Search Results (2,385)

This paper presents a data-driven model-free adaptive containment control (MFACC) scheme for uncertain rehabilitation exoskeleton robots, where the robotic exoskeleton dynamics are uncertain with saturation constraints. To handle uncertainties of the robotic dynamics, a model-free adaptive control (MFAC) strategy is established by linearizing the robotic exoskeleton dynamics into an equivalent data model. Considering the integral additive effect of the traditional MFAC method, an improved MFAC controller is designed in this paper. Since actuators with saturation constraints constantly affect the safety of patients during rehabilitation training, we construct a new criterion function with active constraints for the critical function of the MFAC algorithm and adopt the Hildreth quadratic programming algorithm to find the constrained optimal solution to overcome this limitation. The proposed MFACC scheme is rigorously proven by the compression mapping method to demonstrate model-free stability. Finally, the proposed control scheme is verified to be effective by simulation studies of the robotic SimMechanics model. Full article

In the past five years, the number of articles related to ultrasonic mineral flotation has increased by about 50 per year, and the overall trend is on the rise. The most recent developments in ultrasonics for flotation process intensification are reviewed herein, including effects of ultrasound treatment on an aqueous slurry, improvement in flotation methods and technological processes, device development tracking, and application effects in mineral process engineering. At this point in time, there are pilot-scale flotation tests to evaluate the feasibility of ultrasonic pretreatment technology for industrial use to enhance residue flotation separation, and the results showed that the recovery rate of concentrate is increased by about 10%. Four aspects of ultrasonic flotation process improvement are summarized, namely, changing the ultrasonic parameters, the synergistic effect of ultrasound and reagents, the ultrasonic effect of particles with different-sized fractions, and application to new systems. In addition, the effect of ultrasonic flotation mechanisms is explored through a quadratic model and numerical simulation. The combination of ultrasonic flotation with other fields, such as magnetic fields, to enhance the separation efficiency and recovery of minerals is also a future trend. It is also proposed that ultrasonic flotation technology will be used with big data, industrial Internet of Things, and automatic control technology to achieve deep bundling, optimizing the flotation process by implementing remote monitoring and control of the flotation process. Full article

The effective baryon–baryon potential can be derived in the framework of the quark model. The configurations with different quark spatial distributions are mixed naturally when two baryons get close. The effect of configuration mixing in the chiral quark model (ChQM) is studied by calculating the effective potential between two non-strange baryons in the channels $IJ=01,10$ and 03. For comparison, the results of the color screening model (CSM) are also presented. Generally, configuration mixing will lower the potential when the separation between two baryons is small, and its effect will be ignorable when the separation becomes large. Due to the screened color confinement, the effect of configuration mixing is rather large, which leads to stronger intermediate-range attraction in the CSM, while the effect of configuration mixing is small in the ChQM due to the quadratic confinement and $\sigma$-meson exchange, which is responsible for the intermediate-range attraction. Full article

To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power. Full article

Multispectral remote sensing images contain abundant information about the distribution and reflectance of ground objects, playing a crucial role in target detection, environmental monitoring, and resource exploration. However, due to the complexity of the imaging process in multispectral remote sensing, image blur is inevitable, and the blur kernel is typically unknown. In recent years, many researchers have focused on blind image deblurring, but most of these methods are based on single-band images. When applied to CASEarth satellite multispectral images, the spectral correlation is unutilized. To address this limitation, this paper proposes a novel approach that leverages the characteristics of multispectral data more effectively. We introduce an inter-band gradient similarity prior and incorporate it into the patch-wise minimal pixel (PMP)-based deblurring model. This approach aims to utilize the spectral correlation across bands to improve deblurring performance. A solution algorithm is established by combining the half-quadratic splitting method with alternating minimization. Subjectively, the final experiments on CASEarth multispectral images demonstrate that the proposed method offers good visual effects while enhancing edge sharpness. Objectively, our method leads to an average improvement in point sharpness by a factor of 1.6, an increase in edge strength level by a factor of 1.17, and an enhancement in RMS contrast by a factor of 1.11. Full article

