Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging alo...Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory.However,SSS images often suffer from speckle noise caused by mutual interference between echoes,and limited AUV computational resources further hinder noise suppression.Existing approaches for SSS image processing and speckle noise reduction rely heavily on complex network structures and fail to combine the benefits of deep learning and domain knowledge.To address the problem,Rep DNet,a novel and effective despeckling convolutional neural network is proposed.Rep DNet introduces two re-parameterized blocks:the Pixel Smoothing Block(PSB)and Edge Enhancement Block(EEB),preserving edge information while attenuating speckle noise.During training,PSB and EEB manifest as double-layered multi-branch structures,integrating first-order and secondorder derivatives and smoothing functions.During inference,the branches are re-parameterized into a 3×3 convolution,enabling efficient inference without sacrificing accuracy.Rep DNet comprises three computational operations:3×3 convolution,element-wise summation and Rectified Linear Unit activation.Evaluations on benchmark datasets,a real SSS dataset and Data collected at Lake Mulan aestablish Rep DNet as a well-balanced network,meeting the AUV computational constraints in terms of performance and latency.展开更多
An advantageous porous architecture of electrodes is pivotal in significantly enhancing alkaline water electrolysis(AWE)efficiency by optimizing the mass transport mechanisms.This effect becomes even more pronounced w...An advantageous porous architecture of electrodes is pivotal in significantly enhancing alkaline water electrolysis(AWE)efficiency by optimizing the mass transport mechanisms.This effect becomes even more pronounced when aiming to achieve elevated current densities.Herein,we employed a rapid and scalable laser texturing process to craft novel multi-channel porous electrodes.Particularly,the obtained electrodes exhibit the lowest Tafel slope of 79 mV dec^(-1)(HER)and 49 mV dec^(-1)(OER).As anticipated,the alkaline electrolyzer(AEL)cell incorporating multi-channel porous electrodes(NP-LT30)exhibited a remarkable improvement in cell efficiency,with voltage drops(from 2.28 to 1.97 V)exceeding 300 mV under 1 A cm^(-1),compared to conventional perforated Ni plate electrodes.This enhancement mainly stemmed from the employed multi-channel porous structure,facilitating mass transport and bubble dynamics through an innovative convection mode,surpassing the traditional convection mode.Furthermore,the NP-LT30-based AEL cell demonstrated exceptional durability for 300 h under 1.0 A cm^(-2).This study underscores the capability of the novel multi-channel porous electrodes to expedite mass transport in practical AWE applications.展开更多
This article introduces an underwater robot inspection anomaly localization feedback system comprising a real-time water surface tracking,detection,and positioning system located on the water surface,while the underwa...This article introduces an underwater robot inspection anomaly localization feedback system comprising a real-time water surface tracking,detection,and positioning system located on the water surface,while the underwater robot inspection anomaly feedback system is housed within the underwater robot.The system facilitates the issuance of corresponding mechanical responses based on the water surface’s real-time tracking,detection,and positioning,enabling recognition and feedback of anomaly information.Through sonar technology,the underwater robot inspection anomaly feedback system monitors the underwater robot in real-time,triggering responsive actions upon encountering anomalies.The real-time tracking,detection,and positioning system from the water surface identifies abnormal conditions of underwater robots based on changes in sonar images,subsequently notifying personnel for necessary intervention.展开更多
Cold-junction compensation(CJC)and disconnection detection circuit design of various thermocouples(TC)and multi-channel TC interface circuits were designed.The CJC and disconnection detection circuit consists of a CJC...Cold-junction compensation(CJC)and disconnection detection circuit design of various thermocouples(TC)and multi-channel TC interface circuits were designed.The CJC and disconnection detection circuit consists of a CJC semiconductor device,an instrumentation amplifier(IA),two resistors,and a diode for disconnection detection.Based on the basic circuit,a multi-channel interface circuit was also implemented.The CJC was implemented using compensation semiconductor and IA,and disconnection detection was detected by using two resistors and a diode so that IA input voltage became-0.42 V.As a result of the experiment using R-type TC,the error of the designed circuit was reduced from 0.14 mV to 3μV after CJC in the temperature range of 0°C to 1400°C.In addition,it was confirmed that the output voltage of IA was saturated from 88 mV to-14.2 V when TC was disconnected from normal.The output voltage of the designed circuit was 0 V to 10 V in the temperature range of 0°C to 1400°C.The results of the 4-channel interface experiment using R-type TC were almost identical to the CJC and disconnection detection results for each channel.The implemented multi-channel interface has a feature that can be applied equally to E,J,K,T,R,and S-type TCs by changing the terminals of CJC semiconductor devices and adjusting the IA gain.展开更多
Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides ...Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides detailed and accurate images of marine substrate features.Most of the processing of SSS imagery works around limited sampling stations and requires manual interpretation to complete the classification of seabed sediment imagery.In complex sea areas,with manual interpretation,small targets are often lost due to a large amount of information.To date,studies related to the automatic recognition of seabed sediments are still few.This paper proposes a seabed sediment recognition method based on You Only Look Once version 5 and SSS imagery to perform real-time sedi-ment classification and localization for accuracy,particularly on small targets and faster speeds.We used methods such as changing the dataset size,epoch,and optimizer and adding multiscale training to overcome the challenges of having a small sample and a low accuracy.With these methods,we improved the results on mean average precision by 8.98%and F1 score by 11.12%compared with the original method.In addition,the detection speed was approximately 100 frames per second,which is faster than that of previous methods.This speed enabled us to achieve real-time seabed sediment recognition from SSS imagery.展开更多
Most of existing metasurfaces usually have limited channel behavior,which seriouslyhinders their development and application.In this paper,we propose a multi-channel terahertz focused beam generator based on shared-ap...Most of existing metasurfaces usually have limited channel behavior,which seriouslyhinders their development and application.In this paper,we propose a multi-channel terahertz focused beam generator based on shared-aperture metasurface,and the generator consists of a top square metal strip,a middle layer of silica and a metal bottom plate.By changing the position and size of the shared-aperture array,the designed metasurface can generate any number of multi-channel focusing beams at different predicted positions.In addition,the energy intensity of focusing beams can be controlled.The full-wave simulation results show that the metasurface achieves four-channel vortex focused beam generation with different topological charges,and five-,six-,eight-channel focused beam generation with different energy intensities at a frequency of 1 THz,which are in good agreement with the theoretically calculated predictions.This work can provide a new idea for designing the terahertz multichannel devices.展开更多
Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex str...Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex structure or reflection features, the existing multi-channel inversion methods have to adopt the highly time-consuming strategy of arranging seismic data trace-by-trace, limiting its wide application in pre-stack inversion. A fast pre-stack multi-channel inversion constrained by seismic reflection features has been proposed to address this issue. The key to our method is to re-characterize the reflection features to directly constrain the pre-stack inversion through a Hadamard product operator without rearranging the seismic data. The seismic reflection features can reflect the distribution of the stratum reflection interface, and we obtained them from the post-stack profile by searching the shortest local Euclidean distance between adjacent seismic traces. Instead of directly constructing a large-size reflection features constraint operator advocated by the conventional methods, through decomposing the reflection features along the vertical and horizontal direction at a particular sampling point, we have constructed a computationally well-behaved constraint operator represented by the vertical and horizontal partial derivatives. Based on the Alternating Direction Method of Multipliers (ADMM) optimization, we have derived a fast algorithm for solving the objective function, including Hadamard product operators. Compared with the conventional reflection features constrained inversion, the proposed method is more efficient and accurate, proved on the Overthrust model and a field data set.展开更多
It has remained a hard nut for years to segment sonar images of jacket installation environment,most of which are noisy images with inevitable blur after noise reduction.For the purpose of solutions to this problem,a ...It has remained a hard nut for years to segment sonar images of jacket installation environment,most of which are noisy images with inevitable blur after noise reduction.For the purpose of solutions to this problem,a fast segmen-tation algorithm is proposed on the basis of the gray value characteristics of sonar images.This algorithm is endowed with the advantage in no need of segmentation thresholds.To realize this goal,we follow the undermentioned steps:first,calcu-late the gray matrix of the fuzzy image background.After adjusting the gray value,the image is divided into three regions:background region,buffer region and target regions.Afterfiltering,we reset the pixels with gray value lower than 255 to binarize images and eliminate most artifacts.Finally,the remaining noise is removed by morphological processing.The simulation results of several sonar images show that the algorithm can segment the fuzzy sonar images quickly and effectively.Thus,the stable and feasible method is testified.展开更多
Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the t...Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the target for observers.The target feature information of an SSS image is similar to the background information,and a small target has less pixel information;therefore,accu-rately identifying and locating small targets in SSS images is challenging.We collect the SSS images of iron metal balls(with a diameter of 1m)and rocks to solve the problem of target misclassification.Thus,the dataset contains two types of targets,namely,‘ball’and‘rock’.With the aim to enable AUVs to accurately and automatically identify small underwater targets in SSS images,this study designs a multisize parallel convolution module embedded in state-of-the-art Yolo5.An attention mechanism transformer and a convolutional block attention module are also introduced to compare their contributions to small target detection accuracy.The performance of the proposed method is further evaluated by taking the lightweight networks Mobilenet3 and Shufflenet2 as the backbone network of Yolo5.This study focuses on the performance of convolutional neural networks for the detection of small targets in SSS images,while another comparison experiment is carried out using traditional HOG+SVM to highlight the neural network’s ability.This study aims to improve the detection accuracy while ensuring the model efficiency to meet the real-time working requirements of AUV target detection.展开更多
Due to the scattered nature of the network,data transmission in a dis-tributed Mobile Ad-hoc Network(MANET)consumes more energy resources(ER)than in a centralized network,resulting in a shorter network lifespan(NL).As...Due to the scattered nature of the network,data transmission in a dis-tributed Mobile Ad-hoc Network(MANET)consumes more energy resources(ER)than in a centralized network,resulting in a shorter network lifespan(NL).As a result,we build an Enhanced Opportunistic Routing(EORP)protocol architecture in order to address the issues raised before.This proposed routing protocol goal is to manage the routing cost by employing power,load,and delay to manage the routing energy consumption based on theflooding of control pack-ets from the target node.According to the goal of the proposed protocol techni-que,it is possible to manage the routing cost by applying power,load,and delay.The proposed technique also manage the routing energy consumption based on theflooding of control packets from the destination node in order to reduce the routing cost.Control packet exchange between the target and all the nodes,on the other hand,is capable of having an influence on the overall efficiency of the system.The EORP protocol and the Multi-channel Cooperative Neighbour Discovery(MCCND)protocol have been designed to detect the cooperative adja-cent nodes for each node in the routing route as part of the routing path discovery process,which occurs during control packet transmission.While control packet transmission is taking place during the routing path discovery process,the EORP protocol and the Multi-channel Cooperative Neighbour Discovery(MCCND)protocol have been designed to detect the cooperative adjacent nodes for each node in the routing.Also included is a simulation of these protocols in order to evaluate their performance across a wide range of packet speeds using Constant Bit Rate(CBR).When the packet rate of the CBR is 20 packets per second,the results reveal that the EORP-MCCND is 0.6 s quicker than the state-of-the-art protocols,according to thefindings.Assuming that the CBR packet rate is 20 packets per second,the EORP-MCCND achieves 0.6 s of End 2 End Delay,0.05 s of Routing Overhead Delay,120 s of Network Lifetime,and 20 J of Energy Consumption efficiency,which is much better than that of the state-of-the-art protocols.展开更多
Karst landforms are widely distributed in China,and are most common in Yunnan,Guizhou and Guangxi.If the development of karst caves at the bottom of the piles cannot be accurately ascertained before the construction o...Karst landforms are widely distributed in China,and are most common in Yunnan,Guizhou and Guangxi.If the development of karst caves at the bottom of the piles cannot be accurately ascertained before the construction of bridge pile foundations,accidents such as hole collapse,slurry leakage,and drill sticking will easily occur.In this paper,the principle and method of sonar detection for detecting karst caves at the bottom of bridge piles was introduced,and the sonar detection data and the cave situation at the bottom of the pile during the construction process in combination with the case of Yunnan Zhenguo Highway Project was analyzed,which verifies the practicability and reliability of sonar detection method reliability.展开更多
基金supported by the National Key R&D Program of China(Grant No.2023YFC3010803)the National Nature Science Foundation of China(Grant No.52272424)+1 种基金the Key R&D Program of Hubei Province of China(Grant No.2023BCB123)the Fundamental Research Funds for the Central Universities(Grant No.WUT:2023IVB079)。
文摘Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory.However,SSS images often suffer from speckle noise caused by mutual interference between echoes,and limited AUV computational resources further hinder noise suppression.Existing approaches for SSS image processing and speckle noise reduction rely heavily on complex network structures and fail to combine the benefits of deep learning and domain knowledge.To address the problem,Rep DNet,a novel and effective despeckling convolutional neural network is proposed.Rep DNet introduces two re-parameterized blocks:the Pixel Smoothing Block(PSB)and Edge Enhancement Block(EEB),preserving edge information while attenuating speckle noise.During training,PSB and EEB manifest as double-layered multi-branch structures,integrating first-order and secondorder derivatives and smoothing functions.During inference,the branches are re-parameterized into a 3×3 convolution,enabling efficient inference without sacrificing accuracy.Rep DNet comprises three computational operations:3×3 convolution,element-wise summation and Rectified Linear Unit activation.Evaluations on benchmark datasets,a real SSS dataset and Data collected at Lake Mulan aestablish Rep DNet as a well-balanced network,meeting the AUV computational constraints in terms of performance and latency.
