Optimal Settings Research Articles

Perioperative visual loss and consent for adult spine surgery: a national survey of the practice amongst spine surgeons and anaesthetists †

Background Perioperative Visual Loss (POVL) is a devastating complication for patients undergoing spine surgery. Consent process for POVL amongst spine surgeons and anaesthetist remains variable. The aim of this study is to evaluate their practice and views about it. Methods Two similar questionnaires were distributed to members of the Society of British Neurological Surgeons (SBNS), British Association of Spine Surgeons (BASS), and Neuroanaesthsia and Critical Care Society (NACCS). Results A total of 271 responses were received (SBNS/BASS n = 149, NACCS n = 122). Fewer surgeons considered POVL as a material risk for patients compared to the anaesthetists (57.7 versus 79.7%). Outpatient/pre-assessment clinics were considered as the optimal setting for discussing POVL by the majority of the clinicians (81.2 and 93.4%). POVL should be discussed by both specialists according to 75% of the anaesthetists. Estimated incidence of POVL was considered to be higher by the anaesthetists (0.03–0.2% by 63% of the anaesthetist versus 0.0001–0.004% by 57% of the surgeons). Twenty-three surgeons and 10 anaesthetists had a patient who suffered from POVL, which led to a change of practice in most of them. This questionnaire will lead to a change in practice/consent to 18.1% of the surgeons and 23.5% of the anaesthetists. Conclusions Most of the surgeons and anaesthetist feel that POVL is a material risk that ideally needs to be firstly discussed before the day of surgery, by both specialties. However, a significant number of clinicians have an opposite view. A national guidance from respective societies should encourage POVL to be discussed routinely.

Abstract 13783: Tissue Thickness is an Essential Factor of Higher Risk of Steam Pop During Bipolar Ablation - Ex-Vivo Assessment

Introduction/Backgrounds: For ventricular arrhythmias originating deep within the myocardium, bipolar ablation can be employed. However, there is currently no standardized strategy for bipolar ablation settings such as energy and ablation time. Hypothesis and Aims: We intended to test a hypothesis that the target tissue thickness may determine the optimal bipolar ablation settings of power and duration. Methods: Using porcine cardiac tissues ex-vivo ablation experiments were conducted. Each radiofrequency (RF) ablation catheter was placed on both sides of the myocardial slice at an angle of 45 degrees and 10g contact. We investigated the depth of ablation lesions and the occurrence of steam-pop under the multiple settings of myocardial tissue thickness, energy and ablation duration. Results: We analyzed in total 194 bipolar RF applications. For myocardial slices of 10-, 15- and 20-mm thickness, we performed 60-second RF ablation of 20, 30, 40 and 50W energy settings. No transmural lesion creation was noted on the 20-mm thickness tissues even by 50W RF energy ( Figure 1 ). Transmural lesion was observed with RF energy of 30W or more on the most of the 10-mm thickness tissues. Prolonged duration of 180 seconds with 30W or more accomplished the transmural lesion on the 15-mm thickness tissues. We observed steam-pop in 11 out of 194 applications (5.7%). Steam-pops were exclusively observed on the 10mm tissue with 30W or more. Conclusion: Our data showed that (1) transmural lesion creation for 10-mm tissue is feasible under 30W or more and 60 seconds bipolar ablation, (2) for thicker tissue even 50W application of 60 seconds may not generate transmural lesion, (3) prolonged RF duration enables transmural lesion for thicker tissues, (4) steam-pop can occur on thinner tissues under higher wattage. These results suggest that we should adapt the bipolar RF settings, particularly the power and duration depending on the target tissue thickness.

Abstract 12639: Comparison of Very High-Power Short-Duration, High-Power Short-Duration, and Low-Power Long-Duration Radiofrequency Ablation for Atrial Fibrillation: A Systematic Review and Network Meta-Analysis

