bluuebunny/crossref_metadata_2025 huggingface dataset:
Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows
Authors: Rieger Marcel
Published: July 2020

In particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo event generation. However, physicists performing data analyses are usually required to steer their individual workflows manually, which is time-consuming and often leads to undocumented relations between particular workloads. We present the Luigi Analysis Workflows (Law) Python package, which is based on the open-source pipelining tool Luigi, originally developed by Spotify. It establishes a generic design pattern for analyses of arbitrary scale and complexity, and shifts the focus from executing to defining the analysis logic. Law provides the building blocks to seamlessly integrate interchangeable remote resources without, however, limiting itself to a specific choice of infrastructure. In particular, it encourages and enables the separation of analysis algorithms on the one hand, and run locations, storage locations, and software environments on the other hand. To cope with the sophisticated demands of end-to-end HEP analyses, Law supports job execution on WLCG infrastructure (ARC, gLite) as well as on local computing clusters (HTCondor, LSF), remote file access via most common protocols through the GFAL2 library, and an environment sandboxing mechanism with support for Docker and Singularity containers. Moreover, the novel approach ultimately aims for analysis preservation out-of-the-box. Law is entirely experiment independent and developed open-source.
Faster RooFitting: Automated parallel calculation of collaborative statistical models
Authors: Patrick Bos E G , Burgard Carsten D , Croft Vincent A , Pelupessy Inti , Attema Jisk J , Verkerke Wouter
Published: July 2020

RooFit [1,2] is the main statistical modeling and fitting package used to extract physical parameters from reduced particle collision data, e.g. the Higgs boson experiments at the LHC [3,4]. RooFit aims to separate particle physics model building and fitting (the users’ goals) from their technical implementation and optimization in the back-end. In this paper, we outline our efforts to further optimize this back-end by automatically running parts of user models in parallel on multi-core machines. A major challenge is that RooFit allows users to define many different types of models, with different types of computational bottlenecks. Our automatic parallelization framework must then be flexible, while still reducing run-time by at least an order of magnitude, preferably more. We have performed extensive benchmarks and identified at least three bottlenecks that will benefit from parallelization. We designed a parallelization layer that allows us to parallelize existing classes with minimal effort, but with high performance and retaining as much of the existing class’s interface as possible. The high-level parallelization model is a task-stealing approach. Preliminary results show speed-ups of factor 2 to 20, depending on the exact model and parallelization strategy.
ROOT I/O compression algorithms and their performance impact within Run 3
Authors: Shadura O , Bockelman B
Published: July 2020

The LHC’s Run3 will push the envelope on data-intensive workflows and, since at the lowest level this data is managed using the ROOT software framework, preparations for managing this data are starting already. At the beginning of LHC Run 1, all ROOT data was compressed with the ZLIB algorithm; since then, ROOT has added support for additional algorithms such as LZMA and LZ4, each with unique strengths. This work must continue as industry introduces new techniques - ROOT can benefit saving disk space or reducing the I/O and bandwidth for online and offline needs of experiments by introducing better compression algorithms. In addition to alternate algorithms, we have been exploring alternate techniques to improve parallelism and apply pre-conditioners to the serialized data.

We have performed a survey of the performance of the new compression techniques. Our survey includes various use cases of data compression of ROOT files provided by different LHC experiments. We also provide insight into solutions applied to resolve bottlenecks in compression algorithms, resulting in improved ROOT performance.
Evolution of ROOT package management
Authors: Shadura O , Bockelman B , Vassilev V
Published: July 2020

ROOT is a large code base with a complex set of build-time dependencies; there is a significant difference in compilation time between the “core” of ROOT and the full-fledged deployment. We present results on a “delayed build” for internal ROOT packages and external packages. This gives the ability to offer a “lightweight” core of ROOT, later extended by building additional modules to extend the functionality of ROOT. As a part of this work, we have improved the separation of ROOT code into distinct modules and packages with minimal dependencies. This approach gives users better flexibility and the possibility to combine various build features without rebuilding from scratch.

Dependency hell is a common problem found in software and particularly in HEP software ecosystem. We would like to discuss an improvement of artifact management (“lazy-install”) system as a solution to the “dependency hell” problem.

HEP software stack usually consists of multiple sub-projects with dependencies. The development model is often distributed, independent and non-coherent among the sub-projects. We believe that software should be designed to take advantage of other software components that are already available, or have already been designed and implemented for use elsewhere rather than “reinventing the wheel”.

The main idea is to build the ROOT project and all of its dependencies recursively and incrementally, making it fundamentally different than just adding one external project and rebuilding from scratch. In addition, this allows to keep a list of dependencies to be able to resolve possible incompatibility of transitive dependencies caused by the versions conict.

