Memristors-coupled neuron models with multiple firing patterns and homogeneous and heterogeneous multistability

Memristors are extensively used to estimate the external electromagnetic stimulation and synapses for neurons.In this paper,two distinct scenarios,i.e.,an ideal memristor serves as external electromagnetic stimulation and a locally active memristor serves as a synapse,are formulated to investigate the impact of a memristor on a two-dimensional Hindmarsh-Rose neuron model.Numerical simulations show that the neuronal models in different scenarios have multiple burst firing patterns.The introduction of the memristor makes the neuronal model exhibit complex dynamical behaviors.Finally,the simulation circuit and DSP hardware implementation results validate the physical mechanism,as well as the reliability of the biological neuron model.

A new method for evaluating the firing precision of multiple launch rocket system based on Bayesian theory

How to effectively evaluate the firing precision of weapon equipment at low cost is one of the core contents of improving the test level of weapon system.A new method to evaluate the firing precision of the MLRS considering the credibility of simulation system based on Bayesian theory is proposed in this paper.First of all,a comprehensive index system for the credibility of the simulation system of the firing precision of the MLRS is constructed combined with the group analytic hierarchy process.A modified method for determining the comprehensive weight of the index is established to improve the rationality of the index weight coefficients.The Bayesian posterior estimation formula of firing precision considering prior information is derived in the form of mixed prior distribution,and the rationality of prior information used in estimation model is discussed quantitatively.With the simulation tests,the different evaluation methods are compared to validate the effectiveness of the proposed method.Finally,the experimental results show that the effectiveness of estimation method for firing precision is improved by more than 25%.

基于FireNet的古建筑火灾检测方法研究及改进

针对古建筑火灾检测需要快速、准确及实时的需求,建立了一个专门用于古建筑火灾检测的数据集,用于古建筑火灾检测的深度学习研究。利用CBAM注意力机制模块,结合多尺度特征融合,对FireNet网络进行改进,提出适用于古建筑火灾检测的轻量级FireNet-AMF网络,在FireNet数据集和本文构建的古建筑火灾检测数据集上验证了FireNet-AMF网络的火灾检测能力。与改进前的网络相比,FireNet-AMF网络在FireNet数据集上对火灾识别的准确率达到了95.08%,与原网络相比提高了1.17%,在本文构建的古建筑火灾检测数据集上的准确率达到了95.62%,比原网络提高了1.62%。该网络在保证轻量级的同时也保证了在古建筑火灾检测中较高的检测精度。

基于BIMFire-YOLO的消防监控图像识别方法

在传统条件下,通常存在着火灾现场情况多变,建筑物内构造复杂等问题,使得消防监控系统难以通过现有方法精准检测识别火灾第一现场和所处室内环境。为了优化检测算法在复杂场景下的应用效果,文中提出BIMFire-YOLO检测方法,在以往YOLOv3主干网络中增加了新的卷积方式,在扩大了卷积感受野范围的同时,也保证了输出特征的不变性,对小火焰和烟雾等目标的检测能力有所提升。通过引入新的损失函数,降低了火灾发生的目标漏检率,融合BIM技术不仅提升了定位精度也实现了自动识别视频和图像中失火的场景。该算法不仅提高了检测效率,也实现了火灾的信息共享与管理的统一,在实际医院场景下的综合识别精度达到了45.6%,检测速度和精度均优于其他同类方法。

Enhancing Fire Safety in Commercial Vehicles: Assessing the Efficacy and Advantages of Exploding Fire Extinguishing Balls

