Optimal Output Research Articles

An Obstacle Avoidance Strategy for AUV Based on State-Tracking Collision Detection and Improved Artificial Potential Field

This paper proposes a fusion algorithm based on state-tracking collision detection and the simulated annealing potential field (SCD-SAPF) to address the challenges of obstacle avoidance for autonomous underwater vehicles (AUVs) in dynamic environments. Navigating AUVs in complex underwater environments requires robust autonomous obstacle avoidance capabilities. The SCD-SAPF algorithm aims to accurately assess collision risks and efficiently plan avoidance trajectories. The algorithm introduces an SCD model for proactive collision risk assessment, predicting collision risks between AUVs and dynamic obstacles. Additionally, it proposes a simulated annealing (SA) algorithm to optimize trajectory planning in a simulated annealing potential field (SAPF), integrating the SCD model with the SAPF algorithm to guide AUVs in obstacle avoidance by generating optimal heading and velocity outputs. Extensive simulation experiments demonstrate the effectiveness and robustness of the algorithm in various dynamic scenarios, enabling the early avoidance of dynamic obstacles and outperforming traditional methods. This research provides an accurate collision risk assessment and efficient obstacle avoidance trajectory planning, offering an innovative approach to the field of underwater robotics and supporting the enhancement of AUV autonomy and reliability in practical applications.

Forestry Ecosystem Protection from the Perspective of Eco-civilization Based on Self-Attention Using Hierarchical Dilated Convolutional Neural Network

Ensuring the sustainable protection of forestry ecosystems faces numerous challenges. One significant hurdle is the constant threat of illegal logging and deforestation. Despite various regulations and conservation efforts, enforcing these measures can be difficult, particularly in remote or poorly monitored areas. Additionally, the increasing global demand for timber and other forest products puts immense pressure on these ecosystems, leading to overexploitation and habitat degradation. In this manuscript, Self-Focused Hierarchical Augmented Convolution Neural Network (SAHD-CNN) optimized with Tasmanian Devil Optimization (TDO) algorithm is proposed. Initially data is taken from Global Leaf Area Index (LAI) dataset. Afterward the input data is fed to Adaptive Distorted Quantum Matched-Filter. The pre-processing output is provided to Self-Focused Hierarchical Augmented Convolution Neural Network (SAHD-CNN) to effectively classifying Forestry Ecosystem Protection (FEP) for high, medium, and low. The weight parameters of the SAHD-CNN are optimized using Tasmanian Devil (TD) Optimization method. The proposed method is implemented in MATLAB working platform. The FEP-SAHDCNN technique attains higher accuracy value of 99% than the existing techniques such as Forestry Ecosystem Protection based Particle swarm Optimization (FEP-PSO) Accuracy value is 65%, Forestry Ecosystem Protection using Evaluation-based Neural Network (FEP-EN) Accuracy value is 82%, and FEP-GRS Accuracy value is 79%. Thus, the proposed method gives optimal output than the existing methods.

Performance Evaluation of a Solar Photovoltaic (PV) Module at Different Solar Irradiance

Abstract The existing and available fossil energy sources may not be enough to even out the increasing energy demand. These fossil sources are also depleting and may be exhausted sooner than later. The easiest to access energy source is solar energy. It requires no maintenance and is non-polluting. However, the inefficient conversion of photovoltaic (PV) modules in a PV system remains a major concern. This downside is substantially impacted by the amount of solar irradiance. Using a solar tracking system, the output of solar PV systems can be boosted mechanically or electrically. Their temperature and solar irradiance greatly influence the efficiency of PV modules in various configurations. These operating factors also contribute to the high costs of establishing an optimum PV system power output. In this research was utilized to model and simulate a solar photovoltaic (PV) module at different solar irradiance. This gives the P-V and I-V plots used for performance evaluation. Results of the simulation show that the current drop with the decrease in solar irradiance and the maximum and minimum power by the photovoltaic (PV) module is at 1000 and 400 respectively at a constant temperature of 25OC. The results also revealed a higher significant short-circuit Isc compared to the open-circuit Voc when employing standard test condition (STC) parameters for various PV modules at different operating conditions.

