Real-time Parameters Research Articles

Battery Temperature Prediction Using an Adaptive Neuro-Fuzzy Inference System

Maintaining batteries within a specific temperature range is vital for safety and efficiency, as extreme temperatures can degrade a battery’s performance and lifespan. In addition, battery temperature is the key parameter in battery safety regulations. Battery thermal management systems (BTMSs) are pivotal in regulating battery temperature. While current BTMSs offer real-time temperature monitoring, their lack of predictive capability poses a limitation. This study introduces a novel hybrid system that combines a machine learning-based battery temperature prediction model with an online battery parameter identification unit. The identification unit continuously updates the battery’s electrical parameters in real time, enhancing the prediction model’s accuracy. The prediction model employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) and considers various input parameters, such as ambient temperature, the battery’s current temperature, internal resistance, and open-circuit voltage. The model accurately predicts the battery’s future temperature in a finite time horizon by dynamically adjusting thermal and electrical parameters based on real-time data. Experimental tests are conducted on Li-ion (NCA and LFP) cylindrical cells across a range of ambient temperatures to validate the system’s accuracy under varying conditions, including state of charge and a dynamic load current. The proposed models prioritise simplicity to ensure real-time industrial applicability.

Controller Design of a Novel Interleaved Parallel Bi-Directional DC-DC Converter

To improve the dynamic performance of the output voltage of the new interleaved shunt bi-directional DC-DC converter, a double closed-loop control method of voltage and current based on fuzzy PI control is proposed. Firstly, based on the study of the operating principle and steady-state performance of the new interleaved parallel DC-DC converter, a small-signal mathematical model of the interleaved parallel bidirectional DC-DC converter with different modes is established, and based on the model, the transfer functions from duty cycle to output voltage and inductor current are derived, and accordingly, the double-closed-loop controller of the bidirectional DC-DC converter is designed. Then the design method of the fuzzy PI controller is described in detail, and the fuzzy control is applied to the voltage outer loop of the double closed-loop control so that the system can adjust the PI parameters in real time. Simulation experiments are carried out by Matlab/Simulink to compare the simulation experimental waveforms using fuzzy PI control and conventional PI control, and it is verified that the fuzzy PI control improves the output voltage response speed of the new interleaved shunt bi-directional DC-DC converter, reduces the voltage peak of the system, and reduces the overshoot.

A human-machine interaction method for rock discontinuities mapping by three-dimensional point clouds with noises

Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses. However, existing discontinuity mapping algorithms are susceptible to noise, and the calculation results cannot be fed back to users timely. To address this issue, we proposed a human-machine interaction (HMI) method for discontinuity mapping. Users can help the algorithm identify the noise and make real-time result judgments and parameter adjustments. For this, a regular cube was selected to illustrate the workflows: (1) point cloud was acquired using remote sensing; (2) the HMI method was employed to select reference points and angle thresholds to detect group discontinuity; (3) individual discontinuities were extracted from the group discontinuity using a density-based cluster algorithm; and (4) the orientation of each discontinuity was measured based on a plane fitting algorithm. The method was applied to a well-studied highway road cut and a complex natural slope. The consistency of the computational results with field measurements demonstrates its good accuracy, and the average error in the dip direction and dip angle for both cases was less than 3°. Finally, the computational time of the proposed method was compared with two other popular algorithms, and the reduction in computational time by tens of times proves its high computational efficiency. This method provides geologists and geological engineers with a new idea to map rapidly and accurately rock structures under large amounts of noises or unclear features.

Study of real-time parameter measurement of ring rolling pieces based on machine vision.

Real time parameter measurement cannot be carried out to dynamic ring parts during automation ring rolling processes so that rolling process parameters cannot be adjusted in time. Considering effects of shaping of ring rolling parts, a visual measurement platform was set up and a machine vision-based non-contact real -time measurement method was put forward. This article improves the subpixel level edge extraction algorithm to extract edge data information of circular rolling pieces. Based on the characteristics of circular rolling pieces, an RG-Hough transform method is proposed to fit the detected edge data information. The conversion relationship between pixel and actual sizes were determined in combination with the camera calibration to gain parameters of ring rolling parts. Measurements of ring parts (OD: 462.12mm; and ID: 315.53mm) were applied to verify the effectiveness of our method. Our measurement error is ±0.25mm and our average speed can be up to 104ms/frame. Our study can provide powerful technical supports for intelligent control of ring rolling pieces.

