Tracking Mode Research Articles

Tension-Relaxation In Vivo Computing Principle for Tumor Sensitization and Targeting.

By modeling the tumor sensitization and targeting (TST) as a natural computational process, we have proposed the framework of nanorobots-assisted in vivo computation. The externally manipulable nanorobots are steered to detect the tumor in the high-risk tissue, which is analogous to the process of searching for the optimal solution by the computing agents in the search space. To overcome the constraint of a nanorobotic platform that can only generate a uniform magnetic field to actuate the nanorobots, we have proposed the weak priority evolution strategy (WP-ES) in our previous works. However, these works do not consider the proportions of the nanorobot control and tracking operations, which are part and parcel of in vivo computation as the control operation aims at searching for the tumor effectively while the tracking mode is used for gathering information about the biological gradient function (BGF). Careful planning about the durations spent in these operations is needed for optimal performance of the TST strategy. To account for this issue, in the current article, we propose a novel computational principle, called the tension-relaxation (T-R) principle, to balance the displacements of nanorobots during each control and tracking cycle. Furthermore, we build three tumor vascular models with different sizes to represent three different targeting regions as the morphology of tumor vasculature determined by the tumor growth process is an important factor affecting TST. We carry out the computational experiments for tumors with three different sizes for several representative landscapes by introducing the T-R principle into the WP-ES-based swarm intelligence algorithms and considering the realistic internal constraints. The experimental outcomes demonstrate the effectiveness of the proposed TST strategy.

A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability Model

In this paper, a compact cat swarm optimization algorithm based on a Small Sample Probability Model (SSPCCSO) is proposed. In the same way as with previous algorithms, there is a tracking mode and a searching mode in the processing of searching for optimal solutions, but besides these, a novel differential operator is introduced in the searching mode, and it is proved that this could greatly enhance the search ability for the potential global best solution. Another highlight of this algorithm is that the gradient descent method is adopted to increase the convergence velocity and reduce the computation cost. More importantly, a small sample probability model is designed to represent the population of samples instead of the normal probability distribution. This representation method could run with low computing power of the equipment, and the whole algorithm only uses a cat with no historical position and velocity. Therefore, it is suitable for solving optimization problems with limited hardware. In the experiment, SSPCCSO is superior to other compact evolutionary algorithms in most benchmark functions and can also perform well compared to some population-based evolutionary algorithms. It provides a new means of solving small sample optimization problems.

Global Gravity Field Model from Taiji-1 Observations

Taiji-1 is the first technology demonstration satellite of the Taiji program of China’s space-borne gravitational wave antenna. After the demonstration of the key individual technologies, Taiji-1 continues collecting the data of the precision orbit determinations, satellite attitudes, and non-conservative forces exerted on the S/C. Therefore, during its free-fall, Taiji-1 can be viewed as operating in the high-low satellite-to-satellite tracking mode of a gravity recovery mission. In this work, we have selected and analyzed the one month data from Taiji-1’s observations, and developed the techniques to resolve the long term interruptions and disturbances in the data due to the scheduled technology demonstration experiments. The first global gravity model TJGM-r1911, that independently derived from China’s own satellite mission, is successfully built from Taiji-1’s observations. Compared with gravity models from CHAMP and other satellite gravity missions, the accuracy discrepancies exist, which is mainly caused by the data discontinuity problem. As the extended free-falling phase been approved, Taiji-1 could serve as a gravity recovery mission for China since 2022 and it will provide us the independent measurement of both the static and the monthly time-variable global gravity field.

Rough Sets Theory with Deep Learning for Tracking in Natural Interaction with Deaf

Sign languages commonly serve as an alternative or complementary mode of human communication Tracking is one of the most fundamental problems in computer vision, and use in a long list of applications such as sign languages recognition. Despite great advances in recent years, tracking remains challenging due to many factors including occlusion, scale variation, etc. The mistake detecting of head or left hand instead of right hand in overlapping are, modes like this, and due to the uncertainty of the hand area over the deaf news video frames; we proposed two methods: first, tracking using particle filter and second tracking using the idea of the rough set theory in granular information with deep neural network. We proposed the method for Combination the Rough Set with Deep Neural Network and used for in Hand/Head Tracking in Video Signal DeafNews. We develop a tracking system for Deaf News. We used rough set theory to increase the accuracy of skin segmentation in video signal. Using deep neural network, we extracted inherent relationships available in the frame pixels and generalized the achieved features to tracking. The system proposed is tested on the 33 of Deaf News with 100 different words and 1927 video files for words then recall, MOTA and MOTP values are obtained.

