Adjustment Coefficient Research Articles

Assessment of Boundary Discrimination Performance in a Printed Phantom.

Printed phantoms hold great potential as a tool for examining task-based image quality of x-ray imaging systems. Their ability to produce complex shapes rendered in materials with adjustable attenuation coefficients allows a new level of flexibility in the design of tasks for the evaluation of physical imaging systems. We investigate performance in a fine "boundary discrimination" task in which fine features at the margin of a clearly visible "lesion" are used to classify the lesion as malignant or benign. These tasks are appealing because of their relevance to clinical tasks, and because they typically emphasize higher spatial frequencies relative to more common lesion detection tasks. A 3D printed phantom containing cylindrical shells of varying thickness was used to generate lesions profiles that differed in their edge profiles. This was intended to approximate lesions with indistinct margins that are clinically associated with malignancy. Wall thickness in the phantom ranged from 0.4mm to 0.8mm, which allows for task difficulty to be varied by choosing different thicknesses to represent malignant and benign lesions. The phantom was immersed in a tub filled with water and potassium phosphate to approximate the attenuating background, and imaged repeatedly on a benchtop cone-beam CT scanner. After preparing the image data (reconstruction, ROI Selection, sub-pixel registration), we find that the mean frequency of the lesion profile is 0.11 cyc/mm. The mean frequency of the lesion-difference profile, representative of the discrimination task, is approximately 6 times larger. Model observers show appropriate dose performance in these tasks as well.

Study on Partial Factors and Combination Value Coefficients for Uniform Temperature Action of Large-Span Structures

The closure temperature refers to the temperature at which a structure forms an overall constrained structural system, also called the initial temperature. A reasonable closure temperature can avoid large additional stresses caused by excessive air temperature changes in large-span structures. To study the thermal action of the closure temperature on a structure, a probability distribution function of uniform temperature action is proposed. The effect of closure temperature is studied by introducing an adjustment coefficient. The partial factor and combination value coefficient of thermal action are presented, and their reliabilities are verified by the first-order second-moment method. The results show that with increasing adjustment coefficient, the probability distribution of thermal action tends to be uniformly divergent, the partial factor of thermal action increases, and the combination value coefficient decreases. The optimal closure temperature times are in March, April, October, and November for six cities in China. In this case, an adjustment coefficient of 0.3, a partial factor of 1.5, and the combination value coefficient of 0.45 are recommended. In addition, the partial factor of 1.5 and the combination value coefficient of 0.45 can be verified by reliability verification, which is more economical and reasonable with regard to ensuring safety.

Convergence analysis of hydrodynamic coefficients estimation using regularization filter functions on free-running ship model tests with noise

This paper introduces an effective method with a regularization filter function and conducts stability and convergence analysis for the estimation of the hydrodynamic coefficients of a manoeuvring model. The proposed method is used to estimate the adjustment coefficients of empirical manoeuvring models using free-running manoeuvring ship model tests. The method avoids the recurrent difficulty that parameter estimation based on test data are usually ill-conditioned, and the obtained parameters are unstable and sensitive to noise. The model tests are carried out using a free-running scaled container ship, and the data acquisition and control system is implemented in the LabVIEW platform. Then, the obtained data are used for the parameter estimation using the proposed method and the optimal regularization factor is discussed. Validation is carried out and shows the effectiveness of the proposed method.

Optimal scheduling of integrated energy system under the background of carbon neutrality

In the face of global warming, reducing carbon dioxide emissions has become a common concern. Integrated energy system (IES) is considered to be a supporting technology to increase the proportion of clean energy use and achieve goals of carbon emission reduction and carbon neutrality. By introducing carbon neutral costs and demand response into scheduling strategy, the carbon emission and the economy of IES system operation is optimized in the proposed optimal scheduling. First, modeling of the demand response considering the transferable load and replaceable load, as well as reducible load, is established for guiding users to change their energy consumption ways. Then Structure and components of IES are discussed. Later, the article presents the IES carbon emissions calculation method and model of carbon neutrality cost. Finally, the day ahead optimal scheduling of the IES considering the demand response (DR) and carbon neutrality cost is proposed. The results show that the proposed model is effective in balancing low carbon and economy. Comparative results of different carbon neutralization adjustment coefficients shows that total operating cost as well as carbon emissions is reduced with increasing clean energy penetration and setting moderate carbon neutralization cost coefficients. By adjusting the participation of clean energy, the system cost can be effectively reduced by 48.6%. By adjusting the carbon neutralization cost coefficients, the overall carbon emission can be reduced by 282 kg.