“Below 58 BPM,” involving real-time monitoring and self-medication practices in music performance through IoT technology

The project presented in this paper illustrates the design process for the development of an IoT system that monitors a specific bio-metric parameter (heart rate) in real time and provides feedback for an opera singer, as well as adding effects that manipulate the sounds emitted by the body during a self-healing practice. This allows the singer to rest and alternate opera singing techniques (which is very demanding) with other less demanding singing techniques and even a self-healing session in case of necessity during a performance. The case study presented in this paper has been developed with and for Eleonora Amianto, an opera singer who suffered from a carotid aneurysm. We performed an idiographic design process, closely collaborating with Eleonora, and developed a wearable IoT that suited her health and artistic needs. In the design of the system, we explore the intersection between self-healthcare and performative arts, focusing on the use of an Internet of Musical Things (IoMusT) system to implement medical prevention and treatment practices in an art performance. The system is developed using open-source tools, allowing for easy replication and improvement, as well as reducing risks of obsolescence and costs of updating. We complement a formal evaluation session with field notes collected during the design phase. We could observe a positive effect of the system on Eleonora's practice and its potential applications within different performative scenarios.

An intelligent grasper to provide real-time force feedback to shorten the learning curve in laparoscopic training

BackgroundA lack of force feedback in laparoscopic surgery often leads to a steep learning curve to the novices and traditional training system equipped with force feedback need a high educational cost. This study aimed to use a laparoscopic grasper providing force feedback in laparoscopic training which can assist in controlling of gripping forces and improve the learning processing of the novices.MethodsFirstly, we conducted a pre-experiment to verify the role of force feedback in gripping operations and establish the safe gripping force threshold for the tasks. Following this, we proceeded with a four-week training program. Unlike the novices without feedback (Group A2), the novices receiving feedback (Group B2) underwent training that included force feedback. Finally, we completed a follow-up period without providing force feedback to assess the training effect under different conditions. Real-time force parameters were recorded and compared.ResultsIn the pre-experiment, we set the gripping force threshold for the tasks based on the experienced surgeons’ performance. This is reasonable as the experienced surgeons have obtained adequate skill of handling grasper. The thresholds for task 1, 2, and 3 were set as 0.731 N, 1.203 N and 0.938 N, respectively. With force feedback, the gripping force applied by the novices with feedback (Group B1) was lower than that of the novices without feedback (Group A1) (p < 0.005). During the training period, the Group B2 takes 6 trails to achieve gripping force of 0.635 N, which is lower than the threshold line, whereas the Group A2 needs 11 trails, meaning that the learning curve of Group B2 was significantly shorter than that of Group A2. Additionally, during the follow-up period, there was no significant decline in force learning, and Group B2 demonstrated better control of gripping operations. The training with force feedback received positive evaluations.ConclusionOur study shows that using a grasper providing force feedback in laparoscopic training can help to control the gripping force and shorten the learning curve. It is anticipated that the laparoscopic grasper equipped with FBG sensor is promising to provide force feedback during laparoscopic training, which ultimately shows great potential in laparoscopic surgery.