When dealing with complex models in real situations, many optimization problems require the use of more than one objective function to adequately represent the relevant characteristics of the system under consideration. Multi-objective optimization algorithms that can deal with several objective functions are necessary in order to obtain reasonable results within an adequate processing time. This paper presents the multi-objective version of a recent metaheuristic algorithm that optimizes a single objective function, known as the Majority–minority Cellular Automata Algorithm (MmCAA), inspired by cellular automata operations. The algorithm presented here is known as the Multi-objective Majority–minority Cellular Automata Algorithm (MOMmCAA). The MOMmCAA adds repository management and multi-objective search space density control to complement the performance of the MmCAA and make it capable of optimizing multi-objective problems. To evaluate the performance of the MOMmCAA, results on benchmark test sets (DTLZ, quadratic, and CEC-2020) and real-world engineering design problems were compared against other multi-objective algorithms recognized for their performance (MOLAPO, GS, MOPSO, NSGA-II, and MNMA). The results obtained in this work show that the MOMmCA achieves comparable performance with the other metaheuristic methods, demonstrating its competitiveness for use in multi-objective problems. The MOMmCAA was implemented in MATLAB and its source code can be consulted in GitHub. Full article

Additive Manufacturing (AM) is a pivotal technology for transforming complex geometries with minimal tooling requirements. Among the several AM techniques, Wire Arc Additive Manufacturing (WAAM) is notable for its ability to produce large metal components, which makes it particularly appealing in the aerospace sector. However, precise control of the bead geometry, specifically bead width and height, is essential for maintaining the structural integrity of WAAM-manufactured parts. This paper introduces a methodology using a Deep Neural Network (DNN) model for forecasting the bead geometry in the WAAM process, focusing on gas metal arc welding cold metal transfer (GMAW-CMT) WAAM. This study addresses the challenges of bead geometry prediction by developing a robust predictive framework. Key process parameters, such as the wire travel speed, wire feed rate, and bead dimensions of the previous layer, were monitored using a Coordinate Measuring Machine (CMM) to ensure precision. The collected data were used to train and validate various regression models, including linear regression, ridge regression, regression, polynomial regression (Quadratic and Cubic), Random Forest, and a custom-designed DNN. Among these, the Random Forest and DNN models were particularly effective, with the DNN showing significant accuracy owing to its ability to learn complex nonlinear relationships inherent in the WAAM process. The DNN model architecture consists of multiple hidden layers with varying neuron counts, trained using backpropagation, and optimized using the Adam optimizer. The model achieved mean absolute percentage error (MAPE) values of 0.014% for the width and 0.012% for the height, and root mean squared error (RMSE) values of 0.122 for the width and 0.153 for the height. These results highlight the superior capability of the DNN model in predicting bead geometry compared to other regression models, including the Random Forest and traditional regression techniques. These findings emphasize the potential of deep learning techniques to enhance the accuracy and efficiency of WAAM processes. Full article

Integration of heat pumps combined with thermal energy storage provides a key pathway to decarbonizing the energy supply in the industry when the processes are not operated continuously. Yet, this integration of such novel systems introduces control challenges due to added dependencies between different process streams. This work investigates the control problem of heat pumps coupled to stratified thermal energy storage that is integrated into non-continuous industrial processes. A two-layer control strategy is proposed, where, in the higher level, a model predictive controller is developed for energy management using a linear model of the non-linear process. The resulting optimization problem is a mixed integer quadratic program. The low-level control layer is defined with standard industry controllers. The overall system is tested using a dynamic simulation model for the entire process, demonstrating its performance in three different cases. The control strategy optimizes heat recovery while ensuring system operability. The study demonstrates successful disturbance rejection and cold starts, wherein 100% of the targeted heat recovery can be confirmed under nominal conditions. Further evaluation in laboratory or field trials is recommended, and alternative, yet-to-be-defined, control concepts may be compared to the proposed approach. Full article

