Actual Measurement Data Research Articles

Target Echo Detection Based on the Signal Conditional Random Field Model for Full-Waveform Airborne Laser Bathymetry

Airborne laser bathymetry (ALB) systems with digital full-waveform signal collection can obtain corresponding temporal positions from several backscattering surfaces by laser beam irradiation. This information can help describe the multi-elevation structures of the target and explore the echo signal attenuation response in different nonuniform mediums during laser propagation. Therefore, a full-waveform echo signal is quite practical for integrated water-land detection. However, the wavelength used in the ALB system is generally in the visible band range of 470~580 nm, and the received signal is constantly interfered with by many nontarget factors, such as imperfections in the receiving channel or the strong scattering from the transmission medium. The conventional processing method transforms nontarget interference into noise point cloud filtering or classification extraction, enabling the detection of a single surface or regular geometry. The accuracy of the identification and extraction for multi-elevation target surfaces echo signal is always reduced due to the significant noise signal intensity. We proposed a signal component detection method by constructing the echo signal feature functions and the conditional random field (CRF) model based on the full-waveform decomposition. The processing result for actual measurement data verified that the CRF strategy can effectively reduce the uncertainty of target surface detection. Compared with the single-beam echo sounder, the root mean square errors of the elevation deviation underwater were reduced by 3.2 cm and 4.9 cm respectively in the two different experimental areas.

CFD Simulation of the Airflow Distribution Inside Cube-Grow

Cube-Grow was developed by MARDI to promote urban agriculture to the urban population. The product enables urban people to grow their vegetables with limited space. The initial test run of the system shows that the plant growth inside the structure was below expectation. The problem arises due to a lack of airflow or improper ventilation inside the structure. Optimum ventilation or airflow is crucial for plant growth as it enhances evapotranspiration at the leaf area to promote optimum plant growth. Therefore, this study aims to increase the airflow inside the Cube-Grow and find the best location for the air hole. Computational fluid dynamics (CFD) simulation was used in this study the analyse the effect of adding an air hole to the airflow characteristic inside the Cube-Grow. CFD also was used to select the best location to place the air hole. 3 option of air hole location was analysed and the results were compared with the existing design. The initial CFD simulation results were compared with the actual measurement data before it was used for further analysis. The result shows that adding an air hole increases overall airflow inside the Cube-Grow. Option 3 was chosen as the best location for the air hole as it produces a uniform and higher airflow inside the Cube-Grow. The study proved that CFD was able to be used to optimize the design of Cube-Grow before the actual prototype was built.

Mathematical simulation analysis of optimal detection of shot-putters’ best path

Abstract In order to consider many uncertain factors in the process of shot-put, a fuzzy optimisation model of shot-put is proposed. With the help of fuzzy anthropometric data and strength data, the model calculates the fuzzy solution set of the athlete's best throwing mode and throwing distance with a known probability distribution, which reflects the actual process of shot throwing better than the non-fuzzy optimisation model. Then, using MATLAB6 software, the program design of the model solving and the user interface of optimisation software are developed, which realises fast calculation and good user interaction function. Finally, the actual measurement data of university shot-putters are used to verify the feasibility and effectiveness of the fuzzy optimisation model.

Characteristic Parameter Optimization of Wireless Flaw Sensing System Based on Active Interference Suppression Algorithm

