Underwater acoustic target recognition(UATR)has become increasingly prevalent for ocean detection,localisation,and identification.However,due to the complexity and variability of underwater environments,especially in ...Underwater acoustic target recognition(UATR)has become increasingly prevalent for ocean detection,localisation,and identification.However,due to the complexity and variability of underwater environments,especially in multi ship event environments,where multiple acoustic signals coexist,practical applications face significant challenges.These challenges hinder single-category acoustic recognition algorithms,particularly in extracting time series features and achieving fine-grained or multi-scale feature fusion.This paper innovatively introduce the SKANN framework,which achieve precise submarine sound recognition in underwater mixed ship events environments through timing data enhancement and sampling training module and selective kernel feature extraction module.The timing data enhancement and sampling training module improves time sequence feature extraction through progressive acoustic sampling.The selective kernel feature extraction module effectively fuses multi-scale features by integrating selective kernel(SK)technology.To simulate concurrent ship events,we constructed the mixed ship noise dataset(MDeepShip),providing an experimental basis and test platform for underwater mixed ship event detection.This dataset ensures that the model encounters diverse audio samples during training and validation,improving its ability to extract temporal features.Experimental results show that SKANN achieves a 93.6%recognition rate on the M-DeepShip dataset,demonstrating its effectiveness in recognising underwater mixed ship events.Given the complexity of real underwater environments,this work lays a crucial foundation for the sound recognition of submarine vessels.Future research will focus on real marine environments to validate and refine the models and methods for practical applications.展开更多
This paper proposes a sensor failure detection method based on artificial neural network and signal processing,in comparison with other methods,which does not need any redundancy information among sensor outputs and d...This paper proposes a sensor failure detection method based on artificial neural network and signal processing,in comparison with other methods,which does not need any redundancy information among sensor outputs and divides the output of a sensor into'Signal dominant component'and'Noise dominant component'because the pattern of sensor failure often appears in the'Noise dominant component'.With an ARMA model built for'Noise dominant component'using artificial neural network,such sensor failures as bias failure,hard failure,drift failure,spike failure and cyclic failure may be detected through residual analysis,and the type of sensor failure can be indicated by an appropriate indicator.The failure detection procedure for a temperature sensor in a hovercraft engine is simulated to prove the applicability of the method proposed in this paper.展开更多
In the field of gear fault detection,the symmetrized dot pattern(SDP)technique,combined with a convolutional neural network(CNN),is widely used to classify various types of defects.The SDP-CNN combination is used to t...In the field of gear fault detection,the symmetrized dot pattern(SDP)technique,combined with a convolutional neural network(CNN),is widely used to classify various types of defects.The SDP-CNN combination is used to transform vibration signals and simplify the defect classification process under stationary operating conditions.This work aims to enhance the SDP-CNN combination for detecting incipient defects in gear under variable working conditions.The vibration signals are filtered by Vold-Kalman Filter Multi-Order Tracking to highlight fault characteristics under variable working conditions.Subsequently,the signals are SDP-transformed and are then classified by optimized CNN.The new pipeline has been validated on an experimental dataset and compared with the classical one by developing both two-and multi-class CNNs.The results showed the applicability of the new pipeline in terms of percentage accuracy and ROC curve compared to the classical approach.Finally,the proposed pipeline was compared with other ML literature techniques using the same dataset.展开更多
Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,a...Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.展开更多
DRASTIC is a very simple and common model used for the assessment of groundwater to contamination.This model is widely used across the world in various hydrogeological environments for groundwater vulnerability assess...DRASTIC is a very simple and common model used for the assessment of groundwater to contamination.This model is widely used across the world in various hydrogeological environments for groundwater vulnerability assessment.The Ohio Water Well Association(OWWA)developed DRASTIC model in 1987.Over the years,several modifications have been made in this model as per the need of the regional assessment of groundwater to contamination.This model has fixed weights for its parameters and fixed ratings for the sub-parameters under the main parameters.The weights and ratings of DRASTIC parameters were fixed on the basis of Delphi network technique,which is the best technique for the consensus-building of experts,but it lacks scientific explanations.Over the years,several optimization techniques have been used to optimize these weights and ratings.This work intends to present a critical analysis of decision optimization techniques used to get the optimum values of weights and ratings.The inherent pros and cons and the optimization challenges associated with these techniques have also been discussed.The finding of this study is that the application of MCDA optimization techniques used to optimize the weights and ratings of DRASTIC model to assess the vulnerability of groundwater depend on the availability of hydrogeological data,the pilot study area and the level of required accuracy for earmarking the vulnerable regions.It is recommended that one must choose the appropriate MCDA technique for the particular region because unnecessary complex structure for optimization process takes more time,efforts,resources,and implementation costs.展开更多
This work aims at developing an automatic system for the control of the APS （air plasma spraying） plasma process in which some instability phenomena are present. APS is a versatile technique to produce coatings of p...This work aims at developing an automatic system for the control of the APS （air plasma spraying） plasma process in which some instability phenomena are present. APS is a versatile technique to produce coatings of powder material at high deposition rates. Using this technique, powder particles are injected into a plasma jet, where they are melted and accelerated towards a substrate. The coating microstructures and properties depend strongly on the characteristics of the plasma jet, which can be controlled by the adjustment of the process parameters. However, the imeractions among the spray variables, render optimization and control of this process are quite complex. Understanding relationships between coating properties and process parameters is mandatory to optimize the process technique and the product quality. We are interested in this work to build an on-line control model for the APS process based on the elements of artificial intelligence and to build an emulator that replicates the dynamic behavior of the process as closely as possible.展开更多
基金funded by The National Natural Science Foundation of China under Grant(Nos.62273108 and 62306081)The Youth Project of Guangdong Artificial Intelligence and Digital Economy Laboratory(Guangzhou)(PZL2022KF0006)+6 种基金The National Key Research and Development Program-Research on Key technology of High Frequency broadband mobile communication credit Filter and its Industrialization application-Subproject Circuit Design and Simulation of high frequency broadband Filter(2022YFB3604502)‘New Generation Information Technology’Major Science and Technology Project of Guangzhou Key Field R&D Plan(202206070001)the Special Fund Project of Guangzhou Science and Technology Innovation Development(202201011307)the Guangdong Provincial Department of Education Key construction discipline Scientific research ability Improvement Project,Introduction of Talents Project of Guangdong Polytechnic Normal University of China(99166990222)the Special Projects in Key Fields of General Colleges and Universities in Guangdong Province(2021ZDZX1016)the Natural Science Foundation of Guangdong Province(2024A1515010120)the Special Fund Project of Guangzhou Science and Technology Innovation Development(202201011307).
