Control Signal Research Articles

Closed-loop Speed Control for a Three-Phase Alternating-Current Motor using a Modbus Network

This paper presents a closed-loop controller for speed regulation of a three-phase induction motor by means of a Modbus network, which is compound only by industrial-purpose hardware, namely two programmable logic controllers, a human-machine interface and a variable-frequency drive. This represents a step beyond the state of the art because the results in similar works reported in the literature were obtained using personal computers, academic-purpose hardware or a combination of both academic-industrial technologies. The speed regulation was achieved through a proportionalintegral controller executed in a programmable logic controller, whose feedback was supplied by another similar device completely dedicated to read and decode an industrial optical encoder; furthermore, the control signals were commanded to the alternating current motor using a variable-frequency drive. The proposed control network was tested through diverse regulation experiments, under different conditions, in order to assess its effectiveness and robustness. The obtained results were successful in terms of accuracy of the speed set-point tracking in comparison to the most similar works of the state of the art, namely zero steady-state error was achieved, 12 seconds earlier for 75% larger references and 22% less RMSE than other works, which are the best results reported so far. Such successful achievements encourage the later implementation of more advanced control techniques on three-phase-motor based industrial machinery using only industrial-purpose hardware.

Reversible control of kinase signaling through chemical-induced dephosphorylation

The coordination between kinases and phosphatases is crucial for regulating the phosphorylation levels of essential signaling molecules. Methods enabling precise control of kinase activities are valuable for understanding the kinase functions and for developing targeted therapies. Here, we use the abscisic acid (ABA)-induced proximity system to reversibly control kinase signaling by recruiting phosphatases. Using this method, we found that the oncogenic tyrosine kinase BCR::ABL1 can be inhibited by recruiting various cytoplasmic phosphatases. We also discovered that the oncogenic serine/threonine kinase BRAF(V600E), which has been reported to bypass phosphorylation regulation, can be positively regulated by protein phosphatase 1 (PP1) and negatively regulated by PP5. Additionally, we observed that the dual-specificity kinase MEK1 can be inhibited by recruiting PP5. This suggests that bifunctional molecules capable of recruiting PP5 to MEK or RAF kinases could be promising anticancer drug candidates. Thus, the ABA-induced dephosphorylation method enables rapid screening of phosphatases to precisely control kinase signaling.

Adaptive Fuzzy Filter-based L 1 Adaptive Controller Design for Electromechanical Actuator Containing Uncertainties and Disturbances

Conventional L 1 adaptive controller utilizes high adaptation gain to quickly adapt time varying uncertainties and disturbances. However, it contaminates the control signal with high frequency components. To eliminate this high frequency signal, it uses a low pass filter with fixed structure. Yet, the main problem with this fixed structured filter is that it may cancel the estimated values of uncertainties and disturbances along with the high frequencies. To overcome this problem, in this research work, an adaptive fuzzy low pass filter is proposed in place of the fixed structured filter of the L 1 adaptive controller. In this proposed research work, the filter changes its structure according to the time varying uncertainties and disturbances. Hence, the contributory part of this work is that the proposed adaptive fuzzy filter efficiently cancels out high frequency components without nullifying the estimated values of uncertainties and disturbances from the control signal. The stability of the proposed adaptive fuzzy filter-based L 1 adaptive controller is guaranteed analytically. The proposed method is employed on an electromechanical actuator, viz., DC motor experimental setup to validate its effectiveness. Adequate cancellation of high frequency signal incurs smooth control signal which in turn provides lower control energy.