The Influence of Liquidity, Exchange Rate Profitability and Firm Size on Hedging Decision Making in Bank Companies on the Indonesian Stock Exchange

Risk within banking companies' operational activities is a crucial thing. Investment in the banking financial market has a performance decline as there is uncertainty in gaining higher profits. GDP in the financial service sector declined from 4.49% in the second quarter of 2019 to 1.03% in the second quarter of 2020 with the amount of decline about -77.06% (BPS, 2020). Therefore, banking companies need to do hedging in order to mitigate the risk. This study was quantitative and had a Systematic Literature Review. Moreover, the population was banking companies that had complete financial statements during 2018-2022 and were listed on IDX. Furthermore, the data were secondary and library research. The data analysis technique used discriminant analysis and descriptive analysis. Additionally, the statistical test results showed that liquidity, exchange rate, and firm size of banking companies did not affect hedging decisions. However, profitability which was referred to as ROA affected hedging decisions. It meant the function of discriminant showed that ROA had a strong divide in the companies' tendency of hedging. As a suggestion, the next researcher needed to use other variables outside the study with different years of observation and analysis models; in order to have optimal output

Determining the library size for the optimal output plan in the RapidPlan knowledge-based planning system using multicriteria optimization.

The aim of this study was to determine the number of trade-off explored (TO) library plans required for building a RapidPlan (RP) library that would generate the optimal clinical treatment plan. We developed 2 RP models, 1 each for the 2 clinical sites, head and neck (HN) and cervix. The models were created using 100 plans and were validated using 70 plans (VP) for each site respectively. Each of the 2 libraries comprising 100 TO plans was divided into 5 different subsets of library plans comprising 20, 40, 60, 80, and 100 plans, leading to 5 different RP models for each site. For every validation patient, a TO plan (TO_VP) was created. For every patient, 5 RP plans were automatically generated using RP models. The dosimetric parameters of the 6 plans (TO_VP + 5 RP plans) were compared using Pearson correlation and Greenhouse-Geisser analysis. Planning target volume (PTV) dose volume parameters PTVD95% in 6 competing plans varied between 97.6 ± 0.7% and 98.1 ± 0.6% in HN cases and 98.8 ± 0.3% and 99.0 ± 0.4% in cervix cases. Overall, for both sites, the mean variations in organ at risk (OAR) doses or volumes were within 50 cGy, 0.5%, and 0.2 cc between library plans, and if TO_VP was included the variations deteriorated to 180 cGy, 0.4%, and 15 cc. All OARs in both sites, except D0.1 ccspine, showed a statistically insignificant variation between all plans. Dosimetric variation among various output plans generated from 5 RP libraries is minimal and clinically insignificant. The optimal output plan can be derived from the least-weighted library consisting of 20 plans. This article shows that, when the constituent plans are subjected to trade-off exploration, the number of constituent plans for a knowledge-based planning module is not relevant in terms of its dosimetric output.

Distributed optimal output synchronization of heterogeneous multiagent systems with communication delays

AbstractThe optimal output synchronization problem of heterogeneous multi‐agent systems (HMASs) with communication delays is studied in this article. In order to compensate for communication delays actively, we employ networked data‐driven predictive control method to predict the agents' outputs. Then the output synchronization issue of HAMSs is transformed into the stabilization issue of the local pseudo systems, and model‐free off‐policy reinforcement learning technique is used to learn optimal control policies of the local systems. Combined predicted output information with optimal control policies, we develop a distributed data‐driven optimal output policy for each follower. Finally, a numerical example is given to show the effectiveness of our results.

