Maximum Average Absolute Error Research Articles

Thermal characteristics of solid-state battery and its thermal management system based on flat heat pipe

Solid-state batteries (SSBs) offer promising potential for commercial applications due to their high energy density and elimination of the flammability associated with conventional liquid electrolyte batteries (LBs). Taking into account both experimental and simulated outcomes, the SSB generates more heat than LB, which leads to a higher temperature rise. To effectively address this issue, a hybrid battery thermal management system (BTMS) with micro heat pipe array (MHPA) and air-cooling was developed in this work for the solid-state battery pack (SSBP). In parallel, an electro-thermal coupling model at the pack level considering parallel branch current distribution and an equivalent thermal resistance-based MHPA model were developed. The models were verified by comparing the experimental and simulated results on the 2-parallel and 1-series connected (2P1S) battery pack and the BTMS. A maximum average absolute error (AEave) of 1.11 °C, an average relative error (REave) of 1.62 %, and a root mean square error (RMSE) of 0.88 °C were considered. The simulations were conducted to compare the temperature rise of the battery pack with MHAP and aluminum plate cooling. In the 35 °C-35 °C-6 m/s cooling condition and at 1.5C discharge, the pack temperature reached 52.21 °C with aluminum plate cooling, 8 °C higher than that with MHPA. Moreover, the simulations predicted the temperature, current, and state of charge distributions of 3P4S connected SSBP and LB pack (LBP) based on the hybrid BTMS at different cooling conditions. The results showed that the temperatures of SSBP and LBP were controlled within 43 °C even at 35 °C ambient temperature. Reducing the air temperature had a stronger cooling capacity than increasing the air speed. However, the temperature gradient was exacerbated to 3.29 °C and 1.54 °C in the SSBP and LBP. The impact of temperature difference on the resistance worsened the current inhomogeneity in the parallel branch and led to a maximum state of charge (SOC) difference of 5.59 % within the SSBP and 0.56 % in the LBP. Thus, to achieve an efficient cooling of SSBPs with higher heat loads, a more balanced consideration of the induced thermal-electrical homogeneities is required.

Calculation of ship floating state by quasi-Newton iteration method for onboard loading computer

ABSTRACT Calculating the floating state of a ship is the basic function of onboard loading computer, which is crucial for ship safety. Aiming at the deficiencies in the existing algorithms, we used the quasi-Newton iteration method to calculate the floating state of a ship. Firstly, the section model database of ship's hull was established by cutting the stereolithographic (STL) 3D model which is created by ship design software. Then, Sutherland–Hodgma polygon clipping algorithm and Shoelace Formula were used to calculate the Jacobian matrix. Finally, We selected a 108,000 DWT bulk carrier, ADMIRAL SCHMIDT, as an example to verify the correctness of the algorithm. We performed 100,000 random loading numerical tests, the average number of iterations was 2–3, and the number of convergence failures was zero. We calculated 24 typical standard loading conditions. We found the maximum average absolute error of the draught and trim were 0.0081 and −0.0283 m, respectively. The calculation error was far less than the requirements of the classification society. The results showed that the Quasi-Newton iteration method is simple to implement and easy to program, and has high robustness and accuracy and appropriate convergence. The method is suitable for the real-time calculation of a ship's floating state and has considerable engineering application value.

High accurate detection method for aortic valve opening of seismocardiography signals

