The analysis of critical states during fracture of wood materials is crucial for wood building safety monitoring, wood processing, etc. In this paper, beech and camphor pine are selected as the research objects, and the acoustic emission signals during the fracture process of the specimens are analyzed by three-point bending load experiments. On the one hand, the critical state interval of a complex acoustic emission signal system is determined by selecting characteristic parameters in the natural time domain. On the other hand, an improved method of b_value analysis in the natural time domain is proposed based on the characteristics of the acoustic emission signal. The K-value, which represents the beginning of the critical state of a complex acoustic emission signal system, is further defined by the improved method of b_value in the natural time domain. For beech, the analysis of critical state time based on characteristic parameters can predict the “collapse” time 8.01 s in advance, while for camphor pines, 3.74 s in advance. K-value can be analyzed at least 3 s in advance of the system “crash” time for beech and 4 s in advance of the system “crash” time for camphor pine. The results show that compared with traditional time-domain acoustic emission signal analysis, natural time-domain acoustic emission signal analysis can discover more available feature information to characterize the state of the signal. Both the characteristic parameters and Natural_Time_b_value analysis in the natural time domain can effectively characterize the time when the complex acoustic emission signal system enters the critical state. Critical state analysis can provide new ideas for wood health monitoring and complex signal processing, etc.

For the Internet of Things (IoT) nodes deployed within forested areas, the gathering of energy is of paramount importance. Solar energy possesses a multitude of advantages, such as broad applicability, high energy density, and ease of acquisition. However, the acquisition of solar energy is susceptible to environmental influences, including weather conditions, seasonal factors, and diurnal variations, all of which can impact the efficiency of solar energy harvesting. This study focuses on the specific application of IoT in forestry and conducts an analysis and investigation of the solar cell characteristics, panel inclination, canopy closure, and baseline factors that affect the energy gathering of IoT nodes within forested areas. By designing and deploying an IoT system on the campus of Southwest Forestry University, the operational status of solar cells within forested areas is collected and analyzed based on different solar cell characteristics. Additionally, a fitted analysis is performed to determine the optimal inclination angle for solar cells, utilizing an empirical model calculated based on the latitude of the region. Furthermore, the impact of canopy closure and topography on solar energy collection by nodes is analyzed through the measurement and analysis of real-world data obtained from LoRa sensor network nodes deployed on the campus of Southwest Forestry University. These studies will be beneficial for the practical application of IoT in forestry.

In order to increase the detecting precision of surface defects in industrial hot-rolled strip steel, this paper improves the network model for target detection of strip steel surface defects based on the YOLOv7 algorithm. First, to minimize information loss and enhance global dimensional interaction characteristics, the Global Attention Mechanism (GAM) is applied. Second, the YOLOv7 model incorporates the adaptive spatial feature fusion technique to achieve the fusion of top-level features with bottom-level features. Finally, the regression loss function used in the original model is substituted for SIoU Loss to increase the speed and accuracy of the frame regression. The testing outcomes reveal that the method in this paper has an average accuracy improvement of 3.1% compared to the YOLOv7 algorithm before improvements on the industrial hot-rolled strip data set (NEU-DET).

For the forestry Internet of Things (IoT) systems deployed in the wilderness, the energy consumption of sensor nodes and energy-saving mechanisms are of paramount importance. This study focuses on the energy consumption analysis of various sensors, node controls, and communication devices in the forestry IoT system. Through the design of sensor power shutdown strategies, node sleep strategies, and incremental transmission strategies, it aims to maximize energy savings and improve energy utilization efficiency. By implementing the incremental transmission strategy, data compression can be performed with minimal computational energy expenditure, reducing the number of data transmissions and conserving node energy. Experimental results demonstrate that, when utilizing a 1000mAh lithiumion battery, the implementation of energy-saving strategies enables the node to sustain operation for up to 400 hours, significantly extending the lifespan of the forestry IoT system. The results of this study can be applied to the actual deployment of forestry IoTs, effectively extending node life, and improving network reliability.