An Annual Electric Energy Trade Scheduling Model under the Dual Track Mode

The annual electricity trade scheduling is the basis of long-term power generation scheduling. In recent decades, the ratio of new energy generation in China has increased annually, and the electricity market has operated under the “market electricity” and “planned electricity” double track mode in recent years. However, the existing annual electricity trade scheduling methods are extensive and cannot adapt to the new situation of “market electricity” and large-scale new energy generation. The annual scheduled energy of the power units is set as a decision variable, and a novel annual energy scheduling optimization model based on Gini coefficient of fairness is presented in this paper. In this model, “market electricity” capacity is conversed monthly, considering peaking reserve demand and monthly characteristics of new energy generation. The fairness constraint set based on Gini coefficient is introduced into the optimization model to solve various fairness problems. Simulation results show that the introduction of the Gini coefficient and the optimization model considering the monthly conversion of marketing electricity capacity can obtain more accurate and reasonable electricity distribution results, and the peaking demand can be considered more fairly and effectively. The proposed method provides a feasible solution to the annual electric energy scheduling for dual-track operation country such as China.

Investigation of ACS image stabilization of on-board optoelectronic guidance and tracking devices

Introduction. The movement of the carrier and external factors (the effects of the atmosphere, temperature and pressure) degrade significantly the image quality of the servo optoelectronic systems (OES) and the positioning accuracy of the emitting OES. The issues of image quality improvement and the probability of keeping the image of the observation object (OO) on the optical axis of the servo EOS are considered.Materials and Methods. The development of an automatic control system for an optoelectronic device (ACS OED) involved solving a multi-criteria optimization problem taking into account a number of conflicting technical-andeconomic (TE) requirements. The determination of tolerated dynamic errors (TDE) of image stabilization was a key issue in the development of on-board optoelectronic devices (OOED). Lagrange equations of the second kind and the mixed Gilbert method made it possible to obtain a mathematical model of the CO OED. Then, the decomposition of a two-link ACS with nonlinear cross-couplings in the CO was performed. A functional diagram of the image formation model of the OOED was presented. The parameters of the matrix photodetector and the requirements for the dynamic error of the ACS OED, taking into account the permissible MTF of the OED, were listed. The functions of transferring modulation, as well as linear, harmonic and vibrational shift of the image corresponding to the permissible and achieved TDE were visualized. Logarithmic frequency characteristics were created in the Mathcad environment. The two-link control system of the OED with the specified parameters of the CO for the considered movement was presented as two independent azimuth and elevation control channels.Results. The processes of control of the on-board optoelectronic system in the stabilization and tracking modes were described. To study the dynamics of spatial control of the OOEP in accordance with the ACS methodology, a computer simulation model (CSM) of the digital automatic control systems (DACS) of the OED was developed. It was implemented in the Matlab environment and consisted of CSM CO, drives, proportional-integral-derivative (PID) controllers taking into account non-linearities, a central computing device (CCD), a guidance software device, a CSMcarrier that implemented the equations of motion. Harmonic vibrations of the carrier were described. The errors of tracking and stabilization in the tracking mode with an additional control action introduced in the form of a constant speed were determined. The dynamics of spatial control of the OOED was investigated. A computer simulation model of a digital automatic control system of an optoelectronic device, the results of modeling the DACS OED without considering the board movement, and the processes of OED control subject to movement were visualized.Discussion and Conclusions. The stabilization accuracy was calculated for the studied cases. It was established that the stabilization tens of times exceeded the previously stated indicators, and it tens of times reduced the requirements for the convergence of the laser beam and the laser radiation power when developing the optical path of the product in question. The proposed CSM can be used in the development of the on-board optoelectronic systems. In this case, the application of the presented methodology and CSM will help to reduce labor costs and minimize errors.

