System Output Research Articles

Decoupled level and flow rate control of a two-tank system in beverage production: A comparative analysis of Fuzzy-PID and GA-PID for minimum time operation.

Due to the nonlinear characteristics of the valves and the interactions between the controlled variables, designing a control system for coupled tanks is a difficult task. This paper deals with the comparative study between Fuzzy-PID and GA-PID controllers for decoupling level and flow rate control of two tank systems for beverage factories with minimum time optimal operation. In most process control industries, each process requires multiple control variables. Here two input two output (TITO) systems are considered highly interacting multivariable control systems. The decoupling control scheme (Pre-compensator (dynamic) decoupling) is used to reduce the correlation between the controlled and manipulated variables by diagonalizing the system. The two independent SISO systems are further controlled by different controllers so that the system can trace the set point and yield a good time response. Two radically different control approaches are presented and compared for this system's dynamics, motivated by a desire to provide precise liquid-level control and regulate the flow rate. MATLAB /Simulink model and tuning algorithm (GA) are used for simulation. As the simulation result ensured, the GA-PID controller is the used for the specified system which is based on the transient and steady-state specifications. Quantitatively; the GA-PID controller has 39.167ms rise time, 8.50sec settling time, and -0.393% overshoot; whereas FLC-PID has 118.101ms rise time, 8.65sec settling time, and -0.033% overshoot. But in GA with PID controllers, the external disturbance tolerance capability of the proposed scheme, meaning robustness against external disturbance has a slight difference and FLC-PID has perfectly achieved the robustness. Depending on the result, FLC-PID has more result than GA-PID controller based on the set of specifications. Hence robustness is more important than time performances.

Data‐Driven Dynamic Event‐Triggered Load Frequency Control for Multi‐Area Interconnected Power Systems With Random Delays

ABSTRACTThis paper investigates a load frequency control issue for multi‐area interconnected unknown power systems with random communication delays. First, a model‐free adaptive control scheme is established by building an equivalent data‐relationship model between the area control error and corresponding control input. Then, a dynamic event‐triggered scheme is designed to improve resource utilization and reduce computational burden. Furthermore, random communication delays in the feedback and forward channels are considered. The results show that the proposed method is independent of any model information about the power system, only using the controlled system's control input and output data. Several simulation results validate the effectiveness of the proposed control scheme.

Analysis and Design of Constant Current/Constant Voltage Output of Array Multitransmitter Inductive Power Transfer System

ABSTRACTWireless power transfer has been widely used in unmanned aerial vehicle (UAV) charging due to its convenient and flexible noncontact charging characteristics. The working area of the uncoupler inductive power transfer (IPT) system is limited, and ZVS is easily lost in phase‐shift modulation. This paper proposes a method for constant current (CC) /constant voltage (CV) output in an array multitransmitter IPT system based on HFP modulation in a wide working area. The HFP modulation method easily realizes the current phase synchronization of multiple transmitter coils, and the size can be adjusted. At the same time, an efficient optimization model for a multitransmitter IPT system based on HFP modulation is proposed. Finally, an experimental platform for the array multitransmitter IPT system is built to verify the effectiveness of the CC/CV output realization method and the efficiency optimization model for the multitransmitter IPT system. The maximum errors in charging current and voltage are less than 2.52% and 2.1%, respectively, and the peak efficiency of the IPT system reaches 87.3%.

Modelling and controlling outputs of nonlinear systems using feedback

This study aimed to analyze methods for modeling and controlling the output of nonlinear systems using feedback, analytical methods, mathematical modeling, and differential equation theory. Key findings include the mathematical characterization of equations and the analysis of system stability and asymptotic behavior. The study explored various methods for addressing problems in nonlinear systems, emphasizing the importance of identifying effective solutions. The research highlights the significance of developing effective approaches to solving complex problems involving nonlinear systems. Feedback is essential for controlling and correcting dynamic processes in systems with nonlinearities. The study’s key finding is the mathematical characterization of equations describing nonlinear systems, providing insight into system structure and behavior under different parameters. Analyzing stability and asymptotic behavior allows for assessing system reliability and predicting long-term stability. This study contributes to the scientific understanding and development of methods for modeling and controlling nonlinear systems using feedback.

Exploring Anatomical Links Between the Crow's Nidopallium Caudolaterale and Its Song System.

