Menglei Sun

The High Luminosity phase of the Large Hadron Collider will deliver 10 times more integrated luminosity than the existing collider, posing significant challenges for radiation tolerance and event pileup on detectors, especially for forward calorimetry. As part of its upgrade program, the Compact Muon Solenoid collaboration is designing a high-granularity calorimeter (HGCAL) to replace the existing endcap calorimeters. It will feature unprecedented transverse and longitudinal readout and triggering segmentation for both electromagnetic and hadronic sections. The electromagnetic section and a large fraction of the hadronic section will be based on hexagonal silicon sensors of 0.5–1 cm2 cell size, with the remainder of the hadronic section being based on highly-segmented scintillators with silicon photomultiplier readout. The intrinsic high-precision timing capabilities of the silicon sensors will add an extra dimension to event reconstruction, especially in terms of pileup rejection. First hexagonal silicon modules, using the existing Skiroc2 front-end ASIC developed for CALICE, have been tested in beams at Fermilab and CERN in 2016. We present results from these tests, in terms of system stability, calibration with minimum-ionizing particles and resolution (energy, position and timing) for electrons, and the comparisons of these quantities with GEANT4-based simulation.

Excavator interior noise is composed of the engine order narrowband noise and the impulsive broadband noise caused by working device. In situations where only narrowband reference signal can be obtained, the feedforward active noise control (ANC) system can just eliminate the narrowband component, failing to control the broadband noise. Hybrid feedforward and feedback ANC (HANC) systems are useful for such applications where the reference signals can be partially obtained. However, existing HANC systems have the following drawbacks: coupling between subsystems; inaccurate error and reference signals; high computational complexity; performance degradation in response to the impulsive noise. To solve the above problems, this paper proposes a new feedforward and feedback HANC system consisting of 3 subsystems, namely, a delayed feedforward narrowband subsystem, a robust feedback broadband subsystem, and a supporting error calculating (SEC) subsystem. In the proposed HANC system, not only subsystems are decoupled, but also accurate error and reference signals are provided by SEC subsystem. In addition, calculating efficiency is improved by applying the delayed notch least mean square (DLMS) algorithm to the feedforward subsystem, and the system robustness is promoted by adopting the robust algorithm into the feedback subsystem. Numerous computer simulations are performed on the excavator interior noise measured under actual operating conditions. The corresponding results demonstrate the superior performance of the proposed HANC system in terms of the power spectral density of residual error, average noise reduction, robustness of the system, and calculation efficiency to the existing HANC systems. Real-time experiments on excavator interior noise showed that the proposed HANC system was efficient in eliminating broadband and narrowband mixture noise in practice.

Using intelligent optimization algorithms to retrieve atmospheric duct parameters by monitoring automatic identification system (AIS) signals at sea is a new passive remote sensing technology for atmospheric ducts. To thoroughly compare and analyze the inversion results of different intelligent optimization algorithms and optimize the parameters of the algorithms, this study considered a simulated atmospheric duct environment for atmospheric duct inversion using the genetic, simulated annealing, and particle swarm optimization (PSO) algorithms. The results indicated that the PSO algorithm exhibited the best inversion performance. The inversion results of the simulated annealing particle swarm optimization (SAPSO) and PSO algorithms under different inversion parameters were further statistically analyzed, and the atmospheric duct parameters were obtained from measured AIS signals based on the SAPSO algorithm. The inversion results verified the effectiveness of the proposed algorithm, and they continuously improved with additional calculations in the inversion algorithm. However, the changing trend gradually slowed. Therefore, in practical applications, the inversion time consumption should be balanced with the inversion effect to optimize the inversion parameters.

In this paper, we study the $R^2$ corrections to the spectral functions of heavy mesons in Gauss-Bonnet gravity. We discuss the effect of Gauss-Bonnet parameter $\lambda_{GB}$ on the 1S states and 2S states of charmonium and bottomonium. It is found that $\lambda_{GB}$ reduces the height and increases the width of the 1S states peak. The 2S states of charmonium and bottomonium dissociate gradually as increasing $\lambda_{GB}$. It is obvious that $\lambda_{GB}$ enhances the dissociation of charmonium and bottomonium.

