Automatic fovea detection and choroid segmentation for choroidal thickness assessment in optical coherence tomography

AIM:To develop an automated model for subfoveal choroidal thickness(SFCT)detection in optical coherence tomography(OCT)images,addressing manual fovea location and choroidal contour challenges.METHODS:Two procedures were proposed:defining the fovea and segmenting the choroid.Fovea localization from B-scan OCT image sequence with three-dimensional reconstruction(LocBscan-3D)predicted fovea location using central foveal depression features,and fovea localization from two-dimensional en-face OCT(LocEN-2D)used a mask region-based convolutional neural network(Mask R-CNN)model for optic disc detection,and determined the fovea location based on optic disc relative position.Choroid segmentation also employed Mask R-CNN.RESULTS:For 53 eyes in 28 healthy subjects,LocBscan-3D’s mean difference between manual and predicted fovea locations was 170.0μm,LocEN-2D yielded 675.9μm.LocEN-2D performed better in non-high myopia group(P=0.02).SFCT measurements from Mask R-CNN aligned with manual values.CONCLUSION:Our models accurately predict SFCT in OCT images.LocBscan-3D excels in precise fovea localization even with high myopia.LocEN-2D shows high detection rates but lower accuracy especially in the high myopia group.Combining both models offers a robust SFCT assessment approach,promising efficiency and accuracy for large-scale studies and clinical use.

Automatic detection method of bladder tumor cells based on color and shape features

Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system,and its incidence rate ranks ninth in the world.In recent years,the continuous development of hyperspectral imaging technology has provided a new tool for the auxiliary diagnosis of bladder cancer.In this study,based on microscopic hyperspectral data,an automatic detection algorithm of bladder tumor cells combining color features and shape features is proposed.Support vector machine(SVM)is used to build classification models and compare the classification performance of spectral feature,spectral and shape fusion feature,and the fusion feature proposed in this paper on the same classifier.The results show that the sensitivity,specificity,and accuracy of our classification algorithm based on shape and color fusion features are 0.952,0.897,and 0.920,respectively,which are better than the classification algorithm only using spectral features.Therefore,this study can effectively extract the cell features of bladder urothelial carcinoma smear,thus achieving automatic,real-time,and noninvasive detection of bladder tumor cells,and then helping doctors improve the efficiency of pathological diagnosis of bladder urothelial cancer,and providing a reliable basis for doctors to choose treatment plans and judge the prognosis of the disease.

Study on the Interactivity of Controlled and Automatic Information Processing in L2 Reading

L2 reading is not only an important channel for people to obtain information and knowledge,but also the main way for people to learn a foreign language.Reading information processing can be divided into controlled processing and automatic processing.Controlled information processing is a conscious and resource-intensive processing model,while automatic information processing is an unconscious and automatic processing model.This study investigates the characteristics and interactivity of controlled and automatic information processing in L2 reading,and explores the roles of controlled and automatic information processing strategies in improving L2 reading ability.The findings are as follows:(a)controlled and automatic information processing is interactive in L2 reading;and(b)the uses of controlled and automatic information processing strategies are beneficial to the improvement of the reading ability of L2 learners.This study has important theoretical and practical value in improving the efficiency of L2 reading teaching and learning.

Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.

Fault detection observer design for networked control system with long time-delays and data packet dropout

Focusing on the networked control system with long time-delays and data packet dropout,the problem of observerbased fault detection of the system is studied.According to conditions of data arrival of the controller,the state observers of the system are designed to detect faults when they occur in the system.When the system is normal,the observers system is modeled as an uncertain switched system.Based on the model,stability condition of the whole system is given.When conditions are satisfied,the system is asymptotically stable.When a fault occurs,the observers residual can change rapidly to detect the fault.A numerical example shows the effectiveness of the proposed method.

Fuzzy control on automatic frequency tracking of ultrasonic vibration system with high power and high quality factor Q

In order to realize automatic tracking drift of resonance frequency of ultrasonic vibration system with high power and high quality factor Q, adaptive fuzzy control was studied with a self-fabricated ultrasonic plastic welding machine. At first, relations between amplitude of vibration and frequency as well as main loop current and amplitude of vibration were analyzed. From this analysis, we deduced that frequency tracking process of the vibration system can be concluded as an optimizing problem of one dimensional fluctuant extremum of main loop current in vibration system. Then a method of self-optimizing fuzzy control, used for the realization of automatic frequency tracking in vibration system, is presented on the basis of serf-optimizing adaptive control approach and fuzzy control approach. The result of experiments shows that the fuzzy self-optimizing method can solve the problem of tracking frequency drift very well. Response time of tracking in the system is less than 50 ms, which basically meets the requirements of frequency tracking in ultrasonic plastic welding machine.

