Adjustment Coefficient Research Articles

Hydraulic Planning in Insular Urban Territories: The Case of Madeira Island—Ribeira Brava, Tabua

This study’s primary goal was to conduct an analysis of the flood propensity of the Tabua (Ribeira Brava) drainage basin’s main watercourse. In addition to that, this study also recommends two different methodologies in order to mitigate flood impacts, namely by dimensioning a detention basin and adjusting the riverbed roughness coefficient. Regarding the study on the flood propensity, it was necessary to resort to geomorphological data, which were obtained when characterizing the watershed; these data were crucial to determining the expected peak flow rate, according to the Gumbel distribution methodology and considering a 100-year return period, and to perform necessary tasks in the SIG ArcGIS 10.5 software. Lastly, the drainage capacity of this drainage basin’s river mouth was also analyzed in order to conclude whether it would have the capacity to drain the total volume of rainwater if an extreme flood event were to happen. Indeed, the main results show that this watershed’s river mouth does not have the necessary drainage capacity to cope with an extreme event for the return period that was considered. As a consequence, the two aforementioned mitigation measures were developed considering the Tabua (Ribeira Brava) drainage basin’s specific features. The size of the detention basin was estimated through the Dutch method and the simplified triangular hydrograph method, while the adjustment of the roughness coefficient was considered a valid solution to enhance the drainage capacity of this river mouth.

Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient

The structure of a model has an important impact on information dissemination. Many information models of hypernetworks have been proposed in recent years, in which nodes and hyperedges represent the individuals and the relationships between the individuals, respectively. However, these models select old nodes based on preference attachment and ignore the effect of aggregation. In real life, friends of friends are more likely to form friendships with each other, and a social network should be a hypernetwork with an aggregation phenomenon. Therefore, a social hypernetwork evolution model with adjustable clustering coefficients is proposed. Subsequently, we use the SIS (susceptible–infectious–susceptible) model to describe the information propagation process in the aggregation-phenomenon hypernetwork. In addition, we establish the relationship between the density of informed nodes and the structural parameters of the hypernetwork in a steady state using the mean field theory. Notably, modifications to the clustering coefficients do not impact the hyperdegree distribution; however, an increase in the clustering coefficients results in a reduced speed of information dissemination. It is further observed that the model can degenerate to a BA (Barabási–Albert) hypernetwork by setting the clustering coefficient to zero. Thus, the aggregation-phenomenon hypernetwork is an extension of the BA hypernetwork with stronger applicability.

Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving

For human–machine collaborative driving conditions, a hierarchical chassis multi-objective cooperative control method is proposed in this paper. Firstly, based on the phase plane theory, vehicle dynamics analysis is carried out to complete the definition of vehicle stability region. Secondly, based on the linear time-varying (LTV) system model, a cooperative control strategy combining fuzzy control with model predictive control (MPC) is proposed in the upper layer. In this strategy, the assisted driving weight adjustment coefficient and the stability weight adjustment coefficient are obtained by fuzzy mapping combining human–machine cooperation index and the vehicle stability region, respectively, and the optimization objectives of MPC are designed based on the above coefficients. In the lower layer torque allocation strategy, the stability weight adjustment coefficient is introduced to achieve multi-objective optimization of tire load rate and energy efficiency. For energy efficiency optimization, an optimal energy efficiency point-based tracking method is proposed to avoid nonlinearity caused by the introduction of motor loss models. Simulation analysis results show that the proposed strategy can effectively alleviate human–machine conflicts and improve vehicle handing stability. It also can achieve smaller tire load rate optimization through torque allocation and can reduce energy consumption by approximately 8% compared with the inter-axle torque allocation strategy. This study helps to promote the improvement of the comprehensive performance of assisted driving vehicles in human–machine cooperation, handling stability, and energy-saving torque distribution.

