Acquisition Of Parameters Research Articles

Research on rapid extraction of internal resistance of lithium battery based on short-time transient response

Unlike laboratory measurements and online parameter observations based on long-term continuous operation data, offline fast measurement of battery parameters is widely used for convenient and accurate acquisition of battery parameters in battery production and maintenance processes. However, too short allowable measurement time in engineering results in a small amount of dynamic information that the algorithm can obtain and unclear initial state values, making it difficult to improve the accuracy of the algorithm. This paper employs a local coordinate system established in the vicinity of the measurement moment to extract the transient behavior of battery terminal voltage response under excitation current, and investigates the characterization capability of these transients on ohmic and polarization internal resistance of batteries at different time points. Moreover, based on an equivalent circuit model, it evaluates how initial values of transient voltage affect accuracy in calculating internal resistance. Through customized working condition verification, the selection principles for rapid identification of time points and the basic requirements for excitation conditions have been determined. Comparative verification of online parameter identification results based on continuous time series demonstrates that the fast identification algorithm can accurately identify Ohmic internal resistance and effectively characterize the variation law of battery polarization.

Design and Testing of a Wheeled Crop-Growth-Monitoring Robot Chassis

The high-flux acquisition of crop growth information can be realized using field monitoring robotic platforms. However, most of the existing agricultural monitoring robots have been converted from expensive commercial platforms, and they thus have a hard time adapting to the farmland working environment, let alone satisfying the basic requirements of sensor testing. To address these problems, a wheeled crop-growth-monitoring robot that features the accurate, nondestructive, and efficient acquisition of crop growth information was developed based on the cultivation characteristics of wheat, the obstacle characteristics of the wheat field, and the monitoring mechanism of spectral sensors. By analyzing the phenotypic structural change characteristics and the requirements for the row spacing of different wheat varieties throughout the growth period, a four-wheel mobile chassis was designed with an adjustable wheel track and a high-clearance body structure that can effectively eliminate the risk of the robot destroying the wheat during operation. Moreover, considering the requirements for wheeled robots to overcome obstacles in field operations, a three-dimensional (3D) model of the robot was created in Pro/E. Models of obstacles in the field (e.g., pits and bumps) were created in Adams to simulate the operational stability of the robot. The simulation results showed that the mass center displacement of the robot was smaller than 0.2 cm on flat pavement and the maximum mass center displacement was 1.78 cm during obstacle crossing (10 cm deep pits and 10 cm high bumps). The field test showed that the robot equipped with active-light-source crop growth sensors achieved stable, real-time, nondestructive, and accurate acquisition of the canopy vegetation parameters—NDVI (normalized difference vegetation index) and RVI (ratio vegetation index)—and the wheat growth parameters—LAI (leaf area index), LDW (leaf dry weight), LNA (leaf nitrogen accumulation), and LNC (leaf nitrogen content).

Model-Free Predictive Current Control of Five-Phase PMSM Drives

Model predictive control is highly dependent on accurate models and the parameters of electric motor drives. Multiphase permanent magnet synchronous motors (PMSMs) contain nonlinear parameters and mutual cross-coupling dynamics, resulting in challenges in modeling and parameter acquisition. To lessen the parameter dependence of current predictions, a model-free predictive current control (MFPCC) strategy based on an ultra-local model and motor outputs is proposed for five-phase PMSM drives. The ultra-local model is constructed according to the differential equation of current. The inherent relation between the parameters in the predictive current model and the ultra-local model is analyzed in detail. The unknowns of the ultra-local model are estimated using the motor current and voltage at different time instants without requiring motor parameters or observers. Moreover, space vector modulation technology is employed to minimize the voltage tracking error. Finally, simulations and experiments are conducted to verify the effectiveness of the MFPCC with space vector modulation. The results confirm that the proposed method can effectively eliminate the impact of motor parameters and improve steady-state performance. Moreover, this control strategy demonstrates good robustness against load variations.

