Real-time Dynamic System Research Articles

Real-Time Gesture Recognition in the View of Repeating Characteristics of Sign Languages

Recognizing dynamic gestures from a continuous data stream has been treated as a difficult task. One challenge is the gesture spotting problem, or segmentation of a gesture pattern from a data stream containing consecutive gestures. Although various types of gesture spotting strategies have been introduced so far, all methods has its own limitations to be applied to real world situations such as sign language conversation. One of the conditions that must be satisfied for practical applications is to detect repetitive gestures for emphasizing a certain meaning.In the view of gesture spotting, repetitive gestures are hard to be detected because similar starting and ending moments are repeated without constraints of the number of repetitions. We introduce a data glove-based real-time dynamic gesture recognition system that covers both repetitive and nonrepetitive gestures. The proposed system consists of three steps: Gesture spotting, gesture sequence compression, and gesture recognition. For gesture spotting, we define a quantity termed as a gesture progress sequence, which quantifies a progress of a gesture using numbers from 0 to 1. Gesture sequence compression removes variations including the number of repetitions that seek to impart the same message. At the gesture recognition step, the compressed gesture patterns are classified. The proposed system was evaluated using 17 American sign language (ASL) gestures and some ASL sentences.

Perioperative care of a patient with zygomatic implant placement assisted by a dynamic real-time navigation system

Perioperative care of a patient with zygomatic implant placement assisted by a dynamic real-time navigation system

IoT Speech Recognition Application in Mass Sports Data Monitoring Based on Dynamic Adaptive Recommendation Algorithm.

In this paper, the existing dynamic adaptive recommendation methods are studied, which combine the practical application scheme of transforming the actual dynamic adaptive recommendation problem into user microblog information. After that, a dynamic adaptive weight fusion method is proposed and based on experimental verification, a real-time dynamic adaptive recommendation system is finally designed. The speech recognition of the Internet of Things takes natural language problems as the research object for a long time and takes the sound signal as the research topic. This paper analyzes the application of dynamic adaptive recommendation and Internet of Things speech recognition in mass sports data monitoring. The simulation results show that the system in this paper is convenient for users to monitor the exercise indicators in real time through the mobile client, and at the same time query the exercise historical data and compare the exercise data through the network terminal, thereby improving the exercise method and exercise load. Users can access the motion monitoring module and see the past floating state of motion parameters more intuitively than graphs, contains queries for metrics such as heart rate, body temperature, kinetic energy, pulse, and weight. Due to the diversity and complexity of people's differences, personal characteristics and business environments, sports data monitoring systems also need to be designed according to the scope of use. This paper analyzes the requirements for a motion data monitoring system and provides the system architecture design and basic data for producing detailed information for the system.

A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways

Collision risk identification and prediction is an effective means to prevent truck accidents. However, most existing studies focus only on highways, not on two-lane rural highways. To predict truck collision probabilities and identify high-risk scenes on two-lane rural highways, this study first calculated time to collision and post-encroachment time using high-precision trajectory data and combined them with extreme value theory to predict the truck collision probability. Subsequently, a traffic feature parameter system was constructed with the driving behavior risk parameter. Furthermore, machine learning algorithms were used to identify critical feature parameters that affect truck collision risk. Eventually, extreme value theory based on time to collision and post-encroachment time incorporated a machine learning algorithm to identify high-risk truck driving scenes. The experiments showed that bivariate extreme value theory integrates the applicability of time to collision and post-encroachment time for different driving trajectories of trucks, resulting in significantly better prediction performances than univariate extreme value theory. Additionally, the horizontal curve radius has the most critical impact on truck collision; when a truck is driving on two-lane rural highways with a horizontal curve radius of 227 m or less, the frequency and probability of collision will be higher, and deceleration devices and central guardrail barriers can be installed to reduce risk. Second is the driving behavior risk: the driving behavior of truck drivers on two-lane rural highways has high-risk, and we recommend the installation of speed cameras on two-lane rural roads to control the driving speed of trucks and thus avoid dangerous driving behaviors. This study extends the evaluation method of truck collisions on two-lane rural highways from univariate to bivariate and provides a basis for the design of two-lane rural highways and the development of real-time dynamic warning systems and enforcement for trucks, which will help prevent and control truck collisions and alleviate safety problems on two-lane rural highways.

