Linear Reference Research Articles

Direct electromagnetic force control of linear induction motor with end effects using fuzzy controller

In this article is a present dynamic model of a linen induction motor with end effects, occurs when the electromagnetic system of the motor is open. Instead of the angular speed and electromagnetic torque as a rotation motor, in the linear induction motor we have linear speed and linear electromagnetic force, which are sufficiently influenced by the open electromagnetic system of the primary and secondary elements of the linear motor. The choice of an appropriate control system for a linear induction motor is of particular importance considering the use of this type of motor in manufacturing. There are different types of control schemes for standard rotary motors that have their own advantages and disadvantages. When choosing a motor control scheme, we strive to achieve the following parameters, such as smoothness of regulation, ability to regulate in all four quadrants, accuracy of regulation, range of regulation and last but not least, simplicity of regulation. One such method that meets these criteria for rotary motors is the direct torque control method with space vector modulation. The control system of the linear induction motor proposed in this paper is based on this method, taking into account the open electromagnetic system and the occurrence of the end effects of the linear induction motor. Instead of the angular reference velocity as a rotary motor, in this case we have a linear reference velocity to the input, which is compared to the current linear velocity of the motor. The present paper also uses the vector modulation method by analogy with direct torque control with space vector modulation of the rotary motor. For the conversion of a linear velocity to a linear electromagnetic force, instead of a standard PI controller, it is using a fuzzy controller to convert a linear velocity to a reference electromagnetic force.

Data mining and spatio-temporal characteristics of urban road traffic emissions: A case study in Shijiazhuang, China

Accurate estimation of traffic emissions and analysis of spatio-temporal distribution on urban roads play a crucial role in the development of low-carbon transportation system. Traditionally, a region’s emission characteristics have been studied using numerous emission models with GPS-based spatio-temporal data. Due to the heavy data processing needs of GPS-based data, emission characteristics for a large region have been studied by dividing the region into a limited number of smaller areas or units. Additionally, GPS data are based on a few vehicles in the traffic which does not fully reflect road conditions. This paper proposed an approach that can be used to study and calculate the spatio-temporal emission pattern of a region at a roadway section level by using Baidu’s online traffic data and COPERT model. The proposed method can be used to estimate road-level emission patterns while avoiding the impact of redundant data in large datasets, making the dataset more reliable, applicable, and scalable. The proposed approach has been demonstrated through a study of spatio-temporal emission patterns in the Qiaoxi district within city of Shijiazhuang, China. Online data crawling technology was used to obtain data on urban road traffic speed and driving distance. The linear reference technology was used to construct a two-layer road network model to conduct the coupling and matching of traffic data with the road network data. The COPERT model was implemented to calculate the average traffic emissions on each road in the road network, and a traffic emission intensity index was proposed to quantify the CO, VOC, NOx and CO2 emissions on urban roads in the study area. The analysis results show that the traffic emission intensity of the expressway, trunk road, secondary road, and branch road is high during the morning peak (7 AM-9 AM) and evening peak (5 PM—7 PM). The sections with higher traffic emission intensity are mainly concentrated on the main roads and secondary roads such as Jiefang South Street, Shitong Road and Xinhua Road. Nearly one-third of 2nd Ring and 3rd Ring roads also have relatively high emission intensity. The research results provide new ideas for estimating traffic emissions in urban road networks and analyzing the spatio-temporal distribution of traffic emissions. The research results can also provide a decision-making basis for traffic management departments to formulate energy-saving and emission-reduction measures and promote the development of urban green and low-carbon transportation.