Higher Order Modeling of Reactor Regulating System and Nonlinear Neural Model Predictive Controller Design for a Nuclear Power Generating Station

In the existing instrumentation and control system of an operating Pressurized Heavy Water Reactor (PHWR) based nuclear power plant, conventional controllers are used to control the reactor power. A new idea of Nonlinear Neural Model Predictive Controller (NNMPC) is introduced in this research work. The new 17th order nonlinear higher order model of Reactor Regulating System (RRS) is developed under different plant operating modes and various parametric conditions in Single Input Multi Output (SIMO) configuration with special emphasis on Helium Control Valve Dynamics (HCVD) and Coupled Nonlinear Iodine and Xenon Dynamics (CNIXD). The SIMO RRS model is developed based on first principle. The 17th order model is reduced to 9th order lower dynamic model using Balanced Truncation Method (BTM). The Reduced Order SIMO RRS (RO-SIMO-RRS) model is programmed, simulated and validated in SIMULINK environment. The plant Neural SIMO RRS (N-SIMO-RRS) model is developed using innovative data generated from RO-SIMO-RRS simulations. The plant neural N-SIMO-RRS model is optimized using Levenberg-Marquardt Algorithm (LMA). Using the identified N-SIMO-RRS model, the Nonlinear Neural Model Predictive Controller (NNMPC) is designed, trained, verified, validated, and finally optimized using the backtracking technique in the SIMULINK environment. The optimized results are obtained from designed closed loop RRS and found within the acceptable design limits. The performance of the proposed closed loop RRS is also tested in reference tracking mode with excellent fast tractability near the optimal target demanded power level.

Extended grey wolf optimization–based adaptive fast nonsingular terminal sliding mode control of a robotic manipulator

This article proposes a novel hybrid metaheuristic technique based on nonsingular terminal sliding mode controller, time delay estimation method, an extended grey wolf optimization algorithm and adaptive super twisting control law. The fast convergence is assured by nonsingular terminal sliding mode controller owing to its inherent nonlinear property and no prior knowledge of the robot dynamics is required due to time delay estimation. The proposed extended grey wolf optimization algorithm determines an optimal approximation of the inertial matrix of the robot. Moreover, adaptive super twisting control based on the Lyapunov approach overcomes the disturbances and compensate the higher dynamics not achievable by the time delay estimation method. First, the fast nonsingular terminal sliding mode controller relying on time delay estimation is designed and is combined with super twisting control for chattering attenuation. The constant gain matrix of the time delay is determined by the proposed extended grey wolf optimization algorithm. Second, an adaptive law based on Lyapunov stability theorem is designed for improving tracking performance in the presence of uncertainties and disturbances. The novelty of the proposed method lies in the adaptive law where the prior knowledge of parametric uncertainties and disturbances is not needed. Moreover, the constant gain matrix of time delay estimation method is obtained using the proposed algorithm. The control method has been tested in simulation on a 3-degrees of freedom robotic manipulator in trajectory tracking mode in the presence of control disturbances and uncertainties. The results obtained confirmed the effectiveness, robustness and the superior precision of the proposed control method compared to the classical ones.

Predictive Control of PV/Battery System under Load and Environmental Uncertainty

The standalone microgrids with renewable energy resources (RERs) such as a photovoltaic (PV) system and fast changing loads face major challenges in terms of reliability and power management due to a lack of inherent inertial support from RERs and their intermittent nature. Thus, energy storage technologies such as battery energy storage (BES) are typically used to mitigate the power fluctuations and maintain a power balance in the system. This paper presents a model predictive control (MPC) based power management strategy (PMS) for such standalone PV/battery systems. The proposed method is equipped with an autoregressive integrated moving average (ARIMA) prediction method to forecast the load and environmental parameters. The proposed controller has the capabilities of (1) effective power management, (2) minimization of transients during disturbances, and (3) automatic switching of the operation of the PV between the maximum power point tracking (MPPT) mode and power-curtailed mode that prevents the overcharging of the battery and at the same time maximize the PV utilization. The effectiveness of the proposed method has been verified through a comprehensive simulation-based analysis.

