Search

In view of the different requirements of stability, accuracy and rapidity of different control systems in supercritical CO2 extraction process, an improved whale optimization algorithm objective function is proposed for the parameter tuning of supercritical CO2 extraction. According to the control target of the controlled system, the weight relationship of absolute integral identification, overshoot, residual difference and adjustment time is determined by analytic hierarchy process. The performance index of the control system is normalized by the three fold line method, and the objective function of the whale optimization algorithm is constructed. Taking the pressure and temperature of supercritical CO2 extraction as the controlled objects, the improved whale optimization algorithm objective function and the traditional whale optimization algorithm objective function are used to tune the parameters. The simulation results show that the objective function of the improved whale optimization algorithm can ensure no overshoot in the pressure control system of supercritical CO2 extraction. In the temperature control system of supercritical CO2 extraction, the adjustment time is reduced by 30. 17s, and the tuning optimization of control parameters in the process of supercritical CO2 extraction is realized. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the TRSA-EMD-GAN ship motion attitude prediction model, which utilizes self-attention and generative adversarial networks (GAN) to accurately predict ship motion attitudes. The TRSA mechanism based on time residuals is incorporated into the model to capture the different influences of various attitude points on the prediction and their temporal relationships by using a time mask. Moreover, the model employs a variation mode decomposition generative adversarial network (VMD-GAN) for ship motion attitude prediction through feature fusion. In the VMD-GAN model, VMD is combined with a GRU neural network as the generator, while a convolutional neural network serves as the discriminator. Simulation experiments confirm the effectiveness of the TRSA-VMD-GAN model in predicting ship motion attitudes, resulting in reduced prediction errors and improved accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a novel emergency vehicle path planning approach tailored for university campus traffic is introduced, leveraging reinforcement learning combined with the cuckoo search algorithm. Firstly, an evaluation index system for campus traffic conditions is established, employing the analytic hierarchy process and expert evaluations to assess the prevailing traffic scenarios. Based on these assessments, an objective function is formulated specifically for emergency vehicle path planning within university campuses. Subsequently, the reinforcement learning cuckoo search algorithm is applied to solve this objective function, yielding an optimal path planning strategy. Experimental results demonstrate the efficacy of the proposed method. It achieves a vehicle detour coefficient ranging between 0.01 and 0.13, with an average vehicle travel distance of 5.34 kilometers and an average path planning time of 1.38 seconds. These findings underscore the method's capacity to significantly improve path efficiency and reduce planning time for emergency vehicles navigating university campuses. [ABSTRACT FROM AUTHOR]

People with patellofemoral pain (PFP) may have lower performance during the forward step-down and single-leg hop with their painful (unilateral complaints) or most painful (bilateral complaints) limb when compared with pain-free controls. However, no authors have investigated the appropriateness of using the pain-free or less painful limb as a reference standard in clinical practice or whether deficits might be present depending on the laterality of pain. To compare performance scores and proportion of side-to-side limb symmetry during the forward step-down and single-leg hop tests among people with unilateral and bilateral PFP and pain-free controls. Cross-sectional study. Laboratory. Fifty-two young adults (18–35 years old) with unilateral PFP, 72 with bilateral PFP, and 76 controls. Group × limb interactions on performance during the step-down (repetitions) and single-leg hop (distance [cm] normalized by the limb length) tests were investigated using a repeated-measures analysis of covariance controlling for sex. Pairwise comparisons were interpreted using effect sizes. A χ2 test was used to compare the proportion of symmetry/asymmetry (cutoff point of ≥90% for symmetry indices) across groups and tests. Main effects for groups (small to medium effects) but not limbs indicated lower performance of both limbs of individuals with unilateral and bilateral PFP than controls during forward step-down and single-leg hop tests. No significant differences for the proportion of symmetry/asymmetry were identified across groups (P ≥.05), which further suggests an impaired physical performance of the contralateral limb. Our results indicate bilateral deficits in the physical performance of people with unilateral and bilateral PFP when compared with pain-free controls during the forward step-down and single-leg hop tests. Limb symmetry indices greater than 90% should be interpreted with caution, as they may overstate physical performance by not assuming bilateral deficits. [ABSTRACT FROM AUTHOR]

In garment industries, it is very important to assign fairly workers and tasks to the stations of an assembly line while the precedence relations and resource constraints are satisfied. Many objectives were treated to optimize the working group selection, but consider objectively the workers skills to judge the balancing effectiveness are still non-existent. This paper proposes a new approach for solving the Assembly Line Balancing Problem with workers' competence hierarchical assignment using the ant colony optimization. According to this approach, the workforce's performance index is appreciated with a global Competence Index 'CIg' developed on the basis of measurable criteria to rank hierarchically the workers. The global Competence Index 'CIg' is designed integrating 3 objective criteria: global Quality index 'QIg', global Activity degree 'Ag' and global Attendance Rate 'ARg', which are related to 3 parameters expressing, respectively, the work quality degree, the labour efficiency level and the assiduity for a working group. The global Competence Index is considered as a new 'objective function' for the ant colony optimization algorithm called 'ACO−CIg'. The aim is to find the best solution assigning workers and tasks to an assembly line stations so that the global competence index is maximized. The results showed that the algorithm implementation was useful in real manufacturing process dealing with hierarchical workforce assignments in a reasonable time and allowing to select the best balancing solution, hence, to predict and control the production line's performance. [ABSTRACT FROM AUTHOR]