In our contribution, we will present our approach to artifact management system of ROOT together with a set of examples and use cases.
Nested data structures in array frameworks
Authors: Pivarski Jim , Lange David , Elmer Peter
Published: July 2020

The need for nested data structures and combinatorial operations on arbitrary length lists has prevented particle physicists from adopting array-based data analysis frameworks, such as R, MATLAB, Numpy, and Pandas. These array frameworks work well for purely rectangular tables and hypercubes, but arrays of variable length arrays, called “jagged arrays,” are out of their scope. However, jagged arrays are a fundamental feature of particle physics data, as well as combining them to search for particle decays. To bridge this gap, we developed the awkward-array library, and in this paper we present feedback from some of the first physics groups using it for their analyses. They report similar computational performance to analysis code written in C++, but are split on the ease-of-use of array syntax. In a series of four phone interviews, all users noted how different array programming is from imperative programming, but whereas some found it easier in all aspects, others said it was more difficult to write, yet easier to read.
Vectorization of random number generation and reproducibility of concurrent particle transport simulation
Authors: Jun S Y , Canal P , Apostolakis J , Gheata A , Moneta L
Published: July 2020

Efficient random number generation with high quality statistical properties and exact reproducibility of Monte Carlo simulations are important requirements in many areas of computational science. VecRNG is a package providing pseudo-random number generation (pRNG) in the context of a new library VecMath. This library bundles up several general-purpose mathematical utilities, data structures, and algorithms having both SIMD and SIMT (GPUs) support based on VecCore. Several state-of-the-art RNG algorithms are implemented as kernels supporting parallel generation of random numbers in scalar, vector, and Cuda workflows. In this report, we will present design considerations, implementation details, and computing performance of parallel pRNG engines on both CPU and GPU. Reproducibility of propagating multiple particles in parallel for HEP event simulation is demonstrated, using GeantV-based examples, for both sequential and fine-grain track-level concurrent simulation workflows. Strategies for efficient uses of vectorized pRNG and non-overlapping streams of random number sequences in concurrent computing environments is discussed as well.
Using DODAS as deployment manager for smart caching of CMS data management system
Authors: Tracolli M. , Antonacci M. , Boccali T. , Bonacorsi D. , Ciangottini D , Donvito G. , Duma C. , Gaido L. , Salomoni D. , Spiga D. , Kuznetsov V.
Published: July 2020

DODAS stands for Dynamic On Demand Analysis Service and is a Platform as a Service toolkit built around several EOSC-hub services designed to instantiate and configure on-demand container-based clusters over public or private Cloud resources. It automates the whole workflow from service provisioning to the configuration and setup of software applications. Therefore, such a solution allows using “any cloud provider”, with almost zero effort. In this paper, we demonstrate how DODAS can be adopted as a deployment manager to set up and manage the compute resources and services required to develop an AI solution for smart data caching. The smart caching layer may reduce the operational cost and increase flexibility with respect to regular centrally managed storage of the current CMS computing model. The cache space should be dynamically populated with the most requested data. In addition, clustering such caching systems will allow to operate them as a Content Delivery System between data providers and end-users. Moreover, a geographically distributed caching layer will be functional also to a data-lake based model, where many satellite computing centers might appear and disappear dynamically. In this context, our strategy is to develop a flexible and automated AI environment for smart management of the content of such clustered cache system. In this contribution, we will describe the identified computational phases required for the AI environment implementation, as well as the related DODAS integration. Therefore we will start with the overview of the architecture for the pre-processing step, based on Spark, which has the role to prepare data for a Machine Learning technique. A focus will be given on the automation implemented through DODAS. Then, we will show how to train an AI-based smart cache and how we implemented a training facility managed through DODAS. Finally, we provide an overview of the inference system, based on the CMS-TensorFlow as a Service and also deployed as a DODAS service.
Hardware Accelerated ATLAS Workloads on the WLCG Grid
Authors: Forti A C , Heinrich L , Guth M
Published: July 2020

In recent years the usage of machine learning techniques within data-intensive sciences in general and high-energy physics in particular has rapidly increased, in part due to the availability of large datasets on which such algorithms can be trained, as well as suitable hardware, such as graphic or tensor processing units, which greatly accelerate the training and execution of such algorithms. Within the HEP domain, the development of these techniques has so far relied on resources external to the primary computing infrastructure of the WLCG (Worldwide LHC Computing Grid). In this paper we present an integration of hardware-accelerated workloads into the Grid through the declaration of dedicated queues with access to hardware accelerators and the use of Linux container images holding a modern data science software stack. A frequent use-case in the development of machine learning algorithms is the optimization of neural networks through the tuning of their Hyper Parameters (HP). For this often a large range of network variations must be trained and compared, which for some optimization schemes can be performed in parallel – a workload well suited for Grid computing. An example of such a hyper-parameter scan on Grid resources for the case of flavor tagging within ATLAS is presented.
Using Continous Deployment techniques to manage software change at a WLCG Tier-2
Authors: Roy G , Simili E , Stewart G , Skipsey S C , Britton D
Published: July 2020

