The Deformation Analysis of the 3D Alignment Control Network Based on the Multiple Congruence Models

In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.

Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Beam based alignment using a neural network

Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-based alignment(BBA)is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes.For storage rings with many quadrupoles,the conventional BBA procedure is time-consuming,particularly in the commissioning phase,because of the necessary iterative process.In addition,the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics.In this study,a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy.This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA.The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments.The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA.The development of this new method and the corresponding experiments are reported in this paper.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.

On The Cauchy Problem for A 1D Euler-Alignment System in Besov Spaces

In this paper, we investigate a 1D pressureless Euler-alignment system with a non-local alignment term, describing a kind of self-organizing problem for flocking. As a result, by the transport equation theory and Lagrange coordinate transformation, the local well-posedness of the solutions for the 1D pressureless Euler-alignment in Besov spaces with 1≤p∞ is established. Next, the ill-posedness of the solutions for this model in Besov spaces with 1≤p and is also deduced. Finally, the precise blow-up criteria of the solutions for this system is presented in Besov spaces with 1≤p .

Post-operative strabismus control and motor alignment for basic intermittent exotropia

AIM：To assess strabismus control and motor ocular alignment for basic exotropia surgery at 5y follow-up.METHODS：The medical records of 80 consecutive patients aged less than 17 years of age,who underwent surgery for basic exotropia by a single surgeon between years 2000 to 2009 and completed a minimum of 5y follow-up post-operatively were reviewed.Pre- and post-operative characteristics were documented at 1wk,6mo,1,3 and 5y follow-up.Subjects at 5-year follow-up were assigned to the success group if they had a postoperative angle of deviation within 10 prism diopters of exotropia or within 5 prism diopters of esotropia for distance on prism cover test,and had moderate to good strabismus control.The remaining subjects were assigned to the failure group.RESULTS：Post-operative surgical success at one week was 75%,which decreased to 41% at 5y follow-up.The success group was noted to have more patching pre-operatively（P=0.003）.The duration of patching a day（P=0.020） and total duration of patching preoperatively（P=0.030） was higher in the success group.Surgical success at 1y（P=0.004） and 3y（P=0.002） were associated with higher surgical success at 5y follow-up.CONCLUSION：Post-operative motor alignment and strabismus control for basic exotropia surgery at 1y and beyond is associated with higher exotropia surgery success at 5-year follow-up.There is an association between pre-operative patching and 5-year surgical success of basic intermittent exotropia surgery.

Vector meson spin alignment by the strong force field

Observation of unexpectedly large global spin alignment of φ vector mesons in non-central heavy-ion collisions by STAR experiment may reveal the non-perturbative nature of quark interaction in hot matter through fluctuating strong force field with short correlation length.

Construction and alignment control of the middle steel pylon of Taizhou Bridge

The general construction procedure of the steel middle pylon is briefly introduced. The alignment control of the pylon is carried out during the whole process of the construction. The control concept is extended to the manufacture stage. The manufacturing alignment error is strictly controlled in the segments precast process in factory, and the error is recognized and predicted precisely during the installation stage. The adjusting joints are employed to amend the accumulated error, which ensure that the steel pylon alignment could satisfy the precision requirements after installation.

The Electrical and Mechanical Alignment and Accuracy Detection of Numerial Control Machine Tool

In the work of numerical control reformation of general machine tool, the installation and debugging of machine tool is a crucial part. For the C6132 machine tool, and make the use of electrical and mechanical alignment, parameter adjusting, numerical control lathe accuracy debugging and performance examination has been used to finish a series of tailing in the work of numerical control reformation of general machine tool. In this paper, the detailed process of electrical and mechanical alignment, parameter adjusting, numerical control lathe accuracy debugging and performance examination has been demonstrated, meanwhile, the specific operational approach of these work programs has been discussed. Therefore, the present results provides essential reference and approach for the numerical control reformation of general machine tool.