为了通过声发射信号(AE)对木材内部损伤状态进行评估,本研究选择能够表征信号复杂程度(熵值)以及波形变化(脉冲因子、裕度因子、峰值因子、波形因子)的特征,并通过对木材试样三点加压弯曲实验采集原始声发射信号.另外,为了抑制干扰,提高信号特征对损伤状态的敏感性,提出了一种采用EMD分解方法,基于信号能量、瞬时频率、峭度值的信号预处理方法和信号重构机制.最后,获取重构AE信号的熵值(信息熵、指数熵)和波形特征参数(脉冲因子、裕度因子、峰值因子、波形因子),探索了它们与木材内部损伤与断裂过程的关系,并从中提取6段时序信号进行特征值前后对比分析.结果表明:通过这些特征,可以将木材受力激发出的声发射信号分为4个类别,分别是屈曲AE信号、形变AE信号、微裂AE信号和断裂AE信号,并与木材损坏过程中微观结构变化的4种形式(胞壁屈曲与塌溃、胞壁界面损伤与层裂、微裂隙损伤区的形成与扩展、胞壁断裂)相对应.相比较波形特征,声发射信号的熵值能够更加敏感地反映出木材内部损伤状态的变化.波形特征能够较好地反映出木材断裂后载荷逐渐减小的趋势.本研究提出的信号预处理方法和重构机制,能够提高上述特征对不同损伤状态的区分度,同时本研究所选特征在木材AE信号识别中具有重要作用.

Earthquakes are natural disasters that endanger human life and cause the greatest loss of property. The study of anomalous disturbance of the ionosphere, one of the pre-earthquake anomalies, will help to further study the coupling effect on the ionosphere before the earthquake. This paper focuses on the analysis of the importance and influence of various parameters inside the seismic ionosphere under earthquake conditions, constructs a classification and prediction model of seismic ionospheric anomalies based on the gradient boosting decision tree GBDT algorithm, and analyzes the pre-earthquake ionospheric data. , the results show that NmE, nHe+, foF2 and TEC are important influencing factors, which lays a certain foundation for the further study of the internal structure and parameters of the pre-earthquake ionosphere

The low-velocity blood flow signal close to the blood vessel wall is very important for the early diagnosis of vascular diseases. To obtain more accurate low-velocity blood flow signals from the Doppler ultrasound echo signal, this paper proposes a carotid artery low-velocity blood flow signal extraction method based on local mean decomposition (LMD). First, the LMD decomposes the original signal into a series of product terms. Then, based on the instantaneous frequency and amplitude, the blood flow stratification index is constructed to subdivide the blood flow and blood vessel wall signals in the product component. Finally, the signal is reconstructed, thereby achieving the suppression of the clutter signal of the blood vessel wall. Experimental results show that this method could effectively obtain low-velocity blood flow signals.

传统的人工监测无法实现大规模蓝藻的实时监测预警,该研究运用基于Python的ArcGIS Server自动发布地图服务、多元数据预警分析、人工水质监测数据预警分析等技术,以滇池为研究区,构建了水华预警系统.通过预警体系业务子系统和预警信息共享与发布子系统,结合滇池蓝藻水华监测预警综合数据库,实现了大规模蓝藻预警信息的实时生成、共享和发布,提出了地表变化动态监测预警的思路.系统建成后,通过对滇池流域生态红线预警范围内的地表覆盖变化情况监测,可及时发现导致水华发生的潜在陆源污染因素.同时配合排污点污染成分检测实现预警,从源头防止水华发生.

The nondestructive testing technology of generated acoustic emission(AE) signals for wood is of great significance for the evaluation of internal damages of wood. In order to improve the classification accuracy and adaptability of AE signal, we selected two features(pseudospectrum, entropy) for classify AE signals in the process of wood fracture using SVM classifier. The three-point bending load damage experiment was utilized to generate original AE signals. Evaluation indexes(Precision, Accuracy, Recall, F1-score, Cohen Kappa score, Matthews Corrcoef) were adopted to assess the classification model. The results showed that the overall accuracy of the SVM classification model obtained by the method combining pseudospectrum and entropy features is 89.44%, which indicates that this automatic classification model has good AE signal recognition performance.