Accurately detecting the aortic valve opening (AO) peaks of seismocardiography (SCG) is a challenging problem due to interference of other morphological inflection points. In this paper, a high accurate method is proposed to extract AO peaks based solely upon the SCG data. The raw SCG signal is purified by a simple first order interference cancellation method, resulting in a reduced number of modes to be decomposed. The purified SCG signal is then decomposed into a series of quasi-orthogonal modes by successive variational mode decomposition (SVMD) without any prior about the number of modes. Considering the pulsatile nature of the AO signal, a waveform factor criterion is proposed to reconstruct the AO signal based on the pulsatile level of each mode. A seventh power law detector is designed to amplify the AO peak and suppress spurious peaks. The publicly available combined measurement of electrocardiogram (ECG), breathing and seismocardiograms (CEBS) database is exploited to verify the performance of the proposed method. We show that the average sensitivity of our technique is 99.02%, the prediction rate is 99.06%, and the detection accuracy is 98.10%, which is superior to several state-of-the-art methods. In addition, compared with the ECG reference value, the instantaneous heart rate extracted by the proposed method is in good agreement with that of the ECG, e.g., the maximum average absolute error percentage is as low as 2.11 and the maximum average value relative error is about 0.03, further demonstrating that the proposed method can achieve accurate estimates of heart rate with an accelerometer alone.

Design optimization of soft robotic fingers biologically inspired by the fin ray effect with intrinsic force sensing

Soft grippers display adaptability, dexterity, and safety, garnering increased interest in their applications. However, accurately modeling their nonlinear deformation and sensing the grasping force remain challenging. Here, a generalized closed-form kinetostatic model of a biomimetic FRE soft finger is established based on the chained-beam-constraint model. A methodology combining a geometry-based contact constraint technique is presented, utilizing the minimum strain energy principle for sensorless, precise, and rapid estimation of the deformed contours and contact force when the FRE finger grasps an arbitrary object with a continuous surface. Method verification using FEA demonstrates a maximum error of no more than 1.28% and 2.44% for the contour and contact force estimation, respectively. Subsequently, a multiobjective genetic algorithm is used to optimize the FRE finger design for better adaptability and greater grasping force in a special scenario. Finally, the finger's capability to accurately perceive grasping force and deformed contours was demonstrated by four groups of grasping tests using cylinder and cube objects, with maximum average absolute error and relative error of 0.083 N, 9.84%, and 1.335 mm, 10.06%, respectively.

Real-Time Measurement of Moisture Content of Paddy Rice Based on Microstrip Microwave Sensor Assisted by Machine Learning Strategies

Moisture content is extremely imoprtant to the processes of storage, packaging, and transportation of grains. In this study, a portable moisture measuring device was developed based on microwave microstrip sensors. The device is composed of three parts: a microwave circuit module, a real-time measurement module, and software to display the results. This work proposes an improvement measure by optimizing the thickness of paddy rice samples (8–13 cm) and adding the ambient temperatures and the moisture contents (13.66–27.02% w.b.) at a 3.00 GHz frequency. A random forest, decision tree, k-nearest neighbor, and support vector machine were applied to predict the moisture content in the paddy rice. Microwave characteristics, phase shift, and temperature compensation were selected as the input variables to the prediction models, which have achieved high accuracy. Among those prediction models, the random forest model yielded the best performance with highest accuracy and stability (R2 = 0.99, RMSE = 0.28, MAE = 0.26). The device showed a relatively stable performance (the maximum average absolute error was 0.55%, the minimum absolute error was 0.17%, the mean standard deviation was 0.18%, the maximum standard deviation was 0.41%, and the minimum standard deviation was 0.08%) within the moisture content range of 13–30%. The instrument has the advantages of real-time, simple structure, convenient operation, low cost, and portability. This work is expected to provide an important reference for the real-time in situ measurement of agricultural products, and to be of great significance for the development of intelligent agricultural equipment.

Thermal Management of a 48 V Lithium-Ion Battery Pack by Semiconductor Refrigeration