Continuous Integration (CI) and Continuous Development (CD) are common techniques in software development. Continuous Integration is the practice of bringing together code from multiple developers into a single repository, while Continuous Development is the process by which new releases are automatically created and tested. CI/CD pipelines are available in popular automation tools such as GitLab, and act to enhance and accelerate the software development process. Continuous Deployment, in which automation is employed to push new software releases into the production environment, follows naturally from CI/CD, but is not as well established due to business and legal requirements. Such requirements do not exist in the Worldwide LHC Compute Gird (WLCG), making the use of continuous deployment to simplify the management of grid resources an attractive proposition. We have developed work presented previously on containerised worker node environments by introducing continuous deployment techniques and tooling, and show how these, in conjunction with CI/CD, can reduce the management burden at a WLCG Tier-2 resource. In particular, benefits include reduced downtime as a result of code changes and middleware updates.
Federated data storage evolution in HENP: data lakes and beyond
Authors: Zarochentsev Andrey , Espinal Xavier , Kiryanov Andrey , Schovancová Jaroslava
Published: July 2020

Storage has been identified as the main challenge for the future distributed computing infrastructures: Particle Physics (HL-LHC, DUNE, Belle-II), Astrophysics and Cosmology (SKA, LSST). In particular, the High Luminosity LHC (HL-LHC) will begin operations in the year of 2026 with expected data volumes to increase by at least an order of magnitude as compared with the present systems. Extrapolating from existing trends in disk and tape pricing, and assuming flat infrastructure budgets, the implications for data handling for end-user analysis are significant. HENP experiments need to manage data across a variety of mediums based on the types of data and its uses: from tapes (cold storage) to disks and solid state drives (hot storage) to caches (including world wide access data in clouds and “data lakes”). The DataLake R&D project aims at exploring an evolution of distributed storage while bearing in mind very high demands of the HL-LHC era. Its primary objective is to optimize hardware usage and operational costs of a storage system deployed across distributed centers connected by fat networks and operated as a single service. Such storage would host a large fraction of the data and optimize the cost, eliminating inefficiencies due to fragmentation. In this talk we will highlight current status of the project, its achievements, interconnection with other research activities in this field like WLCG-DOMA and ATLAS-Google DataOcean, and future plans.
Denis Perret-Gallix (1949-2018)
Authors: Werlen Monique
Published: July 2020

Denis Perret-Gallix was the founder of the AIHENP-ACAT workshop series in 1990 and has chaired its international advisory committee until June 2018. He passed away on June 28 during one of the mountain bike climbs he used to enjoy. He was a high energy experimental physicist affiliated to IN2P3-CNRS (research director). He worked at Rutherford Lab., SLAC (DELCO experiment), CERN (CHARM and L3 experiments) and KEK developing low temperature dark matter detectors and event generators for the collider (LEP, LHC, ILC) physics simulations. He was the director of the CNRS Tokyo office from 2000 to 2004 and played a leading role in the France-Japan Particle Physics Laboratory which was established in 2006.
Particle identification using semi-supervised learning in the PICO-60 dark matter detector
Authors: Matusch Brendon , Cao Gevy
Published: July 2020

Astrophysical observations have demonstrated the existence of dark matter over the past decades. Experimental efforts in the search for dark matter are largely focused on the well-motivated weakly interacting massive particles (WIMPs) as a dark matter candidate. Current experiments in direct detection are producing increasingly competitive limits on the cross section of WIMP-nucleon scattering. The main experimental challenge for all direct detection experiments is the presence of background signals. These backgrounds need to be either eliminated by providing sufficient shielding or discriminated from WIMP signals. In this work, semi-supervised learning techniques are developed to discriminate alpha recoils from nuclear recoils induced by WIMPs in the PICO-60 detector. The two semi-supervised learning techniques, gravitational differentiation and iterative cluster nucleation, maximize the effect of the most confidently predicted data samples on subsequent training iterations. Classifications using both techniques can reproduce the traditional acoustic parameter with accuracies over 98%. The best model yields an accuracy of 99.2% and a class-wise standard deviation value of 0.11. These techniques can reliably serve as an intermediate verification tool before the acoustic parameter is constructed in future detectors.
Accelerating dark matter search in emulsion SHiP detector by deep learning
Authors: Shirobokov S K , Ustyuzhanin A E , Golutvin A I
Published: July 2020

We introduce a novel approach for the reconstruction of particle properties for the SHiP detector. The SHiP experiment significantly focuses on finding effects of dark matter particle interaction. A characteristic trace of such an interaction is an electromagnetic shower. Our algorithm aims to reconstruct the energy and origin of such showers using online Target Tracker subdetectors that do not suffer from pile-up. Thus, the online observation of the excess of events with proper energy can be a signal for a dark matter. Two different approaches were applied: classical, using Gaussian Mixtures and machine learning based on a convolutional neural network. We’ve refined the output of the previous step by clusterization techniques to improve transverse coordinate estimation. The obtained results are 25% for energy resolution, 0.8 cm for position resolution in the longitudinal direction and 1 mm in the transverse direction, without any usage of the emulsion.
Reinforced sorting networks for particle physics analyses
Authors: Erdmann Martin , Fischer Benjamin , Noll Dennis
Published: July 2020