Fire incidents in commercial vehicles pose significant risks to passengers, drivers, and cargo. Traditional fire extinguishing systems, while effective, may have limitations in terms of response time, coverage, and human intervention [1]. This study investigates the efficacy of a novel fire suppression technology—the Exploding Fire Extinguishing Ball (EFEB) —as an alternative and complementary fire safety solution for commercial vehicles. The research employs a multidisciplinary approach, encompassing engineering, materials science, fire safety, and human factors analysis. A systematic literature review establishes a comprehensive understanding of existing fire suppression technologies, including EFEBs. Subsequently, this study analyzes the unique features of EFEBs, such as automatic activation, as well as manual activation upon exposure to fire, and their potential to provide rapid, localized, and autonomous fire suppression. The study presents original experimental investigations to assess the performance and effectiveness of EFEBs in various fire scenarios representative of commercial vehicles. Experiments include controlled fires in confined spaces and dynamic simulations to emulate real-world fire incidents. Data on activation times, extinguishing capability, and coverage area are collected and analyzed to compare the efficacy of EFEBs with traditional fire extinguishing methods. Furthermore, this research shows the practical aspects of implementing EFEBs in commercial vehicles. A feasibility study examines the integration challenges, cost-benefit analysis, and potential regulatory implications. The study also addresses the impact of EFEBs on vehicle weight, stability, and overall safety. Human factors and user acceptance are crucial elements in adopting new safety technologies. Therefore, this research utilizes an experimental design to assess the performance and effectiveness of EFEBs in various fire scenarios representative of commercial vehicles. This dissertation presents original controlled experiments to emulate real-world fire incidents, including controlled fires in confined spaces and dynamic simulations. The experimental approach ensures rigorous evaluation and objective insights into EFEBs’ potential as an autonomous fire suppression system for commercial vehicles. This includes the perspectives of drivers, passengers, fleet operators, insurance agencies, and regulatory bodies. Factors influencing trust, perceived safety, and willingness to adopt EFEBs are analyzed to provide insights into the successful integration of this technology. The findings of this research will contribute to the knowledge of fire safety technology and expand the understanding of the applicability of EFEBs in commercial vehicles.

Performance Analysis of Support Vector Machine (SVM) on Challenging Datasets for Forest Fire Detection

This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.

Lightning in a Forest (Wild) Fire: Mechanism at the Molecular Level

The mechanism of lightning that ignites a forest fire and the lightning that occurs above a forest fire are explained at the molecular level. It is based on two phenomena, namely, internal charge separation inside the atmospheric cloud particles and the existence of a layer of positively charged hydrogen atoms sticking out of the surface of the liquid layer of water on the surface of rimers. Strong turbulence-driven collisions of the ice particles and water droplets with the rimers give rise to breakups of the ice particles and water droplets into positively and negatively charged fragments leading to charge separation. Hot weather in a forest contributes to the updraft of hot and humid air, which follows the same physical/chemical processes of normal lightning proposed and explained recently[1]. Lightning would have a high probability of lighting up and burning the dry biological materials in the ground of the forest, leading to a forest (wild) fire. The burning of trees and other plants would release a lot of heat and moisture together with a lot of smoke particles (aerosols) becoming a strong updraft. The condition for creating lightning is again satisfied which would result in further lightning high above the forest wild fire.

基于改进FireNet的轻型火灾实时检测方法

针对火灾检测精确不高,时间长等问题。设计了基于改进FireNet的轻型火灾实时检测方法,通过获取视频图像数据,网络模型进行火灾分析和识别;首先,在FireNet特征提取阶段使用多尺度卷积网络并引入通道注意力机制,以提高回归精度。其次,对全连接层的神经元个数进行压缩优化,减少计算耗时。实验表明,改进的FireNet算法模型检测精度达到96.43%,模型存储空间0.96MB,检测帧率37。相比标准算法精度提高2.5%,存储空间压缩85%,检测帧率提升35%。

The Effects of Firing Temperatures on the Performance of Insulating Firebricks Containing Different Proportions of Alumina and Sawdust

This work studied the effects of firing temperatures on the refractory properties of insulating firebricks produced from a blend of hydrometallurgically purified clay, high alumina cement and sawdust. Twenty grams out of a bulk (1000 Kg) of clay obtained from Ipetumodu in Nigeria was analyzed for size range, consequent upon which the remaining bulk was sieved to 100 μm, being the average size. The bulk was there after leached under a predetermined condition (1.6 mol/dm3 of oxalic acid at 70oC for 150 min and 200 rev/min agitation speed) and cylindrical samples (5 cm diameter by 5 cm high) containing different quantities of high alumina cement (5% - 20%) and sawdust (1% - 5%) were prepared, dried at 110oC and subsequently fired at 900oC, 1100oC, 1300oC and 1500oC, at the rate of 4oC/min and soaked for 2 hrs. These samples were subjected to different refractory tests (permanent linear change, modulus of rupture, bulk density, cold crushing strength and apparent porosity). Even though samples containing more than 20% alumina crumbled at elevated temperatures, it was still observed that the bricks performed to expectations at lower alumina contents, even at 1500oC. The sample containing 3% sawdust and 10% alumina cement however, gave the desired requirement for preparing good insulating firebricks with reliable phase integrity, as revealed by scanning electron microscopy (SEM).