Crows are corvid songbirds that exhibit remarkable cognitive control, including their ability to vocalize on command. The activity of single neurons from the crow's associative telencephalic structure nidopallium caudolaterale (NCL) is correlated with the execution of this vocal and many non-vocal behaviors. However, whether anatomical connections directly link the crow NCL to its "song system" remains unclear. To address this, we used fluorescent tracers along with histological staining methods to characterize the connectivity of the crow's NCL in relation to its song system. Consistent with previous findings in other songbirds, we found that the NCL sends dense projections into the dorsal intermediate arcopallium (AID) directly adjacent to the song system's telencephalic motor output, the robust nucleus of the arcopallium (RA). Similarly, we demonstrate dense NCL projections into the striatum engulfing the basal ganglia song nucleus "area X." Both of these descending projections mirror the projections of the nidopallial song nucleus HVC (proper name) into RA and area X, with extremely sparse NCL fibers extending into area X. Furthermore, we characterized the distribution of cells projecting from the lateral part of the magnocellular nucleus of the anterior nidopallium (MAN) to NCL. Notably, a separate medial population of MAN cells projects to HVC. These two sets of connections-MAN to NCL and MAN to HVC-run in parallel but do not overlap. Taken together, our findings support the hypothesis that the NCL is part of a "general motor system" that parallels the song system but exhibits only minimal monosynaptic interconnections with it.

Data-driven model predictive control for pharmaceutical continuous manufacturing.

This study demonstrates that developing interpretable, data-driven models for pharmaceutical continuous manufacturing is feasible using a machine learning method called Dynamic Mode Decomposition with Control (DMDc). This approach facilitates adoption within Good Manufacturing Practice (GMP)-regulated areas of the pharmaceutical industry. Furthermore, since the pharmaceutical industry needs to be more operationally efficient to be profitable and sustainable, we present a real-time monitoring strategy framework using an interpretable DMDc dynamic model in the design and tuning of a model predictive control (MPC) system for granule size control during a twin-screw granulation process. This model exhibited low computational complexity without requiring first principles knowledge, while effectively capturing nonlinear dynamics of this Multiple input multiple output (MIMO) system, with better performance (e.g., r2 > 0.93 vs.0 for D50 predictions) in the reconstruction of unseen test data in comparison with benchmark data-driven methods for system identification. The DMDc-MPC was implemented and tested on setpoint tracking and disturbance rejection and the proposed advanced process control framework guaranteed both.

WORKLOAD DYNAMICS OF MEDICAL PROFESSIONALS IN BULGARIAN HEALTHCARE SYSTEM: EVIDENCES FROM AGGREGATE DATA

Purpose. This study aims to analyze the tendencies of the workload of medical professionals in Bulgaria after the liberal reforms in the national healthcare system. Material/Methods. Basic econometric methods, such as tests for structural breaks and dynamic rows modeling based on "macro-" or aggregate data, are implemented. The period covered is 1980-2023, twenty years before and twenty-four years after introducing market elements in national healthcare. Variables such as the number of patients discharged, consultations, doctors, and nurses are used to assess the workload level. Workload estimates are derived from a linear regression model of hospital output as the dependent variable with the number of doctors and nurses in the health system as factors. The model is tested for the stability of the regression coefficients (structural breaks) dated 2000. Results. A structural break was found in 2000 in all three dynamic rows of doctors, nurses, and discharges. There was a well-defined increasing trend in the number of patients passing through the hospitals after the liberalization of the national healthcare system. In this condition, the number of medical professionals follows its own dynamics – a slight reduction for the group of doctors and a sharp decline for the nurses in the after-reforms sub-period. These facts indicate the presence of an increasing trend in the workload and intensity of medical labour. The dependence is well established, especially in the case of nurses. Conclusions. This study confirms the hypothesis that the liberalization of the Bulgarian healthcare system, together with open-ended payment systems, caused an intensive growth of inpatient system output and workload. The expansion, especially for hospital facilities, which is not supported by a relevant number of medical professionals, threatens workplace performance and the quality of medical services.

