使用激光雷达在动态场景下实现精确的位姿估计与地图映射是同时定位与建图(simultaneous localization and mapping, SLAM)研究领域的重要内容之一，但动态环境中物体移动会导致SLAM算法精度下降，为此提出一种低成本且可有效剔除动态影响的激光雷达SLAM算法。首先引入深度图投影，通过检测相邻时刻深度图之间的像素值波动，筛选并去除动态点云；然后进行地面点云分割，利用非地面点云的特征实现位姿估计和地图映射，利用地面点云的特征施加地面约束，限制高度漂移；最后引入回环检测矫正全局姿态。实验结果表明，与LOAM、LeGO-LOAM和SuMa算法相比，本文算法可更有效剔除动态目标，提供更优秀的定位建图效果和鲁棒性能。
To achieve LiDAR-based precise pose estimation and mapping in dynamic environments is a big challenge in the field of simultaneous localization and mapping(SLAM).Dynamic objects can significantly interfere with a robot's environmental perception and autonomous navigation.To address the decline in SLAM accuracy caused by moving objects in dynamic environments, this study proposes a low-cost yet effective LiDAR SLAM algorithm to eliminate dynamic interference.The algorithm introduces depth map projection to identify and remove dynamic point clouds by detecting pixel value fluctuations between consecutive depth maps.Ground point cloud segmentation is then performed, leveraging non-ground point cloud features for pose estimation and mapping.Ground point cloud features are utilized to impose ground constraints, mitigating height drift.Additionally, loop closure detection is incorporated to correct global pose drift.Experimental results demonstrate that, compared to LOAM,LeGO-LOAM,and SuMa, the proposed algorithm more effectively filters out dynamic objects, delivering superior localization and mapping performance with enhanced robustness.
为提高电力系统短期电力负荷预测的准确性，提出一种基于混合蜣螂优化(hybrid dung beetle optimization, HDBO)算法优化长短期记忆(long short-term memory, LSTM)网络的预测模型(HDBO-LSTM)。首先，为克服原始蜣螂优化(dung beetle optimization, DBO)算法全局搜索能力较弱且易陷入局部最优的问题，在原始DBO算法的基础上引入随机对立学习策略、哈里斯鹰优化算法、逐维高斯变异策略和动态处理机制，以此形成HDBO算法，增强算法的搜索能力和收敛速度，并通过10个基准函数的对比实验验证HDBO算法的搜索性能；其次，采用HDBO算法优化LSTM网络的超参数，以减小随机超参数对负荷预测精度的影响。使用电工数学建模竞赛的电力负荷数据集对模型进行评估，结果显示，在数据集内随机选取的七天预测任务中，HDBO-LSTM模型的均方根误差(RMSE)、平均绝对百分比误差(MAPE)、平均绝对误差(MAE)和变异系数(COV)较原始LSTM模型分别降低了22.38%、16.33%、19.16%和22.52%,该模型能够有效提升短期电力负荷预测的准确性。
To improve the accuracy of short-term power load forecasting in the power system, a prediction model(HDBO-LSTM) based on hybrid dung beetle optimization(HDBO) algorithm to optimize long short-term memory(LSTM) network is proposed.Firstly, to solve the problem of weak global search ability and susceptibility to local optima in the original dung beetle optimization(DBO) algorithm, the HDBO algorithm is formed by introducing random opposition learning strategy, Harris hawks optimization algorithm, dimension-by-dimension Gaussian variation strategy, and dynamic boundary handling mechanism based on the original DBO algorithm.This enhances the search ability and convergence speed of the algorithm, and the search performance of the HDBO algorithm is verified through comparative experiments with 10 benchmark functions.Secondly, the HDBO algorithm is used to optimize the hyperparameters of the LSTM network, in order to reduce the impact of random hyperparameter selection on the accuracy of load forecasting.Finally, the model is evaluated by using the power load dataset from the electrical mathematics modeling competition.The results show that compared to the original LSTM model, the HDBO-LSTM model reduces root mean square error(RMSE),mean absolute percentage error(MAPE),mean absolute error(MAE),and coefficient of variation(COV) by 22.38%,16.33%,19.16%,and 22.52%,respectively, in the randomly selected seven day prediction task within the dataset.This model can effectively improve the accuracy of short-term power load forecasting.