A fast Fourier transform-based solver for elastic micropolar composites

This work presents a spectral micromechanical formulation for obtaining the full-field and homogenized response of elastic micropolar composites. The algorithm relies on a coupled set of convolution integral equations for the micropolar strains, where periodic Green’s operators associated with a linear homogeneous reference medium are convolved with functions of the Cauchy and couple stress fields that encode the material’s heterogeneity, as well as any potential material nonlinearity. Such convolution integral equations take an algebraic form in the reciprocal Fourier space that can be solved iteratively. In this vein, the fast Fourier transform (FFT) algorithm is leveraged to accelerate the numerical solution, resulting in a mesh-free formulation in which the periodic unit cell representing the heterogeneous material can be discretized by a regular grid of pixels in two dimensions (or voxels in three dimensions). For verification, the numerical solutions obtained with the micropolar FFT solver are compared with analytical solutions for a matrix with a dilute circular inclusion subjected to plane strain loading. The developed computational framework is then used to study length-scale effects and effective (micropolar) moduli of composites with various topological configurations.

Localizing unmapped sequences with families to validate the Telomere-to-Telomere assembly and identify new hotspots for genetic diversity.

Although it is ubiquitous in genomics, the current human reference genome (GRCh38) is incomplete: It is missing large sections of heterochromatic sequence, and as a singular, linear reference genome, it does not represent the full spectrum of human genetic diversity. To characterize gaps in GRCh38 and human genetic diversity, we developed an algorithm for sequence location approximation using nuclear families (ASLAN) to identify the region of origin of reads that do not align to GRCh38. Using unmapped reads and variant calls from whole-genome sequences (WGSs), ASLAN uses a maximum likelihood model to identify the most likely region of the genome that a subsequence belongs to given the distribution of the subsequence in the unmapped reads and phasings of families. Validating ASLAN on synthetic data and on reads from the alternative haplotypes in the decoy genome, ASLAN localizes >90% of 100-bp sequences with >92% accuracy and ∼1 Mb of resolution. We then ran ASLAN on 100-mers from unmapped reads from WGS from more than 700 families, and compared ASLAN localizations to alignment of the 100-mers to the recently released T2T-CHM13 assembly. We found that many unmapped reads in GRCh38 originate from telomeres and centromeres that are gaps in GRCh38. ASLAN localizations are in high concordance with T2T-CHM13 alignments, except in the centromeres of the acrocentric chromosomes. Comparing ASLAN localizations and T2T-CHM13 alignments, we identified sequences missing from T2T-CHM13 or sequences with high divergence from their aligned region in T2T-CHM13, highlighting new hotspots for genetic diversity.

Pangenome graphs in infectious disease: a comprehensive genetic variation analysis of Neisseria meningitidis leveraging Oxford Nanopore long reads.

Whole genome sequencing has revolutionized infectious disease surveillance for tracking and monitoring the spread and evolution of pathogens. However, using a linear reference genome for genomic analyses may introduce biases, especially when studies are conducted on highly variable bacterial genomes of the same species. Pangenome graphs provide an efficient model for representing and analyzing multiple genomes and their variants as a graph structure that includes all types of variations. In this study, we present a practical bioinformatics pipeline that employs the PanGenome Graph Builder and the Variation Graph toolkit to build pangenomes from assembled genomes, align whole genome sequencing data and call variants against a graph reference. The pangenome graph enables the identification of structural variants, rearrangements, and small variants (e.g., single nucleotide polymorphisms and insertions/deletions) simultaneously. We demonstrate that using a pangenome graph, instead of a single linear reference genome, improves mapping rates and variant calling for both simulated and real datasets of the pathogen Neisseria meningitidis. Overall, pangenome graphs offer a promising approach for comparative genomics and comprehensive genetic variation analysis in infectious disease. Moreover, this innovative pipeline, leveraging pangenome graphs, can bridge variant analysis, genome assembly, population genetics, and evolutionary biology, expanding the reach of genomic understanding and applications.

