Accurate Control Model Research Articles

A new wake superposition model and its application in collision prevention control of tri-riser array system

In the field of offshore oil exploitation, the compact multi-riser arrangement increases the risk of collision and has an adverse impact on production and the marine environment. The solution to this problem involves accurate motion prediction and collision prevention control under the effect of flow field superposition between risers, but there is little research on this related theory. This paper improves the traditional wake shielding effect model and further proposes a wake shielding superposition effect model for the mutual influence between risers, in order to establish a comprehensive accurate motion control model for tri-riser array system. A solution method is introduced for this model, both vibration and collision analysis are conducted for the system. The study revealed that solely augmenting the spacing inadequately mitigates system collisions, and active control is an inevitable choice. Based on this, this paper further designs an active boundary controller that is easy to implement in engineering, to achieve collaborative collision prevention control for three risers, and verifies its stability. Compared with traditional PD control, the proposed control method exhibits significant superiority, which can effectively reduce vibration amplitude, significantly increase dynamic spacing, and thus effectively prevent collisions between risers.

Application of Neuro-Fuzzy to the elephant grass production process: A systematic bibliographic review

The transition to renewable energy sources can help combat climate change, as they emit fewer greenhouse gas emissions. Biomass is an important source of energy production, being composed of organic materials, such as residues from agricultural and forestry crops, among others, with emphasis on elephant grass. The application of Neuro-Fuzzy in production processes, especially laboratory ones, is of utmost importance, as it allows the creation of more accurate and efficient prediction and control models in the bioenergetic context. Given this context, the objective of this article is to identify how the state of knowledge is configured regarding the application of the use of Neuro-Fuzzy for the chemical quantification process of elephant grass cellulose. A Systematic Bibliographic Review was carried out to search and map published scientific data to identify numerous applications for the use of Neuro-Fuzzy, mainly in renewable energy. By carrying out the research using the Systemic Bibliographic Review, it was possible to identify several opportunities for the application of Neuro-Fuzzy in the chemical quantification of elephant grass. However, it was observed that this article presents a novelty on the application of the use of Neuro-Fuzzy for the process of chemical quantification of cellulose, in addition to the production of bioethanol from this biomass. Of the 22 documents analyzed in this research, 100% were articles in the form of applied research and literature review, demonstrating great relevance in this line of research, which is the application of Artificial Intelligence in field and laboratory production processes using elephant grass. as biomass to produce bioethanol.

Preparation of pH-sensitive alginate-based hydrogel by microfluidic technology for intestinal targeting drug delivery

Hydrogel microspheres stand out in drug delivery due to their small particle size, biocompatibility and good internal stability. In this paper, pH-sensitive hydrogels are prepared by microfluidic technology for targeted drug delivery in the small intestine. A coaxial dual-channel microfluidic chip is constructed. By analyzing the effects of flow rates and three fracture stages (Rayleigh-Plateau instability crushing stage, pressure difference crushing stage and shear force crushing stage) on the size of hydrogel microspheres, the optimal control stage of the microsphere size is determined (shear force crushing stage). Based on this, the accurate control model of the hydrogel microsphere size is proposed. In addition, based on the coaxial dual channel microfluidic chip, a monolayer hydrogel microcapsule loaded with Indometacin is prepared. The core-shell hydrogel microcapsules loaded with Indometacin are prepared by an improved coaxial three channel microfluidic chip. The swelling rates of both microcapsules in simulated intestinal fluid are significantly higher than those in simulated gastric fluid. The results of in vitro simulated release experiments show that the two hydrogel microcapsules basically do not release in simulated gastric juice. In simulated intestinal fluid, single-layer hydrogel microcapsules show rapid release, while core-shell hydrogel microcapsules showed slow release. In conclusion, the alginate-based hydrogel microcapsules have good stability and pH sensitivity, and are suitable for targeted drug delivery in the small intestine.

RETRACTED ARTICLE: Research on Financial Risk Evaluation and Control of Tourism Enterprises Based on Improved GA Algorithm

With the change and complexity of the tourism market environment, the financial risks faced by tourism enterprises are increasingly diversified. Effectively evaluating and controlling these financial risks has become the key to the development of tourism enterprises. Therefore, this study builds an accurate and real-time enterprise financial risk assessment and control model with the help of genetic algorithm. The results show that compared with other models, the maximum error value of the research model is only 0.12, and the maximum mean square error is only 0.09. The high reliability of the model is verified by simulating the data of selected tourism enterprises. After increasing the number of samples, the accuracy of the model continues to improve, and the predicted financial indicators are more in line with the actual situation. The model achieves the best results in average fitness, and the required error value is reached within 10 iterations. In the goodness of fit comparison, the goodness of fit of the training set, the test set and the verification set of the model are all over 0.7. In the empirical analysis, the ACC of the research model reached 97.4%, the accuracy rate reached 97.1%, the F1 index of the research reached 98.6%, and the other three research models were all lower than 98%. The above shows that the research model has significant advantages and can effectively evaluate and control the financial risk of tourism enterprises.

