Artificial Control Research Articles

Artificial Intelligence Techniques based on aquaculture solar thermal water heating system control

The One of the most promising applications of hot water is aquaculture. Temperature is one of the most principle parameters affects aquaculture life. It can cause stress and mortality or superior environment for growth and reproduction. Shrimp is warm water aquaculture type need hot water supply. To increase pond production it is necessary to keep water temperature at 34oC. Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. In this paper a control system using artificial neural network control (NNC) either adaptive fuzzy logic control (AFLC) are design to control the hot water temperature. A comparison study is applied between the performance of FLC and NNC. The performance of NNC is the best because the control design takes into consideration different variables which give an accurate output than FLC. Also this paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the proposed aquaculture heating system.

Seasonal threshold to reduce false positives for prediction-based outlier detection in building energy data

Recent studies have shown that artificial intelligence-based model predictive control systems have the potential to optimize building operations for energy and cost. But for these systems to function as designed, they require high-quality datasets, which can be challenging when working with actual sensor data that may contain missing or outlier values. Using contaminated data in these carefully tuned machine learning algorithms can cause errors in the prediction results, leading to unexpected building operations. There is a need to clean incoming data in real-time by detecting the outliers in incoming sensor data and correcting them to an appropriate value before incorporating them into the prediction algorithm. This research proposes using a seasonal threshold for prediction-based point outlier detection methods to accurately detect the outliers while maintaining the reliability of the data by reducing false positive detections. The approach combining the global and seasonal threshold showed improvement to the detection accuracy by 83 % and reduced the falsely positives by up to 78 % for the best improvement, making the method a more trustworthy way to detect outliers in building energy data. This method can utilize an already existing model to detect outliers is a promising approach, especially for buildings that have already been implemented or are in the process of implementing an artificial intelligence-based control systems. This will allow the system to predict and control building energy without interruption and with reduced risk of unexpected operation due to corrupted data.

Optical properties of particle dispersed coatings with gradient distribution.

Particle dispersed coatings with gradient distributions, resulting from either gravity or artificial control, are frequently encountered in practical applications. However, most current studies investigating the optical properties of coatings use the uniform model (uniform single layer assumption), overlooking the gradient distribution effects. Given the pervasiveness of gradient distributions and the widespread use of the uniform model, it is imperative to evaluate applicability conditions of the uniform model in practical applications. In this work, we comprehensively investigate the quantitative performance of the uniform model in predicting the infrared optical properties of coatings with gradient distributions of particle volume fraction using the superposition T-matrix method. The results show that the gradient distribution of particle volume fraction has a limited impact on the emissivity properties of T i O 2-PDMS coatings in the midwavelength-infrared (MWIR) and long-wavelength-infrared (LWIR) bands, which validates the uniform model for the gradient coatings with weakly scattering dielectric particles. However, the uniform model can yield significant inaccuracies in estimating the emissivity properties of Al-PDMS coatings with gradient distributions in the MWIR and LWIR bands. To accurately estimate the emissivity of such gradient coatings with the scattering metallic particles, meticulous modeling of the particle volume fraction distribution is essential.

U-Net Convolutional Neural Network for Real-Time Prediction of the Number of Cultured Corneal Endothelial Cells for Cellular Therapy.

Corneal endothelial decompensation is treated by the corneal transplantation of donor corneas, but donor shortages and other problems associated with corneal transplantation have prompted investigations into tissue engineering therapies. For clinical use, cells used in tissue engineering must undergo strict quality control to ensure their safety and efficacy. In addition, efficient cell manufacturing processes are needed to make cell therapy a sustainable standard procedure with an acceptable economic burden. In this study, we obtained 3098 phase contrast images of cultured human corneal endothelial cells (HCECs). We labeled the images using semi-supervised learning and then trained a model that predicted the cell centers with a precision of 95.1%, a recall of 92.3%, and an F-value of 93.4%. The cell density calculated by the model showed a very strong correlation with the ground truth (Pearson's correlation coefficient = 0.97, p value = 8.10 × 10-52). The total cell numbers calculated by our model based on phase contrast images were close to the numbers calculated using a hemocytometer through passages 1 to 4. Our findings confirm the feasibility of using artificial intelligence-assisted quality control assessments in the field of regenerative medicine.