Based on the active interference suppression algorithm, this study combines the radar working mode and the interference type and realizes the effective detection of the flaw detection signal by successively processing the radar receiving signal and the filtering processing. Firstly, this article builds a simulation platform similar to the actual situation to verify the existing conventional active interference suppression algorithms. Secondly, for the detection of chirp active deception jamming signals entering from the main lobe, a radar active deception jamming detection method based on the characteristic parameter matching of the harmonic components of active deception jamming signals is proposed. After that, the spectral characteristics of the harmonic components of the deception interference signal are analyzed, and the center frequency and the tuning frequency of the real target echo are obtained. Finally, by establishing a frequency modulation parameter library for possible interference harmonic signal components, the acquisition phase of the radar gate by the jammer matched analysis with the preestablished frequency modulation parameter library is implemented to achieve active deception interference detection. This method can effectively detect active deception jamming signals in a complex tunnel environment. The interference suppression algorithms verified by simulation include noise FM interference suppression algorithm based on cancellation and distance false target interference suppression algorithm based on LFM radar summary processing. Through actual measurement data processing and analysis, the effectiveness of the method is verified and the idea of interference suppression is expanded. The construction of the simulation platform is obtained by appropriately modifying the actual parameters, a certain type of suppression jammer, and a certain type of deception jammer used in a certain countermeasure field test at a radar station.

A method of determining the parameters in systems with serialized Current-Voltage characteristics

We propose a method of determining the parameters of systems with serialized characteristics, which may suggest the existence of symmetry in the system. The method is demonstrated in extracting the parameters of a metal-semiconductor in the presence of significant series resistance, which is itself important but limits the accuracy of the existing methods in the determination of the other calculated parameters such as barrier height and ideality factor. We show the steps involved in establishing whether symmetry exists, and show that some functional interrelations between the parameters and the independent variables can readily be established. We use actual measurement data from an experimental diode and show that the results outperform the popular Cheung-Cheung approach. This general approach, therefore, represents a significant advancement in the analysis of serialized empirical data.

INTELLIGENT HEALTH STATUS DETECTION METHOD FOR LOCOMOTIVE FUEL CELL BASED ON DATA-DRIVEN TECHNIQUES

Main drawbacks of fuel cell systems, namely, high cost, poor reliability, and short lifespan, limit the large-scale commercial application of fuel cell systems. The health status detection of fuel cell systems for locomotives is of great significance to the safe and stable operation of locomotives. To identify the failure modes of the fuel cell system accurately and quickly, this study proposed an intelligent health status detection method for locomotive fuel cells based on data-driven techniques. In this study, the actual test data of a 150-kW fuel cell system for locomotives was analyzed. The t-distributed stochastic neighbor embedding (t-SNE) algorithm was combined with the general regression neural network (GRNN) to intelligently detect the health status of the fuel cell system for locomotives. Specifically, t-SNE was used to process the high-dimensionality and strong coupling raw data of health status, enabling the dimensional reduction of the raw data to reflect essential features. Then, GRNN was used to identify the feature data to achieve the fast and accurate detection of the health status of the fuel cell system. Results show that the proposed method can effectively detect four health conditions, namely, normal state, high inlet coolant temperature, low air pressure, and low spray pump pressure, with a diagnostic accuracy of 98.75%. This study is applicable to the analysis of the actual measurement data of high-power level fuel cell systems and provides a reference for the health status detection of fuel cell systems for locomotives. Keywords: fuel cell system for locomotive; data-driven; general regression neural network; t-distributed stochastic neighbor embedding; health status detection

Image processing techniques to estimate weight and morphological parameters for selected wheat refractions

The geometric and color features of agricultural material along with related physical properties are critical to characterize and express its physical quality. The experiments were conducted to classify the physical characteristics like size, shape, color and texture and then workout the relationship between manual observations and using image processing techniques for weight and volume of the four wheat refractions i.e. sound, damaged, shriveled and broken grains of wheat variety PBW 725. A flatbed scanner was used to acquire the images and digital image processing method was used to process the images and output of image analysis was compared with the actual measurements data using digital vernier caliper. A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R2 in the range of 0.798–0.947. The individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021–0.045 and 12.56–46.32 g respectively. Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R2 in the range of 0.841–0.920. The sphericity of the refractions varied in the range of 0.52–0.71. Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also. A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R2 in the range of 0.845–0.945. Various color and grey level co-variance matrix texture features were extracted from acquired images using the open-source Python programming language and OpenCV library which can exploit different machine and deep learning algorithms to properly classify these refractions.

