Identification Of Modes Research Articles

Lattice Dynamics of Quinacridone Polymorphs: A Combined Raman and Computational Approach.

Polarized low-frequency Raman microscopy and a posteriori dispersion-corrected density functional simulations are combined to investigate the lattice vibrations of the αI, β, and γ polymorphs of the model organic semiconductor quinacridone, which are known to display different optical and electronic properties. The comparison between experiments and calculations allows for unambiguous mode assignment and identification of the scattering crystal faces. Conversely, the agreement between simulations and experiments validates the adopted computational methods, which correctly describe the intermolecular interaction of the molecular material. The acquired knowledge of quinacridone lattice dynamics is used to describe the αI to β thermal transition and, most consequentially, to reliably characterize the electron-lattice phonon coupling strength of the three polymorphs, obtaining hints about the electrical transport mechanism of the material.

Identification of both orbital angular momentum and radial mode based on multiplexing holography

In recent years, the azimuthal mode, also known as orbital angular momentum (OAM), and the radial mode of Laguerre Gaussian (LG) beams have been applied in many disparate areas of physics. Identification of these modes is a basic technique for these applications. Although there are many recognition methods, each has its own problems. Either it can only be used to distinguish OAM modes or radial modes, or the number of modes that can be distinguished is too small, or the operation is too complex. So, a great challenge is to design a method that is not only simple, but also applicable to the identification of both OAM and radial modes with high dimension. Here, by introducing strong mode selectivity, we propose a convenient method for the identification of OAM and radial modes based on multiplexing holography. In our method, only a single hologram is needed. Experiments on the identification of 100 OAM modes (l = −50, −49, …, +49, +50) have been carried out to demonstrate the feasibility of our design. Additionally, our method is also applicable for the identification of radial modes, and then to the identification of LG modes. In experiment, we have realized the identification of 8 radial modes (p = 1, 2, 3, …, 8) and 120 LG modes (p = 1, 3, 5, and l = −20, −19, …, +20). Our method should have important applications in high-capacity optical communication based on OAM modes, radial modes, or LG modes.

Quantum dot detects Majorana modes of both chiralities

A tunneling junction between normal electrode and a topological superconducting wire, mediated by a quantum dot, is considered theoretically. We show that the presence of the dot in the junction can be advantageous to Majorana zero modes identification. Namely, we demonstrate that for the dot strongly coupled to the wire, the Majorana mode from the upper chiral sub-band ”leaks” into the dot, providing supplementary information on Majorana mode formation. Thus, both the Zeeman-split dot sub-levels detect Majorana partners of a Kramers pair, formed at the wire end. The characteristic three-peak structures in both spin sectors of the spectral density of the dot, distinguish from the trivial scenario of one Andreev resonance at Fermi energy produced exclusively by the dot’s spin sub-levels.

Identification of Various Execution Modes and Their Respective Risks for Public–Private Partnership (PPP) Infrastructure Projects

The public–private partnership (PPP) based model for the execution of infrastructure projects originated from Anglo-Saxon countries and was initially used in 1977 by the United Kingdom (U.K). Since then, its popularity has increased worldwide. Earlier studies by researchers and many other professional sectors and departments have introduced PPP contracts into different execution modes like Build, Operate, and Transfer (BOT); Build, Own, Operate, and Transfer (BOOT); and Build, Lease, and Transfer (BLT), etc. All definitions of PPP contracts are different but have a few common characteristics and risks. Previously, numerous pieces of literature were available on these common risks for various execution modes of PPP contracts. However, each PPP mode still has unique risks that must be identified to understand and successfully implement the PPP projects properly. This paper fills the gap mentioned above and aims to identify various commonly used PPP execution modes in infrastructure projects and their corresponding risks after placing the different PPP execution modes into four (04) different categories. Identified risks for the corresponding PPP categories were also divided into seven (07) stages of the PPP life cycle. Semi-structured interviews were conducted to gather information from thirty-four (34) PPP experts worldwide. Accordingly, interviews are transcribed and processed for thematic analysis in academic NVIVO software. These identified risks are further placed in the respective PPP category for the convenience and better understanding of the study’s outcome to the users and for the subsequent prioritization and allocation of these identified risks accordingly to the PPP parties during the finalization of the PPP execution mode.

