Application Of Control System Research Articles

SEAG: A novel dynamic security risk assessment method for industrial control systems with consideration of social engineering

The development of information and communication technology and its wide application in industrial control systems (ICSs) has brought a growing number of security risks to ICSs. Quantifying and dynamically assessing the security risks of ICSs is of great significance to protect ICSs from cyber attacks. Current risk assessment methods, however, do not take into account social engineering (SE) attacks and the potential cyber-to-physical risks associated with cyber attacks. To address these issues, we propose a novel method for the dynamic security risk assessment of ICSs, called SEAG. Specifically, the scheme first extends and modifies three metrics in the common vulnerability scoring system to four metrics for objectively calculating the exploit probability of SE. Then, we construct the attack graph by relying on the knowledge graph that integrates three kinds of knowledge including SE knowledge, common vulnerability knowledge, and control system knowledge. In addition, we combine real time attack data that causes system performance loss with the industrial protocol function code attack detected by the intrusion detection system to accurately quantify the potential cyber-to-physical risks associated with cyber attacks. This method allows us to dynamically assess the security risks of ICSs in real time. Finally, the method is verified by one simulation testbed, which shows the effectiveness and accuracy of the proposed method for dynamic quantitative evaluating security risks of ICSs.

Application of Medical Record Quality Control System Based on Artificial Intelligence

In this study, we used artificial intelligence (AI) technology to explore for automated medical record quality control methods, standardize the process for medical record documentation, and deal with the drawbacks of manually implemented quality control. In this study, we constructed a medical record quality control system based on AI. We first designed and built, for the system, a quality control rule base based on authoritative standards and expert opinions. Then, medical records data were automatically collected through a data acquisition engine and were converted into structured data through a post-structured engine. Finally, the medical record quality control engine was combined with the rule base to analyze the data, identify quality problems, and realize automated intelligent quality control. This system was applied to the quality control of medical records and five quality control points were selected, including similarities in the history of the present illness, defects in the description of chief complaints, incomplete initial diagnosis, missing in formation in the history of menstruation, marriage, and childbirth, and mismatch between the chief complaints and the history of the present illness. We randomly selected 2 918 medical records of patients discharged in January 2022 to conduct AI quality control. Then we organized medical record quality control experts to conduct an accuracy review, made a comparison with previous manual quality control records, and analyzed the results. The number of quality problems that were verified in the accuracy review was taken as the gold standard and receiver operating characteristic (ROC) curves were drawn for the 5 quality control points. According to the accuracy review performed by medical record quality control experts, the accuracy of AI quality control reached 89.57%. For the sampled medical records, the results of AI quality control were compared with those of previous manually performed quality control and only one problem detected by manual quality control of the sampled medical records was not detected by the AI quality control system. The number of medical record quality problems correctly detected by AI quality control was about 2.97 times that of manual quality control. Analysis of the ROC curves showed that the AUC of the five quality control points of the AI quality control system were statistically significant (P<0.05) and all the AUC values approximated or exceeded 0.9. In contrast, results obtained through manually performed quality control found significant AUC (0.797) for only one quality control point-similarities in the history of present illness (P<0.05). Comparison of the AUC values of the two quality control methods showed that AI quality control system had an advantage over manually performed quality control for the five quality control points. Through the application of medical record quality control system based on AI, efficient full quality control of medical record documentation can be achieved and the detection rate of quality problems can be effectively improved. In addition, the system helps save manpower and improve the quality of medical record documentation.

Neuro-controller implementation for the embedded control system for mini-greenhouse

Control of a certain object can be implemented using different principles, namely, a certain software-implemented algorithm, fuzzy logic, neural networks, etc. In recent years, the use of neural networks for applications in control systems has become increasingly popular. However, their implementation in embedded systems requires taking into account their limitations in performance, memory, etc. In this article, a neuro-controller for the embedded control system is proposed, which enables the processing of input technological data. A structure for the neuro-controller is proposed, which is based on the modular principle. It ensures rapid improvement of the system during its development. The neuro-controller functioning algorithm and data processing model based on artificial neural networks are developed. The neuro-controller hardware is developed based on the STM32 microcontroller, sensors and actuators, which ensures a low cost of implementation. The artificial neural network is implemented in the form of a software module, which allows us to change the neuro-controller function quickly. As a usage example, we considered STM32-based implementation of the control system for an intelligent mini-greenhouse.

