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.

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.

Numerical parametric study on the influence of location and inclination of large-scale asperities on the shear strength of concreterock interfaces of small buttress dams

When assessing the sliding stability of a concrete dam,the influence of large-scale asperities in the sliding plane is often ignored due to limitations of the analytical rigid body assessment methods provided by current dam assessment guidelines.However,these asperities can potentially improve the load capacity of a concrete dam in terms of sliding stability.Although their influence in a sliding plane has been thoroughly studied for direct shear,their influence under eccentric loading,as in the case of dams,is unknown.This paper presents the results of a parametric study that used finite element analysis(FEA)to investigate the influence of large-scale asperities on the load capacity of small buttress dams.By varying the inclination and location of an asperity located in the concrete-rock interface along with the strength of the rock foundation material,transitions between different failure modes and correlations between the load capacity and the varied parameters were observed.The results indicated that the inclination of the asperity had a significant impact on the failure mode.When the inclinationwas 30and greater,interlocking occurred between the dam and foundation and the governing failure modes were either rupture of the dam body or asperity.When the asperity inclination was significant enough to provide interlocking,the load capacity of the dam was impacted by the strength of the rock in the foundation through influencing the load capacity of the asperity.The location of the asperity along the concrete-rock interface did not affect the failure mode,except for when the asperity was located at the toe of the dam,but had an influence on the load capacity when the failure occurred by rupture of the buttress or by sliding.By accounting for a single large-scale asperity in the concrete-rock interface of the analysed dam,a horizontal load capacity increase of 30%e160%was obtained,depending on the inclination and location of the asperity and the strength of the foundation material.

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.

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。

Optimization of Charging/Battery-Swap Station Location of Electric Vehicles with an Improved Genetic Algorithm-Based Model

The joint location planning of charging/battery-swap facilities for electric vehicles is a complex problem.Considering the differences between these two modes of power replenishment,we constructed a joint location-planning model to minimize construction and operation costs,user costs,and user satisfaction-related penalty costs.We designed an improved genetic algorithm that changes the crossover rate using the fitness value,memorizes,and transfers excellent genes.In addition,the present model addresses the problem of“premature convergence”in conventional genetic algorithms.A simulated example revealed that our proposed model could provide a basis for optimized location planning of charging/battery-swapping facilities at different levels under different charging modes with an improved computing efficiency.The example also proved that meeting more demand for power supply of electric vehicles does not necessarily mean increasing the sites of charging/battery-swap stations.Instead,optimizing the level and location planning of charging/battery-swap stations can maximize the investment profit.The proposed model can provide a reference for the government and enterprises to better plan the location of charging/battery-swap facilities.Hence,it is of both theoretical and practical value.

基于 T(X )参与度的负co-location模式挖掘算法

空间co-location模式是一组在空间中频繁并置的空间特征的子集.负co-location模式从非频繁的空间co-location模式中产生.一般来说很难计算和挖掘频繁的负co-location模式.频繁负co-location模式中有较强的应用价值,如发现外来物种入侵,自然界植被生长规律等.现有对负co-location模式研究不全面且挖掘算法的数量屈指可数.针对该问题,提出了T(X)下的负co-location模式的参与度度量方法,并分析了此度量的合理性、可行性和简便性;其次,利用此度量,可以发现负模式中隐含的“团爆炸”现象,而之前的度量方式不能发现此现象.提出了基于T(X)参与度度量的负co-location模式挖掘算法.最后,实验结果表明,在其他条件不变的情况下,该算法可以挖掘数量更少且更具负相关性的频繁负co-location模式.

Research on Leak Location Method of Water Supply Pipeline Based on MVMD

At present,the leakage rate of the water distribution network in China is still high,and the waste of water resources caused by water distribution network leakage is quite serious every year.Therefore,the location of pipeline leakage is of great significance for saving water resources and reducing economic losses.Acoustic emission technology is the most widely used pipeline leak location technology.The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters,ignoring periodic noise and components unrelated to pipeline leakage.Aiming at the above problems,this paper proposes a leak location method for water supply pipelines based on a multivariate variational mode decomposition algorithm.This method combines the two parameters of the energy loss coefficient and the correlation coefficient between adjacent modes,and adaptively determines the decomposition mode number K according to the characteristics of the signal itself.According to the correlation coefficient,the effective component is selected to reconstruct the signal and the cross-correlation time delay is estimated to determine the location of the pipeline leakage point.The experimental results show that this method has higher accuracy than the cross-correlation method based on VMD and the cross-correlation method based on EMD,and the average relative positioning error is less than 2.2%.

Estimation of Lightning-Generated NO_(x) in the Mainland of China Based on Cloud-to-Ground Lightning Location Data

Lightning-generated nitrogen oxides(LNO_(x))have a major influence on the atmosphere and global climate change.Therefore,it is of great importance to obtain a more accurate estimation of LNO_(x).The aim of this study is to provide a reference for the accurate estimation of the total LNO_(x) in the mainland of China based on cloud-to-ground lightning(CG)location data from 2014 to 2018.The energy of each CG flash was based on the number of return strokes per CG flash,the peak current of each return stroke,and the assumed CG breakdown voltage.The energy of intracloud lightning(IC)was based on the estimated frequencies of IC and the assumed energy of each IC flash.Combining the energy of lightning and the number of nitric oxide(NO)molecules produced by unit energy(ρno),the total LNO_(x) production in the mainland of China was determined.The LNO_(x) in the mainland of China estimated in this study is in the range(0.157-0.321)×10^(9) kg per year[Tg(N)yr-1],which is on the high end of other scholars’works.Negative cloud-to-ground lightning(NCG)flashes produce the most moles of NO_(x),while positive cloud-to-ground lightning(PCG)flashes produce the least total moles of NO_(x).The breakdown voltage of PCG is greater than that of IC or NCG,while the latter has a greater output of LNO_(x).

Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.

Microseismic source location using the Log-Cosh function and distant sensor-removed P-wave arrival data

Source location is the core foundation of microseismic monitoring.To date,commonly used location methods have usually been based on the ray-tracing travel-time technique,which generally adopts an L1 or L2 norm to establish the location objective function.However,the L1 norm usually achieves low location accuracy,whereas the L2 norm is easily affected by large P-wave arrival-time picking errors.In addition,traditional location methods may be affected by the initial iteration point used to find a local optimum location.Furthermore,the P-wave arrival-time data that have travelled long distances are usually poor in quality.To address these problems,this paper presents a microseismic source location method using the Log-Cosh function and distant sensor-removed P-wave arrival data.Its basic principles are as follows:First,the source location objective function is established using the Log-Cosh function.This function has the stability of the L1 norm and location accuracy of the L2 norm.Then,multiple initial points are generated randomly in the mining area,and the established Log-Cosh location objective function is used to obtain multiple corresponding location results.The average value of the 50 location points with the largest data field potential values is treated as the initial location result.Next,the P-wave travel times from the initial location result to triggered sensors are calculated,and then the P-wave arrival data with travel times exceeding 0.2 s are removed.Finally,the aforementioned location steps are repeated with the denoised P-wave arrival dataset to obtain a high-precision location result.Two synthetic events and eight blasting events from the Yongshaba mine,China,were used to test the proposed method.Regardless of whether the P-wave arrival data with long travel times were eliminated,the location error of the proposed method was smaller than that of the L1/L2 norm and trigger-time-based location method(TT1/TT2 method).Furthermore,after eliminating the Pwave arrival data with long travel distances,the location accuracy of these three location methods increased,indicating that the proposed location method has good application prospects.

On Fixed-Parameter Solvability of the Minimax Path Location Problem

The minimax path location problem is to find a path P in a graph G such that the maximum distance d_(G)(v,P)from every vertex v∈V(G)to the path P is minimized.It is a well-known NP-hard problem in network optimization.This paper studies the fixed-parameter solvability,that is,for a given graph G and an integer k,to decide whether there exists a path P in G such that max v∈V(G)d_(G)(v,P)≤k.If the answer is affirmative,then graph G is called k-path-eccentric.We show that this decision problem is NP-complete even for k=1.On the other hand,we characterize the family of 1-path-eccentric graphs,including the traceable,interval,split,permutation graphs and others.Furthermore,some polynomially solvable special graphs are discussed.

Overview of the identification of traffic accident-prone locations driven by big data

Effective identification of traffic accident-prone points can reduce accident risks and eliminate safety hazards.This paper first systematically compares the research in Chinese and foreign literature,and proposes three types of identification indicators,namely absolute,relative and comprehensive,according to different reference standards.According to the evaluation indicators and modelling methods,the current status of research and problems in identification theory and methods are systematically summarised in terms of mathematical statistics,cluster analysis,machine learning and conflict technology.The study shows that the foreign literature focuses on the innovation of data and indicators and changes from accident point safety management to road network safety management,while the research in Chinese literature focuses on the integration of multiple identification methods and theoretical innovation.Driven by big data,the identification of traffic accident-prone points has been further developed at the meso-micro scale.Morphological image processing methods are widely used,combined with GIS platforms,to accurately mine the spatial attributes and correlations of accidents.Also,considering the spatial and temporal distribution of accidents,the identification results are also transformed from regions to specific road sections and points to achieve more accurate identification.

Host niche, genotype, and field location shape the diversity and composition of the soybean microbiome

Plant-associated microbes represent a key determinant of plant fitness through acquiring nutrients,promoting growth,and resisting to abiotic and biotic stresses.However,an extensive characterization of the bacterial and fungal microbiomes present in different plant compartments of soybean in field conditions has remained elusive.In this study,we investigated the effects of four niches(roots,stems,leaves,and pods),four genotypes(Andou 203,Hedou 12,Sanning 16,and Zhonghuang 13),and three field locations(Jining,Suzhou,and Xuzhou)on the diversity and composition of bacterial and fungal communities in soybean using 16S and internal transcribed spacer rRNA amplicon sequencing,respectively.The soybean microbiome significantly differed across organs.Host genotypes explained more variation in stem bacterial community composition and leaf fungal community composition.Field location significantly affected the composition of bacterial communities in all compartments and the effects were stronger in the root and stem than in the leaf and pod,whereas field location explained more variation in stem and leaf fungal community composition than in the root and pod.The relative abundances of potential soybean fungal pathogens also differed among host organs and genotypes,reflecting the niches of these microbes in the host and probably their compatibility to the host genotypes.Systematic profiling of the microbiome composition and diversity will aid the development of plant protection technologies to benefit soybean health.