Measuring thermal comfort using wearable technology in transient conditions during office activities

This paper presents an experiment for assessing thermal comfort of occupants in the built environment, from a subjective perspective, focusing on office environment; a dedicated measurement campaign using sensors for acquisition of physiological and environmental parameters was conducted. Skin temperature was measured with two sensors: a minimally invasive sensor for measuring wrist temperature, and a thermal camera to retrieve forehead temperature; simultaneously, heart rate variability was measured using a wearable device. 15 participants were exposed to dynamic changes of air temperature. Data was collected to measure the participants’ thermal sensation vote, with machine learning algorithms. Decision Tree provided higher performances, using a dataset made of wrist temperature, heart rate variability features and air temperature, with mean average error and mean absolute percentage error of 0.86 and 20.9%. The research contributes to thermal comfort personalization in the built environment, to improve well-being and productivity of occupants using minimally invasive sensor network.

Effect of Bergmeister papilla on disc parameters in spectral domain optical coherence tomography

ObjectivesTo investigate the morphological characteristics of Bergmeister papilla (BMP), a persistent hyaloid remnant tissue, and its effects on the measurements and repeatability of spectral-domain optical coherence tomography (OCT).Subjects/MethodsThe subjects of this prospective cross-sectional study including 83 patients with BMP and 76 unaffected individuals, all had open-angle structures. Images, including a 5-line raster and three consecutive optic disc cube scans centred on the optic disc, were acquired using the Cirrus high-definition OCT. BMP’s structural characteristics were classified based on the raster scan images, and repeatability of acquiring optic nerve head and retinal nerve fibre layer parameters acquisition was analysed by calculating the test-retest standard deviation (Sw), coefficient of variance (CV), and intraclass correlation coefficient.ResultsBMPs (n = 83) were categorised into lifting edge (LE) type (63.9%, n = 53), which partially covers the edge of the optic nerve head, and covering disc (CD) type (36.1%, n = 30), which completely covers the cupping area like a cap. The average cup-to-disc ratio (0.58 ± 0.21), vertical cup-to-disc ratio (0.55 ± 0.21), and cup volume (0.22 ± 0.22) of the CD type were lower than those of the LE type (0.66 ± 0.13, 0.64 ± 0.13, and 0.4 ± 0.27, respectively; all P < 0.05). Tolerability indices for repeatability of cup volume (Sw = 0.40 and CV = 0.36) and inferonasal (4 o’clock) retinal nerve fibre layer (Sw = 0.27 and CV = 0.25) in LE-type BMPs exceeded the cut-off value (0.22) and demonstrated stronger correlation with BMP location than that of the controls.ConclusionCaution should be exercised when interpreting OCT findings in eyes with BMP, as BMP can introduce a pitfall in OCT imaging.

A Novel Unsupervised Segmentation Method of Canopy Images from UAV Based on Hybrid Attention Mechanism

Crown segmentation is a pivotal process in the acquisition of tree parameters. In light of the high expenses associated with satellite remote sensing and LiDAR technologies, our study leverages the cost-effective and efficient UAV remote sensing technology for capturing crown images. In addition, considering the expense and sensitivity associated with labeling data for supervised learning and its implications on model generalization and label quality, this paper introduces an innovative unsupervised learning framework based on convolutional neural networks (CNN). To address the limited receptive field of CNN, we have introduced a novel hybrid attention module following each CNN module. This enhancement ensures the integrity of the segmentation results and the coherence of the boundaries. Furthermore, in response to the growing need for user interaction, we have incorporated a scribble interaction function. Through the semantic segmentation of the collected crown images, our proposed method attains remarkable results, achieving an accuracy of 98.15%, an F1_score of 97.01%, and an mIoU of 95.58%. Additionally, we have conducted a comparative analysis of our proposed method with two clustering algorithms, namely K-Means and GMM, and two CNN models, DeepLab and U-Net. The results reveal that our segmentation structures outperform other methods significantly. The experimental findings demonstrate the immense application potential of this method in diverse fields, including forestry management, environmental protection, and ecosystem monitoring.