Economic Operation and Management of Microgrid System Using Deep Reinforcement Learning

The paper presents an adaptive Backstepping sliding mode control (BSMC) management method based on deep reinforcement learning to manage and stabilize DC/DC boost converter with constant power loads (CPLs) in Microgrids market. To perform the BSMC, the system's zero dynamic stability with diverse output functions has been presented via applying the input/output precise feedback linearization. The suggested layout has been modeled in Brunovsky's canonical model to solve the nonlinear issue resulting from the CPLs and the non-minimum phase problem. In this offered controller, the gains of the switching have been considered to being the adjustable controller coefficients that are chosen adaptively via the DRL method by online learning. This topology makes sure the rigid stability of the power electronic system by simultaneous adaptively tuning the gains. Eventually, the results prove the suggested control method owns stronger robustness efficiency and better dynamic regulation in comparison to the nonlinear control strategies.

College English Teaching Quality Monitoring and Intelligent Analysis Based on Internet of Things Technology

With the continuous development of network communication, modern control, information perception, artificial intelligence, and other technologies, the Internet of Things technology has also been more comprehensively developed and grown, and it is playing an increasingly important role in various fields of production and life. Due to the low efficiency and cumbersome procedures of the existing college English teaching quality assessment, this study intends to propose a new method to deal with this problem. Through this research, we get the following: (1) use static weight equalization method, dynamic weight adaptation method, and iterative weight method to filter and analyze the original data received by mobile phones and discard some obviously wrong values in the preprocessing. Analyzing the number requirements of the game model, the supply operation reference model, and the IoT technology upgrade model for the number of demand points, the results show that the IoT technology upgrade model requires the least number of demand points. (2) Optimize the related parameter information of the IoT system model, including candidate numbers, number of students in class, adjustment coefficient, Di, turnover rate, system cost, and total system consumption. The Internet of Things technology upgrade model performed best. Adjustment coefficient = − 102 ; Di = − 99.6 ; turnover = − 100 ; system cost = − 123.2 ; SL = − 123.5 ; SP = − 99.8 . (3) Passed the test of the number of questions and feedback rate, classroom number, classroom test scores, new vocabulary number, system scoring, and comprehensive scoring to judge the model. It is found that the number of questions, feedback rate, class number, and class test scores, number of new vocabularies, system scores, and comprehensive scores of the upgraded and transformed models of the Internet of Things are above the standard values, indicating that the model is highly efficient and intelligent.

Robust method of shared parking allocation based on buffer time

Shared parking platform determines the parking allocation schemes according to the time windows of parking supplies and parking demands. In order to deal with uncertain situations such as early arrival or delayed departure during parking, the robustness of the parking allocation scheme is enhanced by reserving appropriate buffer time. This paper builds a bi-objective optimization model of parking space allocation problem that maximizes the platform revenue and the robustness. The bi-objective function is transformed into a single objective function by the weighted sum method, and the nonlinear model is transformed into a linear model by adding auxiliary decision variables. Finally, the effectiveness of the method is verified by a numerical example, and the sensitivity analysis of key parameters is carried out. The results show that within a reasonable time, the method can obtain the optimal solution and various index values of the example. With the increase of the dual-objective adjustment coefficient, the platform revenue segment increases, while the robustness segment decreases. The non-dominated solutions are screened out from the optimal solutions obtained by different adjustment coefficients, and the approximate Pareto frontier curve is obtained by fitting. The robustness reduction varies in the range of [0.02, 6.75].

Development of the combined approach to the valuation of intellectual property objects

The object of this research is a combined approach to the valuation of intellectual property. The paper compares the traditional approaches to valuation: profitable, market and cost, and identified the shortcomings of each. During the work the methods of analysis and synthesis of scientific literature were used to highlight the disadvantages and advantages of traditional approaches, statistical and comparative methods for estimating the value of patents in one industry. Mathematical modeling was also used to develop the formula of the combined approach. Researchers raised the issue of determining the pricing factors that are taken into account when determining the calculation parameters and dependent coefficients of adjustment of the value of intellectual property. Researchers presented methodological recommendations for calculating the integrated coefficient of influence of qualitative and quantitative factors, taking into account the type of object and the features of standardized approaches to evaluation. As a result of the analysis of approaches the improvement of the combined approach for definition of cost of object of intellectual property is offered. The development takes into account the purpose of the assessment and the availability of complete, reliable and objective information, as well as the affiliation of the intellectual property to a particular industry and the impact of technological development of this industry. On the basis of the proposed coefficients of adjustment of the value of intellectual property objects, the value of industrial property according to the patent of Ukraine UA85101 and some inventions of the power engineering industry of enterprises of Kharkiv region (Ukraine) was calculated. The best result for the patent owner of JSC «Ukrainian Energy Machines» are the calculations obtained on the basis of the combined approach, taking into account the traditional profit approach and the integrated coefficient. According to the studied model, the value of industrial property UA85101 is 26930 USD. These calculations assume the best economic effect of using the rights to this patent in the company, or obtaining income from the sale of a license for this patent using a lump sum or royalty. The practical applicability of the study allows industrial enterprises to assess the possibility of improving the efficiency of intellectual property management, which provides maximum benefit from the commercialization of innovations.