Deep learning architectures in particle physics are often strongly dependent on the order of their input variables. We present a two-stage deep learning architecture consisting of a network for sorting input objects and a subsequent network for data analysis. The sorting network (agent) is trained through reinforcement learning using feedback from the analysis network (environment). The optimal order depends on the environment and is learned by the agent in an unsupervised approach. Thus, the two-stage system can choose an optimal solution which is not known to the physicist in advance. We present the new approach and its application to the signal and background separation in top-quark pair associated Higgs boson production.
Vectorization techniques for probability distribution functions using VecCore
Authors: Chaparro Amaro Oscar R. , Martínez-Castro J. , Yung Jun Soon
Published: July 2020

Probability distribution functions (PDFs) are very used in modeling random processes and physics simulations. Improving the performance of algorithms that generate many random numbers under complex PDFs is often a very challenging task when methods as direct functions are not available. In this work we present general strategies on how to vectorize some PDFs using VecCore library. We show the results for the Exponential, Gaussian, discrete Poisson and Gamma probability distributions.
Jet Grooming through Reinforcement Learning
Authors: Carrazza Stefano , Dreyer Frédéric A.
Published: July 2020

We introduce a novel implementation of a reinforcement learning (RL) algorithm which is designed to find an optimal jet grooming strategy, a critical tool for collider experiments. The RL agent is trained with a reward function constructed to optimize the resulting jet properties, using both signal and background samples in a simultaneous multi-level training. We show that the grooming algorithm derived from the deep RL agent can match state-of-the-art techniques used at the Large Hadron Collider, resulting in improved mass resolution for boosted objects. Given a suitable reward function, the agent learns how to train a policy which optimally removes soft wide-angle radiation, allowing for a modular grooming technique that can be applied in a wide range of contexts. These results are accessible through the corresponding GroomRL framework.
Deep Learning based Algorithms in Astroparticle Physics
Authors: Erdmann Martin , Glombitza Jonas
Published: July 2020

In recent years, great progress has been made in the fields of machine translation, image classification and speech recognition by using deep neural networks and associated techniques (deep learning). Recently, the astroparticle physics community successfully adapted supervised learning algorithms for a wide range of tasks including background rejection, object reconstruction, track segmentation and the denoising of signals. Additionally, the first approaches towards fast simulations and simulation refinement indicate the huge potential of unsupervised learning for astroparticle physics. We summarize the latest results, discuss the algorithms and challenges and further illustrate the opportunities for the astrophysics community offered by deep learning based algorithms.
Making RooFit Ready for Run 3
Authors: Hageböck S , Moneta L
Published: July 2020

RooFit and RooStats, the toolkits for statistical modelling in ROOT, are used in most searches and measurements at the Large Hadron Collider. The data to be collected in Run 3 will enable measurements with higher precision and models with larger complexity, but also require faster data processing.

In this work, first results on modernising RooFit’s collections, restructuring data flow and vectorising likelihood fits in RooFit will be discussed. These improvements will enable the LHC experiments to process larger datasets without having to compromise with respect to model complexity, as fitting times would increase significantly with the large datasets to be expected in Run 3.
GRAAL: Gem Reconstruction And Analysis Library
Authors: Farinelli R. , Alexeev M. , Amoroso A. , Bagnasco S. , Baldini Ferroli R. , Balossino I. , Bertani M. , Bettoni D. , Bortone A. , Bianchi F. , Calcaterra A. , Cerioni S. , Chai J. , Cheng W. , Chiozzi S. , Cibinetto G. , Cossio F. , Cotta Ramusino A. , Cotto G. , Da Rocha Rolo M. , De Mori F. , Destefanis M. , Evangelisti F. , Fava L. , Felici G. , Gaido L. , Garzia I. , Gatta M. , Giraudo G. , Gramigna S. , Greco M. , Lavezzi L. , Lusso S. , Li H. , Maggiora M. , Malaguti R. , Mangoni A. , Marcello S. , Melchiorri M. , Mezzadri G. , Mignone M. , Pacetti S. , Patteri P. , Passalacqua B. , Rivetti A. , Savrié M. , Sosio S. , Spataro S. , Tskhadadze E. , Yan L. , Wheadon R. J.
Published: July 2020