RESEARCH OF PARAMETERS AND MODES OF THE VIBRATION CRUSHER

One of the main directions of research on machines for the production of building ma-terials is the reduction of energy consumption. Solving the problem related to the determination of energy consumption is a rather difficult task, since energy consumption depends on a number of factors that change during the operation of the machine and are difficult to accurately describe. One of the areas of development of machines for the production of construction materials is the creation of resonant vibration machines. Based on the analysis of the latest researches, the prospects of vibrating jaw crushers have been established. The correct selection of parameters that will ensure the operation of the crusher in a stable resonance mode will make it possible to reduce energy costs for crushing. For this purpose, the study of the dynamic parameters of the vibrating jaw crusher is presented in the paper. The efficiency of the double-acting vibratory crusher under study is ensured in the resonance mode with the synchronous adjustment of the movement of the first and third masses and the counterphase movement of the second mass. For this, the stiffness of the elastic connections c1 and c2 is calculated from the condition of resonance of the reduced masses m23 and m12, respectively, and the stiffness of the support springs from the condition of vibration isolation. The physical model of the crusher is presented and the equations of motion of oscillating masses with and without material in the crushing chamber are given. Graphs of the effect on the amplitude of the stiffness adjustment coeffi-cient by frequency were obtained. Based on the equations of motion, the amplitude-frequency characteristics for the corresponding natural angular frequencies (resonance frequencies) are constructed. The mass of the oscillating parts of the crusher has a great influence on the amplitude of oscillations. Rationally selected ratios of oscillating masses provide the required operating mode (in-phase, anti-phase) and provide the re-quired amplitudes of oscillations of these masses. For the given input conditions, the rational limits of the ratios of the oscillating masses are determined to ensure the corresponding frequency regimes. Based on the analysis of the amplitude-frequency characteristics, graphs of the amplitudes of the movement of the oscillating masses of the crusher were constructed. Recommendations for choosing effective frequency modes are given.

A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least Significant Bit Substitution

A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least Significant Bit Substitution

Stabilization of Two Axis Gimbal System with Self Tuning PID Control

Gimbal is a system that provides locking and tracking of the seeker on the target in missiles and increases the angle of view with its mobility in two axes. In this study, stabilization of a two-axis gimbal system used in the missile was carried out. In gimbal stabilization, adaptive controllers are preferred instead of classical controllers due to unbalance, cross-coupling, and unmeasurable disturbances. A Self Tuning PID controller based on Fuzzy Logic was developed for axis controls in the stabilization algorithm. Thanks to this controller, which works with the principle of choosing the most appropriate coefficient at every step, it was possible to control with less than 3% errors in the tests performed with the flight simulator. In addition, a PID controller whose coefficients are optimized with Particle Swarm Optimization is designed for comparison purposes. In experimental studies, it was seen that PID with adjustable coefficients gave better results than the fixed PID.

Axial-flexural strength of steel-concrete composite shear walls

A predictive equation is proposed for the axial-flexural strength of rectangular and flanged Composite Plate Shear Walls-Concrete Filled (C-PSW/CF), with and without endplates and boundary elements. The proposed equation is based on plastic stress distribution, adjusted by coefficients to account for influential design parameters. The adjustment coefficients are established by a regression analysis performed on the data developed using an extensive numerical parametric study. The continuum-based finite element model used for the simulations is first validated using the available test data. The effects of various design variables, including the aspect ratio, slenderness ratio, reinforcement ratio, axial load ratio, infill concrete compressive strength, and yielding strength of the steel faceplates on axial-moment are then numerically examined. Four cross-sections, including double skin plate, double skin plate with endplates, double skin plate with boundary elements, and double skin plate with flanges, are considered. The results are used to develop a new predictive equation to obtain the flexural strength of C-PSW/CFs in presence of axial loading. The proposed equation is finally validated using the experimental test data and its accuracy is evaluated by comparing it to available predictive equations and the method proposed by the 2016 AISC Seismic Provisions. The results show that the proposed design equation can well predict the flexural moment capacity of C-PSW/CFs with various cross-sections in presence of axial loading.

Global path planning with lifetime constraint model-based offline optimized loading strategy for vehicle fuel cell system

To promote the commercialization of fuel cell vehicles, simultaneously improving the dynamic response and durability of fuel cells under loading conditions are extremely important. Since it can seriously affect the system design and lifespan of the fuel cell system. Unfortunately, fast and reliable loading is a pair of contradictory relationships and it needs an optimal loading slope under variable loading conditions. Therefore, this paper proposes a global path planning (GPP) model by considering lifetime limitations to solve the problem. Firstly, the initial GPP model is established according to the initial power and target power, which is exploited to determine exploratory and calibration tests. Subsequently, the trade-off relationship between fast and reliable loading is addressed by the multi-objective cost function based on the dynamic weight coefficient adjustment method to construct a complete GPP model. Eventually, the optimal slopes solution is calculated by the Dijkstra algorithm. The superiority, robustness, and feasibility of the presented method are successfully verified under a 90 kW fuel cell system test bench. The verification test results show that compared with the reference solution, the utilization of optimal solution loading can significantly reduce the dynamic response time by 22.22% and improve the loading reliability by 40.93%. Moreover, the number of calibration tests determined based on the GPP model is 85.19% less than that of the traditional method. Thus, the proposed loading strategy can load quickly and reliably.