Stable, Self‐Adhesive, and High‐Performance Graphene‐Oxide‐Modified Flexible Ionogel Thermoelectric Films

AbstractIonic thermoelectric materials have attracted increasing attention because of their high flexibility and high Seebeck coefficient. However, their insufficient thermoelectric performance and long‐standing processing limit their practical applications. To achieve exotic ionic thermoelectric materials, here, a graphene oxide (GO) modified acrylamide ionogel is designed with high thermoelectric performance and flexibility. Detailed structural characterizations confirm that the uniform dispersion of GO particles in the ionogel structure enables a power factor of 753.0 µW m−1 K−2 and a promising ZT value of 0.19. Additionally, the as‐prepared ionic thermoelectric thin film shows excellent flexibility, stretchability, and self‐adhesiveness. An integrated device, assembled by the as‐prepared ionogel films, can generate an optimal output power density of 1.32 mW cm−2 with a temperature difference of 20 K, indicating great potential for wearable electronics. This work provides insight for searching long‐term, high‐performance ionic thermoelectric materials.

Energy Budgeting and Efficiency Analysis of Organic Cotton: A DEA Approach

Background: Cotton (Gossypium hirsutum L.) is crop of economic importance in global and national scale. India is the greatest producer of quality cotton, the third largest exporter and the second largest consumer. Conventional cotton farming creates environmental pressures and negative impact on public health. Organic farming is the sustainability option in cotton production. Cotton is the energy intensive crop, which uses high amount of input energy to produce seed cotton. Hence, energy budgeting is highly essential for better energy utilization and resource conservation and also measuring the input use efficiency of a farm/agronomic practices is vitally important in present day Indian agriculture. Methods: Sensing the economic importance of cotton, ten number of eco-friendly, ecologically safe organic nutrient management (ONM) practices were framed and field experiment was conducted in split plot design with two main plots (M) and five subplots (S) and replicated thrice. The direct and indirect energy used in different ONM practices were computed and energy coefficients were computed from energy equivalents and that has been used as inputs to generate efficiency coefficients by using an input oriented DEA approach. Result: Energy budgeting on field operation basis reported that, the field preparation and irrigation operation uses most of the energy (in non-renewable forms) which needs attention and there is the scope to find alternative energy conservation systems. Results on input use efficiency reported that the ONM practice, double green manuring followed by cotton and application of well decomposed poultry manure and foliar application of fermented fish extract at 5% concentration at 25 and 35 DAS are 100% efficient in terms of technical CRS, technical VRS, scale, allocative and cost efficiencies. This organic nutrient management practice would produce optimum output from the least amount of input and would be ideal for sustainable cotton production.

Co-Pyrolysis of biomass and plastic waste: Process prediction and optimization based on Artificial Intelligence and response optimizer surrogate model

Co-pyrolysis process prediction and optimization has been done through different artificial intelligence processes and response optimizer surrogate model. Neural network and machine learning-based different prediction models have been evaluated to predict the co-pyrolysis output based on the experimental data from literature. While multi-objective optimization has been done through response optimizer. According to prediction models results, CatBoost Regressor (CatB) and Extreme Gradient Boosting (XGB) models’ performance is better than other (seven) models with CatB co-efficient of determinant (R2) 0.92–0.98 while it is 0.91–0.98 for XGB in different scenarios. Therefore, CatB and XGB both models can provide an optimal result for co-pyrolysis prediction. Surrogate-model for multi-objective optimization results concluded that lower portion of biomass waste and higher plastic waste in the feedstock has the optimum co-pyrolysis process output around 550 °C and 60 min of resident time for higher portion of liquid yield with lower gases production. Overall higher portion of the plastic waste in feedstock at high temperature and low resident time promotes more liquid yield. While solid (biochar) production is optimal when there is a higher amount of biomass waste in the feedstock at lower temperature, and longer resident time.

Finite‐time sliding mode fault‐tolerant control of PEM fuel cell air supply system

AbstractProton exchange membrane (PEM) fuel cell air supply system is primarily responsible for delivering air to the fuel cell at a specific stoichiometric ratio for chemical reaction. The stoichiometric ratio will be impacted by the sudden change in load and system failure. Under the aforementioned circumstances, precise and timely air supply regulation is required to avoid irreparable PEM damage. In order to enhance the robustness and performance of the fuel cell, a cascaded finite time sliding mode fault‐tolerant control method is proposed for the air supply system of PEMFC. The control objective is to adjust the oxygen excess ratio at the cathode flow field, which can avoid the occurrence of oxygen starvation in the PEM fuel cell when the load current changes rapidly or when fault occurs. A cascaded controller structure is introduced, where, in the inner loop, an adaptive sliding mode controller is designed to reconstruct fault signal and track the optimal output trajectory and, in the outer loop, a finite‐time observer is employed to obtain the expected output trajectory. The proposed control scheme is capable of fault‐tolerant tracking of the optimal oxygen excess ratio within finite time. The effectiveness of proposed fault‐tolerant control algorithm is verified through hardware‐in‐the‐loop (HIL) studies.