Artificial intelligence (AI) in games has advanced significantly, notably in perfect information games such as Go and Chess. Imperfect information games, in which participants do not have complete information about the game state, create more difficulties. They incorporate both public and private observations, where strategies must be improved to achieve a Nash equilibrium. This study investigates artificial intelligence and reinforcement learning approaches, in which agents learn to maximize future rewards through interactions with their surroundings. The paper then focuses on card game research platforms such as RLCard and OpenAI Gym. It gives a comprehensive summary of research in No Limit Texas Hold'em, a difficult two-player poker game with a large decision space. DeepStack and Libratus are successful systems that have attained expert-level and superhuman play, respectively. Pluribus, a superhuman artificial intelligence for six-player poker, and DouZero, a pure reinforcement learning technique for the multiplayer card game, DouDiZhu, are both investigated. Overall, this paper provides background information on reinforcement learning and imperfect information games, analyzes commonly used research platforms, evaluates the effectiveness of AI algorithms in various card games, and offers future research areas and directions.

This paper introduces a novel Principal-Weighted Penalized (PWP) regression model, designed for dimensionality reduction in large datasets without sacrificing essential information. This new model retains the favorable features of the principal component analysis (PCA) technique and penalized regression models. It weighs the variables in a large data set based on their contributions to principal components identified by PCA, enhancing its capacity to uncover crucial hidden variables. The PWP model also efficiently performs variable selection and estimates regression coefficients through regularization. An application of the proposed model on high-dimensional economic data is studied. The results of comparative studies in simulations and a real example in economic modeling demonstrate its superior fitting and predictive abilities. The resulting model excels in accuracy and interpretability, outperforming existing methods.

Abstract Active noise control (ANC) is gaining ever-increasing attention owing to its powerful ability to attenuate low-frequency noise. The computational complexity of an ANC system may directly affect its computational efficiency, control performance, and hardware costs. Therefore, the focus of this paper is mainly on discussing the development of ANC systems with emphasis on reducing computational complexity. The ANC systems are classified into two groups of narrowband and broadband systems. The computational complexity analysis is provided to show the computational merit of each system with respect to the conventional ANC systems. In addition, numerical simulations are performed to evaluate the convergence speed and noise reduction performance of the considered systems. The results show that, in the narrowband ANC systems, the LFE-NANC, CFX-NANC and BFX-NANC systems enjoy better overall performance in terms of the computational complexity, convergence speed and steady-state error, and in the broadband ANC systems, the DF-BANC system has the lowest computational complexity but cannot effectively attenuate the broadband noise with high spectral dynamics, whereas the DS-BANC and MDS-BANC systems can. This study provides in-depth insight into current typical low-complexity ANC systems.

Sensitivity analysis and response surface methods were employed to optimize the structural modal of SUV doors. A finite element numerical simulation model was established and was calibrated by restraint modal tests. To screen out highly sensitive panels, a sensitivity analysis for the thickness of door panels was proposed based on the fifth-order modal frequency of the door. Data points were obtained by a faced central composite design with the design variables from the thickness of the highly sensitive panels, and a second-order explicit response surface function of the fifth-order modal frequency of the vehicle door was established. An optimization model was established according to the response surface method. The final results demonstrate that the modal-frequency matching of the door and body in white was optimized after changing the thicknesses, with a 5.74% material reduction.

Gear whine noise has attracted increasing attention from researchers in both the academe and the industry over the past two decades. The wide range of research topics demonstrates that there is a huge technical challenge in understanding the source-path-receiver mechanisms deeply and predicting the gear whine noise precisely. Thoroughly understanding the sources of gear whine noise is the first step to solving this issue. In this paper, the authors summarize a certain number of published articles regarding the sources of gear whine noise. The excitations of gear whine noise are classified into three groups: transmission error along the line of action direction, frictional excitations along the off-line of action direction, and shuttling excitation along the axial direction. The mechanisms, characteristics, predicting approaches, measuring methods, and decreasing strategies for these excitations are summarized. Current research characteristics and future research recommendations are presented at the end.