Optimization of Stable Energy PV Systems Using the Internet of Things (IoT)

The modern power grid faces rapid growth in load demand due to industrialization, leading to an unregulated environment and increasing adoption of renewable energy sources, which presents technical challenges, particularly in terms of stability. Hydrogen conversion technology revolutionizes clean electricity storage with renewable energy, and solar hydrogen is now available in autonomous solar systems. The efficiency of solar photovoltaic systems is closely related to using digital electronic maximum peak power tracking (MPPT) technology. The Internet of Things (IoT) is crucial for performance monitoring and real-time control of PV systems, enhancing the understanding of real-time operating parameters. IoT and wireless sensor networks for distributed solar energy devices and joint building design are essential for developing the photovoltaic construction industry. In this paper, the monitoring system that has been proposed offers a potentially effective solution for the intelligent remote and real-time monitoring of solar photovoltaic (PV) systems. It demonstrated a high level of accuracy, reaching 98.49%, and can transmit graphical representations to a smartphone application within a time frame of 52.34 seconds. Consequently, the battery's longevity was extended, energy consumption was diminished, and the quality of service (QoS) for real-time applications inside the Internet of Things (IoT) was enhanced.

A Digital Twin Platform Integrating Process Parameter Simulation Solution for Intelligent Manufacturing

The present work aims to develop a digital twin system typical of intelligent manufacturing applications, which has integrated visualization technologies, as well as the process parameter simulation solution. The application under consideration is a typical machining process, with a gantry machine tool controlled by Siemens Programmable Logic Controller(PLC) S7-1200. With the establishment of dual-directional data communication between the physical machine tool and its virtual counterpart based on TCP/IP protocol, real-time visualization, monitoring, and control of the entire working process can be achieved. Furthermore, we integrated with the digital twin system as a solution for real-time process parameter simulation based on finite element modeling (FEM), which enables the real-time monitoring of necessary process parameters, e.g., surface deformation, during the machining process. A preliminary experiment was conducted to validate our proposed digital twin system, and the results demonstrated that our proposed method has satisfactory performance in terms of both control and monitoring of the traditional machining process, and synchronization between the physical and virtual models is also proven to be positive with minimal latency.

Energy Management System of Luminosity Controlled Smart City Using IoT

INTRODUCTION: With the escalating rates of urbanization, there is a pressing need for enhanced urban services. The concept of smart cities, leveraging digital technologies, offers a promising solution to elevate urban living. The integration of Internet-of-Things (IoT) in urban infrastructure, particularly on highways, opens avenues for novel services and cross-domain applications through Information and Communication Technologies. However, the efficient functioning of an IoT-enabled smart city necessitates careful energy resource management.&#x0D; OBJECTIVES: Propose a Highway Lighting System (HWLS) integrating IoT technologies to enhance urban services, focusing on significant energy savings and real-time environmental parameter monitoring.&#x0D; METHODS: To achieve the objective of enhancing urban services through the proposed Highway Lighting System (HWLS), the system was designed and implemented by integrating cutting-edge sensors, communication links, and the Blynk IoT app. The deployment involved incorporating IoT technologies for real-time monitoring of air quality, air moisture, and soil moisture, alongside a fault identification system using GSM and GPS modules.&#x0D; RESULTS: The proposed HWLS demonstrates significant energy savings, consuming only 37.6% of the original power consumption. The incorporation of IoT technologies facilitates real-time monitoring of environmental parameters, enabling informed decision-making for urban service optimization. The fault-finding system, utilizing GSM and GPS modules, enhances the reliability of the lighting system.&#x0D; CONCLUSION: In conclusion, the Highway Lighting System (HWLS) represents a novel approach to smart city infrastructure, particularly in the context of urban lighting. The integration of IoT technologies not only contributes to energy savings but also enhances the overall efficiency of urban services. The proposed system's ability to monitor environmental parameters and identify faults demonstrates its potential for sustainable urban development and improved quality of life.

Development of Microclimate Data Recorder on Coffee-Pine Agroforestry Using LoRaWAN and IoT Technology

Microclimate monitoring in agroforestry is very important to understand the complex interactions between vegetation, soil, and the environment. Microclimate parameters include air and soil temperature, air humidity, soil moisture, and light intensity. This research aims to develop a new microclimate data recording system for coffee-pine agroforestry, utilizing LoRaWAN and IoT technology to capture real-time microclimate parameters. Unlike traditional data loggers that require manual download on-site, this innovative system enables instant data download from IoT servers, thereby increasing data efficiency and accessibility. The system proved effective, significantly improving the precision of air temperature and humidity, as well as soil temperature measurements, with an average accuracy of 100%. However, soil moisture and light intensity recorded lower accuracies of 81.23% and 82.56%, respectively, indicating potential areas for future research and system refinement. The system maintains a 15-minute sampling period, aligning with conventional datalogger intervals. This represents an advancement in precision agriculture for microclimate monitoring, enabling the data to be utilized in decision-making for agroforestry management, which involves complex interactions between the local microclimate and the broader ecological system. It underscores the significance of sustainable land use as a response to global climate change.