Design and Analysis of Non-Contact Power Supply System for Wireless Monitoring Node of Ship's Water Lubricated Bearing

The wireless sensor monitoring node for the online monitoring of various parameters of the ship's water-lubricated bearing rotates with the shaft during its operation. Therefore, how to ensure a continuous power supply to the node under the rotating working condition is an urgent problem to be solved. In this paper, a non-contact power supply method is adopted to design a magnetically coupled inductive non-contact power supply system, and experimental verification is carried out. Firstly, the non-contact power supply method is described, a node energy consumption model is established for the existing wireless monitoring nodes, and the energy consumption test is conducted. Then, a magnetically coupled inductive non-contact power supply system is designed, and the output of the system circuit is simulated and analyzed. Finally, the experimental test of the power supply system is carried out to study various factors that affect the performance of the power supply system. The research results show that the rotational speed is not the key factor affecting the power supply effect of the system, and the air gap of the coupling mechanism is the key factor affecting the power of the power supply system. The output power of the power supply system increases with the increase of the air gap, and the output efficiency decreases with the increase of the air gap. Considering the influence of the efficiency factor of the power supply system, the 5mm air gap is the optimal working air gap of the power supply system, and the system output power can reach 15.9 W at this time.

Machine Learning Prediction of Photovoltaic Hydrogen Production Capacity Using Long Short-Term Memory Model

The yield of photovoltaic hydrogen production systems is influenced by a number of factors, including weather conditions, the cleanliness of photovoltaic modules, and operational efficiency. Temporal variations in weather conditions have been shown to significantly impact the output of photovoltaic systems, thereby influencing hydrogen production. To address the inaccuracies in hydrogen production capacity predictions due to weather-related temporal variations in different regions, this study develops a method for predicting photovoltaic hydrogen production capacity using the long short-term memory (LSTM) neural network model. The proposed method integrates meteorological parameters, including temperature, wind speed, precipitation, and humidity into a neural network model to estimate the daily solar radiation intensity. This approach is then integrated with a photovoltaic hydrogen production prediction model to estimate the region’s hydrogen production capacity. To validate the accuracy and feasibility of this method, meteorological data from Lanzhou, China, from 2013 to 2022 were used to train the model and test its performance. The results show that the predicted hydrogen production agrees well with the actual values, with a low mean absolute percentage error (MAPE) and a high coefficient of determination (R2). The predicted hydrogen production in winter has a MAPE of 0.55% and an R2 of 0.985, while the predicted hydrogen production in summer has a slightly higher MAPE of 0.61% and a lower R2 of 0.968, due to higher irradiance levels and weather fluctuations. The present model captures long-term dependencies in the time series data, significantly improving prediction accuracy compared to conventional methods. This approach offers a cost-effective and practical solution for predicting photovoltaic hydrogen production, demonstrating significant potential for the optimization of the operation of photovoltaic hydrogen production systems in diverse environments.

Exhaust waste heat recovery system using thermoelectric generator: a prospective of novel simulation technique toward low-carbon technology

Abstract This study numerically optimizes energy harnessing in vehicle engines using three heat exchanger fin designs: wall to wall, pyramid, and hexagonal. Two thermoelectric generator (TEG) array configurations are compared for electrical power generation. Results show the wall-to-wall fin provides the highest heat transfer, producing 161 W of power from 13 590.53 W of heat. Both TEG configurations generate similar output, with the series offering slightly higher voltage. The flow direction has minimal impact, but increasing the number of heat exchangers boosts efficiency. The total system output reaches 27 763.60 W with a four-parallel exchanger setup and an efficiency of 1.72.

Enhancing Site Reliability Engineering (SRE) Observability: A Comprehensive Approach

The subject of applying software engineering techniques to IT operations to create scalable and highly reliable software systems is known as site reliability engineering, or SRE for short. To preserve system’s health, it heavily emphasizes automation, ongoing improvement, and efficient incident response. Observability, is the tracking and examination of system outputs in the form of metrics, logs, and traces with the goal of providing insight into the system's internal state, is one of the fundamental principles of SRE. This keeps systems reliable and effective by enabling the early detection and resolution of problems. SRE and observability work together to help businesses achieve high IT operations performance and reliability. The contemporary IT environment is becoming more and more complex as industries growing infrastructure use on-premises and cloud hosting, adding microservice design along with traditional monolithic application architectures. In the pursuit of observability, this diversity presents unique difficulties for SRE. This article sheds light on the significance of SRE observability, tools and tactics used by enterprises, and how site reliability engineering could be improved across numerous industries with the use of an open-source framework like Open Telemetry to strengthen observability.