针对遥感图像背景复杂、小目标数量多、目标尺度各异的特点，提出一种基于改进YOLOv8的遥感图像目标检测算法。首先，在主干网络引入深度可分离卷积(DSConv),减少模型的计算量和参数量，提升计算效率；其次，为提升对遥感图像的目标检测能力，在颈部(Neck)添加一种高效通道空间注意力模块(ECSA),通过对通道和空间特征的融合提高模型的识别能力；最后，增加检测尺度为160×160的小目标检测层，提升图像中小目标检测能力，并将边界框损失函数替换为SIoU,关注预测边界框与真实边界框的角度信息，提升检测精度。在遥感图像数据集SIMD上的实验结果表明：改进YOLOv8算法具备更强的特征提取能力，与YOLOv8算法相比，平均精度均值提升了2.63%,模型参数量减少了19.43%,模型计算量减少了28.31%,证明了改进YOLOv8算法的有效性。
Aiming at the characteristics of remote sensing image with complex background, large number of small targets and different target scales, a remote sensing image target detection algorithm based on improved YOLOv8 is proposed.Firstly, the depth separable convolution(DSConv) is introduced to construct the backbone network, which reduces the computational volume and number of parameters of the model and improves the computational efficiency.Secondly, in order to improve the detectability for remote sensing images of the target, an efficient channel spatial attention module(ECSA) proposed is added in the Neck section to improve the recognition ability of the model by fusing the channel and spatial features.Finally, a small target detection layer with a detection scale of 160×160 is added to improve the detectability of small targets in the image; the bounding box loss function is replaced with SIoU,which focuses on the angle information between the predicted bounding box and the real bounding box to improve the detection accuracy.The experimental results on the remote sensing image dataset SIMD show that The optimized YOLOv8 algorithm demonstrates significantly improved feature extraction capacity.Compared with the YOLOv8 algorithm, the average accuracy is improved by 2.63%,the model parameter quantity is reduced by 19.43%,and the model computation is reduced by 28.31%,which proves the effectiveness of the improved YOLOv8 algorithm.
渗铝涂层与高温合金在高温氧化过程中会发生元素互扩散行为，为探究元素互扩散对单晶高温合金基体微观结构和相变的影响，通过高温化学气相渗铝的方法在样品表面上沉积一层铝化物涂层，并使用扫描电镜(SEM)观察沉积后涂层的微观结构，发现涂层与基体之间发生了元素互扩散行为，形成了互扩散区(IDZ)。对沉积后的样品在1 100℃条件下进行500 h的氧化处理，结果表明：IDZ随氧化时间增长逐渐变薄，最终导致拓扑密堆(TCP)相析出，并形成二次反应区(SRZ);难熔元素随氧化时间的延长不断增加，不稳定的γ′-Ni_3Al相也随氧化时间的延长发生转化，最终与难熔元素一起在基体中形成TCP相，影响合金的使用寿命。
Aiming at the fact that element interdiffusion has influence on the microstructure and phase transformation of single crystal superalloy matrix in the high-temperature oxidation process.To investigate this influence, a layer of aluminide coating was deposited on the surface of the sample by high-temperature chemical vapor aluminization, and the microstructure of the deposition coating was observed by scanning electron microscopy(SEM).It was found that the element interdiffusion behavior occurred between the coating and the matrix, forming an interdiffusion zone(IDZ).The samples were oxidized at 1 100 ℃ for 500 h, and the results showed that the IDZ gradually thinned with the oxidation time, which eventually led to the precipitation of the TCP phase and the formation of the secondary reaction zone(SRZ).The refractory elements increase with the oxidation time, and the unstable γ′-Ni_3Al phase also transforms with the oxidation time, and finally forms the TCP phase in the matrix together with the refractory elements, which affects the service life of the alloy.