Optimizing Exoskeleton Assistance: Muscle Synergy-Based Actuation for Personalized Hip Exoskeleton Control

Exoskeleton robots hold promising prospects for rehabilitation training in individuals with weakened muscular conditions. However, achieving improved human–machine interaction and delivering customized assistance remains a challenging task. This paper introduces a muscle synergy-based human-in-the-loop (HIL) optimization framework for hip exoskeletons to offer more personalized torque assistance. Initially, we propose a muscle synergy similarity index to quantify the similarity of synergy while walking with and without the assistance of an exoskeleton. By integrating surface electromyography (sEMG) signals to calculate metrics evaluating muscle synergy and iteratively optimizing assistance parameters in real time, a muscle synergy-based HIL optimized torque configuration is presented and tested on a portable hip exoskeleton. Iterative optimization explores the optimal and suboptimal assistance torque profiles for six healthy volunteers, simultaneously testing zero torque and predefined assistance configurations, and verified the corresponding muscle synergy similarity indices through experimental testing. In our validation experiments, the assistance parameters generated through HIL optimization significantly enhance muscle synergy similarity during walking with exoskeletal assistance, with an optimal average of 0.80 ± 0.04 (mean ± std), marking a 6.3% improvement over prior assistive studies and achieving 96.4% similarity compared with free walking. This demonstrates that the proposed muscle synergy-based HIL optimization can ensure robotic exoskeleton-assisted walking as “natural” as possible.

Photosynthesis promotion mechanisms of artificial humic acid depend on plant types: A hydroponic study on C3 and C4 plants

It is feasible to improve plant photosynthesis to address the global climate goals of carbon neutrality. The application of artificial humic acid (AHA) is a promising approach to promote plant photosynthesis, however, the associated mechanisms for C3 and C4 plants are still unclear. In this study, the real-time chlorophyll synthesis and microscopic physiological changes in plant leave cells with the application of AHA were first revealed using the real-time chlorophyll fluorescence parameters and Non-invasive Micro-test Technique. The transcriptomics suggested that the AHA application up-regulated the genes in photosynthesis, especially related to chlorophyll synthesis and light energy capture, in maize and the genes in photosynthetic vitality and carbohydrate metabolic process in lettuce. Structural equation model suggested that the photodegradable substances and growth hormones in AHA directly contributes to photosynthesis of C4 plants (0.37). AHA indirectly promotes the photosynthesis in the C4 plants by upregulating functional genes (e.g., Mg-CHLI and Chlorophyllase) involved in light capture and transformation (0.96). In contrast, AHA mainly indirectly promotes C3 plants photosynthesis by increasing chlorophyll synthesis, and the Rubisco activity and the ZmRbcS expression in the dark reaction of lettuce (0.55). In addition, Mg2+ transfer and flux in C3 plant leaves was significantly improved by AHA, indirectly contributes to plant photosynthesis (0.24). Finally, the AHA increased the net photosynthetic rate of maize by 46.50 % and that of lettuce by 88.00 %. Application of the nutrients- and hormone-rich AHA improves plant growth and photosynthesis even better than traditional Hoagland solution. The revelation of the different photosynthetic promotion mechanisms on C3 and C4 plant in this work guides the synthesis and efficient application of AHA in green agriculture and will propose the development of AHA technology to against climate change resulting from CO2 emissions in near future.