Pressure-Surface Tension-Temperature Equation of State for n-Alkanes.

Herein, the geometric similitude concept is applied to propose a cubic equation that relates surface tension, saturation pressure, and temperature for n-alkanes. The input properties for each fluid are the molecular mass, pressure, temperature, and compressibility factor at the critical point. The model is applied to temperatures below 0.93·Tc (critical point temperature). A total of 2429 surface tension values have been selected for 32 n-alkanes. The parameters of the model have been obtained with a fit of the surface tension values for 19 pure n-alkanes that are randomly chosen. Then, it is tested for the other 13 pure n-alkanes and used to predict the surface tension for 11 binary and 4 ternary mixtures. These predictions are compared with the reported experimental data. For pure n-alkanes, the overall absolute average deviation is 2.4%, including the correlation and testing sets. No additional adjustable coefficients are used for mixtures, yielding an overall absolute average deviation of 2.98% for the binary systems and 7.97% for the ternary ones. The results show that the model is accurate enough for predictions and that the highest deviations are due to the lack of agreement in the values of surface tension of pure fluids obtained from different sources.

Research on the Mechanism of Anti-Stone-Damage Automotive Coating Based on Computational Fluid Dynamics Method

Damage to the body or critical components caused by stone strikes to automotive coatings affects the safety of the car. More and more attention are being paid to the study of the anti-stone-damage mechanism of coatings. Firstly, a computational fluid dynamics (CFD) model was established based on a single-impact anti-stone tester, and the simulation results show the following: (a) At 90° incidence, the air pressure of the stone impact suddenly and rapidly increases when the semicircular surface projectile velocity is greater than 30 m/s. The air pressure of the conical projectile stone at the distance of 0.3 and 0.15 mm suddenly increases at the projectile velocity of 10 and 20 m/s, respectively. (b) When the projectile velocity exceeds 15 m/s, the pressure of the semicircular surface projectile suddenly increases. (c) When the speed is less than 20 m/s, the shedding area decreases with the increase in the speed, while the stone impact pressure increases with the increase in the speed. Then, the simulation results of the incident velocity of 40 m/s show that the working pressure is 195.48 kPa, and the impact force is 8142.56 N. Finally, the relationship between the impact force and the driving air flow pressure was obtained according to the DIN 55996-1 standard stone impact resistance test and compared with the simulation results, showing that trend of the impact force increasing with the increase in driving air flow pressure is consistent, the simulation results are about 1.40 times the experimental results, and the simulation results are large and can be considered by increasing the test correction coefficient adjustment. Based on the CFD method, research on the mechanism of anti-stone-damage automotive coating can greatly reduce the number of stone strike instrument experiments, shorten the cycle, and reduce the research and development costs.

Prediction of thermodynamic properties for sulfur dioxide

We report a novel formulation representation of the molar Gibbs free energy for sulfur dioxide (SO2), which depends only on experimental values of six molecular constants of SO2, whereas the conventional explicit representations contain many adjustable coefficients determined from fitting a great number of experimental spectroscopic data or calorimetric data. The formulation representation of the molar entropy for SO2 is also developed from the Gibbs free energy representation. The predicted reduced molar Gibbs free energy and entropy values yield the average absolute deviations of 0.0750 % and 0.228 % from the data reported in the National Institute of Standards and Technology database at the pressure of 0.1 MPa and in the temperature range from 298 to 6000 K, respectively. The proposed formulation representation of the Gibbs free energy for SO2 can be valuable in practical engineering applications.