Abstract The Water Cloud Model (WCM) plays a crucial role in active microwave soil moisture inversion applications. Empirical parameters are important factors affecting the accuracy of WCM simulation, but the current evaluation of empirical parameters only considers the forward simulation process, and insufficient consideration is given to the model inversion problem. This study proposes a new estimation method for vegetation parameters in the WCM by combining the soil backscattering model and the objective function. The effectiveness of the method is then verified using measured data. Simultaneously, this study also analyzes the factors influencing the evaluation of vegetation parameters in the WCM, resulting in the following conclusions. First, blindly utilizing vegetation parameters recommended by previous model studies is not advisable. To ensure the accuracy of the simulation, it is necessary to adjust the vegetation parameters appropriately. Second, to ensure the ability of the WCM solving both forward and inverse problems, it is advisable to consider both soil backscatter and surface backscatter simulations in the construction of the cost function. Third, soil backscatter simulations have an impact on the solution of vegetation parameters, and more accurate soil scattering models provide a better representation of the modeled vegetation. This study presents a dependable method for resolving the vegetation parameters of the WCM, thereby offering a valuable reference for the application of the model in surface parameter inversion research.

Energy savings in air-conditioning systems are important for achieving energy efficient buildings. A central air-conditioning system in the large building is installed with air ductwork. Alternative materials are replacing conventional (Galvanized Iron Steel) air ducts for supplying air to the air-conditioned area. This research studies the objective function relationship between exergy and economic variables in the alternative air ductwork compared with conventional air ductwork. The optimization method to identify the optimal type and thickness of air ducts in Thailand's buildings. The result shows that the alternative ductwork has achieved maximum worthiness with more useful exergy than conventional air ductwork. The Pre-insulated duct (PID) with 30.00 mm wall thickness is 82.14 %. It is the maximum of all exergies generated by the Air Handling Unit.

Energy savings in air-conditioning systems are important for achieving energy efficient buildings. A central airconditioning system in the large building is installed with air ductwork. Alternative materials are replacing conventional (Galvanized Iron Steel) air ducts for supplying air to the air-conditioned area. This research studies the objective function relationship between exergy and economic variables in the alternative air ductwork compared with conventional air ductwork. The optimization method to identify the optimal type and thickness of air ducts in Thailand’s buildings. The result shows that the alternative ductwork has achieved maximum worthiness with more useful exergy than conventional air ductwork. The Pre-insulated duct (PID) with 30.00 mm wall thickness is 82.14 %. It is the maximum of all exergies generated by the Air Handling Unit. [ABSTRACT FROM AUTHOR]

Abstract The degradation half-life (DegT50) of a substance in soil plays an important role in the approval process of a plant protection product and is a sensitive input parameter for regulatory models. It is usually derived through least squares optimizations of mathematical models to measured degradation data according to EU FOCUS degradation kinetics guidance. A strong consensus on degradation parameters provides a solid foundation for parts of the environmental risk assessment. The DegT50 of a substance for regulatory modeling is preferably derived from a single first-order (SFO) model as this is currently the only kinetic model implemented in EU regulatory models of the environmental fate of pesticides. However, kinetic optimisation tools do not always provide a regulatory acceptable SFO fit even though a visual inspection of the data suggests it may be possible. It was therefore hypothesized that more acceptable SFO fits might be achieved by adapting the objective function that is minimized during the optimization. Eight objective functions with varying weightings were tested on 29 laboratory soil degradation datasets. A web-based app was developed to allow experts in environmental safety of plant protection products to visually assess the goodness of fits resulting from different objective functions. The visual assessments and a quantitative metric, newly introduced in the proposed update of the FOCUS guidance, show that the acceptability of SFO fits can be increased, but no single objective function exclusively improves all fits. The assessment reveals that expert judgment is very subjective. Participants tended to change their mind when judging the acceptance of a fit, assumingly caused by a learning curve or a period of calibration. It is concluded that different objective functions could be considered in the kinetic assessment as it can improve the acceptability of SFO fits and hence endpoints for regulatory modeling. This study reveals that various qualitative factors influence the visual judgment of experts when performing a kinetic modeling assessment. The proposed quantitative metric seems to be in alignment with the visual assessment of fits to derive modeling endpoints and a promising step toward less subjective kinetic modeling assessments.

