Tracking Ability Research Articles

Determination of the number of 235U target nuclei in the irregular target using a fission time projection chamber

Based on multiple measurements of ionization loss, the Time Projection Chamber (TPC) combines strong tracking ability with particle identification ability in a large momentum range, which is an important advantage of TPC detection technology over traditional ionization measurement technology. According to these two characteristics of TPC, applying it to the measurement of fission cross-section can greatly improve the measurement accuracy. During the measurement of the fission cross-section, the number of target nuclei is required to be accurately measured. So this paper introduces a method for measuring the number of 235U target nuclei using a fission TPC system. The measurement result agrees with the reference value, and relative error is around 1 %.

Sparse mixed attention aggregation network for multimodal images fusion tracking

Recent years have witnessed the exciting performance of trackers based on Transformer. However, they usually separate the process of information extraction and integration, weakening the information interaction between the target and search region. In addition, they depend on traditional Transformer to model the long range dependency, which leads to a lack of focus on the primary information needed by high-accuracy trackers. In this paper, a sparse mixed attention aggregation model is proposed for robust tracking based on visible and thermal infrared images. To be specific, a backbone network composed of sparse mixed attention is designed to achieve information extraction and integration. This is helpful to obtain specific discriminative feature information and enhance their communication. To give full play to the complementary visible and thermal information, a confidence aware aggregation network is designed, which can learn the reliable confidence of visible and thermal branches. Finally, a corner-based localization head is introduced to estimate the target state. Extensive experiments on three large-scale multimodal tracking benchmarks demonstrate the superior tracking ability of the proposed tracker over other advanced trackers.

Differential derivative method for highly accurate calculation of fiber refractive index curves

The refractive index of an optical fiber is an important parameter, which is closely related to the characteristics of dispersion, birefringence, and attenuation. It is vital to accurately calculate the refractive index in designing special optical fibers. However, the refractive index curves for different transmission modes of an optical fiber may be crossed or closed at some specific wavelengths, where the calculator may easily be tricked. A differential derivation method (DDM) is demonstrated to accurately calculate the refractive index curves by enhancing the mode tracking ability. The effectiveness is verified in the calculations of the refractive index curves for the HE11, HE12, TE01, and EH11 modes of a custom-designed fiber.

Parallel Multi-Layer Monte Carlo Optimization Algorithm for Doubly Fed Induction Generator Controller Parameters Optimization

This work proposes a parallel multi-layer Monte Carlo optimization algorithm (PMMCOA) that optimizes proportional–integral parameters for a doubly fed induction generator-based wind turbine controller. The PMMCOA, an improved form of the Monte Carlo algorithm, realizes the optimization process via a parallel multi-layer structure. The PMMCOA includes rough search layers, precise search layers, and re-precise search layers. Each layer of the PMMCOA adopts a multi-region and multi-granularity approach to increase the diversity and randomness of the search samples. The PMMCOA is employed to tune the controller parameters for achieving maximum power point tracking and improving generation efficiency. The controller fitness function reflects the sum of the rotor angular velocity error and the reactive power error. Compared with the five metaheuristic algorithms, the PMMCOA has a higher global convergence and more accurate power tracking ability.

Self-Luminescent Drug Delivery Vehicle: Synthesis, Self-Assembly Behavior, Cysteine-Responsive Property, and Application in the Visualization of Drug Release.

Targeted drug delivery systems have gained great attention from the chemistry and biomedical fields in recent years due to the minimized harm to normal cells. When designing targeted drug delivery systems, the property of harmlessness to normal cells and the tracking ability of the whole process are quite crucial. These two characters can be brought into the related systems by applying a drug carrier that is self-luminescent and its drug release can be induced by the microenvironment of cancer cells. Therefore, the design and synthesis of drug delivery vehicles are significant for the fabrication of target drug delivery systems. Herein, we have synthesized a cysteine-responsive and fluorescent molecule, maleic acid-modified tetraphenylethylene derivative (MATPE), by a facile method. In addition, a drug delivery system with self-luminescence and cysteine-responsiveness based on the self-assembly of MATPE was fabricated. In this system, MATPE and cysteine both played dual roles as cysteine probe/drug carrier and emission-enhanced inducement/drug-release stimulus. The drug-release process was successfully realized in cancer cells and can be visualized, exhibiting great potential in the field of theranostics.

