This paper presents a globe coverage of constellation satellite in one revisit and the regional coverage at defined latitude. This constellation seems to be very close to optimal under the maximum ...revisit time comparing to the results in other papers. They are produced easily by using tables and data achievements. These satellites are fully connected by crosslinks sweeping the Earth. This model is more optimal comparing to the conventional constellations that distribute satellites evenly in the space. Since satellites on the LEO orbit are side by side, in most cases, they can maintain communication continuously. This special feature allows all satellites in the constellation connecting ground at any time when a satellite is available to the stations. Length of the crosslink is allowed to reject location connection or real-time data transmission. For example, six satellites in the constellation cover the whole Earth within one revisit time and all the data are collected by two Earth stations for keeping continuous coverage. Thus, the adjacent satellites may be more efficient and provide more coverage.
•Solubility of oxygen in ionic liquids is modeled.•Advanced smart models comprising DBN, Cat-Boost, MARS and XGB are implemented.•The models’ results were also compared to equations of state (EOSs) ...performance.•The XGB model had the highest accuracy compared to the other approaches.•The Leverage approach confirmed statistically validation of the model.
The solubility of different gases such as O2 in different liquids and finding the proper solvent for the gas separation process is one of the important concerns that has received much attention in recent decades. Ionic liquids (ILs) have received much interest in recent years as a prospective category of suitable solvents for gas separation operations. In this study, the solubility of oxygen in ILs has been estimated using powerful machine learning approaches including Deep belief network (DBN), Categorical boosting (Cat-Boost), Multivariate adaptive regression splines (MARS), and Extreme gradient boosting (XGB). Although temperature, pressure, and critical properties are among the properties of ILs that affect the solubility of gases, especially oxygen, in ILs, the chemical substructure of anions and cations also have a significant effect on the properties of ILs because the substructures can change the properties of ILs and create new solubility properties. In this regard, two different strategies including (I): Chemical structure-based and (II): Thermodynamic properties-based models have been used in model development. The results obtained from two separate methods show that in the first strategy, the DBN model has the most accurate predictions with the coefficient of determination (R2) and the root mean square error (RMSE) values of 0.9976 and 0.00341, respectively, while in the second strategy, the XGB model has the best performance with the R2 and RMSE values of 0.9998 and 0.00095, respectively. Also, the comparison of smart models and equations of state (EOSs) shows that the accuracy of smart models compared to EOSs is remarkable. Sensitivity analysis of the DBN and XGB models in the first and second strategies, respectively, shows that pressure has the greatest effect on the O2 solubility among operational parameters in both strategies, and among ionic liquid type and thermodynamic properties, the –Cl substructure and critical pressure have the greatest effect on the solubility of oxygen in ILs, respectively. Trend model analysis also shows that increasing temperature decreases the O2 solubility while increasing pressure increases the O2 solubility. Additionally, replacing the alkyl group with the ether group in the cation's chain reduces the oxygen solubility. The group error analysis also represents that the proposed models have less accuracy at higher values of the input parameters. Finally, the results of the leverage technique show that more than 98% of the data are in the valid region. The findings of this study can provide a better view to control some chemical reactions such as oxidation and provide suitable solutions.
•Proposing two novel hybrid intelligent systems.•Applying the proposed systems to estimation of the tool-tissue force in robotic laparoscopic surgery.•Evaluating the performance of hyper-level ...supervision for extracting the optimum architecture of fuzzy systems.•Conducting a comparative study to elaborate on the authenticity of the proposed methods.