Impacts of non-linear ITRF2020 on reference frame alignment

The geometric similarity between two sets of coordinates should be assumed for frame alignment. When aligning Global Navigation Satellite System (GNSS) positions with seasonal variations to the linear Terrestrial Reference Frame (TRF), non-negligible aliasing errors of transformation parameters can appear and bias the station positions. The recent release of the seasonally augmented International Terrestrial Reference Frame 2020 (ITRF2020) necessitates an investigation into the impacts of seasonal terms on the alignment of regional networks. Using four-year of GPS data for Crustal Movement Observation Network of China (CMONOC) stations, we realize five sets of regional reference frames (RFs) aligned to the linear ITRF2020 (REF-LIN), the seasonal ITRF2020 (REF-FRQ), the linear ITRF2020 augmented either with seasonal signals or with full signals from loading models (REF-FIT and REF-NTL), and the combination of seasonal displacements from ITRF2020 and sub-seasonal signals from loading model (REF-CMB). Our analysis revealed that the augmented ITRF2020 had significant impact on the global stations used for frame alignment. Aligning daily solutions to the augmented ITRF2020 instead of the linear ITRF2020 resulted in a noticeable root mean square (RMS) decrease for over 70% of global stations. The REF-CMB method was the most preferred for frame alignment of global networks, with the largest decreased RMS of 0.3 and 1.0 mm in horizontal and vertical components. This indicates that most true signals are preserved in the aligned daily solutions. For regional network, we found only marginal impacts on regional stations in terms of linear velocity, residuals of time series stacking between the augmented solutions and REF-LIN. However, the larger discrepancies are noticed between annual estimates and loading model for the vertical component of REF-CMB and REF-FRQ, compared to RER-LIN solutions.

A framework to open the black box of the use phase in circular economy life cycle assessments: The case of shell jacket reuse

AbstractLife cycle assessments of circular economy measures (CE LCA) of consumer products have been criticized for oversimplifying important aspects of the use phase such as user behavior and rebound effects, limiting our understanding of the environmental performance of circular economy measures. This study tests the usefulness of a framework designed to facilitate accounting for such aspects, by applying the framework to a case study of reuse of shell jackets enabled by “premium secondhand” outdoor stores. Methods for collecting use phase data were user surveys and interviews with store managers. Using the framework on this case study generated several novel insights which are interesting in themselves and as inputs to CE LCA. For instance, secondhand shell jackets have a significantly lower frequency of use during their first use span compared to the second and to shell jackets in the linear reference scenario. This implies that reuse in this case does not function as a mere use extension of otherwise similar use phases as is commonly assumed. The generation of such insights, which hitherto have been lacking in CE LCAs, points to the usefulness of the framework as a tool for opening the “black box” of the use phase in CE LCAs to improve understanding of the environmental performance of circular economy measures.

Pangenomic genotyping with the marker array

We present a new method and software tool called rowbowt that applies a pangenome index to the problem of inferring genotypes from short-read sequencing data. The method uses a novel indexing structure called the marker array. Using the marker array, we can genotype variants with respect from large panels like the 1000 Genomes Project while reducing the reference bias that results when aligning to a single linear reference. rowbowt can infer accurate genotypes in less time and memory compared to existing graph-based methods. The method is implemented in the open source software tool rowbowt available at https://github.com/alshai/rowbowt.

Efficient short read mapping to a pangenome that is represented by a graph of ED strings.

A pangenome represents many diverse genome sequences of the same species. In order to cope with small variations as well as structural variations, recent research focused on the development of graph-based models of pangenomes. Mapping is the process of finding the original location of a DNA read in a reference sequence, typically a genome. Using a pangenome instead of a (linear) reference genome can, e.g. reduce mapping bias, the tendency to incorrectly map sequences that differ from the reference genome. Mapping reads to a graph, however, is more complex and needs more resources than mapping to a reference genome. Reducing the complexity of the graph by encoding simple variations like SNPs in a simple way can accelerate read mapping and reduce the memory requirements at the same time. We introduce graphs based on elastic-degenerate strings (ED strings, EDS) and the linearized form of these EDS graphs as a new representation for pangenomes. In this representation, small variations are encoded directly in the sequence. Structural variations are encoded in a graph structure. This reduces the size of the representation in comparison to sequence graphs. In the linearized form, mapping techniques that are known from ordinary strings can be applied with appropriate adjustments. Since most variations are expressed directly in the sequence, the mapping process rarely has to take edges of the EDS graph into account. We developed a prototypical software tool GED-MAP that uses this representation together with a minimizer index to map short reads to the pangenome. Our experiments show that the new method works on a whole human genome scale, taking structural variants properly into account. The advantage of GED-MAP, compared with other pangenomic short read mappers, is that the new representation allows for a simple indexing method. This makes GED-MAP fast and memory efficient. Sources are available at: https://github.com/thomas-buechler-ulm/gedmap.

