森林火灾远程监测及移动预警系统

针对目前林火监测技术存在的不足,提出并实现一种基于无线传感器网络、GPRS和J2ME技术的森林火灾远程监测及移动预警系统,给出系统架构及实现方案.实验测试表明,系统性能稳定,节点的灵敏度和网络传输达到理想效果,实现了实时监测和移动预警,对林火监测提供了有益的思路.     

物联网视觉中森林小距离火点定位算法仿真

研究物联网视觉中森林小距离火点定位优化问题.在森林火点定位中,物联网视觉遥感图像采集规范不同,图像特征存在质量差异,森林内不同区域、不同质量的图像在经过统一几何校正后,获取的定位特征残差扩大.传统算法应用转换后的图像进行定位,会导致火点对地定位精度大幅下降,小距离内的火点定位结果失真.为了避免上述缺陷,提出了一种空间矩阵亚像素的森林小距离火点定位算法.利用卡尔曼滤波方法,对采集的森林视觉遥感图像进行滤波处理,去除图像中的干扰因素.利用物联网视觉技术,计算森林火点的空间位置,从而实现物联网视觉中的森林小距离火点定位.实验结果表明,利用算法进行森林小距离火点定位,能够避免由于遥感图像采集规范差异造成的定位特征残差扩大的缺陷,从而提高了定位的准确性.     

基于植物间互利作用的森林生长模型快速计算

植物间互利作用的研究与探索有助于加强林业生产中的混交林建设,从而提高林分生产力。为了快速地模拟植物间互利作用下的生长情况,提出了一种利用植物间互利生长模型进行森林场景快速可视化的方法。该方法通过植物间互利指数、植物自身的生长率、植物当前生物量、环境条件等参数将Lotka-Volterra种间互利模型与植物个体生长模型进行融合,并采用像素化方法进行快速计算,以模拟互利植物的生长情况。通过水曲柳和落叶松混交林在不同的环境资源条件、分布密度、生长时间等条件下的可视化仿真,验证了基于植物间互利作用的森林生长模型快速计算方法的有效性和实用性。     

一种像素与对象相结合的林区建筑物识别方法

针对林区建筑物遥感监测技术需求,为构建GF-2数据在林区建筑物识别中的应用方法,选取蜀南竹海风景名胜区为研究区,根据所选区域建筑物的GF-2影像特征,研究形成了像素级和对象级相结合的林区建筑物识别方法。首先利用基于递归特征消除法的随机森林算法对预处理后的GF-2影像进行特征筛选;然后通过对比支持向量机和随机森林分类器识别的建筑物结果,选用支持向量机分类器所得研究区建筑物作为像素级识别结果;融合像素级建筑物识别结果和多尺度分割得到的影像对象,识别出该研究区建筑物目标。结果表明：利用支持向量机分类器进行像素级建筑物识别,其结果的正确率、完整率和质量均高于随机森林分类器;提出的像素级和对象级相结合的建筑物识别方法既保留了简单易行的优势,也避免了椒盐现象,在正确率、完整率和质量上均比像素级方法和对象级方法有所提高,在质量上分别比像素级方法和对象级方法提高了0.20和0.13,该方法可为主管单位有效监管林区内违规建筑物提供技术支撑。     

冠顶式步道景观环境感知评价研究 ——以福州“福道”为例

冠顶式步道是新兴的城市步道类型,因其独特的架空结构,给使用者带来与众不同的观景游憩体验。因此从人体感知的角度切入,依托韦伯-费希纳定律的函数框架及理论内涵,对中国首条冠顶式步道——福州"福道"及其所属环境对民众的心理所产生的刺激和感知强度进行量化,获取目前民众对于该步道的各种感知反馈,分析其景观环境现状。通过研究发现,公众对于步道设计建造品质的感知、游憩健身体验、观景体验以及整体景观营造的感知突出且良好,最后针对架空行走过程中存在的一系列问题,提出相应的优化建议。     

Energy consumption mode identification and monitoring method of process industry system under unstable working conditions