At present, 48 V mild hybrid battery systems are widely used in hybrid electric vehicles to reduce fuel consumption and emissions. The battery pack often operates at high discharge/charge rates and requires an efficient and compact battery thermal management system (BTMS) to control its temperature, improve its electrical performance and extend its life. Due to their short start-up times and simple structures, semiconductors can provide rapid refrigeration and cool a battery quickly in response to sudden high current rates. Therefore, semiconductors were applied to the BTMS of a 48 V battery. The performance of the semiconductor-based BTMS was studied by simulation and experiment at high discharge rates (up to 9.375 C). Firstly, a thermal model of the BTMS was developed that integrates a resistance-based battery thermal model, a semiconductor thermal model and a three-dimensional fluid-solid coupled heat transfer model. Unlike a traditional thermal model, the proposed model considers the joint influences of SOC, temperature and current on battery resistance and improves the predictive precision of the battery’s thermal behaviour. The thermal model was verified by an experiment, with the results showing that it could precisely describe the temperature increase in the battery (maximum average absolute error within 0.9°C). Finally, the BTMS thermal model was applied to predict the cooling performance of the semiconductor BTMS at an ambient temperature of 37°C and high current rates (up to 9.375 C), which was compared with that of an air-cooled BTMS. The results demonstrate that the semiconductor-based BTMS achieves lower battery temperature than the air-cooled BTMS and ensures a temperature difference within the 48 V pack of <1.6°C.

Uncertainty prediction method for traffic flow based on K-nearest neighbor algorithm

In order to overcome the problem of low fitting between traffic uncertainty prediction results and actual values in existing research methods, a traffic flow uncertainty prediction method based on K-nearest neighbor algorithm is proposed. The original database, classification center database, k-nearest neighbor database and intermediate search database are used to construct the database needed in the prediction process. Based on the database, multivariate linear regression is used to assign weights to state variables, and k-nearest neighbor algorithm and Kalman filter are used to update the weights to adapt to the uncertainties of traffic flow until the predicted values are obtained, and the uncertainties of traffic flow are predicted. The experimental results show that the maximum average absolute error and average relative error of the proposed method are 0.018 and 0.02, respectively. Compared with the traditional method, the proposed method has higher overall prediction accuracy, higher fitting degree, and is feasible.

A new model of emulsion flow in porous media for conformance control

Emulsion flow in porous media is of paramount importance to the use of emulsions in the conformance control and enhanced oil recovery processes. In this paper, a new theoretical model, incorporating physical properties of porous media, physicochemical properties of the emulsion system, injection strategy, and the interactions between porous media and emulsion, was developed to quantitatively describe flow behaviors of emulsions in porous media. The resistance factor of an emulsion when transported in porous media was first derived through a-two phase flow method. The strong interaction between emulsion droplets and porous media was characterized by the capillary resistance force in the model. A non-uniform capillary model which considers size differences of the pore-body and pore-throat in porous media was proposed to represent the complicated real porous media. By analyzing the adsorption and plugging properties of different emulsion droplets in the non-uniform capillary model, the capillary resistance force was finally determined. To describe emulsion flow in the subsequent water flooding process after emulsion injection, an emulsion dilution factor was introduced into the model. A set of experimental data of emulsion flow in sandpacks was used to validate the reliability of the newly proposed model. The validation results show that by appropriately choosing coefficients, the simulated results are in good agreement with experimental values, with a maximum average absolute error less than 10%, and the developed theoretical flow model can be used to describe emulsion flow behavior in porous media.

Constrained and network multi-receiver single-epoch RTK positioning

This work presents the concept of RTK positioning based on two or three rover GNSS receivers from two or three different reference stations. Additionally, detailed mathematical models of constrained least squares adjustments of RTK positioning based on two or three different RTK/GNSS receivers are presented. The models were tested on real RTK data using three rover Trimble RTK receivers and three reference stations of the ASG-EUPOS system. Practical calculations obtained from the adjustments showed improved accuracy over traditional RTK positioning when reference GNSS stations were located far away (about 30 km from the mobile RTK receivers). The maximum average absolute error for horizontal and vertical coordinates in constrained RTK positioning was over two times lower than in the single baseline RTK positioning, and reached 0.021 and 0.046 m, respectively. Since the concept of constrained and redundant, single-epoch RTK adjustment can be used for static or kinematic applications in real-time positioning, it can also be widely used in geoscience surveying and remote sensing.

