The indoor accelerated corrosion of 09CuPCrNi weathering steel in simulated industrial atmosphere was investigated by the corrosion weight-loss method, field emission scanning electron microscopy ...(FE-SEM), x-ray diffraction (XRD), and Raman spectroscopy. The results indicated that the indoor accelerated corrosion kinetics of 09CuPCrNi weathering steel could be divided into two stages with different log (thickness loss, D)-log (time, t) fitting functions. The second stage of corrosion corresponded to the protective rust layer that formed. Three correlation models involving atmospheric corrosion and accelerated indoor corrosion were developed based on the kinetic model of the second stage of the accelerated indoor corrosion. Those three models corresponded to three atmospheric corrosion stations located in Beijing, Jiangjin, and Guangzhou (China), respectively. This indoor accelerated method presented a better accelerated effect for Beijing atmospheric environment.
In order to solve the problem that the stem nodes are difficult to identify in the process of sugarcane seed automatic cutting, a method of identifying the stem nodes of sugarcane based on the ...extreme points of vertical projection function is proposed in this paper. Firstly, in order to reduce the influence of light on image processing, the RGB color image is converted to HSI color image, and the S component image of the HSI color space is extracted as a research object. Then, the S component image is binarized by the Otsu method, the hole of the binary image is filled by morphology closing algorithm, and the sugarcane and the background are initially separated by the horizontal projection map of the binary image. Finally, the position of sugarcane stem is preliminarily determined by continuously taking the derivative of the vertical projection function of the binary image, and the sum of the local pixel value of the suspicious pixel column is compared to further determine the sugarcane stem node. The experimental results showed that the recognition rate of single stem node is 100%, and the standard deviation is less than 1.1 mm. The accuracy of simultaneous identification of double stem nodes is 98%, and the standard deviation is less than 1.7 mm. The accuracy of simultaneous identification of the three stem nodes is 95%, and the standard deviation is less than 2.2 mm. Compared with the other methods introduced in this paper, the proposed method has higher recognition and accuracy.
RESUMO: Para resolver o problema que os nós do caule são difíceis de identificar no processo de corte automático de sementes de cana-de-açúcar, é proposto, neste artigo, um método para identificar os nós do colmo da cana-de-açúcar com base nos pontos extremos da função de projeção vertical. Em primeiro lugar, a fim de reduzir a influência da luz no processamento da imagem, a imagem de cor RGB foi convertida em imagem de cor HSI, e a imagem de componente S do espaço de cores HSI é extraída como um objeto de pesquisa. Em seguida, o método Otsu foi usado para binarizar o mapa do componente S, e a operação morfológica fechada foi usada para preencher os espaços da imagem binária, e a projeção horizontal da imagem binária foi usada para separar a cana de açúcar do fundo. Finalmente, a posição do caule de cana-de-açúcar foi preliminarmente determinada através da tomada contínua do derivado da função de projeção vertical da imagem binária, e a soma do valor pixel local da coluna de pixel suspeito foi comparada para determinar ainda mais o nódulo da cana-de-açúcar. Os resultados experimentais mostram que a taxa de reconhecimento do nó de haste única foi de 100%, o desvio padrão foi inferior a 1.1 mm. A precisão da identificação simultânea de nós de haste dupla foi de 98%, o desvio padrão foi menor que 1.7 mm. A precisão da identificação simultânea dos três nós de haste é de 95%, o desvio padrão foi inferior a 2.2 mm. Comparado com os outros métodos introduzidos neste artigo, o método proposto possui maior reconhecimento e precisão.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Heat pipe cooling for battery thermal management systems (BTMSs) in electric vehicles (EVs) is growing due to its advantages of high cooling efficiency, compact structure and flexible geometry. ...Considering the transient conduction, phase change and uncertain thermal conditions in a heat pipe, it is challenging to obtain the dynamic thermal characteristics accurately in such complex heat and mass transfer process. In this paper, a “segmented” thermal resistance model of a heat pipe is proposed based on thermal circuit method. The equivalent conductivities of different segments, viz. the evaporator and condenser of pipe, are used to determine their own thermal parameters and conditions integrated into the thermal model of battery for a complete three-dimensional (3D) computational fluid dynamics (CFD) simulation. The proposed “segmented” model shows more precise than the “non-segmented” model by the comparison of simulated and experimental temperature distribution and variation of an ultra-thin micro heat pipe (UMHP) battery pack, and has less calculation error to obtain dynamic thermal behavior for exact thermal design, management and control of heat pipe BTMSs. Using the “segmented” model, the cooling effect of the UMHP pack with different natural/forced convection and arrangements is predicted, and the results correspond well to the tests.