Micro Pattern Gas Detectors (MPGD) are the new frontier in gas trackers. Among this kind of devices, the Gas Electron Multiplier (GEM) chambers are widely used. The experimental signals acquired with the detector must obviously be reconstructed and analysed. In this contribution, a new offline software to perform reconstruction, alignment and analysis on the data collected with APV-25 and TIGER ASICs will be presented. GRAAL (Gem Reconstruction And Analysis Library) is able to measure the performance of a MPGD detector with a strip segmented anode (presently). The code is divided in three parts: reconstruction, where the hits are digitized and clusterized; tracking, where a procedure fits the points from the tracking system and uses that information to align the chamber with rotations and shifts; analysis, where the performance is evaluated (e.g. efficiency, spatial resolution,etc.). The user must set the geometry of the setup and then the program returns automatically the analysis results, taking care of different conditions of gas mixture, electric field, magnetic field, geometries, strip orientation, dead strip, misalignment and many others.
A multifunctional silk coating on additively manufactured porous titanium to prevent implant-associated infection and stimulate bone regeneration
Authors: Gorgin Karaji Z , Jahanmard F , Mirzaei A H , van der Wal B , Amin Yavari S
Published: July 2020

Despite tremendous progress in the design and manufacturing of metallic implants, they do not outlive the patient. To illustrate, more than half of hip replacements will fail, mainly due to implant infection and loosening. Surface engineering approaches and, in particular, coatings can facilitate implant bio-functionality via the recruitment of more host cells for new bone formation and inhibition of bacterial colonization. Here, we used electrophoretic deposition to apply a silk fibroin solution consisting of tricalcium phosphate (TCP) and vancomycin as a coating on the surface of additively-manufactured porous titanium. Furthermore, the surface properties of the coatings developed and the release kinetics of the vancomycin were studied to evaluate the applied coating. The in vitro antibacterial behavior of the multifunctional coating, as well as the cell viability and osteogenic differentiation of the MC3T3-E1 cell line were extensively studied. The biomaterials developed exhibited an antibacterial behavior with a reduction of up to four orders of magnitude in both planktonic and adherent bacteria for 6 h and 1 d. A live-dead assay, the Alamar Blue activity, the DNA content, and cytoskeleton staining demonstrated a significant increase in the cell density of the coated groups versus the as-manufactured ones. The significantly enhanced calcium deposition and the increase in mineralization for the groups with TCP after 21 and 28 d, respectively, demonstrate upregulation of the MC3T3 cells’ osteogenic differentiation. Our results collectively show that the multifunctional coating studied here can be potentially used to develop a new generation of orthopedic implants.
Study on the Optimal Travel Route Selection Based on Ant Colony Algorithm Model
Authors: Sang Senyao
Published: July 2020

Based on the perspective of travel agencies, this paper focuses on the optimization of tourist traffic routes, and divides the optimization of tourist traffic routes into “one day tour”, the optimization of tourist traffic routes and the optimization of “multi day tour” tourist traffic routes. The objective function of tourism traffic route optimization is improved by using principal component analysis. Finally, the ant colony algorithm is used to model, which completes the research based on the ant colony algorithm model to select the best tourism route, so that the travel agency get the most benefits and the tourists enjoy the most humane experience.
Research on the Monitoring and Response Mechanism of Earthquake Public Opinion
Authors: Gao Shuang , Li Xiaojun , Bo Tao
Published: July 2020

Earthquake public opinion along with the development of online media platforms has become an important part of earthquake public opinion monitoring. In order to strengthen the monitoring of public opinion on the seismic network, scientifically respond to the public opinion on the seismic network, and effectively disseminate seismic information, we analyze the content of the public opinion on the seismic network, the characteristics of the public opinion, the monitoring of the public opinion, and the response mechanism of the public opinion on the earthquake.
Air Quality Analysis of Wuhan from the Perspective of Functional Data
Authors: Chen Guici , Zhang Zuo , Zuo Qian
Published: July 2020

In recent years, with the continuous advancement of Wuhan’s industry, manufacturing and engineering construction, the consumption of energy is also increasing, and the air quality problems caused by it have also received attention from all walks of life. In order to explore the characteristics of air pollution in Wuhan, this paper uses functional data analysis methods to study air quality (AQI) from the perspective of space and time. The results show that 13 prefecture-level cities in Wuhan can be divided into 4 levels according to the quality of air quality. In general, Xinzhou District, Huang pi District, Jiangxia District and Hannan District have the best air quality, while Qingshan District and Dongxihu District have the worst air quality; all have significant seasonal characteristics in the time dimension, that is, winter is a high-frequency band with serious air pollution.
Screening of rhizosphere growth promoting bacteria and their growth promoting ability of sunflower in cold black soil area
Authors: Zou Danzhi , Zheng Hongjuan , Zhang Yaosheng , Gu Ye , Cao Yajie , Song Yujun , Li Lan
Published: July 2020