为提高采摘设备的执行效率,采用六自由度机械臂、树莓派、Android手机端和服务器设计了一种智能果实采摘系统,该系统可自动识别不同种类的水果,并实现自动采摘,可通过手机端远程控制采摘设备的起始和停止,并远程查看实时采摘视频.提出通过降低自由度和使用二维坐标系来实现三维坐标系中机械臂逆运动学的求解过程,从而避免了大量的矩阵运算,使机械臂逆运动学求解过程更加简捷.利用Matlab中的Robotic Toolbox进行机械臂三维建模仿真,验证了降维求解的可行性.在果实采摘流程中,为了使机械臂运动轨迹更加稳定与协调,采用五项式插值法对机械臂进行运动轨迹规划控制.基于Darknet深度学习框架的YOLO v4目标检测识别算法进行果实目标检测和像素定位,在Ubuntu 19.10操作系统中使用2000幅图像作为训练集,分别对不同种类的果实进行识别模型训练,在GPU环境下进行测试,结果表明,每种果实识别的准确率均在94％以上,单次果实采摘的时间约为17 s.经过实际测试,该系统具有良好的稳定性、实时性以及对果实采摘的准确性.

围绕一种集火焰识别与定位、路线规划、自动巡逻、环境参数检测、险情预警等功能于一体的室内智能安防车设计,提出将测距法与地磁计相结合的路线规划导航方法,结合PID算法和椭圆拟合算法实现了小车自动巡逻时走直线与转向的精准控制.基于OpenCV库的Haar-Like特征训练分类器,实现对火焰及入室人员的智能识别,结合传感器采样信息实现对火焰位置的精准定位.系统由手机端、智能小车、网关、云平台组成.该设计解决了传统室内安防设备安装位置固定导致检测死角的问题,给未来的室内智能移动安防设计提供了一定的参考价值.

为实现水泵自动控制及泵站智能调度,进而达到泵房无人值守运行、提高供水保障能力的目的,提出了一种结合物联网技术、边缘计算技术、嵌入式技术及数据可视化技术的水泵智能控制系统构架.该系统采用LoRa低功耗物联网技术配合STM32F103微处理器构建网络节点及数据网关,使用C#编写后台管理平台和Django搭建数据展示平台;针对泵房工作环境特点综合考虑了系统的成本、可靠性、扩展性等.通过该系统的设计与实验,能够培养学生的团队合作意识,促使学生综合运用电子线路设计、物联网、嵌入式开发、.NET程序设计等知识.该系统的部署及运用在保障学校供水安全的同时,也为学生提高实践动手能力提供了真实的物联网系统运维环境及原始数据.

文中设计了一种由Android手机端控制的具有视频采集功能的智能车,STM32F103RCT6作为智能车主控板,树莓派实现视频采集,Android手机端可选择语音、按键、重力感应等模式对小车进行控制.提出了在小车行走过程中基于四元数算法获取每个时刻的偏航角,并配合P ID算法,从而实现小车自动走直线的精确控制.经测试,系统软硬件配合良好,小车能及时执行Android手机端发送的各种控制指令,实时视频传输稳定.

森林环境的特殊性导致物联网技术在林业的实际应用中还存在诸多困难.该文提出一种结合物联网技术、嵌入式技术、网络技术的智慧森林监测系统构架.该系统采用SX1278芯片设置LoRa收发器配合STM32F103微处理器构建监测节点,采用SX1301配合树莓派构建多业务网关,并完成组网及对森林环境因子的采集、传输和汇集实验;后台服务软件支持用户通过PC机网页端或手机端访问,完成所有监测数据的实时显示、历史查询、数据分析等功能.在设计过程中,针对林区特点综合考虑了系统的成本、低功耗、可靠性、扩展性等.该系统已部署在哀牢山区,并稳定运行了1年半.

在基于Django框架构建的古茶树数据平台收集临沧、文山、德宏、普洱、西双版纳5个州市14个县不同茶区的64份普洱茶种古茶树资源的16个主要表型性状数据,提出一种基于Python的Anaconda来统计古茶树描述型性状的方法,并采用相关性分析、主成分分析、聚类分析等方法对古茶树表型性状数据进行分析研究.结果表明,64份云南普洱茶种古茶树资源的描述型性状变异丰富,遗传多样性指数为0.6937～1.1967.按照表型性状遗传多样性进行聚类,临翔区(邦东乡)古茶树种类较丰富,在选种培育上具有优势.