The proposed role of MSL-lncRNAs in causing sex lability of female poplars

Labile sex expression is frequently observed in dioecious plants,but the underlying genetic mechanism remains largely unknown.Sex plasticity is also observed in many Populus species.Here we carried out a systematic study on a maleness-promoting gene,MSL,detected in the Populus deltoides genome.Our results showed that both strands of MSL contained multiple cis-activating elements,which generated long non-coding RNAs(lncRNAs)promoting maleness.Although female P.deltoides did not have the male-specific MSL gene,a large number of partial sequences with high sequence similarity to this gene were detected in the female poplar genome.Based on sequence alignment,the MSL sequence could be divided into three partial sequences,and heterologous expression of these partial sequences in Arabidopsis confirmed that they could promote maleness.Since activation of the MSL sequences can only result in female sex lability,we propose that MSL-lncRNAs might play a role in causing sex lability of female poplars.

Role of highest occupied molecular orbitals in molecular field-free alignment by a femtosecond pulse

This paper studies the molecular rotational excitation and field-free spatial alignment in a nonresonant intense laser field numerically and analytically by using the time-dependent SchrSdinger equation. The broad rotational wave packets excited by the femtosecond pulse are defined in conjugate angle space, and their coefficients are obtained by solving a set of coupled linear equations. Both single molecule orientation angles and an ensemble of O2 and CO molecule angular distributions are calculated in detail. The numerical results show that, for single molecule highest occupied molecular orbital （HOMO） symmetry σ tends to have a molecular orientation along the laser polarization direction and the permanent dipole moment diminishes the mean of the orientation angles; for an ensemble of molecules, angular distributions provide more complex and additional information at times where there are no revivals in the single molecule plot. In particular, at the revival peak instant, with the increase of temperature of the molecular ensemble, the anisotropic angular distributions with respect to the laser polarization direction of the πg orbital gradually transform to the symmetrical distributions regarding the laser polarization vector and for two HOMO configurations angular distributions of all directions are confined within a smaller angle when the temperature of the molecular ensemble is higher.

Coherent Control of Molecular Alignment and Orientation by a Femtosecond Two-Color Laser Pulse

The coherent control of molecular alignment and orientation by a femtosecond two-color laser pulse is studied theoretically.The effect of the carrier-envelope phase of the femtosecond two-color laser pulse on molecular alignment and orientation is discussed,and it is shown that the enhancement or suppression of the molecular orientation can be coherently manipulated by precisely controlling the carrier-envelope phase of the femtosecond two-color laser pulse.In addition,the time-dependent angular distributions of the molecular axis are presented.

Precision measurement and suppression of low-frequency noise in a current source with double-resonance alignment magnetometers

Low-noise high-stability current sources have essential applications such as neutron electric dipole moment measurement and high-stability magnetometers. Previous studies mainly focused on frequency noise above 0.1 Hz while less on the low-frequency noise/drift. We use double resonance alignment magnetometers(DRAMs) to measure and suppress the low-frequency noise of a homemade current source(CS) board. The CS board noise level is suppressed by about 10 times in the range of 0.001-0.1 Hz and is reduced to 100 n A/√Hz at 0.001 Hz. The relative stability of CS board can reach2.2 × 10^(-8). In addition, the DRAM shows a better resolution and accuracy than a commercial 7.5-digit multimeter when measuring our homemade CS board. Further, by combining the DRAM with a double resonance orientation magnetometer,we may realize a low-noise CS in the 0.001-1000 Hz range.