Introduction: The optimal power and duration settings for radiofrequency (RF) atrial fibrillation (AF) ablation is unclear. We compared low-power long-duration (LPLD), high-power short-duration (HPSD), and very HPSD (vHPSD) settings for AF ablation. Hypothesis: Ablation utilizing HPSD and vHPSD are expected to have shorter procedural times than LPLD and may have improved arrhythmic outcomes. Aims: To compare LPLD, HPSD, and vHPSD ablation by network meta-analysis Methods: This network meta-analysis (NMA) was structured according to the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. Medline, Scopus and Cochrane Central Register of Controlled Trials were systematically searched to identify relevant studies. Observational and randomized studies were included. Eligible studies compared outcomes in AF patients who underwent first-time RF ablation with the following settings: vHPSD (70-90 Watts for 3-10 seconds), HPSD (45-60 W, 5-10 s), or LPLD (20-40 W, 20-60 s). Results: Thirty-six studies comprising 10375 patients were included (33% female). Frequentist NMA showed LPLD tended towards a lower odds of freedom from arrhythmia (FFA) vs. HPSD (OR 0.93, 95% CI 0.86-1.00). There was no difference in FFA between vHPSD vs. HPSD. Splitwise interval estimates showed a lower odds of FFA in LPLD vs. vHPSD on direct (OR 0.78, 95% CI 0.65-0.93) and network estimates (OR 0.85, 95% CI 0.73-0.98). Frequentist NMA showed less total procedural (TP) time with HPSD vs. LPLD (generic variance 1.06, 95% CI 0.83-1.29) and no difference between HPSD vs. vHPSD. Conclusion: This NMA shows improved procedural times in HPSD and vHPSD vs. LPLD. Although HPSD tended towards improved odds of FFA compared to LPLD, the result was not statistically significant. The odds of FFA in LPLD was lower vs. vHPSD on direct and network estimates on splitwise interval analysis. Randomized clinical trials are needed to validate HPSD and vHPSD settings.

Experimental investigation and multi-performance optimization of the leachate recirculation based sustainable landfills using Taguchi approach and an integrated MCDM method

Landfill leachates contain harmful substances viz. chemicals, heavy metals, and pathogens, that pose a threat to human health and the environment. Unattended leachate can also cause ground water contamination, soil pollution and air pollution. This study focuses on management of leachate, by recirculating the rich, nutrient-filled fluid back into the landfills, turning it to a bioreactor, thereby maximising the performance parameters of landfills favourable for electricity production by the waste to energy plants. This study demonstrates a sustainable alternative method for utilising the fluid, rather than treating it using an extremely expensive treatment process. Further, it also experimentally investigates the effect of varying levels of five input parameters of the landfill including waste particle size, waste addition, inorganic content in waste, leachate recirculation rate, and landfill age, each at five levels, on the multiple performance of the landfill using Taguchi’s L25 standard orthogonal array. Experimental results are analysed using an integrated MCDM approach i.e. MEREC-PIV method and statistical techniques such as analysis of mean (ANOM) and analysis of variance (ANOVA). The results indicate that the optimal setting of the input parameters is waste particle size at 9 ppm, waste addition at 80 Ktoe, inorganic content in waste at 2%, leachate recirculation rate at 250 l/day and landfill age at 3 years. Further, inorganic content waste is found to be the most significant parameter for the multiple performance of the landfill. This study presents a novel approach to produce input parameters for power plants which may enhance their profitability and sustainability.

Coordination of protective relays in distribution systems considering uncertainty caused by renewables and demands based on orthogonal experiments

The operations of distribution systems are more challenging than in past decades because of the penetration of distributed generation (DG). Several serious problems may be caused by DG, such as bi-directional power flow in a distribution line, different levels of fault currents and uncertainty in power generation. This paper investigates the coordination of inverse-time overcurrent relays (OCRs) in distribution systems with uncertain distributed power generation and loads. A novel method is presented in this work, considering different scenarios along with two levels of uncertainty caused by DGs (photovoltaics and wind) and bus loads, to determine fault currents using Taguchi orthogonal experiments. Nonlinear optimization is then employed to find the optimal settings of inverse-time OCRs in distribution systems and to identify infeasible current transformer ratios. Simulation results obtained from a 9-bus system and a modified IEEE 33-bus system show that the proposed method outperforms the traditional point-estimation method (PEM). For the 33-bus system, the mean values of similarity indices based on the probability density function are obtained by the proposed method, and they are 0.979 and 0.975, which are closer to unity, for time multiplier setting (TMS) and pick-up setting (PS), respectively, than those (0.953 and 0.951 for TMS and PS, respectively) obtained by the PEM. The mean values of error indices based on the cumulative distribution function are obtained by the proposed method, and they are only 0.0007 and 0.0085 for TMS and PS, respectively, while those obtained by the PEM are 0.0011 and 0.0110 for TMS and PS, respectively.