基金financial support from the National Key R&D Program(2023YFE0108000)the Academy of Sciences Project of Guangdong Province(2019GDASYL-0102007,2021GDASYL-20210103063)+1 种基金GDAS’Project of Science and Technology Development(2022GDASZH-2022010203-003)financial support from the China Scholarship Council(202108210128)。
文摘An advantageous porous architecture of electrodes is pivotal in significantly enhancing alkaline water electrolysis(AWE)efficiency by optimizing the mass transport mechanisms.This effect becomes even more pronounced when aiming to achieve elevated current densities.Herein,we employed a rapid and scalable laser texturing process to craft novel multi-channel porous electrodes.Particularly,the obtained electrodes exhibit the lowest Tafel slope of 79 mV dec^(-1)(HER)and 49 mV dec^(-1)(OER).As anticipated,the alkaline electrolyzer(AEL)cell incorporating multi-channel porous electrodes(NP-LT30)exhibited a remarkable improvement in cell efficiency,with voltage drops(from 2.28 to 1.97 V)exceeding 300 mV under 1 A cm^(-1),compared to conventional perforated Ni plate electrodes.This enhancement mainly stemmed from the employed multi-channel porous structure,facilitating mass transport and bubble dynamics through an innovative convection mode,surpassing the traditional convection mode.Furthermore,the NP-LT30-based AEL cell demonstrated exceptional durability for 300 h under 1.0 A cm^(-2).This study underscores the capability of the novel multi-channel porous electrodes to expedite mass transport in practical AWE applications.
文摘This article introduces an underwater robot inspection anomaly localization feedback system comprising a real-time water surface tracking,detection,and positioning system located on the water surface,while the underwater robot inspection anomaly feedback system is housed within the underwater robot.The system facilitates the issuance of corresponding mechanical responses based on the water surface’s real-time tracking,detection,and positioning,enabling recognition and feedback of anomaly information.Through sonar technology,the underwater robot inspection anomaly feedback system monitors the underwater robot in real-time,triggering responsive actions upon encountering anomalies.The real-time tracking,detection,and positioning system from the water surface identifies abnormal conditions of underwater robots based on changes in sonar images,subsequently notifying personnel for necessary intervention.
文摘Cold-junction compensation(CJC)and disconnection detection circuit design of various thermocouples(TC)and multi-channel TC interface circuits were designed.The CJC and disconnection detection circuit consists of a CJC semiconductor device,an instrumentation amplifier(IA),two resistors,and a diode for disconnection detection.Based on the basic circuit,a multi-channel interface circuit was also implemented.The CJC was implemented using compensation semiconductor and IA,and disconnection detection was detected by using two resistors and a diode so that IA input voltage became-0.42 V.As a result of the experiment using R-type TC,the error of the designed circuit was reduced from 0.14 mV to 3μV after CJC in the temperature range of 0°C to 1400°C.In addition,it was confirmed that the output voltage of IA was saturated from 88 mV to-14.2 V when TC was disconnected from normal.The output voltage of the designed circuit was 0 V to 10 V in the temperature range of 0°C to 1400°C.The results of the 4-channel interface experiment using R-type TC were almost identical to the CJC and disconnection detection results for each channel.The implemented multi-channel interface has a feature that can be applied equally to E,J,K,T,R,and S-type TCs by changing the terminals of CJC semiconductor devices and adjusting the IA gain.
