Objective:To evaluate the relationship between the clinical and imaging features of ground glass opacity(GGO)localized using a preoperative Hook-wire guidewire and postoperative pathology.Method:Preoperative Hook-wire...Objective:To evaluate the relationship between the clinical and imaging features of ground glass opacity(GGO)localized using a preoperative Hook-wire guidewire and postoperative pathology.Method:Preoperative Hook-wire guidewire localization was performed in 83 patients with GGO less than 2 cm,and their clinical data,imaging data,and postoperative pathology findings were retrospectively analyzed.The images were classified as pure GGO(pGGO) or mixed GGO(mGGO).The relationship between clinical and imaging features and postoperative pathology was analyzed.Result:The 83 cases were colocalized,and the success rate of the guidewire positioning was 100%.Complications included pneumothorax(19.2% [16/83]) and the incidence of minor bleeding(30.2 [25/83]).Forty-seven patients had mGGO and 36 had pGGO.Among the 47 cases of mGGO,18(38.3%) were invasive adenocarcinoma(IAC),18(38.3%) were microinvasive adenocarcinoma(MIA),8(17.0%) were adenocarcinoma in situ(AIS),2(4.3%) were atypical adenomatous hyperplasia(AAH),and 1(2.1%) was benign.Among the 36 cases of pGGO,6(16.7%) were IAC,13(36.1%) were MIA,8(22.2%) were AIS,2(5.6%) were AAH,and 7(19.4%) cases were benign lesions.A significantly higher proportion of patients with IAC had mGGO than pGGO(21.7% vs.7.2%,respectively;p=0.004).Among patients with mGGO,a higher proportion of them had a nodule diameter of ≥1 cm than those with a diameter of <1 cm(25.5% vs.12.8%,respectively;p=0.003).There was no significant difference in age,location distribution,or pathological type.Conclusion: Preoperative CT-guided Hook-wire guidewire positioning was safe with minor complications.A significantly higher proportion of patients with IAC had mGGO than pGGO.Patients with mGGO and a nodule diameter ≥1 cm require active treatment.展开更多
Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techni...Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.展开更多
Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using a...Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction techniques.In the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management system.Moreover,an audio-based location distinction method is designed to detect the position change of the devices.Specifically,the audio frequency response(AFR)of several frequency points is utilized as a device signature.The device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different directions.Based on the features above,amovement detection algorithmfor device identification and location distinction is designed.Moreover,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and Severs.Extensive experiments are conducted to evaluate the performance of the proposed method.The experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.展开更多
Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for ...Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.展开更多
BACKGROUND Gastric cancer(GC)is a highly prevalent gastrointestinal tract tumor.Several trials have demonstrated that the location of GC can affect patient prognosis.However,the factors determining tumor location rema...BACKGROUND Gastric cancer(GC)is a highly prevalent gastrointestinal tract tumor.Several trials have demonstrated that the location of GC can affect patient prognosis.However,the factors determining tumor location remain unclear.AIM To investigate the tumor location of patients,we went on to study the influencing factors that lead to changes in the location of GC.METHODS A retrospective evaluation was carried out on 3287 patients who underwent gastrectomy for GC in Zhejiang Cancer Hospital.The patients were followed up post-diagnosis and post-gastrectomy.The clinicopathological variables and overall survival of the patients were recorded.By analyzing the location of GC,the tumor location was divided into four categories:“Upper”,“middle”,“lower”,and“total”.Statistical software was utilized to analyze the relationship of each variable with the location of GC.RESULTS A total of 3287 patients were included in this study.The clinicopathological indices of gender,age,serum levels of carcinoembryonic antigen(CEA),carbohydrate antigen(CA19-9)and CA72-4 levels,were significantly associated with tumor location in patients with GC.In addition,there was a strong correlation between GC location and the prognosis of postoperative patients.Specifically,patients with“lower”and“middle”GC demonstrated a better prognosis than those with tumors in other categories.CONCLUSION The five clinicopathological indices of gender,age,CEA,CA19-9 and CA72-4 levels exhibit varying degrees of influence on the tumor location.The tumor location correlates with patient prognosis following surgery.展开更多
Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times beca...Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times because they are easily affected by tides,currents,and other factors in the shallow sea environment during long-term acquisition.If uncorrected,then the imaging quality of subsequent processing will be affected.The conventional secondary positioning does not consider the case of multiple movements of the receivers,and the accuracy of secondary positioning is insufficient.The first arrival wave of OBN seismic data in shallow ocean mainly comprises refracted waves.In this study,a nonlinear model is established in accordance with the propagation mechanism of a refracted wave and its relationship with the time interval curve to realize the accurate location of multiple receiver movements.In addition,the Levenberg-Marquart algorithm is used to reduce the influence of the first arrival pickup error and to automatically detect the receiver movements,identifying the accurate dynamic relocation of the receivers.The simulation and field data show that the proposed method can realize the dynamic location of multiple receiver movements,thereby improving the accuracy of seismic imaging and achieving high practical value.展开更多
To enhance the understanding of the geometry and characteristics of seismogenic faults in the Beijing-Tianjin-Hebei region,we relocated 14805 out of 16063 earthquakes(113°E-120°E,36°N-43°N)that occ...To enhance the understanding of the geometry and characteristics of seismogenic faults in the Beijing-Tianjin-Hebei region,we relocated 14805 out of 16063 earthquakes(113°E-120°E,36°N-43°N)that occurred between January 2008 and December 2020 using the double-difference tomography method.Based on the spatial variation in seismicity after relocation,the Beijing-Tianjin-Hebei region can be divided into three seismic zones:Xingtai-Wen'an,Zhangbei-Ninghexi,and Tangshan.(1)The Xingtai-Wen'an Seismic Zone has a northeastsouthwest strike.The depth profile of earthquakes perpendicular to the strike reveals three northeast-striking,southeast-dipping,high-angle deep faults(>10 km depth),including one below the shallow(<10 km depth)listric,northwest-dipping Xinghe fault in the Xingtai region.Two additional deep faults in the Wen'an region are suggested to be associated with the 2006 M 5.1 Wen'an Earthquake and the 1967 M 6.3 Dacheng earthquake;(2)The Zhangbei-Ninghexi Seismic Zone is oriented north-northwest.Multiple northeast-striking faults(10-20 km depth),inferred from the earthquake-intensive zones,exist beneath the shallow(<10 km depth)Xiandian Fault,Xiaotangshan Fault,Huailai-Zhuolu Basin North Fault,Yangyuan Basin Fault and Yanggao Basin North Fault;(3)In the Tangshan Seismic Zone,earthquakes are mainly concentrated near the northeast-striking Tangshan-Guye Fault,Lulong Fault,and northwest-striking Luanxian-Laoting Fault.An inferred north-south-oriented blind fault is present to the north of the Tangshan-Guye Fault.The 1976 M 7.8 Tangshan earthquake occurred at the junction of a shallow northwest-dipping fault and a deep southeast-dipping fault.This study emphasizes that earthquakes in the region are primarily associated with deep blind faults.Some deep blind faults have different geometries compared to shallow faults,suggesting a complex fault system in the region.Overall,this research provides valuable insights into the seismogenic faults in the Beijing–Tianjin–Hebei region.Further studies and monitoring of these faults are essential for earthquake mitigation efforts in this region.展开更多
Location prediction in social media,a growing research field,employs machine learning to identify users' locations from their online activities.This technology,useful in targeted advertising and urban planning,rel...Location prediction in social media,a growing research field,employs machine learning to identify users' locations from their online activities.This technology,useful in targeted advertising and urban planning,relies on natural language processing to analyze social media content and understand the temporal dynamics and structures of social networks.A key application is predicting a Twitter user's location from their tweets,which can be challenging due to the short and unstructured nature of tweet text.To address this challenge,the research introduces a novel machine learning model called the location-aware attention LSTM(LAA-LSTM).This hybrid model combines a Long Short-Term Memory(LSTM) network with an attention mechanism.The LSTM is trained on a dataset of tweets,and the attention network focuses on extracting features related to latitude and longitude,which are crucial for pinpointing the location of a user's tweet.The result analysis shows approx.10% improvement in accuracy over other existing machine learning approaches.展开更多