Mutual Information-Based Non-Local Total Variation Denoiser for Low-Dose Cone-Beam Computed Tomography.

Conventional non-local total variation (NLTV) approaches use the weight of a non-local means (NLM) filter, which degrades performance in low-dose cone-beam computed tomography (CBCT) images generated with a low milliampere-seconds (mAs) parameter value because a local patch used to determine the pixel weights comprises noisy-damaged pixels that reduce the similarity between corresponding patches. In this paper, we propose a novel type of NLTV based on a combination of mutual information (MI): MI-NLTV. It is based on a statistical measure for a similarity calculation between the corresponding bins of non-local patches vs. a reference patch. The weight is determined in terms of a statistical measure comprising the MI value between corresponding non-local patches and the reference-patch entropy. The MI-NLTV denoising process is applied to CBCT images generated by the analytical reconstruction algorithm using a ray-driven backprojector (RDB). The MI-NLTV objective function is minimized based on the steepest gradient descent optimization to augment the difference between a real structure and noise, cleaning noisy pixels without significant loss of the fine structure and details that remain in the reconstructed images. The proposed method was evaluated using patient data and actual phantom measurement data acquired with lower mAs. The results show that integrating the RDB further enhances the MI-NLTV denoising-based analytical reconstruction algorithm to achieve a higher CBCT image quality when compared with those generated by NLTV denoising-based approach, with an average of 15.97% higher contrast-to-noise ratio, 2.67% lower root mean square error, 0.12% lower spatial non-uniformity, 1.14% higher correlation, and an average of 18.11% higher detectability index. These quantitative results indicate that the incorporation of MI makes the NLTV more stable and robust than the conventional NLM filter for low-dose CBCT imaging. In addition, achieving clinically acceptable CBCT image quality despite low-mAs projection acquisition can reduce the burden on common online CBCT imaging, improving patient safety throughout the course of radiotherapy.

Signature network-based survey of the effects of a traditional Chinese medicine on heart failure

Ethnopharmacological relevanceHeart failure (HF) after myocardial infarction (MI) is one of the most common disabling and painful diseases. A traditional Chinese medicine (TCM) formula, Shengmaisan, is known as a multitarget medicine that is widely used clinically to treat heart failure (HF) in Asian countries. However, its mechanism has not been comprehensively demonstrated. Aim of the studyTo use a prediction network to figure out which disease link SMZ mainly alleviates in HF and find biomarkers related to myocardial fibrosis in the serum for clinical reference. Materials and methodsIn this article, we collected a large amount of actual measurement data and our own proteomics data, along with the biomarkers of heart failure staging under study to establish a precise network. Then, we tested and verified the medicinal effect of SMZ in treatment of HF after MI by Measurement of left ventricular wall thickness and ejection fraction by echocardiography. Then we tested the serum level of the potential targets of SMZ predicting by the network we developed using ELISA.Results: the cardiac ejection fraction and retarding the thinning of the anterior wall of the left ventricle increased after treating with SMZ. The serum level of EGFR and MAPK1 decreased in the groups treated with SMZ. ConclusionSMZ can improve the cardiac function of rats with MI by increasing the cardiac ejection fraction and retarding the thinning of the anterior wall of the left ventricle. In addition, SMZ could delay heart failure mainly by inhibiting the progression of myocardial fibrosis through decreasing the EGFR and MAPK1 levels.

A millennium-long climate history of erosive storms across the Tiber River Basin, Italy, from 725 to 2019 CE