A synthetic human-centric dataset generation pipeline for active robotic vision

Active vision aims to equip computer vision methods with the ability to dynamically adjust the capturing sensor’s viewpoint, position, or parameters in real time. This dynamic capability allows for improving the accuracy of the perception process. However, training and evaluating an active vision model often requires a large number of annotated images captured under different sensor and environmental settings, in order to emulate actions like moving around, approaching, or moving away from a person and thus effectively model the active perception dynamics. Obviously, collecting and annotating such datasets is a challenging and expensive task. To overcome these limitations, this paper introduces a synthetic image generation pipeline specifically designed to support active vision tasks. The pipeline is developed using a highly realistic simulation framework based on Unity and allows for the generation of images depicting humans, captured at varying view angles, distances, illumination conditions, and backgrounds, supporting a wide range of different tasks. Two annotated datasets, namely ActiveHuman and ActiveFace, are generated using the pipeline and the effectiveness of the proposed approach is demonstrated by a solid use case that involves training and evaluating an embedding-based active face recognizer. Furthermore, we demonstrate how the proposed generation approach enables expanding existing active face recognition methods by training models that control both the left/right movements, as well as the distance to a subject, leveraging the additional information provided by ActiveFace dataset. To facilitate replication and encourage the use of the generated datasets for training and evaluating other active vision approaches, the associated assets and the developed dataset generation pipeline is to become publicly available.

Self-tuning PID feedback control method for magnetic suspension active vibration isolation system with parameters uncertainty

A self-tuning PID (Proportion-Integral-Derivative) control method based on the adaptive mechanism was proposed for the magnetic suspension active vibration isolation system with the characteristics of nonlinear, time-varying, and model parameter uncertainty. The controller identified the controlled object parameters online and solved the controller parameters in real time. To test the effectiveness of the proposed method, a six degree-of-freedom (DOF) magnetic suspension active vibration isolation system was designed and built. The six DOF dynamic model of the vibration isolation system with uncertain model parameters was derived for control. Experiments were carried out and compared with the control effects of the tradition PID control and cascade PID control algorithm. The experimental results show that the proposed self-tuning PID controller has more outstanding practicality and effectiveness in solving the model parameter uncertainty problem compared with the existing studies at home and abroad, and it also proves that the control method has a good isolation effect on the wide band disturbances.

Data assimilation for online model calibration in discrete event simulation

The increasing availability of real-time data collected from dynamic systems brings opportunities for simulation models to be calibrated online for improving the accuracy of simulation-based studies. Systematical methods are needed for assimilating real-time measurement data into simulation models. This paper presents a particle filter-based data assimilation method to support online model calibration in discrete event simulation. A joint state-parameter estimation problem is defined, and a particle filter-based data assimilation algorithm is presented. The developed method is applied to a discrete event simulation of a one-way traffic control system. Experiments results demonstrate the effectiveness of the developed method for calibrating simulation models’ parameters in real time and for improving data assimilation results.

Identification of Multi-Innovation Stochastic Gradients with Maximum Likelihood Algorithm Based on Ship Maneuverability and Wave Peak Models

This paper investigates the problem of real-time parameter identification for ship maneuvering parameters and wave peak frequency in an ocean environment. Based on the idea of Euler discretion, a combined model of ship maneuvering and wave peak frequency (ship–wave) is made a discretion, and a discrete-time auto-regressive moving-average model with exogenous input (ARMAX) is derived for parameter identification. Based on the ideas of stochastic gradient identification and multi-innovation theory, a multi-innovation stochastic gradient (MI-SG) algorithm is derived for parameter identification of the ship–wave discretion model. Maximum likelihood theory is introduced to propose a maximum likelihood-based multi-innovation stochastic gradient (ML-MI-SG) algorithm. Compared to the MI-SG algorithm, the ML-MI-SG algorithm shows improvements in both parameter identification accuracy and identification convergence speed. Simulation results verify the effectiveness of the proposed algorithm.