Integration of Vis–NIR Spectroscopy and Machine Learning Techniques to Predict Eight Soil Parameters in Alpine Regions

Visible and near-infrared spectroscopy (Vis–NIR, 350–1100 nm) has great potential for predicting soil properties. However, current research on the hyperspectral prediction of soil parameters in agricultural areas of alpine regions and the types of parameters included is limited, and optimal spectral treatments and predictive models applicable to different parameters have not been sufficiently investigated. Therefore, we evaluated the accuracy of predicting total nitrogen (TN), phosphorus pentoxide (TP2O5), total potassium oxide (TK2O), alkali-hydrolyzable nitrogen (AHN), effective phosphorus (AP), effective potassium (AK), soil organic matter (SOM), and pH in the Qinghai–Tibet Plateau using the Vis–NIR technique in combination with spectral transformations, correlation analysis, feature selection, and machine learning. The results show that spectral transformations improve the correlation between spectra and parameters but are dependent on the parameter type and the method used. Continuum removal (CR), logarithmic first-order differential (FDL), and inverse first-order differential (FDR) had the most significant effects. The feature bands were extracted using the SPA and modeled using partial least squares (PLSR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and backpropagation neural networks (BPNNs). The accuracy was evaluated based on R2, RMSE, RPD, and RPIQ. We found that the PLSR model only enables the prediction of SOM and pH with lower accuracy than the remaining models. XGBoost can predict all of the parameters but only for AHN; the prediction performance is better than other methods (R2 = 0.776, RMSE = 0.043 g/kg, and RPIQ = 2.88). The RF, SVM, and BPNN models cannot predict AK, AP, and AHN, respectively. In addition, TP2O5, AP, and pH are best suited for modeling using RF (RPIQ = 2.776, 3.011, and 3.198); TN, AK, and SOM are best suited for modeling using BPNN (RPIQ = 2.851, 2.394, and 3.085); and AHN and TK2O are best suited for XGBoost and SVM, respectively (RPIQ = 2.880 and 3.217). Therefore, this study can provide technical and data support for the accurate and efficient acquisition of soil parameters in alpine agriculture.

A novel method of Gas-Path health assessment based on exhaust electrostatic signal and performance parameters

Gas-path monitoring plays a crucial role in ensuring the safety of aero-engines during flight. Utilizing advanced electrostatic monitoring technology, this study introduces a novel method for health assessment of the gas-path system. To conduct the experiment, an aero-engine test bench was employed, enabling the acquisition of both electrostatic signals and performance parameters. Two innovative electrostatic signal features were devised, leveraging the energy measurements from the time-domain and frequency-domain analyses. Subsequently, the electrostatic features and performance parameters were standardized in alignment with the control parameters. In order to quantify the health of the gas-path system, a logistic regression algorithm was employed in combination with the available data. The results substantiate that the electrostatic monitoring information effectively reflects the condition of the gas-path system. This proposed method not only provides early detection of gas-path faults or performance degradation, but also serves as a critical foundation for maintenance decision-making. Notably, it offers distinct advantages over the sole reliance on performance parameters.

Current status and applications of photovoltaic technology in wearable sensors: a review

The rapid development of wearable sensor technology can be attributed to developments in materials, microelectronics, fabrication, communication systems, and Artificial Intelligence (AI). The use of wearable sensors enables continuous acquisition and monitoring of the pathophysiological parameters of a person in real time. The global market for health-related wearables has experienced significant growth, particularly in response to the COVID-19 pandemic. A wearable sensor module is comprised of various components, including a powering unit, sensor(s), acquisition unit, communication unit, and processing unit. The non-fluctuating power source with a long life is of utmost significance to the continuous and real-time operation of a wearable sensor. A wearable device can be powered by a rechargeable battery, such as a lithium-ion battery, which can be charged from a standard power source but requires regular recharging after depletion and has a negative environmental impact. This necessitates using green renewable energy sources like photovoltaic cells, piezoelectric generators, wind energy converters, and thermoelectric generators for powering wearable sensor modules. The photovoltaic cell that converts photonics into electrical energy is deemed a viable green energy source for wearable sensor modules. This article reviews the progress and application of photovoltaic technology in wearable sensor modules.

