Artificial Control Research Articles

A safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning.

Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL. It employed ten tricks to enhance the proximal policy optimization (PPO) algorithm, improving training efficiency. Additionally, a dual safety mechanism of 'proactive guidance + reactive correction' was introduced to reduce the risks of hyperglycemia and hypoglycemia and to prevent emergencies. Performance evaluations in the Simglucose simulator demonstrate that the proposed controller achieved an 87.45% time in range (TIR) median, superior to baseline methods, with a lower incidence of hypoglycemia, notably eliminating severe hypoglycemia and treatment failures. These encouraging results indicate that the DRL-based fully closed-loop AP controller has taken an essential step toward clinical implementation.

MeStanG-Resource for High-Throughput Sequencing Standard Data Sets Generation for Bioinformatic Methods Evaluation and Validation.

Metagenomics analysis has enabled the measurement of the microbiome diversity in environmental samples without prior targeted enrichment. Functional and phylogenetic studies based on microbial diversity retrieved using HTS platforms have advanced from detecting known organisms and discovering unknown species to applications in disease diagnostics. Robust validation processes are essential for test reliability, requiring standard samples and databases deriving from real samples and in silico generated artificial controls. We propose a MeStanG as a resource for generating HTS Nanopore data sets to evaluate present and emerging bioinformatics pipelines. MeStanG allows samples to be designed with user-defined organism abundances expressed as number of reads, reference sequences, and predetermined or custom errors by sequencing profiles. The simulator pipeline was evaluated by analyzing its output mock metagenomic samples containing known read abundances using read mapping, genome assembly, and taxonomic classification on three scenarios: a bacterial community composed of nine different organisms, samples resembling pathogen-infected wheat plants, and a viral pathogen serial dilution sampling. The evaluation was able to report consistently the same organisms, and their read abundances as provided in the mock metagenomic sample design. Based on this performance and its novel capacity of generating exact number of reads, MeStanG can be used by scientists to develop mock metagenomic samples (artificial HTS data sets) to assess the diagnostic performance metrics of bioinformatic pipelines, allowing the user to choose predetermined or customized models for research and training.

Atomically Precise Fabrication of Ultranarrow Zigzag CuTe Nanoribbons via Dimensional Regulation.

Artificial dimension control has been playing a vital role in electronic structure manipulation and properties generation. However, systematic investigations into the dimensional regulation, such as transformation from two-dimensional (2D) materials to well-controlled one-dimensional (1D) ribbons, remain insufficient via molecular beam epitaxy. Here, high-quality ultranarrow zigzag CuTe nanoribbons are atomically precisely prepared via the dimensional regulation induced by adjusting the Te chemical potential, utilizing CuSe monolayer as the starting 2D template. Introducing Te atoms into the CuSe monolayer and subsequent annealing, Te atoms replace Se atoms within CuSe lattice. As the Te substitution ratio increases, strain accumulates and elongated nanopores emerge, which expand and interconnect to form 1D CuSe1-xTex (0 ≤ x ≤ 1) nanoribbons and ultimately coalesce into a 1D ultranarrow zigzag CuTe nanoribbons with a honeycomb lattice. The entire structural transformation is verified through scanning tunneling microscopy (STM) and density functional theory (DFT). Contrary to the 2D semiconducting nature of CuSe and CuSe1-xTex monolayers, newly formed 1D CuTe nanoribbons exhibit metallic properties. Intriguingly, DFT calculations further reveal spin-polarized states at the zigzag edges of CuTe nanoribbons. Our proposed dimensional regulation strategy from 2D materials to well-controlled 1D nanoribbons presents avenues for refining and enhancing the synthesis process.