AI-based small arms firing skill evaluation system in the military domain

Evaluation system of small arms firing has an important effect in the context of military domain. A partially automated evaluation system has been conducted and performed at the ground level. Automation of such system with the inclusion of artificial intelligence is a much required process. This papers puts focus on designing and developing an AI-based small arms firing evaluation systems in the context of military environment. Initially image processing techniques are used to calculate the target firing score. Additionally, firing errors during the shooting have also been detected using a machine learning algorithm. However, consistency in firing requires an abundance of practice and updated analysis of the previous results. Accuracy and precision are the basic requirements of a good shooter. To test the shooting skill of combatants, firing practices are held by the military personnel at frequent intervals that include 'grouping' and 'shoot to hit' scores. Shortage of skilled personnel and lack of personal interest leads to an inefficient evaluation of the firing standard of a firer. This paper introduces a system that will automatically be able to fetch the target data and evaluate the standard based on the fuzzy systems.Moreover it will be able to predict the shooter performance based on linear regression techniques.Thereby, it compares with recognized patterns to analyze the individual expertise and suggest improvements based on previous values. The paper is developed on a Small Arms Firing Skill Evaluation System, which makes the whole process of firing and target evaluation faster with better accuracy. The experiment has been conducted on real-time scenarios considering the military field and shows a promising result to evaluate the system automatically.

Temporal and spatial analysis of vegetation fire activity in the circum-Arctic during 2001–2020

Vegetation fires become the concern worldwide due to their substantial impacts on climate and environment,and in particular in the circum-Arctic.Assessing vegetation fires and associated emissions and causes can improve understanding of fire regime and provide helpful information for vegetation fires solution.In this study,satellitebased vegetation fires and emissions during 2001–2020 were investigated and contributions of different types of fires were analyzed.Furthermore,climate anomalies related to extreme vegetation fires were explored.The main results showed that the region south of the Arctic circle(50°N-67°N)experienced a greater number of vegetation fires compared to the Arctic(north of 67°N).During 2001–2020,interannual variability of vegetation fires between 50°N and 67°N appeared to be decreasing while emissions(including carbon,dry matter,PM_(2.5),and BC)appeared to be increasing overall,which were contributed by the increasing summer boreal forest fires in this region largely.In the Arctic,vegetation fires and emissions increased in recent years distinctly,and those were dominated by the summer forest fires.Spatially,large increases of vegetation fires were located in the eastern Siberia and northern North America while large decreases were located in the northwestern Eurasia mainly.Additionally,in the Arctic,the unprecedented vegetation fires were observed in the eastern Siberia and Alaska in 2019 and in the eastern Siberia in 2020,which could be attributed to high pressure,high near-surface temperature,and low air moisture anomalies.Meanwhile,obvious anticyclonic anomalies in Alaska in 2019 and in the eastern Siberia in 2020 and cyclonic anomalies in the western Siberia in 2019,also played an important role on fire occurrences making drier conditions.

A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.

Firing activities in a fractional-order Hindmarsh–Rose neuron with multistable memristor as autapse

Considering the fact that memristors have the characteristics similar to biological synapses, a fractional-order multistable memristor is proposed in this paper. It is verified that the fractional-order memristor has multiple local active regions and multiple stable hysteresis loops, and the influence of fractional-order on its nonvolatility is also revealed. Then by considering the fractional-order memristor as an autapse of Hindmarsh–Rose(HR) neuron model, a fractional-order memristive neuron model is developed. The effects of the initial value, external excitation current, coupling strength and fractional-order on the firing behavior are discussed by time series, phase diagram, Lyapunov exponent and inter spike interval(ISI) bifurcation diagram. Three coexisting firing patterns, including irregular asymptotically periodic(A-periodic)bursting, A-periodic bursting and chaotic bursting, dependent on the memristor initial values, are observed. It is also revealed that the fractional-order can not only induce the transition of firing patterns, but also change the firing frequency of the neuron. Finally, a neuron circuit with variable fractional-order is designed to verify the numerical simulations.