Numerical modelling of the mode I fracture behavior in composite laminates with significant R-curve effect

Significant R-curve effect is very common in the mode I fracture of composite laminates. Accurate simulation of fracture with R-curve effect is important for damage tolerance design of composite structures. However, traditional numerical methods usually do not consider the R-curve effect. In this work, four methods, including three phenomenological methods and cohesive zone model with multi-linear constitutive relation are presented. The three phenomenological methods are based on bilinear cohesive zone model, virtual crack closure technique and extended finite element method respectively with the consideration of R-curve. It is interesting to know which method is more suitable for modelling delamination growth with significant R-curve effect. To achieve this goal, double cantilever beam specimen is modeled and delamination growth is simulated using above four methods. Comparisons between the numerical results and the experimental results of laminates with different material types and stack sequences are made for these methods. Finally, the accuracy, computational efficiency and parameter acquisition of these methods are discussed with the goal of finding a suitable method for delamination modelling in laminated composites. It is shown that the cohesive zone model with multi-linear constitutive relation shows good accuracy in all studied cases.

Simulation study on fire and evacuation of super high-rise commercial building

The construction of high-rise and multi-functional commercial buildings has emerged as a global trend, but it also introduces significant fire hazards. Consequently, there is a pressing need to examine the fire development trends in super high-rise commercial buildings and establish logical evacuation strategies. This study utilizes the Pyrosim program to simulate a typical fire scenario at the Yangtze River International Conference Center, allowing for the acquisition of essential fire parameters and available evacuation time. Subsequently, based on the complex functional zones and personnel distribution within the building, appropriate evacuation plans are devised and simulated using the Pathfinder software. The results reveal that by effectively utilizing stairs, elevators, and refuge floors in accordance with personnel distribution, evacuation efficiency can be substantially enhanced. Furthermore, the required evacuation time during nighttime fires is merely 36.6%–55.3% compared to unorganized evacuation. The findings of this study offer valuable theoretical guidance for the evacuation of super high-rise commercial buildings, holding immense significance in minimizing fire-related casualties within such structures.

Evaluation of personal dose equivalents during positron emission tomography and computed tomography imaging

This study aimed to evaluate the annual occupational radiation exposure of technologists during PET/CT imaging in 2020 and 2021. Eleven nuclear medicine technologists were monitored at the PET/CT department. The personal dose equivalents (PDEs) for staff members, measured in terms of Hp (10) and Hp (0.07), were assessed annually, considering both deep and surface doses. PDEs were quantified using a thermoluminescent detector (LiF:Mg:Ti)). The average and range of PDEs and extremity doses (mSv) for the technologists were as follows: 4.5 (0.1–13.4) for Hp (10), 4.63 (0.1–13.9) for Hp (0.07), and 2.5 (0.2–17.5) for extremity, respectively. The results indicated that staff members are exposed to high-energy gamma rays while preparing radiopharmaceuticals, injections, and image acquisition. Therefore, proper handling of radiopharmaceuticals and patient management during the acquisition of parameters are crucial for maintaining the occupational dose within the defined limit.

MRI in rectal cancer: An institutional real life analysis of technical parameters

MRI plays a key role in the preoperative staging of rectal cancers and choice of neoadjuvant radiochemotherapy. Yet, the acquisition and interpretation of rectum magnetic resonance imaging (MRI) turn out to be unequal, impacting patients'care. The present study aims at evaluating the quality of the acquisition of technical parameters of the rectal MRI performed by comparing them according to the various guidelines. The medical MRI reports of all consecutive patients with locally advanced rectal cancer treated in a curative intent, by preoperative RCT and completion surgery were retrospectively reviewed over two periods (January 2010-December 2014 and January 2018 and December 2020) according to international 2012 and 2016 ESGAR and 2017 SAR MRI recommendation reports. During the first period (69 MRI performed), 58% of these MRI abided by the recommendations and 75% of essential criteria could be found in 25.5% of MRI reportings. During the second period (73 MRI performed), the protocol used by 6.8% of MR images abided by the 2016 Society of Gastrointestinal and Abdominal Radiology(ESGAR) recommendations and 39.7% abided by the Society of Abdominal Radiology (SAR) recommendations. 75% of essential criteria could be found in 52.3% of MRI reportings and 90% of essential criteria could be found in 6.2% of MRI reportings. In an era of increasing individualized patient care and conservative treatment focused on tumour response and prognostic factors, the present study showed that compliance to MRI protocols and reporting guidelines needs improving to upgrade patient care.