High Gain Quasi Z-Source Converters with Artificial Bee Colony Control for Grid-Integrated Solar-Wind Energy Sources

High Gain Quasi Z-Source Converters with Artificial Bee Colony Control for Grid-Integrated Solar-Wind Energy Sources

Artificial Intelligence-Based Direct Power Control for Power Quality Improvement in a WT-DFIG System via Neural Networks: Prediction and Classification Techniques

This paper discusses the improvement of power quality injected into the AC grid. This approach is achieved by enhancing the quality of injected power signals and mastering the active and reactive power exchanged between the DFIG based wind turbine (WT-DFIG) and the electrical grid, resulting in an improvement of the overall system performance and efficiency. This study includes all WT-DFIG operating modes, successively and continuously, as well as all local reactive power compensation modes. Therefore, novel control strategies are proposed in this paper for wind energy conversion systems based on artificial intelligence techniques. These techniques include Neural Network Prediction (PNN-DPC) and Classification (CNN-DPC). They aim to eliminate the drawbacks and difficulties associated with conventional Direct Power Control (C-DPC), while retaining its advantages. The paper also provides a thorough explanation of the mathematical models for neural network techniques and WT-DFIG system models. The MATLAB/Simulink environment is used to investigate the performance of the proposed techniques under different conditions and operating modes related to different scenarios. The results reveal a significant reduction in the ripple of the generated active power and the compensated local reactive power, better quality of the generated signal currents and a remarkable reduction in the current total harmonic distortion (THD). Furthermore, compared to C-DPC, PNN-DPC achieves a reduction of 72.07% in active power ripples, 77.07% in reactive power ripples, and 76.79% in current Total Harmonic Distortion (THD). CNN-DPC shows similar improvements with 72.04%, 77.13%, and 76.54% of reductions respectively. In addition, CNN-DPC slightly outperforms PNN-DPC. Nevertheless, both proposed control techniques show significant improvements in all characteristics compared to other methods. Consequently, the proposed control strategies indicate that artificial intelligence has the potential to improve the power quality and performance of wind power conversion system.

«Кібернетика та системний аналіз»: 60 років інновацій та розвитку

This year, we are celebrating a remarkable event: the 60th anniversary of the first issue of the “Kibernetyka ta Systemnyi Analiz” (“Cybernetics and Systems Analysis”) journal. Founded in January 1965 at the Institute of Cybernetics of the Academy of Sciences of the Ukrainian SSR (and initially named “Kibernetika” (“Cybernetics” in Russian)), this journal has become an important scientific publication that has brought together experts from all over the world in the field of cybernetics and information technology. Since its first issues, the journal has become a platform for exchanging ideas and research results and for discussions about the future of artificial intelligence, automation, and control of complex systems.

Power Quality Enhancement Using Artificial Neural Network-Proportional Integral Controller and Fuzzy Granular Controller for DSTATCOM Integrated with Renewable Energy and Battery Storage System

Power Quality Enhancement Using Artificial Neural Network-Proportional Integral Controller and Fuzzy Granular Controller for DSTATCOM Integrated with Renewable Energy and Battery Storage System

Optomechatronic system for robotic arm using neural networks for educational inclusion

The artificial vision system is crucial in technological advancements, offering innovative solutions for assisting people with disabilities. This study presents an optomechatronic system designed to control a robotic hand prosthesis using artificial vision to interpret hand movements and translate sign language into voice commands. The prosthesis operates through artificial vision-based control that maps hand positions to prosthetic fingers. This technology aids in direct communication between people with hearing impairments and others, enhancing inclusivity. The project emphasizes the use of low-cost and open-source software, making it accessible to a broader audience.

Advanced Bending and Forming Technologies for Bimetallic Composite Pipes.