Research on Coordinated Control Strategy of DFIG-ES System Based on Fuzzy Control

As the penetration rate of wind power systems is rising, which causes the overall system’s inertia to decline, the power system’s capacity to regulate frequency will be negatively affected. Therefore, this paper investigates the inertia control of doubly fed induction generation, and an energy storage system is installed in the wind farm to respond to the frequency deviation. First, a fuzzy control-based virtual inertia adaptive control strategy is presented. The goal of dynamic adjustment of the virtual inertia coefficient is realized by taking into account the uncertain factors of wind speed and frequency change rate. A recovery strategy based on the energy storage system’s level of charge is employed to prevent overcharging and over-discharging of the battery. Then, a weight factor based on frequency deviation is introduced to combine the droop output of the energy storage system with the virtual inertia output of the doubly fed induction generation, and the joint output mode of the wind storage system is determined in each stage of primary frequency regulation. Finally, the simulation verification is performed using the wind storage system simulation model created by MATLAB. The comparison results with other control methods prove that the proposed method is effective.

Dynamic Optimization of Emergency Logistics for Major Epidemic Considering Demand Urgency

In recent years, epidemic disasters broke through frequently around the world, posing a huge threat to economic and social development, as well as human health. A fair and accurate distribution of emergency supplies during an epidemic is vital for improving emergency rescue efficiency and reducing economic losses. However, traditional emergency material allocation models often focus on meeting the amount of materials requested, and ignore the differences in the importance of different emergency materials and the subjective urgency demand of the disaster victims. As a result, it is difficult for the system to fairly and reasonably match different scarce materials to the corresponding areas of greatest need. Consequently, this paper proposes a material shortage adjustment coefficient based on the entropy weight method, which includes indicators such as material consumption rate, material reproduction rate, durability, degree of danger to life, and degree of irreplaceability, to enlarge and narrow the actual shortage of material supply according to the demand urgency. Due to the fact that emergency materials are not dispatched in one go during epidemic periods, a multi-period integer programming model was established to minimize the adjusted total material shortage based on the above function. Taking the cases of Wuhan and Shanghai during the lockdown and static management period, the quantitative analysis based on material distribution reflected that the model established in this paper was effective in different scenarios where there were significant differences in the quantity and structure of material demand. At the same time, the model could significantly adjust the shortage of emergency materials with higher importance and improve the satisfaction rate.

Reprint of: Physical modeling for digital twin of Continuous Damping Control damper

Continuous Damping Control (CDC) damper is a kind of active damper with adjustable damping coefficients, which has a broad application prospect for vibration attenuation in robots and manufacturing systems. To develop a digital twin of CDC damper for monitoring its working conditions, it is necessary to develop a physical model describing the characteristics of CDC damper. However, the existing physical models of CDC damper have some disadvantages such as expensive computing costs and low accuracy, which make it difficult to apply them in the digital twin. In order to solve these issues, this paper proposes a hybrid model which can accurately describe the physical characteristics of CDC damper by combining a lumped parameter model and a neural network model. First, the physical characteristics of a CDC damper are measured on a test rig under harmonic excitations and random excitations. Then the modeling and parameter identification methods for hybrid models are proposed to obtain model parameters. Finally, the output force of the CDC damper under random excitation is calculated using the hybrid model, and the accuracy of the hybrid model is validated after comparing the calculated results with the measured results. The proposed hybrid model has high accuracy and low computational cost, and is applicable to developing digital twins of CDC dampers which require high calculation accuracy and efficiency.

Cointegration between Agricultural GDP and Inputs in India: An Empirical Analysis

Using Johansen cointegration test, the study analyzes the long-term relationship between the agricultural GDP and its factors (agricultural land, irrigation, fertilizer use, and absolute residual of rainfall and temperature) in India during the period 1960-61 to 2019-20. The analysis considers the absolute residual values of rainfall and temperature as relevant factors. Overall, agricultural production demonstrates a long-term correlation with rainfall and temperature trends due to its capacity to adapt to these variables over extended periods. The findings indicate a significant long-run relationship between these variables, with at least two cointegrating equations. The normalized cointegrating coefficients reveal a positive relationship between agricultural productivity and the size of agricultural land, irrigation, and fertilizer use, while the absolute residual of rainfall and temperature have a negative impact. The adjustment coefficients show that the speed of adjustment to the long-run equilibrium is relatively slow, with the short-term dynamics driven by changes in the residuals. Additionally, the results suggest that policies aimed at increasing agricultural land, irrigation, and fertilizer use in India could positively impact agricultural productivity in the long run. Nevertheless, policymakers should be cautious about the potential adverse effects of policies that impact the residuals, and the implementation of such policies should be done with prudence and patience. These results can provide valuable insights for policymakers and stakeholders in the agricultural sector in India.