In this paper, two different hybrid intelligent systems are applied to develop practical soft identifiers for modeling the tool-tissue force as well as the resulted maximum local stress in laparoscopic surgery. To conduct the system identification process, a 2D model of an in vivo porcine liver was built for different probing tasks. Based on the simulation, three different geometric features, i.e. maximum deformation angle, maximum deformation depth and width of displacement constraint of the reconstructed shape of the deformed body are extracted. Thereafter, two different fuzzy inference paradigms are proposed for the identification task. The first identifier is an adaptive co-evolutionary fuzzy inference system (ACFIS) which takes advantage of bio-inspired supervisors to be reconciled to the characteristics of the problem at hand. To learn the fuzzy machine, the authors propose a co-evolutionary technique which uses a modified optimizer called scale factor local search differential evolution (SFLSDE) as the core metaheuristic. The concept of co-evolving is implemented through a consequential optimization procedure in which the degree of optimality of the ACFIS architecture is evaluated by sharing the characteristics of both antecedent and consequent parts between two different SFLSDEs. The second identifier is an adaptive neuro-fuzzy inference system (ANFIS) which is based on the use of some well-known neuro computing concepts, i.e. back-propagation learning and synaptic nodal computing, for tuning the construction of the fuzzy identifier. The two proposed techniques are used to identify the force and maximum local stress of tool-tissue. Based on the experiments, the authors have observed that each of the identifiers have their own advantages and disadvantages. However, both ACFIS and ANFIS succeed to identify the model outputs precisely. Moreover, to ascertain the veracity of the derived systems, the authors adopt a Pareto-based hyper-level heuristic approach called synchronous self-learning Pareto strategy (SSLPS). This technique provides the authors with good information regarding the optimum controlling parameters of both ACFIS and ANFIS identifiers.
This research will analyze the tradeoffs between coverage optimization based on Position dilution of precision (PDOP) and cost of the launch vehicle. It adopts MATLAB and STK tools along with ...multiple objective genetic algorithms (MOGA) to explore the trade space for the constellation designs at different orbital altitudes. The objective of optimal design solutions is inferred to determine the economic and efficient LEO, MEO, HEO or hybrid constellations and simulation results are presented to optimize the design of satellite constellations. The benefits of this research are the optimization of satellite constellation design, which reduces costs and increases regional and global coverage with the least number of satellites. The result of this project is the optimization of the number of constellation satellites in several orbital planes in LEO orbit. Validations are based on reviewing the results of several simulations. The results of graphs and tables are presented in the last two sections and are taken from the results of several simulations.
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•Solubility of CO2-N2 gas mixture is modeled using a large data bank.•Two white-box approaches (GMDH and GEP) are used for modeling.•The results of proposed models are compared to ...those of equations of state.•GMDH shows the best performance among all the applied models.•Leverage approach demonstrated that the data are valid and modeling is statistically correct.
The solubility of CO2-N2 gas mixtures in water is important for CO2/flue gas sequestration in aquifer. Having precise thermodynamic measurements as well as credible prediction models to utilize in innovative carbon capture and storage (CCS) systems is critical. In this study, two simple-to-use white-box models, including Gene Expression Programming (GEP) and Group Method of Data Handling (GMDH) models, have been developed using 289 experimental data to predict the solubility of CO2-N2 gas mixtures in aqueous solutions. Four tuned equations of state (EOSs), namely Peng-Robinson (PR), Soave-Redlich-Kwong (SRK), Zudkevitch-Joffe (ZJ), and Redlich-Kwong (RK), as well as the outcomes of the GEP and GMDH, were compared. The results show that the tuned EOSs perform much better than the untuned EOSs. The results show that the GMDH model has the best predictive performance such that the obtained values of root mean square error (RMSE) and coefficient of determination (R2) are 0.000564 and 0.9792, respectively. It should be noted that the GEP model also has acceptable accuracy, with RMSE and R2 values of 0.00081 and 0.9465, respectively. The SRK model obtained the best outcomes among the EOSs for the solubility of the CO2-N2 gas mixture in aqueous solution, with RMSE and R2 values of 0.00128 and 0.9561, respectively. The results also show that the solubility of CO2 in aqueous solutions is much higher than N2, and increasing the pressure increases the solubility of CO2 and N2 in aqueous solutions, while increasing the CO2 content increases and decreases the solubility of CO2 and N2, respectively. Group error analysis also shows that the developed models have less error in low values of temperature, pressure, and CO2 content. Finally, in order to validate the results of the GMDH and GEP models, the leverage technique has been utilized, which illustrated that 95% of the data are in the valid region, thus, the developed models are statistically reliable. The findings of this study can help for better understanding the solubility process of CO2 and N2 in water to overcome thermodynamic and environmental challenges.
