexploitation. This study analyzes the basin’s environmental conditions from 2016 to 2020, identifies key restoration factors, and examines practical restoration projects. Investigating five major pollutants (permanganate index, chemical oxygen demand (COD), biochemical oxygen demand, ammonia nitrogen, total phosphorus) in eight sections, the study finds the Xicun section most polluted, mainly from Baishan City’s industrial and domestic discharges. The ammonia nitrogen concentration at the Zian section also shows deterioration. Using a DPSIR (Driving forces, Pressures, State, Impacts, Responses) framework, the study elucidates the relationship between environmental and socio-economic issues. Results indicate that population changes, industrial development, and water resource management have complex ecological impacts. Evaluating the urban water resource carrying capacity with the entropy weight method and correlation coefficient weighting method, the study finds that increasing forest coverage, improving wastewater treatment efficiency, and reducing COD emissions are crucial. Quantitative assessment of integrated protection and restoration projects involving mountains, rivers, forests, farmlands, lakes, and grasslands demonstrates their positive impact. This research reveals the interplay between the ecological environment and social factors, proposes practical restoration measures, and clarifies project effects, providing reliable decision-making schemes for policymakers.

Research into multi-directional high stability techniques in the same plane for satellite static testing

In order to ensure the structural strength reliability of spacecraft base plate during launch and operation, it is necessary to perform the structural strength test of load synchronous loading on the base plate in the same plane to evaluate its structural bearing capacity under certain axial and shear loads. In order to meet the test requirements and simulate the real stress condition of the base plate, a reliable implementation scheme is proposed in this paper. Multi-directional uniform loading of a satellite base plate is completed by using the same plane multi-directional high stability test technology. The mechanical test of a satellite using this technology shows that the test strain is consistent with the theoretical simulation deformation. This technology effectively solves the problem of uniform loading in the same plane of the satellite base plate in mechanical testing.

Borel control and efficient numerical techniques to solve the Allen–Cahn equation governed by temporal multiplicative noise

In this manuscript, we investigate a stochastic version of the Allen–Cahn equation, which is a nonlinear partial differential equation from mathematical physics. The stochastic model considers temporal multiplicative noise in the form of the derivative of the standard Wiener process. The existence and the uniqueness of solutions for this stochastic model is established rigorously using the theory of distributions. As a corollary from these analytical results, some a priori optimal estimates for the solutions of this model are constructed. In this work, we develop reliable numerical schemes which possess similar features as those of the solutions for the analytical model. Moreover, we establish mathematically the von Neumann stability and the consistency for the schemes proposed in this paper. Both the analytical and numerical results derived in this work are computationally verified through some simulations.

Core-winding of Sodium Cold Fast Reactor Based on CFD Method Numerical Study of Flow Characteristics

Abstract Liquid metal cooled fast reactor often uses spiral winding wire as the positioning component of fuel rod bundle. Besides fixing, spiral winding has the functions of strengthening subchannel mixing and heat transfer. These characteristics have a very important impact on the internal flow temperature characteristics of the fuel assembly and the safety of the core system, so it is necessary to conduct in-depth research on the flow field distribution of the fuel rod bundle channel with wire wound, the transverse and axial flow characteristics of the edge channel and its internal channel, the flow characteristics of the circulation and eddy current, the local temperature field, and the outlet temperature distribution characteristics. In the actual research, there are some problems such as high cost, difficulty and high risk to carry out thermal hydraulic characteristic experiment directly. This paper uses CFD fine simulation technology to study 7 rod bundles with wire winding, uses Fluent meshing to partition high-quality polyhedral unstructured mesh, uses ANSYS Fluent to conduct numerical simulation, studies the influence of the presence of rod bundles with wire winding on the distribution of flow field and local temperature field, and forms a mature and reliable simulation experiment scheme. The temperature change curve and lateral mixing phenomenon obtained by CFD simulation were investigated. The outlet temperature differences of side channel, inner channel and corner channel were investigated, and the convective heat transfer characteristics between channels were investigated due to the existence of wire winding.

An implicit adaptive unified gas-kinetic scheme for steady-state solutions of nonequilibrium flows

In recent years, nonequilibrium flows have been frequently encountered in various aerospace engineering and micro-electro-mechanical systems applications. To understand nonequilibrium physics, multiscale effects, and the dynamics in these applications, a reliable multiscale scheme for all flow regimes is required. Following the direct modeling methodology, the adaptive unified gas-kinetic scheme employs discrete velocity space to accurately capture the nonequilibrium physics, recovering the original unified gas-kinetic scheme (UGKS). By adaptively employing continuous distribution functions based on the Chapman–Enskog expansion, it efficiently handles near-equilibrium flow regions. The two regions are dynamically coupled at the cell interface through the fluxes from the discrete and continuous gas distribution functions, thereby avoiding any buffer zone between them. In this study, an implicit adaptive unified gas-kinetic scheme (IAUGKS) is constructed to further enhance the efficiency of steady-state solutions. The current scheme employs implicit macroscopic governing equations and couples them with implicit microscopic governing equations within the nonequilibrium region, resulting in high convergence efficiency in all flow regimes. To validate the efficiency and robustness of the IAUGKS, a series of numerical tests were conducted for high Mach number flows around diverse geometries. The current scheme can capture the nonequilibrium physics and provide accurate predictions of surface quantities. In comparison with the original UGKS, the velocity space adaptation, unstructured discrete velocity space, and implicit iteration significantly improve the efficiency by one or two orders of magnitude. Given its exceptional efficiency and accuracy, the IAUGKS serves as an effective tool for nonequilibrium flow simulations.