基金funded by the Natural Science Foundation of Fujian Province(No.2018J01063)the Project of Deep Learning Based Underwater Cultural Relics Recognization(No.38360041)the Project of the State Administration of Cultural Relics(No.2018300).
文摘Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides detailed and accurate images of marine substrate features.Most of the processing of SSS imagery works around limited sampling stations and requires manual interpretation to complete the classification of seabed sediment imagery.In complex sea areas,with manual interpretation,small targets are often lost due to a large amount of information.To date,studies related to the automatic recognition of seabed sediments are still few.This paper proposes a seabed sediment recognition method based on You Only Look Once version 5 and SSS imagery to perform real-time sedi-ment classification and localization for accuracy,particularly on small targets and faster speeds.We used methods such as changing the dataset size,epoch,and optimizer and adding multiscale training to overcome the challenges of having a small sample and a low accuracy.With these methods,we improved the results on mean average precision by 8.98%and F1 score by 11.12%compared with the original method.In addition,the detection speed was approximately 100 frames per second,which is faster than that of previous methods.This speed enabled us to achieve real-time seabed sediment recognition from SSS imagery.
基金Project supported by the National Natural Science Foundation of China (Grant No.62271460)the Zhejiang Key Research and Development Project,China (Grant Nos.2021C03153 and 2022C03166)。
文摘Most of existing metasurfaces usually have limited channel behavior,which seriouslyhinders their development and application.In this paper,we propose a multi-channel terahertz focused beam generator based on shared-aperture metasurface,and the generator consists of a top square metal strip,a middle layer of silica and a metal bottom plate.By changing the position and size of the shared-aperture array,the designed metasurface can generate any number of multi-channel focusing beams at different predicted positions.In addition,the energy intensity of focusing beams can be controlled.The full-wave simulation results show that the metasurface achieves four-channel vortex focused beam generation with different topological charges,and five-,six-,eight-channel focused beam generation with different energy intensities at a frequency of 1 THz,which are in good agreement with the theoretically calculated predictions.This work can provide a new idea for designing the terahertz multichannel devices.
基金We would like to acknowledge the sponsorship of the National Natural Science Foundation of China(42004092,42030103,41974119)Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant No.2021QNLM020001-6)Young Elite Scientists Sponsorship Program by CAST(2021QNRC001).
文摘Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex structure or reflection features, the existing multi-channel inversion methods have to adopt the highly time-consuming strategy of arranging seismic data trace-by-trace, limiting its wide application in pre-stack inversion. A fast pre-stack multi-channel inversion constrained by seismic reflection features has been proposed to address this issue. The key to our method is to re-characterize the reflection features to directly constrain the pre-stack inversion through a Hadamard product operator without rearranging the seismic data. The seismic reflection features can reflect the distribution of the stratum reflection interface, and we obtained them from the post-stack profile by searching the shortest local Euclidean distance between adjacent seismic traces. Instead of directly constructing a large-size reflection features constraint operator advocated by the conventional methods, through decomposing the reflection features along the vertical and horizontal direction at a particular sampling point, we have constructed a computationally well-behaved constraint operator represented by the vertical and horizontal partial derivatives. Based on the Alternating Direction Method of Multipliers (ADMM) optimization, we have derived a fast algorithm for solving the objective function, including Hadamard product operators. Compared with the conventional reflection features constrained inversion, the proposed method is more efficient and accurate, proved on the Overthrust model and a field data set.
基金supported by Open Fund Project of China Key Laboratory of Submarine Geoscience(KLSG1802)Science&Technology Project of China Ocean Mineral Resources Research and Development Association(DY135-N1-1-05)Science&Technology Project of Zhoushan city of Zhejiang Province(2019C42271,2019C33205).