LNA linearization solution for direct conversion receiver using under-sampling technique in reference receiver

The paper proposes a method for linearizing low noise amplifiers (LNAs) in multichannel direct conversion receivers. The proposed direct conversion receiver (DCR) uses a linear reference receiver to extract distortion information, which is then fed to an adaptive circuit for linearizing the main channel signal. The proposed DCR differs from prior LNA linearization techniques in that the reference channel in the proposed DCR uses analog to digital converter (ADC) with an undersampling technique to extract reference information. The low-speed ADC also serves as a downconverter, shifting radio frequency (RF) signal to baseband and allowing for all further linearization processing to be performed digitally at a low-sampling data rate. This significantly reduces cost, design complexity, and energy consumption. The effectiveness of the proposed design is theoretically verified through MATLAB simulation and practically measured for a 65 Mhz band of ultra-high frequency (UHF) DCR capable of simultaneously receiving four 16–QAM channels of the same bandwidth of 4 Mhz. The MATLAB software simulation results show that the proposed approach significantly improved the signal-to-noise and distortion ratio (SNDR) for the channel of interest by approximately 30 dB in the worst distorted channel. For hardware implementation, the distorted signals are sampled from a commercial LNA (ZFL–500LN+) by a customized FPGA board. Results from measurements show an improvement of 14.6% for error vector magnitude (EVM) in a strong distortion scenario of 16–QAM modulation signal.

Efficient Trajectory Clustering with Road Network Constraints Based on Spatiotemporal Buffering

The analysis of individuals’ movement behaviors is an important area of research in geographic information sciences, with broad applications in smart mobility and transportation systems. Recent advances in information and communication technologies have enabled the collection of vast amounts of mobility data for investigating movement behaviors using trajectory data mining techniques. Trajectory clustering is one commonly used method, but most existing methods require a complete similarity matrix to quantify the similarities among users’ trajectories in the dataset. This creates a significant computational overhead for large datasets with many user trajectories. To address this complexity, an efficient clustering-based method for network constraint trajectories is proposed, which can help with transportation planning and reduce traffic congestion on roads. The proposed algorithm is based on spatiotemporal buffering and overlapping operations and involves the following steps: (i) Trajectory preprocessing, which uses an efficient map-matching algorithm to match trajectory points to the road network. (ii) Trajectory segmentation, where a Compressed Linear Reference (CLR) technique is used to convert the discrete 3D trajectories to 2D CLR space. (iii) Spatiotemporal proximity analysis, which calculates a partial similarity matrix using the Longest Common Subsequence similarity indicator in CLR space. (iv) Trajectory clustering, which uses density-based and hierarchical clustering approaches to cluster the trajectories. To verify the proposed clustering-based method, a case study is carried out using real trajectories from the GeoLife project of Microsoft Research Asia. The case study results demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art clustering-based methods.

The Effect of Leadership Style on Employee Performance at CV. Production Coalition

This study aims to determine the effect of leadership style on employee performance at Cv. Production Coalition. In this study using primary data research methods. The sample in this study were employees of Cv. Coalesi Production, totaling 35 respondents. The data was tested using SPSS Statistics 25. The sample data in this study used a questionnaire. The research analysis method used simple linear reference analysis. Based on the results of the t test, for the variable Building Organization (X) t count 7.081> t table (1.69236) with a significance of 0.000 <0.05. This shows that H0 is rejected and H1 is accepted, which means that there is a significant influence of leadership style on employee performance in cv. production coalition..