声学的无损检测方法具有成本低、易于携带、无辐射、检测速度快等优点,在木材领域内广泛应用.阐述了基于声学的常见6种无损检测方法,包括冲击应力波法、超声波速法、共振法、声发射、声-超声和层析成像技术的基本原理,并对其特点进行了分析与比较.介绍了这些方法在木材工业中的应甩,包括对木材的物理力学特性的评估、木材内部缺陷的检测.综述了提高检测精度的研究现状,分析了基于声学的木材无损检测研究中存在的困难.展望了木材无损检测设备在信号源、信号传输机理、信号分析与处理、便携性与实时性等方面的发展趋势.

实践教学作为提升学生实践创新能力的关键环节,是新工科建设背景下教学改革的重中之重.学院改革课程体系,形成一体两翼的三课堂实践创新能力培养模式;构建多层次实践教学体系,联系校内校外,搭建创新基地和平台,实施产教融合,科教协同,以赛促学等一系列的措施,使学生不断将理论知识转化为技能,提升了学生实践创新能力.

The disturbances caused by the instability of probe hold and the breathing exercise from subjects,are introduced during B-ultrasound examination.It decreases the measuring accuracy of the arterial wall displacement obtained from the B-Mode images by speckle tracking method.Thus,we propose a global rigid-registration method based on phase images of B-Mode images to eliminate these external disturbances.The phase features are obtained from B-Mode images via Riesz transform and difference of Gaussian filters.To improve the speed of searching optimization and the registration accuracy,a stepwise registration method is applied based on position weighted principal axis and centroid method combined with mutual information.The results show that,compared with these schemes of extracting wall displacement directly from B-Mode or phase images by speckle tracking method,this scheme can effectively remove these disturbances,and significantly improve the estimation accuracy.The proposed method can also be used in image registration of cardio-cerebro diseases.

Objective. This paper presents an assessment of physical meanings of parameter and goodness of fit for homodyned K (HK) distribution modeling ultrasonic speckles from scatterer distributions with wide- varying spatial organizations. Methods. A set of 3D scatterer phantoms based on gamma distributions is built to be implemented from the clustered to random to uniform scatterer distributions continuously. The model parameters are obtained by maximum likelihood estimation (MLE) from statistical histograms of the ultrasonic envelope data and then compared with those by the optimally fitting models chosen from three single distributions. Results show that the parameters of the HK distribution still present their respective physical meanings of independent contributions in the scatterer distributions. Moreover, the HK distribution presents better goodness of fit with a maximum relative MLE difference of 6.23% for random or clustered scatterers with a well-organized periodic structure. Experiments based on ultrasonic envelope data from common carotid arterial B-mode images of human subjects validate the modeling performance of HK distribution. Conclusion. We conclude that the HK model for ultrasonic speckles is a better choice for characterizing tissue with a wide variety of spatial organizations, especially the emphasis on the goodness of fit for the tissue in practical applications.

This paper presents an ultrasound simulation model for pulsatile blood flow, modulated by the motion of a stenosed vessel wall. It aims at generating more realistic ultrasonic signals to provide an environment for evaluating ultrasound signal processing and imaging and a framework for investigating the behaviors of blood flow field modulated by wall motion. This model takes into account fluid-structure interaction, blood pulsatility, stenosis of the vessel, and arterial wall movement caused by surrounding tissue's motion. The axial and radial velocity distributions of blood and the displacement of vessel wall are calculated by solving coupled Navier-Stokes and wall equations. With these obtained values, we made several different phantoms by treating blood and the vessel wall as a group of point scatterers. Then, ultrasound echoed signals from oscillating wall and blood in the axisymmetric stenotic-carotid arteries were computed by ultrasound simulation software, Field II. The results show better consistency with corresponding theoretical values and clinical data and reflect the influence of wall movement on the flow field. It can serve as an effective tool not only for investigating the behavior of blood flow field modulated by wall motion but also for quantitative or qualitative evaluation of new ultrasound imaging technology and estimation method of blood velocity.