Artificial Potential Field techniques (APF) have become a popular path planning technique due to their simplicity and fast-execution nature. APF techniques have been widely known for their outstanding performance against the path quality and computational time trade-of problem for path planning techniques. However, Since APF techniques are optimization problems, they suffer from well-known problems such as the minima trap problem and the narrow passage problem. The APF planner can fail to find an obstacle-firee path to the final goal point and simply be trapped in a local minima for some map configurations. This paper proposes a new path planning technique that aims to take the advantage of the APF technique as well as solving the APF problems of local trap and narrow passage problems. The proposed technique is inspired by the fluid dynamic concepts and adopts an artificial "flushing" operation. The robot and the obstacles will be placed in an artificial pool. The robot will have negligible weight while the obstacles will be fixed in the pool. Once the "flushing" operation starts, the robot will be automatically pulled by the water to the flushing point which is the goal point. It is guaranteed that the water in the whole map will be absorbed into the flushing point and no particles will be trapped in any region. The simulation results have proven the effectiveness of the proposed technique and demonstrated its ability to overcome the challenge of local minima traps in potential field path planning. [ABSTRACT FROM AUTHOR]

The degradation half-life (DegT50) of a substance in soil plays an important role in the approval process of a plant protection product and is a sensitive input parameter for regulatory models. It is usually derived through least squares optimizations of mathematical models to measured degradation data according to EU FOCUS degradation kinetics guidance. A strong consensus on degradation parameters provides a solid foundation for parts of the environmental risk assessment. The DegT50 of a substance for regulatory modeling is preferably derived from a single first-order (SFO) model as this is currently the only kinetic model implemented in EU regulatory models of the environmental fate of pesticides. However, kinetic optimisation tools do not always provide a regulatory acceptable SFO fit even though a visual inspection of the data suggests it may be possible. It was therefore hypothesized that more acceptable SFO fits might be achieved by adapting the objective function that is minimized during the optimization. Eight objective functions with varying weightings were tested on 29 laboratory soil degradation datasets. A web-based app was developed to allow experts in environmental safety of plant protection products to visually assess the goodness of fits resulting from different objective functions. The visual assessments and a quantitative metric, newly introduced in the proposed update of the FOCUS guidance, show that the acceptability of SFO fits can be increased, but no single objective function exclusively improves all fits. The assessment reveals that expert judgment is very subjective. Participants tended to change their mind when judging the acceptance of a fit, assumingly caused by a learning curve or a period of calibration. It is concluded that different objective functions could be considered in the kinetic assessment as it can improve the acceptability of SFO fits and hence endpoints for regulatory modeling. This study reveals that various qualitative factors influence the visual judgment of experts when performing a kinetic modeling assessment. The proposed quantitative metric seems to be in alignment with the visual assessment of fits to derive modeling endpoints and a promising step toward less subjective kinetic modeling assessments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hydrological model is an important tool for flood simulation, and most of the models contain a certain number of parameters. There? fore, parameter calibration is one of the keys to improve the precision of hydrological model runoff simulation and ensure the accuracy of mod? el results. As the optimization standard of parameter calibration process, there is currently no detailed classification system to provide a clear selection basis for the selection of objective function. In order to explore the effects of different types of objective functions on the calibration results of hydrological model parameters and the precision of runoff simulation, this paper comprehensively considered various types of objectives, such as Nash efficiency coefficient, Kling-Gupta coefficient, flood relative error, etc., and constructed an objective function classification system that includes high flow target, low flow target, flood volume target, flood process target and weighted target. According to the requirements of the current hydrological forecast information standard, some criteria were provided for the selection of objective function. Further, the Xinanjiang model was established, and the optimization algorithms of SCE-UA and NSGA-II were introduced. A variety of objective function selection schemes were set up for different algorithms, and the influence of different objective function schemes and optimization algorithms on the precision of runoff simulation was analyzed by taking Muma River in Hanjiang River basin as an example. The results show that using a single objective function can improve the simulation accuracy of hydrological elements embodied by the objective function; Selecting the appropriate objective function is the key to improve the precision of runoff simulation. Under the requirements of existing norms, selecting flood process target and high flow target is the best choice to ensure the accuracy of hydrological model. Compared with Nash efficiency coefficient, Kling Gupta coefficient has better performance in flood volume simulation accuracy and is a better choice for flood process target. The simulation results of weighted single target method and multi-target method are different in the high-flow part. [ABSTRACT FROM AUTHOR]

Oilfield development optimization plays a vital role in maximizing the potential of hydrocarbon reservoirs. Decision-making in this complex domain can rely on various objective functions, including net present value (NPV), expected monetary value (EMV), cumulative oil production (COP), cumulative gas production (CGP), cumulative water production (CWP), project costs, and risks. However, EMV is often the main function when optimization is performed under uncertainty. The behavior and performance of different objective functions has been investigated in this paper, when EMV is the primary criterion for optimization under reservoir and economic uncertainty. One of the goals of this study is to provide insights into the advantages and limitations of employing EMV as the sole objective function in oil field development decision-making. The designed optimization problem included sequential optimization of design variables including well positions, well quantity, well type, platform capacity, and internal control valve placements. A comparative analysis is presented, contrasting the outcomes obtained from optimizing the EMV-based objective function against traditional objective functions. The study underscores the importance of incorporating multiple objective functions alongside EMV to guide decision-making in oilfield development. Potential benefits in minimizing CGP and CWP are revealed, aiding in the mitigation of environmental impact and optimization of resource utilization. A strong correlation between EMV and COP is identified, highlighting EMV's role in improving COP and RF. [ABSTRACT FROM AUTHOR]