A hybrid YDSE - THDCNN approach based multi objective optimization of energy management for renewable energy sources with electric vehicles

Thermal power plants have long been essential to the fossil fuel-based electricity supply chain, according to statistics studies. Nevertheless, due to the enormous initial and ongoing expenses of modern power plants as well as their negative environmental effects. This manuscript proposes a hybrid technique for grid-connected load-following hybrid photovoltaic and wind microgrid with a grid-connected electric vehicle charging system. The proposed hybrid technique is the joint execution of both Young's double-slit experiment optimizer (YDSE) and the Tree Hierarchical Deep Convolutional Neural Network (THDCNN). Hence, it is named as YDSE-THDCNN. The major objective of the proposed technique is to reduce the fuel, operation, and maintenance costs, as well as to reduce the environmental costs and maximize the efficiency of the system. The proposed YDSE technique is utilized to generate the control signal of the inverter and the optimal signal will be predicted by the THDCNN. By then, the MATLAB working platform has adopted the proposed way, and the existing method is used to compute its execution. The proposed method outperforms any existing techniques, including the Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Particle Swarm Optimization (PSO) Algorithm. The existing method shows the cost of 2.6$, 3.8$, 4.6$, and the proposed method shows the cost of 1.3$ which is lower than the other existing method.

Evaluation of Traffic Congestion in an Urban Roads: A Review

Abstract: Urban traffic congestion significantly impacts economic productivity, environmental health, and residents' quality of life. This study provides a comprehensive evaluation of traffic congestion in urban areas, focusing on recent innovations in traffic management, urban planning, public transportation, and technological advancements. Intelligent Transportation Systems (ITS), incorporating real-time data and adaptive signal controls, have proven effective in reducing delays and improving traffic flow. The study underscores the importance of mixed-use urban planning in minimizing travel distances and traffic volumes. Expanding public transportation networks is highlighted as a critical strategy for alleviating congestion, with evidence indicating that cities with extensive transit systems experience lower congestion levels. Additionally, the environmental and health implications of traffic congestion are considered, linking high congestion levels to increased pollution and negative health outcomes. . Road construction as a policy intervention to reduce congestion is debated, with the understanding that road widening may sometimes increase traffic. Evaluating the technologies contributing to congestion is crucial for devising effective solutions. Urban transport is central to economic activity and the well-being of urban residents. The rising population and car ownership rates, coupled with unchanged natural land areas, exacerbate traffic issues, leading to accidents and increased carbon dioxide emissions. The article explores the complexities of urban road traffic, addressing transport infrastructure, organization, and the high share of car traffic. It discusses theoretical and practical aspects of traffic flow and design, as well as the role of driver behavior in traffic jams.

Design and development of an intelligent zone based master electronic control unit for power optimization in electric vehicles

The development of electric vehicles (EVs) has been incremental because EVs satisfy a significant demand for energy sources. Electronic control unit (ECU) is an important component that processes the electric signals received from various sensors for generating the control signals for the actuators. Automotive control systems were initially operated manually throughout the automotive revolution based on the responses of input signals received from ECUs and drivers. Most of the functions in EV are controlled by the ECU and every ECU consumes power at all times even if it is not in use. The larger power consumption of passive ECUs like adaptive lighting systems (ALS), automatic wiper systems (AWS) brake light systems (BLS), etc., affect the life of ECUs and the range of EVs. This article is primarily concerned with limiting power consumption by switching the power supply to the passive ECUs based on their requirements. Hence, to achieve the objective, the intelligent zone (i-zone) based master ECU is triggered to activate the slave ECUs. Designing suites including Proteus and KiCAD were used for designing the circuits including master as well as slave ECU. This prototype is built using three secondary ECUs such as ALS & AWS and BLS which are controlled using i-zone-based master ECU. The performance of this implemented design is evaluated, and it is discovered that almost 40% of the battery consumption is reduced. This i-zone-based master ECU and all its slave ECUs manage power while ensuring the safety and reliability of EVs.

An Integrated Hybrid Convolutional-Support Vector Machine (CSVM) Model with the CWT Method for Hearing Disorder Detection using AEP Signals

