Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data

Environmental assessments are critical for ensuring the sustainable development of human civilization.The integration of artificial intelligence(AI)in these assessments has shown great promise,yet the"black box"nature of AI models often undermines trust due to the lack of transparency in their decision-making processes,even when these models demonstrate high accuracy.To address this challenge,we evaluated the performance of a transformer model against other AI approaches,utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments,enabling the identification of individual indicators'contributions to the model's predictions.We find that the transformer model outperforms others,achieving an accuracy of about 98%and an area under the receiver operating characteristic curve(AUC)of 0.891.Regionally,the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas,level IV in the northern region,and level V in the western region.Through explainability analysis,we identify that water hardness,total dissolved solids,and arsenic concentrations are the most influential indicators in the model.Our AI-driven environmental assessment model is accurate and explainable,offering actionable insights for targeted environmental management.Furthermore,this study advances the application of AI in environmental science by presenting a robust,explainable model that bridges the gap between machine learning and environmental governance,enhancing both understanding and trust in AI-assisted environmental assessments.

Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

Hybrid Block Diagonalization Precoding for Multi-User Weighted Sum-Rate Maximization

This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.

Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

Risk Analysis Using Multi-Source Data for Distribution Networks Facing Extreme Natural Disasters

Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.

Evaluation and Improvement Strategies for Slow Traffic Systems Based on Multi-source Big Data:A Case Study of Shijingshan District of Beijing City

The slow traffic system is an important component of urban transportation,and the prerequisite and necessary condition for Beijing to continue promoting“green priority”are establishing a good urban slow traffic system.Shijingshan District of Beijing City is taken as a research object.By analyzing and processing population distribution data,POI data,and shared bicycle data,the shortcomings and deficiencies of the current slow traffic system in Shijingshan District are explored,and corresponding solutions are proposed,in order to provide new ideas and methods for future urban planning from the perspective of data.

Classification of Beijing Line 10 Subway Living Circle Based on Multi-source Big Data

In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.

Joint Multi-User Detection with Weighting Factors for Unsourced Multiple Access

Multi-user detection techniques are currently being studied as highly promising technologies for improving the performance of unsourced multiple access systems. In this paper, we propose joint multi-user detection schemes with weighting factors for unsourced multiple access. First, we introduce bidirectional weighting factors in the extrinsic information passing process between the multi-user detector based on belief propagation (BP) and the LDPC decoder. Second, we incorporate bidirectional weighting factors in the message passing process between the MAC nodes and the user variable nodes in BP- based multi-user detector. The proposed schemes select the optimal weighting factors through simulations. The simulation results demonstrate that the proposed schemes exhibit significant performance improvements in terms of block error rate (BLER) compared to traditional schemes. .

Closed BER expression of multi-user DS UWB systemsin indoor fading channel

The bit error rate （BER） performance of multi-user direct spreading bi-phase shift keying （DSBPSK） direct impulse ultra wideband （UWB） systems is analyzed and simulated based on a statistical indoor multi-path fading channel model. The BER of the system is theoretically derived and given in closed form, which is expressed in terms of channel parameters and system parameters such as pulse width parameter, pulse repeat period, user number and pulse waveform. With this BER expression, the effect of these parameters on the system performance can be evaluated in a uniform way. Simulation results well match the theory numerical results, and prove that the multi-access interference （MAI） of DS-BPSK UWB is a normal distribution.

Separation method for multi-source blended seismic data

Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of blended seismic data is a linear inverse problem. According to the relationship between the shooting number and the simultaneous source number of the acquisition system, this separation of blended seismic data is divided into an easily determined or overdetermined linear inverse problem and an underdetermined linear inverse problem that is difficult to solve. For the latter, this paper presents an optimization method that imposes the sparsity constraint on wavefields to construct the object function of inversion, and the problem is solved by using the iterative thresholding method. For the most extremely underdetermined separation problem with single-shooting and multiple sources, this paper presents a method of pseudo-deblending with random noise filtering. In this method, approximate common shot gathers are received through the pseudo-deblending process, and the random noises that appear when the approximate common shot gathers are sorted into common receiver gathers are eliminated through filtering methods. The separation methods proposed in this paper are applied to three types of numerical simulation data, including pure data without noise, data with random noise, and data with linear regular noise to obtain satisfactory results. The noise suppression effects of these methods are sufficient, particularly with single-shooting blended seismic data, which verifies the effectiveness of the proposed methods.

Adaptive resource allocation in downlink multi-user MC-CDMA systems

The joint channel and power allocation in the downlink transmission of multi-user multi-carrier code division multiple access（MC-CDMA） systems are investigated and the throughput maximization problem is considered as a mixed integer optimization problem. For simplicity of analysis, the problem is divided into two less complex sub-problems： power allocation and channel allocation, which can be solved by a suboptimal adaptive power allocation （APA）algorithm and an optimal adaptive channel allocation （ACA） algorithm, respectively. By combining APA and ACA algorithms, an adaptive channel and power allocation scheme is proposed. The numerical results show that the proposed APA algorithm is more suitable for MC-CDMA systems than the conventional equal power allocation algorithm, and that the proposed channel and power allocation scheme can significantly improve the system throughout performance.

Optimal subcarrier allocation for cognitive radio with multi-user OFDM and distributed antenna

The subcarrier allocation problem in cognitive radio（CR）networks with multi-user orthogonal frequency-division multiplexing（OFDM）and distributed antenna is analyzed and modeled for the flat fading channel and the frequency selective channel,where the constraint on the secondary user（SU）to protect the primary user（PU）is that the total throughput of each PU must be above the given threshold instead of the ＂interference temperature＂.According to the features of different types of channels,the optimal subcarrier allocation schemes are proposed to pursue efficiency（or maximal throughput）,using the branch and bound algorithm and the 0-1 implicit enumeration algorithm.Furthermore,considering the tradeoff between efficiency and fairness,the optimal subcarrier allocation schemes with fairness are proposed in different fading channels,using the pegging algorithm.Extensive simulation results illustrate the significant performance improvement of the proposed subcarrier allocation schemes compared with the existing ones in different scenarios.