This paper presents an optimal energy control strategy for hybrid solar thermal/heat pump water heaters to address the issue of energy conservation commonly associated with conventional water heaters in residential applications. The pervasive use of fossil-fuel-based water heating technology incurs significant energy costs and contributes to the climate crisis. These have sparked a lot of interest in using renewable technology such as solar water heaters (SWHs). Amidst this, solar radiation intermittent and low efficiency due to PV cell temperature rise have been identified as a limitation to using SWHs. One potential solution to these issues is to explore hybrid technology, which would replace conventional heating technology with a more energy-efficient hybrid solar/heat pump water heater. The main objective of the controller is to reduce the electricity cost by optimizing the operation of the heat pump water heater. A medium-density family in the southwest of Nigeria is selected for the study. The mathematical model was formulated based on the energy balance equations of the sub-system components and then discretized using the Euler forward method. Furthermore, a cost optimization problem with continuous and binary control variables was developed, resulting in a mixed integer problem. This optimization problem was solved using the OPTI toolbox's mixed integer nonlinear programming (MINLP) optimization solver and the solving constraint integer programs (SCIP) algorithm. Simulations were run in MATLAB, and the results show that the optimal control (OC) has a potential energy savings of 61.14% and a cost savings of approximately 70.83% compared to the baseline model. Better utilization of the hybrid system with the OC strategy saves 45.4% more energy than conventional water heaters. [ABSTRACT FROM AUTHOR]

Designing a PEM fuel cell model is exceedingly challenging because of its multivariate in nature. Optimization is required to achieve highest operating condition. Neural Network Model is one of the possible methods to solve complex problems. The polarisation curve of a PEMFC (Proton Exchange Membrane Fuel Cell) is investigated in this paper in relation to the effects of seven parameters, including temperature, relative humidity in the cathode, relative humidity in the anode, anode stoichiometry, cathode stoichiometry, partial pressure of H2, and partial pressure of O2, using an ANN (artificial neural network) model. Where model geometric parameters i.e. Channel width, Channel depth, Channel length, Rib width, Cell width, GDL thickness, CL thickness, Membrane thickness of PEMFC was constant. Initially single Objective Function (Output Power) is predicted. The research presented here makes predictions about a PEMFC stack's electrical performance under multiple operating conditions. Mathematical model was further verified using laboratory data. Co-efficient of Determination (R2), Mean Square Error (MSE), and Mean Absolute Error (MAE) was determined using the fuel cell stack voltage model and stack power model. The model results show the possibility of using ANN in the implementation of such models to predict the PEMFC system's steady-state behaviour. Highlights: Experimentally data useful for investigation and work on PEMFCs. ANN models to predict the steady state behaviour of the PEMFC system for different operating conditions Single Objective Function (Output Power Predicted) ANN-Multi Objective function is presented to predict Efficiency and output Power simultaneously ANN-MOO model is Validated using Laboratory Data [ABSTRACT FROM AUTHOR]

The current resource allocation in 5G vehicular networks for mobile cloud communication faces several challenges, such as low user utilization, unbalanced resource allocation, and extended adaptive allocation time. We propose an adaptive allocation algorithm for mobile cloud communication resources in 5G vehicular networks to address these issues. This study analyzes the components of the 5G vehicular network architecture to determine the performance of different components. It is ascertained that the communication modes in 5G vehicular networks for mobile cloud communication include in-band and out-of-band modes. Furthermore, this study analyzes the single-hop and multi-hop modes in mobile cloud communication and calculates the resource transmission rate and bandwidth in different communication modes. The study also determines the scenario of one-way and two-way vehicle lane cloud communication network connectivity, calculates the probability of vehicle network connectivity under different mobile cloud communication radii, and determines the amount of cloud communication resources required by vehicles in different lane scenarios. Based on the communication status of users in 5G vehicular networks, this study calculates the bandwidth and transmission rate of the allocated channels using Shannon's formula. It determines the adaptive allocation of cloud communication resources, introduces an objective function to obtain the optimal solution after allocation, and completes the adaptive allocation process. The experimental results demonstrate that, with the application of the proposed method, the maximum utilization of user communication resources reaches approximately 99%. The balance coefficient curve approaches 1, and the allocation time remains under 2 s. This indicates that the proposed method has higher adaptive allocation efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic optimization method capable of optimizing the dynamic responses of a compliant parallel mechanism (CPM), in terms of its multiple primary resonant modes, is presented. A novel two-term objective function is formulated based on the specific frequency range and targeted ratios. The first term of the function is used to optimize the first resonant mode of the CPM, within a specific frequency range. The obtained frequency value of the first mode is used in the second term to define the remaining resonant modes to be optimized in terms of targeted ratios. Using the proposed objective function, the resonant modes of a CPM can be customized for a specific purpose, overcoming the limitations of existing methods. A 6-degree-of-freedom (DoF) CPM with decoupled motion is synthesized, monolithically prototyped, and investigated experimentally to demonstrate the effectiveness of the proposed function. The experimental results showed that the objective function is capable of optimizing the six resonant modes within the desired frequency range and the targeted ratios. The highest deviation between the experimental results and the predictions among the six resonant modes is found to be 9.42%, while the highest deviation in the compliances is 10.77%. The ranges of motions are found to be 10.0 mm in the translations, and 10.8° in the rotations. [ABSTRACT FROM AUTHOR]