为解决传统差分混沌移位键控(differential chaotic shift keying, DCSK)系统安全性较差及误码性能受限的问题，提出一种结合脉冲位置调制与动态帧变换技术的DCSK(frame-transform DCSK system based on pulse position modulation, FT-PPM-DCSK)系统，实现安全性与误码性能的协同优化。通过推导系统在加性高斯白噪声(additive white Gaussian noise, AWGN)信道下的误比特率(BER)表达式，基于蒙特卡洛仿真验证其性能。实验结果表明，与基准DCSK系统相比，FT-PPM-DCSK系统提高了误码性能且具有更高的安全性。
To solve the problems of poor security and limited bit error performance of traditional differential chaotic shift keying system(DCSK),a differential chaotic shift keying system combining pulse position modulation and dynamic frame transform technology(FT-PPM-DCSK)is proposed.Based on the hybrid modulation strategy of frame transformation, the system uses pulse position modulation(PPM)to modulate part of the information bits to achieve the collaborative optimization of security and bit error performance.The bit error rate(BER)expression of the system in additive white Gaussian noise(AWGN)channel is derived, and its performance is verified by Monte Carlo simulation.The experimental results show that, compared with the benchmark DCSK system, the FT-PPM-DCSK system improves the BER performance and has higher security.
采用有限元软件ABAQUS对圆端形钢管混凝土柱局压力学性能及受力机理进行数值模拟，对试件受力全过程、接触力及参数进行分析。结果表明：同一截面钢管圆弧段相比平直段约束作用更强，接触力更大，且自端部向下，截面各点处的接触力逐渐减小；当局压面积比由1.44分别增大至4、9、16时，圆端形钢管混凝土柱局压承载力分别降低33.6%、54.7%、66.1%;钢管屈服强度由235 MPa增加至345、390、420 MPa时，试件刚度无明显变化，局压承载力分别提高33.7%、47.3%、56.4%;当混凝土立方体抗压强度由30 MPa分别增加至35、40、45、50 MPa时，试件刚度逐渐增大，局压承载力分别提高2.2%、4.1%、6.4%、11.2%;含钢率由0.05增加至0.1、0.15、0.2时，试件刚度增大，局压承载力分别提高57.7%、114.6%、165.3%。
A numerical simulation of the local compressive mechanical properties and force mechanism of concrete-filled steel tubular(CFST) columns with rounded ends was conducted using the finite element software ABAQUS.The entire loading process, contact forces, and relevant parameters of the specimens were analyzed.The results indicate that, within the same cross-section, the circular arc segment of the steel tube provides stronger confinement and experiences greater contact forces compared to the straight segment.Moreover, the contact forces gradually decrease from the end downwards along the column.When the local compressive area ratio increases from 1.44 to 4,9,and 16,the local compressive bearing capacity of the CFST columns with rounded ends decreases by 33.6%,54.7%,and 66.1%,respectively.As the yield strength of the steel tube increases from 235 MPa to 345,390,and 420 MPa, there is no significant change in the stiffness of the specimens, but the local compressive bearing capacity increases by 33.7%,47.3%,and 56.4%,respectively.When the compressive strength of the concrete cube increases from 30 MPa to 35,40,45,and 50 MPa, the stiffness of the specimens gradually increases, and the local compressive bearing capacity improves by 2.2%,4.1%,6.4%,and 11.2%,respectively.As the steel ratio increases from 0.05 to 0.1,0.15,and 0.2,the stiffness of the specimens increases, and the local compressive bearing capacity enhances by 57.7%,114.6%,and 165.3%,respectively.