•A “segmented” thermal resistance model of a heat pipe is proposed.•Accuracy of “segmented” model is verified by comparing with “non-segmented” model.•Ultra-thin micro heat pipe(UMHP) is compact and effective for EV battery cooling.•The cooling effect of an UMHP pack with natural/forced convection is evaluated.•The thermal performance of an UMHP pack with different arrangements is compared.
This article investigates the adaptive fuzzy prescribed time H∞ congestion control problem for a class of high speed transmission control protocol/active queue management (HSTCP/AQM) systems subject ...to asymmetric input saturation and external disturbance. Firstly, a saturation-based finite-time performance function (SFTPF) is constructed by introducing a nonnegative modified signal associated with input saturation errors into user-defined performance boundaries, which features the capability of ensuring the user-appointed tracking performance when no saturation exists and degrading it once saturation occurs, thereby effectively increasing the flexibility and reliability of the control scheme. Then, by designing a novel shifting function, the condition that the initial value of the queue tracking error remains within that of SFTPF is relaxed, which avoids the SFTPF being redeveloped for different initial queue tracking errors. Thereafter, an auxiliary system and H∞ control theory are employed to eliminate the adverse effect caused by input saturation and external disturbance, respectively. Moreover, through the Lyapunov stability analysis, it is proven that, under our proposed adaptive fuzzy congestion control scheme, all the closed-loop signals are bounded, and the connection between input saturation and performance-related constraint is established. Finally, a set of simulation results are presented to demonstrate the feasibility and effectiveness of the designed congestion controller.
•A new shifting function is developed to relax the initial condition restriction.•An error transformation function is designed to solve asymmetric constrained issues.•The performance constraint boundaries can be resiliently enlarged and recovered.•An adaptive fuzzy practical prescribed time congestion control scheme is proposed.
Coasting in gear is a common driving mode for the conventional vehicle equipped with the internal combustion engine（ICE）, and the assistant braking function of ICE is utilized to decelerate the ...vehicle in this mode. However, the electric vehicle（EV） does not have this feature in the coasting mode due to the relatively small inertia of the driving motor, so it will cause the driver cannot obtain the similar driving feeling to that of the conventional vehicle, and even a traffic accident may occur if the driver cannot immediately adapt to the changes. In this paper, the coasting control for EV is researched based on the driving feeling. A conventional vehicle equipped with continuously variable transmission（CVT） is taken as the reference vehicle, and the combined simulation model of EV is established based on AVL CRUISE and MATLAB/Simulink. The torque characteristic of the CVT output shaft is measured in coasting mode, and the data are smoothed and fitted to a polynomial curve. For the EV in coasting mode, if the state of charge（SOC） of the battery is below 95%, the polynomial curve is used as the control target for the torque characteristic of the driving motor, otherwise, the required torque is replaced by hydraulic braking torque to keep the same deceleration. The co-simulation of Matlab/Simulink/Stateflow and AVL CRUISE, as well as the hardware-in-loop experiment combined with d SPACE are carried out to verify the effectiveness and the real-time performance of the control algorithm. The results show that the EV with coasting braking control system has similar driving feeling to that of the reference vehicle, meanwhile, the battery SOC can be increased by 0.036% and 0.021% in the initial speed of 100 km/h and 50 km/h, respectively. The proposed control algorithm for EV is beneficial to improve the driving feeling in coasting mode, and it also makes the EV has the assistant braking function.