Cost function criteria using muscle synergies: Exploring the potential of muscle synergy hypothesis

Background and objectiveSolving the redundant optimization problem for human muscles depends on the cost function. Choosing the appropriate cost function helps to address a specific problem. Muscle synergies are currently limited to those obtained by electromyography. Furthermore, debate continues regarding whether muscle synergy is derived or real. This study proposes new cost functions based on the muscle synergy hypothesis for solving the optimal muscle force output problem through musculoskeletal modeling. MethodsWe propose two new computational cost functions involving muscle synergies, which are extracted from muscle activations predicted by musculoskeletal modelling rather than electromyography. In this study, we constructed a musculoskeletal model for simulation using the “Grand Challenge Competition to Predict In Vivo Knee Loads” dataset. Muscle synergies were obtained using non-negative matrix factorization. Two cost functions with muscle synergies were constructed by integrating the polynomial and min/max criterion. Two new functions were verified and validated in normal, smooth, and bouncy gaits. ResultsThe muscle synergies based on normal gaits were classified into four modules. The cosine similarities of the first three modules were all >0.9. In the normal and smooth gaits, the forces in most muscles predicted using the two new functions were within three standard deviations of the root mean square error for electromyographic comparisons. Predicted muscle force curves using the four methods as well as characteristic points (i.e., time points in the gait cycle when the significant difference was observed between normal and bouncy gaits) were obtained to validate their predictive capabilities. ConclusionsThis study constructed two new cost functions involving muscle synergies, verified and validated the ability, and explored the potential of muscle synergy hypothesis.

Calculation on maximum output power of wave energy-PTO system

The energy conversion efficiency of wave energy device is one of the key problems in the large-scale utilization of wave energy. The study of the maximum output POWER of PTO (Power-Take-Off) system provides a theoretical reference for the efficient utilization of energy. In this paper, genetic algorithm and adaptive differential evolution algorithm based on neighborhood search are used to optimize the two-objective multi-order differential equations for the maximum output power of PTO system, and the global optimal solution is obtained. Compared with the traversal algorithm, the algorithm involved in this paper is efficient and the optimal output power obtained can provide a scientific basis for the structural optimization design and material selection of the wave energy device.

Environmental and economic scheduling for wind-pumped storage-thermal integrated energy system based on priority ranking

With the rapid access of wind power clusters (WPC), it is difficult for the traditional active scheduling mode to take into account the security, economy and environmental protection of the power system. Pumped storage power station (PSPS), with its flexible regulation characteristics, can reduce the volatility of wind power and enhance the capacity of wind power accommodation to a certain extent. Therefore, this paper proposes an environmental and economic scheduling strategy for the wind-pumped storage-thermal integrated energy system, and rationally arranges the sequence of the units based on the principle of energy saving and emission reduction. The scheduling strategy involves optimizing operational decision-making for pumped storage units to enhance their regulation performance. Firstly, a pre-scheduling of the wind-pumped storage bundling system is carried out, aiming at achieving the highest wind power accommodation rate, the smallest output fluctuation and the lowest pumped storage scheduling cost. Next, the joint scheduling of the wind-pumped storage-thermal system is carried out, and the priority ranking method is used to arrange the generation schedule of the thermal units according to the optimal wind-pumped storage joint output. Finally, the proposed environmental-economic scheduling model is solved by the improved NSGA-II algorithm to verify the effectiveness of the scheduling strategy.