Multi-objective optimal dispatch strategy for power systems with Spatio-temporal distribution of air pollutants

Accurate and effective pollutant emission control models are key in mitigating the environmental impact of energy supply systems. This paper proposes a novel high-dimensional multi-objective optimal dispatching strategy for power systems considering the spatial and temporal distribution of multiple pollutants. Firstly, a spatial and temporal distribution model of pollutants in thermal power plants is constructed, which is truly applicable to power dispatch. For modeling the pollution situation, the daily changes in the atmospheric boundary layer are taken into account, fully reflecting the pollutant dispersion characteristics of thermal power plants and improving the result accuracy. Next, a high-dimensional multi-objective optimization model is developed to simultaneously reduce the cost of power generation, carbon emission and the impact air quality from VOCs (volatile organic compounds), SO2 and NO2. This model combines the spatial and temporal distribution characteristics of various pollutants and environmental capacity. Finally, a representative high-dimensional multi-objective optimization algorithm is employed to obtain an approximate Pareto-optimal solution set. And a multi-objective decision method is proposed to filter the compromise solution. The results show that the APCs of VOCs, SO2 and NO2 are reduced by 27.5%, 10.13% and 23.16%, respectively, in the pollution day model. On non-pollution days, the proposed scheduling method not only effectively improves air quality, but also achieves cost savings of $1276.59 and reduces CO2 emission by 0.168 × 104t compared to the emission limitation method. The proposed scheduling method can not only effectively improve air quality, but also make corresponding adjustments according to the spatial and temporal changes of environmental capacity, which can truly realize economic and environmental-friendly power scheduling.

FAST AND ACCURATE DOCUMENTATION OF ARCHITECTURAL HERITAGE WITH LOW-COST SPHERICAL PANORAMIC PHOTOGRAPHS FROM 360 CAMERAS

Abstract. This research wants to show the possibility of quickly making high-quality photogrammetric models using 360-degree panoramic photographs, but made using low-cost, single-shot cameras. Currently there is a wide variety of single shot cameras, in which the shape of the camera can vary according to the number of lenses that it incorporates in the camera mount, but this will directly affect the quality of the photography. result and in the price of the team.The fundamental characteristic of single-shot cameras is that they are capable of capturing a 360-degree panoramic photo at the very moment of shooting, which allows to greatly reduce data collection times compared to the normal method of taking photos spherical. This occurs thanks to the fact that they have several lenses that allow them to capture everything that happens around them in a fraction of a second. In addition, the composition times of the final image are also reduced, since they incorporate a processor that allows them to stitch the panorama automatically. These features make data capture very fast, greatly reducing work times.To demonstrate the efficiency of this equipment, several tests have been carried out in different spaces, interior and exterior, of the Cathedral of Plasencia. For each data set, the object documentation was performed twice. The first time it was done with a Faro Focus 3D laser scanner to generate the highly metrically accurate control model. And the second time a Xiaomi Mi Sphere camera was used to generate the comparison model. The photogrammetric models were processed with the Agisoft Metashape software and the comparison of the models in the CloudCompare software.The results obtained in these comparisons have been very promising, showing the effectiveness of these cameras to quickly document heritage. All the data on working times, geometric precision of the models, metric deviations, etc. They are shown below in the document.

Analysis of the Influencing Factors of Grinding Uniformity under Three-Body Coupling Grinding Mode