Process industry systems under unstable working conditions are prone to potential anomalies, deviating from the original transition trajectory, and taking longer than expected to return to stability due to persistent disturbances from uncertainties and experience-based regulation errors. The energy waste caused by this situation has not received sufficient attention, and cannot be addressed by existing energy consumption monitoring methods. Herein, an energy consumption mode (ECM) identification and monitoring method under unstable working conditions is proposed, consisting of ECM identification model and multi-mode dynamic monitoring model, focusing on the variation rules of the correlation between energy consumption and other states of the system. In the ECM identification stage, the ECM correlation parameters that reflect the comprehensive production information are selected. Then, given the transfer characteristics of ECM, a Hidden Semi-Markov Model (HSMM) is constructed to fit the migration between modes and the duration within modes. The Variational Bayesian Gaussian Mixture Model is introduced to improve the HSMM, which solves the problem of lacking prior knowledge of ECM and achieves the automatic classification and online identification of ECM. In the dynamic monitoring stage of multi-ECMs, a series of dynamic kernel principle component analysis models are established, and the corresponding monitoring thresholds are set for each ECM. By calculating the maximum of the posteriori probability and the mode thresholds, the ECMs under unstable conditions can be accurately identified and automatically monitored. Compared with previous methods, the proposed method reduces the false detection rate and missed detection rate of abnormal ECM identification to 1.04% and 1.31% in the actual slag grinding production process, which proves its effectiveness.     

Exploring high hydrostatic pressure for enhancing the preservation of white and dark muscle fish fillets stored at different packaging systems under refrigeration

High hydrostatic pressure (HHP) is an old preservation method. However, there is still a gap in the literature about their promising conditions for improving the shelf life and quality of different fish groups (dark and white muscle) at different packaging systems (air and vacuum) stored under refrigeration. Since it is crucial to boost optimization research and enable HHP industrial commercialization, this study aimed to identify promising high hydrostatic pressure (HHP) conditions to extend the maximum fresh fish quality of dark and white muscle fillets stored under air-packing and vacuum-packing (DM-AP, DM-VP, WM-AP, and WM-VP, respectively), considering the bacteriological (total viable aerobic count), physicochemical (lipid oxidation, lightness, redness, yellowness, total color changes, and hardness or firmness), and sensory (appearance, color, odor, texture, and acceptability) characteristics during refrigerated storage. The systematic literature search was carried out in four databases (PubMed, Science Direct, Scopus, and Web of Science) to retrieve articles from 2001 to 2023 published in English. A total of 3,922 articles were retrieved, of which 49 were eligible for systematic review and 47 for meta-analysis. Overall, the best HHP conditions for DM-AP (400 MPa/5 min/5 °C) and DM-VP (400 MPa/5 min/20 °C) extended the microbial and sensory shelf life from about 6 to ≥ 9 days with a positive effect on color (ΔE ≤ 4). Concerning WM-AP, HHP at 220 MPa/10 min/25 °C and 250 MPa/5 min/3 °C did not affect lipid oxidation and extended the sensory acceptability by 2–4 days but negatively influenced hardness and color (ΔE > 5). For WM-VP, HHP at 300 MPa/3 or 10 min/10–20 °C could prolong the microbial and sensory shelf life by 7 days without affecting hardness, decreased lipid oxidation, and color changes (ΔE < 4). Therefore, HHP may enable the shelf life extension of dark and white muscle fish fillets stored under refrigeration in aerobiosis and vacuum while retaining their quality.     

Intelligent Deep Learning Enabled Wild Forest Fire Detection System

The latest advancements in computer vision and deep learning (DL) techniques pave the way to design novel tools for the detection and monitoring of forest fires. In this view, this paper presents an intelligent wild forest fire detection and alarming system using deep learning (IWFFDA-DL) model. The proposed IWFFDA-DL technique aims to identify forest fires at earlier stages through integrated sensors. The proposed IWFFDA-DL system includes an Integrated sensor system (ISS) combining an array of sensors that acts as the major input source that helps to forecast the fire. Then, the attention based convolution neural network with bidirectional long short term memory (ACNN-BLSTM) model is applied to examine and identify the existence of danger. For hyperparameter tuning of the ACNN-BLSTM model, the bacterial foraging optimization (BFO) algorithm is employed and thereby enhances the detection performance. Finally, when the fire is detected, the Global System for Mobiles (GSM) modem transmits messages to the authorities to take required actions. An extensive set of simulations were performed and the results are investigated interms of several aspects. The obtained results highlight the betterment of the IWFFDA-DL technique interms of various measures.