Hearing disorder is the most widespread sensory disability worldwide, impairing human communication and learning. An early and accurate hearing disorder detection system using an electroencephalogram (EEG) is the appropriate technique for dealing with this concern. The most significant modality for diagnosing hearing deficiency among EEG control signals is the auditory evoked potential (AEP), which is generated in the cortical region of the brain through auditory stimulus. This study aims to develop an efficient approach for detecting hearing disorders. For this purpose, this study has designed a hybrid model based on the convolutional operation of CNN and the SVM classifier. Initially, the CWT method was utilized to transform the raw AEP signals into time-frequency images. Then, the extracted features were classified using the proposed CSVM model. To test the robustness of the proposed model, this study also implemented a convolutional neural network (CNN) and support vector machine (SVM) with the same parameters. The experimental results with the hybrid CSVM model showed superior performance on the publicly available AEP dataset by achieving 94.48% testing accuracy, 96.40% precision, 92.96% recall, 94.65% F1 score, 88.95% Cohen Kappa score, which indicates that the proposed hybrid model could be used for early hearing disorder detection. Future enhancements will concentrate on identifying different hearing-signal-based data and the cloud-based, automated classification of AEP signals.

A Carbon Benefits-Based Signal Control Method in a Connected Environment

A Carbon Benefits-Based Signal Control Method in a Connected Environment

Minimizing Intersection Delays: A Novel Fuzzy Logic-Based Architecture for Traffic Signal Control

Minimizing Intersection Delays: A Novel Fuzzy Logic-Based Architecture for Traffic Signal Control

ALL-OPTICAL LOGIC GATE OPERATION WITH A MULTI-CORE NONLINEAR PHOTONIC CRYSTAL FIBER

In this paper, we propose the design of a photonic crystal fiber with four central cores infiltrated by a high-index liquid to achieve highly efficient control of light guidance. We analyze the field distribution, effective mode area, and dispersion characteristics of the fundamental guided modes of the fiber. Within the coupled-mode theory, the pulse propagation in the fiber is governed by coupled nonlinear Schrödinger equations. We use the split-step Fourier method to simulate the propagation of pulses numerically. The results show three features of the dynamics: oscillation, switching, and self-trapping. We predict that the fiber could operate as a logic-gate device by introducing suitable input and control signals.

Evaluating the performance of implementing regionally coordinating bus priority signals under different control schemes

Bus Rapid Transit (BRT) proves its effectiveness in alleviating traffic congestion, especially in urban areas. The implementation of Transit Signal Priority (TSP) for BRT has shown a significant reduction in delays. However, in densely populated urban areas, this priority can inadvertently cause additional delays for other modes of transportation. In this paper, we propose a control strategy for Regionally Coordinating Bus Priority Signals Control (RCBPSC) at urban intersections. The aim is not only to reduce bus delays but also to consider minimizing delays for pedestrians and other vehicles. To achieve this, we modeled two consecutive intersections along the Amman BRT. Essentially, we evaluated three different control scenarios in addition to the current base scenario. These scenarios include adaptive traffic signal control, RCBPSC with no signal timing optimization, and RCBPSC with signal optimization. Simulation results indicate that the adaptive traffic signal timing has the worst operational performance in terms of average delay and Level of Service (LOS) compared to the base scenario. Additionally, the results show that BRT delays significantly decrease at both intersections when we implement RCBPSC scenarios. When implementing RCBPSC with optimization scenarios, the results show an average reduction of more than 60% in intersection delay, a decrease in emissions of more than 50%, and an improved LOS for system users compared to the base scenario. The findings of this work can help agencies improve the current operational condition of BRT when implementing RCBPSC.

Developmental signals control chromosome segregation fidelity during pluripotency and neurogenesis by modulating replicative stress

Human development relies on the correct replication, maintenance and segregation of our genetic blueprints. How these processes are monitored across embryonic lineages, and why genomic mosaicism varies during development remain unknown. Using pluripotent stem cells, we identify that several patterning signals—including WNT, BMP, and FGF—converge into the modulation of DNA replication stress and damage during S-phase, which in turn controls chromosome segregation fidelity in mitosis. We show that the WNT and BMP signals protect from excessive origin firing, DNA damage and chromosome missegregation derived from stalled forks in pluripotency. Cell signalling control of chromosome segregation declines during lineage specification into the three germ layers, but re-emerges in neural progenitors. In particular, we find that the neurogenic factor FGF2 induces DNA replication stress-mediated chromosome missegregation during the onset of neurogenesis, which could provide a rationale for the elevated chromosomal mosaicism of the developing brain. Our results highlight roles for morphogens and cellular identity in genome maintenance that contribute to somatic mosaicism during mammalian development.

