Modular Extremely Large-Scale Array Communication:Near-Field Modelling and Performance Analysis

This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced modules.Each module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical deployment.By accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array communications.Based on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)location.In addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are revealed.Furthermore,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of onedimension.Extensive simulation results are presented to validate our findings.

Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling

Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS abutments.Five other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO model.The predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature.The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models.Therefore,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments.Finally,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.

Comparison of Pile-Soil-Structure Interaction Modeling Techniques for A 10-MW Large-Scale Monopile Wind Turbine Model Under Wind and Wave Conditions

Considering the large diameter effect of piles,the influence of different pile-soil analysis methods on the design of monopile foundations for offshore wind turbines has become an urgent problem to be solved.Three different pile-soil models were used to study a large 10 MW monopile wind turbine.By modeling the three models in the SACS software,this paper analyzed the motion response of the overall structure under the conditions of wind and waves.According to the given working conditions,this paper concludes that under the condition of independent wind,the average value of the tower top x-displacement of the rigid connection method is the smalle st,and the standard deviation is the smallest under the condition of independent wave.The results obtained by the p-y curve method are the most conservative.

Long Short-Term Memory Recurrent Neural Network-Based Acoustic Model Using Connectionist Temporal Classification on a Large-Scale Training Corpus

A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.

AI赋能智慧城市数字化治理的关键技术与典型应用

[研究目的]数字化治理是指利用数字技术和数据来提高治理效率和效果,提高公共服务和治理的质量、效率和可持续性。当前,人工智能(AI)技术赋能智慧城市数字化治理已成为不可逆转的时代发展趋势,而对于AI赋能的技术路径,还存在进一步解读的必要。[研究方法]在总结智慧城市相关共性需求的基础上,阐述了人工智能如何与数字化治理关键技术相结合,并给出了相关人工智能技术赋能数字化治理的典型案例。[研究结论]重点阐述人工智能技术在这些领域中进一步提升智能分析、视觉识别、智能监管、辅助决策效能的具体方法,表明人工智能技术与数字化治理技术的结合,能够为智慧城市应用带来更高效、更精准的工具及方法,尤其在基于自然语言处理以及跨模态应用的领域中能够发挥重要作用。

AI语音助手用户虚拟在线体验影响因素

在以ChatGPT为代表的ICT技术快速发展和社会变革环境下,以AI语音助手为例的智能应用对用户体验和行为的影响成为重要研究问题。文章基于认知情感意图(Cognition-Affect-Co⁃nation,CAC)框架,从AI语音助手技术特征出发分析感知拟人性、感知关系性、感知娱乐性对于用户认知评价的感知服务质量和感知使用成本,以及在认知评价作用下的情感体验对用户采纳意愿的影响。利用结构方法和fsQCA混合研究方法,通过对328份受访者的问卷调查进行实证检验。研究发现:AI语音助手的感知拟人性增加用户的感知使用成本,感知关系性和感知娱乐性有利于感知服务质量;情感体验在用户认知评价与用户行为意图结果之间起中介作用;根据fsQCA数据结果总结并诠释5种AI语音助手用户在线虚拟体验组态路径,验证并补充结构方程研究发现。文章为AI语音助手用户虚拟在线体验影响因素研究提供了新的理论研究视角,深化了客户旅程理论在用户虚拟在线体验中理论应用,为各界利用AI语音助手增强用户体验提供参考。

基于AIS数据的船舶风险领域模型

为构建船舶风险领域及分析其特征,基于船舶自动识别系统(Automatic Identification System,AIS)数据提出一种具有风险级别的船舶领域模型。首先,根据预处理后的AIS数据,获取他船相对于本船的位置。然后,采用椭圆领域边界对船舶相对位置数据进行筛选,同时获取到代表风险级别的临界点,并使用最小二乘法对其进行拟合,从而得到船舶风险领域。最后,利用老铁山水道中149 m、190 m、229 m、300 m船舶的AIS数据对所提方法进行验证,并分析船舶风险领域的特征。结果表明,该方法可以较好地反映船舶的风险级别;在同一风险级别时,不同尺度船舶间的风险领域长、短半轴与船长之比差异较小;风险级别为1的船舶领域边界接近于圆形;他船在本船周围分布的密集程度不同。本研究所提模型对航行安全保障、航行风险研究有一定的参考意义。