Time-resolved FTIR difference spectroscopy for the study of photosystem I with high potential naphthoquinones incorporated into the A1 binding site 2: Identification of neutral state quinone bands.

Time-resolved step-scan FTIR difference spectroscopy at 77K has been used to study photosystem I (PSI) from Synechocystis sp. PCC 6803 with four high-potential, 1,4-naphthoquinones (NQs) incorporated into the A1 binding site. The incorporated quinones are 2-chloro-NQ (2ClNQ), 2-bromo-NQ (2BrNQ), 2,3-dichloro-NQ (Cl2NQ), and 2,3-dibromo-NQ (Br2NQ). For completeness 2-methyl-NQ (2MNQ) was also incorporated and studied. Previously, PSI with the same quinones incorporated were studied in the, so-called, anion spectral region between 1550 and 1400cm-1 (Agarwala et al. in Biochim Biophys Acta 1864(1):148918, 2023). Here we focus on spectra in the previously unexplored 1400-1200cm-1 spectral region. In this region several bands are identified and assigned to the neutral state of the incorporated quinones. This is important as identification of neutral state quinone bands in the regular 1700-1600cm-1 region has proven difficult in the past. For neutral PhQ in PSI a broad, intense band appears at ~ 1300cm-1. For the symmetric di-substituted NQs (Cl2NQ/Br2NQ) a single intense neutral state band is found at ~ 1280/1269cm-1, respectively. For both mono-substituted NQs, 2ClNQ and 2BrNQ, however, two neutral state bands are observed at ~ 1280 and ~ 1250cm-1, respectively. These observations from time-resolved spectra agree well with conclusions drawn from absorption spectra of the NQs in THF, which are also presented here. Density functional theory based vibrational frequency calculations were undertaken allowing an identification of the normal modes associated with the neutral state quinone bands.

The Microstructural Origins of Acoustic Emission Signatures Encountered During Fracture Cutting of Bovine Cortical Bone

Abstract This paper is aimed at studying the acoustic emission signatures of dominant failure mechanisms encountered during fracture cutting of bovine cortical bone. This is achieved through an orthogonal cutting study performed in a sensor-rich environment comprising a cutting force sensor, acoustic emission sensor, and a high-speed camera. The synchronization of these three sensing modalities allows for the visual identification of the dominant failure modes, while also mapping them to their corresponding acoustic and cutting force metrics. Given their distinctly different underlying microstructures, the haversian and plexiform components of the bovine cortical bone are investigated separately. A total of six dominant failure mechanisms have been confirmed across the haversian and plexiform bone types. Osteon fracture and trans-lamellar fracture have been identified as the mechanisms expending the maximum energy during the fracture cutting of haversian and plexiform bone, respectively. Overall, the acoustic emission and the cutting force metrics are seen to be complementary in characterizing the six failure mechanisms. The findings of this work have implications for tool-mounted sensing modalities that could be used to detect “in-process” failure mechanisms during bone surgical procedures.

Genetically Encoded Crosslinking Enables Identification of Multivalent Ubiquitin-Deubiquitylating Enzyme Interactions.

Ubiquitin (Ub) proteoforms control nearly every aspect of eukaryotic cell biology through their diversity. Inspired by the widely used Ub C-terminal electrophiles (Ub-E), here we report the identification of multivalent binding of Ub with deubiquitylating enzymes (Dubs) using genetic code expansion (GCE) and crosslinking mass spectrometry. While the Ub-Es only gather structural information with the S1 Dub sites, we demonstrate that GCE of Ub with p-benzoyl-L-phenylalanine enables identification of interaction modes beyond the S1 site with a panel of Dubs of both eukaryotic and prokaryotic origin. Collectively, this represents the next generation of Ub-based affinity probes with a unique ability to unravel Ub interaction landscapes beyond what is afforded by cysteine-based chemistries.