Study on sustainable developments in Guangdong Province from 2013 to 2018 based on an improved ecological footprint model

Aiming at the ecological footprint model, the traditional trade adjustment method only considered the international trade process at the urban scale, ignoring the trade footprint generated by domestic trade and indirect trade in various products. This paper adopts the urban-scale ecological footprint model based on the macro-trade adjustment method to calculate the trade adjustment coefficient of biological products and the energy trade adjustment coefficient respectively to correct the trade footprint. The results showed that the per capita ecological deficit showed a straight upward trend, from 0.07351 hm2 in 2013 to 0.15472 hm2 in 2018. From 2013 to 2018, the per capita ecological footprint of Guangdong Province was greater than the per capita ecological carrying capacity, and the ecological economic system of Guangdong Province was in an unsustainable state. According to the trade ecological footprint, Guangdong Province was a completely foreign resource and service exporting city, which was consistent with Guangdong Province’s own economic development direction; the analysis results of the ecological product trade footprint were more consistent with the current city positioning of biological resource products of each city, and the energy indirect trade footprint. The improved ecological footprint model could more accurately assess the true status of ecological vitality above the urban scale.

Neural network method for solving nonlinear fractional advection-diffusion equation with spatiotemporal variable-order

In this article, neural network method (NNM) is presented to solve the spatiotemporal variable-order fractional advection-diffusion equation with a nonlinear source term. The network is established by using shifted Legendre orthogonal polynomials with adjustable coefficients. According to the properties of variable fractional derivative, the loss function of neural network is deduced theoretically. Assume that the source function satisfies the Lipschitz hypothesis, the reasonable range for learning rate is discussed in details. Then neural networks are trained repeatedly on the training set to reduce the loss functions for two numerical examples. Numerical results show that the neural network method is better than the finite difference method in solving some nonlinear variable fractional order problems. Finally, several graphs and some numerical analysis are given to confirm our conclusions.

Calibration of Adjustment Coefficient of the Viscous Boundary in Particle Discrete Element Method Based on Water Cycle Algorithm

The viscous boundary has a direct influence on the accuracy of structural dynamic response analysis, and the absorbing effect of the viscous boundary is controlled by the adjustment coefficient. Therefore, a calibration model of the viscous boundary’s adjustment coefficient based on the water cycle algorithm is established for the particle discrete element to improve the accuracy of dynamic response analysis. First, the traditional viscous boundary theory is utilized to realize the viscous boundary’s application method in the particle discrete element via programming. This avoids the reflection and superposition of seismic waves at the boundary and makes the structural dynamic response with the particle discrete element more real and accurate. Second, for the complex and time-consuming adjustment coefficients determination, a calibration model based on the water cycle algorithm and Latin hypercube sampling is established for the adjustment coefficients in the particle discrete element method. Finally, this calibration model is employed for the seismic response analysis of a rockfill slope, the maximum velocity of rock in this rockfill slope being about 1.30 times that of a seismic wave. Comparing the rockfill slope response with fixed and viscous boundaries, the calibration’s accuracy and the viscous boundary’s feasibility are demonstrated, further expanding the research and application of the particle discrete element method in dynamic response analysis.

Reliable EM-Driven Size Reduction of Antenna Structures by Means of Adaptive Penalty Factors

Miniaturization has become of paramount importance in the design of modern antenna systems. In particular, compact size is essential for emerging application areas such as Internet of Things (IoT), wearable and implantable devices, 5G technology, or medical imaging. On the other hand, reduction of physical dimensions generally has a detrimental effect on antenna performance. From the perspective of numerical optimization, miniaturization is a heavily constrained problem, with most constraints being expensive to evaluate due to involving full-wave electromagnetic (EM) simulation. A convenient way of handling such a task is a penalty function approach where constraint violations contribute, upon suitable scaling, to the primary objective, i.e., the antenna size. The penalty coefficients determining the aforementioned contributions are normally adjusted through engineering experience, which does not allow for a reliable control of antenna performance figures. This article proposes an efficient management scheme for adaptive adjustment of penalty coefficients, which eliminates the need for objective function setup by trial and error, and ensures precise control of the design constraints. Our approach is demonstrated using three broadband antennas optimized for minimum size with acceptance thresholds imposed on the in-band matching level. The adaptive adjustment of penalty coefficients is shown to outperform experience-driven setups in terms of constraint control precision and the final design quality.