Friction stir processing of AZ91 hybrid composites with exfoliated multi-layered graphene: A Taguchi-Grey relational analysis

Friction Stir Processing (FSP) involves the use of Silicon Carbide (SiC) and Graphite (Gr) nanoparticles as reinforcements to enhance the properties of AZ91 Mg alloy composites. The relationship between FSP processing parameters and surface properties of Mg alloy composites with SiC and Gr nanoparticles is poorly understood. The present study addresses this and investigates the impact of SiC and Gr volume percentages (%vol), tool traverse speed (TTS), and tool rotational speed (TRS) on the corrosion behavior and microhardness of AZ91 alloy composites. FSP turns graphite into layered graphene, making it more corrosion-resistant and stronger through various strengthening mechanisms. Taguchi optimization yielded optimal settings in runs H7 (1500 rpm-20 mm/min-13 %vol) and H8 (1500 rpm-40 mm/min-7 %vol) for high microhardness and low corrosion rate. Conversely, runs H1 (500 rpm-20 mm/min-7 %vol) and H3 (500 rpm-60 mm/min-13 %vol) resulted in reduced microhardness and increased corrosion rate. Integrating Taguchi and Grey Relational Analysis (GRA) optimized the multi-objective enhancement of microhardness and corrosion resistance, achieving 3rd-level values for TRS, TTS, and %vol. Notably, the reinforcement percentage, followed by TRS and TTS, simultaneously improved these responses.

TENSILE STRENGTH AND HARDNESS OF POLYPROPYLENE-BAMBOO/KENAF HYBRID COMPOSITE AFTER OPTIMIZATION OF PRODUCTION PROCESS

While in use and when not, engineering materials occasionally exhibit poor characteristics and perform poorly as a result of internal or external factors. Therefore, there is a need to improve the protection of the composites from the various agents and improve their mechanical properties. This study's objective was to assess the impact of bamboo/kenaf hybrid fibres, Maleic Anhydride Grafted Polypropylene (MAHgPP), Hinder Amine Light Stabiliser (HALS), and Calcium Carbonate (CaCO3) additions on tensile strength and hardness at various levels, as well as their interactions on polypropylene (PP) composites. The manufacturing of the composites was accomplished through the compression molding process. The study was conducted using Taguchi orthogonal array L27 with fiber, MAHgPP, HALS, and CaCO3 as input parameters. A robust optimization technique for product and process design (Taguchi method ) was used to optimize the parameter settings for the tensile strength and hardness of the composite. Results showed that the tensile strength of the resultant composite ranged from 37.60 MPa to 53.57 MPa; while the hardness value ranged from 70.32 (Shore A) to 93.03 (Shore A). The optimized composite had a tensile strength of 49.12 MPa and a hardness of 79.85 (Shores A). The factors settings that gave the combined optimum tensile strength and hardness for the bamboo/kenaf fiber are 36.87% fiber, 5% MAHgPP, 1% HALS, and 3% CaCO3. therefore the best tensile strength and hardness performance of the composite was obtained from the optimum setting.

Optimization and experimental analysis of mechanical properties and porosity on FDM based 3D printed ABS sample

A basic thermoplastic used in Fused Deposition Modeling (FDM) additive manufacturing methods is acrylonitrile–butadiene–styrene (ABS). However, the mechanical qualities of ABS components may be regulated by their porosity. This study aims to optimize the FDM process parameters to minimize porosity and increase ABS parts' tensile and compressive strengths. The experiment has been designed based on Taguchi's philosophy. The machining parameters have been considered as Extrusion multiplier, Temperature (oC), and Layer thickness (mm). The machining output has been analyzed using the mean effect plot. Furthermore, a hybrid optimization called grey relational analysis (GRA) and genetic algorithm (GA) is used to obtain the optimum parameter setting. the multi-optimization parameter setting is achieved as extrusion multiplier = 1, temperature = 240 °C, and layer thickness = 0.1 mm. The confirmation is also conducted to verify the optimum parameter setting. The findings showed that the suggested approach effectively enhanced the FDM process parameters for ABS components, leading to better mechanical characteristics and decreased porosity.

Tri-Parametric Fractional-Order Controller Design for Integrating Systems With Time Delay

This work introduces a new fractional-order integral derivative (FOIλD1-λ) controller for a class of integrating systems involving time delays. The stability region is explored through the complex root boundary (CRB) analysis which explicitly provides the search space of FOIλD1-λ parameters for integrating time delay systems of any order. The optimal settings are obtained through a three-step optimization algorithm based on the CRB knowledge and the desired performance measures. Comparisons with other recent sophisticated tuning methods are also provided to demonstrate the effectiveness of the suggested control scheme through numerical study. Finally, a case study on a non-linear continuously stirred reactor under model uncertainties is presented to check the efficacy of the new method.