文摘Underwater acoustic target recognition(UATR)has become increasingly prevalent for ocean detection,localisation,and identification.However,due to the complexity and variability of underwater environments,especially in multi ship event environments,where multiple acoustic signals coexist,practical applications face significant challenges.These challenges hinder single-category acoustic recognition algorithms,particularly in extracting time series features and achieving fine-grained or multi-scale feature fusion.This paper innovatively introduce the SKANN framework,which achieve precise submarine sound recognition in underwater mixed ship events environments through timing data enhancement and sampling training module and selective kernel feature extraction module.The timing data enhancement and sampling training module improves time sequence feature extraction through progressive acoustic sampling.The selective kernel feature extraction module effectively fuses multi-scale features by integrating selective kernel(SK)technology.To simulate concurrent ship events,we constructed the mixed ship noise dataset(MDeepShip),providing an experimental basis and test platform for underwater mixed ship event detection.This dataset ensures that the model encounters diverse audio samples during training and validation,improving its ability to extract temporal features.Experimental results show that SKANN achieves a 93.6%recognition rate on the M-DeepShip dataset,demonstrating its effectiveness in recognising underwater mixed ship events.Given the complexity of real underwater environments,this work lays a crucial foundation for the sound recognition of submarine vessels.Future research will focus on real marine environments to validate and refine the models and methods for practical applications.
文摘This paper proposes a sensor failure detection method based on artificial neural network and signal processing,in comparison with other methods,which does not need any redundancy information among sensor outputs and divides the output of a sensor into'Signal dominant component'and'Noise dominant component'because the pattern of sensor failure often appears in the'Noise dominant component'.With an ARMA model built for'Noise dominant component'using artificial neural network,such sensor failures as bias failure,hard failure,drift failure,spike failure and cyclic failure may be detected through residual analysis,and the type of sensor failure can be indicated by an appropriate indicator.The failure detection procedure for a temperature sensor in a hovercraft engine is simulated to prove the applicability of the method proposed in this paper.
文摘In the field of gear fault detection,the symmetrized dot pattern(SDP)technique,combined with a convolutional neural network(CNN),is widely used to classify various types of defects.The SDP-CNN combination is used to transform vibration signals and simplify the defect classification process under stationary operating conditions.This work aims to enhance the SDP-CNN combination for detecting incipient defects in gear under variable working conditions.The vibration signals are filtered by Vold-Kalman Filter Multi-Order Tracking to highlight fault characteristics under variable working conditions.Subsequently,the signals are SDP-transformed and are then classified by optimized CNN.The new pipeline has been validated on an experimental dataset and compared with the classical one by developing both two-and multi-class CNNs.The results showed the applicability of the new pipeline in terms of percentage accuracy and ROC curve compared to the classical approach.Finally,the proposed pipeline was compared with other ML literature techniques using the same dataset.
文摘Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.
文摘DRASTIC is a very simple and common model used for the assessment of groundwater to contamination.This model is widely used across the world in various hydrogeological environments for groundwater vulnerability assessment.The Ohio Water Well Association(OWWA)developed DRASTIC model in 1987.Over the years,several modifications have been made in this model as per the need of the regional assessment of groundwater to contamination.This model has fixed weights for its parameters and fixed ratings for the sub-parameters under the main parameters.The weights and ratings of DRASTIC parameters were fixed on the basis of Delphi network technique,which is the best technique for the consensus-building of experts,but it lacks scientific explanations.Over the years,several optimization techniques have been used to optimize these weights and ratings.This work intends to present a critical analysis of decision optimization techniques used to get the optimum values of weights and ratings.The inherent pros and cons and the optimization challenges associated with these techniques have also been discussed.The finding of this study is that the application of MCDA optimization techniques used to optimize the weights and ratings of DRASTIC model to assess the vulnerability of groundwater depend on the availability of hydrogeological data,the pilot study area and the level of required accuracy for earmarking the vulnerable regions.It is recommended that one must choose the appropriate MCDA technique for the particular region because unnecessary complex structure for optimization process takes more time,efforts,resources,and implementation costs.
文摘This work aims at developing an automatic system for the control of the APS （air plasma spraying） plasma process in which some instability phenomena are present. APS is a versatile technique to produce coatings of powder material at high deposition rates. Using this technique, powder particles are injected into a plasma jet, where they are melted and accelerated towards a substrate. The coating microstructures and properties depend strongly on the characteristics of the plasma jet, which can be controlled by the adjustment of the process parameters. However, the imeractions among the spray variables, render optimization and control of this process are quite complex. Understanding relationships between coating properties and process parameters is mandatory to optimize the process technique and the product quality. We are interested in this work to build an on-line control model for the APS process based on the elements of artificial intelligence and to build an emulator that replicates the dynamic behavior of the process as closely as possible.