Estimation of Heat Generation in Semiconductors Centrally Mounted on Printed Circuit Boards

This work proposes a method to estimate the heat dissipation of components that are centrally soldered to printed circuit boards (PCBs). The power losses of a ball grid array (BGA) package and a MOSFET are estimated via a 1-D thermal model in tandem with a heat flux sensor. The former device is soldered to a multilayer PCB with complex copper traces, whereas the latter is mounted on a PCB without internal copper layers and relatively simple trace patterns. Thermal analyses demonstrate that the 1-D model is capable of representing heat conduction with good agreement in the board without internal copper layers. However, the multilayer PCB requires an adjustment coefficient based on the thermal conductance of the board. Said coefficient is employed to adjust the effective heat transfer coefficient of the multilayer PCB. With the aid of the adjustment coefficient, results show that the 1-D thermal model in combination with a heat flux sensor is capable of predicting heat dissipation within 15% of the actual power loss for both devices.

Refinements of bounds for tails of compound distributions and ruin probabilities

In this paper we derive lower bounds for right-tails of compound geometric distributions and ruin probabilities in the classical compound Poisson risk model in the heavy-tailed cases using the truncated Lundberg condition, which improve all the corresponding known lower bounds. Some upper bounds are also derived. Examples are given and numerical comparison for ruin probabilities when the adjustment coefficient does not exist are also considered, illustrating the effectiveness of the proposed new bounds. In addition, several bounds for tails of negative binomial distributions are obtained in terms of the tail of compound geometric distributions as well as bounds in the heavy-tailed cases. Also, some bounds for the stop-loss premium associated with compound negative binomial distributions are given. Using Chernoff's upper bounds we derive two-sided bounds for tails of compound Poisson distributions. Finally, two-sided bounds are given for tails of compound logarithmic distributions in the heavy-tailed cases.

Optimization design of a fully variable valve system based on Nelder-Mead algorithm

This paper presents an optimal design for the soft-landing of the fully variable valve system in internal combustion engines based on Nelder-Mead algorithm and integrated simulation. The system is used to enhance the engine performance in terms of emissions and fuel economy. The electromagnetic linear actuator (EMLA) and the magnetorheological (MR) buffer are modelled with COMSOL Multiphysics, and a co-simulation platform is constructed with MATLAB/Simulink. The prototype test results are basically consistent with the integrated simulation, indicating the feasibility of the integrated simulation platform. After that, the structure of the magnetorheological buffer is optimized based on the Nelder-Mead algorithm. The optimization objective is the adjustable coefficient and the optimized parameters are the effective length, the annular gap width, the piston diameter, the groove depth and length of the coil. The adjustable coefficient of the optimized MR buffer was increased from 8.8 to 25.46. And the co-simulation results showed that the optimized system has a great improvement in soft-landing performance, with the valve seat velocity reduced from 0.51 m/s to 0.03 m/s and the bounce height reduced from 0.6 mm to nearly 0 when the lift is 8 mm.

Corresponding-states model for the correlation and prediction of the surface tension of hydrocarbons

In this paper, a new corresponding state principle (CSP)-based model is proposed to calculate the surface tension of hydrocarbons based on the National Institute of Standards and Technology (NIST) database. This model requires the critical point temperature, the lowest temperature available for surface tension data and the corresponding maximum surface tension value as inputs. It includes two adjustable coefficients which are fitted by using NIST data of three hydrocarbons. To test the applicability of this model, we applied it to predict the surface tension values of other 16 hydrocarbons. For all the 19 hydrocarbons available in NIST database, the average absolute deviations of 16 hydrocarbons are less than 5%. We also compared this new proposed model with other three existing corresponding state models, and found that this model is the best in predicting the surface tension data of hydrocarbons.

An extended VIKOR method based on particle swarm optimization and novel operations of probabilistic linguistic term sets for multicriteria group decision‐making problem

This paper proposes an extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method based on the particle swarm optimization (PSO) algorithm for solving multicriteria group decision-making problems with probabilistic linguistic information. First, we define the novel operations of probabilistic linguistic term sets and then prove the corresponding properties. Second, we apply a modified PSO algorithm to the consensus reaching process to improve the collective consensus level. In the context of probabilistic linguistic information, each participant can be recognized as a particle moving towards the best position. The consensus level can be regarded as the objective function that is used to construct the fitness function. In the update function, the trust relationship and the similarity measure between experts are exploited to determine the adjustment coefficient. The new consensus model based on PSO can ensure that the ultimate evaluation achieves a high level of consensus. Afterward, we propose the extended VIKOR method to obtain the optimal solution, which not only avoids the loss of decision information, but also considers the separation of each alternative from the positive ideal solution and the negative ideal solution when criteria are interactive. The advantages of the proposed method are highlighted through a numerical example. Finally, we perform a comparative analysis and a sensitivity analysis to reveal the effectiveness and applicability of the method.