The Use of Hierarchical Temporal Memory and Temporal Sequence Encoder for Online Anomaly Detection in Industrial Cyber-Physical Systems

This study introduces a novel, practical approach for designing a hierarchical online anomaly detection system for industrial cyber-physical systems. The proposed method utilizes the Hierarchical Temporal Memory (HTM) unsupervised learning algorithm, which requires data to be encoded as sparse binary distributed representations (SDRs). A new SDR encoding method termed the temporal sequence encoder (TSSE) is presented to convert system outputs into SDRs. This approach enables HTM to retain high memory capacity and robust performance when processing data streams of slowly varying physical measurements, typical of many industrial processes. The effectiveness of the proposed system is demonstrated on the Secure Water Treatment (SWaT) dataset, which comprises data collected from a fully operational, scaled-down water treatment plant. The system achieves a recall of 0.906, a precision of 0.935, and an F1 score of 0.92 on SWaT. Compared to previous methods, our approach achieves state-of-the-art recall (~5.3% improvement), along with competitive precision and F1 score, by learning in an online manner without the need for expensive dataset collection, labeling, or retraining phases. These findings suggest that the proposed online anomaly detection method can be effectively applied to a broad range of water treatment and large-scale industrial cyber-physical systems.

Green Economic Efficiency and Coordinated Development in the Bohai Rim Region: Addressing Regional Disparities for Sustainable Innovation and Economic Transformation

The Bohai Rim Region plays a crucial role in the economy of northern China. Historically, the area’s development has been driven by resource-intensive industries, necessitating urgent structural transformation. In response, the government has actively promoted a green economic model. This study evaluates the efficiency of green economic performance and total factor productivity (TFP) across five provinces in the region, incorporating regional innovation capabilities and green innovation outputs into the green economy input–output system. The results show that green economic efficiency (GEE) has improved across regions, with higher technological advancement leading to greater improvements in green TFP. Additionally, the economic disparity between provinces and municipalities has been decreasing. This study indicates that, while inter-provincial differences are widening, intra-regional disparities are narrowing. Meanwhile, this study provides a foundation for regional economic integration and policymaking in the Bohai Rim, offering insights into balancing economic growth with environmental sustainability.

A Model for Sustainable Development in Territorial Production Systems

ABSTRACTThe territorial production system demand innovation and adaptability to provide the majority of decisions, especially when splitting up available resources among productive sectors by the lens of sustainable development. The system's economic units evolve in dimensional profile, technical execution, and management of productive process to approach the worldwide benchmark for sustainability, taking into account not only the environment, society, and economy, but also the twin transition to digitalisation. The study addresses the issue of allocation resources across territory's productive sectors by an inter‐sectorial model that accounts for both external and endogenous sustainability performance criteria. The model is developed starting from the Leontief input–output system and tested in the Italian production system. The sustainability perspective's individualization of the sector's top performer and the quantity of resources to be allocated in response to their respectable sustainable performance are provided as outputs. The political repercussions, model's potential and strengths, and its limits with regard to the sustainable development of territorial production frame are discussed in the conclusions.

Output‐Based Dual Dynamic Periodic Event‐Triggered Control for Nonlinear Interconnected Cyber‐Physical Systems Under Deception Attacks

ABSTRACTThis article considers the problem of output‐based dual dynamic periodic decentralized event‐triggered control (DPDETC) for nonlinear interconnected cyber‐physical systems (CPSs) subject to deception attacks. Taking into account the conflict between the limited network resources and the growing bandwidth demand, a dual dynamic periodic event‐triggered scheme (DPETS) is designed to reasonably decrease unnecessary data transmission, which includes sensor‐to‐controller (S‐C) and controller‐to‐actuator (C‐A) communication channels. For resisting the impact of attacks, a deception attacks model following Bernoulli distribution is introduced for nonlinear interconnected CPSs. Then, a new decentralized sampled‐data observer is proposed based on output‐dependent periodic event‐triggered conditions, such that the system output under deception attacks can be estimated. By means of decentralized observer and observer‐dependent DPETS, a new decentralized output feedback controller is established. The designed DPDETC strategy can guarantee that the nonlinear interconnected CPSs are mean square stability. Finally, simulation results are performed to validate the superiority of the designed DPDETC scheme.

Output tracking problem for a class of 3-D hyperbolic PDEs

The output tracking problem (OTP) refers to the procedure of determining an input control that ensures the system output follows a specified trajectory. In this paper, we investigate the OTP for controlled systems governed by a class of 3-D hyperbolic partial differential equations (PDEs), aiming for an exact alignment between the system output and the desired trajectory. Utilizing backstepping techniques, semigroup theory, the Hahn-Banach theorem, Laplace transforms, and Green's functions, our approach begins by establishing the OTP solution as a bounded feedback law, with Green's functions allowing the solution of a Cauchy-Euler equation. This is followed by deriving a detailed formulation for the complete state of the corresponding control problem. Finally, the approach provides an explicit formula for the OTP solution, using the desired trajectory and the solutions of specific kernel PDEs.