Functionalized alginate-based bioinks for microscale electrohydrodynamic bioprinting of living tissue constructs with improved cellular spreading and alignment

Bioprinting has been widely investigated for tissue engineering and regenerative medicine applications.However,it is still difficult to reconstruct the complex native cell arrangement due to the limited printing resolution of conventional bioprinting techniques such as extrusion-and inkjet-based printing.Recently,an electrohydrodynamic(EHD)bioprinting strategy was reported for the precise deposition of well-organized cell-laden constructs with microscale filament size,whereas few studies have been devoted to developing bioinks that can be applied for EHD bioprinting and simultaneously support cell spreading.This study describes functionalized alginate-based bioinks for microscale EHD bioprinting using peptide grafting and fibrin incorporation,which leads to high cell viability(>90%)and cell spreading.The printed filaments can be further refined to as small as 30μm by incorporating polyoxyethylene and remained stable over one week when exposed to an aqueous environment.By utilizing the presented alginate-based bioinks,layer-specific cell alignment along the printing struts could be observed inside the EHD-printed microscale filaments,which allows fabricating living constructs with cell-scale filament resolution for guided cellular orientation.

Photo-induced flexible semiconductor CdSe/CdS quantum rods alignment

The anisotropic absorption and emission from semiconductor CdSe/CdS quantum rods(QRs)provide extra benefits among other photoluminescence nanocrystals.Using photo-induced alignment technique,the QRs can be oriented in liquid crystal polymer matrix at a large scale.In this article,a 2D Dammann grating pattern,within“SKL”characters domains aligned QRs in composite film,was fabricated by multi-step photo exposure using several photo masks,and a continuous geometric lens profile pattern aligned QRs was realized by the single step polarization converting holographic irradiation method.Both polarized optical microscope and fluorescence microscope are employed to determine the liquid crystal director profiles and QRs anisotropic excitation properties.We have been able to orient the QRs in fine binary and continuous patterns that confirms the strong quantum rod aligning ability of the proposed method.Thus,the proposed approach paves a way for photoinduced flexible QRs alignments to provide a highly specific and difficult-to-replicate security application at a large scale.

Deep learning for estimation of Kirkpatrick-Baez mirror alignment errors

A deep learning-based automated Kirkpatrick-Baez mirror alignment method is proposed for synchrotron radiation.We trained a convolutional neural network(CNN)on simulated and experimental imaging data of a focusing system.Instead of learning directly from bypass images,we use a scatterer for X-ray modulation and speckle generation for image feature enhancement.The smallest normalized root-mean-square error on the validation set was 4%.Compared with conventional alignment methods based on motor scanning and analyzer setups,the present method simplified the optical layout and estimated alignment errors using a single-exposure experiment.Single-shot misalignment error estimation only took 0.13 s,significantly outperforming conventional methods.We also demonstrated the effects of the beam quality and pretraining using experimental data.The proposed method exhibited strong robustness,can handle high-precision focusing systems with complex or dynamic wavefront errors,and provides an important basis for intelligent control of future synchrotron radiation beamlines.

Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

With the rapid development of emerging 5G and beyond(B5G),Unmanned Aerial Vehicles(UAVs)are increasingly important to improve the performance of dense cellular networks.As a conventional metric,coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous environment.In recent years,stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network systems.In this paper,an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been proposed.An assumption was considered that all users are distributed according to Poisson Cluster Process(PCP)around base stations,in particular,Thomas Cluster Process(TCP).Using thismodel,the impact of beam alignment errors on the coverage probabilitywas investigated.Initially,the ProbabilityDensity Function(PDF)of directional antenna gain between the user and its serving base station was obtained.Then,association probability with each tier was achieved.A tractable expression was derived for coverage probability in both Line-of-Sight(LoS)andNon-Line-of-Sight(NLoS)condition links.Numerical results demonstrated that at low UAVs altitude,beam alignment errors significantly degrade coverage performance.Moreover,for a small cluster size,alignment errors do not necessarily affect the coverage performance.

Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.

Anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base

The performance of a strapdown inertial navigation system(SINS)largely depends on the accuracy and rapidness of the initial alignment.A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper.The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions,the SINS alignment is heuristically established as an optimiza-tion problem of finding the minimum eigenvector.In order to further improve the alignment precision,an adaptive recursive weighted least squares(ARWLS)curve fitting algorithm is used to fit the translational motion interference-contaminated reference vectors according to their time domain characteristics.Simulation studies and experimental results favorably demonstrate its rapidness,accuracy and robustness.