Clinical study of dynamic real-time navigation assisted immediate implant without flapping in the esthetic zone

ObjectiveThe purpose of this study is to investigate the clinical effect of Dynamic real-time navigation to assist immediate implant without flapping in the esthetic zone. MethodsEight patients who underwent immediate implantation in the aesthetic area were included. A total of 11 implants were implanted using dynamic real-time navigation system combined with non-flap technology. Clinical indicators including implant deviation, initial stability, alveolar bone absorption, implant success rate, pink esthetic score (PES), Papilla index score (PIS), and the thickness of labial side bone plate of the implant were recorded. ResultsThe deviation between the actual implant position and the preoperative design was (0.76±0.08) mm at the top, (1.11±0.18) mm at the root, (0.90±0.16) mm at the depth, and (1.48±0.91)°at the Angle. ISO values of all implants were greater than 59. PES was greater than 8. PIS index was 2 or 3. The average alveolar bone absorption was (0.34±0.09) mm and the thickness of bone plate on the lip of implant was greater than 1.6 mm. The success rate of implantation was 100%. ConclusionThe use of dynamic real-time navigation assisted non-flap implantation in the aesthetic area can effectively reduce implant deviation and improve the aesthetic effect.

Researching a Simulation of Real-Time Nonlinear Dynamical Systems for Digital Power Grids in Massive IoT

“Digital real-time simulation” refers to the replication of output waveforms with the required accuracy, which duplicates the behavior of a real power system that is being simulated. A variety of problems relating to the functioning of power systems can be effectively solved using real-time simulators. Fortunately, modern state-of-the-art technological advancements have solved the energy problems. When used as a real-time application of electromagnetic transient-type simulation, the real-time digital simulator takes advantage of the traveling wave properties of cables and transmission lines in conjunction with a parallel processing platform. Power Control (PC), Hardware Under Test (HUT), Time Scale of Events (TSE), and Control Action (CA) are used as independent variables and the Real-Time Dynamic Simulation (RTDS) has been used as dependent variables. The objective of study improves the energy production in Digital Power Grids using Real-time Dynamic Nonlinear System Simulation System. The data was collected by using questionnaires based on 5-Likert Scale. The data has been analyzed using smart PLS 3. The questionnaire were filled from 50 technicians of power grids. The results are that Real-Time Dynamic Simulation improves the energy production in Digital Power Grids.

Real-Time Reconstruction of the Complex Field of Phase Objects Based on Off-Axis Interferometry

Quantitative phase imaging (QPI) can acquire dynamic data from living cells without the need for physical contact. We presented a real-time and stable dynamic imaging system for recording complex fields of transparent samples by using Fourier transform based on off-axis interferometry. We calculated and removed the system phase without sample to obtain the real phase of the sample, so as to ensure that the system has the ability to accurately measure the phase. The temporal and spatial phase sensitivity of the system was evaluated. Benefit from the ability to record the dynamic phase and phase profile of a specimen, a standard sample (polystyrene microspheres) is investigated to demonstrate the efficiency of this imaging system and we have observed the variation of erythrocyte membrane during Red Blood Cells (RBCs) spontaneous hemolysis with different mediums. Experimental results indicate that the phase of non-anticoagulant RBC changed apparently than anticoagulant RBC and the system could be applied to real-time noninvasive and label-free identification of living cells.

Voltage abnormality-based fault diagnosis for batteries in electric buses with a self-adapting update model

This study aims to solve the key issue for electric buses on how to improve the accuracy and reliability of battery fault diagnosis with the emerging intelligence technology on battery management. The battery fault diagnosis method needs to fuse both the physic and cyber systems, reflecting the real-time dynamic battery system in the physical-layer, as well as taking full advantage of battery historical data and outside information in the cyber-layer. Given that, this paper proposes a fault diagnosis method based on the physical-layer model updated by cyber-layer deep learning algorithms. Long short-term memory network (LSTMN) and back propagation neural network (BPNN) are used in the proposed framework to anticipate present aging conditions and update physical-layer model parameters. As a result, the battery model may self-update in response to the environment and aging circumstances, increasing the accuracy of problem detection and prediction. Finally, data gathered from electric buses in real-world conditions is utilized to validate the proposed method's accuracy and dependability in normal voltage and failure prediction. In addition, compared with the traditional model-based method, the fault warning time can be advanced by 51 s on average. And the misdiagnosis rate and the diagnosis failure rate can decrease by 15.8 % and 11.6 % respectively through the proposed system.