Fuzzy-based maximum power point tracking (MPPT) control system for photovoltaic power generation system

The ability of the Maximum Power Point Tracking (MPPT) technology to prevent losses by stabilizing power fluctuations during severe weather conditions is critical in improving photovoltaic power generation systems. Overall system stability is improved by carefully tracing the maximum power point (MPP). This research focuses on improving MPPT performance in solar systems by employing the "Fuzzy Logic" control method. The simulation, which is run in MATLAB/Simulink, includes a detailed model of the entire system. The primary circuit is designed with a DC-DC Boost architecture and a single MOSFET transistor. The Fuzzy Logic Controller (FLC) unit in MATLAB/Simulink generates an output variable led by two input variables via the Fuzzy Logic Controller unit. The Fuzzy Logic Controller (FLC) unit generates an output variable led by two input variables in MATLAB/Simulink. The simulation model, called "Fuzzy Disturbance," combines Perturb & Observe with Fuzzy Logic and runs for 3 s. The results show an essential increase in system efficiency to 97%. The simulation results show that the suggested approach tracks MPP, reduces output power fluctuations, and improves system efficiency.

Rotor cross-tilt optimization for yaw control improvement of multi-rotor eVTOL aircraft

Manned multi-rotor electric Vertical Takeoff and Landing (eVTOL) aircraft is prone to actuator saturation due to its weak yaw control efficiency. To address this inherent problem, a rotor cross-tilt configuration is applied in this paper, with an optimization method proposed to improve the overall control efficiency of the vehicle. First, a flight dynamics model of a 500-kg manned multi-rotor eVTOL aircraft is established. The accuracy of the co-axial rotor model is verified using a single arm test bench, and the accuracy of the flight dynamics model is verified by the flight test data. Then, an optimization method is designed based on the flight dynamics model to calculate an optimal rotor cross-tilt mounting angle, which not only improves the yaw control efficiency, but also basically maintains the efficiency of other control channels. The ideal rotor cross-tilt mounting angle for the prototype is determined by comprehensively considering the optimal results with different payloads, forward flight speeds, and rotor mounting angle errors. Finally, the feasibility of the rotor cross-tilt mounting angle is proved by analyzing the control derivatives of the flight dynamics model, the test data of a ground three Degree-of-Freedom (3DOF) platform, and the actual flight data of the prototype. The results show that a fixed rotor cross-tilt mounting angle can achieve ideal yaw control effectiveness, improving yaw angle tracking and hold ability, increasing endurance time, and achieving good yaw control performance with different payloads and forward speeds.

Fast polarization state tracking for dual-polarization direct detection with Jones-space field recovery.

Direct detection system is expected to possess the phase and polarization diversity in order to achieve high spectral efficiency and fiber impairment compensation such as chromatic dispersion and polarization rotation. In this Letter, we theoretically extend the concept of the proposed Jones-space field recovery (JSFR) to include a dynamic polarization rotation matrix and experimentally demonstrate the rapid polarization state tracking ability of the JSFR receiver based on a 3 × 3 optical coupler. Under a rotation of the state of polarization at a rate of 1 Mrad/s, we successfully transmit 59-GBd dual-polarization 16-ary quadrature-amplitude-modulation signals over an 80-km standard single-mode fiber based on a decision-directed least mean square (DD-LMS) or a recursive least square (DD-RLS), with a bit-error rate below the 14% hard-decision forward error correction threshold of 1 × 10-2. The experimental results indicate that the legacy polarization tracking algorithms designed for coherent optical communication are also applicable for this direct detection scheme. To our best knowledge, this work demonstrates the first polarization rotation-tolerant direct detection system with phase and polarization diversity, providing a low-cost and high-speed solution for short-reach communications.

