A feedback control method for phase signal demodulation in fber-optic hydrophones

In the realm of acoustic signal detection,the identification of weak signals,particularly in the presence of negative signal-to-noise ratios,poses a significant challenge.This challenge is further heightened when signals are acquired through fiber-optic hydrophones,as these signals often lack physical significance and resist clear systematic modeling.Conventional processing methods,e.g.,low-pass filter(LPF),require a thorough understanding of the effective signal bandwidth for noise reduction,and may introduce undesirable time lags.This paper introduces an innovative feedback control method with dual Kalman filters for the demodulation of phase signals with noises in fiber-optic hydrophones.A mathematical model of the closed-loop system is established to guide the design of the feedback control,aiming to achieve a balance with the input phase signal.The dual Kalman filters are instrumental in mitigating the effects of signal noise,observation noise,and control execution noise,thereby enabling precise estimation for the input phase signals.The effectiveness of this feedback control method is demonstrated through examples,showcasing the restoration of low-noise signals,negative signal-to-noise ratio signals,and multi-frequency signals.This research contributes to the technical advancement of high-performance devices,including fiber-optic hydrophones and phase-locked amplifiers.

Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review

Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors.Because of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal control demonstrates significant improvements over existing conventional signal control systems.Though various CV-based signal control systems have been investigated in the past decades,these approaches still have many issues and drawbacks to overcome.We summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital concepts.Last,future research directions are discussed with some suggestions.We hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.

An integrated and cooperative architecture for multi-intersection traffic signal control

Traffic signal control(TSC)systems are one essential component in intelligent transport systems.However,relevant studies are usually independent of the urban traffic simulation environment,collaborative TSC algorithms and traffic signal communication.In this paper,we propose(1)an integrated and cooperative Internet-of-Things architecture,namely General City Traffic Computing System(GCTCS),which simultaneously leverages an urban traffic simulation environment,TSC algorithms,and traffic signal communication;and(2)a general multi-agent reinforcement learning algorithm,namely General-MARL,considering cooperation and communication between traffic lights for multi-intersection TSC.In experiments,we demonstrate that the integrated and cooperative architecture of GCTCS is much closer to the real-life traffic environment.The General-MARL increases the average movement speed of vehicles in traffic by 23.2%while decreases the network latency by 11.7%.

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal

This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.

Controller Design for Induction and Brushless Motors Using Matlab with Digital Signal Processor (DSP)

The automation process is a very important pillar for Industry 4.0.One of the first steps is the control of motors to improve production efficiency and generate energy savings.In mass production industries,techniques such as digital signal processing(DSP)systems are implemented to control motors.These systems are efficient but very expensive for certain applications.From this arises the need for a controller capable of handling AC and DC motors that improves efficiency and maintains low energy consumption.This project presents the design of an adaptive control system for brushless AC induction and DC motors,which is functional to any type of plant in the industry.The design was possible by implementing Matlab software and tools such as digital signal processor(DSP)and Simulink.Through an extensive investigation of the state of the art,three models needed to represent the control system have been specified.The first model for the AC motor,the second for the DC motor and the third for the DSP control;this is done in this way so that the probability of failure is lower.Subsequently,these models have been programmed in Simulink,integrating the three main models into one.In this way,the design of a controller for use in AC induction motors,specifically squirrel cage and brushless DC motors,has been achieved.The final model represents a response time of 0.25 seconds,which is optimal for this type of application,where response times of 2e-3 to 3 seconds are expected.

A Survey of Model Predictive Control Methods for Traffic Signal Control

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

A new approach to detecting weak signal in strong noise based on chaos system control

In this paper, a chaos system and proportional differential control are both used to detect the frequency of an unknown signal. In traditional methods the useful signal is obtained through the Duffing equation or other chaotic oscillators. But these methods are too complex because of using a lot of chaos oscillators. In this paper a new method is presented that uses the Rossler equation and proportional differential control to detect a weak signal frequency. Substituting the detected signal frequency into the RSssler equation leads the Rossler phase state to be considerably changed. The chaos state can be controlled through the proportional differential method. Through its phase diagram and spectrum analysis, the unknown frequency is obtained. The simulation results verify that the presented method is feasible and that the detection accuracy is higher than those of other methods.

