Vector Table Research Articles

Fault diagnosis of mine main ventilator based on multi-eigenvalue selection and data fusion

As a strong nonlinear complex system, the mine main ventilator is difficult to select eigenvalues of the ventilator fault. This study proposed a multiple eigenvalue selection method based on ensemble empirical modal decomposition (EEMD). Firstly, an experimental platform of axial flow fan was built to simulate the bearing fault and fan blade fault. Secondly, the vibration and wind pressure data of the above three faults and the normal state were collected, and the vibration signals were decomposed by EEMD. The normalized Root Mean Square Error was used to evaluate the signal separation degree of different faults under the same eigenvalue and to select the eigenvalue. Then, combined with wind pressure information, multi-data information fusion was used to construct the failure indicator vector table. Finally, the BP neural network was trained and tested with a vector table, and the fault diagnosis of the mine main ventilator was achieved. The experiment results showed that the diagnostic accuracy of test results was 98.87% after eigenvalue selection and data fusion. The method has high accuracy and adaptability, and is very suitable for mechanical equipment such as mine ventilator.

Reliability Allocation Method Based on 2-Tuple Linguistic Weighted Muirhead Mean Operator and 2-Tuple Linguistic Best-Worst Method

Reliability allocation is a significant link in product design. To solve the problems of poor rationality of data use, considerable difficulty of calculation using existing methods, low accuracy of results, and weak ability of experts to express and process fuzzy information, this article proposes a reliability allocation method for the initial stage of product design based on the 2-tuple linguistic weighted Muirhead mean (2TLWMM) operator and 2-tuple linguistic best-worst method (2TLBWM). The 2TLBWM introduces 2-tuple linguistic to enhance the experts’ ability to express fuzzy information. The 2TLWMM operator is used to judge the reliability index ranking of each subsystem, thereby improving the rationality of scoring data, providing a basis for experts to establish the comparison vector table, and reducing the influence of experts’ subjective factors. Experts use 2TLBWM, refer to the ranking results of subsystem reliability index, only need to establish a contrast vector table, and can calculate the weight of the subsystem, thereby reducing the number of comparisons between elements and computational complexity. The advantages of the proposed method are illustrated by a specific case.

Adjacent-Vector-Based Model Predictive Control for Permanent Magnet Synchronous Motors With Full Model Estimation

The finite-control-set model predictive control (FCS-MPC) shows appealing features in permanent magnet synchronous motor control but suffers from high current ripples. Multiple-vector-based MPC (MVB-MPC) methods reduce the ripples but may lose some of the merits possessed by the FCS-MPC. Furthermore, the performance of both the FCS-MPC and the MVB-MPC is dependent on the accurate knowledge of the system parameters. This article proposes an adjacent-vector-based MPC (AVB-MPC) combining the FCS-MPC and the MVB-MPC. Instead of finding a solution with minimum cost, the AVB-MPC searches from a lookup table of adjacent vectors for the solution with the least switching actions to keep the cost within a tolerable band. Hence, the current ripple is reduced under the same switching frequency, while the merits of fast dynamic responsiveness and inherent overmodulation ability are retained. In addition, a full model estimation method is proposed so that the knowledge of the machine parameters is not required. The rank deficiency problem of multiparameter identification is also solved by the tolerable band. And these benefits are gained without adding much computational effort. Finally, the effectiveness and benefits of the AVB-MPC are validated by comparative experimental results comparing with several other typical predictive controllers.

Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information

Hash is one of the most widely used methods for computing efficiency and storage efficiency. With the development of deep learning, the deep hash method shows more advantages than traditional methods. This paper proposes a method to convert entities with attribute information into embedded vectors (FPHD). The design uses the hash method to quickly extract entity features, and uses a deep neural network to learn the implicit association between entity features. This design solves two main problems in large-scale dynamic data addition: (1) The linear growth of the size of the embedded vector table and the size of the vocabulary table leads to huge memory consumption. (2) It is difficult to deal with the problem of adding new entities to the retraining model. Finally, taking the movie data as an example, this paper introduces the encoding method and the specific algorithm flow in detail, and realizes the effect of rapid reuse of dynamic addition data model. Compared with three existing embedding algorithms that can fuse entity attribute information, the deep hash embedding algorithm proposed in this paper has significantly improved in time complexity and space complexity.