预测方法对AI的限制

今天的预测技术和人类一直依赖的推理一样,正在成为科学研究中重要的独立的驱动力。本文对二者进行比较,并指出了预测的不足,正是这种不足造就了今日AI技术发展中存在的一些天生的缺陷,最终导致实现不了agent建立完备世界模型的目标。但是,不能忽视的是,目前的AI技术已经具有缩小人与人之间智力水平的能力。

Validating the Runoff from the PRECIS Model Using a Large-Scale Routing Model

The streamflow over the Yellow River basin is simulated using the PRECIS （Providing REgional Climates for Impacts Studies） regional climate model driven by 15-year （1979-1993） ECMWF reanalysis data as the initial and lateral boundary conditions and an off-line large-scale routing model （LRM）. The LRM uses physical catchment and river channel information and allows streamflow to be predicted for large continental rivers with a 1°×1° spatial resolution. The results show that the PRECIS model can reproduce the general southeast to northwest gradient distribution of the precipitation over the Yellow River basin, The PRECIS- LRM model combination has the capability to simulate the seasonal and annual streamflow over the Yellow River basin. The simulated streamflow is generally coincident with the naturalized streamflow both in timing and in magnitude.

SNWPM:A Siamese Network Based Wireless Positioning Model Resilient to Partial Base Stations Unavailable

Artificial intelligence(AI)models are promising to improve the accuracy of wireless positioning systems,particularly in indoor environments where unpredictable radio propagation channel is a great challenge.Although great efforts have been made to explore the effectiveness of different AI models,it is still an open problem whether these models,trained with the data collected from all base stations(BSs),could work when some BSs are unavailable.In this paper,we make the first effort to enhance the generalization ability of AI wireless positioning model to adapt to the scenario where only partial BSs work.Particularly,a Siamese Network based Wireless Positioning Model(SNWPM)is proposed to predict the location of mobile user equipment from channel state information(CSI)collected from 5G BSs.Furthermore,a Feature Aware Attention Module(FAAM)is introduced to reinforce the capability of feature extraction from CSI data.Experiments are conducted on the 2022 Wireless Communication AI Competition(WAIC)dataset.The proposed SNWPM achieves decimeter-level positioning accuracy even if the data of partial BSs are unavailable.Compared with other AI models,the proposed SNWPM can reduce the positioning error by nearly 50%to more than 60%while using less parameters and lower computation resources.

关于AI大模型技术赋能船舶领域的认识

概述了人工智能(artificial intelligence,AI)大模型研究的焦点、发展趋势及其技术本质,分析了国家层面的人工智能发展战略、国防领域的紧迫需求以及船舶领域应用的基础。从智能绿色船舶的发展、防务装备体系的革新、管控体系的建设以及知识密集产业的转型等方面,探讨了将人工智能大模型技术应用于船舶领域的广阔前景。人工智能大模型技术与“平行系统”“知识工厂”和“数字员工”等理念相结合,能够催生“AI设计”+“数字工厂”+“平行验证”等新型设计、研发和验证手段。此外,人工智能大模型技术可以从船体设计、船舶建造、航运管理、节能减排等方面为船舶行业注入智能和绿色元素,优化船舶各项功能,提高企业运营的效率,提升经济性和环保性。与新能源、新材料、先进制造和电子信息等战略新兴产业结合后,人工智能大模型技术能够基于新理念和新形态塑造未来海洋防务装备体系。同时,人工智能大模型技术能够赋能船舶管控体系的建设,优化规划计划、助力科技创新、提高管理效率和改善用装质效等。特别是随着船舶领域知识工厂的建立、数字人员工的培训、工业机器人的推广以及对深远海领域的拓展,人工智能大模型技术将能够推动船舶领域“自然人”“机器人”和“数字人”的有机结合和密切协同,加速船舶行业向知识密集型和智能密集型升级,使产业生态和价值创造模式向高端化、智能化和绿色化转变,实现更注重质量和效率的发展方式。

On the Assessment of Generative AI in Requirements Analysis and Modeling Tasks with UML:An Exploratory Study

Generative AI is rapidly employed by software developers to generate code or other software artifacts.However,the analysis and assessment of generative AI with respect to requirements analysis and modeling tasks,especially with UML,has received little attention.This paper investigates the capabilities of generative AI to aid in the creation of three types of UML models:UML use case models,class diagrams,and sequence diagrams.For this purpose,we designed an AI-aided UML modeling task in our course on software requirements modeling.50 undergraduates who majored in Software Engineering at Wuhan University completed the modeling task and the corresponding online survey.Our findings show that generative AI can help create these three types of UML models,but its performance is limited to identifying essential modeling elements of these UML models.

Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus

BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive impairment(MCI),and these patterns predicted their cognitive performance.It has been reported that patients with type 2 diabetes mellitus(T2DM)may develop MCI that could progress to dementia.We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM,using connectome-based predictive modeling(CPM)and a support vector machine.AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls.Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA).Patients with T2DM were divided into two groups,according to the presence(T2DM-C;n=16)or absence(T2DM-NC;n=26)of MCI.Brain regions were marked using Harvard Oxford(HOA-112),automated anatomical labeling(AAL-116),and 264-region functional(Power-264)atlases.LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique.Subsequently,we used a support vector machine based on LSFC patterns for among-group differentiation.The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTS CPM could predict the MoCA scores in patients with T2DM(Pearson’s correlation coefficient between predicted and actual MoCA scores,r=0.32,P=0.0066[HOA-112 atlas];r=0.32,P=0.0078[AAL-116 atlas];r=0.42,P=0.0038[Power-264 atlas]),indicating that LSFC patterns represent cognition-level measures in these patients.Positive(anti-correlated)LSFC networks based on the Power-264 atlas showed the best predictive performance;moreover,we observed new brain regions of interest associated with T2DM-related cognition.The area under the receiver operating characteristic curve values(T2DM-NC group vs.T2DM-C group)were 0.65-0.70,with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value(0.70).Most discriminative and attractive LSFCs were related to the default mode network,limbic system,and basal ganglia.CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

AI大模型驱动的智慧图书馆服务体系研究

构建AI大模型驱动的智慧图书馆服务体系,能够提高服务质量和效率,为读者提供更加优质的服务体验。文章在探讨AI大模型驱动的智慧图书馆服务变革方向的基础上,结合智慧图书馆服务需求,构建包括图书馆数据管理、图书馆AI大模型调度、图书馆平台管理、图书馆交互服务、制度标准和安全保障等服务体系,建立人员管理、资源管理、技术管理和服务管理等管理机制,同时指出AI大模型驱动的智慧图书馆服务中的信息安全、版权、隐私和伦理等问题,以推动图书馆发展。

Impacts of a Large-scale Adaptive Blending Scheme for CMA-MESO on Regional Forecasts-A Case Study of Typhoon Haima

Large-scale atmospheric information plays an important role in the regional model for the forecasts of weather such as tropical cyclone(TC).However,it is difficult to be fully represented in regional models due to domain size and a lack of observation data,particularly at sea used in regional data assimilation.Blending analysis has been developed and implemented in regional models to reintroduce large-scale information from global model to regional analysis.Research of the impact of this large-scale blending scheme for the Global/Regional Assimilation and PrEdiction System(CMA-MESO)regional model on TC forecasting is limited and this study attempts to further progress by examining the adaptivity of the blending scheme using the two-dimensional Discrete Cosine Transform(2D-DCT)filter on the model forecast of Typhoon Haima over Shenzhen,China in 2016 and considering various cut-off wavelengths.Results showed that the error of the 24-hour typhoon track forecast can be reduced to less than 25 km by applying the scale-dependent blending scheme,indicating that the blending analysis is effectively able to minimise the large-scale bias for the initial fields.The improvement of the wind forecast is more evident for u-wind component according to the reduced root mean square errors(RMSEs)by comparing the experiments with and without blending analysis.Furthermore,the higher equitable threat score(ETS)provided implications that the precipitation prediction skills were increased in the 24h forecast by improving the representation of the large-scale feature in the CMA-MESO analysis.Furthermore,significant differences of the track error forecast were found by applying the blending analysis with different cut-off wavelengths from 400 km to 1200 km and the track error can be reduced less than by 10 km with 400 km cut-off wavelength in the first 6h forecast.It highlighted that the blending scheme with dynamic cut-off wavelengths adapted to the development of different TC systems is necessary in order to optimally introduce and ingest the large-scale information from global model to the regional model for improving the TC forecast.In this paper,the methods and data applied in this study will be firstly introduced,before discussion of the results regarding the performance of the blending analysis and its impacts on the wind and precipitation forecast correspondingly,followed by the discussion of the effects of different blending scheme on TC forecasts and the conclusion section.