煤层瓦斯含量是煤矿日常生产、瓦斯动力灾害防治和瓦斯资源利用的基础数据。为提高煤层瓦斯含量测定结果的准确性，通过分析现行测定方法存在的问题，基于区域定点和原位环境保持的技术要求，研制了定点保压取样装置，提出了一种基于定点保压取样的煤层瓦斯含量精准测定方法，测定过程包括装置连接、钻孔钻进、钻孔取芯、钻孔退钻、煤层瓦斯含量测定等5个步骤。采用现场试验的方法将定点保压取样与传统取样方式进行对比分析，结果表明：采用传统取样方式测得的二_1煤层瓦斯含量为2.61～3.09 m~3/t,平均瓦斯含量为2.83 m~3/t;采用定点保压取样方式测得的二_1煤层瓦斯含量为3.09～3.63 m~3/t,平均瓦斯含量为3.32 m~3/t,较传统取样方式平均增加了17.31%。基于定点保压取样的煤层瓦斯含量精准测定技术无孔壁杂质干扰，取样过程中煤样暴露时间极短，提高了煤矿井下大范围区域煤层瓦斯含量测定准确度，为煤层区域瓦斯治理和瓦斯抽采达标评判等提供了技术支撑。
The determination of coal seam gas content is fundamental to daily production in coal mines, prevention and control of gas dynamic disasters, and the utilization of gas resources.To enhance the accuracy of coal seam gas content measurements, a position fixed and pressure maintenance sampling device was developed based on the technical requirements for regional fixed-point and in-situ environmental preservation.This development was based on an analysis of existing challenges in current measurement methods.A precise method for determining coal seam gas content using position fixed and pressure maintenance sampling was proposed, with the process comprising five steps: device connection, drilling, core sampling, drill withdrawal, and gas content determination.Field experiments were conducted to compare the position fixed and pressure maintenance sampling method with traditional sampling techniques.The results indicated that the gas content of the No.2-1 coal seam measured by the traditional method ranged from 2.61 to 3.09 m~3/t, with an average of 2.83 m~3/t.In contrast, the position fixed and pressure maintenance sampling method yielded a range of 3.09 to 3.63 m~3/t, with an average of 3.32 m~3/t, representing a 17.31% increase over the traditional method.The precise determination technology based on position fixed and pressure maintenance sampling eliminates interference from hole wall impurities and minimizes coal sample exposure time during sampling, thereby improving the accuracy of large-scale regional coal seam gas content determinations in underground coal mines.This technology provides essential technical support for regional gas control and gas drainage compliance evaluation in coal seams.
为消除滑模控制中的振颤问题，设计一种基于超螺旋算法的T-S模糊广义系统动态滑模控制器。首先，构造一个新型的动态滑模面，利用广义系统描述滑模运动的动态过程，同时使用基于输出反馈动态滑模的控制方法进行系统设计，并利用凸优化与状态输入增强相结合的方法分析出系统渐近稳定的充分条件；其次，采用多变量超螺旋算法设计二阶且连续控制器，可有效减缓滑模控制系统中存在的振颤问题。实验结果表明，所提出的控制器可以使系统状态在有限时间内到达滑模面，并保持稳定的滑动模态，同时也有效消除系统中的振颤问题，提升系统的控制性能。
To address the chattering problem in sliding mode control, a dynamic sliding mode controller for T-S fuzzy descriptor system based on the super-twisting algorithm is designed.Firstly, a novel dynamic sliding surface is constructed.The process of dynamic sliding mode motion is described using a descriptor system.Additionally, a control method based on output feedback of dynamic sliding mode is employed for system design.The algorithm combines convex optimization with status-input augmentation to analyze sufficient conditions for asymptotic stability of the system.Secondly, a second-order and continuous controller is designed using a multivariable super-twisting algorithm, which effectively alleviates the chattering inherent in sliding mode control systems.Experimental results demonstrate that the proposed controller enables the system state to reach the sliding surface within a finite time and maintain in a stable sliding mode, effectively eliminating the chattering and enhancing the control performance of the system.