Fast nonlinear model predictive controller using parallel PSO based on divide and conquer approach

The high computationally expensive nature of nonlinear optimisation algorithms leads to limitations of its use in a real-time era. So, this is the main challenge against researchers to develop a fast algorithm that is used in real-time computations. This paper proposes a fast nonlinear model predictive control algorithm that utilises a parallel particle swarm optimisation with synchronous and asynchronous methods inclusion, that handles nonlinear optimisation problems with constraints. The additional divide and conquer approach of the proposed algorithm improves the speed of computation and disturbance rejection capability which proves its efficacy in real-time applications. A highly nonlinear fast dynamic real-time inverted pendulum system with hybrid embedded hardware platform (ARM + FPGA) is used to validate the performance of this algorithm under constraints. The solution presented in the paper is computationally feasible for smaller sampling times i.e. in miliseconds and it gives promising results with synchronous and asynchonous parallel PSO compared to the state-of-art PSO algorithm.

Real-time 3-dimensional Dynamic Navigation System in Endodontic Microsurgery: A Cadaver Study

This study evaluated the accuracy and efficiency of the 3-dimensional dynamic navigation system (3D-DNS) to perform minimally invasive osteotomy (MIO) and root end resection (RER) in endodontic microsurgery (EMS) and investigated the viability of root end cavity preparation (RECP) and root end fill (REF) in MIO. Forty-eight tooth roots were divided in cadaver heads into 2 groups: 3D-DNS (n=24) and freehand (n=24). Cone-beam computed tomographic scans were taken before and after surgery. First, virtual 3D-DNS accuracy was verified using 3 outcome measures: 2-dimensional and 3-dimensional virtual deviations and angular deflection. Second, the accuracy of 3D-DNS for performing MIO was investigated in 2 outcome measures: osteotomy size and volume. Third, the 3D-DNS accuracy was determined for RER in 3 outcomes: resected root length, root length after resection, and resection angle. The viability of RECP and REF was investigated and REF depth and volume measured as well, and procedural time and the number of mishaps were recorded. Two- and 3-dimensional virtual deviations and the angular deflection were lower in the 3D-DNS group than the freehand group (P<.05). Osteotomy height, length, and volume were all reduced when using 3D-DNS (P<.05). The resection angle was lower for 3D-DNS (P<.05). RECP and REF were completed in 100% of the roots. The REF depth achieved was ∼3mm. Osteotomy time, RER time, and the total procedure time were all significantly shortened using 3D-DNS (P<.05). 3D-DNS enabled our surgeon to perform accurate and efficient EMS with minimally invasive osteotomy and RER. The surgeon was also able to conduct RECP with adequate REF in minimally invasive osteotomy performed using 3D-DNS guidance.

Schedulability analysis of dynamic priority real-time systems with contention

In multicore scheduling of hard real-time systems, there is a significant source of unpredictability due to the interference caused by the sharing of hardware resources. This paper deals with the schedulability analysis of multicore systems where the interference caused by the sharing of hardware resources is taken into account. We rely on a task model where this interference is integrated in a general way, without depending on a specific type of hardware resource. There are similar approaches but they consider fixed priorities. The schedulability analysis is provided for dynamic priorities assuming constrained deadlines and based on the demand bound function. We propose two techniques, one more pessimistic than the other but with a lower computational cost. We evaluate the two proposals for different task allocators in terms of the increased estimated utilization. The results show that both bounds are valid for ensuring schedulability although, as expected, one is tighter than the other. The evaluation also serves to compare allocators to see which one produces less interference.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

The application and development of a wide-area measurement system (WAMS) has enabled many applications and led to several requirements based on dynamic measurement data. Such data are transmitted as big data information flow. To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS, this study performs real-time traffic monitoring and analysis of the data network of a power information system, and establishes corresponding network optimization strategies to solve existing transmission problems. This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy, covering the optimization in three progressive levels: the underlying communication protocol, source data, and transmission process. Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

Reliability and accuracy of dynamic navigation for zygomatic implant placement.