The effect of digestive bypass macronutrients and age on stool quality (moisture and firmness) in dogs and cats is not well understood. Data were analyzed from digestibility tests (n = 2020, 361 dogs and 536 cats) including dry and wet product types. Both food and feces were measured for moisture and nutrients according to standard protocols; stool firmness was graded. Linear mixed modeling was used to evaluate the associations between nutrient bypass, age and stool quality. Bypass protein increased stool moisture (dog, cat p < 0.0001) and decreased firmness (dog p = 0.01, cat p < 0.0001), while bypass fiber decreased stool moisture and increased firmness (dog, cat p < 0.0001 for both). Both species manifested a negative quadratic effect of advanced age on stool firmness (dog p < 0.0001 and cat p = 0.02). However, the association of advanced age (quadratic effect) with metabolizable energy required to maintain body weight was different between species; dogs had a positive association (p = 0.028), while it was negative for cats (p < 0.0001). Taken together, these data may aid in the development of food formulations for companion animals, which can better meet changing nutritional needs across life stages. Full article

To reduce the wear of the motor rotor punching punch and ensure the efficiency is the highest in actual production, the finite element analysis software Deform-3Dv11 is used to simulate the punch wear based on the Archard model theory. With punch wear as the response target and punch speed, punch clearance, and punch edge fillet as the main factors, 17 groups of response surface Box–Behnken test designs are established, as well as a quadratic polynomial regression model between the main factors and the response. The results revealed that: the influence of various parameters on punch wear is in the order of punch edge fillet C > punch clearance B > punch speed A; the order of the interactive influence of various factors is as follows: punch speed and punch edge fillet AC > punch speed and punch clearance AB > punch clearance and punch edge fillet BC. The optimal blanking process combination obtained by using Design-Expert13 software is as follows: blanking speed 50 mm/s, blanking clearance 0.036 mm, and die cutting edge rounded corner 0.076 mm; the predicted response surface value is 6.95 × 10⁻¹² mm. Through simulation verification, the actual optimized simulation value is 6.93 × 10⁻¹² mm, with an absolute relative error of 2.5% for the predicted response value. Moreover, the optimized simulation value is reduced by 30.4% compared to the one before optimization, effectively reducing the punch wear of the motor rotor punching forming and providing a theoretical foundation for further wear optimization. Full article

Predicting the International Roughness Index (IRI) is crucial for maintaining road quality and ensuring the safety and comfort of road users. Accurate IRI predictions help in the timely identification of road sections that require maintenance, thus preventing further deterioration and reducing overall maintenance costs. This study aims to develop robust predictive models for the IRI using advanced machine learning techniques across different climatic conditions. Data were sourced from the Ministry of Energy and Infrastructure in the UAE for localized conditions coupled with the Long-Term Pavement Performance (LTPP) database for comparison and validation purposes. This study evaluates several machine learning models, including regression trees, support vector machines (SVMs), ensemble trees, Gaussian process regression (GPR), artificial neural networks (ANNs), and kernel-based methods. Among the models tested, GPR, particularly with rational quadratic specifications, consistently demonstrated superior performance with the lowest Root Mean Square Error (RMSE) and highest R-squared values across all datasets. Sensitivity analysis identified age, total pavement thickness, precipitation, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors influencing the IRI. The results indicate that pavement age and higher traffic loads significantly increase roughness, while thicker pavements contribute to smoother surfaces. Climatic factors such as temperature and precipitation showed varying impacts depending on the regional conditions. The developed models provide a powerful tool for predicting pavement roughness, enabling more accurate maintenance planning and resource allocation. The findings highlight the necessity of tailoring pavement management practices to specific environmental and traffic conditions to enhance road quality and longevity. This research offers a comprehensive framework for understanding and predicting pavement performance, with implications for infrastructure management both locally and worldwide. Full article