Design and Test of Hydraulic Bottom Seal Fracturing Tool for Coiled Tubing

Abstract As the main tool for reservoir reconstruction of Longdong shale reservoir in Changqing Oilfield, the coiled tubing bottom seal drag-segment fracturing tool is currently in the stage of scale promotion, providing tool support for the efficient development of Longdong shale oil. However, in the process of horizontal construction with large water vertical ratio and super long horizontal section, there are construction problems such as natural depth limitation of the tool and difficulty in setting the bottom packer, which seriously affect the construction efficiency and quality. In view of the above problems, this paper carried out the development of pumped coiled tubing hydraulic bottom seal drag-segment fracturing tool. With the technical ideas of pumping cup deepening and hydraulic setting bottom packer, the hydraulic setting type bottom packer, soluble cup pumping sub and supporting tools were designed and developed. By using the method of annular pumping and tubing accompanying injection, the tail cup pumping sub was used to generate throttle and larger cup outer diameter, forming a large hydraulic traction force to carry the tool string to break the depth limit. Without applying drilling pressure, the bottom packer was set by oil injection and displacement, and the setting state was lifted and lifted after the fracturing construction, providing a reliable tool scheme for solving the above engineering problems. The indoor evaluation results show that the traction effect of the bowl pumping short joint with small displacement of water is good, the bottom packer is smooth in the setting and unpacking action at 120°C, and the sealing is reliable under 70MPa pressure. The overall test of the tool string can realize the setting and unpacking action without the intervention of drill pressure. Six well tests were carried out in the field. The pumping depth was 430m under the condition of 740L/min water pumping displacement, and the oil injection displacement was increased to 550L/min. The hydraulic setting was fast and reliable, which effectively improved the construction capacity of the coiled tubing fracturing tool in the ultra-long horizontal well, greatly shortened the time consumption of interlayer conversion, and improved the speed and efficiency significantly.

Experimental research of helicopter blade load in flight based on optical sensing

Abstract The actual flight measurement of blade load is an extremely important part of the new helicopter. In order to solve the problems existing in the traditional electrical measurement method, such as weak anti-interference ability, short life, and large added mass, a flight measurement method of blade load based on optical sensing is proposed. Firstly, the strain mode distribution law is obtained by calculating the dynamic response of the real blade. The optimal layout of the optical fiber measurement points is determined. Through theoretical analysis of strain transferring and experimental verification, a highly reliable fusion scheme of blade and fiber is designed to meet the flight requirements. Based on the power line carrier technology, the optical sensing test system for helicopter flight test is developed. The flight test of blade load is completed successfully. The results of optical fiber measurement are consistent with the results of traditional electrical measurement, which indicates that the optical sensing technology has good applicability in the measurement of helicopter blade load. At the same time, it has been proved that the optical measurement system set is reliable and durable. It is of great significance for the leap from electric measurement to optical measurement.

Natural and anthropogenic factors that determine the activity of geological processes of the Abakan agglomeration

Relevance. As a result of the construction and operation of buildings and structures within the Abakan agglomeration, there is an activation of existing and the formation of new engineering and geological processes accompanied by accidental destruction of constructions. In order to minimize economic and socio-environmental damage, timely development of scientifically sound urban planning and architectural planning solutions is necessary. It would take into account the results of design studies of engineering protection of the territory from dangerous manifestations of engineering and geological processes. Generalization of the available survey results in order to typify the engineering and geological conditions of the urban area will optimize the construction development of urban areas, develop reliable schemes of engineering protection against the manifestations of dangerous engineering and geological processes. Aim. To establish natural and anthropogenic factors that determine the activity of dangerous geological processes in the territory of the Abakan agglomeration. Methods. Collection, systematization and analysis of available territorial geological, hydrological, hydrogeological, environmental information, results of engineering surveys. Results and conclusions. At a qualitatively new level, a set of maps has been compiled on the factors that determine the development of dangerous geological processes. The authors have carried out a GIS analysis of morphometric indicators of the relief of the agglomeration territory: maps of the steepness and exposure of the slopes were constructed. The analysis of the collected data made it possible to establish the regional characteristics of the components of the geological environment and propose a zoning scheme for the territory for its construction development and engineering protection. The authors identified four taxa during zoning: territories favorable for construction that do not require significant engineering training; territories conditionally favorable for construction, requiring preliminary engineering training; territories conditionally unfavorable for construction, requiring significant costs for engineering training; territories unfavorable for construction. The boundaries of the taxa are based on the analysis of the geomorphological features of the territory. The conducted research was performed for the agglomeration territory for the first time.