Bimetallic composite pipes, as critical components, effectively integrate the superior properties of diverse materials to meet the growing demand for lightweight, high-strength, and corrosion-resistant solutions. These pipes find extensive applications in petrochemical, power generation, marine engineering, refrigeration equipment, and automotive manufacturing industries. This paper comprehensively reviews advanced bending and forming technologies, with a focus on challenges such as wrinkling, excessive wall thinning, springback, cross-sectional distortion, and interlayer separation. The review combines theoretical analysis, experimental findings, and numerical simulations to provide insights into defect prevention strategies and process optimization. It also evaluates emerging technologies such as artificial neural networks and intelligent control systems, which demonstrate significant potential in enhancing bending accuracy, reducing defects, and improving manufacturing efficiency. Additionally, this work outlines future research directions, emphasizing innovations required to meet the stringent performance standards of bimetallic composite pipe components in high-end applications.

Micro-Grid Wind Energy Conversion Systems: Conventional and Modern Embedded Technologies - A Review

This study assesses the effectiveness of an electric microgrid wind energy conversion system using both traditional techniques and contemporary embedded systems, such as artificial neural network-based control mechanisms and fuzzy logic control. The text compares and lists the advantages and disadvantages of various types of wind turbines (WTs). Moreover, this control falls into one of two groups: conventional power control or non-traditional power control. On the other side, conventional control describes methods of control such as manually controlling the turbine rotor's rotation speed and using computational analysis. The current work, in contrast, investigates and evaluates contemporary embedded control techniques used in wind energy conversion systems (WECS), including maximum power point tracking, Artificially intelligent control systems, in relation to the control mechanism, provide complete control over the pitch angle, power coefficient, and tip speed ratio for the best possible wind energy extraction. This makes a direct comparison possible. Nonetheless, there are a few drawbacks and difficulties with the two widely utilized contemporary techniques for power quality extractions: artificially intelligent neural networks and their embedded control systems. However, combining contemporary technology with integrated artificial intelligence controllers may be a workable strategy to lessen and even eliminate these difficulties, as well as advantageous for upcoming studies.

Impact of Insect Foliar Herbivory on Soil N₂O Emission and Nitrogen Dynamics in Subtropical Tree Species

Insect foliar herbivory is ubiquitous in terrestrial ecosystems, yet its impacts on soil nitrogen cycling processes remain not yet well known. To examine the impacts of insect foliar herbivory on soil N2O emission flux and available nitrogen (N), we conducted a pot experiment to measure soil available N content and soil N2O emission flux among three treatments (i.e., leaf herbivory, artificial defoliation, and control,) in two broad-leaved trees (Cinnamomum camphora and Liquidambar formosana) and two conifer trees (Pinus massonianna and Cryptomeria fortunei). Our results showed that insect foliar herbivory significantly increased soil inorganic N (i.e., NH4+–N and NO3−–N), dissolved organic nitrogen (DON) and microbial biomass nitrogen (MBN) contents, and urease activity compared to control treatment. However, there were no differences in soil available N contents and urease activity between artificial defoliation and control treatments, implying that insect foliar herbivory had greater impacts on soil available N contents compared to physical damage of leaves. Moreover, soil N2O emission fluxes were increased by insect foliar herbivory in Cinnamomum camphora and Pinus massonianna, but not for the other two tree species, indicating various effect of insect foliar herbivory on soil N2O emission among tree species. Furthermore, our results showed the positive correlations between soil N2O emission flux and soil NO3−–N, DON, MBN, and acid protease activity, and soil inorganic N, pH, and MBN mainly explained soil N2O emission. Our results implied that insect foliar herbivory can speed up soil nitrogen availability in subtropical forests, but the impacts on soil N2O emission are related to tree species.