Concrete may be loaded at an early age for a variety of reasons. This loading can have negative and sometimes destructive effects on the hardened properties of concrete. Therefore, in the present ...study, the mechanical properties of fiber-reinforced geopolymer concrete after loading at an early age have been investigated. In the present study, the effect of preload on compressive strength at the ages of 28 and 90 days for geopolymer concrete containing fibers has been investigated. For this purpose, the samples were loaded at ages of 1, 3, and 7 days, equivalent to 30 and 70% of their compressive strength at the same age. The samples were then treated again in a humid environment and subjected to compressive loading at 28 and 90 days of age. The effect of preload on flexural strength as well as energy absorption of geopolymer concrete containing fibers was also investigated. Steel fibers with volumetric percentages of 0.25, 0.5, 0.75, and 1 and polypropylene fibers with volumetric percentages of 0.25, 0.5, and 0.75 were used in fabricating laboratory samples. The results demonstrate the positive effect of fibers on reducing the destructive effects of preload on compressive and flexural strength. The effect of fibers on reducing the destructive effects of 1-day preload is higher at higher loading percentage (70% pre-loading), such that the samples containing fibers with preload of 30% at the age of one day experienced a 28.8% increase in 28-day compressive strength, while this increase was 33.2% for the samples with preload of 70%. Samples containing 0.75% polypropylene fibers at 28 and 90 days of age compared to those containing 0.25 and 0.5% polypropylene showed less energy absorption on average due to preloading. In general, the design containing 0.25 polypropylene fiber and 1% steel fiber had the best result of flexural strength among preloaded samples.
•Deep learning methods comprising CNN, RNN, DBN and DJINN are implemented to predict H2S solubility in ionic liquids.•The CNN model provides more accurate, rapid, flexible, and inexpensive ...estimations for H2S solubility.•The CNN model successfully captures the physically expected trends of H2S solubility.
Hydrogen sulfide (H2S) is a toxic, flammable, corrosive, and acidic gas that can have harmful impacts on the environment. Ionic liquids (ILs) are relatively new solutions that have desirable characteristics such as high thermal and electrochemical stabilities, negligible vapor pressure, non-combustibility, and high solubility, allowing them to be an appropriate choice of liquid solvents in gas removal processes. In this study, deep learning (DL) approaches were utilized to predict the H2S solubility in ionic liquids (ILs). The proposed models include convolutional neural network (CNN), recurrent neural networks (RNNs), deep belief networks (DBN), and deep neural network initialization with decision trees (DJINN). To this end, a large databank in a broad range of pressure and temperature encompassing 1516 data points of H2S solubility in 37 various ILs was used for developing models. In these models, the chemical structure of ILs, temperature, and pressure were considered as the model’s inputs, while H2S solubility was the model’s output. The results reveal that the CNN model provides more accurate, rapid, flexible, and inexpensive estimations for H2S solubility in ILs with an average absolute percent relative error (AAPRE) of 2.92% and a determination coefficient (R2) of 0.99. The results were then compared to those of previously proposed techniques in the literature. According to the results of the sensitivity analysis, it was found that pressure and OH (as substructure) impose the highest positive and the highest negative impacts on the solubility of H2S in ILs, respectively. Also, based on sensitivity analysis, temperature has a reverse effect on the solubility of H2S and with increasing temperature, the H2S solubility decreases. The Taylor diagram illustrated that the CNN approach is very efficient, accurate, and realistic for predicting the solubility of H2S in diverse ILs based on the chemical structure, pressure, and temperature. Finally, trend analysis revealed that the trends of CNN predictions are in great agreement with the measured data of H2S solubility in ILs as a function of pressure. The findings of this work may be used not only to overcome the difficulties of calculating H2S solubility in ILs, but also to develop novel and accurate forecasting algorithms for a large dataset that cover a broad range of temperatures and pressures.
Space radiation in the Van Allen radiation belt reduces the life time of the satellite. Also deployment in LEO, below the Van Allen belts, reduces the delay associated with sending signals to and ...from satellites. In these type orbits one can improve the design of satellite constellations by devising ways to reduce the number of satellites in constellations. Such an optimized design lowers the cost of construction and launch. This paper analyzes ways to optimize the design of constellations to lower costs. The timing of the launching and replacing of unhealthy satellites to avoid breakdown of a constellation also can have a major cost impact and also improve the level of system. Thus an improved method to design LEO constellations for communication and navigation is proposed in this paper, it takes coverage capability and precession into consideration to create an improved design for a LEO constellation, The issue of optimizing the number of satellites and other effective ways to improve an LEO constellation design is discussed and analyzed. It provides the optimal solutions for enhancing the constellation capability for either communication and navigation. The simulation results confirm the performance of the proposed algorithm and indicates that it is feasible and effective.
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