The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optim...The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.展开更多
The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-bran...The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system.In this paper,the multi-branch mainline is decomposed into single branch lines,transforming the complex multi-branch fault location problem into a double-ended fault location problem.Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines,the endpoint reference time difference matrix S and the fault time difference matrix G were established.The time variation rule of the fault-traveling wave arriving at each endpoint before and after a fault was comprehensively utilized.To realize the fault segment location,the least square method was introduced.It was used to find the first-order fitting relation that satisfies the matching relationship between the corresponding row vector and the first-order function in the two matrices,to realize the fault segment location.Then,the time difference matrix is used to determine the traveling wave velocity,which,combined with the double-ended traveling wave location,enables accurate fault location.展开更多
As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not...As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not only the academic, integrity-based concerns of students using AI for their homework, but also as the forebearers of new learning and technology, how AI will change their students’ futures and careers. In this study, we will explore the different factors, such as Computer Science Score and location, that might affect how much a college discusses AI, ChatGPT specifically. To demonstrate the validity of our research, we used self-collected data with our methods detailed below.展开更多
Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications i...Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.展开更多
文摘Objective:To evaluate the relationship between the clinical and imaging features of ground glass opacity(GGO)localized using a preoperative Hook-wire guidewire and postoperative pathology.Method:Preoperative Hook-wire guidewire localization was performed in 83 patients with GGO less than 2 cm,and their clinical data,imaging data,and postoperative pathology findings were retrospectively analyzed.The images were classified as pure GGO(pGGO) or mixed GGO(mGGO).The relationship between clinical and imaging features and postoperative pathology was analyzed.Result:The 83 cases were colocalized,and the success rate of the guidewire positioning was 100%.Complications included pneumothorax(19.2% [16/83]) and the incidence of minor bleeding(30.2 [25/83]).Forty-seven patients had mGGO and 36 had pGGO.Among the 47 cases of mGGO,18(38.3%) were invasive adenocarcinoma(IAC),18(38.3%) were microinvasive adenocarcinoma(MIA),8(17.0%) were adenocarcinoma in situ(AIS),2(4.3%) were atypical adenomatous hyperplasia(AAH),and 1(2.1%) was benign.Among the 36 cases of pGGO,6(16.7%) were IAC,13(36.1%) were MIA,8(22.2%) were AIS,2(5.6%) were AAH,and 7(19.4%) cases were benign lesions.A significantly higher proportion of patients with IAC had mGGO than pGGO(21.7% vs.7.2%,respectively;p=0.004).Among patients with mGGO,a higher proportion of them had a nodule diameter of ≥1 cm than those with a diameter of <1 cm(25.5% vs.12.8%,respectively;p=0.003).There was no significant difference in age,location distribution,or pathological type.Conclusion: Preoperative CT-guided Hook-wire guidewire positioning was safe with minor complications.A significantly higher proportion of patients with IAC had mGGO than pGGO.Patients with mGGO and a nodule diameter ≥1 cm require active treatment.
基金financial support of the Fundamental Research Funds for the Central Universities(Grant No.2022XSCX35)the National Natural Science Foundation of China(Grant Nos.51934007 and 52104230).
文摘Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.
基金This work is supported by Demonstration of Scientific and Technology Achievements Transform in Sichuan Province under Grant 2022ZHCG0036National Natural Science Foundation of China(62002047).