An Algorithm to Detect Endangered Cultural Heritage by Agricultural Expansion in Drylands at a Global Scale

This article presents AgriExp, a remote-based workflow for the rapid mapping and monitoring of archaeological and cultural heritage locations endangered by new agricultural expansion and encroachment. Our approach is powered by the cloud-computing data cataloguing and processing capabilities of Google Earth Engine and it uses all the available scenes from the Sentinel-2 image collection to map index-based multi-aggregate yearly vegetation changes. A user-defined index threshold maps the first per-pixel occurrence of an abrupt vegetation change and returns an updated and classified multi-temporal image aggregate in almost-real-time. The algorithm requires an input vector table such as data gazetteers or heritage inventories, and it performs buffer zonal statistics for each site to return a series of spatial indicators of potential site disturbance. It also returns time series charts for the evaluation and validation of the local to regional vegetation trends and the seasonal phenology. Additionally, we used multi-temporal MODIS, Sentinel-2 and high-resolution Planet imagery for further photo-interpretation of critically endangered sites. AgriExp was first tested in the arid region of the Cholistan Desert in eastern Pakistan. Here, hundreds of archaeological mound surfaces are threatened by the accelerated transformation of barren lands into new irrigated agricultural lands. We have provided the algorithm code with the article to ensure that AgriExp can be exported and implemented with little computational cost by academics and heritage practitioners alike to monitor critically endangered archaeological and cultural landscapes elsewhere.

ПРОФЕССИОНАЛЬНЫЕ ПРИВЕРЖЕННОСТЬ И САМОИДЕНТИФИКАЦИЯ СТУДЕНТОВ: МЕТОДИЧЕСКИЕ ПОДХОДЫ К АНАЛИЗУ УРОВНЕЙ И ФАКТОРОВ

The relevance of promoting the effective-ness of the professionalization of students is shown. The statistical data of the labor mar-ket are presented. It demonstrates an in-crease in the share of citizens who do not use their professional education in the course of their work. A variant of forming a battery of tests designed to identify the influ-ence of personal characteristics on the lev-els of professional commitment and self-identification at the adept and adaptant stages is proposed. The choice of test meth-ods combined into two blocks is justified. They are designed to analyze personality traits and to assess the level of professional-ization. A table of probable vectors of pro-fessional development determined by the ratios of values of commitment and identity of a person in relation to the profession is presented.

Instant neural graphics primitives with a multiresolution hash encoding

Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations: a small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through stochastic gradient descent. The multiresolution structure allows the network to disambiguate hash collisions, making for a simple architecture that is trivial to parallelize on modern GPUs. We leverage this parallelism by implementing the whole system using fully-fused CUDA kernels with a focus on minimizing wasted bandwidth and compute operations. We achieve a combined speedup of several orders of magnitude, enabling training of high-quality neural graphics primitives in a matter of seconds, and rendering in tens of milliseconds at a resolution of 1920×1080.

NATURAL AND CULTURAL HERITAGE IN THE INTERACTIVE ATLAS "THE POPULATION OF UKRAINE AND ITS NATURAL AND CULTURAL HERITAGE"

The reports of The Club of Rome, the Agenda 21, Global Development Goals 2030 focus on dangerous trends in the development of Society, which can lead to the disappearance of life on planet Earth. Particular attention is paid to the preservation of the quality of the natural environment. 
 The purpose of this study is to consider the significance, interconnectedness, and interdependence of natural and cultural heritage in line with the general trends of nature conservation and digitalization of various spheres of human activity.
 In complex studies of Geospace, as you know, the integration of its results should be ensured, which in current conditions is ensured by geocoding data, automatic processing, storage, and visualization. A recognized direction is the use of electronic atlas systems. In Ukraine, over the past 5 years, a study has been carried out concerning the inventory and assessment of natural and cultural heritage against the background of the formation of the Ukrainian ethnos, starting from the Stone and Copper-Bronze Ages. The absence of the concept of "natural heritage" in the legislative field of Ukraine has led to the analysis of international experience and the adoption of appropriate classifications.
 The research resulted in an interactive atlas "The Population of Ukraine and its Natural and Cultural Heritage", containing about 4 gigabytes of various information, which is focused on 98 vector maps (366 information layers), texts, photos, and tables.