The Mars Oxygen Visible Dayglow: A Martian Year of NOMAD/UVIS Observations

AbstractThe Ultraviolet and Visible Spectrometer Ultraviolet (UVIS UV) and Visible Spectrometer channel of the Nadir and Occultation for MArs Discovery spectrometer aboard the ExoMars Trace Gas Orbiter has made limb observations of the Martian dayglow during more than a Martian year. Two pointing modes have been applied: (a) In the inertial mode, the spectrometer scans the atmosphere twice down to near the surface and provides altitude profiles of the dayglow; (b) in the tracking mode, the atmosphere is scanned at varying latitudes at a nearly constant altitude through the entire observation. We present a statistical study of the vertical and seasonal distribution of the recently discovered oxygen green and red lines at 557.7 nm and 630 nm. It indicates that the brightness of the green line emission responds to changes in the Lyman‐α flux. The peak altitude of the green line emission increases seasonally when the Sun‐Mars distance decreases. The lower peak of the green line statistically drops by 15–20 km between perihelion and aphelion at mid‐to high altitude. The main lower peak intensity shows an asymmetry between the two hemispheres. It is significantly brighter and more pronounced in the southern hemisphere than in the north. This is a consequence of the stronger Lyman‐α solar flux near perihelion. The second component of the oxygen red line at 636.4 nm is also detected for the first time in the Martian atmosphere. A photochemical model is used to simulate the variations of the green dayglow observed along limb tracking orbits.

Unified control scheme for a dual-stage grid-connected PV system with mode change

This paper proposes a unified control scheme for a dual-stage grid-connected PV system to achieve both the maximum power point tracking (MPPT) mode and the droop mode of operation with mode change. The DC-bus voltage regulation is achieved through the DC–DC converter control, and different modes of operation and the MPPT function are implemented in the DC–AC converter control, where three digital switches are designed to achieve seamless mode change. To enhance system stability and flexibility, a droop coefficient self-tuning algorithm is proposed to handle the scenario with insufficient solar irradiation in the droop mode. The effectiveness of the proposed methods is validated through both experimental and simulation studies.

Voltage-Integral-based Reference Tracking Modulation Method for High-Efficiency Motor Drive

Voltage-Integral-based Reference Tracking Modulation Method for High-Efficiency Motor Drive

Artificial Intelligence-Based Automated Treatment Planning of Postmastectomy Volumetric Modulated Arc Radiotherapy.

As a useful tool, artificial intelligence has surpassed human beings in many fields. Artificial intelligence-based automated radiotherapy planning strategies have been proposed in lots of cancer sites and are the future of treatment planning. Postmastectomy radiotherapy (PMRT) decreases local recurrence probability and improves overall survival, and volumetric modulated arc therapy (VMAT) has gradually become the mainstream technique of radiotherapy. However, there are few customized effective automated treatment planning schemes for postmastectomy VMAT so far. This study investigated an artificial intelligence based automated planning using the MD Anderson Cancer Center AutoPlan (MDAP) system and Pinnacle treatment planning system (TPS), to effectively generate high-quality postmastectomy VMAT plans. In this study, 20 patients treated with PMRT were retrospectively investigated, including 10 left- and 10 right-sided postmastectomy patients. Chest wall and the supraclavicular, subclavicular, and internal mammary regions were delineated as target volume by radiation oncologists, and 50 Gy in 25 fractions was prescribed. Organs at risk including heart, spinal cord, left lung, right lung, and lungs were also contoured. All patients were planned with VMAT using 2 arcs. An optimization objective template was summarized based on the dose of clinical plans and requirements from oncologists. Several treatment planning parameters were investigated using an artificial intelligence algorithm, including collimation angle, jaw collimator mode, gantry spacing resolution (GSR), and number of start optimization times. The treatment planning parameters with the best performance or that were most preferred were applied to the automated treatment planning method. Dosimetric indexes of automated treatment plans (autoplans) and manual clinical plans were compared by the paired t-test. The jaw tracking mode, 2-degree GSR, and 3 rounds of optimization were selected in all the PMRT autoplans. Additionally, the 350- and 10-degree collimation angles were selected in the left- and right-sided PMRT autoplans, respectively. The uniformity index and conformity index of the planning target volume, mean heart dose, spinal cord D0.03cc, mean lung dose, and V5Gy and V20Gy of the lung of autoplans were significantly better compared with the manual clinical plans. An artificial intelligence-based automated treatment planning method for postmastectomy VMAT has been developed to ensure plan quality and improve clinical efficiency.