A reliable Bayesian regularization neural network approach to solve the global stability of infectious disease model

The purpose of this study is to perform the numerical results of the global stability of infectious disease mathematical model by using the stochastic computing scheme. The design of proposed solver is presented by one of the efficient and reliable schemes named as Bayesian regularization neural network (BRNN). The global stability of infectious disease mathematical nonlinear model is categorized into susceptible, infected, recovered and vaccinated. The construction of dataset is performed through the Runge-Kutta scheme in order to lessen the mean square error (MSE) by dividing the statics as training 74 %, while 13 % for both testing and endorsement. The proposed stochastic process contains log-sigmoid merit function, twenty neurons and optimization through RBNN for the numerical solutions of the global stability of infectious disease mathematical system. The best training values for each model's case are performed around 10–11. The scheme's correctness is performed by the matching of the results and the minor calculated absolute error performances. Moreover, the regression, state transmission, error histogram and MSE indicate the trustworthiness of the designed solver.

A Novel Scalable and Reliable Control for DC Microgrids With Varying Number of Agents.

Existing scalable control methods mainly rely on a fixed block-diagonal structure for the Lyapunov matrix, potentially resulting in numerical infeasibility issues. To overcome this limitation, this article proposes a novel scalable and reliable control scheme for dc microgrids. Initially, a general model for dc microgrids is established to enhance reliability, considering scenarios involving loss of control effectiveness (LoCE) and offset faults. Subsequently, a structured free-weight matrix technique is introduced to mitigate negative coupling effects of power lines, and to address numerical infeasibility by avoiding the assumption about the Lyapunov matrix. Furthermore, the stability of the entire dc microgrid is guaranteed by checking local agent conditions, independently of power line couplings. Therefore, the proposed control scheme ensures plug-and-play scalability with varying number of agents. Finally, theoretical results are validated through numerical simulations using the MATLAB/SimPowerSystems toolbox.

Modeling of cotton yield responses to different irrigation strategies in Southern Xinjiang Region,China

Southern Xinjiang region represents a prototypical oasis agricultural area, and cotton is the primary cash crop. The current cotton water productivity for this area remains room for improvement. In order to refine cotton drip irrigation strategy in Southern Xinjiang, this study combined field experiment with crop model simulation and prediction. In this experiment, two irrigation methods, furrow irrigation and drip irrigation, and different degrees of deficit irrigation were set up. The impacts of soil moisture on cotton yield and quality were assessed, with the additional utilization of the Root Zone Water Quality Model (RZWQM2) for simulating irrigation scenarios and predicting cotton yield field data showed that during the 2021–2022 growing season, drip irrigation significantly increased boll weight, yield, and Water Productivity compared to furrow irrigation. The deficit irrigation with 90 % of full irrigation during the flowering period resulted in the highest yield among all drip irrigation treatments, while simultaneously improving the fiber quality. The RZWQM2 model was calibrated and validated using 2021 and 2022 cotton field experimental data, respectively. The calibrated and verified RZWQM2 model shows good performance. In terms of soil water storage, cotton yield and evapotranspiration. The simulated value output by the model is close to the measured value in the field. Model prediction data suggest that there is still room for improvement in cotton yield, accompanied by an increase in evapotranspiration. But deficit irrigation below 80 % of the full irrigation quotas poses a notable risk of yield reduction. Targeting higher yields, the predicted scenario suggests an increase in water application (irrigation quota:314.8 mm). Additionally, this study proposes irrigation regimes with water savings of 10 % and 20 %, with irrigation quotas of 290.5 mm and 266.5 mm during the growth period, respectively. Based on this study, we provide a more detailed and reliable water-saving scheme for cotton drip irrigation in southern Xinjiang region.

COLOR IMAGE ENCRYPTION THROUGH MULTI-S-BOX GENERATED BY HYPERCHAOTIC SYSTEM AND MIXTURE OF PIXEL BITS

The growing demand for maintaining secure communication channels with the advancements in digital technologies has led to an intensified interest in designing reliable image encryption schemes. Despite various encryption schemes that have been used, some are considered to have insecurity regarding data transmission and multimedia. Motivated by this concern, this paper proposes a new color image encryption algorithm of a multi-key generation-based nD-Hyperchaotic (HC) system. The new algorithm achieves Shannon’s confusion and diffusion principles using a two S-box generation approach, where the first S-box is generated from the proposed nD-HC system and the resulting sequence is used to increase encryption complexity, while the second S-box is generated from ([Formula: see text])D-HC system. Afterward, mathematical analysis is carried out to showcase the robustness and efficiency of the proposed algorithm, as well as its resistance to visual, statistical, differential, and brute-force attacks. The proposed scheme successfully passes all NIST SP 800 suite tests. The cryptographic system demonstrated by the proposed scheme has proven to have outstanding performance through simulation tests, which indicates promising potential applicability aspects in secure and real-time image communication settings.