Multiphysics simulations of uniaxial compression applied to various rock samples subject to microwave pretreatment

PurposeAmong the various applications involving the use of microwave energy, its growing utility within the mining industry is particularly noteworthy. Conventional grinding processes are often overburdened by energy inefficiencies that are directly related to machine wear, pollution and rising project costs. In this work, we numerically investigate the effects of microwave pretreatment through a series of compression tests as a means to help mitigate these energy inefficiencies.Design/methodology/approachWe investigate the effects of microwave pretreatment on various rock samples, as quantified by uniaxial compression tests. In particular, we assign sample heterogeneity based on a Gaussian statistical distribution and invoke a damage model for elemental tensile and compressive stresses based on the maximum tensile stress and the Mohr–Coulomb theories, respectively. We further couple the electromagnetic, thermal and solid displacement relations using finite element modeling.Findings(1) Increased power intensity during microwave pretreatment results in decreased axial compressive stress. (2) Leveraging statistics to induce variable compressive and tensile strength can greatly facilitate sample heterogeneity and prove necessary for damage modeling. (3) There exists a nonlinear trend to the reduction in smax with increasing power levels, implying an optimum energy output efficiency to create the maximum degradation-power cost relationship.Originality/valuePrevious research in this area has been largely limited to two-dimensional thermo-electric models. The onset of high-performance computing has allowed for the development of high-fidelity, three-dimensional models with coupled equations for electromagnetics, heat transfer and solid mechanics.

Multi-Objective Optimization of Vortex Magnetohydrodynamics (MHD) Generator using Response Surface Methodology

The introduction of electromagnetic fields in fluid dynamics in magnetohydrodynamics (MHD), particularly when those fields are vector and non-uniform, complicates its application in vortex geometry. The imperative to optimize MHD generators arises from the inherent trade-off between voltage and pressure drop in energy conversion systems, to maximize voltage output while minimizing associated pressure drop. This study focuses on optimizing vortex MHD generators by applying Response Surface Methodology (RSM), which is based on mathematical models that capture the complex relationships between factor and response variables. This method offers a comprehensive approach to obtaining the optimum solution to the objectives, voltage and pressure drop, based on fluid velocity and magnetic field strength input parameters. Numerical optimization RSM generates 11 solutions. The optimum solutions obtained are a velocity of 1.415 m/s, and magnetic field strength of 0.43 T, and the corresponding optimum output voltage and pressure drop will be 4.264 mV and 4.254 psi, respectively, with a desirability level of the selected solution is 0.770. This study suggests the RSM method shows a good measurement of R2 and RSME. Our findings contribute to the understanding of optimizing vortex MHD generators and offer insights into achieving efficient energy conversion systems of a set of optimum generator operating parameters.

Exploiting the Capabilities of Classifiers to Examine a Website Defacement Data Set

Website defacement is the illegal electronic act of changing a website. In this paper, the capabilities of robust machine learning classifiers are exploited to select the best input feature set for evaluation of a website’s defacement risk. A defacement mining data set was obtained from Zone-H, a private organization, and a sample consisting of 93,644 data points was pre-processed and used for modelling purposes. Using multi-dimensional features as input, enormous modelling computations were carried out to determine the optimal outputs, in terms of performance. Reason and hackmode presented the highest contributions for the evaluation of website defacement, and were thus chosen as outputs. Various machine learning models were examined, and decision tree (DT), k-nearest neighbours (k-NN), and random forest (RF) were found to be the most powerful algorithms for prediction of the target model. The input variables 'domain', 'system', 'web_server', 'redefacement', 'type', 'def_grade', and 'reason/hackmode' were tested and used to shape the final model. Using the cross-validation (CV) technique, the key performance factors of the models were calculated and reported. After calculating the average scores for the hyperparameter metrics (i.e., max-depth, min-sample-leaf, weight, max-features, and CV), both targets were evaluated, and the learning algorithms were ranked as RF > DT > k-NN. The reason and hackmode variables were thoroughly analysed, and the average score accuracies for the reason and hackmode targets were 0.85 and 0.585, respectively. The results comprise a significant development, in terms of modelling and optimizing website defacement risk. This study successfully addresses key cybersecurity concerns, particularly website defacement.