Combating the devastating effects of cotton bollworm: case of Uzbekistan

Bollworm poses a significant threat to cotton crops worldwide, including in the Fergana province of Uzbekistan. This study aims to explore innovative approaches to bollworm control and contribute to the global understanding of integrated pest management strategies (IPM). Using an extensive literature review and field observations, the study evaluates the effectiveness of an integrated protection system in reducing reliance on chemical inputs while increasing overall efficiency. The results highlight the multigenerational life cycle of bollworm, its impact on various crops, and the need for artificial biological control methods and targeted insecticide applications. In addition, the study highlights the need to adopt globally accepted IPM practices to ensure sustainable pest management. The findings underscore the importance of collaboration among researchers, farmers, policymakers, and industry representatives in developing and implementing advanced control measures. Through integrated approaches and the incorporation of scientific advances, bollworm infestations can be contained while safeguarding agricultural productivity and minimizing environmental risks. Preprint of this article is available at: https://www.preprints.org/manuscript/202306.1512/v1

Smart lattice light-sheet microscopy for imaging rare and complex cellular events.

Light-sheet microscopes enable rapid high-resolution imaging of biological specimens; however, biological processes span spatiotemporal scales. Moreover, long-term phenotypes are often instigated by rare or fleeting biological events that are difficult to capture with a single imaging modality. Here, to overcome this limitation, we present smartLLSM, a microscope that incorporates artificial intelligence-based instrument control to autonomously switch between epifluorescent inverted imaging and lattice light-sheet microscopy (LLSM). We apply this approach to two unique processes: cell division and immune synapse formation. In each context, smartLLSM provides population-level statistics across thousands of cells and autonomously captures multicolor three-dimensional datasets or four-dimensional time-lapse movies of rare events at rates that dramatically exceed human capabilities. From this, we quantify the effects of Taxol dose on spindle structure and kinetochore dynamics in dividing cells and of antigen strength on cytotoxic T lymphocyte engagement and lytic granule polarization at the immune synapse. Overall, smartLLSM efficiently detects rare events within heterogeneous cell populations and records these processes with high spatiotemporal four-dimensional imaging over statistically significant replicates.

Development of Rapid and Affordable Virus-Mimicking Artificial Positive Controls.

A major bottleneck in the development of detection assays is the availability of positive controls. Their acquisition can be problematic, their maintenance is expensive, and without them, assays cannot be validated. Herein, we present a novel strategy for the development of virus-mimicking artificial positive controls (ViMAPCs). The time between design and application is less than 5 days, unlike alternatives which normally take several weeks to obtain and implement. The ViMAPCs provide a realistic representation of natural infection unlike alternatives and allow for an effortless recognition of laboratory-based contamination. The feasibility and adaptability of the strategy was evaluated using several RNA and DNA plant viruses. ViMAPCs can be used in diagnostics laboratories but also in the monitoring of pathogen outbreaks where rapid response is of utmost importance.

Application of artificial intelligence control in the control system of cooling and heating energy stations