为了更精准、高效地预测煤层底板突水量等级，提出了基于CPO-LSSVM的煤层底板突水量等级预测模型。首先利用冠豪猪优化(CPO)算法对最小二乘支持向量机(LSSVM)的参数进行优化，再通过LSSVM的最小二乘损失函数优化预测模型，运用LSSVM在特征空间中划分最优超平面的方式对煤层底板突水量等级进行预测。根据收集的突水事故数据及查阅的文献，选取影响煤层底板突水的关键因素作为模型的输入指标，对突水量等级进行划分。选取27组样本数据，通过数据增强的方式扩充至152组，划分对应的训练集及测试集，并将CPO-LSSVM与CPO-BPNN、OOA-LSTM及PSO-BPNN模型的预测结果进行对比分析。结果表明：相较于其他三种模型，CPO-LSSVM模型的预测准确率分别提高了15.00%、2.22%、24.33%,宏精确率分别提高了9.26%、1.01%、31.80%,宏召回率分别提高了12.06%、2.86%、20.50%,宏F1分数分别提高了10.66%、1.94%、26.15%。将CPO-LSSVM模型实际应用于杨庄煤矿4个巷道，其预测结果与工程实际情况相符合，验证了模型的稳定性与适用性。
In order to predict the inrush water level of coal floor more accurately and efficiently, a prediction model of inrush water level of coal floor based on CPO-LSSVM was proposed.Firstly, the crested porcupine optimizer(CPO) algorithm was used to optimize the parameters of the least squares support vector machine(LSSVM),and then the LSSVM was used to optimize the prediction model through the least squares loss function of LSSVM.The LSSVM was used to divide the optimal hyperplane in the feature space to predict the water burst level of coal seam floor.According to the collected data of water inrush and the literature reviewed, the key factors affecting water inrush from coal seam floor were selected as the input indexes of the model, and the water inrush levels were divided.Twenty-seven groups of sample data were selected and expanded to 152 groups by data enhancement, and the corresponding training set and test set were divided.The prediction results of CPO-LSSVM,CPO-BPNN,OOA-LSTM and PSO-BPNN models were compared and analyzed.The results show that compared with the other three models, the prediction accuracy of the CPO-LSSVM model is increased by 15.00%,2.22%,24.33%,the macro precision is increased by 9.26%,1.01%,31.80%,the macro recall rate is increased by 12.06%,2.86%,20.50%,and the macro F1-score is increased by 10.66%,1.94%,26.15%.The CPO-LSSVM model is applied to four laneways in Yangzhuang coal mine, and the prediction results are consistent with the actual engineering situation, which verifies the stability and applicability of the model.
为提高纤维素对Ni(II)的吸附效果，通过环氧氯丙烷交联和苹果酸羧基化改性，制备具有良好吸附性能的羧基改性纤维素气凝胶。采用扫描电子显微镜、傅里叶变换红外光谱仪和X射线衍射仪系统表征材料羧基化前后的变化，结果表明，改性过程实现了纤维素表面羟基与苹果酸中羧基的有效酯化，所得气凝胶呈现典型的三维蜂窝状多孔结构。通过条件优化实验发现，当苹果酸与纤维素质量比为1.75∶1、磷酸二氢钠用量为0.5 g/g、130℃反应210 min时，制备的改性纤维素气凝胶在pH为7.0、吸附时间为120 min条件下，对Ni(II)的去除率可达92.61%。动力学及热力学分析结果表明，苹果酸改性纤维素气凝胶对Ni(II)的吸附过程可由准二级动力学模型描述，符合Langmuir等温吸附模型，且为自发进行的吸热过程。
To improve the adsorption effect of cellulose on Ni(II),carboxyl-modified cellulose aerogels with good adsorption properties were prepared by epichlorohydrin cross-linking and carboxylation modification of malate.Scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction were used to characterize the changes before and after carboxylation.Through the condition optimization experiment, it was found that, when the mass ratio of malic acid to cellulose was 1.75∶1,the amount of sodium dihydrogen phosphate was 0.5 g/g, and the reaction was 130 ℃ for 210 min, the removal rate of Ni(II)in the prepared modified cellulose aerogel could reach 92.61% under the conditions of pH=7.0 and adsorption time of 120 min.The results of kinetic and thermodynamic analysis showed that the adsorption process of Ni(II)by malic acid-modified cellulose aerogel could be described by the quasi-second-order kinetic model, which was in line with the Langmuir isothermal adsorption model and was a spontaneous endothermic process.