Retraction Note to: Analysis of PM2.5 air pollution sources based on image recognition and tracking of sports training process

With the rapid development of social economy and the acceleration of urbanization, air quality is also facing many challenges. Recently, through effective control of pollutant emissions, the number of foggy days and PM2.5 concentration in city T tends to decrease, and air quality has been greatly improved, but haze pollution still occurs occasionally in this area. Analyzing the sources of PM2.5 by industry is particularly important for further improving the air environment quality in T city and implementing targeted industry control. To this end, this study simulated the PM2.5 pollution characteristics of city T in January, April, July, and October 2019 based on the Models-3/CMAQ 3D air quality model and compared and verified the PM2.5 concentration. In practical applications, we will analyze and study the sources of PM2.5 pollution in T cities in the industry based on the “return to zero method” of source analysis based on image recognition and within the framework of a unified model. It is especially important to correctly understand the impact of the target direction on the academic performance of sports training students. The sports field goal setting vector table deeply understands the achievement goal orientation and academic performance of sports training students through literature data methods, questionnaire survey methods, and mathematical statistics methods and provides empirical evidence for teachers to track education. Reference materials are provided by professional students, who manage and develop sports training in a more targeted way in the sports academy. Through the research of image recognition, this paper applies it to the research of PM2.5 air pollution sources and the tracking of sports training process, thereby promoting the better development of air quality and sports training.

Hybrid intelligent controller based torque ripple minimization in switched reluctance motor drive

An inherent torque ripple characterizes switched reluctance technology from conventional technology. The ultimate aim of this paper is to reduce the torque ripple of the switched reluctance motor drive using genetic neural network controller based direct torque scheme. In the proposed controller network appropriate bits of data are chosen for training and testing. The proper selection of the learning rate and momentum will help in weight adjustment. Here the error is reduced which proves that the selection of voltage vectors from the vector table is precise and its results in better torque response over a wide range of speed. The simulation results reveal that the torque ripples vary between 3.25% to 1.7% for the variation in load torque and the drive speed. The experimental results for the proposed controller reveal that the torque ripple varies between 3.7% to 2.1%. Both the simulation and hardware results illustrate the efficiency of the controller.

Torque Ripple Minimization in Switched Reluctance Motor using ANFIS Controller

An inborn torque swell portrays changed hesitance innovation from traditional innovation. A definitive target of this paper is to minimize the torque wave of the exchanged hesitance engine drive utilizing Artificial Network Fuzzy Inference System based direct torque conspire. In the proposed controller arrange proper bits of information are picked for preparing and testing. The best possible choice of the learning rate and energy will help in weight change. The Intelligent controller gives high power over motor torque and speed, lessens rise time just as overshoot. Here the blunder is decreased which demonstrates that the determination of voltage vectors from the vector table is exact and its outcomes in better torque reaction over a wide scope of speed. The reenactment results uncover that the torque swells fluctuate between 3.75% to 2.25% for the variety in load torque and the drive speed. The experimental results for the proposed controller reveal that the torque ripple varies between 3.9% to 2.4% for the variation in speed. Both the recreation and equipment results delineate the effectiveness of the controller.

TEDL: A Text Encryption Method Based on Deep Learning

Recent years have seen an increasing emphasis on information security, and various encryption methods have been proposed. However, for symmetric encryption methods, the well-known encryption techniques still rely on the key space to guarantee security and suffer from frequent key updating. Aiming to solve those problems, this paper proposes a novel symmetry-key method for text encryption based on deep learning called TEDL, where the secret key includes hyperparameters in the deep learning model and the core step of encryption is transforming input data into weights trained under hyperparameters. Firstly, both communication parties establish a word vector table by training a deep learning model according to specified hyperparameters. Then, a self-update codebook is constructed on the word vector table with the SHA-256 function and other tricks. When communication starts, encryption and decryption are equivalent to indexing and inverted indexing on the codebook, respectively, thus achieving the transformation between plaintext and ciphertext. Results of experiments and relevant analyses show that TEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution. Especially, as a supplement to current encryption methods, the time-consuming process of constructing a codebook increases the difficulty of brute-force attacks, meanwhile, it does not degrade the efficiency of communications.