ObjectivesTo assess the accuracy of a real‐time dynamic navigation system applied in zygomatic implant (ZI) surgery and summarize device‐related negative events and their management.Material and methodsPatients who presented with severely maxillary atrophy or maxillary defects and received dynamic navigation‐supported ZI surgery were included. The deviations of entry, exit, and angle were measured after image data fusion. A linear mixed‐effects model was used. Statistical significance was defined as p < .05. Device‐related negative events and their management were also recorded and analyzed.ResultsTwo hundred and thirty‐one zygomatic implants (ZIs) with navigation‐guided placement were planned in 74 consecutive patients between Jan 2015 and Aug 2020. Among them, 71 patients with 221 ZIs received navigation‐guided surgery finally. The deviations in entry, exit, and angle were 1.57 ± 0.71 mm, 2.1 ± 0.94 mm and 2.68 ± 1.25 degrees, respectively. Significant differences were found in entry and exit deviation according to the number of ZIs in the zygomata (p = .03 and .00, respectively). Patients with atrophic maxillary or maxillary defects showed a significant difference in exit deviation (p = .01). A total of 28 device‐related negative events occurred, and one resulted in 2 ZI failures due to implant malposition. The overall survival rate of ZIs was 98.64%, and the mean follow‐up time was 24.11 months (Standard Deviation [SD]: 12.62).ConclusionsThe navigation‐supported ZI implantation is an accurate and reliable surgical approach. However, relevant technical negative events in the navigation process are worthy of attention.

Interfacing a real-time dynamic clamp system with neuron simulation software in living cells

The leading in silico software for building/testing neuronal computational models is NEURON (neuron.yale.edu). We combined NEURON models with a real-time, dynamic clamp system (Cybercyte DC1) which enables us to interface NEURON models with cells during patch-clamp electrophysiology experiments. Simulated action potentials (AP) generated by NEURON were used to voltage clamp HEK cells expressing the cloned HERG ion channel currents. The resulting cloned ion channel current measured during the AP was fed back in real time to the simulated NEURON model AP.

Robust Information Filtering Under Model Mismatch for Large-Scale Dynamic Systems

The Kalman filter (KF) loses its optimality properties when the system model is misspecified to a certain extent, that is, when the assumed system knowledge does not perfectly match the true system dynamics, which in turn can severely impact its performance in real-life applications. The same goes for its extended counterpart (EKF). Linear constraints were recently shown to be a possible way to mitigate the impact of system model mismatch, which led to the derivation of a linearly constrained KF and EKF (LC-KF and LC-EKF). In this contribution, we build on these new robust filters to provide their information form counterparts, the so-called linearly constrained (extended) information filters (LC-IF and LC-EIF). In the unconstrained case, both IF and EIF are mathematically equivalent to the KF and EKF, respectively, but better suited to systems without priors or with a measurement dimension much larger than the state one. Actually only the IF and EIF can be used for large-scale real-time dynamic systems in which computational time and memory are at a premium. Hence the essential need of IF forms of the LC-KF and LC-EKF for such systems, which are introduced in this article. Furthermore, LC-(E)IF still adapts to systems without priors, even though some restrictions apply. Their computational advantages are shown through representative applications, i.e., large array processing and multi-sensor localisation.

Численный метод обработки результатов динамических измерений

The problem of processing data obtained during dynamic measurements is one of the central problems in measuring technology. Purpose of the study. The article is devoted to the study of the stability of the method for solving the problem of processing the results of dynamic measurements with respect to the error in the initial data. Therefore, an urgent task is the development of algorithms for processing the results of dynamic measurements. Materials and methods. This article proposes an algorithm for processing the data obtained during dynamic measurements based on the finite-difference approach. The main prerequisites of the mathematical model of the problem of dynamic measurements associated with the processes of restoration of the input signal in conditions of incomplete and noisy initial data are as follows. Initially, the function of the noisy output signal is known. The restoration of the input signal is carried out using the transfer function of the sensor. The transfer function of the sensor is presented in the form of a differential equation. This equation describes the state of a dynamic system in real time. The proposed computational scheme of the method is based on finite-difference analogs of partial derivatives and the Tikhonov regularization method was used to construct a numerical model of the sensor. The problem of stability of the method for solving high-order differential equations is also one of the central problems of data processing in automatic control systems. Based on the approach of the generalized quasi-optimal choice of the regularization parameter in the Lavrent'ev method, the dependence of the regularization parameter, the parameters of the dynamic measuring system, the noise index and the required level of accuracy was found. Results. The main goal of the computational experiment was to construct a numerical solution to the problem under consideration. Standard test functions were considered as input signals. Test signals simulating various physical processes were used as an input signal. The function of the output signal was found using the proposed numerical method, the found function was noisy with an additive noise of 5 %. Conclusion. The input signal was restored from the noisy signal. The deviation of the reconstructed signal from the initial one in all experiments was no more than 0.05, which indicates the stability of this method with respect to noisy data.