•A prediction point Hough transform with a maximum error of 0.5°is proposed.•Based on the G-B and 2G-B-R grayscale factors, a new grayscale factor is proposed.•The least square method is used to ...optimize the traditional Hough transform.
Accurate extraction of navigation path is very important for automated navigation of agricultural robots. Based on the machine vision system, this paper proposes a new algorithm for the fitting of navigation path of greenhouse cucumber-picking robots. Aiming at the problems that the traditional Hough transform has a large amount of calculation and the least square method has a low precision, the prediction point Hough transform algorithm is proposed to extract the navigation path. The prediction-point Hough transform contains 4 parts: intercept area of interest, image segmentation, navigation point extraction, navigation path fitting. In this paper, only the final 160-pixel rows of image captured by the camera are taken as the region of interest. In the image segmentation stage, this paper proposes a new graying factor. For navigation path extraction, a regression equation is used to determine the prediction point, and finally the proposed prediction point Hough transform is used to fit the navigation path. The experimental results show that the proposed grayscale factor can well segment cucumber plants and soil, and the segmentation effect is better than 2G-B-R and G-B grayscale factors. The proposed prediction point Hough transform fits the navigation paths with an average error less than 0.5°, which is 10.25° lower than the average error of the least-square method. Also, the computation time of the proposed Hough transform is 17.92 ms. Compared with the traditional Hough transform, it saves 35.20 ms.
The existing investigations on thermal comfort mostly focus on the thermal environment conditions, especially of the air-flow field and the temperature distributions in vehicle cabin. Less attention ...appears to direct to the thermal comfort or thermal sensation of occupants, even to the relationship between thermal conditions and thermal sensation. In this paper, a series of experiments were designed and conducted for understanding the non-uniform conditions and the occupant’s thermal responses in vehicle cabin during the heating period. To accurately assess the transient temperature distribution in cabin in common daily condition, the air temperature at a number of positions is measured in a full size vehicle cabin under natural winter environment in South China by using a discrete thermocouples network. The occupant body is divided into nine segments, the skin temperature at each segment and the occupant’s local thermal sensation at the head, body, upper limb and lower limb are monitored continuously. The skin temperature is observed by using a discrete thermocouples network, and the local thermal sensation is evaluated by using a seven-point thermal comfort survey questionnaire proposed by American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc(ASHRAE) Standard. The relationship between the skin temperature and the thermal sensation is discussed and regressed by statistics method. The results show that the interior air temperature is highly non-uniform over the vehicle cabin. The locations where the occupants sit have a significant effect on the occupant’s thermal responses, including the skin temperature and the thermal sensation. The skin temperature and thermal sensation are quite different between body segments due to the effect of non-uniform conditions, clothing resistance, and the human thermal regulating system. A quantitative relationship between the thermal sensation and the skin temperature at each body segment of occupant in real life traffic is presented. The investigation result indicates that the skin temperature is a robust index to evaluate the thermal sensation. Applying the skin temperature to designing and controlling parameters of the heating, ventilation and air conditioning(HVAC) system may benefit the thermal comfort and reducing energy consumption.
•A semicircular baffle flow field with helical structure is proposed.•Considering the anisotropic mass and heat transfer properties of the porous layers.•Considering the actual agglomerate structure ...of the cathode catalyst layer.•The optimized flow field increases the fuel cell’s net power density by 11.42 %.
To effectively improve the fuel cell (FC) mass transport capacity, a new flow field (FF) design with helical baffle at the cathode is proposed, which facilitates gas flow and mass transfer in both through- and in-plane directions. To fully understand the influence of various design parameters on the FC performance, a series of studies were carried out with a semicircular baffle as an example. The effect of the baffle structure on the complex heat and mass transport process is studied in detail to obtain the optimal baffle structure parameters. To simulate the complete transport process, a three-dimensional, multiphase, non-isothermal steady-state model was developed, embedding the anisotropic transport properties caused by the porous layer structures and the heterogeneous model of the actual agglomerate structure of the catalyst layer in the model. The results show that the helical baffles induce cross flow under the ribs while inducing forced convection, enhancing the oxygen supply in both directions. The FF structure with baffle height and pitch of 0.4 mm and 1.0 mm respectively has the maximum net power density. Taking the relative humidity = 50 % and 100 % as an example, the net power density is increased by 11.42 % and 5.72 % respectively compared with the original FF.