A predictive assessment of households' risk against disasters caused by cold waves using machine learning

This paper trains a household-level disaster risk classifier based on supervised machine learning algorithms for cold wave-related disasters. The households' features considered for this task proxy multiple dimensions of vulnerability to disasters accounting for economic, health, social, and geographical conditions. These features are theoretically hypothesized to explain disaster risk classification. We test our predictive model based on the case of Puno, Peru, where cold wave-related disasters (e.g., −28°in 2003 and -35° in 2004) are recurrent and overwhelming. Two supervised learning algorithms were tested to build the classifiers: Logistic Regression and Random Forest Classifier. Hyperparameters of such models were optimized through Bayesian Optimization heuristic. Random Forest Classifier outperformed Logistic Regression by 1.16 % in MCC and 2.34 % in Sensitivity. In the test dataset, Random Forest Classifier achieved an MCC of 48.64 % and a Sensitivity of 80.9 %. After statistical analysis and threshold tunning, both MCC and Sensitivity increased to 51.05 % and 86.52 % in the optimal setting, respectively. Feature importance drawn from features' contribution to a reduction in entropy in the construction of the forest suggests that per capita expenditure, household localization in a rural area, altitude, and access to public goods such as paths and concrete walls drive the disaster risk classification. The model can be used to identify the demand for humanitarian aid and build operational plans to aid the most affected by cold wave-related disasters.

The Influence of Latitude on the Climatic Characteristics of Classroom in Campus 1 and Campus 2 of Khairun University in Achieving Adaptive Thermal Comfort

Microclimate variables are important factors that affect adaptive thermal comfort. These factors include air temperature, solar radiation, air humidity, and wind speed. This study focuses on discussing microclimate variables, specifically air temperature and air humidity. This is based on previous research indicating that the most influential climate variables are air temperature, solar radiation, and air humidity. This study is a field research aimed at comparing air temperature and air humidity between campus 1 and campus 2 of Khairun University, due to differences in geographical elevation. Measurements were taken in March. The variables of air temperature and air humidity were measured inside the building. The research results show significant differences between campus 1 and campus 2. These differences can be associated with the perceived adaptive thermal comfort by the users. The thermal sensation results direct that the majority of users in campus 2 feel cool, while the majority of users in campus 1 feel comfortable or neutral. These findings provide a strong basis for improving adaptive thermal comfort in both campuses by considering optimal air temperature and air humidity settings.

Data-driven boiler efficiency and NOx characteristic variable selection and combustion optimization

In the process of comprehensively promoting carbon peak and carbon neutralization, thermal power units need to adapt to the needs of high efficiency and cleanliness, so as to play the role of ballast stone in the transformation and upgrading of energy structure. When the traditional analysis method can not be comprehensive, real-time and efficient analysis, the introduction of data science is an option. Therefore, this paper is based on the data-driven method to model and optimize the combustion of thermal power plant boilers. The second-order characteristic variable selection algorithm is used to screen the key variables of boiler efficiency and NOx emission. Based on the multi-objective optimization algorithm of NSGA-II, the pareto frontier solution and the optimal adjustable parameter setting under different boundary conditions are determined. The example shows that the optimization scheme proposed in this paper can effectively improve the efficiency of power plant boilers and reduce NOx emissions.

Artificial Intelligence (AI) in Islamic Ethics: Towards Pluralist Ethical Benchmarking for AI

This paper explores artificial intelligence (AI) ethics from an Islamic perspective at a critical time for AI ethical norm-setting. It advocates for a pluralist approach to ethical AI benchmarking. As rapid advancements in AI technologies pose challenges surrounding autonomy, privacy, fairness, and transparency, the prevailing ethical discourse has been predominantly Western or Eurocentric. To address this imbalance, this paper delves into the Islamic ethical traditions to develop a framework that contributes to the global debate on optimal norm setting for designing and using AI technologies.The paper outlines Islamic parameters for ethical values and moral actions in the context of AI's ethical uncertainties. It emphasizes the significance of both textual and non-textual Islamic sources in addressing these uncertainties while placing a strong emphasis on the notion of "good" or "maṣlaḥa" as a normative guide for AI's ethical evaluation. Defining maṣlaḥa as an ethical state of affairs in harmony with divine will, the paper highlights the coexistence of two interpretations of maṣlaḥa: welfarist/utility-based and duty-based. Islamic jurisprudence allows for arguments supporting ethical choices that prioritize building the technical infrastructure for AI to maximize utility. Conversely, it also supports choices that reject consequential utility calculations as the sole measure of value in determining ethical responses to AI advancements.