Inverse Kinematics Solution Method of an Adaptive Piecewise Geometry for Cable-Driven Hyper-Redundant Manipulator

Abstract Cable-driven hyper-redundant manipulator (CDHM) with flexible and compliant configuration has high maneuverability in a tight space owing to its multiple degrees of freedom (DOFs). However, an increase in the DOFs of the manipulator makes it very challenging to solve its inverse kinematics. The present work proposes a novel adaptive piecewise geometry method to solve the inverse kinematics of the CDHM. The corresponding computation efficiency will be much lower for traditional methods, i.e., the generalized inverse of the Jacobian matrix and artificial neural network method. When the end-effector of the manipulator is required to move with a larger range, Joint angle physical limit needs to be considered and the proposed method can select the optimal arc configuration to solve the inverse kinematics aiming at reducing joint overrun. An adaptive adjustment coefficient is further introduced to optimize the double-arc configuration so that joint motion is more reasonable as well as avoiding singular configuration. The geometry and joint parameters solved with the proposed novel method are then compared to those of the existing method with the same desired target position to verify the effectiveness of the proposed novel method. Finally, a 12-DOFs hyper-redundant manipulator physical prototype is built, and corresponding experimental results show that with the novel solution method, the manipulator end can precisely reach the expected target position with significantly less computational complexity, which is beneficial to improve real-time control efficiency of the CDHM in practical applications.

Data generation for digital simulators of metallurgical process operators

The article deals with the formation of model implementations of time series of data (based on in-situ data) of controlled and uncontrolled impacts in simulator-training and digital modeling systems. Such simulators are becoming increasingly widespread due to the development of information and computer technologies, automated research systems, training systems, digital modeling technologies (APM modeling), as well as digital counterparts and advanced control systems. The formed implementations of impacts can characterize situations of normal process flow, emergency and pre-emergency states, or specific representative situations for training operators and technological personnel, software testing, research and tuning of algorithms and search for optimal control actions. Using examples from the metallurgical industry, the possibility of forming several interrelated impacts based on models of nonlinear dynamics and multivariate dynamic databases is shown. The Lorentz system describing the thermal convection of a fluid medium is considered as a model of the impacts formation. The model parameters for the low- and high-frequency components are determined separately, by processing in-situ data. Next, a training sample is formed using normalization and relay-exponential smoothing operations. The implementations of the actions are formed taking into account the mutual correlation of data based on models of chemical dynamics and are adjusted to the specified properties on a limited sample of a given volume with the required accuracy using a generator in the form of a closed dynamic system. The generator in form of a closed dynamic system is built on the basis of a multidimensional generating autoregressive model with adjustable coefficients. An example of the formation of data series on technological parameters of a blast furnace (the degree of wear of the furnace lining, temperature sensor readings and heat flux density) is shown.

Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China’s Diverse Latitudes

Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iForest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iForest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthermore, we mixed the six image pairs of the three latitudes to obtain the adjustment coefficients derived for integrated L8 and S-2 data with high-spatial-resolution. The significance and accuracy quantification of the adjustment coefficients were thoroughly examined from three dimensions: qualitative and quantitative analyses, and spatial heterogeneity assessment. The obtained results were highly satisfactory, affirming the validity and precision of the adjustment coefficients. Finally, we applied the adjustment coefficients to crop monitoring in three latitudes. The normalized difference vegetation index (NDVI) time-series curves drawn by the integrated dataset could accurately describe the cropping system and capture the intensity changes of crop growth within the high, middle, and low latitudes of China. This study provides valuable insights into enhancing the application of multi-source remote sensing satellite data for long-term, continuous quantitative inversion of surface parameters and is of great significance for crop remote sensing monitoring.