Tail-welded blanks (TWBs) are widely used in automotive bodies to improve the structural performance and reduce weight. The stiffness and modal lightweight design optimisation of TWBs for automotive doors was performed in this study. The finite element model was validated through physical experiments. An L27 (312) Taguchi orthogonal array was used to collect the sample points. The multi-objective optimisation problem was transformed into a single-objective optimisation problem based on the grey relational degree. The optimal combination of structural design parameters was obtained for a tail-welded door using the proposed method, and the weight of the door structure was reduced by 2.83 kg. The proposed optimisation method has fewer iterations and a lower computational cost, enabling the design of lightweight TWBs.

In automotive industries, panel acoustic contribution analysis (PACA) is used to investigate the contributions of the body panels to the acoustic pressure at a certain point of interest.Currently, PACA is implemented mostly by either experiment-based methods or traditional numerical methods.However, these schemes are effort-consuming and inefficient in solving engineering problems, thereby restraining the further development of PACA in automotive acoustics.In this work, we propose a PACA scheme using discontinuous isogeometric boundary element method (IGABEM) to build an easily implementable and efficient method to identify the relative acoustic contributions of each automotive body panel.Discontinuous IGABEM is more accurate and converges faster than continuous BEM and IGABEM in the interior sound pressure evaluation of automotive compartments.In this work, a contribution ratio is defined to estimate the relative acoustic contribution of the structure panels; it can be calculated by reusing the coefficient matrix that has already been generated in the sound pressure evaluation process.The utilization of the parallel technique enables the proposed method to be more efficient than conventional methods; it is validated in two numerical examples, including a car passenger compartment subjected to realistic boundary conditions.A sound pressure response experiment based on a steel box is conducted to verify the accuracy of the interior sound pressure calculation using discontinuous IGABEM.This work is expected to promote the practical process of IGABEM for application in automotive acoustic problems.

We examine the reasons for discrepancies between two alternative approaches to modeling small-amplitude tides in binary systems. The 'direct solution' (DS) approach solves the governing differential equations and boundary conditions directly, while the 'modal decomposition' (MD) approach relies on a normal-mode expansion. Applied to a model for the primary star in the heartbeat system KOI-54, the two approaches predict quite different behavior of the secular tidal torque. The MD approach exhibits the pseudosynchronization phenomenon, where the torque due to the equilibrium tide changes sign at a single, well-defined and theoretically predicted stellar rotation rate. The DS approach instead shows 'blurred' pseudosynchronization, where positive and negative torques intermingle over a range of rotation rates. We trace a major source of these differences to an incorrect damping coefficient in the profile functions describing the frequency dependence of the MD expansion coefficients. With this error corrected some differences between the approaches remain; however, both are in agreement that pseudosynchronization is blurred in the KOI-54 system. Our findings generalize to any type of star for which the tidal damping depends explicitly or implicitly on the forcing frequency.

Abstract This paper is aimed to analyze and control the structure vibration of a vehicle steering wheel by using the acceleration transfer path analysis (TPA) method and vibration sensitivity method. A steering wheel vibration test is conducted to obtain the critical engine speed and corresponding to the frequency. The acceleration-acceleration transfer function from mounts body side to steering wheel is deducted and tested, in conjunction with the actual acceleration spectrums of mounts body side, the acceleration spectrum of steering wheel is synthesized and the accuracy of the synthetic results is evaluated high. Then the vibration transmitted by each path is investigated and the main vibration participation paths are picked out from numerous paths. The interaction mechanisms between the path transfer vibration, path vibration participation and the total vibration are further obtained. The critical parameters (mounting stiffnesses and vibration sensitivity) of the main vibration participation paths are matched, modified and the vibration control effect is experimental verified. The results indicate that, by pertinently matching the stiffness parameters of the mounts and reducing vibration sensitivity of structure, the dynamic force transmitted by the paths are decreased, the vibration transfer functions from mounts body side to steering wheel are decreased, and then the actual steering wheel vibration is controlled.