A hybrid clustering-based type-2 adaptive neuro-fuzzy forecasting model for smart control systems

The practical applicability of smart control systems suffers from the existence of uncertainty in the physical parameters, sensor measurements and external excitations. Effective methods and its ease of implementation are required in the real-time active control strategies. To this end, in this research, we introduce an overlapping clusters and Multilayer Extreme Learning Machine (ML-ELM)-aided interval type-2 Takagi-Sugeno fuzzy inference system for vibration mitigation of smart structures. The combined Fuzzy C-Means (FCM) and imperialist competitive algorithm (ICA) with respect to degree of fuzzy overlap between clusters was utilized for characterizing the upper and lower membership functions forming the antecedent part. Furthermore, indeterminacy was modeled as footprint of uncertainty (FOU) and automatically formed based on statistical characteristics of soft clusters. The consequent parameters were adjusted by hybridization of ML-ELM and overlapping clusters, then the adaptivity was guaranteed. The predictive accuracy and generalization capability of the proposed method were demonstrated over the estimation of active control effort. Finally, to prove the practicability of the implemented framework, it was applied to four high-rise, mid-rise and low-rise benchmark building structures equipped with active mass damper (AMD), active tendon and active bracing system (ABS). The obtained results demonstrated the higher predictive accuracy and generalization ability of the presented data-driven control scheme compared to the other standard technical machine learning strategies. This practical guideline is easy to implement and automates control engineering tasks in real-time applications, solving one of the most challenging aspects of smart control systems.

Experimental study of heat rejection performance of water membrane evaporator in vacuum environment

The water membrane evaporator (WME) is a kind of consumable heat sink proposed for application in spacecraft thermal control systems. It has great potential to be applied as a primary or auxiliary heat sink in the thermal control systems of spacecraft such as the Mars extravehicular spacesuit, the near space vehicle and others. In the space high vacuum pressure atmosphere, the water evaporates to the environment and absorbs the latent heat of vaporization to achieve the purpose of cooling the fluid. The heat rejection of WME is a very complex physical process of heat and mass transfer, and its performance is affected by the coupling of several parameters. However, the influence laws of various operating and structural parameters of the WME on its performance have not been adequately investigated in previous studies. Thus leading to the considerable challenge of optimizing design of WME and its usage strategies. In this paper, five groups of tests were conducted on three WME prototypes with different structures and the effect laws of temperature, backpressure, circulating water flow rate, and hollow fiber (HoFi) tube distance on their heat rejection and water consumption rate were obtained. The WME based on porous HoFi membranes has a very considerable heat rejection capacity. Several WME prototypes tested in this study obtained a maximum heat rejection of 589 W/m2 at the inlet water temperature of 45 °C. The structure of HoFi membrane tubes and parameters such as inlet flow rate have a significant effect on its performance. The heat rejection per unit area for Case 1 is about 3.76 times that of Case 4 at the inlet water temperature of about 45 °C and the backpressure of about 700 Pa. In addition, the dimensionless analysis was also carried out, and the correlations that can be used to predict the heat rejection performance were obtained. These results are of guiding significance for optimizing the structural design, heat rejection performance and operating strategy of WME.

Research on seat active noise control of driver's cab ground platform based on far-point secondary sound sources

Driver's cab noise has an impact on the long-term driving environment and physical health of drivers. The locomotive driver's cab has significant low-frequency noise due to structure-borne sound, which is suitable for effective suppression by the active noise control system. However, on the one hand, the seat headrest system hinders the driver's movement space, on the one hand, the active noise control for space zones in the driver's cab has limited noise reduction effect due to the large control area. Therefore, a ground research test platform of driver's cab for active noise control was designed, and its structure and control method were introduced in this study. Based on the near-point seat active noise reduction control system, the coherence of the reference acoustic signal of the chair to the acoustic signal at the driver's left ear is very close with the one on the floor, and both of their active noise reduction in the full frequency band from 50 Hz to 5000 Hz are very close (about 4.5 dB (A)). In order to alleviate the interference of the headrest system installed with secondary sound sources on the driver's operating space, a far-point active noise reduction control system was proposed. The results showed that when two reference microphone signals from the seat back and floor position are used at the same time, the active noise reduction next to the driver's left ear in the full frequency band of 50 Hz to 5000 Hz is 4.0 dB(A), which is still significant. This provides a useful reference for the engineering application of active noise control systems in rail driver's cabs.