Reduction of torque and flux ripples in direct torque control for three‐level open‐end winding PMSM drive

The direct torque control (DTC) of permanent magnet synchronous motor (PMSM) drives is a noticeable practice in industrial applications due to its quick dynamic performance. However, due to the presence of hysteresis controllers and inaccurate switching tables especially for the multilevel fed DTC drives are facing higher ripples in torque and flux response. An improved DTC for three-level open-end winding PMSM drive is proposed in this article and features reduction of flux and torque ripples. The torque and flux performances of the PMSM are directly controlled by the calculated reference voltage vector in the stationary reference frame. The voltage vector tables are proposed based on the length of the voltage space vectors and the actual voltage vector nearer to the calculated reference voltage vector is selected so that the minimum ripples exist in flux and torque responses. The steady-state and dynamic conditions of the proposed DTC method are verified in the real-time experiment and also compared with the conventional DTC as well as the recently developed control methods.

An Improved Direct Torque Control for a Single-Winding Bearingless Switched Reluctance Motor

The direct torque control (DTC) and direct force control (DFC) method were introduced to reduce the torque and levitation force ripple in single-winding bearingless switched reluctance motors (SWBSRMs). However, it still has some disadvantages. Firstly, the flux-linkage control is not suitable for the DTC method in SWBSRMs. On the one hand, it can increase the torque ripple. On the other hand, the RMS current can be increased and then the torque-ampere ratio is decreased. Secondly, the vectors selection is also unreasonable, which can increase the torque ripple further. In order to solve these problems, an improved control method based on DTC and DFC method for SWBSRMs is proposed in this paper, which can obtain high torque-ampere ratio and low torque ripple simultaneously. In the proposed method, the flux-linkage loop control is not needed and the space voltage vector table is improved. The experimental results show that the torque ripple is reduced by 66.7%, the torque-ampere ratio is increased by 200% and the switching times in one electrical period are reduced by 47.3%.

НАСЕЛЕННЯ УКРАЇНИ ТА ЙОГО ПРИРОДНА І КУЛЬТУРНА СПАДЩИНА В АТЛАСНІЙ ІНФОРМАЦІЙНІЙ СИСТЕМІ

The purpose of the publication is to highlight the main results of the development of the first Ukrainian interactive atlas “Population of Ukraine and its natural and cultural heritage”. It was created in accordance with the Memorandum of Cooperation between the NAS of Ukraine and the Ministry of Culture and Information Policy of Ukraine. According to analysis of previous researches, there have been no attempts to map the complex and long-term processes of interaction between the population and its natural and cultural heritage so far in Ukraine and abroad. The Atlas provides knowledge and information about the historical stages of development of the population of Ukraine and the formation of its cultural and natural heritage in the following sections: 1. Ukraine in the European space - 3 maps; 2. Population - the subject of heritage formation - 57 maps; 3. Natural and cultural heritage - 28 maps; 4. Protection, preservation, restoration and using of natural and cultural heritage - 9 maps. The Atlas was developed using the updated AtlasSF Atlas Solutions Framework, one of the previous versions of which was used to create the Electronic version of the National Atlas of Ukraine. The modern architecture of AtlasSF allows integrating the developed atlas both in the atlas information system, and in the atlas geoinformation system of cultural heritage. All types of information resources were used to create the Atlas: maps, texts, photos, statistics. The interactive atlas contains 97 vector maps, texts, photos and tables.