Stochastic-robust planning optimization method based on tracking-economy extreme scenario tradeoff for CCHP multi-energy system

The load tracking performance of combined cooling, heating, and power multi-energy system (CCHP-MES) greatly depends on the equipment capacity configuration. And the frequent fluctuations in the source-load uncertainty puts higher demands on the load tracking ability of CCHP-MES. For this reason, this paper proposes a novel tracking-economy extreme scenario tradeoff model by comparing the differences between tracking ability and economic benefit. The corresponding extreme scenarios based on these two objectives are characterized and the concept of uncertainty adjustment parameter is introduced to weigh the preference of extreme scenarios. On this basis, a multi-objective two-stage stochastic-robust optimization approach is developed, where the first stage optimizes the investment cost and the second stage optimizes the operating cost. Case study using typical load demand and renewable energy data in Nanjing verify the efficacy of the optimization model. The results indicate that the proposed tracking-economy extreme scenario can better characterize the frequent fluctuations of uncertainties compared to the traditional definition of extreme scenario. The equipment capacity configuration obtained under tracking-economy extreme scenario tradeoff model can better cope with the uncertainty fluctuations under extreme weather. Depending on different optimization preference, the economic robustness and tracking robustness can be improved by 13.80% and 36.06%, respectively.

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters.

The Single-Stage Grid-Connected Solar Photovoltaic (SSGC-SPV) topology has recently gained significant attention, as it offers promising advantages in terms of reducing overall losses and installation costs. We provide a comprehensive overview of the system components, which include the photovoltaic generator, the inverter, the Incremental Conductance Maximum Power Point Tracking (IC-MPPT) algorithm, and the PI regulator for DC bus voltage control. Moreover, this study presents detailed system configurations and control schemes for two types of inverters: 2L-3PVSI and 3L-3PNPC. In order to perform a comparative study between the two structures, we subjected them to the same irradiation profile using the same grid configuration. The Photovoltaic Array (PVA) irradiance is increased instantaneously, in 0.2 s, from 400 W/m2 to 800 W/m2, is kept at 800 W/m2 for 0.2 s, is then gradually decreased from 800 W/m2 to 200 W/m2 in 0.2 s, is then kept at 200 W/m2 for 0.2 s, and is then finally increased to 1000 W/m2 for 0.2 s. We explain the operational principles of these inverters and describe the various switching states involved in generating output voltages. To achieve effective control, we adopt the Finite Set-Model Predictive Control (FS-MPC) algorithm, due to the benefits of excellent dynamic responsiveness and precise current tracking abilities. This algorithm aims to minimise the cost function, while taking into account the dynamic behaviour of both the PV system and the inverter, including any associated delays. To evaluate the performance of the FS-MPC controller, we compare its application in the three-level inverter configuration with the two-level inverter setup. The DC bus voltage is maintained at 615 V using the PI controller. The objective is to achieve a Total Harmonic Distortion (THD) below 5%, with reference to the IEEE standards. The 2L-3PVSI inverter is above the threshold at an irradiance of 200 W/m2. The 3L-3PNPC inverter offers a great THD percentage, meaning improved quality of the power returned to the grid.