Structural and magnetic properties of M-type hexaferrite nanoparticles: A comparative analysis of BaFe12O19 and SrFe12O19 synthesized via chemical co-precipitation

In this work, a comprehensive analysis of the structural and magnetic properties of the M-type hexaferrite (BaFe12O19 and SrFe12O19) was conducted using the chemical co-precipitation approach. The analytical techniques employed include powder X-ray diffraction, Transmission Electron Microscopy, Fourier Transform Infrared Spectroscopy, and Thermogravimetric Analysis. Powder Neutron Diffraction was utilized to analyse the magnetic ordering and crystal structure. A hexagonal crystal structure with space group P63/mmc was obtained from to structural analysis. Additionally, the investigation encompassed an assessment of the maximum energy product (BH)max and magnetic properties utilizing Vibrating Sample Magnetometry across all synthesized materials. Magnetic investigations of the studied materials showed several interesting characteristics: SrM showed a maximum saturation magnetization of 49.67 emu/g and a maximum coercivity of 4874.55 Oe. Remarkably, SrFe12O19 showed twice as much saturation magnetization and remnant magnetization than BaFe12O19, suggesting more robust and effective magnetic parameters. The overarching objective of this study is to discern the optimal hexaferrite material, evaluating magnetism, coercivity, thermal stability, and energy output through a systematic exploration of crystal structure, magnetic properties, thermal behaviour, manufacturing methodologies, and microstructure. This research endeavour not only contributes crucial insights into the potential applications of both BaM and SrM but also considers their environmental implications. The study further provides valuable recommendations for selecting the most efficacious material for specific permanent magnet applications, thereby contributing to the advancement on the field of magnetic engineering. The culmination of this work is poised to facilitate informed decision-making and technological progress in the realm of hexaferrite materials with a deliberate consideration of their environmental impact.

The Problem Statement of Optimal Petroleum Engineering Output with Respect to Various Techniques and Materials Used

The Problem Statement of Optimal Petroleum Engineering Output with Respect to Various Techniques and Materials Used

Model predictive control for Vienna rectifier with constant frequency based on inductance parameters online identification

When the finite control set model predictive control (FCS-MPC) is applied to the three-level converter, there are problems such as large current harmonics, high requirements for the computing efficiency of the microcontroller, complex multi-objective optimization and limited output vector switching. In addition,the mismatch of inductance parameter may directly affect the observation accuracy of FCS-MPC. To solve the above problem,a double vector model predictive control strategy with constant frequency strategy based on the inductance parameters on-line identification is presented.First, a single objective cost function based on the direct power is constructed by optimizing the redundant vector which is selected to balance the neutral-point potential, the design of weighting factor is avoided.At the same time, in order to effectively reduce the grid current ripple under single vector modulation, space vector pulse width modulation (SVPWM) is realized by combining with zero vector control mode. And, a parameter identification method based on model reference adaptive system(MARS) is proposed to overcome the effect of model parameter mismatch.Finally, the results show that the proposed F-MPCCF has good steady-state and dynamic performance from the static, transient and neutral-point potential control.

Comparative Analysis of Two-Stage and Single-Stage Models in Batteryless PV Systems for Motor Power Supply

Implementing photovoltaic (PV) systems as direct power sources for motors without batteries is a complex process that requires a sophisticated control mechanism. The crucial aspect of PV systems is the Maximum Power Point Tracking (MPPT) process, which ensures that the installed PV system generates optimal energy output. A recent study has analyzed research related to PV systems supplying power to pump motors, and the results have successfully classified these systems into two main models: the two-stage and the single-stage. The two-stage model involves separate power tracking and load consumption control processes, while the single-stage model integrates power tracking and load consumption control into a single process. A comparative analysis of these two models has revealed that the two-stage model exhibits higher stability due to the separate power tracking and load consumption control processes. Aspects such as the MPPT process, motor power consumption, and the utilization of DC-link capacitors were examined in this study. The findings of this comparative study contribute valuable insights into the effectiveness and stability of two-stage and single-stage models in PV systems supplying power to motors without batteries. The results will significantly interest researchers and practitioners working in Photovoltaic systems and motor control, providing helpful information for designing and implementing more efficient and reliable PV systems.