Fault detection and optimization for networked control systems with uncertain time-varying delay

The observer-based robust fault detection filter design and optimization for networked control systems （NOSs） with uncer- tain time-varying delays are addressed. The NCSs with uncertain time-varying delays are modeled as parameter-uncertain systems by the matrix theory. Based on the model, an observer-based residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of the linear matrix inequality. Furthermore, a time domain opti- mization approach is proposed to improve the performance of the fault detection system. To prevent the false alarms, a new thresh- old function is established, and the solution of the optimization problem is given by using the singular value decomposition （SVD） of the matrix. A numerical example is provided to illustrate the effectiveness of the proposed approach.

Chaos detection and control in a typical power system

In this paper, a new chaotic system is introduced. The proposed system is a conventional power network that demonstrates a chaotic behavior under special operating conditions. Some features such as Lyapunov exponents and a strange attractor show the chaotic behavior of the system, which decreases the system performance. Two different controllers are proposed to control the chaotic system. The first one is a nonlinear conventional controller that is simple and easy to construct, but the second one is developed based on the finite time control theory and optimized for faster control. A MATLAB-based simulation verifies the results.

Gas concentration monitoring system for small and medium-sized coal mines based on gas-sensing detection and single-chip control

This paper is aimed at the actual conditions of disaster caused by gas in small and medium-sized coal mines. A new gas concentration monitoring system for coal mines is developed on the basis of gas-sensing detection and single-chip control. The monitoring system uses the tin oxide as the main material of N-type semiconductor gas sensors, be- cause it has good sensitive characteristics for the flammable and explosive gas （ such as methane, carbon monoxide）. The QM-N5-semiconductor gas sensor is adopted to detect the output values of the resistance under the different gas con- centrations. The system, designedly, takes the AT89C51 digital chip as the core of the circuit processing hardware structure to analyze and judge the input values of the resistance, and then achieve the control and alarm for going beyond the limit of gas concentration. The gas concentration monitoring system has man）, advantages including simple in struc- ture, fast response time, stable performance and low cost. Thus, it can be widely used to monitor gas concentration and provide early wamings in small and medium-sized coal mines.

Intrusion Detection System with Remote Signalling for Vehicles Using an Arduino Controller and Radio-Frequency Technology

Malicious activities or policy violations have been a concern for the past years. For example, many people have been victims of robbery on vehicles. A conceptual diagram of an Intrusion Detection System (IDS) [1] [2] for vehicles with remote signaling using an Arduino controller and radio-frequency technology is proposed in this paper. To address malicious activities on vehicles, two aspects are considered here, namely: notifier and detector. Firstly, an object-oriented C module that puts on and off a controller (installed inside the vehicle) and an anti-theft electronic editing that powered using an alternator and supported by a back-up battery are implemented. Secondly, a magnetic intrusion sensor, controlled by a proximity detector using radio-frequency technology, has been installed on each vehicle door. To enable IDS, a user needs to activate the monitoring system when leaving their vehicle. This is done using a remote system. In case the user does not activate the monitoring system while leaving the vehicle, a 5-meter-proximity detector will automatically lock the system and set off the monitoring system whenever the user is outside the detection zone. The detection zone is a 5-meter radius area centered at the controller. Here, monitoring consists of geolocating any intruders within the detection zone. This means, if any of the vehicle doors is opened while the system is still locked, the controller will activate the vehicle alarm for a few seconds, thereafter send an SMS notification to the owner. The system automatically unlocks as soon as the proximity detector is within the detection zone. The contribution of this paper, as compared to other similar work, is to reinforce the electronic implementation of IDS.

A hybrid stochastic fractal search and pattern search technique based cascade PI-PD controller for automatic generation control of multi-source power systems in presence of plug in electric vehicles

A hybrid Stochastic Fractal Search plus Pattern Search （hSFS-PS） based cascade PI-PD controller is suggested in this paper for Automatic Generation Control （AGC） of thermal, hydro and gas power unit based power systems in presence of Plug in Electric Vehicles （PEV）. Firstly, a single area multi-source power system consisting of thermal hydro and gas power plants is considered and parameters of Integral （I） controller is optimized by Stochastic FractaI Search （SFS） algorithm. The superiority of SFS algorithm over some recently proposed approaches such as optimal control, differential evolution and teaching learning based optimization techniques is demonstrated by comparing simulation results for the identical power system. To improve the system performance further, Pattern Search （PS） is subsequently employed. The study is further extended for different controllers like PI, PID, and cascaded PI-PD controller and the superiority of cascade PI-PD controller over conventional controllers is demonstrated. Then, cascade PI- PD controller parameters of AGC searched using the proposed hSFS-PS algorithm in presence of plug in electric vehicles. The study is also extended to an interconnected power system. It is seen from the comparative analysis that hSFS-PS tuned PI-PD controller in single and multi-area with multi sources improves the system frequency stability in complicated situations. Lastly, a three area interconnected system with PEVs with dissimilar cascade PI-PD controller in each area is considered and proposed hSFS- PS algorithm is used to tune the controller parameters in presence of nonlinearities like rate constraint of units, dead zone of governor and communication delay.