Development of a tomato harvesting robot used in greenhouse

A tomato harvesting robot was developed in this study, which consisted of a four-wheel independent steering system, a 5-DOF harvesting system, a navigation system, and a binocular stereo vision system. The four-wheel independent steering system was capable of providing a low-speed steering control of the robot based on Ackerman steering geometry. The proportional-integral-derivative (PID) algorithm was used in the laser navigation control system. The Otsu algorithm and the elliptic template method were used for the automatic recognition of ripe tomatoes, and obstacle avoidance strategies were proposed based on the C-space method. The maximum average absolute error between the set angle and the actual angle was about 0.14°, and the maximum standard deviation was about 0.04°. The laser navigation system was able to rapidly and accurately track the path, with the deviation being less than 8 cm. The load bearing capacity of the mechanical arm was about 1.5 kg. The success rate of the binocular vision system in the recognition of ripe tomatoes was 99.3%. When the distance was less than 600 mm, the positioning error was less than 10 mm. The time needed for recognition of ripe tomatoes and pitching was about 15 s per tomato, with a success rate of about 86%. This study provides some insights into the development and application of tomato harvesting robot used in the greenhouse. Keywords: tomato harvesting robot, four-wheel independent steering, automatic navigation, binocular stereo vision system, obstacle avoidance, greenhouse DOI: 10.25165/j.ijabe.20171004.3204 Citation: Wang L L, Zhao B, Fan J W, Hu X A, Wei S, Li Y S, et al. Development of a tomato harvesting robot used in greenhouse. Int J Agric & Biol Eng, 2017; 10(4): 140–149.

WE‐C‐108‐03: CT‐Based 3D Dose Calculation Method Using Artificial Neural Networks (ANN)

Purpose: In this study Artificial Neural Networks (ANN) are trained to develop a fast CT based dose calculation method. The ANN is capable of calculating the 3D dose distribution inside a voxel‐based phantom for prostate I–125 seed implants. The speed allows for accurate CT‐based calculation of the dose distributions during implantation in real time due to plan modifications. Methods: Training the ANN needs a set of accurate sample dose distributions in heterogeneous CT based phantoms. For the purpose of this study, 60000 sample cubicle phantoms were extracted from 3 patient CT studies. The dose distributions of the cubicle phantoms were calculated using MCNP5. The absorption coefficient of each voxel of the cubicle phantom was calculated by analyzing the CT scan. This information was then given to the ANN as the input. The calculated dose by MCNP5 was given to it as the sample output for the purpose of training. The dose distributions calculated by ANNs were then tested for 7000 cubicle phantoms against the MCNP5 results. Finally, the ANN was used to calculate the dose distribution in two real treatment plans. The results were compared against the MCNP5 voxel based results. Results: The regression graphs for individual ANNs show a regression of R=0.99543 for the worst ANN The regression of the best Trained ANN was equal to R=0.9997 for the 7000 cases. The maximum average absolute error of the ANN dose calculation was 3% in comparison with MCNP5. Conclusion: The results of individual ANN calculations and the complete treatment plan calculations verified the accuracy of the ANN method. Capability of the ANN method to calculate the dose distributions for 500 seed positions in less than 10 minutes suggests that this method can be used for model based dose calculation for real‐time plan modifications in the case of LDR brachytherapy.