Targeting Lane-Level Map Matching for Smart Vehicles: Construction of High-Definition Road Maps Based on GIS

The development of smart vehicles has increased the demand for high-definition road maps. However, traditional road maps for vehicle navigation systems are not sufficient to meet the requirements of intelligent vehicle systems (e.g., autonomous driving). The present work comes up with a method of generating high-definition map models based on the geographic information system (GIS). A systematic map construction framework including the road layer, intersection connection layer, and lane layer is proposed based on the GIS database. Specifically, the constrained Delaunay triangular network method is applied to extract road layer network models, which are then used as linear reference networks to construct lane-level road maps. To further examine the feasibility of the proposed framework, a field experiment is then conducted to build a high-definition road map. Furthermore, a lane-level map matching test is conducted in the constructed road map using the trajectory data collected from a probe vehicle. The results show that the proposed method provides an efficient way of extracting lane-level information from urban road networks and can be applied for lane-level map matching with good performance.

PanPA: generation and alignment of panproteome graphs.

Compared to eukaryotes, prokaryote genomes are more diverse through different mechanisms, including a higher mutation rate and horizontal gene transfer. Therefore, using a linear representative reference can cause a reference bias. Graph-based pangenome methods have been developed to tackle this problem. However, comparisons in DNA space are still challenging due to this high diversity. In contrast, amino acid sequences have higher similarity due to evolutionary constraints, whereby a single amino acid may be encoded by several synonymous codons. Coding regions cover the majority of the genome in prokaryotes. Thus, panproteomes present an attractive alternative leveraging the higher sequence similarity while not losing much of the genome in non-coding regions. We present PanPA, a method that takes a set of multiple sequence alignments of protein sequences, indexes them, and builds a graph for each multiple sequence alignment. In the querying step, it can align DNA or amino acid sequences back to these graphs. We first showcase that PanPA generates correct alignments on a panproteome from 1350 Escherichia coli. To demonstrate that panproteomes allow comparisons at longer phylogenetic distances, we compare DNA and protein alignments from 1073 Salmonella enterica assemblies against E.coli reference genome, pangenome, and panproteome using BWA, GraphAligner, and PanPA, respectively; with PanPA aligning around 22% more sequences. We also aligned a DNA short-reads whole genome sequencing (WGS) sample from S.enterica against the E.coli reference with BWA and the panproteome with PanPA, where PanPA was able to find alignment for 68% of the reads compared to 5% with BWA. PanPA is available at https://github.com/fawaz-dabbaghieh/PanPA.

Consensus of multiple manipulators with elastic joints under a directed graph

We investigate the consensus problem of multiple manipulators with elastic joints under a directed graph in this paper. We use model reference adaptive consensus to transform the consensus problem into two subproblems: reference model consensus and trajectory tracking for a single manipulator. We then propose a linear reference model without relative velocity information because determining relative joint angular velocity information among agents is difficult. We finally develop a trajectory tracking algorithm based on the backstepping method and demonstrate that the joint angles of the manipulators asymptotically achieve consensus.

Модификация алгоритма выравнивания коротких прочтений для повышения качества пайплайна обработки данных полногеномного секвенирования человека

This study emphasizes the importance of aligning short reads in the analysis of human whole-genome sequencing data. The alignment process involves determining the positions of short genetic sequences relative to a known reference genome sequence of the human genome. Traditional alignment methods use a linear reference sequence, but this can lead to incorrect alignment, especially when short reads contain genetic variations. In this work, the index file of the reference sequence was modified using the minimap2 tool. Experimental results showed that adding information about frequently occurring genetic variations to the minimap2 index increases the number of correctly identified genetic variants, which affects the quality of subsequent data analysis.

Thermodynamic characterization of a macrocyclic Zika virus NS2B/NS3 protease inhibitor and its acyclic analogs.

Cyclization of small molecules is a widely applied strategy in drug design for ligand optimization to improve affinity, as it eliminates the putative need for structural preorganization of the ligand before binding, or to improve pharmacokinetic properties. In this work, we provide a deeper insight into the binding thermodynamics of a macrocyclic Zika virus NS2B/NS3 protease inhibitor and its linear analogs. Characterization of the thermodynamic binding profiles by isothermal titration calorimetry experiments revealed an unfavorable entropy of the macrocycle compared to the open linear reference ligands. Molecular dynamic simulations and X-ray crystal structure analysis indicated only minor benefits from macrocyclization to fixate a favorable conformation, while linear ligands retained some flexibility even in the protein-bound complex structure, possibly explaining the initially surprising effect of a higher entropic penalty for the macrocyclic ligand.