Fabrication of Robust and Porous Lead Chloride-Based Metal-Organic Frameworks toward a Selective and Sensitive Smart NH3 Sensor.

Organolead halide materials have shown promising optoelectronic properties that are suitable for light-emitting diodes (e.g., strong photoluminescence, narrow emission width, and high charge carrier mobility). However, the vast majority of them have no open porosity or open metal sites for host-guest interactions and are therefore not widely applicable in intrinsic fluorescent sensing of small molecules. Herein, we report a lead chloride-based metal-organic framework (MOF) with high porosity and stability and promising photoluminescent characteristics, performing as a sensitive, selective, and long-term stable fluorescence probe for NH3. For the first time, a homemade dynamic real-time photoluminescence monitoring system was developed, which showed that our haloplumbate-based MOF has an immediate response and an extremely low limit of detection (12 ppm) toward NH3. A variety of experimental characterization and theoretical calculations evidenced that the photoluminescence quenching was ascribed to the coordination between NH3 guests and exposed Pb2+ centers in MOFs. Moreover, a portable on-site smart NH3 detector was designed based on this haloplumbate-MOF using a 3D printer, and the quantitative recovery experiment demonstrated the effective detection of NH3 in the range of 15-150 ppm. This study opens a new pathway to design organolead halide-based MOFs to perform on-site chemical sensing of small molecules and shows their high potential to monitor safety concentrations of NH3 in different industrial sites.

Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla.

Zygomatic implants (ZIs) are an ideal way to address cases of a severely atrophic edentulous maxilla and maxilla defects because they replace extensive bone augmentation and shorten the treatment cycle. However, there are risks associated with the placement of ZIs, such as penetration of the orbital cavity or infra-temporal fossa. Furthermore, the placement of multiple ZIs makes this surgery risky and more difficult to perform. Potential intraoperative complications are extremely dangerous and may cause irreparable losses. Here, we describe a practical, feasible, and reproducible protocol for a real-time surgical navigation system for precisely placing quad-zygomatic implants in the severely atrophic maxilla of patients with residual bone that does not meet the requirements of conventional implants. Hundreds of patients have received ZIs at our department based on this protocol. The clinical outcomes have been satisfactory, the intraoperative and postoperative complications have been low, and the accuracy indicated by infusion of the designed image and postoperative three-dimensional image has been high. This method should be utilized during the entire surgical procedure to ensure ZI placement safety.

Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla

Zygomatic implants (ZIs) are an ideal way to address cases of a severely atrophic edentulous maxilla and maxilla defects because they replace extensive bone augmentation and shorten the treatment cycle. However, there are risks associated with the placement of ZIs, such as penetration of the orbital cavity or infra-temporal fossa. Furthermore, the placement of multiple ZIs makes this surgery risky and more difficult to perform. Potential intraoperative complications are extremely dangerous and may cause irreparable losses. Here, we describe a practical, feasible, and reproducible protocol for a real-time surgical navigation system for precisely placing quad-zygomatic implants in the severely atrophic maxilla of patients with residual bone that does not meet the requirements of conventional implants. Hundreds of patients have received ZIs at our department based on this protocol. The clinical outcomes have been satisfactory, the intraoperative and postoperative complications have been low, and the accuracy indicated by infusion of the designed image and postoperative three-dimensional image has been high. This method should be utilized during the entire surgical procedure to ensure ZI placement safety.

Research on BeiDou Satellite Positioning Algorithm Based on GPRS Technology

The BeiDou Satellite Navigation System of China can provide users with high precision, as well as all-weather and real-time positioning and navigation information. It can be widely used in many applications. However, new challenges appear with the expansion of the 5G communication system. To eradicate or weaken the influence of various errors in BeiDou positioning, a BeiDou satellite positioning algorithm based on GPRS technology is proposed. According to the principles of the BeiDou Satellite navigation system, the navigation and positioning data are obtained and useful information are extracted and sent to the communication network through the wireless module. The error is corrected by establishing a real-time kinematic (RTK) mathematical model, and the pseudorange is calculated by carrier phase to further eliminate the relativistic and multipath errors. Based on the results of error elimination, the BeiDou satellite positioning algorithm is improved and the positioning error is corrected. The experimental results show that the positioning accuracy and efficiency of the algorithm can meet the actual needs of real-time dynamic positioning systems.