Development of trajectory-tracking maneuvering system for automatic berthing/unberthing based on double deep Q-network and experimental validation with an actual large ferry

The development of an automatic berthing and unberthing system is essential for realizing autonomous ship navigation systems. Although numerous berthing/unberthing methods using neural networks (NNs) have been proposed in recent years, whether NNs trained via numerical simulations including modeling errors can be used for the automatic berthing/unberthing of actual ships in natural external disturbances is not clear. Hence, in this study, a maneuvering system for automatic berthing/unberthing was developed using a NN that solves the trajectory-tracking problem. The proposed method considered future targets as discrete points in learning, such as to absorb the modeling error and reduce the influence of external disturbances; thus, it achieved a more robust control compared to path planning methods. The system achieved trajectory tracking at low speeds by offline learning without teaching data to follow a target ship with the same maneuverability as its own ship and operated randomly under external disturbances. Furthermore, an online target correction logic function that locally corrects targets was developed to increase the tracking ability in external disturbances. Subsequently, a berthing and unberthing experiment was conducted using an actual large ferry to ensure the applicability of simulation-based NNs to the trajectory-tracking control of an actual ship in a real-life environment.

A New Predictive Current Control With Reduced Current Tracking Error and Switching Frequency for Multilevel Inverters

The forward Euler's integration method is widely used to compute the trajectories of the control variables in conventional predictive current control (PCC) techniques. However, the computational error caused by the forward Euler's method proportionally increases with the sampling time. Hence, the current tracking ability of conventional PCC techniques deteriorates when operating with a large sampling time. Furthermore, the conventional PCC methods produce more switching transitions to reduce the current tracking error, thereby leading to a high switching frequency operation. To overcome these problems, Heun's integration method is proposed for the PCC in this article. The proposed method has predictor and corrector stages to minimize the computational error caused by the large sampling time during the prediction process of the control variables. Thereby, the proposed Heun's method-based PCC techniques produce fewer switching transitions to minimize the current tracking error. Consequently, this results in a decrease in switching frequency. The proposed PCC method is applied to a four-level multilevel inverter (4L-MLI). The discrete-time system models are developed using Heun's integration method to implement the proposed PCC. The working philosophy of the proposed method is demonstrated through the dSPACE/DS1103 controlled 4L-MLI laboratory prototype. The experimental performance of the proposed PCC is compared with that of the conventional PCC in terms of average switching frequency, current tracking error, current harmonic distortion, and transient response time.

Bivalent Gadolinium Ions Forming Injectable Hydrogels for Simultaneous In Situ Vaccination Therapy and Imaging of Soft Tissue Sarcoma.

Doxorubicin (DOX) is the classic soft tissue sarcomas (STS) first-line treatment drug, while dose-dependent myelosuppression and cardiotoxicity limit its application in clinic. This research intends to apply DOX, which is also an inducer of immunogenic cell death as a part for "in situ vaccination" and conjointly uses PD-1 inhibitors to enhance antitumor efficacy. In order to achieve the sustained vaccination effect and real-time monitoring of distribution in vivo, the in situ forming and injectable hydrogel platform with the function of visualization is established for local delivery. The hydrogel platform is synthesized by hyaluronic acid-dopamine coordinated with gadolinium ions (Gd2+ ). Gd2+ provides the ability of magnetic resonance imaging, meanwhile further cross-linking the hydrogel network. Experiments show excellent ability of sustained release and imaging tracking for the hydrogel platform. In mouse STS models, the "in situ vaccination" hydrogels show the best effect of inhibiting tumor growth. Further analysis of tumor tissues show that "in situ vaccination" group can increase T cell infiltration, promote M1-type macrophage polarization and block elevated PD-1/PD-L1 pathway caused by DOX. These results are expected to prove the potential for synthesized hydrogels to achieve a universal platform for "in situ vaccination" strategies on STS treatments.

Autonomous Trajectory Tracking and Collision Avoidance Design for Unmanned Surface Vessels: A Nonlinear Fuzzy Approach

An intelligent fuzzy-based control system that consists of several subsystems—a fuzzy collision evaluator, a fuzzy collision avoidance acting timing indicator, a collision-free trajectory generator, and a nonlinear adaptive fuzzy robust control law—is proposed for the collision-free condition and trajectory tracking of unmanned surface vessels (USVs). For the purpose of ensuring that controlled USVs are capable of executing tasks in an actual ocean environment that is full of randomly encountered ships under collision-free conditions, the real-time decision making and the desired trajectory arrangements of this proposed control system were developed by following the “Convention on the International Regulations for Preventing Collisions at Sea” (COLREGs). From the simulation results, several promising properties were demonstrated: (1) robustness with respect to modeling uncertainties and ocean environmental disturbances, (2) a precise trajectory tracking ability, and (3) sailing collision avoidance was shown by this proposed system for controlled USVs.

