Wireless sensor networks are useful complements to existing monitoring systems in underground mines. They play an important role of enhancing and improving coverage and flexibility of safety monitoring systems.Regions...Wireless sensor networks are useful complements to existing monitoring systems in underground mines. They play an important role of enhancing and improving coverage and flexibility of safety monitoring systems.Regions prone to danger and environments after disasters in underground mines require saving and balancing energy consumption of nodes to prolong the lifespan of networks.Based on the structure of a tunnel,we present a Long Chain-type Wireless Sensor Network（LC-WSN）to monitor the safety of underground mine tunnels.We define the optimal transmission distance and the range of the key region and present an Energy Optimal Routing（EOR）algorithm for LC-WSN to balance the energy consumption of nodes and maximize the lifespan of networks.EOR constructs routing paths based on an optimal transmission distance and uses an energy balancing strategy in the key region.Simulation results show that the EOR algorithm extends the lifespan of a network,balances the energy consumption of nodes in the key region and effectively limits the length of routing paths,compared with similar algorithms.展开更多
Resource management in Underground Wireless Sensor Networks(UWSNs)is one of the pillars to extend the network lifetime.An intriguing design goal for such networks is to achieve balanced energy and spectral resource ut...Resource management in Underground Wireless Sensor Networks(UWSNs)is one of the pillars to extend the network lifetime.An intriguing design goal for such networks is to achieve balanced energy and spectral resource utilization.This paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data,received from the buried source nodes through a lossy soil medium,to the aboveground base station.A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover(HCSSC)algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource efficiency.The proposed algorithm improves the standard Salp Swarm Algorithm(SSA)by considering a chaotic map to initialize the population along with performing the crossover technique in the position updates of salps.Through experimental results,the HCSSC algorithm proves its outstanding superiority to the standard SSA for resource efficiency optimization.Hence,the network’s lifetime is prolonged.Indeed,the proposed algorithm achieves an improvement performance of 23.6%and 20.4%for the resource efficiency and average remaining relay battery per transmission,respectively.Furthermore,simulation results demonstrate that the HCSSC algorithm proves its efficacy in the case of both equal and different node battery capacities.展开更多
Karstic aquifers in Southwest China are largely located in mountainous areas and groundwater level observation data are usually absent. Therefore, numerical groundwater models are inappropriate for simulation of groun...Karstic aquifers in Southwest China are largely located in mountainous areas and groundwater level observation data are usually absent. Therefore, numerical groundwater models are inappropriate for simulation of groundwater flow and rainfall-underground outflow responses. In this study, an artificial neural network （ANN） model was developed to simulate underground stream discharge. The ANN model was applied to the Houzhai subterranean drainage in Guizhou Province of Southwest China, which is representative of karstic geomorphology in the humid areas of China. Correlation analysis between daily rainfall and the outflow series was used to determine the model inputs and time lags. The ANN model was trained using an error backpropagation algorithm and validated at three hydrological stations with different karstic features. Study results show that the ANN model performs well in the modeling of highly non-linear karstic aquifers.展开更多
A prediction method of strata movement in underground metal mines is put forward, in which fuzzy BP neural network is applied. The results show that there is a strong nonlinear relation between the selected factors an...A prediction method of strata movement in underground metal mines is put forward, in which fuzzy BP neural network is applied. The results show that there is a strong nonlinear relation between the selected factors and strata movement angle, the anticipant and the actual output results are very similar. It is proved that the numerical value of movement angle is correlated with the selected factors in theory. The scope of strata and surface movement due to mining can be predicted. This research provides a thought to study the movement scope of strata due to mining.展开更多