A novel data acquisition approach for valves in industrial refrigeration

In this paper, a novel method is introduced for the acquisition and transmission of critical parameters from valves used in industrial refrigeration. The parameter sensing provides local intelligence to the valve, which can be later used as a feedback signal for the controller. The hardware design includes ICS (Industrial Control Servo) valve with modifications to connect a Linear Variable Transformer (LVT) position sensor, pressure and temperature sensors. Also, a circuit board carrying the phase shift oscillator, Root Mean Square (RMS) to Direct Current (DC) converter, power supply and a dedicated Bluetooth Low Energy (BLE) module for transmitting the collected data is designed. The transmitted data can be received in a mobile phone android application and used for a secondary data calculation, alarm notification and alerting service technician. The sensor integration along with BLE connectivity creates a novel platform for data acquisition in Industrial Refrigeration (IR). The proposed method tested in real time to regulate the Helium gas flow rate and measured valve parameters is monitored by the dedicated mobile application.

Improved parameter acquisition of generalized Maxwell model in asphalt mixtures by continuous relaxation spectrum

The generalized Maxwell (GM) model is widely used to describe the viscoelastic behavior and predict performance of asphalt mixtures. However, the existing methods may not provide satisfactory accuracy because relaxation spectral distribution features are not considered. In this study, the methods (area equalization (AE) and peak equalization (PE) method) consider the distribution characteristics of the relaxation spectrum were proposed to obtain the Prony series parameters of the GM model. Two typical asphalt mixtures, HMA (hot mix asphalt mixture) and WMA (warm mix asphalt mixture), were used as case studies. The Prony series parameters of the GM model obtained by the proposed methods and existing methods (traditional fitting (TF) method and Limit equalization (LE) method) were discussed and analyzed. The derived Prony series parameters were also employed in finite element (FE) simulations to inversely predict dynamic modulus, and the simulated results were then compared with the experimental results. The results indicate that the spring stiffnesses of Maxwell elements in the Prony series parameters obtained by different methods have different distribution features. For the TF method, the Maxwell element with larger relaxation time has smaller spring stiffness. In the LE and PE method, the spring stiffness of the Maxwell element first increases and then decreases as the relaxation time increases. The spring stiffness is the same for the different Maxwell elements in the AE method. More importantly, the number of branches of the GM model is increased from 6 to 12, the relative error of the predicted dynamic modulus of HMA by the PE, AE, LE and TF method is reduced from 29.8% to 4.8%, from 49.9% to 25.9%, from 61.4% to 32.7%, from 159.1% to 133.9% respectively. The PE method has the best accuracy in FE analysis, even with a smaller number of branches of the GM model. The similar conclusion can also be summarized in the dynamic modulus prediction of WMA. Therefore, the PE method that considers the rate of change of the relaxation spectrum is preferentially recommended to obtain the Prony series parameters of the GM model.

Inverse design of optical lenses enabled by generative flow-based invertible neural networks

Developing an optical geometric lens system in a conventional way involves substantial effort from designers to devise and assess the lens specifications. An expeditious and effortless acquisition of lens parameters satisfying the desired lens performance requirements can ease the workload by avoiding complex lens design process. In this study, we adopted the Glow, a generative flow model, which utilizes latent Gaussian variables to effectively tackle the issues of one-to-many mapping and information loss caused by dimensional disparities between high-dimensional lens structure parameters and low-dimensional performance metrics. We developed two lenses to tailor the vertical field of view and magnify the horizontal coverage range using two Glow-based invertible neural networks (INNs). By directly inputting the specified lens performance metrics into the proposed INNs, optimal inverse-designed lens specifications can be obtained efficiently with superb precision. The implementation of Glow-assisted INN approach is anticipated to significantly streamline the optical lens design workflows.

Identification method of raindrops and hailstones based on digital holographic interference.

The identification of raindrops and hailstones is of great significance to the study of precipitation characteristics from the aspect of microphysics and can provide important data support for weather modification. In this paper, an identification method of raindrops and hailstones based on digital holographic interference is proposed. The grayscale gradient variance method is used to obtain the focus position of the particles. By means of binarization and morphological processing, digital holograms are processed to obtain clear profiles of the particles. Then the contour parameters of the particles are used to obtain the equivalent volume diameter and roundness. Finally, according to the equivalent volume diameter, roundness and lens-like effect of the particles, the phase states of the raindrop and hailstone are identified by the algorithm. Experiments show that the method proposed in this paper has a good identification effect on raindrops and hailstones. The research results can provide reference for the research of the identification method of raindrops and hailstones and the acquisition of accurate characteristic parameters.