The observation of randomness patterns serves as guidance for the craft of probabilistic modelling. The most used count models—Binomial, Poisson, Negative Binomial—are the discrete Morris’ natural exponential families whose variance is at most quadratic on the mean, and the solutions of Katz–Panjer recurrence relation, aside from being members of the generalised power series and hypergeometric distribution families, and this accounts for their many advantageous characteristics. Some other basic count models are also described, as well as models with less obvious but useful randomness patterns in connection with maximum entropy characterisations, such as Zipf and Good models. Simple tools, such as truncation, thinning, or parameter randomisation, are straightforward ways of constructing other count models. Full article

To understand the processes behind pollinator declines and for the conservation of pollination services, we need to understand fundamental drivers influencing pollinator behaviour. Here, we aimed to elucidate how wild bumblebees interact with three plant species and investigated their foraging behaviour with varying flower densities. We video-recorded Bombus terrestris in 60 × 60 cm quadrats of Lotus creticus, Persicaria capitata, and Trifolium pratense in urban areas of Terceira (Azores, Portugal). For the automated bumblebee detection and counting, we created deep learning-based computer vision models with custom datasets. We achieved high model accuracy of 0.88 for Lotus and Persicaria and 0.95 for Trifolium, indicating accurate bumblebee detection. In our study, flower cover was the only factor that influenced the attractiveness of flower patches, and plant species did not have an effect. We detected a significant positive effect of flower cover on the attractiveness of flower patches for flower-visiting bumblebees. The time spent per unit of inflorescence surface area was longer on the Trifolium than those on the Lotus and Persicaria. However, our result did not indicate significant differences in the time bumblebees spent on inflorescences among the three plant species. Here, we also justify computer vision-based analysis as a reliable tool for studying pollinator behavioural ecology. Full article

This study aimed to optimize modified starch from Mangifera indica (mango) fruit using acid hydrolysis and pre-gelatinization via computer-assisted techniques as a substituent for pharmaceutical tableting excipients. The hydrolysis and microwave-assisted pre-gelatinization time and temperature were optimized using a three-level factorial design. The modified starches were characterized for flowability, compressibility, and swelling properties. It was found that all parameters fit a quadratic model, which can be used to predict the properties of the modified starch. The optimized hydrolysis reaction was 3.8 h at 56.4 °C, while the pre-gelatinization reaction was 3 min at 150 °C. Structural changes were found, ascertaining that starch modification was successful. The optimized hydrolyzed starch showed superior properties in relative to unmodified M. indica fruit starch and comparable characteristics to conventional excipients. The optimized pre-gelatinized starch presented an excellent enhancement in the flow and compression properties, with %swelling greatly augmented 3.95-fold and 1.24-fold compared to unmodified starch and SSG, respectively. Additionally, the pre-gelatinized starch presented comparable binding effect, while the hydrolyzed powder had reduced binding capacity due to shorter chains. The findings revealed that the use of software-assisted design of experiment facilitated a data-driven approach to optimize the modifications. The optimized modified mango starch demonstrated potential as a multifunctional excipient, capable of functioning as binder, disintegrant, and diluent. Full article

This contribution provides a detailed comparison of the impact of various rheological models on the filling phase of injection molding simulations in order to enhance the accuracy of flow predictions and improve material processing. The challenge of accurately modeling polymer melt flow behavior under different temperature and shear rate conditions is crucial for optimizing injection molding processes. Therefore, the study examines commonly used rheological models, including Power-Law, Second-Order, Herschel-Bulkley, Carreau and Cross models. Using experimental data for validation, the accuracy of each model in predicting the flow front and viscosity distribution for a quadratic molded part with a PA66 polymer is evaluated. The Carreau-WLF Winter model showed the highest accuracy, with the lowest RMSE values, closely followed by the Carreau model. The Second-Order model exhibited significant deviations in the edge region from experimental results, indicating its limitations. Results indicate that models incorporating both shear rate and temperature dependencies, such as Carreau-WLF Winter, provide superior predictions compared to those including only shear rate dependence. These findings suggest that selecting appropriate rheological models can significantly enhance the predictive capability of injection molding simulations, leading to better process optimization and higher quality in manufactured parts. The study emphasizes the significance of comprehensive rheological analysis and identifies potential avenues for future research and industrial applications in polymer processing. Full article