There are fundamentally two different communication media in wireless underground sensor networks. The first of these is a solid medium where the sensor nodes are buried underground and wirelessly transmit data from u...There are fundamentally two different communication media in wireless underground sensor networks. The first of these is a solid medium where the sensor nodes are buried underground and wirelessly transmit data from underground to aboveground. The second is an underground medium such as tunnel, cave etc. and the data is transmitted from underground to the aboveground through partially solid medium. The quality of communication is greatly influenced by the humidity of the soil in both environments. The placement of wireless underground sensor nodes at hard-to-reach locations makes energy efficient work compulsory. In this paper, rule based collector station selection scheme is proposed for lossless data transmission in underground sensor networks. In order for sensor nodes to transmit energy-efficient lossless data, rulebased selection operations are carried out with the help of fuzzy logic. The proposed wireless underground sensor network is simulated using Riverbed software, and fuzzy logic-based selection scheme is implemented utilizing Matlab software. In order to evaluate the performance of the sensor network;the parameters of delay, throughput and energy consumption are investigated. Examining performance evaluation results, it is seen that average delay and maximum throughput are accomplished in the proposed underground sensor network. Under these conditions, it has been shown that the most appropriate collector station selection decision is made with the aim of minimizing energy consumption.展开更多
Based on the analysis of main causes of rockburst,the compressive strength,tensile strength,elastic energy index of rock and the maximum tangential stress of the cavern wall are chosen as the criterion indexes for roc...Based on the analysis of main causes of rockburst,the compressive strength,tensile strength,elastic energy index of rock and the maximum tangential stress of the cavern wall are chosen as the criterion indexes for rockburst prediction.A new approach using neural method is proposed to predict rockburst occurrence and its intensity.The prediction results show that it is feasible and appropriate to use artificial neural network model for rockburst prediction.展开更多
基于深度学习的探地雷达目标识别方法已广泛应用于地质勘探、基础设施检测等领域.然而,现有方法存在三方面局限:(1)多数探地雷达采用单极化工作模式,目标散射信息获取不完整;(2)一些目标的Bscan图像由于具有相似的双曲线特征,传统深度...基于深度学习的探地雷达目标识别方法已广泛应用于地质勘探、基础设施检测等领域.然而,现有方法存在三方面局限:(1)多数探地雷达采用单极化工作模式,目标散射信息获取不完整;(2)一些目标的Bscan图像由于具有相似的双曲线特征,传统深度学习方法存在误判风险;(3)直接将二维B-scan图像输入卷积神经网络,数据运算量大.为解决上述问题,本文提出一种基于轻量级MobileNetV3网络的多极化分解融合探地雷达目标识别方法.该方法首先采用全极化探地雷获取地下目标的HH、VH和HH三种极化数据.接着对其进行H-Alpha分解、Freeman分解和Pauli分解,获得目标的八种极化参数.将八种极化参数融合组成八维特征矩阵后,输入一个增加了注意力机制SE(Squeeze and Excitation)模块的MobileNetV3网络进行地下目标识别.为验证方法有效性,实验中对四种典型目标进行分类识别,结果表明将八维特征矩阵作为网络输入能提高目标识别率.网络中由于增加了SE模块,目标识别率可进一步提升.此外,与常见的ResNet18网络和VGG16网络相比,改进MobileNetV3网络的目标识别率最高(98.75%),而其网络的参数量和模型大小显著降低.实验结果证明将包含目标极化信息的八维特征矩阵作为网络输入,不仅可以提供更丰富的目标信息,还能有效减小输入网络的冗余信息,提高了目标识别率的同时减少输入网络的矩阵规模.此外,基于MobileNetV3构建轻量化主干网络,并引入通道注意力机制SE模块,提高对关键信息的提取能力,提升了对目标的识别能力.论文有效解决了探地雷达目标识别中特征鉴别力不足与计算负载过高的双重挑战.展开更多
基金Financial support for this work,provided by the National Natural Science Foundation of China(No.50904070)the Science and Technology Foundation of China University of Mining & Technology (Nos.2007A046 and 2008A042)the Joint Production and Research Innovation Project of Jiangsu Province (No.BY2009114)
文摘Wireless sensor networks are useful complements to existing monitoring systems in underground mines. They play an important role of enhancing and improving coverage and flexibility of safety monitoring systems.Regions prone to danger and environments after disasters in underground mines require saving and balancing energy consumption of nodes to prolong the lifespan of networks.Based on the structure of a tunnel,we present a Long Chain-type Wireless Sensor Network（LC-WSN）to monitor the safety of underground mine tunnels.We define the optimal transmission distance and the range of the key region and present an Energy Optimal Routing（EOR）algorithm for LC-WSN to balance the energy consumption of nodes and maximize the lifespan of networks.EOR constructs routing paths based on an optimal transmission distance and uses an energy balancing strategy in the key region.Simulation results show that the EOR algorithm extends the lifespan of a network,balances the energy consumption of nodes in the key region and effectively limits the length of routing paths,compared with similar algorithms.
文摘Resource management in Underground Wireless Sensor Networks(UWSNs)is one of the pillars to extend the network lifetime.An intriguing design goal for such networks is to achieve balanced energy and spectral resource utilization.This paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data,received from the buried source nodes through a lossy soil medium,to the aboveground base station.A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover(HCSSC)algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource efficiency.The proposed algorithm improves the standard Salp Swarm Algorithm(SSA)by considering a chaotic map to initialize the population along with performing the crossover technique in the position updates of salps.Through experimental results,the HCSSC algorithm proves its outstanding superiority to the standard SSA for resource efficiency optimization.Hence,the network’s lifetime is prolonged.Indeed,the proposed algorithm achieves an improvement performance of 23.6%and 20.4%for the resource efficiency and average remaining relay battery per transmission,respectively.Furthermore,simulation results demonstrate that the HCSSC algorithm proves its efficacy in the case of both equal and different node battery capacities.