Notch signaling controls chondrocyte hypertrophy via indirect regulation of Sox9

RBPjk-dependent Notch signaling regulates both the onset of chondrocyte hypertrophy and the progression to terminal chondrocyte maturation during endochondral ossification. It has been suggested that Notch signaling can regulate Sox9 transcription, although how this occurs at the molecular level in chondrocytes and whether this transcriptional regulation mediates Notch control of chondrocyte hypertrophy and cartilage development is unknown or controversial. Here we have provided conclusive genetic evidence linking RBPjk-dependent Notch signaling to the regulation of Sox9 expression and chondrocyte hypertrophy by examining tissuespecific Rbpjk mutant（Prx1Cre;Rbpjkf/f）, Rbpjk mutant/Sox9 haploinsufficient（Prx1Cre;Rbpjkf/f;Sox9f/1）,and control embryos for alterations in SOX9 expression and chondrocyte hypertrophy during cartilage development. These studies demonstrate that Notch signaling regulates the onset of chondrocyte maturation in a SOX9-dependent manner, while Notch-mediated regulation of terminal chondrocyte maturation likely functions independently of SOX9. Furthermore, our in vitro molecular analyses of the Sox9 promoter and Notch-mediated regulation of Sox9 gene expression in chondrogenic cells identified the ability of Notch to induce Sox9 expression directly in the acute setting, but suppresses Sox9 transcription with prolonged Notch signaling that requires protein synthesis of secondary effectors.

Adaptive Learning with Large Variability of Teaching Signals for Neural Networks and Its Application to Motion Control of an Industrial Robot

Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.

Gα_s signaling controls intramembranous ossification during cranial bone development by regulating both Hedgehog and Wnt/β-catenin signaling

How osteoblast cells are induced is a central question for understanding skeletal formation. Abnormal osteoblast differentiation leads to a broad range of devastating craniofacial diseases. Here we have investigated intramembranous ossification during cranial bone development in mouse models of skeletal genetic diseases that exhibit craniofacial bone defects. The GNAS gene encodes Gαs that transduces GPCR signaling. GNAS activation or loss-of-function mutations in humans cause fibrous dysplasia(FD) or progressive osseous heteroplasia(POH) that shows craniofacial hyperostosis or craniosynostosis, respectively. We find here that, while Hh ligand-dependent Hh signaling is essential for endochondral ossification, it is dispensable for intramembranous ossification, where Gαsregulates Hh signaling in a ligand-independent manner. We further show that Gαscontrols intramembranous ossification by regulating both Hh and Wnt/β-catenin signaling. In addition, Gαsactivation in the developing cranial bone leads to reduced ossification but increased cartilage presence due to reduced cartilage dissolution, not cell fate switch. Small molecule inhibitors of Hh and Wnt signaling can effectively ameliorate cranial bone phenotypes in mice caused by loss or gain of Gnas function mutations, respectively. Our work shows that studies of genetic diseases provide invaluable insights in both pathological bone defects and normal bone development, understanding both leads to better diagnosis and therapeutic treatment of bone diseases.

Consensus intuitionistic fuzzy group decision-making method for aircraft cockpit display and control system evaluation

A novel group decision-making （GDM） method based on intuitionistic fuzzy sets （IFSs） is developed to evaluate the ergonomics of aircraft cockpit display and control system （ACDCS）. The GDM process with four steps is discussed. Firstly, approaches are proposed to transform four types of common judgement representations into a unified expression by the form of the IFS, and the features of unifications are analyzed. Then, the aggregation operator called the IFSs weighted averaging （IFSWA） operator is taken to synthesize decision-makers’ （DMs’） preferences by the form of the IFS. In this operator, the DM’s reliability weights factors are determined based on the distance measure between their preferences. Finally, an improved score function is used to rank alternatives and to get the best one. An illustrative example proves the proposed method is effective to valuate the ergonomics of the ACDCS.