In order to understand the application of artificial intelligence in the control system of cold and hot energy station, the author proposes a research based on artificial intelligence control in the control system of cooling and heating energy station. The author first introduces artificial intelligence technology on the basis of heating and precision heating, relies on the ?Internet plus? and IoT platform, applies indoor temperature measurement technology, intelligent control and information docking technology, and establishes a heating system based on artificial intelligence technology. This system has improved the level of enterprise management, promoted the further development of heating and intelligent energy conservation, reduced energy consumption, reduced pollution, and achieved the need for precise heating of the pipe-line network. Secondly, combined with the application case of a district, this district is selected as the application pilot to describe the role and effect of this system in the Hydronics. The experimental results indicate that the heat consumption of the residential area is 27.07 W/m2, while the heat consumption of the comparison residential area is 28 W/m2. Through indoor temperature measurement, it was found that the average indoor temperature of the comparison residential area is 22?C. Therefore, the application of artificial intelligence heating systems has improved the thermal comfort of residents, bringing users a high quality heating experience while relatively reducing energy consumption. The intelligent application of the Hydronics can more accurately grasp the heating situation, timely and efficiently adjust the heating scheme, achieve heating balance, and improve the utilization efficiency of heat sources. At the same time, it can also effectively improve the troubleshooting ability of the hydronics, prevent safety risks, improve management efficiency, and will certainly provide more powerful support for the energy-saving society and environment-friendly society.

Artificial intelligence control learning for autonomous industrial robots

Artificial intelligence control learning for autonomous industrial robots

An Artificial Intelligent Hybrid Controller for Solar and Battery Fed Five-Level UPQC for Power Quality Improvement

An Artificial Intelligent Hybrid Controller for Solar and Battery Fed Five-Level UPQC for Power Quality Improvement

Artificial Intelligence-Enhanced Peak Current Mode Control for Enhanced Power Regulation in High Power High Frequency Electrosurgical Generators

Artificial Intelligence-Enhanced Peak Current Mode Control for Enhanced Power Regulation in High Power High Frequency Electrosurgical Generators

Using Artificial Intelligence and Numerical Control in the Design and Production of Ceramics

Using Artificial Intelligence and Numerical Control in the Design and Production of Ceramics

Optimize the energy storage system with an artificial intelligence-based control strategy

Abstract Currently, energy storage systems adopt control strategies based on the crossover approach despite their limited generalization performance. To improve the control effect of the control strategy, the limitation of the SOC of the energy storage element is added. This adds smoothness to the system’s output power, enabling the energy storage element to distribute the power reasonably. Meanwhile, we propose and introduce a DPI control strategy based on PSO optimization into the control strategy of the second-order low-pass filtering method, thereby further enhancing and optimizing the original control strategy. Taking the investment cost of the energy storage system and the stable operation of the system as the objective functions, the constraint function is determined according to the parameters, the PSO algorithm is used to construct a two-layer optimization model of the energy storage system, and the FCEM is introduced to determine the objective weights. Based on the constructed model, an arithmetic example analysis of the energy storage system is carried out using artificial intelligence. Under the steady-state analysis of Buck mode, the low voltage side voltage adjustment time based on the PID control strategy is about 0.108s, and the low voltage side voltage adjustment time based on PSOPID is 0.032s, which is reduced by 0.070s compared with the PID control strategy. Also, when optimizing a system that generates electricity from renewable sources on its own, the two goals of minimizing the investment in the energy storage system and making sure it runs smoothly are met when the system’s fixed power is equal to 6.58 MW and its capacity is less than 267 MWh.

Empowering Microgrid Energy Management with Artificial Intelligence and Model Predictive Control

This paper presents an innovative approach to microgrid energy management by integrating Model Predictive Control (MPC) with Artificial Intelligence (AI), focusing on the application of Long Short-Term Memory (LSTM) networks for load forecasting. We show that AI-enhanced MPC can significantly improve the efficiency and reliability of microgrid energy management. The fundamental results of the LSTM models highlight the effectiveness of our methodology in improving predictive accuracy and operational performance.

Closed Loop Control of Blood Glucose Levels in Diabetes Using an Artificial Neural Network Controller

Regulating blood glucose levels (BGL) is of paramount significance for those with Type 1 Diabetes (T1D), as it can lead to severe health issues, including cardiovascular complications. Our research is centred around the development of an automated closed-loop system, rooted in the minimal Bergman model and driven by a carefully trained artificial neural network (ANN) controller. This approach, evaluated on three T1D patients, effectively keeps blood glucose within the desirable range of 70 to 140 mg/dl, holding the promise of enhancing the overall health and well-being of individuals living with T1D. The potential impact of this research extends to an improved quality of life and a reduction in healthcare burdens.