基金supported by the National Basic Research Program of China (973 Program, Grant No 2006CB403200)the National Natural Scientific Foundation of China (Grant No 50679025)the 111 Project of the Ministry of Education and the State Administration of Foreign Expert Affairs, China (Grant No. B08048)
文摘Karstic aquifers in Southwest China are largely located in mountainous areas and groundwater level observation data are usually absent. Therefore, numerical groundwater models are inappropriate for simulation of groundwater flow and rainfall-underground outflow responses. In this study, an artificial neural network （ANN） model was developed to simulate underground stream discharge. The ANN model was applied to the Houzhai subterranean drainage in Guizhou Province of Southwest China, which is representative of karstic geomorphology in the humid areas of China. Correlation analysis between daily rainfall and the outflow series was used to determine the model inputs and time lags. The ANN model was trained using an error backpropagation algorithm and validated at three hydrological stations with different karstic features. Study results show that the ANN model performs well in the modeling of highly non-linear karstic aquifers.
文摘A prediction method of strata movement in underground metal mines is put forward, in which fuzzy BP neural network is applied. The results show that there is a strong nonlinear relation between the selected factors and strata movement angle, the anticipant and the actual output results are very similar. It is proved that the numerical value of movement angle is correlated with the selected factors in theory. The scope of strata and surface movement due to mining can be predicted. This research provides a thought to study the movement scope of strata due to mining.
文摘There are fundamentally two different communication media in wireless underground sensor networks. The first of these is a solid medium where the sensor nodes are buried underground and wirelessly transmit data from underground to aboveground. The second is an underground medium such as tunnel, cave etc. and the data is transmitted from underground to the aboveground through partially solid medium. The quality of communication is greatly influenced by the humidity of the soil in both environments. The placement of wireless underground sensor nodes at hard-to-reach locations makes energy efficient work compulsory. In this paper, rule based collector station selection scheme is proposed for lossless data transmission in underground sensor networks. In order for sensor nodes to transmit energy-efficient lossless data, rulebased selection operations are carried out with the help of fuzzy logic. The proposed wireless underground sensor network is simulated using Riverbed software, and fuzzy logic-based selection scheme is implemented utilizing Matlab software. In order to evaluate the performance of the sensor network;the parameters of delay, throughput and energy consumption are investigated. Examining performance evaluation results, it is seen that average delay and maximum throughput are accomplished in the proposed underground sensor network. Under these conditions, it has been shown that the most appropriate collector station selection decision is made with the aim of minimizing energy consumption.
基金Supported by Chinese National Natural Science Foundaion(49972091)
文摘Based on the analysis of main causes of rockburst,the compressive strength,tensile strength,elastic energy index of rock and the maximum tangential stress of the cavern wall are chosen as the criterion indexes for rockburst prediction.A new approach using neural method is proposed to predict rockburst occurrence and its intensity.The prediction results show that it is feasible and appropriate to use artificial neural network model for rockburst prediction.
文摘基于深度学习的探地雷达目标识别方法已广泛应用于地质勘探、基础设施检测等领域.然而,现有方法存在三方面局限:(1)多数探地雷达采用单极化工作模式,目标散射信息获取不完整;(2)一些目标的Bscan图像由于具有相似的双曲线特征,传统深度学习方法存在误判风险;(3)直接将二维B-scan图像输入卷积神经网络,数据运算量大.为解决上述问题,本文提出一种基于轻量级MobileNetV3网络的多极化分解融合探地雷达目标识别方法.该方法首先采用全极化探地雷获取地下目标的HH、VH和HH三种极化数据.接着对其进行H-Alpha分解、Freeman分解和Pauli分解,获得目标的八种极化参数.将八种极化参数融合组成八维特征矩阵后,输入一个增加了注意力机制SE(Squeeze and Excitation)模块的MobileNetV3网络进行地下目标识别.为验证方法有效性,实验中对四种典型目标进行分类识别,结果表明将八维特征矩阵作为网络输入能提高目标识别率.网络中由于增加了SE模块,目标识别率可进一步提升.此外,与常见的ResNet18网络和VGG16网络相比,改进MobileNetV3网络的目标识别率最高(98.75%),而其网络的参数量和模型大小显著降低.实验结果证明将包含目标极化信息的八维特征矩阵作为网络输入,不仅可以提供更丰富的目标信息,还能有效减小输入网络的冗余信息,提高了目标识别率的同时减少输入网络的矩阵规模.此外,基于MobileNetV3构建轻量化主干网络,并引入通道注意力机制SE模块,提高对关键信息的提取能力,提升了对目标的识别能力.论文有效解决了探地雷达目标识别中特征鉴别力不足与计算负载过高的双重挑战.