Structural and Parametric Synthesis of Neural Network Controllers for Control Objects with Limiters

The article presents a methodology for the synthesis of digital control systems for nonlinear objects with limiters under conditions of incomplete information. Closed-loop tracking systems with negative feedback are considered. Artificial neural networks are proposed to build a controller, which is included in series with the control object. This approach is effective when known classical methods do not allow to synthesize control. This is the case, for example, if the mathematical model is essentially nonlinear and is not fully defined. The developed methods allow us to expand the class of technical systems, for which the direct (without using various kinds of simplifications) synthesis of control laws that are close to optimal is possible. In addition, neural network controllers possess the properties of robustness, adaptivity, and are initially digital, i.e. those qualities, which are very much in demand in practice. In article main attention is given to such problems, as a choice of neural network structure for neural simulator and neural controller, construction of training sample, ensuring convergence of the process of weights correction. For training neural networks the method of back propagation of error is used as a basic one. The effectiveness of the proposed technique is demonstrated by the example of the synthesis of a neuroregulator for a specific technical object and its comparison with classical control systems. It should be noted that today neural network technologies are widespread enough in various spheres of activity. The successes demonstrated in sound processing, image processing, automatic translation, in navigation systems, in big data processing are impressive. However, their application in automatic control systems is not so widespread. The authors of this article believe that the potential of artificial neural networks can be used in this direction. It should be understood that the use of neural networks is effective only under certain conditions and properties of the control object.

Android Application for Automatic Door Control System using Body Temperature Measuring Sensor

One way for people to prevent the transmission of Covid-19 in indoor and outdoor activities is by using body temperature detection. People who have a body temperature higher than the measurement limit are prohibited from mingling with other people because they have a high probability of being infected with the virus and can transmit it to other people. Manual checks in public places can cause officers to potentially contract the virus and can spread it to other visitors more quickly. Therefore, it is necessary to develop an automatic body temperature measurement system by displaying the results of the measurements on a screen and automatic door as a further prevention step, and temperature data can be stored in a database to help make the right decisions in preventing the spread of Covid-19 in that place. The implementation stages of the activities include: Data Collection, Preparation of Technology Design and Purchase of Materials, Product Manufacturing, Product Testing, Product Evaluation. An Android application for an automatic door control system using a body temperature measurement sensor can be developed into an even better tool. The development of this prototype will be developed by adding a PIR sensor and system modifications. The addition of a PIR sensor is used to increase the security of the tool to detect only human body temperature which can be recorded in order to minimize data errors. The system modifications made can be used for initial examination assessments with body temperature and also the addition of a system for measuring body weight and height to complete the assessment data.

Tikhonov-Tuned Sliding Neural Network Decoupling Control for an Inverted Pendulum

This paper introduces the concept of intelligent control using Tikhonov regularization for nonlinear coupled systems. This research is driven by the increasing demand for advanced control techniques and aims to explore the impact of Tikhonov regularization on these systems. The primary objective is to determine the optimal regularization term and its integration with other control methods to enhance intelligent control for nonlinear coupled systems. Tikhonov regularization is a technique employed to adjust neural network weights and prevent overfitting. Additionally, the incorporation of ReLU activation function in the neural network simplifies thearchitecture, avoiding issues like gradient explosion, and optimizes controller performance. Furthermore, sliding surfaces are designed to improve control system stability and robustness. The proposed Tikhonov-tuned sliding neural network (TSN) controller ensures both stability and superior system performance. The methodology emphasizes the importance of determining optimal neural network weights and regularization terms to prevent overfitting, facilitating accurate predictions in inverted pendulum control system applications. To assess the functionality and stability of TSN, this paper employs simulations and experimental implementations to control both the rotary inverted pendulum and the arm-driven inverted pendulum. The results indicate that the proposed TSN methodologies are effective and feasible.

Feasibility analysis of the application of building automation and control system and their interaction with occupant behavior

Occupant behavior is among the main causes for the mismatch between simulated and in-use energy performance of buildings. One of the strategies considered capable of reducing user’s behavior induced energy consumption, while increasing indoor environmental quality is the application of Building Automation and Control Systems (BACS). In this study, three building user’s profiles have been considered depending on their energy consumption. The energy savings due to BACS class increase have been calculated, and a cost–benefit analysis (CBA) has been performed to evaluate the feasibility of different scenarios. Additional co-benefits perceived by the individuals have been accounted for in the form of willingness-to-pay (WTP). The methodology is applied to two case studies: a nearly zero-energy building (NZEB) rural single-family house and a recently renovated dwelling in an apartment block. The results show that the main reductions are achieved by the users’ behavior alone, and the adoption of BACS is economically feasible only when an incentive program is in place, and the WTP is repeated as a recurrent co-benefit over the years. In particular, relying only on energy reduction due to higher BACS class introduction is not economically desirable. The greatest savings are achieved by the behavioral change of the user when coupled with BACS, supporting their potential role in improving user’s energy literacy. Finally, incentive schemes are necessary to reduce the investment costs of such projects, being these the most influential variables in the feasibility of BACS applications.