Proposal of a Correlation Model Integrating FDRM and CLSCM Practices and Performance Measures: A Case Study from the Automotive Battery Industry in Brazil

The field of closed-loop supply chain management (CLSCM) seeks to replace the linear flow of materials and energy with a cyclical model in which the outputs of the production system become inputs to the same system, thus closing the cycle of materials and energy within the supply chain. Current literature on CLSCM reports a wide variety of practices, and combining these practices with environmental performance measures is an ongoing challenge, mainly because results from these practices are often diffuse and linking them with performance results is not a straightforward task. This paper addresses this problem by proposing a model to prioritize CLSCM practices and performance measures. The correlation model integrating the fuzzy direct rating method (FDRM) and CLSCM practices and performance measures was tested in a real company that is part of a closed-loop supply chain that recycles lead obtained from automotive batteries in Brazil. The results allowed the identification of which management practices are more relevant to the organization by correlating their impact with performance measures. The most relevant practices for the company under study were demand forecasting, with 21.68% of relative importance, followed by reverse logistics practices (21.15%) and production planning and control (18.16%). Another relevant finding is that upstream performance measures account for 77.72% of the company’s CLSCM performance.

Estimation of powder factor in mine blasting: feasibility of tree-based predictive models

Drilling and blasting is a process frequently used in rock-surface and deep excavation. For a proper drilling plan, accurate prediction of the amount of explosive material is essential to reduce the environmental effects associated with blasting operations. This study introduces a series of tree-based models, namely extreme gradient boosting machine (XGBoost), gradient boosting machine (GBM), adaptive boosting machine (AdaBoost), and random forest (RF), for predicting powder factor (PF) values obtained from blasting operations. The predictive models were constructed based on geomechanical characteristics at the blasting site, blasting pattern parameters, and rock material properties. These tree-based models were designed and tuned to minimize system error or maximize accuracy in predicting PF. Subsequently, the best model from each category was evaluated using various statistical metrics. It was found that the XGBoost model outperformed the other implemented techniques and exhibited outstanding potential in establishing the relationship between PF and input variables in the training set. Among the input parameters, hole diameter received the highest significance rating for predicting the system output, while the point load index had the least impact on the PF values.

Design and Implementation of a Wireless Power Transfer System Using LCL Coupling Network with Inherent Constant-Current and Constant-Voltage Output for Battery Charging

The constant current followed by constant voltage (CC-CV) charging method is commonly employed for battery charging, effectively extending battery life and reducing charging time. However, as charging progresses, the battery’s internal resistance increases, complicating the charging circuit. This paper designs a wireless power transfer system utilizing an LCL coupling network for battery charging. The system employs frequency modulation (FM) to manage its CC and CV output characteristics at two fixed frequency points. The influence of the LCL coupling network parameters on system output characteristics was investigated using MATLAB. A simulation model was developed in the PSIM environment to validate the CC and CV output characteristics. The simulation results show that the system has load-independent constant-current and constant-voltage characteristics at two different frequency points. In order to verify the theoretical analysis, an experimental platform was also established. Experimental results demonstrate that the proposed system operates effectively in CC mode, maintaining constant output current across various loads, while in CV mode, it effectively regulates output voltage for different loads. The designed frequency modulation controller ensures a rapid response to sudden changes in load resistance, regardless of operating in CC or CV modes.

Modelling of the gas purification system for flue gas acid production

AbstractEstablishing a gas pressure model of the purification system to accurately estimate the system output pressure is a powerful guarantee to ensure the stable and efficient operation of the acid production process. This paper presents a study on the establishment of gas pressure models for three facilities in the purification system at the Guixi Smelter in China, including a pulse jet fabric filter, an electrostatic precipitator, and a drying tower. Through analysis of the operating mechanisms of these facilities, pressure mechanism models are established for the gas flows. Considering the nonlinearity and time‐varying nature of the model, an improved extended Kalman filter (EKF) algorithm is proposed to perform online identification of the unknown parameters within the model. Compared to the first‐order EKF, the improved algorithm achieves significantly better performance without incurring additional computational overhead. Field experiments on pressure estimation at the acid production site validate the reasonableness of the established models, as well as the efficacy of the proposed identification algorithm.