Numerical models play a primary role in Cultural Heritage preservation. Nevertheless, the design of a realistic model remains challenging due not only to the complex behavior of masonry but also to the asynchronous building phases, the damage induced by natural and anthropic aggression, and the associated repairs. This paper discusses the impact of the information provided by an in-depth analysis of the construction history on the updating process of a Finite Element building model. The case study is the church of Sant'Agata del Mugello (Italy); for this building, a previous historical–archaeological study identified and recorded the asynchronous construction phases, the repair techniques, and the damage induced by three historical earthquakes (1542, 1611, and 1919) – moreover, a dense ambient vibration survey allowed to identify the modal parameters. The information from previous works is summarized in five Finite Element models with increasing complexity. A vibration-based model updating methodology based on a Particle Swarm Algorithm is developed. This work shows that the best minimization of the difference between the numerical and experimental modal parameters is obtained with the numerical model considering the identified construction techniques, repair phases, and connection relations between the bell tower and the nave. [ABSTRACT FROM AUTHOR]

Abstract Oilfield development optimization plays a vital role in maximizing the potential of hydrocarbon reservoirs. Decision-making in this complex domain can rely on various objective functions, including net present value (NPV), expected monetary value (EMV), cumulative oil production (COP), cumulative gas production (CGP), cumulative water production (CWP), project costs, and risks. However, EMV is often the main function when optimization is performed under uncertainty. The behavior and performance of different objective functions has been investigated in this paper, when EMV is the primary criterion for optimization under reservoir and economic uncertainty. One of the goals of this study is to provide insights into the advantages and limitations of employing EMV as the sole objective function in oil field development decision-making. The designed optimization problem included sequential optimization of design variables including well positions, well quantity, well type, platform capacity, and internal control valve placements. A comparative analysis is presented, contrasting the outcomes obtained from optimizing the EMV-based objective function against traditional objective functions. The study underscores the importance of incorporating multiple objective functions alongside EMV to guide decision-making in oilfield development. Potential benefits in minimizing CGP and CWP are revealed, aiding in the mitigation of environmental impact and optimization of resource utilization. A strong correlation between EMV and COP is identified, highlighting EMV’s role in improving COP and RF.

Kernel matching pursuit (KMP) requires every step of the searching process to be global optimal searching in the redundant dictionary of functions in order to select the best matching signal structure. Namely, the dictionary learning time of KMP is too long. To solve the above drawbacks, a rough dataset was divided into some small-sized dictionaries to substitute local searching for global searching by using the property superiority of dynamic clustering performance, which is also superior in the intuitionistic fuzzy c-means (IFCM) algorithm. Then, we proposed a novel technique for KMP based on IFCM (IFCM-KMP). Subsequently, three tests including classification, effectiveness, and time complexity were carried out on four practical sample datasets, the conclusions of which fully demonstrate that the IFCM-KMP algorithm is superior to FCM and KMP. [ABSTRACT FROM AUTHOR]

Routing protocols for Internet of Things (IoT) play a major role in the performance of the network. The standard Routing Protocol for Low-Power and Lossy Networks (RPL) suffers from a number of limitations including congestion of higher-level nodes and unbalanced topology. This paper proposes a novel Objective Function called Load Balanced Minimum Rank with Hysteresis Objective Function (LB_MRHOF), which assigns child nodes to the most suitable parent in the topology. The Objective Function utilizes a weight of the Expected Transmission Count (ETX) and number of children to calculate the Composite ETX and Number of Children (CENOC) which estimates the load on each node. The attained CENOC is used to select the optimum parent for each node in the topology, where nodes with high CENOC are avoided in the parent selection process. The proposed Objective Function has been evaluated under random and hierarchical network topologies. In addition, the evaluation has investigated the influence of the number of nodes by testing for small, medium and large-scale networks. Results have shown that the proposed Objective Function outperforms MRHOF, OF_FUZZY and OF-EC in terms of Packet Delivery Ratio (PDR) and reduces nodal hop-count under all tested scenarios, with no compromise in energy consumption. They have also revealed that the best performance achieved by LB_MRHOF is attained under large-scale networks. The resulting network topology which is formed by the proposed Objective Function has shown improved balance and more depth. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Considering the high importance of the optimization problem, this study evaluated mathematical programming models by considering various methods of estimating model coefficients. Correct and accurate data must be entered into the model to get accurate and robust result from the model. Most input data to the presented model are technical and objective function coefficients. Therefore, it is necessary to determine the information related to these coefficients with the utmost precision and, as much as possible, to develop a suitable scientific method to estimate the value of these coefficients [5]. Finding the best method for estimating the coefficients of mathematical programming models can significantly optimize the final values of the variables extracted from the mathematical programming model. For this reason, it is essential to study the methods used so far in this field and examine their advantages and disadvantages. This review study investigated various methods of estimating technical coefficients of mathematical planning models in the conditions of possible decision-making and uncertainty after reviewing 117 articles published between 1955 and 2022. These methods include fuzzy methods, statistical methods, and data analysis methods. Statistical methods such as regression methods, time series methods, exponential smoothing, and linear non-linear and non-parametric, machine learning and data mining methods were investigated in this article. The methods of data-driven analysis explained in this article can be referred to as decision trees, random forests and the Lasso methods. After evaluating and comparing these methods, suggestions for choosing the best method were provided. [ABSTRACT FROM AUTHOR]