Rainfall erosivity drives damaging hydrological events with significant environmental and socio-economic impacts. This study presents the world’s hitherto longest time-series of annual rainfall erosivity (725–2019 CE), one from the Tiber River Basin (TRB), a fluvial valley in central Italy in which the city of Rome is located. A historical perspective of erosive floods in the TRB is provided employing a rainfall erosivity model based on documentary data, calibrated against a sample (1923–1964) of actual measurement data. Estimates show a notable rainfall erosivity, and increasing variability, during the Little Ice Age (here, ~ 1250–1849), especially after c. 1495. During the sixteenth century, erosive forcing peaked at > 3500 MJ mm hm–2 h–1 yr–1 in 1590, with values > 2500 MJ mm hm–2 h–1 yr–1 in 1519 and 1566. Rainfall erosivity continued into the Current Warm Period (since ~ 1850), reaching a maximum of ~ 3000 MJ mm hm–2 h–1 yr–1 in the 1940s. More recently, erosive forcing has attenuated, though remains critically high (e.g., 2087 and 2008 MJ mm hm–2 h–1 yr–1 in 1992 and 2005, respectively). Comparison of the results with sediment production (1934–1973) confirms the model’s ability to predict geomorphological effects in the TRB, and reflects the role of North Atlantic circulation dynamics in central Italian river basins.

Development of in‐wheel receiver coil for dynamic wireless power transfer

AbstractShort cruise range is one of the major limitations of electric vehicles. To address this problem, dynamic wireless power transfer has been proposed. However, the eddy current loss in foreign matter is one of the greatest disadvantages of dynamic wireless power transfer. Eddy current loss decreases efficiency of wireless power transfer and increases temperature of the foreign matter, which causes burning of the combustible materials around the foreign matter in the worst case. Herein, we propose a wireless power transfer system named in‐wheel coil wherein the magnetic flus in concentrated in the tires and wheels. In addition, we propose suitable materials for the tires and the wheels of the system, to reduce the eddy current loss, are proposed using actual measurement data.

Development and Analysis of a Dynamic Energy Model of an Office Using a Building Management System (BMS) and Actual Measurement Data

Calibration of the energy model of a building is one of the essential tasks required to determine the efficiency of building management systems, and both their own and other systems’ improvement potential. In order to make the building energy model as accurate as possible, it is necessary to collect comprehensive data on its operation and sometimes to assess the missing information. This paper represents the process of developing an energy model for an administrative building and its calibration procedure, using detailed long-term measurement and building management system (BMS) data. Indoor air temperature, CO₂ concentration, and relative humidity were experimentally measured and evaluated separately. Such dual application of data reduces the inaccuracy of the assumptions made and assesses the model’s accuracy. The DesignBuilder software developed the building model. During the development of the model, it was observed that the actual energy consumption needs to be assessed, as the assumptions made during the design about the operation and management of HVAC systems often do not coincide with the actual situation. After integrating BMS information on HVAC management into the building model, the resulting discrepancy between the model results and the actual heat consumption was 6.5%. Such a model can be further used to optimize management decisions and assess energy savings potential.

The Effect of Sea Level Rise on Adjacent Land and the Resulting Land Value Assessment Method

Joo, S.-M. and Kim, J.-H., 2021. The effect of sea level rise on adjacent land and the resulting land value assessment method. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 479–483. Coconut Creek (Florida), ISSN 0749-0208. This study aims to infer the possibility of inundation of coastal areas due to sea level rise caused by climate change. In addition, it seeks to present a necessary survey method and value calculation method to be reflected on the land value. To this end, GIS spatial analysis was performed using tidal observation data and flooding maps. The analysis of IPCC sea level rise assuming the worst-case scenario (Scenario 1) and the gradual-case scenario (Scenario 2) of the actual measurement data of the Busan Tide Observatory showed that 3.8%–5.5% of the total land in Haeundae-gu will be at risk of flooding within the next 100 years. Particularly, lands with a relatively high value at present are more vulnerable. If the method of calculating flood-prone areas presented in this study is introduced in the Government's Land Characteristic Evaluation and the standard land value for areas expected to be flooded is accurately assessed, then the economic loss and social confusion caused by future sea level rises can be prevented.