High efficient growth promoting bacteria were screened from the rhizosphere soil of sunflower in the cold black soil area, and their growth promoting ability was tested to obtain the strains with comprehensive growth promoting ability. The phosphate solubilizing bacteria were screened out by inorganic phosphate bacteria culture medium, the phosphate solubilizing ability of the strain was determined by mo sb anti chromogenic method, the indoleacetic acid producing ability of the strain was determined by salkowski method, the iron carrier producing ability of the strain was determined by CAS method, and the growth promoting ability of the strain was evaluated comprehensively. Eleven strains of phosphate solubilizing bacteria were isolated and screened from soil samples. Among them, strain WP2 had the strongest phosphate solubilizing ability, and the concentration of phosphate radical was 12.75 μg/mL; strain WP1 had the strongest indoleacetic acid producing ability, and the concentration of indoleacetic acid was 25.37μg/mL; strain WP1 had the strongest iron producing ability, with Su = 72.76%, and the strength was 4+. It was found that WP1, WP2 and WP10 had strong ability of promoting growth and had strong potential of development and utilization. The above results can provide excellent strains for the development and promotion of special bio fertilizer suitable for the cold black soil.
Test and Analysis of Vibration Mode of Distribution Transformer Tank Structure
Authors: Wang Guangzhou , Yao Degui , Ma Yuchao , Zhang Songyang , Wang Guangke , Xu Lianhuan , Wang Leilei
Published: July 2020

Along with the social and economic development and continuous increasing of electrical load, power distribution network has its scale continuously expanded, and more and more distribution transformers go deep into the urban residential areas, leading to increasingly serious vibration and noise problems. Relying on a typical 35 kV distribution transformer, a vibration mode test was done on the tank structure of the distribution transformer, and PolyLSCF fitting method was adopted to obtain the first 10-order modal shape in this paper. Meanwhile, finite element model was built for distribution transformer tank, and contrastive analysis were done in combination with the measured data to obtain the rules of influence on vibration mode of distribution transformer by tank wall thickness. The vibration characteristics acquired by measured mode of distribution transformer are consistent with those obtained by numerical simulation, and the central vibration amplitude of the tank is the maximum. Along with the increase of wall thickness, the low-order mode of the tank structure shows insignificant change, and high-order mode frequency reduces, thus providing measured data reference for optimizing the tank structure of distribution transformer.
Study on Support Optimization of mining roadway in deep steeply dipping coal seam
Authors: Sang Fengyu , Huang jing , Zhao Baoxiang
Published: July 2020

In order to solve the problem of the failure phenomenon of roof fall and the inward extrusion of two roadsides in main roadway of 31205 working face of Zhubai coal mine, the theoretical analysis, field measurement and numerical simulation were adopted to study the rock breakage and instability of roadway’s and put forward the differential anchor cable and bolt targeted support solution. Industrial tests and the roadway pressure observation were carried out in 31205 main roadway. The field application results showed that the deformation and break of roadway surrounding rock is effectively controlled, the supporting cost was reduced and the supporting effect was remarkable.
A New Method for Predicting Water Breakthrough Time in the Edge Water Condensate Gas Reservoir
Authors: Huang Quanhua , Lin Xingyu , Xu Qinglong , He Yunjun
Published: July 2020

The influence of condensate oil is often ignored when people predict the water breakthrough time of condensate gas reservoirs, leading to a certain difference between the prediction and the actual results. In order to efficiently and scientifically develop the edge water condensate gas reservoir, it is necessary to consider the influence of the precipitated condensate oil when making reasonable predictions on the water breakthrough time. Based on the basic law of gas-water two-phase percolation, this paper simplified the water inlet process as the migration of water particles to the well bottom, with the influence of condensate oil precipitation taken into consideration. In this meantime, this paper derived a new model for predicting the breakthrough time of the edge water condensate gas reservoir based on the idea that the change of condensate saturation varies with time and the distance from well bottom. Case studies show that the lower the permeability, the greater the influence of condensate oil precipitation on water breakthrough time; the farther the well bottom is from the initial gas-water boundary, the longer the water breakthrough time; the effect of condensate oil cannot be ignored during the prediction of the water breakthrough time in the edge water condensate gas reservoir. The new model considers the dual effects of time and location on the condensate saturation, which is more in line with the actual situation of the change of condensate saturation in the formation.
GSM-R wireless field strength coverage prediction algorithm based on GA-BP algorithm
Authors: Jin Xiang , Zheng Yunshui
Published: July 2020

With the opening of multiple railway lines, newly-built routes are adjacent to, close to, and overcoming existing routes. If you do not consider the network coverage of adjacent lines in future planning in the early construction of the lines, you need to adjust the original The reconstruction of the line network coverage has increased the difficulty of construction and investment costs. In this paper, based on the genetic algorithm and BP artificial neural network, a GSM-R wireless network communication field strength prediction model is established. Combined with the field strength measured data of the base station, the GA-BP neural network model is compared with the traditional HATA model and BP neural network model. The comparison and verification show that the model has greatly improved the accuracy of prediction compared with the HATA model and the BP neural network model. It provides a feasible theoretical basis for the subsequent construction of railway parallel GSM-R wireless network coverage design.
Structure Design of Multi-joint Bionic Robot Based on Solidworks
Authors: Xu KaiJie , Wang Rihe , Lu Yaping
Published: July 2020