When developing control systems, an important issue arises of choosing an operator, which forms the control function. The standard approach is to use a PI- or PID-controller, a more advanced approach involves ANNs training for this purpose. A comparative analysis of the PI- and neurocontroller performances makes it possible to establish the disadvantages and advantages of each of the compared controllers, which is an important scientific and applied problem. The purpose of the work was to conduct a comparative analysis of the performance of the PI-controller and the neurocontroller based on a set of evaluation indicators for plants of the second and third orders. Such a comparison was carried out by using an approach to the synthesis of both controllers, which involved the minimization of a complex objective function. The latter is obtained as a result of reducing the problem of optimal control with constraints to the problem of unconstrained optimization. The analysis showed that according to the settling time indicator (optimization criterion), the neurocontroller has an advantage of 6.1...96.2% for the modelled plants. At the same time, according to other indicators of the control quality, the PI-controller has an advantage. In addition, the synthesis of a neurocontroller in terms of finding the minimum of the objective function is a more difficult problem. For its solution, a bigger number of iterations of the VCT-PSO optimization algorithm is required. It is rationally to set more than 1000 iterations and swarm population in the range 30...50 particles. A comparative analysis by the settling time of the neurocontroller and PI-controller, which is tuned according to engineering methods, showed significant reserves for improving this indicator. Thus, if the requirements for settling time minimization are quite strict, then it is advisable to use a neurocontroller. The obtained results will make it possible to develop recommendations for the rational choice of the control operator when solving practical problems of the control systems synthesis. [ABSTRACT FROM AUTHOR]

The object of this study is the process of image segmentation from the First Person View (FPV) of an unmanned aerial vehicle (UAV). The main hypothesis of the study assumes that the use of a simple ant algorithm could ensure the necessary quality of the segmented image. The segmentation method, unlike the known ones, takes into account the number of ants in the image, weight, initial amount and evaporation rate of the pheromone, the "greediness" of the algorithm and provides: – preliminary selection of individual channels of the Red-Green-Blue (RGB) color space; – preliminary placement of ants according to the uniform law; – determining the routes of ants; – taking into account the attractiveness of the route for each ant; – change (adjustment) in the concentration of ant pheromones; – calculation of the probability of movement (transition) of the ant on the movement route; – determination of the objective function at the j-th iteration and its minimization; – determining the coordinates of the route of movement (movement) of ants; – verification of the fulfillment of the stop condition; – determination of the best routes found by ants; – calculation of the brightness of the pixels of the segmented image in each channel of the RGB color space; – further combining the results of channel segmentation. An experimental study of image segmentation from UAV FPV based on a simple ant algorithm was conducted. The specified object of interest on the segmented image has a certain structure, unevenness of the contours, and can be further used for decoding, categorization, etc. Unlike the object of interest, the background ("garbage" objects) in the segmented image do not have a stable structure and can be further filtered out. It has been established that the segmented image by the known method based on the gradient module has a low contrast value, there are gaps in the segmented pixels of the object of interest. A segmented image using a method based on a simple ant algorithm is free from that drawback. [ABSTRACT FROM AUTHOR]

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated. [ABSTRACT FROM AUTHOR]

The supply chain regards suppliers, producers, and consumers as an organic whole, unifying and coordinating the information flow, logistics, and capital flow of all members, and achieving the goal of win-win for all members in the overall operation of cross organization. Demand forecasting is an important factor driving the entire supply chain, and low error rates in forecasting are a common goal pursued by the industry. In order to improve the quality of demand forecasting, enhance the efficiency of supply chain operations, and leverage the important role of machine learning in the era of artificial intelligence, this paper conducts research based on LSTM. Firstly, this paper determines the objective function and constraints for supply chain demand forecasting; Then, this paper constructs a supply chain demand prediction model, based on the LSTM network structure, determine the network training method and model construction process; Finally, this paper conducts simulation experiments and result analysis, configure LSTM parameters, determine model performance evaluation indicators, and compare and analyze actual values with predicted values. The results indicate that the supply chain demand prediction model constructed in this article has very good performance and has promotional value in practice. [ABSTRACT FROM AUTHOR]