The three-body coupling grinding mode (3B-CGM) of spheres not only guarantees lot diameter variation and batch consistency of precision ball processing on the balling principle but also makes structural control easy and feasible. To assure balling quality under the three-body coupling grinding mode, it must improve dynamic mechanical precision control ability during processing. This study detailed contents covered the following three aspects: (1) The velocity equilibrium equation under the three-body coupling grinding mode was established under ideal conditions. This velocity equilibrium equation was transformed into the velocity equilibrium equation in the form of θ, Ωb, and ωb, thus laying the foundations to analyze the influencing degree of grinding uniformity; (2) On the basis of the velocity equilibrium equation, the characteristics of various sliding states were analyzed. Moreover, the sliding–friction ratio (Sc) was established to analyze the variation laws of the sliding state. The acquired mathematical model of evaluation indexes could realize optimization of the system control precision; (3) A multibody dynamics analysis software, i.e., ADMAS, was applied, and the standard deviation of uniformity of spherical track points in the simulation was created to evaluate influences of subsequent mechanical structural errors, including excessive loads, run-out, the tilt and out-of-roundness of upper and lower grinding discs, the diameter of grinding discs, and the V-shaped groove angle of grinding discs. This study establishes an accurate motion control model, as well as the optimal parameter analysis method. It improved the fine control over motion states. These models and indexes lay theoretical foundations for the realization of approximately ideal grinding effect in practical mass production.

Modeling the Bio-Inspired Wing-Tail Interaction Mechanism and Applying It in Flapping Wing Aircraft Pitch Control

Birds produce tail control torques to maintain their attitude by unfolding their tails in the wing induced flow at hover. This mechanism is termed as wing-tail interaction. This study aims to apply this mechanism to the pitch control of a flapping wing aircraft. Momentum theory is adapted to analytically calculate the flapping wing induced flow and its distribution along the body is also presented. The proposed theory is experimentally tested on our self-designed flapping wing aircraft and it holds when the tail is installed within 1.5 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> (wing chord) distance from the leading edge at frequency above 10 Hz. The tail of our aircraft is mounted at the experimentally identified maximum tail control torque position to enlarge the control effect. An accurate tail control model of the aircraft is established with the validated theory and it is utilized to design frequency dependent tail controller. The controller can realize good pitch stabilization by including the flapping wing induced flow. The proposed theory can be easily incorporated into the tail controller design of flapping wing aircraft and has promising application potential in the similar tailed aircraft.

A framework for analyzing the accuracy, complexity, and long-term performance of cable-driven parallel robot models

Cable-driven parallel robots require accurate and reliable control models to satisfy the demands of their diverse applications. The models’ usefulness in practice depends on their structure, the considered effects, and the ability to determine their best parameter values. This work establishes a unified model framework, containing eleven of the most commonly used cable robot models. The framework can optimize, evaluate, and compare models based on measurement datasets that capture the behavior of the robot. The models are evaluated based on their accuracy in the platform pose and the cable forces. Their computational costs, optimization process indicators, and the influence of the dataset size are also studied. This analysis is exercised for a large-scale cable robot based on two measurement datasets that are recorded 1.5 months apart and cover different regions of the robot’s state–space. The results show that a catenary model with force offsets offers the best overall accuracy for the given cable robot. While the models’ relative behavior is consistent in both datasets, their accuracy is significantly reduced on the second dataset due to long-term effects acting on the robot.

A predictive model of indoor PM2.5 considering occupancy level in a hospital outpatient hall

The hospital outpatient hall is more complex and sensitive than other indoor places because of its high density, flow of patients, and risk of infection. The prediction of indoor pollutants, such as PM2.5, is a critical health risk factor and an important topic in the study of indoor air quality. Numerous black-box models have been built to predict PM2.5, which are prone to overfitting and low precision in long sequence time prediction due to their limited weighting calculation and factors considered In this study, subject-object weighting incorporates a long sequence time-series model that considers occupancy (SO-LSTS) to predict PM2.5 concentrations in a hospital outpatient hall. First, the occupancy level was obtained using image recognition technology. Second, both the subjective (improved AHP) and objective (entropy weight) information were coupled by a distance function and then integrated into the LSTS model. Finally, the model performance was compared to six traditional models and the impact on the output length and hyper-parameter confirmation was assessed. The results demonstrate that the occupancy factor can improve the model performance by 54 %, and the model accuracy is improved by 89 % compared to the traditional Informer method. Our study considers real-time environmental and occupancy levels, which can compensate for the difficulty of interpreting the black-box model and identifying an accurate and resource-efficient proactive control model for hospital environmental management compared to conventional approaches.