The body structure of the multi joint bionic robot is designed and analyzed. According to the movement form of snake and caterpillar, a multi joint bionic robot is designed and manufactured based on the principle of bionics. Using SolidWorks software to establish the model, there are three parts: head joint, body joint and tail joint. The bionic robot uses 3D technology to be printed and make a physical prototype, and experiment with the prototype. It can imitate the movement of snakes and caterpillars, and realize snake movement, insect movement, turning, curling, lurking and other movements. Through the structural design, processing and manufacturing of the multi joint bionic robot, the movement shape is analyzed and adjusted to make its movement attitude completely similar to the biological shape.
Safety Evaluation of Dry-fasten Stone Curtain Wall Structure of Existing Building
Authors: Liu Zhong
Published: July 2020

Stone curtain walls are more and more widely used due to their good decorative effect and hard texture. However, with the increase of their service life, many safety issues have gradually appeared and caused widespread concern from all walks of life. The dry-fasten stone curtain wall of an office building has been used for more than 12 years. Through on-site inspection and testing of the verticality of the stone curtain wall, the connection node between the stone curtain wall and the main structure, the stone curtain wall framework system, the curtain wall stone panel and weather resisting silicone sealant, some suggestions for transformation are proposed for the existed problems.
Research on Cold Chain Transport of Vaccines in Major Outbreaks
Authors: Li Gang , Wang Gangyi , Ma Yuanbo , Yue Lulu , Zhao Wenting
Published: July 2020

In the face of the sudden outbreak of new type of coronavirus, the virus is spreading at a faster rate than expected, which seriously threatens people’s life and health and national security. According to the World Organization, infectious diseases have been at a high incidence in the last two decades. How to transport kits and vaccines safely and efficiently to suspected cases and patients in a short period of time is related to the speed of national control of the epidemic. This paper first analyzes the scale of the pharmaceutical cold chain market in China, analyzes the policy of the pharmaceutical cold chain industry issued by the state, and analyzes the problems existing in the pharmaceutical cold chain industry; then it analyzes the influence of epidemic situation on cold chain transportation, and finally puts forward the safe and efficient transportation scheme of vaccine under epidemic situation.
Rsearch on Load Adaptability of Existing Hollow Slab Bridge of Ji-Qing Highway
Authors: Hao Long , Liu Guiru , Wang Shan , Fang Hua
Published: July 2020

Throughout the reconstruction and expansion process of the Jinan and Qingdao Expressway, there is a need for the existing Bridge to be demolished first and then reinforced for a better utilization. In order to better evaluate the load capability of the Level 1 lane, the load ability of the Seven-Ministry loads adaptability model and the current Ji-Qing traffic model, this paper examines three models of the load adaptability of existing small span Bridges. The load adaptability of the three load models on the old bridge of Jiqing existing line is analysed by comparing the bearing capacity level of the three load models in the three spans of 5m, 8m, and 13m of the hollow slab bridge.
EEG Neuro - processing for the development of neurointerfaces
Authors: Chervyakov N I , Lyakhov P A , Kiladze M R , Kovalets A A , Lyakhova U A
Published: July 2020

Fast and accurate processing of electroencephalogram (EEG) signals is necessary for correct realization of neurointerfaces. Neural network is one of the ways of its processing. In this article the method of EEG processing with the help of multilayer perceptron trained by the algorithm of error backpropagation is considered. The results of modeling during training of the neural network showed the minimum value of the Mean Squared error.
Role of innovative digital technologies in transformation of Agricultural and Industrial Complex
Authors: Strizhakova E A , Nazarova Yu N , Zayats O A , Zadorozhneva Yu V , Shumakova R I
Published: July 2020

The conditions of the digital economy development are studied in the article taking into account the application of innovative technologies. The authors considered features and the role of their influence on the digital transformation of the Agricultural and Industrial Complex. The review of the main government programs to intensify the industry development by using digital technologies is presented here.
The assessment of gait features according to the data of a portable acceleration sensor in an intelligent monitoring system
Authors: Dorofeev N V , Grecheneva A V
Published: July 2020

The article is devoted to assessing the possibility of identifying individual characteristics of a person’s gait according to the data of one accelerometric sensor, as well as the possibility of assessing impaired functioning of the musculoskeletal system with subsequent use in intelligent expert systems based on neural network algorithms. The article notes the prospects of using dynamic measurements to improve the effectiveness of orthopedic diagnostics. The prospects of using accelerometry along with complex medical motion capture systems are noted. The results of distinguishing characteristic gait indicators among a group of subjects according to one accelerometer sensor, as well as an assessment of the distribution structure of the articular angle when walking, are described. As subjects, people with impaired functioning of the musculoskeletal system and people without disorders are involved. The results obtained are in good agreement with the data of goniometric control systems built on the basis of several sensors. Thus, the results of the research show the possibility of obtaining a detailed picture of gait. The results obtained will allow us to form a preliminary structure of the neural network and evaluate its coefficients with the further development of automated control and monitoring systems based on existing portable wearable devices (phone, watch).
Development of Telecommunication System Units in Parallel Neural Network Basis
Authors: Smirnov A A , Sakhnyuk P A
Published: July 2020