A novel optimized artificial neural networks-based controller for smart morphing wings

A novel optimized artificial neural networks-based controller for smart morphing wings

Energy efficiency optimization analysis of a ground source heat pump system based on neural networks and genetic algorithms

This paper reports on the performance of a ground source heat pump (GSHP) system located in Shandong Province, China. The system operation data were monitored and collected by a data collection system. According to the analysis of the accumulated operational data, it was found that the GSHP system showed a relative higher COP in cooling season of 2023 than that of 2022 due to the change of supplying water temperature at ground-source side. Based on the analyzed data, a BP neural network model for energy consumption prediction was established. Furthermore, genetic algorithm (GA) was used to optimize the control strategy on the basis of the energy consumption prediction model. Comparison between the artificial experience control strategy and the one optimized by the genetic algorithm was conducted. The results show that the optimization strategy of the genetic algorithm is superior in terms of energy saving, particularly in the load rate higher than 50%, in which, the average energy-saving rate reaches 39.66%. Within the load rate range of 30–50%, the energy-saving rate could also reach 7.84%.

Artificial intelligent fuzzy control and LAPO algorithm for enhancement LVRT and power quality of grid connected PV/wind hybrid systems

Low Voltage Ride Through (LVRT) is considered one of the main and serious problems facing the electrical grid. It occurs due to three-phase symmetric faults and asymmetric faults such as a double line to ground fault that applies in this system. This paper applies Static Synchronous Compensators (STATCOM) to improve the LVRT capability and dynamic performance of an electrical grid linked to a Photovoltaic (PV)/Wind hybrid system through grid disturbances. A hybrid power system containing a PV station that produces 1 MW and a wind farm from type Doubly Fed Induction Generator (DFIG) that produces 9 MW is connected to STATCOM with 48 pulses at PCC bus and energized load. It compensates reactive power to improve LVRT that occurred due to fault. The applied STATCOM controller adjusts the voltage of the PCC bus during an occuring fault on the grid by compensating reactive power. STATCOM is controlled by a Proportional–Integral–Derivative (PID) and is compared with STATCOM controlled by Artificial Intelligence Control (AIC)-based on Proportional—Integral Fuzzy Logic Control (PI FLC). The Lightning Attachment Procedure Optimization Algorithm (LAPO) optimization method is used to adjust the parameters of the PI controller to reduce error signals. A simulation model of the suggested hybrid power system has been performed using Matlab/Simulink. The simulation results of STATCOM proved powerful and the effectiveness of STATCOM with PI FLC in reducing voltage dip, compensating active power of wind and PV farm, protecting DC-link voltage of PV and wind from overvoltage and oscillation that happens at three-phase fault and double line to ground fault as compared with PID STATCOM in enhancement LVRT capability, and power quality.

Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System

Intelligent lighting control systems are essential for regulating office illumination. Both illuminance levels and uniformity are important factors influencing the comfort of the office lighting environment. Thus, designing automatic control systems to regulate lighting is essential. This study addresses the issue of natural glare by proposing a method that uses a genetic algorithm (GA) to optimize a backpropagation (BP) neural network model. The model predicts the angle of window slats, with the Sun altitude and azimuth angles as inputs, and the slat angle as the output. For artificial lighting control, a linear function is proposed to manage the relationship between work plane illuminance, natural light intensity, occupancy rates, adjacent luminaire illuminance, and the dimming factor (K). The optimal K value for each luminaire is determined using the least squares method in MATLAB. The intelligent lighting system transmits dimming factors via a ZigBee tree network structure to achieve target illuminance levels. The system’s effectiveness is validated through simulations in DIAlux software, demonstrating that the workplace illuminance in occupied areas reaches 500 lx, while, in unoccupied areas, it reaches 300 lx, with an illuminance uniformity greater than 0.7. This addresses the issue of low illuminance uniformity during daytime. Additionally, the lighting power densities (LPDs) of 1.53 W/m2 and 3.8 W/m2 are well below the specified threshold of 6 W/m2, indicating significant energy savings while maintaining compliance with office lighting standards.