Voltage Control of PEM Fuel Cell in a DC Microgrid Using Optimal Artificial Rabbits Algorithm-Based Fractional Order PID Controller

Voltage Control of PEM Fuel Cell in a DC Microgrid Using Optimal Artificial Rabbits Algorithm-Based Fractional Order PID Controller

Active control for a low-drag Ahmed vehicle model based on steady blowing

An experimental investigation is conducted on the active drag reduction (DR) of an Ahmed body with a rear slant angle of 35°, corresponding to the low-drag regime, using single and combined actuations at the Reynolds number Re = 1.7 × 105. Five different actuations, produced by steady blowing jets, are deployed independently around the edges of the rear slant surface and vertical base, achieving the maximum DR of 1%–9%. An artificial intelligence control system based on ant colony algorithm is used for finding near-optimal control laws of the combined jets. With both DR and control power input considered in the cost function, the maximum DR obtained reaches 18%, though the corresponding control efficiency η (≡ES/EI, where ES and EI are the saved power due to DR and the total input power of the actuations, respectively) is only 0.13. However, η may go up greatly, climbing to 5.8, given a 3% sacrifice of DR. Extensive flow measurements are conducted, with and without control, to understand the flow physics and mechanisms under the control of individual and combined actuations. A linear regression model is established to describe the correlation between the control efficiency and parameters under the combined actuations.

Hybrid ANFIS‐PI‐Based Robust Control of Wind Turbine Power Generation System

This paper introduces a novel hybrid controller designed for a wind turbine power generation system (WTPGS) that utilizes a permanent magnet synchronous generator (PMSG). This hybrid controller combines the adaptability of an adaptive neuro‐fuzzy inference system (ANFIS) with the simplicity of a proportional‐integral (PI) controller. The PI controllers are traditionally used for stability and noise handling. ANFIS adds adaptability, making it more suitable to cope with the variable nature of wind energy. The primary objective of this hybrid strategy is to augment the overall control performance and reliability of PMSG‐based WTPGS when encountered with continuous variable wind conditions. However, implementing the PI controller alone with the WTPGS often suffers from high overshoot and sluggish response in nonlinear systems like WTPGS. In contrast, ANFIS controllers offer superior performance to PI and other artificial intelligence controllers but are still susceptible to noise issues. In this paper, the proposed WTPGS system is designed in MATLAB/Simulink software where a hybrid controller (ANFIS‐PI) is implemented in the machine‐side converter (MSC) and grid‐side converter (GSC) of a variable speed PMSG‐based wind turbine to enhance its performance subjected to wind variations. The hybrid controller is implemented in such a way that the ANFIS controller is implemented in the outer layers while the PI controller is applied in the inner layers of both MSC and GSC. The simulation results for this hybrid controller in the MSC outperform those of the conventional PI controller. They demonstrate minimal overshooting and settling time, maintaining consistent stability even when subjected to various test signals at different intervals. Similarly, the GSC also surpasses conventional PI controllers, achieving a significant 6.4% reduction in maximum overshoot and a decrease of 4.36 seconds in settling time. This highlights its strong suitability for wind turbine applications.

Adaptive Artificial Neural Network-Based Proportional Integral Controllers and Extremum Seeking Energy Optimizer for Wind Systems

Adaptive Artificial Neural Network-Based Proportional Integral Controllers and Extremum Seeking Energy Optimizer for Wind Systems

Modeling and Control of a 3DOF Robot Manipulator Using Artificial Fuzzy-Immune FOPID Controller

Modeling and Control of a 3DOF Robot Manipulator Using Artificial Fuzzy-Immune FOPID Controller