Multi-Variable Flocking Control for Multi-Agent Systems via a Received Signal Strength Indicator

A novel flocking control approach is proposed for multi-agent systems by integrating the variables of velocities, motion directions, and positions of agents. A received signal strength indicator （RSSI） is applied as a variable to estimate the inter-distance between agents. A key parameter that contains the local information of agents is defined, and a multi-variable controller is proposed based on the parameter. For the position control of agents, the RSSI is introduced to substitute the distance as a control variable in the systems. The advantages of RSSI include that the relative distance between every two agents can be adjusted through the communication quality under different environments, and it can shun the shortage of the limit of sensors. Simulation studies demonstrate the effectiveness of the proposed control approach.

INTELLIGENT CONTROL SYSTEM OF PULSED MAG WELDING INVERTER BASED ON DIGITAL SIGNAL PROCESSOR

A fuzzy logic intelligent control system of pulsed MAG welding inverter based on digital signal processor （DSP） is proposed to obtain the consistency of arc length in pulsed MAG welding. The proposed control system combines the merits of intelligent control with DSP digital control. The fuzzy logic intelligent control system designed is a typical two-input-single-output structure, and regards the error and the change in error of peak arc voltage as two inputs and the background time as single output. The fuzzy logic intelligent control system is realized in a look-up table （LUT） method by using MATLAB based fuzzy logic toolbox, and the implement of LUT method based on DSP is also discussed. The pulsed MAG welding experimental results demonstrate that the developed fuzzy logic intelligent control system based on DSP has strong arc length controlling ability to accomplish the stable pulsed MAG welding process and controls pulsed MAG welding inverter digitally and intelligently.

FL-FN-MOGA Based Traffic Signal Control

In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes.The 'urgency degree' term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objective genetic algorithm ( MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.

Real-Time Discrete Adaptive Control of Robot Arm Based on Digital Signal Processing

A discrete model reference adaptive controller of robot arm is obtained by integrating the reduced dynamic model of robot, model reference adaptive control （MRAC） and digital signal processing （DSP） computer system into an electromechanical system. With the DSP computer system, the control signal of each joint of the robot arm can be processed in real time and independently. The simulation and experiment results show that with the control strategy, the robot achieved a good trajectory following precision, a good decoupling performance and a high real-time adaptivity.

Signal averaging for noise reduction in anesthesia monitoring and control with communication channels

This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.

The Implementation and Lateral Control Optimization of a UAV Based on Phase Lead Compensator and Signal Constraint Controller

Unmanned Aero Vehicles (UAV) has become a useful entity for quite a good number of industries and facilities. It is an agile, cost effective and reliable solution for communication, defense, security, delivery, surveillance and surveying etc. However, their reliability is dependent on the resilient and stabilizes performance based on control systems embedded behind the body. Therefore, the UAV is majorly dependent upon controller design and the requirement of particular performance parameters. Nevertheless, in modern technologies there is always a room for improvement. In the similar manner a UAV lateral control system was implemented and researched in this study, which has been optimized using Proportional, Integral and Derivative (PID) controller, phase lead compensator and signal constraint controller. The significance of this study is the optimization of the existing UAV controller plant for improving lateral performance and stability. With this UAV community will benefit from designing robust controls using the optimized method utilized in this paper and moreover this will provide sophisticated control to operate in unpredictable environments. It is observed that results obtained for optimized lateral control dynamics using phase lead compensator (PLC) are efficacious than the simple PID feedback gains. However, for optimizing unwanted signals of lateral velocity, yaw rate, and yaw angle modes, PLC were integrated with PID to achieve dynamical stability.

Tri-level programming model for combined urban traffic signal control and traffic flow guidance

In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.