Machine Learning-based Dispatching for a Wet Clean Station in Semiconductor Manufacturing

The concept of cyber manufacturing has become a critical element in semiconductor fabrication environments, where automation and systemization are integral, for addressing the growing complexity of processes and facilitating predictive capabilities through data integration. This study deals with the dispatching problem to minimize makespan at a wet clean station in semiconductor fabrication using artificial intelligence-enabled manufacturing control techniques. The wet clean station is comprised of sequential chemical and rinsing baths for cleaning wafer lots and multiple robot arms for lot handling. In the station, wafer lots are sequentially immersed in several baths for cleaning to eliminate residual contaminants and stains that cause defects on wafer surfaces. The station can process various types of products, and the specific order of immersion differs depending on the product type. Unlike typical dispatching problems, the information required for dispatching, such as processing times and sequences inside the station, is not available. The only available data are historical logs that record when each lot enters and leaves the station. However, even when cleaning the same product type, the duration that lots spend in the station may vary based on the combination of product types being cleaned simultaneously and the settings of the station. Thus, using the time records, this study proposes a dispatching method based on machine learning models (multiple linear regression, deep neural network, and convolutional neural network). The proposed algorithms were evaluated and verified by comparing them with CPLEX solving a mixed integer programming and dispatching methods used in a semiconductor fab in Korea. Through this experiment, we observed that the proposed models can provide dispatching solutions that are practical and effective in a rapidly changing production setting. These models have the potential to enhance the capacity of a wet clean station and will contribute to artificial intelligence-based manufacturing system control.

Light Pollution Monitoring Through Zenithal Sky Brightness at Selected Malaysian Observatories

Abstract Sky brightness magnitude is crucial for optical astronomical observations that require the dark skies area. This study focuses on the effect of artificial light pollution on astronomical observations by measuring the zenithal brightness at some selected observatories in Malaysia. Data were collected from eight observatories representing both pristine and rural areas using the Sky Quality Meter (SQM). The results show huge differences in sky brightness levels like pristine areas such as Balai Cerap KUSZA, Terengganu and the suggestion site Pusat Astronomi Borneo, Sarawak have less light pollution, making them ideal for astronomical observatory. On the other hand, due to surrounding urban growth and artificial light, rural observatories such as Stesen Hilal Permandangan Indah in Langkawi and Tapak Cerapan Bukit Geliga, Terengganu have low sky brightness. The study shows how urbanization has a significant impact and how artificial light pollution control are needed to maintain the quality of astronomical observations in Malaysia.

IoT enabled microgrid framework using a novel dispersal diffusion artificial neural network controller for PV systems and wind energy to minimize electrical faults

IoT enabled microgrid framework using a novel dispersal diffusion artificial neural network controller for PV systems and wind energy to minimize electrical faults

Performance Analysis of DC Motors With Integrated Proportional-Integral and Artificial Neural Network Control

Direct current (DC) motors are frequently utilized in various applications, and the motor's pace is affected by applied loads as it fluctuates. A power converter must be employed to control the velocity of the motor by varying the armature voltage. One of the options for the power converter is the one-quadrant DC chopper. In this case, the investigation will turn the one-quadrant chopper into a system by merging velocity and current control into the DC motor. The speed is regulated by controlling the armature voltage. This may be accomplished using a controlled rectifier. The contribution of the research is to test the effectiveness of Artificial Neural Network Control (ANN) and Proportional-Integral (PI) controllers to control the speed of a DC motor using a one-quadrant DC chopper. Therefore, due to technological advancements, the authors will utilize the training data of the artificial neural network of Proportional- Integral controllers in MATLAB's Simulink. Test results demonstrate the artificial neural network (ANN's) superior ability to regulate system response, showing enhancements in delay time, rise time, overshoot, and steady-state error compared to the PI controller. These findings underscore the potential of ANN as a more sophisticated choice for DC motor control, although further research is required to finetune its performance through rigorous training.

Improvement of the Transient Levitation Response of a Magnetic Levitation System Using Hybrid Fuzzy and Artificial Neural Network Control

Improvement of the Transient Levitation Response of a Magnetic Levitation System Using Hybrid Fuzzy and Artificial Neural Network Control