Design and research of digital twin system for multi-environmental variable mapping in plant factory

Internet of Things (IoT) technology develops rapidly and is increasingly used in the field of facility agriculture, the emergence of digital twin technology provides a possibility for the intelligent management of facility agriculture. In this study, IoT and digital twin technologies are combined to design a digital twin system for mapping multiple environmental variables in plant factories to realize the intelligent management and help plant factory managers make intelligent decision-making management schemes. The system includes a multi-environmental variable parameter acquisition system and an environment variable control system based on Arduino, a multi-environmental variable data monitoring and remote environment control system based on Android, and a multi-environmental variable mapping digital twin model. IoT and computer development technologies are used to monitor environmental parameters and control the state of various equipment in plant factories and realize the connection between things and networks. Digital twin technology maps the multi-environmental variables of plant factories to digital twin and intuitively feeds back the parameters of the multi-environmental variables to the plant factory managers, completing the connection between people and things. By combining the IoT technology with digital twin technology, a digital twin system based on multi-environmental variable mapping in plant factory is established, so that the parameters of environmental variables such as CO2, temperature and humidity in plant factory can be accurately adjusted according to the different needs of different stages of plant growth, and it can help plant factory managers to realize intelligent decision-making.

Legal custom and other rules established by practice: issues of correlation

The article is devoted to the issues of the comparison between legal custom and other rules, formed by practice. In particular, the correlation of legal custom with the practice of state authorities, legal precedent and personal business practice is considered. Identifying the relationships between them makes it possible to understand the meaning of the corresponding phenomena. Differences in the legal nature of the mentioned phenomena cause the fact that some are sources of law, while others are not. The acquisition or loss of certain parameters (features) can lead to the transition (transformation) of one phenomenon into another with corresponding consequences.&#x0D; It is explained, that the practice of state authorities is not an independent form of law, but can create such forms - legal custom, legal precedent, etc. It can also create quasi-legal phenomena – agreements between governing subjects, clerk habits, etc. The criteria for distinguishing between legal custom and legal precedent are defined: subjects, the nature of law-making practice, the intensity of such practice, the justification of validity and content, the level of certainty and formalization, as well as the scope of legal action. A legal custom is a general social practice, which consists in repeating certain actions several times or refraining from them by a wide range of subjects. Instead, legal precedent is formed by the special practice of the competent authority in the course of law enforcement, which is most often expressed in a specific judicial or administrative decision.&#x0D; It has been found, that business practice is able to create two basic types of rules for contracting parties – legal customs and rules of personal business practice. The first of them are characterized by generality and in terms of legal consequences are equated to the norms, contained in the legislation. Instead, the latter bind only specific counterparties, and therefore are equivalent to the terms of the contract, concluded between them. The use of the term "custom" to delineate the rules, formed by personal business practice, is not only contrary to its nature, but also creates a terminological imbalance in law of obligations

The 103Rh NMR spectroscopy and relaxometry of the rhodium formate paddlewheel complex.

The nuclear magnetic resonance (NMR) spectroscopy of spin-1/2 nuclei with low gyromagnetic ratio is challenging due to the low NMR signal strength. Methodology for the rapid acquisition of 103Rh NMR parameters is demonstrated for the case of the rhodium formate "paddlewheel" complex Rh2(HCO2)4. A scheme is described for enhancing the 103Rh signal strength by polarization transfer from 1H nuclei, which also greatly reduces the interference from ringing artifacts, a common hurdle for the direct observation of low-γ nuclei. The 103Rh relaxation time constants T1 and T2 are measured within 20minby using 1H-detected experiments. The field dependence of the 103Rh T1 is measured. The high-field relaxation is dominated by the chemical shift anisotropy mechanism. The 103Rh shielding anisotropy is found to be very large: |Δσ| = 9900 ± 540ppm. This estimate is compared with density functional theory calculations.