An envelope-based machine learning workflow for locating earthquakes in the southern Sichuan Basin

The development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. To address the issues of sparse event distribution and inaccurate ground truth in historical seismic datasets, we expand the training dataset by using a large number of synthetic envelopes that closely resemble real data and build an earthquake location model named ENVloc. We propose an envelope-based machine learning workflow for simultaneously determining earthquake location and origin time. The method eliminates the need for phase picking and avoids the accumulation of location errors resulting from inaccurate picking results. In practical application, ENVloc is applied to several data intercepted at different starting points. We take the starting point of the time window corresponding to the highest prediction probability value as the origin time and save the predicted result as the earthquake location. We apply ENVloc to observed data acquired in the southern Sichuan Basin, China, between September 2018 and March 2019. The results show that the average difference with the catalog in latitude, longitude, depth, and origin time is 0.02°,0.02°, 2 km, and 1.25 s, respectively. These suggest that our envelope-based method provides an efficient and robust way to locate earthquakes without phase picking, and can be used in earthquake monitoring in near-real time.

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance.Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel,addressing a crucial gap in the integration of maintenance personnel dispatching and station selection.Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness.The core of our methodology is the NSGA Ⅲ+Dispatch,an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ),meticulously designed for the selection of maintenance stations and effective operator dispatching.This method integrates a comprehensive coding process,crossover operator,and mutation operator to efficiently manage multiple objectives.Rigorous empirical testing,including a detailed analysis from a taiwan region electronic equipment manufacturer,validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations.The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23%in low-frequency and 28.23%in high-frequency machine failure scenarios,leading to notable improvements in efficiency and cost reduction.Additionally,it demonstrates significant improvements in oper-ational efficiency,particularly in selective high-frequency failure contexts,while ensuring substantial manpower cost savings without compromising on operational effectiveness.This research significantly advances maintenance strategies in production environments,providing the manufacturing industry with practical,optimized solutions for diverse machine malfunction situations.Furthermore,the methodologies and principles developed in this study have potential applications in various other sectors,including healthcare,transportation,and energy,where maintenance efficiency and resource optimization are equally critical.

Reviewer Locator模块在科技期刊中的应用实践与发展前景——以《中国肺癌杂志》为例

科技期刊作为科技传播的重要力量,其对重要科研成果的发布速度影响着科学技术的传播效力,而编辑工作效率直接决定稿件发表周期,提高编辑工作效率是科技期刊发展的推进器。为此,从同行评议审稿人储备和遴选方面入手,重点阐述如何准确高效地多维度遴选优质审稿人,归纳并探析Reviewer Locator模块在科技期刊办刊工作中的实际应用经验,助力出版业实现质量更好、效率更高的发展。

An online fast multi-track locating algorithm for high-resolution single-event effect test platform

To improve the efficiency and accuracy of single-event effect(SEE)research at the Heavy Ion Research Facility at Lanzhou,Hi’Beam-SEE must precisely localize the position at which each heavy ion hitting the integrated circuit(IC)causes SEE.In this study,we propose a fast multi-track location(FML)method based on deep learning to locate the position of each particle track with high speed and accuracy.FML can process a vast amount of data supplied by Hi’Beam-SEE online,revealing sensitive areas in real time.FML is a slot-based object-centric encoder-decoder structure in which each slot can learn the location information of each track in the image.To make the method more accurate for real data,we designed an algorithm to generate a simulated dataset with a distribution similar to that of the real data,which was then used to train the model.Extensive comparison experiments demonstrated that the FML method,which has the best performance on simulated datasets,has high accuracy on real datasets as well.In particular,FML can reach 238 fps and a standard error of 1.6237μm.This study discusses the design and performance of FML.