An electric-field high energy harvester from medium or high voltage power line with parallel line

Abstract In order to effectively monitor transmission lines and transmission towers, a number of different types of sensors are needed. A lot of times, these sensors along with the transmission lines and transmission towers are in inaccessible or hard-to-access areas and replacing their batteries is difficult. Yet they need consistent power supply. By harvesting energy directly from these medium and high voltage power lines, these devices can become self-sustaining while the overall system is more friendly to the environment. This paper presents a novel approach to high energy harvesting based on capacitive coupling between the power line and the harvesting line. This technique has several advantages, namely high output voltage, easy adjustment of coupling coefficient, and low cost. The validation and implantation of this harvesting system are proposed with the support of experimental results. This energy-harvesting ability of W and mA levels is achieved for the power line monitoring devices, with higher power output depending on the length and the size of the harvesting line.

Performance evaluation method of oil and gas pipeline integrity management

The performance evaluation of integrity management has become the focus of attention because integrity management has become widely accepted by pipeline operators. This paper proposes a three-index system of pipeline integrity management performance evaluation (TISPIMPE), which includes system construction, implementation process, and pipeline health status, comprehensively evaluating the status, adequacy, and effectiveness of integrity management. It helps the pipeline operators to determine the weakness in each step. Based on the improved analytic hierarchy process (AHP), the different backgrounds of experts are fully considered, and experts' opinions are revised. The adjustment coefficient of performance evaluation is set up simultaneously, and the evaluation results are adjusted to make the results more reasonable. Taking the performance evaluation of pipeline integrity management of an oil and gas company as an example, it is shown that TISPIMPE has reasonable practicability and can accurately reflect the shortcomings of pipeline integrity management. TISPIMPE can effectively help pipeline managers comprehensively and systematically evaluate the performance of pipeline integrity management and gain an in-depth understanding of pipeline operation status.

Act Before Another Is Aware: Safeguarding Backscatter Systems With Covert Communications

This letter investigates and analyzes covert communications to safeguard backscatter systems, i.e., how to hide backscattering transmission for privacy and security, which is rarely a concern yet. Specifically, we consider a backscatter communication system in the presence of an eavesdropper (Eve), in which a passive tag supported by the power source (Alice) attempts to communicate reliably while simultaneously guaranteeing covertness to Eve. Furthermore, artificial noise (AN) is also injected from Alice to mislead Eve for security purposely. Our objective is to maximize the transmission rate via joint power control, AN injection, and reflection coefficient (RC) adjustment while satisfying the physical constraints on the total power budget, the circuit energy consumption, the minimum transmission rate, and the covertness requirement. To deal with this intractable non-convex problem, we reformulate the problem by utilizing the Kullback-Leibler divergence (KLD) and Lambert W function. Subsequently, an alternative iterative algorithm is designed to solve it. Besides, we derive the closed forms for RC and transmit power for more insight. Simulation results show that the proposed algorithm outperforms the benchmark algorithms in terms of both security and effectiveness.

Photovoltaic MPPT Control Strategy Based on Improved Conductance Increment Method

In this research, a new incremental conductance approach with variable step size is suggested in order to address the inadequacies of the conventional incremental conductance method in tracking accuracy and dynamic reaction speed. In this paper, an absolute value of dP / (dV – dI) is used as the change in step size, and the step size of the tracking process is dynamically adjusted using the exponential function as the adjustment coefficient to reduce the maximum power point’s tracking duration. Finally, on the Matlab platform, a simulation system was created. According to the simulation findings, the suggested approach has less steady-state oscillations and can track the maximum power point more quickly than the conventional conductance increment method, which increases the efficiency of solar power generation.

High Performance FPGA Implementation of Single MAC Adaptive Filter for Independent Component Analysis

Blind source separation (BSS) is the process of extracting sources from mixed data without or with limited awareness of the sources. This paper uses field programmable gate array (FPGA) to create an effective version of the Blind source separation algorithm (ICA) with a single Multiply Accumulate (MAC) adaptive filter and to optimize it. Recently, space research has paid a lot of attention to this technique. We address this problem in two sections. The first approach is ICA, which seeks a linear revolution that can enhance the mutual independence of the mixture to distinguish the source signals from mixed signals. The second is a powerful flexible finite impulse response (FIR) filter construction that makes use of a MAC core and is adaptable. The adjustable coefficient filters have been used in the proposed study to determine the undiscovered system utilizing an optimal least mean square (LMS) technique. The filter tap under consideration in this paper includes 32 taps, and hardware description language (HDL) and FPGA devices were used to carry out the analysis and synthesis of it. When compared to the described architecture, the executed filter architecture uses 80% fewer resources and increases clock frequency by nearly five times, and speed is increased up to 32%.