The growing ubiquity of Internet of Things (IoT) devices within smart homes demands the use of advanced strategies in IoT implementation, with an emphasis on energy efficiency and security. The incorporation of Artificial Intelligence (AI) within the IoT framework improves the overall efficiency of the network. An inefficient mechanism of parent selection at the network layer of IoT causes energy drain in the nodes, particularly near the sink node. As a result, nodes die earlier, causing network holes that further increase the control message overhead as well as the energy consumption of the network, compromising network security. This research introduces an AI‐based approach to parent selection of the Routing Protocol for Low Power and Lossy networks (RPL) at the network layer of IoT to enhance security and energy efficiency. A novel objective function, named Energy and Parent Load Objective Function (EA‐EPL), is also proposed that considers the composite metrics, including energy and parent load. Extensive experiments are conducted to assess EA‐EPL against OF0 and MRHOF algorithms. Experimental results show that EA‐EPL outperformed these algorithms in improving energy efficiency, network stability, and packet delivery ratio. The results also demonstrate a significant enhancement in the overall efficiency of IoT networks and increased security in smart home environments. [ABSTRACT FROM AUTHOR]

In this work, we address the issue of insufficient accuracy in the gradient projection algorithm and propose a closed-loop control dynamic obstacle avoidance algorithm that relies on a machine learning objective function. We initially establish a reasonable objective function and employ the gradient descent algorithm to enable dynamic obstacle avoidance in each bar. We then separate the end trajectory of the manipulator into multiple trajectory points and use the actual and expected positions of the end as the starting and ending points to significantly enhance the end tracking accuracy of the manipulator. Finally, we conduct simulation and real experiments on planar four degrees-of-freedom redundant manipulators to validate the efficacy of the algorithm. Moreover, the algorithm is proven to be applicable to dynamic obstacle avoidance under various trajectory tracking scenarios. It also exhibits advantages, such as smooth and continuous avoidance states and low computational costs. [ABSTRACT FROM AUTHOR]

The accurate modelling of streamflow is highly significant for hydrological monitoring, water resource management, and climate change studies. Streamflow simulation with lumped hydrological models has been widely performed by researchers. However, the parameter calibration process is a major obstacle in these models. In the present study, a conceptual rainfall-runoff model (TUW model) was used to simulate streamflow in the sub-basin of the Upper Euphrates Basin during the time period 1991-2009. The Differential Evolution Optimization (DEoptim) algorithm were tested for the automatic parameter calibration of the lumped version of TUW model, in the study area. The model is calibrated using two objective function named and Nash-Sutcliffe efficiency (NSE) and Kling-Gupta Efficiency (KGE). Additionally, percent bias (PBias) was used to evaluate the performance of the model. For the objective function NSE, calibration and validation results indicated good agreement between observed and simulated streamflow data with NSE, 0.76 and 0.76 and KGE, 0.73 and 0.75 and PBias (%), -0.8 and -7.5, respectively. Similarly for KGE objective function, the calibration results produced a NSE of 0.71, KGE of 0.85, and PBias (%) of -0.9, while validation results revealed a NSE of 0.72, KGE of 0.84, and PBias (%) of -7.2. It can be concluded that the applicability of the DEoptim algorithm for the estimation of the parameters of the TUW model is confirmed by the case study. The findings of the study can serve as a guide for researchers and be useful in achieving watershed management goals. [ABSTRACT FROM AUTHOR]

Reliability-based design optimization (RBDO) is a valuable tool for optimizing while considering the impact of uncertainties. However, its application in engineering, specifically in pile foundation design, is complicated due to high computational costs and the potential for nonlinear iteration misconvergence. To address these challenges, we propose a modified optimization calculation method utilizing the mean first-order reliability method (MFORM). The revised function of the reliability index is introduced to ensure computational accuracy and linear regression is employed for its calculation. The results of the case study demonstrate that the modified optimization calculation method not only improves computational efficiency but also enhances computational accuracy. While the form of the performance function significantly influences initial and local optimizations, it has minimal impact on global optimization. Through local and global optimizations, the objective function values are reduced by 20.2% and 24.9%, respectively, for the first form of the performance function. For the second form of the performance function, reductions of 15.0% and 24.9%, respectively, are achieved through local and global optimizations. [ABSTRACT FROM AUTHOR]

In this study, we address a critical aspect of the routing protocol for low-power and lossy Networks (RPL), namely the selection of the parent node, which plays a pivotal role. As IoT networks expand rapidly, tackling data congestion becomes increasingly crucial. The conventional RPL algorithm, initially designed for smaller networks, lacks mechanisms for balancing loads during parent–child node assignment and does not consider congestion scenarios. To overcome these limitations, we propose a novel objective function (OF) tailored specifically for the RPL algorithm. This OF integrates network load and congestion conditions using an Adaptive fuzzy multi-criteria decision-making approach, combining fuzzy analytic hierarchy process and technique for order of preference by similarity to ideal solution (TOPSIS) techniques. By redefining the process of selecting parent nodes, our approach enhances the efficiency of data transmission, alleviates congestion, and optimizes the performance of IoT networks. Our method introduces a multi-criteria decision-making framework for parent node selection, ensuring that the chosen parent node is both free of congestion and balanced in load, resulting in efficient forwarding of data packets. We prioritize parent node selection based on five crucial criteria, directly addressing load and congestion challenges in IoT networks. Through fuzzy AHP, we determine the relative importance of each criterion, while the TOPSIS method aids in ranking alternatives. This comprehensive approach provides a robust solution to mitigate network congestion, optimize load distribution, and enhance IoT network performance amidst dynamic growth. Implementing the algorithm using Contiki OS and the Cooja simulator, our results demonstrate 0–15% reduction in delay, 20–30% lesser energy consumption, and 10–25% reduction in packet overflow rate while maintaining network throughput by 15% as compare to CQARPL, CAFOR and QHCA and enhancing overall performance. [ABSTRACT FROM AUTHOR]