Data-driven models for train control dynamics in high-speed railways: LAG-LSTM for train trajectory prediction

The construction of an accurate train control model (TCM) is crucial to the design of automatic train operation and real-time traffic management systems in high-speed railways. Traditional physical-driven models usually fail to reflect the “true” dynamics of high-speed trains (HSTs) because of the strong nonlinearity and uncertainty due to air resistance, frequently switching working conditions, and variations in external influencing factors such as weather and temperature. Although some data-driven deep learning models have recently been proposed for environmental adaptation, they are all “black-box” models, which cannot explain how the input of the models affects the HST output. To overcome these issues, this study constructs a novel long short-term memory with lagged information (LAG-LSTM) model by combining the physical-driven HST model and an “interpretable” deep learning model. Specifically, our LAG-LSTM model contains three modules: a time-delay variable module to model the transform delay of control variables, state variable enhancement module to extract the key features among high-dimensional input data, and Pre-LSTM module to predict the future train trajectory with given control variables. We collected field data from the Beijing-Shanghai high-speed railway and developed a data filter method and a normalization procedure to overcome the positioning errors of HSTs and construct a standard data set. Finally, we tested the effectiveness of our LAG-LSTM by comparing it with six deep learning structures, including fully connected neural network, recursive neural network, standard LSTM, and LSTM with convolutional layers. The results show that LAG-LSTM can accurately predict the trajectories of HSTs and outperforms other deep learning models. Regarding prediction accuracy, LAG-LSTM improved the performance of the traditional LSTM by 13.5% to 23.3%.

Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment

For soft continuum arms, visual servoing is a popular control strategy that relies on visual feedback to close the control loop. However, robust visual servoing is challenging as it requires reliable feature extraction from the image, accurate control models and sensors to perceive the shape of the arm, both of which can be hard to implement in a soft robot. This letter circumvents these challenges by presenting a deep neural network-based method to perform smooth and robust 3D positioning tasks on a soft arm by visual servoing using a camera mounted at the distal end of the arm. A convolutional neural network is trained to predict the actuations required to achieve the desired pose in a structured environment. Integrated and modular approaches for estimating the actuations from the image are proposed and are experimentally compared. A proportional control law is implemented to reduce the error between the desired and current image as seen by the camera. The model together with the proportional feedback control makes the described approach robust to several variations such as new targets, lighting, loads, and diminution of the soft arm. Furthermore, the model lends itself to be transferred to a new environment with minimal effort.

Data-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study

We use Koopman theory for data-driven model reduction of nonlinear dynamical systems with controls. We propose generic model structures combining delay-coordinate encoding of measurements and full state decoding to integrate reduced Koopman modeling and state estimation. We present a deep-learning approach to train the proposed models. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column.

Tongue image quality assessment based on a deep convolutional neural network

BackgroundTongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Therefore, an automatic, efficient and accurate quality control model is of significance to the development of intelligent tongue diagnosis technology for TCM.MethodsMachine learning methods, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (Adaboost), Naïve Bayes, Decision Tree (DT), Residual Neural Network (ResNet), Convolution Neural Network developed by Visual Geometry Group at University of Oxford (VGG), and Densely Connected Convolutional Networks (DenseNet), were utilized to identify good-quality and poor-quality tongue images. Their performances were made comparisons by using metrics such as accuracy, precision, recall, and F1-Score.ResultsThe experimental results showed that the accuracy of the three deep learning models was more than 96%, and the accuracy of ResNet-152 and DenseNet-169 was more than 98%. The model ResNet-152 obtained accuracy of 99.04%, precision of 99.05%, recall of 99.04%, and F1-score of 99.05%. The performances were better than performances of other eight models. The eight models are VGG-16, DenseNet-169, SVM, RF, GBDT, Adaboost, Naïve Bayes, and DT. ResNet-152 was selected as quality-screening model for tongue IQA.ConclusionsOur research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and indicate that applying deep learning methods, specifically deep CNNs, to evaluate poor-quality tongue images is feasible.

Deep Reinforcement Learning for Vectored Thruster Autonomous Underwater Vehicle Control

Autonomous underwater vehicles (AUVs) are widely used to accomplish various missions in the complex marine environment; the design of a control system for AUVs is particularly difficult due to the high nonlinearity, variations in hydrodynamic coefficients, and external force from ocean currents. In this paper, we propose a controller based on deep reinforcement learning (DRL) in a simulation environment for studying the control performance of the vectored thruster AUV. RL is an important method of artificial intelligence that can learn behavior through trial-and-error interactions with the environment, so it does not need to provide an accurate AUV control model that is very hard to establish. The proposed RL algorithm only uses the information that can be measured by sensors inside the AUVs as the input parameters, and the outputs of the designed controller are the continuous control actions, which are the commands that are set to the vectored thruster. Moreover, a reward function is developed for deep RL controller considering different factors which actually affect the control accuracy of AUV navigation control. To confirm the algorithm’s effectiveness, a series of simulations are carried out in the designed simulation environment, which is a method to save time and improve efficiency. Simulation results prove the feasibility of the deep RL algorithm applied to the control system for AUV. Furthermore, our work also provides an optional method for robot control problems to deal with improving technology requirements and complicated application environments.