Data-Driven Iterative Learning Control of Nonlinear Systems by Adaptive Model Matching

Iterative learning control (ILC) has proven successful in the industry for enhancing tracking performance of repetitive tasks. The high accuracy and fast convergence of ILC algorithms hinge on: 1) the knowledge of the system model and 2) an effective learning algorithm. For general industrial systems with nonlinear dynamics, this raises technical challenges because acquiring a nonlinear dynamical model or several linearized models at different operating points may be difficult and costly. It is also nontrivial to determine the learning algorithm for the complex model obtained. To address these challenges, this article proposes a novel data-driven ILC algorithm for single-input-single-output nonlinear systems. Without explicit nonlinear models, our algorithm treats the nonlinear system as an unknown linear time-varying system linearized on a specified input–output (I/O) trajectory in each ILC iteration. A linearly parameterized time-varying adaptive filter is constructed in each ILC iteration so that, when cascading with the nonlinear plant, the I/O dynamics around the specified trajectory follow a linear time-invariant reference model. The ILC error trajectory is then filtered by the adaptive time-varying filter, which represents the inverse dynamics with a bandwidth specified by the reference model, to render fast convergence. The benefits of the proposed data-driven algorithm is demonstrated by comparing to a gradient and Hessian-based data-driven ILC algorithm on a prototypical two-degree-of-freedom nonlinear pendulum.

쿼드로터 UAV 의 PPO 기반 모델 참조 추종 제어

This paper presents a proximal policy optimization (PPO)-based model reference tracking controller design for quadrotor unmanned aerial vehicles (UAVs). First, the quadrotor UAV is divided into the attitude and position systems, and each system is expressed as a nonlinear state-space equation. Thereafter, we design a linear reference model, which is used to derive the tracking error dynamics. The proposed neural network model implements a PPO-based reinforcement learning algorithm consisting of an actor approximating a policy and a critic corresponding to a state-value function. The actor receives the state variables of the tracking error dynamics as input and outputs the thrust values to be applied to each axis. Further, we decentralize the PPO-based controller into the attitude and position controllers, which are then trained separately. For learning purposes, we implement an environment code that expresses the tracking error dynamics of the quadrotor by extending the OpenAI Gym environment. Finally, a simulation example is provided to show the position tracking performances of up to 0.0166 m and 0.0254 m for the horizontal and vertical axes, respectively.

Strengthened adsorption films of double antibiotic medicines skeletons-based dendrimers on copper surface: Molecular dynamics simulation and intensified anti effects of algae, bacteria and corrosion

In this study, an approach was proposed to achieve super anti-effects of alga, bacteria and corrosion for copper surface by employing strengthened adsorption films of dendritic compounds having double antibiotic medicine xacins skeletons. Thus, two new target dendritic compounds based on goal-orientation were prepared with the linear reference molecules carrying a single xacin segment. The adsorption of the studied compounds on copper surface was analyzed based on molecular modeling and material dynamic simulation. Furthermore, the spontaneous adsorption of copper surface of these molecules was performed in mixed DMSO/H2O (80/20, v/v). The copper surface adsorbed by the target molecules showed superior antialgae performance of Chlorella, and the maximum diameters of bacteriostatic zones against E. coli and S. aureus reached about 3.5 cm and 2.8 cm. Besides, the dynamic polarization and electrochemical impedance spectroscopy show that the target compounds achieved over 96% corrosion inhibition efficiency at 0.100 mM in 0.5 M H2SO4 solution. The adsorption of copper surface of the target molecules was deeply characterized such as X-ray photoelectron spectroscopy (XPS), which revealed that the target molecules are chemically adsorbed on the metal surface through the complexation of heteroatoms with metals.