文摘Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction techniques.In the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management system.Moreover,an audio-based location distinction method is designed to detect the position change of the devices.Specifically,the audio frequency response(AFR)of several frequency points is utilized as a device signature.The device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different directions.Based on the features above,amovement detection algorithmfor device identification and location distinction is designed.Moreover,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and Severs.Extensive experiments are conducted to evaluate the performance of the proposed method.The experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.
基金supported by the NationalNatural Science Foundation of China(No.61866023).
文摘Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.
基金Supported by the National Natural Science Foundation of China,No.82473195Natural Science Foundation of Zhejiang Province,No.LTGY23H160018+2 种基金Zhejiang Medical and Health Science and Technology Program,No.2024KY789Beijing Science and Technology Innovation Medical Development Foundation,No.KC2023-JX-0270-07National Natural Science Foundation of China,No.32370797.
文摘BACKGROUND Gastric cancer(GC)is a highly prevalent gastrointestinal tract tumor.Several trials have demonstrated that the location of GC can affect patient prognosis.However,the factors determining tumor location remain unclear.AIM To investigate the tumor location of patients,we went on to study the influencing factors that lead to changes in the location of GC.METHODS A retrospective evaluation was carried out on 3287 patients who underwent gastrectomy for GC in Zhejiang Cancer Hospital.The patients were followed up post-diagnosis and post-gastrectomy.The clinicopathological variables and overall survival of the patients were recorded.By analyzing the location of GC,the tumor location was divided into four categories:“Upper”,“middle”,“lower”,and“total”.Statistical software was utilized to analyze the relationship of each variable with the location of GC.RESULTS A total of 3287 patients were included in this study.The clinicopathological indices of gender,age,serum levels of carcinoembryonic antigen(CEA),carbohydrate antigen(CA19-9)and CA72-4 levels,were significantly associated with tumor location in patients with GC.In addition,there was a strong correlation between GC location and the prognosis of postoperative patients.Specifically,patients with“lower”and“middle”GC demonstrated a better prognosis than those with tumors in other categories.CONCLUSION The five clinicopathological indices of gender,age,CEA,CA19-9 and CA72-4 levels exhibit varying degrees of influence on the tumor location.The tumor location correlates with patient prognosis following surgery.
基金funded by the National Natural Science Foundation of China (No.42074140)the Scientific Research and Technology Development Project of China National Petroleum Corporation (No.2021ZG02)。
文摘Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times because they are easily affected by tides,currents,and other factors in the shallow sea environment during long-term acquisition.If uncorrected,then the imaging quality of subsequent processing will be affected.The conventional secondary positioning does not consider the case of multiple movements of the receivers,and the accuracy of secondary positioning is insufficient.The first arrival wave of OBN seismic data in shallow ocean mainly comprises refracted waves.In this study,a nonlinear model is established in accordance with the propagation mechanism of a refracted wave and its relationship with the time interval curve to realize the accurate location of multiple receiver movements.In addition,the Levenberg-Marquart algorithm is used to reduce the influence of the first arrival pickup error and to automatically detect the receiver movements,identifying the accurate dynamic relocation of the receivers.The simulation and field data show that the proposed method can realize the dynamic location of multiple receiver movements,thereby improving the accuracy of seismic imaging and achieving high practical value.
基金supported by the Natural Science Foundation of China(U2034207)the Natural Science Foundation of Hebei Province(E2021210099)the Technical Development Project of Shuohuang Railway Development Co.,Ltd.(GJNY-20-230).