2-D Actuator based Shape Memory Alloy using PID controller

Over the past years, researchers have been focusing on development the robotics and actuation due to increase demand for these applications like industrial engineering, oil industry, healthcare, aerospace … etc. This work involves the design, construction and control of the Shape Memory Alloy (SMA) actuator. The industrial actuator has many characteristics able to be measured, which have an impact on the efficiency and effectiveness of the actuator while the execution of its tasks. The most important measurable characteristics are repeatability and accuracy. The current system typically is using Nitinol (Nickle Titanium Naval Ordinance Lab), which is one of the Shape Memory Alloy that contract when applying specific heat on it, and it can be used as an actuator. This work presents SMA in the shape of a spring to operate and control the accurate position of the 2-D system which containing four SMA springs, two SMA springs for the x-axis and two SMA springs for the y-axis. The theoretical design and calculations for SMA springs have been presented to collect information about the SMA springs. In a practical manner, the SMA spring characteristic like force and displacement were collected by a test bed that was designed and constructs before making the final rig. The setting shape of the SMA spring was presented and done as per the theoretical calculations. In the rig, each axis works as a two-direction actuator, the actuator is not prone to precise position points due to hysteresis and temperature variation. The SMA spring exhibited hysteresis and imprecise pointing, for that employing PID (Proportional Integral Derivative) with tracking mode controller to compensate the hysteresis. PID control system is played a decisive role with tracking mode model that achieves the aim behind the construction of the experimental rig. Good results have been obtained presented in three cases of drawing different shapes.

A novel smart energy network paradigm integrating combined heat and power, photovoltaic and electric vehicles

Smart energy networks, based on the integration of thermal and electric technologies, are becoming more attractive to match the increasing energy demand with minimum environmental impact. The design and optimization of a smart energy network based on a combined heat and power system, a photovoltaic solar field, an electric energy storage system and electric vehicles is presented in this work. The investigated system is designed to match the power, heating and cooling demands of a district of 50 residential buildings. The cogeneration system, operating in thermal tracking mode, is based on a reciprocating internal combustion engine, fuelled by natural gas. The engine waste heat is used to match the heating and domestic hot water demands of the residential district. The power produced by both engine and photovoltaic panels is used to match the power demand of the district, including the electric vehicles. The electric energy storage system based on lithium-ion technology is included to shave the peaks of power demand and to decouple demand from supply. The whole system, including all final users, was dynamically simulated in TRNSYS 18. The model was used to develop a case study regarding a district of 50 residential buildings located in Naples (South of Italy). A thermoeconomic optimization of the layout was also performed, using Genopt software. The results of the case study showed that the proposed system exhibits promising performance, reducing CO2 emissions by 62%, with a payback period of 6.39 years. In the optimal scenario, the peak power of the photovoltaic field was found equal to 3.50 MW, with a capacity of the electric energy storage system of 6.70 MWh. The corresponding payback period was equal to 6.31 years.

Cat swarm optimization algorithm based on the information interaction of subgroup and the top-N learning strategy

Abstract Because of the lack of interaction between seeking mode cats and tracking mode cats in cat swarm optimization (CSO), its convergence speed and convergence accuracy are affected. An information interaction strategy is designed between seeking mode cats and tracking mode cats to improve the convergence speed of the CSO. To increase the diversity of each cat, a top-N learning strategy is proposed during the tracking process of tracking mode cats to improve the convergence accuracy of the CSO. On ten standard test functions, the average values, standard deviations, and optimal values of the proposed algorithm with different N values are compared with the original CSO algorithm and the adaptive cat swarm algorithm based on dynamic search (ADSCSO). Experimental results show that the global search ability and the convergence speed of the proposed algorithm are significantly improved on all test functions. The proposed two strategies will improve the convergence accuracy and convergence speed of CSO greatly.

WashU Epigenome Browser update 2022.