•A multi-scale feature extraction module (MFE) is designed by using multi-layer dilated convolution to obtain the multi-scale feature information of pest image with a large receptive field without ...increasing the amount of model parameters.•A deep feature extraction module (DFE) is proposed. Firstly, the deep feature information of pest image is obtained by multiple convolution operations, and then the obtained deep feature image is restored to the size of the original image by deconvolution. At the same time, the deep and shallow feature information are fused by multiple skip-connection structure (Zhang et al., 2019) to reduce the loss of feature information.•We propose a network model based on multi-scale feature fusion to accurately identify and classify crop pests. Experiments on 12 types of pest datasets show that our method has better performance in pest identification and classification than other advanced methods, its classification accuracy (ACC) reaches 98.2%, and the model training time is only 197 min.
Crop diseases and insect pests are a serious natural disaster, which needs to be predicted and monitored in time to ensure the output of crops. Due to the wide variety of pests and the similar morphology of crops in the early stages of growth, it is difficult for agricultural workers to accurately identify various types of pests. Crop insects have brought huge challenges to the prevention and control of plant diseases and insect pests. In response to this problem, we propose a way of classification of crop pests based on multi-scale feature fusion(MFFNet) to accurately recognizes and classifies crop pests. First, the multi-scale feature extraction module (MFE) is designed by using dilated convolution to obtain the multi-scale feature map of the pest image. At the same time, extracted the deep feature information of the image by the feature extraction module (DFE). Finally, the features extracted separately by the multi-scale feature extraction module (MFE) and the feature extraction module (DFE) were fused thus achieving accurately classified and identified the crops insects by the way of end-to-end. Experiments show that our proposed method has obtained excellent classification performance on the dataset of 12 types of pests, its classification accuracy rate (ACC) reached 98.2%.
•Modeling algorithms for human thermal sensation integrating physiological responses.•The change rate reveals TS and avoids the inconsistency of raw physiological levels.•CRPR and CRMAP are ...significantly different in warm chamber than in the cool.•Case analysis verifies feasibility of proposed thermal sensation model with 0.8 R2.
Background and Objectives: Thermal conditions are changeable in cabin space, where occupants could suffer consecutive self-thermoregulation to such changing thermal stresses. Thermal environment management is expected to be purposefully auto-adjustable for the environment by recognizing individual real-time thermal sensations. Current thermal sensation evaluation models are developed for virtual simulations rather than for realistic scenarios, challenging to evaluate human thermal sensation in the field surveys.
Methods: The study constructs a human thermal sensation model via human physiological responses to evaluate the human thermal sensation in the actual vehicle environment. The thermal sensation model forms with exponential functions to clarify the relationship between thermal sensation and pulse rate and blood pressure, which successfully expresses the approximately linear trend around neutral sensation and compensates for the end-points bias. The study set up experimental cases to determine the parameter states in the thermal sensation model. Firstly, subjective thermal sensation scoring was performed by combing with an established seven-point-scale questionnaire survey system for human thermal sensation. Wearable sensors are then applied to measure the human physiological response, including blood pressure BP, pulse rate PR and blood oxygen saturation SpO2.
Results: The subjects revealed significantly higher pulse rates (positively correlated) and lower blood pressure (negatively correlated) in the warm chamber than in the cool chamber. The defined parameter change rate effectively reveals the trend of human thermal sensation and avoids the inconsistency of raw physiological response levels. The change rate in PR and MAP between the thermal sensation in cold -3 and hot +3 is about a 10% difference.
Conclusions: Based on the thermal sensation model algorithm, model parameters were fitted by the subjects’ thermal sensation voting and the change rate of their physiological responses. With the coefficient of determination (R2) of the regression over 0.8, the proposed thermal sensation model can be employed for human thermal sensation evaluation. The physiological thermoregulatory responses effectively indicate the thermal state of the human body and can be used in thermal environments in conjunction with human smart wearable devices.
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