Prediction of forest fire occurrence in China under climate change scenarios

Climate change has an impact on forest fire patterns.In the context of global warming,it is important to study the possible effects of climate change on forest fires,carbon emission reductions,carbon sink effects,forest fire management,and sustainable development of forest ecosystems.This study is based on MODIS active fire data from 2001-2020 and the influence of climate,topography,vegetation,and social factors were integrated.Temperature and precipitation information from different scenarios of the BCC-CSM2-MR climate model were used as future climate data.Under climate change scenarios of a sustainable low development path and a high conventional development path,the extreme gradient boosting model predicted the spatial distribution of forest fire occurrence in China in the 2030s(2021-2040),2050s(2041-2060),2070s(2061-2080),and2090s(2081-2100).Probability maps were generated and tested using ROC curves.The results show that:(1)the area under the ROC curve of training data(70%)and validation data(30%)were 0.8465 and 0.8171,respectively,indicating that the model can reasonably predict the occurrence of forest fire in the study area;(2)temperature,elevation,and precipitation were strongly correlated with fire occurrence,while land type,slope,distance from settlements and roads,and slope direction were less strongly correlated;and,(3)based on future climate change scenarios,the probability of forest fire occurrence will tend to shift from the south to the center of the country.Compared with the current climate(2001-2020),the occurrence of forest fires in 2021-2040,2041-2060,2061-2080,and 2081-2100 will increase significantly in Henan Province(Luoyang,Nanyang,S anmenxia),Shaanxi Province(Shangluo,Ankang),Sichuan Province(Mianyang,Guangyuan,Ganzi),Tibet Autonomous Region(Shannan,Linzhi,Changdu),Liaoning Province(Liaoyang,Fushun,Dandong).

A Survey of the Machine Learning Models for Forest Fire Prediction and Detection

Forest fires are a significant threat to the environment, causing ecological damage, economic losses, and posing a threat to human life. Hence, timely detection and prevention of forest fires are critical to minimizing their impact. In this paper, we review the current state-of-the-art methods in forest fire detection and prevention using predictions based on weather conditions and predictions based on forest fire history. In particular, we discuss different Machine Learning (ML) models that have been used for forest fire detection. Further, we present the challenges faced when implementing the ML-based forest fire detection and prevention systems, such as data availability, model prediction errors and processing speed. Finally, we discuss how recent advances in Deep Learning (DL) can be utilized to improve the performance of current fire detection systems.

Influence of Firing Temperature on Properties of High Alumina Bricks

To reduce production costs and make full and reasonable use of raw materials,high alumina bricks were prepared using tabular corundum and mullite as aggregates,sillimanite as intermediate particles,and white fused corundum powder,α-alumina micropowder,and Suzhou soil as the matrix,firing at different temperatures(1420,1440,1460,1480,1500 and 1520℃)for 4 h.The apparent porosity(AP),the bulk density(BD),the cold crushing strength(CCS),the thermal shock resistance(TSR),the refractoriness under load(RUL)and the creep rate of the samples were tested.The effects of the firing temperature on the creep rate(1450℃×50 h,under a load of 0.2 MPa)of the samples were studied.The results show that with the sillimanite addition of 22.5 mass%,the sample fired at 1460℃for 4 h performs the best comprehensive properties:the AP of 17.5%,the BD of 2.75 g·cm^(-3),the CCS of 100.5 MPa,the TSR number of 35 cycles,the RUL of 1682℃,and the creep rate of-0.428%,which can prolong the service life of furnaces.