輝尽性蛍光体板を用いた Winston-Lutzテスト自動解析法の開発

Stereotactic radiosurgery (SRS) and radiotherapy (SRT) are intricate techniques that deliver a highly precise radiation dose to a localized target, usually a tumor. At our hospital, we perform SRS and SRT on brain tumors using a linear accelerator (linac) mounted with an external micro multi-leaf system. The Task Group TG-142 Report by the American Association of Physicists in Medicine recommends the coincidence of the radiation and mechanical isocenter to be within ±1 mm. The Winston-Lutz test is commonly used to verify the linac isocenter position: it has the advantages of being a simple method that uses a film or electronic portal imaging device (EPID). However, the film method requires a higher radiation dose, which makes it more time-consuming than the EPID method, and the results are highly dependent on the skills of the observer. The EPID method has certain advantages over the film method, but it has low resolution and can only be used for a few combinations of gantry and couch angles. This prompted us to develop an in-house-designed radiation receptor system based on digital radiography, using a photostimulable storage phosphor and automated analysis algorithm for Winston-Lutz test images using a template-matching technique based on cross-correlation coefficients. Our proposed method shows a maximum average absolute error of 0.222 mm (less than 2 pixels) for 0.5 mm and 1.0 mm displacement from the isocenter toward the inline and crossline directions. Our proposed method is thus potentially useful for verifying the Linac isocenter position with a small error and good reproducibility, as demonstrated by improved accuracy of evaluation.

SVM Analysis Method for Infrared Spectra of Mixed Gas

In order to solving the problem that the mass samples of mixed gas spectra data samples being unable to obtain, characteristic absorption spectrum line of the component gas for mixed gas being overlap, and the problem of randomness of component concentration distribution for mixed gas and so on, support vector machine is introduced for the infrared spectra analysis for the mixed gas. Key technologies as feature selection of spectra data samples, data preprocessing, SVM calibration model parameters optimization and level structure for spectrum analysis of a mixed gas is proposed in the paper. The influence of above-mentioned four key technologies to the analysis results is discussed by using experimental means. The experimental result shows that with adoption of the key technologies, the maximum absolute error of component concentration analysis for the mixed gas is 2.93%, and the maximum average absolute error is of 0.73%. The method can also be used for infrared spectra analysis for other mixed gas, and it has practical application value.

ESTIMATED INFILTRATION PARAMETERS AND MANNING ROUGHNESS IN BORDER IRRIGATION

ABSTRACTBased on the volume balance (VB) equation, the surface water profile was shown to be a power function and Philip equation equal to the Kostiakov–Lewis equation when the infiltration parameter a = 0.5. With this assumption, an equation was proposed for estimating infiltration parameters together with Manning roughness. The method requires only multi‐point water advance data of border irrigation. Solving the equation requires use of the nonlinear least‐squares fitting technique by Matlab software. The proposed method was validated with field experimental data and some reported border tests. The solution procedure was analysed, the results showing that the values of infiltration parameters S and A are not changed, but Manning roughness n varies when σh were given different values (0.70, 0.75, 0.80, respectively). Alternative measures of subsurface water storage and water advance trajectory were used to validate the method. Errors between measured and estimated were 2.0–13% for subsurface water storage, in which the infiltration parameters S and A were estimated with the proposed method. The maximum average absolute error was 6.07% for water advance in all border tests when different Manning roughness n was used (for an n value was estimated when different the shape factor of surface profile σh values were adopt by the proposed method in this paper). Copyright © 2011 John Wiley & Sons, Ltd.

Measurement and Correlation of Solubility of Cefuroxime Acid in Pure and Binary Solvents at Various Temperatures

The solubility of cefuroxime acid in four pure solvents and three binary solvents formed by water + acetone, ethanol + acetone, and 2-propanol + acetone at atmospheric pressure has been measured via a laser monitoring observation technique over the temperature range from (278.5 to 313.4) K. The solubility of cefuroxime acid increased with increasing temperature in water, 2-propanol, and ethanol but decreased in acetone. The heat flux measurements by a Differential Scanning Calorimeter (DSC) and solubility with temperature behavior were consistent. The solubility data of cefuroxime acid in pure solvents were correlated by the modified Apelblat equation, and the maximum average absolute error (AAE) was 0.07. The solubility data of cefuroxime acid in binary solvents at various temperatures were fitted by the Jouyban−Acree model, and the maximum AAE was 0.06. The solubility of cefuroxime acid in binary solvents was significantly higher than that in pure solvents and showed a maximum at a specific volume fraction of acetone.