Ultra-Short-term Power Prediction of the Wind Farm Based on Multivariate Data Combination

Accurate wind power prediction is an important way to promote large-scale wind power grid connection. First, to address the abnormal wind farm actual measurement data caused by wind abandonment and power limitation, the DBSCAN method is used to pre-process the wind farm actual measurement data and eliminate the abnormal data. Then, a short-term wind power prediction model with a combination of GA-LSSVM and ARIMA weights is established, and the Lagrange multiplier algorithm is used to obtain the weighted values of each single model in the combined model to further obtain the wind power prediction results. Finally, the effectiveness of the proposed method is verified by arithmetic examples, and the results show that the proposed model and method can effectively improve the prediction accuracy of short-term wind power.

Sizing of an Islanded Wind-Solar-Battery Hybrid Power System for a Zero-Energy House in Afyon Turkey

Renewable energy systems offer sustainable solutions for the energy need of the world. Hybrid systems can be an alternative for remote off-grid houses that do not have connection to the electricity grid. This study examines sizing of an islanded wind-solar-battery power system for a zero-energy house that does not have a grid connection in Afyon Turkey. For this remote house, continuous power supply is an essential requirement and the designed system must guarantee reliable uninterrupted supply of power to the house. In an off-grid wind-solar-battery hybrid system, if different rated wind turbines and/or different number of solar panels are used in the system, the minimum number of batteries needed in the hybrid system would be different in order the meet the uninterrupted continuous power supply requirement. In practice it is the usual case that, due to incompatible hybrid system components the consumers suffer from no electric power occasionally. In this study, using power consumption data of the considered remote house and actual wind and solar measurement data of Afyon site, sizing analysis for a hybrid wind-solar-battery power system is done. For a sizing analysis, calculations that use actual small time interval data are essential to ensure uninterrupted continuous power from a hybrid system.

전기추진 선박의 연료비용 분석에 따른 최적 발전시스템 구성방안 및 에너지 저장장치 적용의 효용성 분석

This paper presents how to design the configuration of generation system for shipboard power system based on fuel cost analysis. We propose a sophisticated economic analysis method with detailed fuel cost evaluation, which can be obtained from actual specific fuel consumption (SFC) characteristics of the gas-turbine and diesel generators installed in existing ships. Using the proposed method, we analyze economic benefits for optimal configurations of shipboard generation system for both hybrid propulsion ships such as CODLOG (COmbined Diesel-eLectric Or Gas-turbine) and COGLOG (COmbined Gas-turbine eLectric Or Gas-turbine) and full electric propulsion ships. In addition, we analyze the effects of energy storage system (ESS) in shipboard power systems in terms of spinning reserve management and single generator operation mode to meet the reliability and survivability requirement. The effectiveness of the proposed method is verified through multiple case studies with actual measurement data in ships that are currently in operation.

Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms

Rapid and accurate estimation of rice Nitrogen Nutrition Index (NNI) is beneficial for management of nitrogen application in rice production. Traditional estimation methods required manual actual measurement data in the field, which was time-consuming and cost-expensive, and RGB images from unmanned aerial vehicle (UAV) provided an alternative option for nitrogen nutrition index (NNI) monitoring. In this study, RGB images from unmanned aerial vehicle (UAV) were obtained from each growth period of rice, and six machine learning (ML) algorithms, i.e., adaptive boosting (AB), artificial neural network (ANN), K-nearest neighbor (KNN), partial least squares (PLSR), random forest (RF) and support vector machine (SVM), were used to extract target information for estimating NNI as well as vegetation index (VI). Results showed that most UAV VIs were significantly correlated with rice NNI at the key growing periods; the estimation results of rice NNI using six ML algorithms showed that the RF algorithms performed the best at each growth period with the determination coefficient (R2) ranged from 0.88 to 0.96 and room mean square error (RMSE) ranged from 0.03 to 0.07, in which the estimation of NNI was the best in filling period and the early jointing stage. Rice NNI at the early jointing stage was significantly correlated with soil available nitrogen (AN) with the R2 of 0.84 in Pukou and 0.72 in Luhe, respectively, and rice NNI was significantly correlated with the yield with the R2 of more than 0.7 in Pukou at the whole period and more than 0.7 in Luhe from late jointing to maturity stage. Therefore, the combination of RGB images from UAV and ML algorithms was a scalable, simple and inexpensive method for rapid qualification of rice NNI, which effectively improved nitrogen use efficiency and provided guidance for precision fertilization in rice production.