文摘It has remained a hard nut for years to segment sonar images of jacket installation environment,most of which are noisy images with inevitable blur after noise reduction.For the purpose of solutions to this problem,a fast segmen-tation algorithm is proposed on the basis of the gray value characteristics of sonar images.This algorithm is endowed with the advantage in no need of segmentation thresholds.To realize this goal,we follow the undermentioned steps:first,calcu-late the gray matrix of the fuzzy image background.After adjusting the gray value,the image is divided into three regions:background region,buffer region and target regions.Afterfiltering,we reset the pixels with gray value lower than 255 to binarize images and eliminate most artifacts.Finally,the remaining noise is removed by morphological processing.The simulation results of several sonar images show that the algorithm can segment the fuzzy sonar images quickly and effectively.Thus,the stable and feasible method is testified.
基金supported by the National Key Research and Development Program of China(No.2016YFC0301400).
文摘Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the target for observers.The target feature information of an SSS image is similar to the background information,and a small target has less pixel information;therefore,accu-rately identifying and locating small targets in SSS images is challenging.We collect the SSS images of iron metal balls(with a diameter of 1m)and rocks to solve the problem of target misclassification.Thus,the dataset contains two types of targets,namely,‘ball’and‘rock’.With the aim to enable AUVs to accurately and automatically identify small underwater targets in SSS images,this study designs a multisize parallel convolution module embedded in state-of-the-art Yolo5.An attention mechanism transformer and a convolutional block attention module are also introduced to compare their contributions to small target detection accuracy.The performance of the proposed method is further evaluated by taking the lightweight networks Mobilenet3 and Shufflenet2 as the backbone network of Yolo5.This study focuses on the performance of convolutional neural networks for the detection of small targets in SSS images,while another comparison experiment is carried out using traditional HOG+SVM to highlight the neural network’s ability.This study aims to improve the detection accuracy while ensuring the model efficiency to meet the real-time working requirements of AUV target detection.
文摘Due to the scattered nature of the network,data transmission in a dis-tributed Mobile Ad-hoc Network(MANET)consumes more energy resources(ER)than in a centralized network,resulting in a shorter network lifespan(NL).As a result,we build an Enhanced Opportunistic Routing(EORP)protocol architecture in order to address the issues raised before.This proposed routing protocol goal is to manage the routing cost by employing power,load,and delay to manage the routing energy consumption based on theflooding of control pack-ets from the target node.According to the goal of the proposed protocol techni-que,it is possible to manage the routing cost by applying power,load,and delay.The proposed technique also manage the routing energy consumption based on theflooding of control packets from the destination node in order to reduce the routing cost.Control packet exchange between the target and all the nodes,on the other hand,is capable of having an influence on the overall efficiency of the system.The EORP protocol and the Multi-channel Cooperative Neighbour Discovery(MCCND)protocol have been designed to detect the cooperative adja-cent nodes for each node in the routing route as part of the routing path discovery process,which occurs during control packet transmission.While control packet transmission is taking place during the routing path discovery process,the EORP protocol and the Multi-channel Cooperative Neighbour Discovery(MCCND)protocol have been designed to detect the cooperative adjacent nodes for each node in the routing.Also included is a simulation of these protocols in order to evaluate their performance across a wide range of packet speeds using Constant Bit Rate(CBR).When the packet rate of the CBR is 20 packets per second,the results reveal that the EORP-MCCND is 0.6 s quicker than the state-of-the-art protocols,according to thefindings.Assuming that the CBR packet rate is 20 packets per second,the EORP-MCCND achieves 0.6 s of End 2 End Delay,0.05 s of Routing Overhead Delay,120 s of Network Lifetime,and 20 J of Energy Consumption efficiency,which is much better than that of the state-of-the-art protocols.
文摘Karst landforms are widely distributed in China,and are most common in Yunnan,Guizhou and Guangxi.If the development of karst caves at the bottom of the piles cannot be accurately ascertained before the construction of bridge pile foundations,accidents such as hole collapse,slurry leakage,and drill sticking will easily occur.In this paper,the principle and method of sonar detection for detecting karst caves at the bottom of bridge piles was introduced,and the sonar detection data and the cave situation at the bottom of the pile during the construction process in combination with the case of Yunnan Zhenguo Highway Project was analyzed,which verifies the practicability and reliability of sonar detection method reliability.