Numerical Modelling and Kinetic Effects of Synthesized Allium Cepa- Zinc Oxide Nanoparticles for the Degradation of Sodium Lauryl Sulphate-Box Behnken Design

SEM, XRD, and FTIR methods were used to create and characterise Allium cepa Zinc Oxide Nanoparticles. The goal of the current study was to determine the ideal circumstances for Sodium Lauryl Sulphate (SLS) removal by ZnO-NP utilising onion peels. Utilising the precipitation process and metallic precursor sulphates, ZnO nanoparticles were created. SEM analysis was used to measure the particle size, which ranged from 180.4 to 284.1 nm. The crystalline structure of the Synthesised ZnO-NPs' hexagonal wurtzite structure is amply verified by XRD, and FTIR discloses the numerous functional groups that are present. The research's findings demonstrate how the Bio-nanocomposites can effectively remove SLS by varying various factors (such as time, dosage, pH, temperature, and initial concentration) and achieving the best results possible through experimentation, such as 35 min, 0.35 g/lit, pH 9, 313 K, and an initial concentration of 100 ppm of SLS in an aqueous solution. These Optimum settings increase the effectiveness of removing.

High purity galacto-oligosaccharides: Optimal process design and prebiotic effect

Prebiotic unpurified commercial galacto-oligosaccharides (GOS) often contain significant amounts of glucose, galactose and lactose. Current purification processes remove these digestible components, especially lactose with a high risk of affecting functionality.In the current study a high purity GOS (HP-GOS) was prepared by treating GOS with β-galactosidase to hydrolyze the remaining lactose into glucose and galactose. Subsequently mono sugars were removed by sequential simulated moving bed (SSMB) chromatography. To this end, a design of experiments (DOE) approach was applied to determine the dry matter content and β-galactosidase concentration that optimally support lactose hydrolysis in such a way that the detrimental effects on GOS composition are minimized to preserve its original oligosaccharide composition. The HP-GOS product obtained using the identified optimal settings for lactose hydrolysis contained more than 75% of the original DP2 GOS fraction while the fraction with a DP above 2 was unaffected by the purification. Using fecal fermentations, it was demonstrated that the GOS parts of the HP-GOS and the parent GOS have comparable effects on gut microbiota composition. Both GOS products equally increased the relative abundances of Bifidobacterium. Furthermore, there were no differences in alpha diversity and in the distribution of the different bifidobacterial species. Based on these outcomes it was concluded that the lactose hydrolysis step of the purification process did not influence gut microbiota modulating effects of GOS.

A sustainable model using RSM and MCDM techniques to evaluate performance and emission characteristics of a diesel engine fueled with diphenylamine antioxidant and CeO2 nanoparticle additive biodiesel blends

Overconsuming fossil fuels has worsened global warming and air pollution, requiring us to investigate alternate fuels for compression ignition engines. Biodiesel is a renewable fuel and environmentally favorable. Biodiesel's most significant disadvantage is increased nitrogen oxide (NOx) emissions. The intent of the present study was to examine the impact of antioxidant diphenylamine (DPA) and nanoparticle ceria (CeO2) additive inclusion in a B30 blend on engine performance and exhaust emission characteristics. For this study, diesel, Jatropha biodiesel (B30), 100 ppm of antioxidant diphenylamine (50 ppm) with ceria nanoparticle (50 ppm) is added to the B30 blend named as B30+DPA100 and antioxidant diphenylamine (50 ppm) with ceria nanoparticle (50 ppm) is added to the B30 blend named as B30+DPA50+CeO250 fuel blends has been used. A hybrid response surface methodology and multi-criteria decision-making techniques (entropy method, TOPSIS, and VIKOR) have been used to develop a sustainable model and find the optimal setting of input parameters in terms of ranking. From experimental findings, the inclusion of antioxidants (DPA) and nanoparticle (CeO2) at 50 ppm to B30 significantly reduced NOx emission. The brake-specific fuel consumption and NOx have been found reduced by 5.67% and 18.87%, respectively, for B30+DPA50+CeO250 as compared to B30. At the same time, brake thermal efficiency increased by 1.01%. The brake mean effective pressure and maximum cylinder pressure) have been found increased by 0.68% and reduced by 4.52%, respectively, for B30+DPA50+CeO250 as compared to B30. The alternative ranking of the input parameters has been found fuel injection pressure (300), compression ratio (17), and load (12) as Rank 1 for TOPSIS and VIKOR. Therefore, the B30+DPA50+CeO250 blend is appropriate for improving diesel engine performance and diminishing exhaust emissions.