Prediction of Bearing Vibration Fault State based on Fused Bi-LSTM and SVM

Machinery failure prediction and equipment health management is the key to reduce even prevent the occurrence of significant failures. In recent years, deep learning based intelligent fault diagnosis methods have become a hot topic of research in the field of machinery and equipment health management, especially the evaluation of the equipment health indicators and failure mode identification issues in complex environments. In this paper, a fused bidirectional long and short term memory (BiLSTM) and support vector machine (SVM) recurrent neural network deep learning algorithm is proposed to solve the problem of fast fault diagnosis of mechanical bearing vibration in complex environments. The algorithm uses a BiLSTM memory network as a feature extractor to capture fault features in the raw one dimensional (1-D) time series data, and then a SVM is used at the end of the network instead of the traditional softmax function as a classifier discriminator to further improve the accuracy of diagnosis. The experimental results indicate that the proposed algorithm model has higher diagnostic accuracy and faster diagnosis speed, and more suitable for fast diagnosis and real time detection of faults in complex environments.

Incremental capacity analysis (dQ/dV) as a tool for analysing the effect of ambient temperature and mechanical clamping on degradation

This work presents a comprehensive degradation study of two types of large lithium-ion pouch cells; 26 NMC532/Graphite (64 Ah) and 9 NMC433/Graphite (31 Ah) pouch cells. The cells were degraded under different cycling conditions and periodically characterized at room temperature. Specifically, the effect of different ambient temperatures and constraining the cells by clamping was studied. Incremental capacity analysis is an in situ, non-invasive characterization technique that allows the identification of battery degradation modes, and is a technique that does not require additional and advanced equipment. Therefore, in this study we also look into applying the analysis technique on an existing data set. This is done by combining incremental capacity analysis on a qualitative level with the tracking of features of interest in the incremental capacity curve as a function of State of Health and utilizing the simulation of different degradation modes for a more in-depth analysis. We combine simulation and experimental incremental capacity analysis with conclusions from capacity loss and resistance changes with a focus on understanding the benefit and limitations of the incremental capacity analysis for large cells. This is important, as incremental capacity analysis is a relatively fast analysis to qualify large commercial batteries for 2nd life applications. Specifically in this study, we found that degradation and capacity loss do not always correlate. For the 64 Ah Cells cycled at 15 °C and 25 °C, the rate of capacity loss appeared to be similar, although the degradation modes and mechanisms are found to be very different. The clamping was the most important factor for impeding degradation. The 31 Ah Cell cycled at low temperatures showed a very poor cycling performance, where the incremental capacity analysis revealed that Loss of Lithium Inventory from fast and irreversible plating was responsible.

Development mechanism and technological innovation of hydrogen energy: evaluating collaborative innovation based on hydrogen patent data

The identification of the technical cooperation mode in the field of hydrogen energy is of great importance in guiding technological innovation. Based on the social network analysis (SNA) and spatial network method, this paper constructs the technical cooperation graph of China's industry-university-research (IUR) tripartites in the field of hydrogen technology. Based on the performed graphs, it shows the game and evolution of hydrogen technology innovation in three stages, respectively 2000–2010, 2011–2015 and 2016–2019. Meanwhile, this paper calculates the changes of network indicators and found that: (1) the network density showed a downward trend, from the initial 0.062 to 0.003. (2) The degree centrality of the network decreased from 14.7% to 5.7%. (3) The average path length and clustering coefficient indicate that the internal connections within the network were relatively close from 2000 to 2010, and had small-world characteristics. From 2016 to 2019, although the clustering coefficient increased, the average path length also increased significantly, indicating that the internal connections of the network did not become closer. From the network characteristics, the collaborative innovation network of China's hydrogen energy industry continues to expand in scale and scope, and the cooperation relationship is stable. However, it shows the characteristics of low network density and loose cooperation relationship, gradually developing into a scale-free network. Developed provinces and cities in North and East China occupy core positions in the industry-university-research network, while other regions are gradually expanding their cooperation efficiency; however, the overall cooperation frequency is relatively low in Northeast China, and most provinces and cities in South and Northwest China are in edge positions with poor collaboration capabilities.