Surface Performance of Titanium Alloy Brake Shell Polished by Industrial Robot Based on Digital Twin

The titanium alloy brake shell is an important component used in aviation, but its surface polishing is mostly done manually, making it difficult to ensure surface quality and consistency. As a result, an industrial robot polishing system based on digital twin is proposed, which can realize the interaction between physical and virtual platforms by using digital twin technology, acquire various parameters in real time, and monitor the polishing process. Based on this system, a removal depth model was established, and the polishing parameters to be analyzed were determined by combining the removal depth model. On this basis, the influence law of polishing parameters on surface roughness is analyzed through physical tests, and orthogonal experiments are used to optimize the polishing parameters. The results show that the surface roughness is reduced to 0.171 μm after optimization. Finally, the reliability of the polishing system is verified through the polishing machining test, and the surface quality of titanium alloy brake shell is significantly improved.

Real-time estimation of barycenter position for fuel cell distributed drive electric tractor based on derived UKF

ABSTRACT The FCDET is considered as a promising and advanced agricultural machinery due to its convenience and controllability. To enhance the control performance of the FCDET unit dynamic system, understanding the accurate position of the center of gravity (CoG) of the tractor unit in longitudinal motion and the operating environment is essential. In this paper, we proposed a real-time estimation method of CoG position of longitudinal motion for FCDET plowing and transportation conditions. CoG height and longitudinal axis distance distribution coefficients are used as state estimation variables, and the data obtained from displacement and velocity sensors are used as state observation variables, establishing a discrete system state space model and proving the observability. The proposed Derived UKF algorithm introduces the Sage-Husa adaptive noise equation within the Square-Root UKF framework to address the non-positive definite matrix problem. The research shows that the precise CoG longitudinal position effectively reflects changes in operational pavement load. The Derived UKF algorithm exhibits high state estimation accuracy in plowing and transportation conditions, with mean absolute errors of 14.2% and 5.0% of the state amplitude, respectively. This study supports the practical application of real-time state parameter estimation algorithms in controlling advanced agricultural machinery distributed drive systems.

Deep learning for parameter estimation of supermassive binary black holes with simulated LISA data

Previous studies have demonstrated that deep learning is an effective approach for processing gravitational wave (GW) data obtained from ground-based detectors, as it can enhance the efficiency of data processing and offer great potential for real-time parameter estimation. In this paper, we explore three different deep learning architectures (MCD, TFP, and VI) for inferring the chirp mass (Mc) and the luminosity distance (DL) of supermassive binary black holes (SMBBHs) using simulated data from the Laser Interferometer Space Antenna (LISA). We train the neural networks with the simulated data and evaluate their performance on predicting the parameters (Mc,DL). The results show that more than 97.5% of the true values fall within the 3σ confidence interval of the predicted values with the optimal network. To verify the accuracy of the network on parameter estimation, we also calculate the estimation error of the parameters using the Fisher Information Matrix (FIM) with the same simulated data. By comparing the root-mean-square error (RMSE) between deep learning and FIM, we find that the two methods are comparable, which implies that deep learning can be reliable for GW parameter estimation.

Tremor Suppression Using Functional Electrical Stimulation.

Parkinson's disease (PD) and essential tremor are two major causes of pathological tremor among people over 60 years old. Due to the side effects and complications of traditional tremor management methods such as medication and deep brain surgery, non invasive tremor suppression methods have become more popular in recent years. Functional electrical stimulation (FES) is one of the methods used to reduce tremor in several studies. However, the effect of different FES parameters on tremor suppression and discomfort level, including amplitude, the number of pulses in each stimulation burst, frequency, and pulse width is yet to be studied for longer stimulation durations. Therefore, in this work, experiments were performed on 14 participants with PD to evaluate the effect of thirty seconds of out-of-phase electrical stimulation on wrist tremor at rest. Trials were conducted by varying the stimulation amplitude and the number of pulses while keeping the frequency and pulse width constant. Each test was repeated three times for each participant. The results showed an overall tremor suppression for 11 out of 14 participants and no average positive effects for three participants. It is concluded that despite the effectiveness of FES in tremor suppression, each set of FES parameters showed different suppression levels among participants due to the variability of tremor over time. Thus, for this method to be effective, an adaptive control system would be required to tune FES parameters in real time according to changes in tremor during extended stimulation periods.