In order to accurately predict the minimum creep rate under the actual working conditions of compacted graphite cast iron cylinder head, this paper conducted creep experiments in the temperature range of 723.15–823.15 K and the stress range of 100–300 MPa. The creep damage evolution and damage mechanisms under test conditions were also analyzed. Then the data fitting performances were analyzed and compared from three aspects: the optimization‐seeking algorithm, the form of the objective function and the construction of the creep constitutive model. Finally, the chosen optimization algorithm combined with the proposed objective function and creep constitutive model resulted in 100% of the predicted values within the 3 times error band and 26.67% of the predicted values within the 1.5 times error band. This improves the prediction accuracy while reducing the effect of data overfitting. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks always operate within the context of diverse and constrained characteristics of the devices. Low-Power and Lossy Networks (LLNs) constitute a network architecture commonly utilized in IoT application deployments, facilitating networking and the establishment of paths for data transmission. The Routing Protocol for Low-Power and Lossy Networks (RPL) demonstrates promising capabilities for LLN network operations, supporting IPv4 and IPv6-enabled services. The RPL protocol constructs a Destination Oriented Directed Acyclic Graph (DODAG) logical routing topology based on defined Objective Function (OF) metrics. Routing operations within the DODAG utilize these metrics and constraints to select parent nodes and calculate optimal routes between two nodes. Standardized OFs have traditionally focused on either parent node selection or routing objectives within the DODAG, often treating load balancing and bottleneck optimization separately. However, their combined impact on RPL's effectiveness has been overlooked. This paper introduces an Adaptively Composite Objective Function (AC-OF) approach that considers the combined objectives of DODAG load balancing and optimized routing operations. Through simulation evidence, the paper presents improved network parameters. The AC-OF implementation brings out significant results in the form of a balanced DODAG topology and it has good impacts on data transmission, control overhead messages, parent switching, delay, energy consumption, and node lifetime. [ABSTRACT FROM AUTHOR]

Purpose: Calculation of the uncertainty of the individual time-integrated activity coefficient (TIACs) is desirable in molecular radiotherapy. However, the calculation of TIAC's uncertainty in single-time-point (STP) method has never been reported in the literature. This study presents a method based on the Bayesian fitting (BF) to calculate the standard deviation (SD) of individual TIACs in the STP dosimetry. Methods: Biokinetic data of 177Lu-DOTATATE in kidneys were obtained from PMID33443063. BF methods with extended objective function, which optimize the fitting using prior knowledge of the function's parameters, were used. Reference TIACs (rTIACs) were calculated by fitting a mono-exponential function to the all-time-point data. The goodness of fit was checked based on the visual inspection and the coefficient of variations (CV) of the fitted parameters < 0.5. BF with relative (BFr) and absolute-based (BFa) variance methods were used to obtain the calculated TIACs (cTIACs) from the STP dosimetry. Performance of the STP method was obtained by calculating the relative deviation (RD) between cTIACs and rTIACs. Results: Visual inspection showed a good fit for all patients with CV of fitted parameters less than 50%. The mean ± SD of cTIAC's %RD were 7.0 ± 25.2 for BFr and 2.6 ± 8.9 for BFa. The range of %CV of the individual cTIAC's SD for BFr and BFa methods was 36–78% and 22–33%, respectively, while the %CV of the rTIAC SD was 0.8–49%. Conclusion: We introduce the BF method to calculate the SD of individual TIACs in STP dosimetry. The presented method might be used as an alternative method for uncertainty analysis in STP dosimetry. [ABSTRACT FROM AUTHOR]

In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information‐theoretic paradigm, the "best" objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should prefer one objective versus another, and the log of that ratio represents the relative information loss (or gain) from one objective to another. In plain terms, minimizing information loss is equivalent to minimizing uncertainty, as well as maximizing probability and general utility. We argue that this paradigm is well‐suited to models that have many uses and no definite utility like the complex Earth system models used to understand the effects of climate change. Furthermore, the benefits of "maximizing information and general utility" extend beyond model accuracy to other important considerations including how efficiently the model calibrates, how well it generalizes, and how well it compresses data. Key Points: A basic problem in modeling is the choice of objective function (or performance metric)According to information theory, the "best" objective function minimizes information loss, which we evaluate using the AICLike friction or inefficiency in a system, information loss incurs additional cost however the model is used [ABSTRACT FROM AUTHOR]