Multi-objective optimisation of ionic liquid parameters for hydrogen compressors by Taguchi and grey relational analysis methods

The ionic liquid compressor developed in recent decades possesses a prospective application for hydrogen storage. The design of this new compressor considering multiple design parameters and objectives is critical for product development. This study investigated the percentage contribution of four design parameters of the ionic liquid to compressor performance by Taguchi and ANOVA. A two-objective optimisation was conducted to identify the near-optimal setting of ionic liquid parameters combining the Taguchi and grey relational analysis (GRA) methods considering both the liquid mass taken off and the specific energy consumption. It was found that the liquid height was the most influential parameter with percentage contributions of 84.65 % and 90.08 % to the liquid mass taken off and specific energy consumption, respectively. The optimal setting is obtained as the liquid height of 15 mm, liquid density of 1600 kg/m3, liquid viscosity of 50 mPa∙s and surface tension of 0.052 N/m.

Performance evaluation of resolution and sensitivity of C-SPECT's variable slat-stack collimator.

Myocardial perfusion imaging is commonly performed using SPECT, where both general-purpose and dedicated scanners are available. A limitation with general-purpose systems has been the inability to image dynamically since different projections are obtained far apart in time due to scanner rotation. Dedicated systems can have this capability since they acquire completely sampled projections (i.e., those with enough angular views for reconstruction) with short time frames. C-SPECT, does not need any scanner or patient motion to obtain complete projections, allowing fast dynamics. When imaging fast dynamics, the optimal collimator settings are not necessarily the same as for static imaging, where longer acquisitions can be utilized. Thus, C-SPECT offers adaptive collimation in the transverse and axial directions. The performance of adaptation in the axial direction was characterized herein. The ratio of the resolution metric in high-sensitivity mode to that in the high-resolution mode, termed resolution boost factor, was determined. Analogously, the sensitivity boost factor was also determined. Comparisons were made with theory and simulations. The boost factors for resolution and sensitivity, averaged over the 14 modules of the system, were determined to be 1.72 and 1.75, respectively. The boost factors, which ideally would be two, were between 10% and 15% below optimal values and tracked each other, suggesting mechanical challenges in the apparatus, such as incomplete closure of adjacent slats, but show reasonably successful adaptation between modes.

Controlling the operation of the dc motor by using pid with metaheuristic technology

This paper presents an investigation into precise trajectory tracking and synchronization of two-axes direct current (DC) motor control, with an emphasis on a cascade proportional-plus-integral (P-PI) controller to regulate the speed and position of a single-axis permanent magnet DC (PMDC) motor. Various methods were explored for the controller’s design process, including classical methods (CM) and three optimization strategies: genetic algorithm (GA), dandelion optimization algorithm (DOA), and butterfly optimization algorithm (BOA), with the latter found to be the most effective. Simulation was a crucial component in assessing the efficiency of these methods. A comparative analysis of four tuning strategies (CM, GA, BOA, and DOA) was conducted to ascertain optimal settings for the P-PI cascade controller. The DOA outperformed the others, providing accurate tracking with no deviation from the reference location or speed overshoot. Moreover, DOA ensured safety by limiting voltage and current to prevent potential damage to the motor. The findings thus suggest that the proposed P-PI controller with DOA can serve as a reliable solution for speed and position control in single-axis PMDC motors.

Data-driven modeling for complex contacting phenomena via improved neural networks considering link switches

Recent years saw tremendous developments of data-driven modeling in various engineering fields. As for the contact modeling between complex surfaces, the utilization of neural networks successfully eliminates the limitations encountered by the traditional physics-based contact modeling strategy. However, contrary to its increasingly extensive applications, very little attention has been paid to the role of network hyper-parameters in reducing the model redundancy and improving its training efficiency. In this work, a novel neural network considering link switches has been presented for the data-driven modeling of complex contact phenomena. In order to further boost its prediction performance, genetic algorithm (GA) is employed for the optimal settings of relevant hyper-parameters. An indoor experimental setup is utilized to demonstrate the effectiveness of the presented methodology. Comprehensive comparisons with the base models indicate the superiorities of the established locally-connected-neural-network-based contact force model for complex geometries.