Wi-CL: Low-Cost WiFi-Based Detection System for Nonmotorized Traffic Travel Mode Classification

Traffic travel mode identification and classification are crucial for the development of intelligent transportation systems (ITSs). At present, scholars have investigated the classification of motorized and nonmotorized traffic travel in various road environments; however, the classification of walking and bicycle modes in nonmotorized travel has been largely ignored. Therefore, in this paper, we investigate nonmotorized traffic travel and propose a new low-cost nonmotorized traffic travel mode classification system, known as the Wi-Fi classification (Wi-CL) system that uses Wi-Fi signal detectors and the refined characteristics of nonmotorized travel modes. The Wi-CL system includes four modules: data acquisition module, data processing module, feature extraction module, and mode classification module. In the data acquisition module, the proposed system detects the Wi-Fi signals of traffic participants in road environments. In addition, we propose a received signal strength indicator (RSSI) filtering algorithm for hybrid traffic networks that effectively addresses surrounding obstacles and environmental noise. In the feature extraction module, we extract relevant traffic features to construct a mode classification model. Finally, a recurrent neural network (RNN) framework based on the long short-term memory (LSTM) algorithm is successfully implemented in the mode classification module for traffic travel mode identification. To validate the effectiveness of the Wi-CL system, extensive experiments were conducted using field data collected by Wi-Fi detectors installed at the South China University of Technology (SCUT). The experimental results show that the proposed RSSI filtering algorithm achieves excellent signal filtering results in real road traffic environments. In addition, the constructed travel speed estimation algorithm outperforms other baseline models in four different scenarios (flat-peak walking, midday peak walking, flat-peak cycling, and midday peak cycling), achieving an overall classification accuracy of 97.92%. In summary, our Wi-CL system is a feasible approach for nonmotorized traffic travel mode classification.

A Study on the Ultimate Strength and Failure Mode of Stiffened Panels

Different buckling failure modes of stiffened panels will result in differences in ultimate strengths. In this study, the benchmark model of the bottom of bulk carrier with appropriate initial imperfections, boundary conditions and mesh size are selected for a series of non-linear FE analyses. A failure mode discrimination method considering the change rule of ultimate strength and the stress and strain distribution of stiffened plates in ultimate limit state is proposed, and then the boundary function of the failure modes composed of four key parameters, λ, β, hw/tw, λe, is established. Based on this boundary function, the rapid identification of failure modes and the classification of ultimate strength under different failure modes can be realized. Furthermore, the ultimate strength formulas of stiffened panels for different failure modes are obtained by data analysis from many nonlinear finite element analyses.

An ensemble federated learning framework for privacy-by-design mobility behaviour inference in smart cities

Inferring the travel behaviour of users in their GPS trajectories, while protecting their privacy is a significant issue for smart and sustainable cities. To address this challenge, we use Federated Learning (FL), a privacy-preserving machine learning technique that aims at collaboratively training a robust global model by accessing users’ locally trained models, but not their data. Specifically, we design a novel eNsemble federATed leArning for mobiLity InfErence (NATALIE) framework. The ensemble method combines the outputs from different DNN models learned via FL and shows an accuracy that surpasses comparable models reported in the literature. Extensive benchmarking experiments on open-access MTL Trajét and GeoLife GPS datasets demonstrate that the proposed inference model can achieve comparable accuracy in the identification of mode of travel without compromising privacy. The evaluation of the proposed model against non-i.i.d. data at varying sample sizes and different worker numbers shows improved performance. Findings are expected to contribute to the advancement of the transportation sector in smart and sustainable cities.

