Numerical Investigation on the Urban Heat Island Effect by Using a Porous Media Model

The urban heat island (UHI) effect resulted from urbanization as well as industrialization has become a major environmental problem. UHI effect aggravates global warming and endangers human health. Thus, mitigating the UHI effect has become a primary task to address these challenges. This paper verifies the feasibility of a three-dimensional turbulent porous media model. Using this model, the authors simulate the urban canopy wind-heat environment. The temperature and flow field over a city with a concentric circular structure are presented. The impact of three factors (i.e., anthropogenic heat, ambient crosswind speed, and porosity in the central area) on turbulent flow and heat transfer in the central business district of a simplified city model with a concentric circular structure were analyzed. It is found that the three-dimensional turbulent porous media model is suitable for estimating the UHI effect. The UHI effect could be mitigated by reducing the artificial heat and improving the porosity of the central city area

Robust Linear Quadratic Regulator via Sliding Mode Guidance for Spacecraft Orbiting a Tumbling Asteroid

Aiming to ensure the stability of the spacecraft with multiuncertainties and mitigate the threat of the initial actuator saturation, a Robust Linear Quadratic Regulator (RLQR) via sliding mode guidance (SMG) for orbiting a tumbling asteroid is proposed in this paper. The orbital motion of the spacecraft near a tumbling asteroid is modelled in the body-fixed frame considering the sun-relative effects, and the orbiting control problem is formulated as a stabilization of a nonlinear time-varying system. RLQR based on the adaptive feedback linearization is proposed to stabilize the spacecraft orbiting with the uncertainties of the asteroid’s rotation and gravitational field. In order to avoid the initial actuator saturation, SMG is applied to generate the transition process trajectory of the closed-loop system. The effectiveness of the proposed control scheme is verified by the simulations of orbiting the asteroid Toutatis 4179

Fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid ship

In this paper, energy management strategy based on fuzzy logic is proposed for a fuel cell hybrid ship, combining proton exchange membrane fuel cell (PEMFC), battery and ultra-capacitor (UC). This hybrid system aims to optimize power distribution among each energy unit. The simulation model of the fuel cell hybrid power system is established in the MATLAB/SIMULINK simulation environment. The fuzzy logic energy strategy is verified by simulation according to the typical drive cycle of ship. The simulation results show that the proposed energy management strategy is able to satisfy power required by the ship, reduce the dynamic load of fuel cell, maintain the state of charge (SOC) of battery and SOC of the UC, and optimize the performance, fuel economy and efficiency of the hybrid systemThe research is supported by the program of the National Natural Science Foundation (No.61304186 and No.51007056)

Design and Application of Simulating Cutting Experiment System for Drum Shearer

When the shearer cuts coal or rock with different hardness, it will produce corresponding cutting state information. This paper develops a simulation cutting experiment system for the drum shearer based on similarity theory. It took the spiral cutting drum of a shearer as the research target and derived the principal similarity coefficients through the dimensional analysis method. Meanwhile, this paper designed the structure of the cutting power system and hydraulic system. Then, it chose a certain amount of coal powder as an aggregate, cement 325# as cementing material, sand, and water as auxiliary materials to prepare simulated coal samples. The paper adopted the orthogonal experiment method and used a proportion of cement, sand, and water as the influencing factors in designing a simulated coal sample preparation plan. In addition, it utilized the range analysis method to research the influence of various factors on the density and compressive strength of simulated coal samples. Finally, it conducted simulated coal sample cutting tests. The results show that the density of the simulated coal samples is between 1192.59 Kg/m3–1483.51 Kg/m3, and the compressive strength range reaches 0.16 MPa–3.94 MPa. The density of the simulated coal sample is related to the mass proportion of cement and sand. When the ratio gradually increases, the influence of sand increases. Furthermore, the compressive strength is linearly proportional to the proportion of cement. The self-designed simulation cutting experiment system could effectively carry out the relevant experiments and obtain the corresponding cutting condition signals through the sensors. There are differences in vibration signals generated by cutting different strength materials. Extracting the kurtosis value as the characteristic value can distinguish various cutting modes, which can provide a reliable experimental solution for the research of coal-rock identification

Object Detector with Residual Learning and Multi-scale Feature Enhancement

At present, deep learning has achieved great success in the field of computer vision, but small object detection is still a challenging problem in the field of object detection. Aiming at the problems of low resolution of small objects, blurred images, and less information carried, one object detector that introduces residual learning and multi-scale feature enhancement is proposed. Firstly, an enhanced feature mapping block based on residual learning is introduced into the backbone network. Through channel averaging and normalization, the model more focuses on the object area instead of the background, and it provides additional semantics information for the effective feature layer while taking into account the detection speed. Then the feature map increases the receptive field of the effective feature map through feature fusion block sensitive to context information, and fuses the shallow feature layer and the deep feature layer used for prediction to improve the detection performance at low resolution. Finally, a dual attention block is used to suppress background noise, and key features are embedded in attention. While preserving spatial information, it strengthens the information association between channels, thereby enhancing the expressive ability of features. In order to better detect small objects, the number of a priori boxes for shallow feature mapping is also adjusted. Experimental results show that on the dataset of PASCAL VOC2007, the detection accuracy (mAP) of the algorithm for 300×300 input scale is 79.9%, which is 2.7 percentage points higher than that of SSD, and the detection accuracy of small objects bird, bottle, chair, and plant is improved 5.1 percentage points, 7.5 percentage points, 3.9 percentage points, 7.2 percentage points，respectively. The detection accuracy (mAP) on the OAP self-made aerial dataset is 82.7%

A Cutting Pattern Recognition Method for Shearers Based on ICEEMDAN and Improved Grey Wolf Optimizer Algorithm-Optimized SVM

When the shearer is cutting, the sound signal generated by the cutting drum crushing coal and rock contains a wealth of cutting status information. In order to effectively process the shearer cutting sound signal and accurately identify the cutting mode, this paper proposed a shearer cutting sound signal recognition method based on an improved complete ensemble empirical mode decomposition with adaptive noise (ICCEMDAN) and an improved grey wolf optimizer (IGWO) algorithm-optimized support vector machine (SVM). First, the approach applied ICEEMDAN to process the cutting sound signal and obtained several intrinsic mode function (IMF) components. It used the correlation coefficient to select the characteristic component. Meanwhile, this paper calculated the composite multi-scale permutation entropy (CMPE) of the characteristic components as the eigenvalue. Then, the method introduced a differential evolution algorithm and nonlinear convergence factor to improve the GWO algorithm. It used the improved GWO algorithm to realize the adaptive selection of SVM parameters and established a cutting sound signal recognition model. According to the proportioning plan, the paper made several simulation coal walls for cutting experiments and collected cutting sound signals for cutting pattern recognition. The experimental results show that the method proposed in this paper can effectively process the cutting sound signal of the shearer, and the average accuracy of the cutting pattern recognition model reached 97.67%

The first complete mitochondrial genome of Macalpinomyces bursus (Ustilaginales: Ustilaginaceae) and insights into its phylogeny

In the present study, the complete mitochondrial genome of Macalpinomyces bursus (Berk.) Vanky 2002 was sequenced and assembled. The complete mitochondrial genome of M. bursus was 49,024 bp in length, with the GC content of 30.4%. The M. bursus mitochondrial genome contained 27 protein-coding genes, 2 ribosomal RNA (rRNA) genes, and 22 transfer RNA (tRNA) genes. Phylogenetic analysis based on combined mitochondrial gene dataset indicated that the M. bursus exhibited a close relationship with species from the genera Ustilago, Sporisorium, and Anthracocystis