Robust Control of EHCS of Intelligent Commercial Vehicle under Load Change

Electro Hydraulic Coupling Steering (EHCS) system is a new type of intelligent commercial vehicle steering system, having strong nonlinear characteristics. Besides, the change of load would cause the change of control system parameters, making not easy to establish an accurate control model of it. To realize the robustness of EHCS under the change of load, the controller based on the adaptive control method is proposed in this paper. To this end, the cosimulation model of EHCS is first established, where the constructed control model is simplified to a 2‐degree‐of‐freedom model under reasonable simplification and assumption. Then, the steering angle controller is designed based on the model reference adaptive theory. Finally, some simulations are given to show the effectiveness of the proposed control method.

Use of Tobacco Products in Turkish Children and Young People: Is there an Alarm for Hookah Use?

In the recent years, it has been observed that the use of hookah, a tobacco product, has increased in children and young people in Turkey. This study was conducted to determine the prevalence of cigarette and hookah use in children and young people specific to age and gender and to define the factors relevant to the use of hookah. The study was conducted with secondary/high school students between 11 and 17 years of age (Survey 1, n=4718) and with university students between 18 and 23 years of age (Survey 2, n=1588) as two groups, which made a total of 6306 students. The students were asked to fill in a questionnaire which consisted of questions about socio-demographic data and the use of tobacco and tobacco products. It was determined that the use of cigarette and hookah was high in males, and it increased as the age increased in both genders (p<0.05). It was also determined that parents and friends using cigarette and hookah affected the use of cigarette and hookah in the study groups in both surveys (p<0.05). Additionally, it was determined that the educational level of the parents and being of a high social class were the social determiners of hookah use (p<0.05). It is important that we fight against the use of all tobacco products, especially in young people. Education about the damages of tobacco and applying tobacco products should start at an early age, and accurate tobacco control models may provide support in this field.

High-Speed Wavelength Switching at TDA-DFB Laser Based on a Nonlinear Model

In order to realize a high-speed optical switch for optical packet switching (OPS), we have been focused on a wavelength routing optical switch consisting of tunable lasers and an arrayed waveguide grating (AWG), and have studied high-speed wavelength switching of a tunable distributed amplification (TDA)-distributed feedback (DFB) laser. Previously, we achieved high-speed wavelength switching by feedforward control based on linear control theory. In this article, to realize higher-speed wavelength switching, we proposed a more accurate control model based on the nonlinearity of the TDA-DFB laser. We applied our nonlinear model to 400-GHz-wide and 600-GHz-wide wavelength switching and realized high-speed wavelength switching times of 33 ns and 39 ns, respectively.

EOQ inventory model for perishable products under uncertainty

Perishable products require accurate inventory control models as their effect on operations management can be critical. This assumption is particularly relevant in highly uncertain and dynamic markets, as for the ones generated by the pandemic era. This paper presents an inventory control model for perishable items with a demand rate variable over time, and dependent on the inventory rate. The model also considers the potential for backlogging and lost sales. Imperfect product quality is included, and deterioration is modelled as a time-dependent variable. The framework envisages the possibility to define variables affected by uncertainty in terms of probability distribution functions, which are then jointly managed via a Monte Carlo simulation. This paper is intended to provide an analytical formulation to deal with uncertainty and time-dependent inventory functions to be used for a variety of perishable products. The formulation is designed to support decision-making for the identification of the optimal order quantity. A numerical example exemplifies the outcomes of the paper and provides a cost-based sensitivity analysis to understand the role of main parameters.

High precision control model of large-sized gantry-type linear motor slider

Large-sized gantry-type linear motor sliders are widely used in industry (e.g. in liquid crystal panel production equipment). An appropriate control model is important for deriving control methods to improve control performance. Although various control models of large-sized gantry-type linear motor sliders have been developed in previous studies, no results are yet reported regarding control models which precisely reproduce characteristics of a large-sized gantry-type linear motor slider. In general, a method to derive a transfer function by using frequency responses can be employed to obtain an accurate control model, however, large sized gantry-type linear motor sliders have unique characteristics, namely “distortion” and “coupling”, thus the transfer function derived using only the frequency response cannot reproduce the experimental results with high accuracy. In this paper we report on a method to obtain a highly precise control model of a large sized gantry-type linear motor slider.