AI在大数据技术中的创新与应用

当前我国正在经历着数字化转型,大数据受到各行各业的重点关注,对大数据技术人才的需求正在不断增长。AI技术的突破和发展使得传统大数据处理技术的不足和局限性进一步被放大,探讨AI在大数据技术中的创新与应用势在必行。针对这一问题,该文首先在分析使用大数据技术面临的问题时提出使用AI的解决方案,然后从大数据处理流程出发,提出增加AI数据感知阶段,最后展望AI的优势和存在的不足之处。

AI智算发展对高速光模块的应用需求研究

AI智算技术的高速发展驱动高速光模块需求量激增,大规模数据处理、大模型训练和推理等任务对高速光模块提出了前所未有的高要求。通过分析大模型训练的分布式并行计算需求,建立通信模型,并以GPT-3为例定量分析大模型通信量,由于通信量巨大,完成大模型训练的数据通信时间远高于并行计算时间。因此,在不降低计算性能的前提下,降低通信时间成为AI智算对通信网络的核心诉求,而采用更高速率的光模块互联、提升有效带宽是解决问题的主要途径。AI智算对高速光模块技术的需求将主要体现在更高速率、更大规模、高集约化、低功耗、高稳定性以及可管可控等方面。

Improved Diurnal Cycle of Precipitation on Land in a Global Non-Hydrostatic Model Using a Revised NSAS Deep Convective Scheme

In relatively coarse-resolution atmospheric models,cumulus parameterization helps account for the effect of subgridscale convection,which produces supplemental rainfall to the grid-scale precipitation and impacts the diurnal cycle of precipitation.In this study,the diurnal cycle of precipitation was studied using the new simplified Arakawa-Schubert scheme in a global non-hydrostatic atmospheric model,i.e.,the Yin-Yang-grid Unified Model for the Atmosphere.Two new diagnostic closures and a convective trigger function were suggested to emphasize the job of the cloud work function corresponding to the free tropospheric large-scale forcing.Numerical results of the 0.25-degree model in 3-month batched real-case simulations revealed an improvement in the diurnal precipitation variation by using a revised trigger function with an enhanced dynamical constraint on the convective initiation and a suitable threshold of the trigger.By reducing the occurrence of convection during peak solar radiation hours,the revised scheme was shown to be effective in delaying the appearance of early-afternoon rainfall peaks over most land areas and accentuating the nocturnal peaks that were wrongly concealed by the more substantial afternoon peak.In addition,the revised scheme enhanced the simulation capability of the precipitation probability density function,such as increasing the extremely low-and high-intensity precipitation events and decreasing small and moderate rainfall events,which contributed to the reduction of precipitation bias over mid-latitude and tropical land areas.

U-Net Models for Representing Wind Stress Anomalies over the Tropical Pacific and Their Integrations with an Intermediate Coupled Model for ENSO Studies

El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.

Advances of Model Order Reduction Research in Large-scale System Simulation

Model Order Reduction (MOR) plays more and more imp or tant role in complex system simulation, design and control recently. For example , for the large-size space structures, VLSI and MEMS (Micro-ElectroMechanical Systems) etc., in order to shorten the development cost, increase the system co ntrolling accuracy and reduce the complexity of controllers, the reduced order model must be constructed. Even in Virtual Reality (VR), the simulation and d isplay must be in real-time, the model order must be reduced too. The recent advances of MOR research are overviewed in the article. The MOR theor y and methods may be classified as Singular Value decomposition (SVD) based, the Krylov subspace based and others. The merits and demerits of the different meth ods are analyzed, and the existed problems are pointed out. Moreover, the applic ation’s fields are overviewed, and the potential applications are forecaste d. After the existed problems analyzed, the future work is described. There are som e problems in the traditional methods such as SVD and Krylov subspace, they are that it’s difficult to (1)guarantee the stability of the original system, (2) b e adaptive to nonlinear system, and (3) control the modeling accuracy. The f uture works may be solving the above problems on the foundation of the tradition al methods, and applying other methods such as wavelet or signal compression.