In this paper multilinear mean component analysis (MMCA) is introduced as a new algorithm for gait recognition. Compared with traditional PCA and MPCA, the new method MMCA is based on dimensionality reduction by preserving the squared length, and implicitly also the direction of the mean vector of the each mode's original input. The solution is not necessarily corresponding to the top eigenvalues. MMCA improved the clustering results and reduced the small sample size (SSS) problem and has great convergence. MMCA as a feature extraction tool provides stable recognition rates and the MMCA-based approaches we proposed achieves better performance for gait recognition based on the University of South Florida (USF) HumanID Database.

Many physics analyses using the Compact Muon Solenoid (CMS) detector at the LHC require accurate, high resolution electron and photon energy measurements.Particularly important are decays of the Higgs boson resulting in electromagnetic particles in the final state.Di-photon events in CMS are also a very important channel in the search for Higgs boson production in association with other particles or in the search for possible new resonances of higher mass.The requirement for high performance electromagnetic calorimetry therefore remains high during LHC Run II.Following the excellent performance achieved in Run I at a center of mass energy of 7 and 8 TeV, the CMS electromagnetic calorimeter (ECAL) started operating at the LHC in Spring 2015 with proton-proton collisions at 13 TeV center-of-mass energy.The instantaneous luminosity delivered by the LHC during Run II is expected to exceed the levels achieved in Run I, using 25 ns bunch spacing.The average number of concurrent proton-proton collisions per bunchcrossing (pileup) is expected to reach up to 40 interactions in 2016.These high pileup levels necessitate a retuning of the ECAL readout and trigger thresholds and reconstruction algorithms, to maintain the best possible performance in these more challenging conditions.The energy response of the detector must be precisely calibrated and monitored to achieve and maintain the excellent performance obtained in Run I in terms of energy scale and resolution.A dedicated calibration of each detector channel is performed with physics events exploiting electrons from W and Z boson decays, photons from π 0 /η decays and from the azimuthally symmetrical energy distribution of minimum bias events.This paper describes the new reconstruction algorithm and calibration strategies that we have implemented to maintain the excellent performance of the CMS ECAL throughout Run II.We will show performance results from the 2015 and 2016 data taking periods and provide an outlook on the expected Run II performance in the years to come.

Results of a search for new physics in events with a photon, an electron or muon, and large missing transverse momentum (pTmiss ) is presented. The studyis based on a sample of proton-proton collisions at sqrt(s)= 13 TeV corresponding to an integrated luminosity of 35.9 fb-1 collected with the CMS detector in 2016. Many models of new physics predict events with significant pTmiss in addition to electroweak gauge bosons. Models of supersymmetry (SUSY) with gauge-mediated supersymmetry breaking naturally yield events with photons in the final state. Searches for events with both a photon and a lepton enhanced the sensitivity to electroweak production of supersymmetric particles. No significant excess above the standard model background is observed in the signalregion. We interpret the results of our search in the context of SUSY with gauge-mediated supersymmetry breaking as well as simplified SUSY models.

Abstract Tail-welded blanks (TWBs) are widely used in automotive bodies to improve structural performance and reduce weight. The stiffness and modal lightweight design optimisation of TWBs for automotive doors was performed in this study. The finite element model was validated through physical experiments. An L27 (3 12 ) Taguchi orthogonal array was used to collect the sample points. The multi-objective optimisation problem was transformed into a single-objective optimisation problem based on the grey relational degree. The optimal combination of structural design parameters was obtained for a tail-welded door using the proposed method; the weight of the door structure was reduced by 2.83 kg. The proposed optimisation method has fewer iterations and a lower computational cost, enabling the design of lightweight TWBs.