Development and application of novel sun-tracking control system for parabolic trough solar collector on distributed energy

Development and application of novel sun-tracking control system for parabolic trough solar collector on distributed energy

Research and application of main control system for 2MW direct-driven PMSG-based wind turbine

Research and application of main control system for 2MW direct-driven PMSG-based wind turbine

Active Reduction of Apparent Cable Capacitance.

This paper presents a method for reducing apparent cable capacitance seen by sensors. The proposed method is designed for sensing in extreme environments including ultra-high temperatures, but can be applied in benign environments as well. By reducing the cable capacitance, high speed signals can be transmitted over longer distances, allowing the application of advanced control systems that require high bandwidth data for stable operation. A triaxial cable with an associated guard circuit is developed that actively reduces cable capacitance while also rejecting extraneous electric and magnetic interference on the signal. The active capacitance reduction method developed is tested experimentally and shown to reduce apparent cable capacitance to two percent of the static value.

GWO based Robust Stabilization of DC Motor Fractional Order Speed Control System with Interval Coefficients

Robust stability analysis (RSA) is of significant concern for the robust behaviour of real-world control system applications. A stabilization strategy that assures stability and exhibits robust performance for a specified limit of system perturbations is necessary. This article presents an optimal robust stabilization method for a closed loop fractional order proportional integral derivative (FOPI^λD^µ) system involving DC motor with interval parametric uncertainty. To determine the optimum value of parameters for a FOPI^λD^µ controller to control the speed of a DC motor, Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Nelder-Mead (NM), Jaya and Whale Optimizer Algorithm (WOA) are applied with the same objective function involving ITAE criterion. FOPI^λD^µ offers two additional tuning parameters unlike a nominal PID controller and hence the former gives more flexibility in controller design than the latter in terms of transient response. The FOPID controller provides a faster closed-loop output augmented with improved robust properties of the system. Despite inherent non-linearities and time variation in system parameters, FOPI^λD^µ controllers depict enhanced performance. Using the concept of conformal mapping, robust stability analysis of fractional order polynomials is done with uncertain interval structure using Vertex and Edge theorem. Based on the value set, this paper demonstrates numerical and graphical optimal robust stability analysis of a system with variations observed in five parameters, considering the minimum argument root of the polynomial of the aforementioned closed-loop system.

Analisis Sistem Pengendalian Piutang Terhadap Piutang Tak Tertagih pada PT. Abadi Sakti Mitra Mandiri

Uncollectible accounts are one of the causes of losses in the company because they can hinder the process of entering finance into the company. Required control of good accounts receivable so that the company does not experience losses. This study aims to determine the application of a COSO-based internal control system for accounts receivable at PT. Abadi Sakti Mitra Mandiri. The method used in this study uses a grounded research approach. Grounded research is a research method on facts and uses comparative analysis with the aim of establishing concepts, proving theories, and developing theories, collecting, and analyzing data at the same time. Based on the results of research conducted, the internal control system implemented by PT. Abadi Sakti Mitra Mandiri has not been good enough. This is indicated by the increase in the level of bad debts caused by the low level of internal control in the elements of control activity, namely the existence of multiple jobs so that the risk assessment in the provision of receivables becomes ineffective. Therefore, it is necessary to have additional employees in marketing and selling products as well as a special section for the analysis of receivables, so that the receivables control process can run effectively and efficiently. The company should also provide fines to customers who delay payment of their receivables.

EP02.24: Application of prenatal ultrasonic artificial intelligence quality control system in Shenzhen

EP02.24: Application of prenatal ultrasonic artificial intelligence quality control system in Shenzhen

Analysis of Internal Control on Bai' Taqsith Financing in Sharia Cooperatives

This study aims to analyze the application of the internal control system in bai' taqsith financing activities at the Ammar Madani Ummah Sharia Consumer Cooperative by discussing in detail the following elements of internal control: elements of separation of duties and responsibilities, authority systems and recording procedures, healthy practices and employees of appropriate quality. This research is a type of case study research with a qualitative approach with the type of data used, namely primary data and secondary data. Data collection methods through observation, interviews, documentation and literature studies. The results showed that the internal control system applied to the Ammar Madani Ummah Sharia Consumer Cooperative was quite good and fulfilled the elements of internal control in the form of elements of separation of duties and responsibilities, authority systems and recording procedures, healthy practices and employees of appropriate quality. However, there are several weaknesses that occur, namely the task trap by the service function which doubles into the cashier function. The occurrence of inefficiencies in the procedures carried out due to data re-input. Authorization systems are less reliable, usually only through whatsapp media.