Fault locating for traveling-wave accelerators based on transmission line theory

Radio-frequency(RF)breakdown analysis and location are critical for successful development of high-gradient traveling-wave(TW)accelerators,especially those expected to generate high-intensity,high-power beams.Compared with commonly used schemes involving dedicated devices or complicated techniques,a convenient approach for breakdown locating based on transmission line(TL)theory offers advantages in the typical constant-gradient TW-accelerating structure.To deliver such an approach,an equivalent TL model has been constructed to equate the TW-accelerating structure based on the fun-damental theory of the TL transient response in the time domain.An equivalence relationship between the TW-accelerating structure and the TL model has been established via analytical derivations associated with grid charts and verified by TL circuit simulations.Furthermore,to validate the proposed fault-locating method in practical applications,an elaborate analysis via such a method has been conducted for the recoverable RF-breakdown phenomena observed at an existing prototype of a TW-accelerating-structure-based beam injector constructed at the Huazhong University of Science and Technology.In addition,further considerations and discussion for extending the applications of the proposed method have been given.This breakdown-locating approach involving the transient response in the framework of TL theory can be a conceivable supple-ment to existing methods,facilitating solution to construction problems at an affordable cost.

Performance Evaluation of Three-Dimensional UWB Real-Time Locating Auto-Positioning System for Fire Rescue

Fire rescue challenges and solutions have evolved from straightfor-ward plane rescue to encompass 3D space due to the rise of high-rise city buildings.Hence,this study facilitates a system with quick and simplified on-site launching and generates real-time location data,enabling fire rescuers to arrive at the intended spot faster and correctly for effective and precise rescue.Auto-positioning with step-by-step instructions is proposed when launching the locating system,while no extra measuring instrument like Total Station(TS)is needed.Real-time location tracking is provided via a 3D space real-time locating system(RTLS)constructed using Ultra-wide Bandwidth technology(UWB),which requires electromagnetic waves to pass through concrete walls.A hybrid weighted least squares with a time difference of arrival(WLS/TDOA)positioning method is proposed to address real path-tracking issues in 3D space and to meet RTLS requirements for quick computing in real-world applications.The 3D WLS/TDOA algorithm is theoretically constructed with the Cramer-Rao lower bound(CRLB).The computing complexity is reduced to the lower bound for embedded hardware to directly compute the time differential of the arriving signals using the time-to-digital converter(TDC).The results of the experiments show that the errors are controlled when the positioning algorithm is applied in various complicated situations to fulfill the requirements of engineering applications.The statistical analysis of the data reveals that the proposed UWB RTLS auto-positioning system can track target tags with an accuracy of 0.20 m.

Microseismic source location using deep learning:A coal mine case study in China

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.

A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

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.

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

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.

Prognostic impact and reasons for variability by tumor location in gastric cancer

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.

Dynamic Location Method for Shallow Ocean Bottom Nodes Using the Levenberg-Marquart Algorithm

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.

Earthquake relocation using a 3D velocity model and implications on seismogenic faults in the Beijing-Tianjin-Hebei region

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.

Location Prediction from Social Media Contents using Location Aware Attention LSTM Network

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.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

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.

Multi-Branch Fault Line Location Method Based on Time Difference Matrix Fitting

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.

How Does a University’s Computer Science Strength and Location Impact Its Total ChatGPT News?

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.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

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.

An intersection method for locating earthquakes in complex velocity models

The intersection method is one of the basic approaches for locating earthquakes and is not only robust but also efficient. However, its location accuracy is not high, especially for focal depth because the velocity model used for the conventional intersection method is based on homogeneous or laterally homogeneous media, which is too simple. In order to improve the accuracy, we have modified the existing intersection method. In the modified approach, the earthquake loci are not assumed to be circular or hyperbolic and calculation accuracy is improved using a minimum traveltime tree algorithm for tracing rays. The numerical model shows that the modified method can locate earthquakes in complex velocity models.

WORKPIECE LOCATING AND POST PROCESSING SYSTEMS ON 6-DOF CNC MILLING MACHINE

A conventional non-computerized numerical control （CNC） machine is updated by mounting a six degree-of-free （DOF） parallel mechanism on it, thus obtaining a new CNC one. The structure of this CNC milling machine is introduced, and the workpiece locating system and the post processing system of the cutter location （CL） data file are analyzed. The new machine has advantages of low costs, simple structure, good rigidity, and high precision. It is easy to be transformed and used to process the workpiece with a complex surface.

无线网络优化LOCATING过程中ERICSSON3算法的应用

介绍在LOCATING过程中处理基本排队时所用到的一种算法——ERICSSON 3算法,探讨通过该算法控制切换的方法(只涉及爱立信设备)。