Underground Goaf Parameters Estimation by Cross-Iteration with InSAR Measurements

Determining the geographic location and spatial distribution of underground goaf is of great significance for the prevention of mining subsidence hazards and the detection of illegal mining. However, traditional goaf detection techniques mainly focus on geophysical methods that are labor intensive, have low efficiency, and are expensive. Due to the large range and off-site monitoring capability of interferometric synthetic aperture radar (InSAR) techniques, research on goaf location detection based on InSAR measurements has been increasing. This paper proposes a new method for locating underground goaf based on cross-iteration and InSAR measurements. Firstly, the functional relationship between the geometric parameters of the goaf and the line of sight (LOS) deformation retrieved by InSAR techniques is constructed. Then, the three initial model parameters of the probability integration method (PIM) are determined by mining geological conditions. Finally, the cross-iteration method is used to determine the parameters to characterize the spatial location of underground goaf. The experimental results show that the average relative errors of the simulated experiment and the real experiment are 1.5% and 5.1%, respectively, and the inverted goaf parameters are in good agreement with the real values. Moreover, the proposed method only requires the main lithology of the overlying rock in the goaf and does not depend on the accuracy of PIM model parameters. Therefore, this method has engineering application value for the detection of goaf lacking actual measurement data or that caused by illegal mining.

Rabi-oscillation spectroscopy of the hyperfine structure of muonium atoms

As a new method to determine the resonance frequency, Rabi-oscillation spectroscopy has been developed. In contrast to the conventional spectroscopy which draws the resonance curve, Rabi-oscillation spectroscopy fits the time evolution of the Rabi oscillation. By selecting the optimized frequency, it is shown that the precision is twice as good as the conventional spectroscopy with a frequency sweep. Furthermore, the data under different conditions can be treated in a unified manner, allowing more efficient measurements for systems consisting of a limited number of short-lived particles produced by accelerators such as muons. We have developed a fitting function that takes into account the spatial distribution of muonium and the spatial distribution of the microwave intensity to apply the new method to ground-state muonium hyperfine structure measurements at zero field. This was applied to the actual measurement data and the resonance frequencies were determined under various conditions. The result of our analysis gives $\nu_{\rm HFS}=4\ 463\ 301.61 \pm 0.71\ {\rm kHz}$, which is the world's highest precision under zero field conditions.

Errors of Airborne Bathymetry LiDAR Detection Caused by Ocean Waves and Dimension-Based Laser Incidence Correction

Ocean waves are a vital environmental factor that affects the accuracy of airborne laser bathymetry (ALB) systems. As the regional water surface undulates with randomness, the laser propagation direction through the air–water surface will change and impact the underwater topographic result from the ALB system, especially for the small laser divergence system. However, the natural ocean surface changes rapidly over time, and uneven ocean surface point clouds from ALB scanning will cause an uncertain estimation of the laser propagation direction; therefore, a self-adaptive correction method based on the characteristics of the partial wave surface is key to improving the accuracy and applicability of the ALB system. In this paper, we focused on the issues of spatial position deviation caused by surface waves and position correction of the underwater laser footprint, and the dimension-based adaptive method is applied to attempt to correct the laser incidence angle. Simulation experiments and analysis of the actual measurement data from different ALB systems verified that the method can effectively suppress the influence of ocean waves. Furthermore, the inversion result of sea surface inclination changes is consistent with the surface wind wave reanalysis products. Based on the laser underwater propagation model in the strategy, we also quantitatively analyzed the influence of surface waves on laser bathymetry, which can guide the operation selection and data processing of the ALB system at specific water depths and under dynamic ocean conditions.