Comparative Study of E-beam and Optical Probing Approaches in Attacking the ICs

AbstractOptical probing methods through the chip backside have been demonstrated to be powerful attack techniques in the field of electronic security. However, these attacks typically require specific circuit conditions, such as enforcing gate or register switching at certain frequencies or repeating measurements over multiple executions to achieve an acceptable signal-to-noise ratio (SNR). Meeting these requirements can pose challenges, such as low-frequency switching or inaccessibility of the control signals. This study evaluates these requirements for contactless electron- and photon-based probing attacks by performing extensive experiments and discussing the advantages and drawbacks of each approach. Our findings demonstrate that E-beam probing has the potential to outperform optical methods in scenarios involving static or low-frequency circuit activities. Nevertheless, E-beam probing requires the assistance of optical techniques for area localization and requires aggressive thinning and trenching to the STI level.

Sensorimotor control of robots mediated by electrophysiological measurements of fungal mycelia.

Living tissues are still far from being used as practical components in biohybrid robots because of limitations in life span, sensitivity to environmental factors, and stringent culture procedures. Here, we introduce fungal mycelia as an easy-to-use and robust living component in biohybrid robots. We constructed two biohybrid robots that use the electrophysiological activity of living mycelia to control their artificial actuators. The mycelia sense their environment and issue action potential-like spiking voltages as control signals to the motors and valves of the robots that we designed and built. The paper highlights two key innovations: first, a vibration- and electromagnetic interference-shielded mycelium electrical interface that allows for stable, long-term electrophysiological bioelectric recordings during untethered, mobile operation; second, a control architecture for robots inspired by neural central pattern generators, incorporating rhythmic patterns of positive and negative spikes from the living mycelia. We used these signals to control a walking soft robot as well as a wheeled hard one. We also demonstrated the use of mycelia to respond to environmental cues by using ultraviolet light stimulation to augment the robots' gaits.

Overview of knowledge graph construction in the field of fully electronic computer interlocking systems

As a core technology in railway signal control, the fully electronic computer interlocking system achieves the control of railway signals and switches through computerization, ensuring the safety and efficiency of train operations. With the increasing complexity of railway systems, traditional management and maintenance face significant challenges. Knowledge graphs, as an advanced data management and representation method, can effectively organize and utilize large-scale domain knowledge, enhancing the system's intelligence. This paper introduces the construction methods, core concepts, and applications of knowledge graphs in the field of fully electronic computer interlocking systems, providing references for research and practice in this area.

Role of Mitofusin 1 in mediating reactive oxygen species in alveolar macrophages during Streptococcuspneumoniae

Alveolar macrophages (AM) are key effectors of the immune response and are essential for host responses to S. pneumoniae. Mitochondria are highly dynamic organelles whose function aids in regulating the cell cycle, innate immunity, autophagy, redox signaling, calcium homeostasis, and mitochondrial quality control in AM. In response to cellular stress, mitochondria can engage in stress-induced mitochondrial hyperfusion (SIMH). The current study aimed to investigate the role of Mfn1 on mitochondrial control of reactive oxygen species (ROS) in AMs and the role of Mfn1 deficiency on immune responses to S. pneumoniae. Compared to Mfn1FloxCre− controls, there were distinct histological differences in lung tissue collected from Mfn1Floxed; CreLysM mice, with less injury and inflammation observed in mice with Mfn1 deficient myeloid cells. There was a significant decrease in lipid peroxidation and ROS production in Mfn1 deficient AM that was associated with increased superoxide dismutase (SOD) and antioxidant activity. Our findings demonstrate that Mfn1 deficiency in myeloid cells decreased inflammation and lung tissue injury during S. pneumoniae infection.