Design and realization of the automatic control system for the bell anneal furnace production

In accordance with the technical requirements of the full hydrogen bell anneal furnace(BAF)used for the annealing of stainless steel coils in the Baosteel Stainless Steel Business Unit,the design concept for the automatic control system was introduced in the paper.The major points described concerned the infrastructure of computers and networks,system interfaces,control functions,etc.The process control system of the full hydrogen BAF(L2)was the topic and its seven main functions were introduced.The expected output was achieved and manufacturing efficiency was enhanced within three months after the BAF units were put into operation,which demonstrated that the design concept of the automatic control system introduced in the paper was correct and that its functionalities were effective.

Design of a Modularized Automatic Measurement and Control System for LaserInduced Breakdown Spectrometer

In order to improve the compatibility of laser-induced breakdown spectroscopy( LIBS) instrument for different types of parts and optimize the analysis and testing processes,a modularized automatic measurement and control system was developed. Based on the characteristics of each LIBS component, the following development steps have been performed:( 1) a summary of characteristic parameters of the component are established;( 2) the integration mechanism of multiple electrical interfaces is designed;( 3) the component control instruction library is developed. The experimental results indicate that the measurement and control system is compatible with most LIBS parts in the market.Spectrometer and laser can be compatible with at least three different types of parts. In addition,a multilayer iterative testing process is designed to improve the efficiency of optimization process of LIBS parameters. The experimental results have shown that the automatic optimization of the delay time compared to the manual testing provides significant gain in testing efficiency. The range of delay time in the experiments is 1. 28 to 10. 28 μs and the step value is 1,0. 5,0. 2 and 0. 1 μs. The gain in testing efficiency has been found to be increased by 73. 76%,75. 93%,78. 81% and 80. 42%,respectively.

OPTIMUM DESIGN AND ANALYSIS OF THE SIZE CONTROL SYSTEM OF AUTOMATIC REELING MACHINE

This paper sets up a mathematical model of size variation to derive theobjective function, and constructs the constrained conditions In accordance withtheories of the raw silk production process and probability. On the above basis,the optimal feasible solutions to the parameters of different types of size controlsystem are searched out.

Predictive Control and Implementation of Mooring Automatic Positioning System for Deepwater Semi-Submersible Platform

The automatic positioning control of mooring system for deepwater semi-submersible platform has become a key issue in the research and development field of deep-sea resources. The Dual-Stage Actuator (DSA) proposed in this paper can replace the single actuator to achieve the high speed and high precision positioning by cooperative control. The relative model and control algorithm of motion trajectory (CAMT) are designed and validated, which proves that the method proposed in this paper is effective.

Quality Control System of Meteorological Real-time Data from Automatic Weather Stations in Shandong

The quality control system for meteorological real-time data from automatic weather stations in Shandong realized integration of communi- cation system and provincial quality control system, and an interaction platform which was mainly created by Web was set up. The system not only was fully guaranteed for the funning of basic business, also improved the reliability of the data.

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.

Fuzzy Controller-Based Design and Simulation of an Automatic Parking System

With the development of automatic driving and fuzzy theory, people pay more and more attention to the application of fuzzy logic in engineering technology. The automatic parking module in the automatic driving system has always been the focus of research. Automatic parking modules can greatly assist drivers in parking operations, greatly reduce parking difficulties and make people more convenient and fast parking. In this paper, an automatic parking system based on the fuzzy controller is proposed. The fuzzy controller of automatic parking system is constructed by using fuzzy theory, and the robustness of the whole system is examined by fuzzy logic. Firstly, the vehicle motion rules and trajectory changes are analyzed in detail, and the real parking lot model is simulated. Then, the input and output variables of the whole system are analyzed by fuzzy theory and the membership function is constructed. Based on the experience of human experts, the parking rules are tested and summarized, and a reasonable and practical rule base is established. Finally, MATLAB is used to code, build the visual interface of parking lot and vehicles, and draw the cyclic iterative function to detect the vehicle position and direction angle, so as to act as a sensor. The results show that using a fuzzy controller to construct an automatic parking system can effectively improve the parking level.

An automatic seismic signal detection method based on fourth-order statistics and applications

Real-time, automatic, and accurate determination of seismic signals is critical for rapid earthquake reporting and early warning. In this study, we present a correction trigger function（CTF） for automatically detecting regional seismic events and a fourth-order statistics algorithm with the Akaike information criterion（AIC） for determining the direct wave phase, based on the differences, or changes, in energy, frequency, and amplitude of the direct P- or S-waves signal and noise. Simulations suggest for that the proposed fourth-order statistics result in high resolution even for weak signal and noise variations at different amplitude, frequency, and polarization characteristics. To improve the precision of establishing the S-waves onset, first a specific segment of P-wave seismograms is selected and the polarization characteristics of the data are obtained. Second, the S-wave seismograms that contained the specific segment of P-wave seismograms are analyzed by S-wave polarization filtering. Finally, the S-wave phase onset times are estimated. The proposed algorithm was used to analyze regional earthquake data from the Shandong Seismic Network. The results suggest that compared with conventional methods, the proposed algorithm greatly decreased false and missed earthquake triggers, and improved the detection precision of direct P- and S-wave phases.