文摘To enhance the understanding of the geometry and characteristics of seismogenic faults in the Beijing-Tianjin-Hebei region,we relocated 14805 out of 16063 earthquakes(113°E-120°E,36°N-43°N)that occurred between January 2008 and December 2020 using the double-difference tomography method.Based on the spatial variation in seismicity after relocation,the Beijing-Tianjin-Hebei region can be divided into three seismic zones:Xingtai-Wen'an,Zhangbei-Ninghexi,and Tangshan.(1)The Xingtai-Wen'an Seismic Zone has a northeastsouthwest strike.The depth profile of earthquakes perpendicular to the strike reveals three northeast-striking,southeast-dipping,high-angle deep faults(>10 km depth),including one below the shallow(<10 km depth)listric,northwest-dipping Xinghe fault in the Xingtai region.Two additional deep faults in the Wen'an region are suggested to be associated with the 2006 M 5.1 Wen'an Earthquake and the 1967 M 6.3 Dacheng earthquake;(2)The Zhangbei-Ninghexi Seismic Zone is oriented north-northwest.Multiple northeast-striking faults(10-20 km depth),inferred from the earthquake-intensive zones,exist beneath the shallow(<10 km depth)Xiandian Fault,Xiaotangshan Fault,Huailai-Zhuolu Basin North Fault,Yangyuan Basin Fault and Yanggao Basin North Fault;(3)In the Tangshan Seismic Zone,earthquakes are mainly concentrated near the northeast-striking Tangshan-Guye Fault,Lulong Fault,and northwest-striking Luanxian-Laoting Fault.An inferred north-south-oriented blind fault is present to the north of the Tangshan-Guye Fault.The 1976 M 7.8 Tangshan earthquake occurred at the junction of a shallow northwest-dipping fault and a deep southeast-dipping fault.This study emphasizes that earthquakes in the region are primarily associated with deep blind faults.Some deep blind faults have different geometries compared to shallow faults,suggesting a complex fault system in the region.Overall,this research provides valuable insights into the seismogenic faults in the Beijing–Tianjin–Hebei region.Further studies and monitoring of these faults are essential for earthquake mitigation efforts in this region.
文摘Location prediction in social media,a growing research field,employs machine learning to identify users' locations from their online activities.This technology,useful in targeted advertising and urban planning,relies on natural language processing to analyze social media content and understand the temporal dynamics and structures of social networks.A key application is predicting a Twitter user's location from their tweets,which can be challenging due to the short and unstructured nature of tweet text.To address this challenge,the research introduces a novel machine learning model called the location-aware attention LSTM(LAA-LSTM).This hybrid model combines a Long Short-Term Memory(LSTM) network with an attention mechanism.The LSTM is trained on a dataset of tweets,and the attention network focuses on extracting features related to latitude and longitude,which are crucial for pinpointing the location of a user's tweet.The result analysis shows approx.10% improvement in accuracy over other existing machine learning approaches.
基金the National Natural Science Foundation of China(52177074).
文摘The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.
基金This work was funded by the project of State Grid Hunan Electric Power Research Institute(No.SGHNDK00PWJS2210033).
文摘The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system.In this paper,the multi-branch mainline is decomposed into single branch lines,transforming the complex multi-branch fault location problem into a double-ended fault location problem.Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines,the endpoint reference time difference matrix S and the fault time difference matrix G were established.The time variation rule of the fault-traveling wave arriving at each endpoint before and after a fault was comprehensively utilized.To realize the fault segment location,the least square method was introduced.It was used to find the first-order fitting relation that satisfies the matching relationship between the corresponding row vector and the first-order function in the two matrices,to realize the fault segment location.Then,the time difference matrix is used to determine the traveling wave velocity,which,combined with the double-ended traveling wave location,enables accurate fault location.
文摘As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not only the academic, integrity-based concerns of students using AI for their homework, but also as the forebearers of new learning and technology, how AI will change their students’ futures and careers. In this study, we will explore the different factors, such as Computer Science Score and location, that might affect how much a college discusses AI, ChatGPT specifically. To demonstrate the validity of our research, we used self-collected data with our methods detailed below.
基金This research was partly supported by the National Science and Technology Council,Taiwan with Grant Numbers 112-2221-E-992-045,112-2221-E-992-057-MY3 and 112-2622-8-992-009-TD1.
文摘Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.