Application of Artificial Intelligence in the Management of Coagulation Treatment Engineering System

In this paper, the application of artificial intelligence, especially neural networks, in the field of water treatment is comprehensively reviewed, with emphasis on water quality prediction and chemical dosage optimization. It begins with an overview of machine learning and deep learning concepts relevant to water treatment. Key advances and challenges in using neural networks for coagulation processes are thoroughly analyzed, including the automation of coagulant dosing, dosage level optimization, and efficiency comparisons of modeling approaches. Applications of neural networks in predicting pollutant levels and supporting water quality monitoring are explored. The review identifies avenues for improving coagulation-based modeling with neural networks, such as enhancing data quality, employing feature engineering, refining model selection criteria, and improving cross-validation methods. The necessity of continuous monitoring and adaptive optimization strategies is emphasized. Challenges such as the complexity of coagulation processes, feedback control signal acquisition, and model adaptability from simulations to real-world settings are discussed. Cost control and resource management in water treatment are also highlighted, emphasizing the optimized chemical dosage to reduce expenses while maintaining water quality compliance. In summary, this review provides valuable insights into the current state of neural network applications in water treatment and highlights key areas for further research and development. Integrating AI into coagulation processes has the potential to enhance the efficiency and sustainability of drinking water treatment.

A multilayer neural‐network‐based fault estimation and fault tolerant control scheme for uncertain system

AbstractThis work introduces a new actuator fault estimation approach coupled with a fault‐tolerant control (FTC) strategy for uncertain systems in an output feedback framework. The proposed method involves constructing a Multi‐Layer Neural Network (MLNN) observer‐based fault estimation unit to accurately predict system states and potential faults in the actuator channel. An online control allocation (CA) scheme is then developed, utilizing the derived estimates to actively reconfigure the virtual control signals among the healthy redundant actuators in the event of actuator malfunction. Furthermore, an adaptive neural network‐based output integral sliding mode control scheme is designed based on the virtual control. This integration enhances the overall system's robustness and significantly reduces the chattering effect. The stability analysis of the proposed fault estimations scheme is initially performed using MLNN structure, followed by a comprehensive closed‐loop stability analysis to establish the stability of the entire system. Finally, the effectiveness of the proposed method is validated on a nonlinear six‐degree‐of‐freedom model of multirotor unmanned aerial vehicle aircraft. Numerical simulations under different fault and failure scenarios validate the efficacy of the proposed method. The comparative analysis of the proposed scheme is conducted with the static output feedback control allocations and adaptive allocation strategy. This analysis focuses on evaluating performance using metrics such as root mean square error and mean square deviation, particularly in the presence of faults and failures. The results demonstrate the superior performance of the proposed scheme in fault/failure conditions.

Urban traffic signal control optimization through Deep Q Learning and double Deep Q Learning: a novel approach for efficient traffic management

Urban traffic signal control optimization through Deep Q Learning and double Deep Q Learning: a novel approach for efficient traffic management

Enhancing 5G massive MIMO systems with EfficientNet‐B7‐powered deep learning‐driven beamforming

AbstractThe development of wireless communication systems is a challenging and constantly evolving field and the issue of gaining optimal performance is of utmost importance. This work intends to give a thorough and detailed description of massive MIMO technology and its properties, with a significant emphasis on digital beamforming (FDB) and hybrid beamforming (HBF) techniques and the potential of combining them with the most recent and exciting frontier of research: deep learning. On one hand, FDB provides accurate signal control but, on the other hand, it deals with substantial needs like high‐power consumption. This challenge makes the focus shift to HBF—the innovative technology successfully coupled with deep learning's powerful potential. The chosen research explores extensively the major areas of application and compatibility of this operating mode in a diverse range of operational situations in the interference environment as well as in different levels of noise conditions. Moreover, the study offers a comprehensive comparison, which is highly effective in exploring further methods that focus on improving spectral efficiency. Significantly, the “Proposed Method” is suggested to be at the leading position, which demonstrates superior performance. Showing outstanding generalization capability, versatile robustness, and efficiency of usage in the proposed framework rely on EfficientNet‐B7 as the major portion. This makes it adaptive to its dynamic surroundings and puts it as a powerful tool in the world of advanced connectivity and massive MIMO technology. Due to its core ability to respond to changes in conditions effectively and efficiently, the proposed framework is seen as one of the most powerful approaches that could be used to change wireless communication systems.