Multi-objective high-dimensional multi-fractional-order optimization algorithm for multi-objective high-dimensional multi-fractional-order optimization controller parameters of doubly-fed induction generator-based wind turbines

Aiming at the operation control of doubly-fed induction generator-wind energy systems, this study firstly introduces multi-dimensional information feedback and fractional-order theory and designs a high-dimensional multi-fractional-order controller in the rotor side converter. Secondly, for the tuning optimization of controller parameters of doubly-fed induction generators, this study proposes a high-dimensional multi-fractional-order optimization. Finally, this study proposes a multi-objective high-dimensional multi-fractional-order optimization algorithm for the multi-objective optimization problems of control parameters tuning and optimization. This study adopts an adaptive grid strategy to update non-dominated solutions for external archives with crowded conditions. Furthermore, this study adopts a roulette strategy to select the optimal global position in the iterative process. This study utilizes multi-objective test functions to verify the effectiveness of the proposed multi-objective optimization. The Pareto optimal solutions and five types of statistical data from two indexes show that the proposed multi-objective optimization has improved convergence, diversity, and operational stability. Compared with five algorithms, the proposed multi-objective optimization can obtain Pareto optimal solutions with uniform distribution and good diversity in the multi-objective tuning of control parameters of the designed multi-objective controllers for doubly-fed induction generators. Therefore, decision-makers can choose the control parameters according to the actual needs. The test results show that the designed multi-objective controller optimized by the proposed multi-objective optimization has more accurate power tracking ability and superior control performance.

Force Control of a Grinding Robotic Manipulator With Floating Base Via Model Prediction Optimization Control

In this article, a grinding robot for large area and an impedance-based force control method applied to the robot are described. With the development of science and technology, the demand for various industrial robots increased, and among them, the demand and importance of grinding robots that require high risk and precision increased. In particular, research on grinding robots targeting large areas has not been conducted relatively, and accordingly, the design and control mechanism of robots specialized in large areas was needed. A robot consisting of a manipulator and a grinding module with a 2-DOF parallel structure is proposed as the design of a new grinding robot. The control method is based on impedance force control mainly used in existing grinding robots, but to overcome the limitations of using only impedance control, the impedance control via model-based prediction optimization (MPO) is proposed as a control technique for grinding robots. Experiments were conducted to verify the force tracking ability of the proposed control, resulting in a 28.1% improvement in settling time for the desired force. Even for disturbance, more improved recovery performance than conventional controllers has been verified. As a result, proposed impedance force control via MPO shows improved force tracking performance over conventional impedance control, and is presented as one of the appropriate control methods for grinding robots targeting large areas.

High-Quality Positioning Strategy for Biaxial Contour Machining System With Nonlinear Model Predictive Control

The trajectory tracking ability is an important index to measure the computer numerical control (CNC) machining tools. In this paper, the nonlinear model predictive control strategy (NMPC) is proposed to improve the trajectory tracking ability of the biaxial contour machining system (BCMS). The position-, velocity-, and current-loop of the permanent magnet synchronous motors (PMSMs) of the BCMS are considered as the complete control object to construct a single-loop nonlinear model predictive controller. Meanwhile, the matching non-linear disturbance observer (NL-DOB) is designed to estimate the uncertainty disturbances caused by the parameters mismatch, external load changes and harmonic disturbances in real-time. And the estimated disturbance values are used as feed-forward compensations, which can improve the steady-state performance of the system. At last, the positioning performance comparison between the proposed strategy and the conventional ones are carried on the testbench. The experimental results indicate that the positioning errros can be reduced by up to 40% with the proposed algorithm.

Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles

Powertrain electrification incorporating advanced combustion-based dedicated hybrid engines (DHEs) is an effective and affordable approach to automotive energy saving. To explore the concealed fuel-saving potential of connected plug-in hybrid electric vehicles (CPHEVs) and manage engine dynamics, a data-driven predictive energy consumption minimization strategy (D-PECMS) is proposed in a hierarchical framework. The strategy relies on multi-source trip information provided by advanced driving assistance systems (ADAS) combined with maps and realizes power demand prediction by designing a multivariable long-term and short-term memory (M-LSTM) network. The upper level adopts dynamic programming (DP) to realize SOC planning, while the bottom layer utilizes D-PECMS to achieve computationally-efficient energy management with the engine combustion process being regulated in transient. This strategy is featured with predictive SOC tracking ability with less computational burden and look-ahead engine start-stop control. To ensure the credibility of validation, bench test data are used from a high-efficiency spark-induced compression ignition (SICI) engine to model the CPHEV, and the real-world driving scenarios are reconstructed based on real-time traffic data collected in China. The proposed D-PECMS strategy is tested through comprehensive experiments and compared against both the adaptive ECMS and offline DP. The results demonstrate that the proposed strategy effectively reduces fuel consumption by 3.1% and 13.2% in contrast to the adaptive ECMS and rule-based control respectively. Moreover, the D-PECMS strategy successfully avoids frequent engine operation mode switching as well as engine startup and shutdown.

A novel feature extraction method based on symbol-scale diversity entropy and its application for fault diagnosis of rotary machines

Multiscale entropy-based methods have made great progress in the field of health condition monitoring and fault diagnosis of machines due to their powerful feature representation capabilities. However, existing multiscale entropy methods suffer from three major obstacles: high fluctuation under large scale-factor, loss of high-frequency information, and poor robustness to noises. Thus, this work proposes a symbol-scale analysis method to deal with the above problems. In one aspect, to capture fault features from the time series over multiple time scales, time-delay process of different intervals is utilized to obtain long-term features and short-term features. In the other aspect, symbol-scale analysis introduces a symbolization procedure and maps time series into a corresponding sequence of symbols to overcome the limitation of weak fault extraction under a low-signal-to-noise ratio environment. Moreover, the symbol-scale entropy approach is developed by integrating with diversity entropy, called symbol-scale diversity entropy. The effectiveness of the proposed strategy is intensively validated using two simulated signals and experimental cases. Results demonstrate its advantages in dynamic change tracking ability and calculation efficiency by comparing it with other state-of-the-art entropy methods. Apart from diversity entropy, the versatility of incorporating the proposed symbol-scale analysis and other entropy methods is also verified using experimental data.

Parameter Adaptive Research of Automatic Train Control Algorithm Based on Sliding Mode PID

In existing urban rail systems, most trains are operated by automatic control, which places high demands on the control effectiveness of automatic train operation (ATO). In this study, a train operation model considering the response delay is firstly constructed. Subsequently, by analyzing and comparing the existing mainstream research methods, neural network and sliding mode control techniques were selected and incorporated into the speed control of the train. Among them, the dynamic sliding mode technique, is used to optimize the PID control effect of ATO. Single neuron and back propagation (BP) neural network algorithms are applied to the selection of PID control parameters. The study selects the difference between the actual speed of the control train and the target speed as the control objective. Through continuous optimization and iteration of the control parameters, the control accuracy of the train operation was improved. Finally, this study validates and simulates different model control methods using the actual operation data of urban rail transit. The results show that the sliding mode PID control model optimized with BP neural network performs better in terms of error distribution, average error value, and control effect variance under different simulation scenarios, showing good tracking ability and robustness. The related research results are expected to be applied to the initial selection of rail vehicle control parameters, dynamic adaptive operation control, and other fields, providing practical help to rail operators and rail signaling companies.