The article suggests the integration of a neural network as a parallel element base in a telecommunication system. In this case, the ability to learn or adapt to external conditions is applied as the main advantage. For telecommunication systems in conditions when it is possible, this ability will improve noise immunity, reliability, operability, etc. The article considers an example of the integration of a neural network into a discrete matched signal filter. It is noted that the use of parallel mathematical methods in signal processing leads to the maximum effect of increasing the quality parameters of such telecommunication elements.
Simulator of the navigation equipped with LIDAR of the mobile robot based on the neural network
Authors: Faizullin R V
Published: July 2020

The paper considers the issue of using the robot simulators equipped with LIDAR (Light Detection and Ranging). The simulator application allows decreasing the experiment costs, their time and risks or negative aftereffects of accidents, but, at the same time, the simulated environment should identical to the real one. In practice, the simulated environment can have differences and there comes the issue of estimating the robot ability to adapt to new conditions. Neural networks are used to teach the robots, due to which the robots reach the target in the shortest or the fastest way, at the same time, there should be no accident/collision. The initial series represents the multiple time series. To teach the neural networks to control the robots, the paper proposes to introduce the additional index in the form of the minimal distance to the nearest object in the actual time moment. The approach novelty in using the new penalty function for teaching the neural network is the speed index of approaching the nearest object/obstacle. The neural networks were taught on the maps without dynamic objects, but tested in the conditions when several mobile robots were used on the map, thus, they became the dynamic obstacles for each other. The accident risk for robots without the proposed penalty function during testing was 4.5% against 0.3% - with the penalty function.
Anthropomorphic manipulator master-slave teleoperation using 3D depth sensor and color camera
Authors: Petrenko V I , Tebueva F B , Gurchinskiy M M , Svistunov N Yu , Nestulya A K
Published: July 2020

The article proposes a method for anthropomorphic manipulator teleoperation based on data processing using depth and color cameras of the Kinect controller. Processing data with Kinect allows to determine the coordinates of the nodal points of the operator’s body, which, after recalculation, can be used to generate control signals for the manipulator. The article describes the algorithm and the mathematical apparatus for determining the angles of the operator’s arm joints in real time. The proposed method can be used to control the joints of the manipulator of an anthropomorphic robot, and can also be adapted to control manipulators with a kinematic structure different from the structure of human arm, or to implement other control methods such as gesture control.
Cognitive modeling of the development of complex managed systems
Authors: Baidakov A N , Nazarenko A V , Babkina O N
Published: July 2020

The purpose of this research is to form a set of methodological provisions related to the use of cognitive modeling in the management of complex systems in conditions of high dynamism of studied processes. The rapid development of information and analytical tools provides new opportunities for applying cognitive modeling to a wider range of systems. The methodological basis of the study is the system methodology and its direct implementation is based on the system sequence “mission – goal – functions - structure – behavior – result”. Cognitive modeling in the management of complex systems takes into account their hierarchical structure, which entails the hierarchy of the corresponding models, which is directly reflected in the generated models or considered when building local models for systems of a certain hierarchy level. Complex systems also determine the complexity of the models that represent them. Therefore, the paper proposes stratification of the modeling process and its results on five levels: the representation model, the understanding model, the interaction model, the management model and the behavior model. The dynamics of simulated systems and their external environment assumes an iterative (cyclic) nature of cognitive modeling. The paper presents a structural cognitive model of managing the functioning and development of complex systems as its result.
Lattice thermal conductivity of NaCoO2 and LiCoO2 intercalation materials studied by hybrid density functional theory
Authors: Mattila Nina , Karttunen Antti J
Published: July 2020

We have studied the lattice dynamics and lattice thermal conductivity of NaCoO2 intercalation material with first-principles hybrid density functional methods. The lattice thermal conductivity has been obtained using linearized Boltzmann transport theory and the contributions to the lattice thermal conductivity have been analyzed in detail. The results obtained for NaCoO2 have been systematically compared with LiCoO2 to shed light on the effect of the alkali metal atom. The room-temperature in-plane lattice thermal conductivities within relaxation time approximation are 78 Wm−1K−1 and 46 Wm−1K−1 for NaCoO2 and LiCoO2, respectively. The respective room-temperature cross-plane lattice-thermal conductivities are 25.0Wm−1K−1 and 6.6 Wm−1K−1. The predicted lattice thermal conductivities for fully alkali-occupied single crystals are clearly larger in comparison to the experimental values obtained for single-crystal NaCoO2 and polycrystalline LiCoO2. Analysis of the lattice thermal conductivity reveals that the differences between NaCoO2 and LiCoO2 can be explained by significantly shorter phonon lifetimes in LiCoO2.