WashU Epigenome Browser (https://epigenomegateway.wustl.edu/browser/) is a web-based genomic data exploration tool that provides visualization, integration, and analysis of epigenomic datasets. The newly renovated user interface and functions have enabled researchers to engage with the browser and genomic data more efficiently and effectively since 2018. Here, we introduce a new integrated panel design in the browser that allows users to interact with 1D (genomic features), 2D (such as Hi-C), 3D (genome structure), and 4D (time series) data in a single web page. The browser can display three-dimensional chromatin structures with the 3D viewer module. The 4D tracks, called ‘Dynamic’ tracks, animatedly display time-series data, allowing for a more striking visual impact to identify the gene or genomic region candidates as a function of time. Genomic data, such as annotation features, numerical values, and chromatin interaction data can all be viewed in the dynamic track mode. Imaging data from microscopy experiments can also be displayed in the browser. In addition to software development, we continue to service and expand the data hubs we host for large consortia including 4DN, Roadmap Epigenomics, TaRGET and ENCODE, among others. Our growing user/developer community developed additional track types as plugins, such as qBed and dynseq tracks, which extend the utility of the browser. The browser serves as a foundation for additional genomics platforms including the WashU Virus Genome Browser (for COVID-19 research) and the Comparative Genome Browser. The WashU Epigenome Browser can also be accessed freely through Amazon Web Services at https://epigenomegateway.org/.

Adaptive power management algorithm for multi-source DC microgrid system

Abstract In this work, an adaptive power management algorithm is developed for a multi-source dc microgrid system. The proposed algorithm is tested with a dc microgrid system consisting of PV, wind, batteries, supercapacitors, and variable load. The PV and wind energy systems are operated in maximum power point tracking mode to extract maximum energy from the available wind power and solar irradiation. The proposed algorithm has considered wide variation in weather conditions like zero wind velocity, zero solar irradiation, wind gust, and dynamic load change at the dc bus. For these cases, the proposed algorithm maintains the dc bus voltage constant without using a communication link. The efficacy of the proposed algorithm is tested using MATLAB-Simulink. Also, the feasibility and effectiveness of the proposed algorithm are validated using OPAL-RT control platform-based experimental studies.

Adaptive hybrid formation-search and track controller of UAVs

In this paper, a novel search and track scheme is developed through adaptive hybrid formation- search and track controller of networked unmanned aerial vehicles (UAVs). The search and the track algorithm is carried out with antennas placed on at least four UAVs that kept the ‘+’ formation on the x-y plane. The x-axis is assumed to lie in the east–west direction and the y-axis in the north–south direction. UAVs flying in the ‘+’ formation are divided into 4 main groups: east, west, north, and south. UAVs on the x-axis are driven from the difference in signal strengths of the east group and the west group, and on the y-axis from the difference in signal strengths of the north and south groups respectively. The hybrid controller consists of two discrete modes: (1) formation mode (2) search and track mode. The back-stepping formation controller is derived through Lyapunov stability criteria to make the UAVs fly in ‘+’shape and at a fixed altitude. Then, received signal power consensus-based search and track controller is developed for networked UAV groups flying in a desired ‘+’ formation. Simulation results verify our theoretical claims.

Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2 Capture Plants

The purpose of this paper is to investigate the possible implementation of the Fast model predictive control (MPC) scheme for chemical systems. Due to the difficulties associated with complicated dynamic behavior and model sensitivity, which results in considerable offsets, the Fast MPC controller has not been implemented on the CO2 capture plant based on the absorption/stripping system. The main objective of this work is to evaluate the most appropriate model for implementing the Fast MPC control strategy, which results in fast output responses, negligible offsets, and minimum errors. The steady-state and dynamic simulation models of the CO2 capture plant are designed in Aspen PLUS. In the System Identification Toolbox, multiple state-space models are identified to achieve a highly accurate model for the Fast MPC controller. The Fast MPC controller is then implemented to evaluate the performance under a setpoint tracking mode with ±5 and ±15% step changes. The results showed that the Fast MPC based on the state-space prediction focus model has on average 7.9 times lower offset than the simulation focus model and 10.4 times lower integral absolute error values. The comparison study concluded that the Fast MPC control strategy performs efficiently using prediction-based focus state-space models for CO2 capture plants using the absorption/stripping system with minimum offsets and errors.