不同草原防火政策下内蒙古草原火灾发生风险及其驱动因素的研究

草原火灾是草原生态系统中重要的干扰因子之一,不同时期的草原防火政策可能会导致草原火灾发生概率及驱动因素发生变化。本研究基于内蒙古1981~2020年草原火灾数据,以新旧《草原防火条例》实施时间(旧《草原防火条例》1993年10月5日颁布并实施,新《草原防火条例》2008年11月19日颁布并于2009年1月1日起实施)为界线,通过随机森林模型分4个时期(1981~2020年、1981~1993年、1994~2008年、2009~2020年)对内蒙古草原火灾发生概率与驱动因素进行比较与分析,并绘制草原火灾风险等级区划图。结果表明:(1)4个时期建模的全样本AUC在0.930~0.940之间,精度优异。(2)在不同时期,气象因素(日平均相对湿度、气温日较差等)始终是影响草原火灾的主导因素,海拔、距火点最近公路距离等因素也是内蒙古草原火灾发生的重要驱动因素;(3)1981~1993年和1981~2020年草原火灾风险区基本相似,中、高、极高草原火灾风险区主要集中在呼伦贝尔市大部分地区和兴安盟北部,1994~2008年中、高、极高草原火灾风险区主要集中在呼伦贝尔市,而2009~2020年中、高、极高草原火灾风险区主要集中在呼伦贝尔市西部、锡林郭勒盟北部、阿拉善盟东南部、乌海市和鄂尔多斯市东部。

钢壳沉管隧道耐火极限标准与防火保护技术研究——以深中通道沉管隧道为例

为提高我国超大断面钢壳沉管隧道火灾防控水平,以深中通道海底沉管隧道为例,采用热力耦合模拟和材性试验等方法开展钢壳沉管隧道耐火极限标准研究,考虑到龙骨、拼缝等防火构造薄弱环节,进行管节结构和管节接头耐火试验,并提出相应的防火保护技术方案。主要结论如下:1)管节结构的耐火极限标准为火灾时,应将结构内侧钢壳最高温度控制在300℃以内;2)OMEGA橡胶管节接头的耐火极限标准为OMEGA橡胶超过70℃的时间小于2 h,超过100℃的时间小于1 h,且最高温度不得超过150℃;3)GINA橡胶管节接头的耐火极限标准为GINA橡胶超过100℃的时间小于1 h,且最高温度不得超过150℃;4)管节结构可采用35 mm单层防火板L型拼缝,对接面涂热膨胀胶;5)管节接头可采用35 mm防火板+50 mm陶瓷纤维毯双层防火保护体系,龙骨自由端取500 mm,拼缝处采用防火胶封堵。

基于网络开放数据的区域消防救援总体有效覆盖率评估

高效准确地掌握消防救援有效覆盖率是优化消防站点资源配置的前提。将判断是否有效覆盖的消防救援行程时间阈值定为240 s,建立了基于时间加权的区域消防救援总体有效覆盖率评估模型。基于网络开放数据收集了长沙的50座消防站作为消防救援供给点、5746家被抽查到的社会单位作为消防救援需求点,并调用网络地图应用程序编程接口基于实时路况来仿真消防救援行程时间。在连续7 d内设置197个评估场景,共获得了1131962个有效样本,进而得出:长沙消防救援总体有效覆盖率为21.22%,结果等级为“C”,消防救援水平一般,需加强消防站建设。这种基于网络开放数据的区域消防救援有效覆盖率评估,具备高效和准确的特点,可为优化消防站点资源配置、提升公共安全水平提供关键理论和方法支撑。

火源深度对深井立式空间火灾烟气流动特性及火灾探测方式的影响分析

为掌握深井立式车库、深地矿井等深井立式空间内火灾烟气运动规律,进而对此类火灾进行预警,以某深井地下车库为原型,构建深井立式空间模型,通过数值模拟分析火源深度和热释放速率对深井立式空间火灾烟气流动特性的影响,并掌握温度场和速度场的变化规律。研究结果表明:当火源深度足够大时,由于向上的浮力和速度减小,烟气可能无法到达井口,火源位置越深,其上方热烟气温度越高;并且火源深度对烟气流动的影响存在临界值,即临界深度,当火源高于临界深度时,火源可以从深井出口外卷吸空气,当火源低于临界深度时,火源仅能从深井内部卷吸空气。研究结果可为深井立式空间火灾的预测预警提供科学参考。