Risk identification of PPP mode in stadiums and gymnasiums by ant colony algorithm

Risk identification of PPP mode in stadiums and gymnasiums by ant colony algorithm

Vibration mode identification method for structures using image correlation and compressed sensing

All objects are subject to deformations of their shapes that correspond to their natural vibration modes. The analysis of these modes is extremely useful for structural design and structural health monitoring. Traditionally, expensive measurement equipment and many vibration sensors have been required to analyze vibration modes. Recently, a method that measures displacement from images (digital image correlation) has enabled the identification of vibration modes with a single camera. However, the identification of high-frequency vibration modes requires a high-speed camera, which increases costs and reduces spatial resolution. In this study, a low-cost and highly accurate method for identifying vibration modes and frequencies, randomized single-exposure sampling (RSES), was developed by applying compressed sensing to images captured with a strobe flash and a low-speed camera. This method extracts vibration modes using proper orthogonal decomposition and applies compressed sensing to the time function to recover high-speed vibrations from low-speed measurement results. The performance of RSES was evaluated by conducting vibration experiments on beams and comparing the results with the theoretical values using acceleration sensor measurements as boundary conditions. The results demonstrated that images captured at a shutter speed of 10 fps could measure the vibration modes and amplitudes of beams vibrating at a single frequency (ranging 170–3210 Hz). From 40 images, the amplitude of vibration could be measured with errors of less than 1 %, 2 %, and 26 % for vibrational frequencies of 170, 1130, and 3210 Hz, respectively. The larger error at 3210 Hz was attributed to the amplitude of vibration being only a few micrometers owing to the shaker limitations, a value close to the resolution of digital image correlation. This method can be used to accurately analyze structures that vibrate at high speeds, which is useful for vibration control design and other applications.

Structural modal identification and enhancement of low-energy modes by successively variational extraction of high-energy modes

Accurate identification of modal properties for as much as possible modes from structural monitoring responses is an important part for structural health monitoring systems. Limited by the energy of external excitations, or influenced by the heavy noise, the energies of some modal components may be weak or buried in the energy of noise, giving rise to the difficulty for accurate modal identification. Variational mode decomposition method is a non-recursive decomposing procedure to decompose a signal into narrow-band sub-components. However, the difficulty for the decomposition of low-energy mode is determining the corresponding initial center frequencies. In this study, a new method for low-energy mode identification is proposed using the idea of locating, identifying, and subtracting the high-energy component to gradually enlarge the relative energy level of low-energy mode by substituting the original signal with residual signal repeatedly. First, the initial center frequency of the mode with maximum energy is determined by autoregressive power spectrum; second, the modal parameters including frequency, damping ratio and mode shape are identified by the singular value decomposition of Hankel matrix with low system order; third, vibrational mode extraction method is proposed to extract the modal component with maximum energy; subsequently, the modal component with maximum energy is subtracted from the original signal to construct the residual signal for the identification of next maximum energy component. The relative energy level of low-energy mode is enlarged gradually by repeating the subtraction of high-energy modal component. Then, the accuracy of low-energy mode identification is improved. The effectiveness of the proposed method is verified by the simulated structures under ambient and earthquake excitations with high-level noises. Besides, the capability for the application of practical structure is verified by the experimental data of a real bridge.

A SVM-based method for identifying fracture modes of rock using WVD spectrogram features of AE signals

In order to achieve the highly efficient and accurate identification of fracture modes including tension or shear fractures during rock failure, an intelligent identification method based on Wigner-Ville distribution (WVD) spectrogram features of acoustic emission (AE) signals was proposed. This method was mainly constructed by the following steps: Firstly, AE hits corre-sponding to tension and shear fractures were obtained through conducting the Brazilian disc test (tension fracture) and direct shear test (shear fracture) of limestone. Secondly, the WVD spectro-grams of these tensile-type and shear-type AE hits were respectively extracted and then trans-formed into the image features of relatively low-dimension as the sample set based on the gray-level cooccurrence matrix (GLCM) and histogram of oriented gradient (HOG). Finally, on the basis of the processed and classified sample set of the WVD spectrogram features, an identifica-tion model of rock fracture modes was established by a support vector machine (SVM) learning algorithm. To verify this method, the fracture modes of limestone subjected to biaxial compres-sion were identified by the method. The results showed that the method not only can greatly re-veal the fracture modes change from tension-dominated to shear-dominated fractures, but also has advantages over the RA-AF value method, such as applicability, accuracy and practicality.

Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVM

AbstractFaults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non‐stationary and often contaminated by noise. Therefore, in order to effectively extract the random variation and non‐linear dynamic variation characteristics of vibration signals, a new method of rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy (GMMPE) and grey wolf optimized least squares support vector machine (GWO‐LSSVM) is put forward in this paper. Based on the multiscale permutation entropy (MPE), the multiscale equalization is firstly used to replace the coarse grained process, and the value of mean is extended to variance to avoid the dynamic mutation of the original signal. Finally, the parameters of LSSVM are optimized by the grey wolf optimization algorithm to achieve accurate identification of fault modes. The results of simulation and experiment show that applying the proposed GMMPE to rolling bearing fault feature extraction is feasible and superior, and the method based on GMMPE and GWO‐LSSVM has better noise robustness, which can effectively achieve rolling bearing fault diagnosis.

A Framework of Travel Mode Identification Fusing Deep Learning and Map-Matching Algorithm

The ubiquity of mobile phone signaling data (MPSD) allows us to study travel mode identification (TMI) of a larger scale of population in cities than GPS data and travel survey. Existing studies suffers from deficient data cleaning, ignoring low spatial accuracy of data and lack of the common datasets labeled with travel modes. This study proposes a novel TMI framework with MPSD and pseudo MPSD by designing hidden markov model (HMM)-based map-matching algorithm and bi-directional long short-term memory (Bi-LSTM)-based deep neural network (DNN). In this framework, conventional data cleaning method is modified to eliminate outliers in raw MPSD to improve data quality. In addition, HMM-based map-matching algorithm is designed to estimate the actual position of travelers from MPSD for spatial-temporal accuracy improvement. Specifically, pseudo MPSD is reconstructed by multi-source data aiming at overcoming the facts lacking of labeled MPSD. Both MPSD and labeled pseudo MPSD are processed to features, where the latter is used to train model based on Bi-LSTM, and the former is input to the trained model for TMI application validation. To evaluate the framework performance, a case study is carried out. Experimental results show that our framework outperforms baseline models on the same datasets. Furthermore, the accuracy improvement of designed approaches is demonstrated by a series of algorithm comparisons. The framework has good generality and flexibility, and can be extended to other cities for TMI.

Inversion of multimodal dispersion curves from distributed acoustic sensing measurements for subsurface imaging: A field case of Garner Valley, California

Utilizing the surface-wave (Rayleigh-wave) dispersion properties to estimate subsurface S-wave velocity structure has become a popular method due to simple data collection and analysis procedure during the past decades. In particular, the promotion of distributed acoustic sensing (DAS) technique enhances its popularity due to the low-cost of ultra-dense observations. Moreover, under the same exploration requirement, the cost of the DAS data collection is much lower than that of the traditional acquisition implemented by the nodal seismographs. Therefore, it is necessary to expand the DAS application. Here, the multimodal surface-wave dispersion curves are successfully extracted from the phase-weighted stack of cross-correlation functions (CCFs) of the DAS data collected at Garner Valley Downhole Array field site, California by the proposed cylindrical-wave phase-shift. Then, the dispersion curves are inverted by the inversion workflow (expected to avoid the mode-misidentification) proposed by Yan et al. (2022a), consisting of staging strategy and pattern search with embedded Kuhn-Munkres (PSEKM) algorithm. Compared with other inversion schemes, the novel inversion method allows the presence of the observed values without prior mode-order definition in the inversion system, which omits the tedious and risky manual mode identification, and the mode-orders would be predicted dynamically during the inversion. The dispersion fittings for certain survey points indicate that the mode losses and aliasings appear to be widespread for the surface waves recorded on the free-surface. Finally, the S-wave velocity tomogram consistent with the geological materials of survey area is revealed. Our study demonstrates the feasibility of extracting the DAS-derived multimodal dispersion information and consequently obtaining a physical-reasonable subsurface S-wave velocity model.