An Assessment of The Effect of The Average Speed Enforcement Systems on Lithuanian Roads

The speeding is a major road safety problem on the roads of Lithuania and many other countries. To reduce and control the speed of vehicles, engineering measures are installed on the roads of Lithuania and other countries – intelligent instantaneous and average speed enforcement systems, as well as many other engineering and structural speed reduction measures. The article presents good practices in the use of average speed enforcement systems, assessment of their effect on road safety. The article analyses the assessment results of the effect of the average speed enforcement systems installed on 25 sections of roads in Lithuania that have been operating for four years. Calculations of the effect factor of the installed average speed enforcement systems on road safety were performed using the before-and-after (B&amp;A) method with a comparison group. Studies on the effect of application of speed control systems provide conditions for evaluating road safety, i.e., changes in road accidents linked to the effectiveness of implemented measures. The conclusions of the conducted study presented the calculations of the efficiency coefficients of the average speed enforcement systems on Lithuanian main and national roads. The analysis of the results of the conducted study allows stating that the installed average speed enforcement systems on 25 sections of Lithuanian roads increase overall road safety by 47%, reduce the number of collisions with animals by 80%, and the number of collisions with vehicles by 35%, as well as the number of other road accidents by 56%.

Research into the Beetle Antennae Optimization-Based PID Servo System Control of an Industrial Robot

Industrial robot speed control remains a critical aspect for efficient operations, especially given the challenges of nonlinearity and multivariable characteristics inherent to servo motor control systems, as well as energy inefficiencies due to a lack of automatic speed control. This study refines an existing control algorithm, beetle antennae optimization (BAO), by integrating elements of particle swarm optimization (PSO) and a beetle antennae search algorithm (BAS), further enhanced by chaos mapping and an adaptive weighting factor. These modifications aim to improve the algorithm’s search capabilities and mitigate the risks of settling into local optima. Unlike previous iterations, this study includes rigorous dynamic and stability analyses focusing on key performance metrics such as settling time, overshoot, and steady-state error. Comparative Simulink/MATLAB modeling demonstrates that the enhanced BAO algorithm significantly outperforms traditional PID control, BAS, and adaptive weighted-PSO in reducing static error, overshoot, and adjustment time under various conditions, including scenarios with external disturbances. Our results indicate a 60% improvement in the optimization performance of speed curve metrics, confirming the enhanced efficacy and robustness of the robotic control system. This research offers valuable insights into the advantages of the refined BAO algorithm, providing a comprehensive basis for its practical application in industrial robotic control systems.

Artificial neural network approach for MHD mixed convection and entropy generation in a vertical annulus with time periodic thermal boundary conditions in the presence of radial and induced magnetic field

Entropy generation with time periodic thermal boundary conditions have numerous applications in heat exchangers, electronic devices, automatic and thermal control systems. The present research focuses on mixed convective hydromagnetic electrically conducting fluid flow in a vertical annulus using thermodynamic concepts, in the presence of induced magnetic field along with entropy generation and time periodic boundary conditions. The governing equations related to the present model are solved using finite element method. Also to predict heat transport features in the vertical annulus, artificial neural network – backpropogated Levenberg Marquardt algorithm is adopted. The steady and periodic profiles of velocity, skin friction, temperature and induced magnetic field are illustrated graphically for specific range of pertinent parameters to demonstrate significant aspects of the results. It is noted that increase in Hartmann number enhances the entropy generation near the outer cylinder, which illustrates that the fluid friction and magnetic field influence is maximum at the outer cylinder surface. The Bejan number in the annulus reduces with increase in viscous heating parameter. Results indicate that raise in the Prandtl number diminishes the thermal boundary layer thickness. As a result of decrease in the intensity of heating the boundary walls, a raise in Strouhal number lowers the fluid temperature profile. The generalized correlation is expressed to predict heat transport features in the annulus using artificial neural network – backpropogated Levenberg Marquardt algorithm.