Face Recognition Based on Histogram Equalization and LBP Algorithm

In the recent time bioinformatics take wide field in image processing. Face recognition which is basically the task of recognizing a person based on its facial image. It has become very popular in the last two decades, mainly because of the new methods developed and the high quality of the current visual instruments. There are different types of face recognition algorithms, and each method has a different approach to extract the image features and perform the matching with the input image.
 In this paper the Local Binary Patterns (LBP) was used, which is a particular case of the Texture Spectrum model, and powerful feature for texture classification. The face recognition system consists of recognizing the faces acquisition from a given data base via two phases. The most useful and unique features of the face image are extracted in the feature extraction phase. In the classification the face image is compared with the images from the database.
 The proposed algorithm for face recognition in this paper adopt the LBP features encode local texture information with default values. Apply histogram equalization and Resize the image into 80x60, divide it to five blocks, then Save every LBP feature as a vector table.
 Matlab R2019a was used to build the face recognition system. The Results which obtained are accurate and they are 98.8% overall (500 face image).

Dual-Vector Predictive Torque Control of Permanent Magnet Synchronous Motors Based on a Candidate Vector Table

In order to reduce the torque ripple of permanent magnet synchronous motors (PMSMs), this paper proposes a dual-vector predictive torque control strategy based on a candidate vector table. The main feature of this strategy is that two vectors are acted in a control period to form a vector combination, and the vector combination can be either an effective-zero combination or an effective-effective combination. In the process of establishing the vector combinations, the switching frequency is also taken into account, therefore avoiding a high switching frequency, while effectively reducing the motor torque ripple. The candidate vector table is constructed offline, and three sets of candidate vectors and their duty cycles can be determined by looking up the table. Then the cost function is used to screen the action vectors from the three sets candidate vectors, so the two vectors acted in one control period and their duty cycles can be obtained simultaneously. Finally, the feasibility and effectiveness of the proposed method are verified on a 5.2 kW two-level inverter-fed PMSM drive system.

MixProTool: A Powerful and Comprehensive Web Tool for Analyzing and Visualizing Multigroup Proteomics Data.

Deciphering and visualizing proteomics data are a big challenge for high-throughput proteomics research. In this work, we develop a free interactive web software platform, MixProTool, for processing multigroup proteomics data sets. This tool provides integrated data analysis workflow, including quality control assessment, normalization, soft independent modeling of class analogy, statistics, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. This software is also highly compatible with the identification and quantification results of various frequently used search engines, such as MaxQuant, Proteome Discoverer, or Mascot. Moreover, all analyzed results can be visualized as vector graphs and tables for further analysis. MixProTool can be conveniently operated by users, even those without bioinformatics training, and it is extremely useful for mining the most relevant features among different samples. MixProTool is deployed at the public shinyapps io server.

A vector table model-based systematic analysis of spectral fault localization techniques

Spectral fault localization (SFL) is an automatic fault-localization technique, which uses risk evaluation formula to rank the risk of fault existence in each program entity after collecting the testing information dynamically. To provide insight into SFL techniques, the evaluation method is an important research topic. In this paper, we present a uniformly systematic investigation framework to evaluate and compare SFL techniques with fixed formulas considering both single-fault and multiple-fault scenarios. Particularly, we design a generic vector table model called VTM as a novel measurement model to thoroughly understand various SFL techniques. By defining different types of faulty statements and investigating suspiciousness factors’ mathematical expression of statements on the basis of VTM, the effectiveness of different SFL techniques could be systematically analyzed and compared. Under the VTM-based evaluation framework, the latest formula D* and the optimal formula O considered before as examples are explored: (1) under a single-fault scenario, O has a better performance than D*, and O is the best SFL technique at present; (2) O shows stable performance and the performance of D* fluctuates in a range under double-fault scenarios: O outperforms D* in three cases, and the performance of O is between the worst performance and the best performance of D* in the other three cases that are less likely to happen; and (3) sample programs are presented to explain such observations. The VTM-based method overcomes the limitations of existing empirical and systematic approaches, which enables a systematic evaluation for SFL techniques with fixed formulas under both single-fault and multiple-fault cases.

A Security and Efficient Routing Scheme with Misbehavior Detection in Delay-Tolerant Networks

Due to the unique network characteristics, the security and efficient routing in DTNs are considered as two great challenges. In this paper, we design a security and efficient routing scheme, called SER, which integrates the routing decision and the attacks detection mechanisms. In SER scheme, each DTNs node locally maintains a one-dimensional vector table to record the summary information about the contact with other nodes and the trust degree of other nodes. To obtain the global status and the contact relationship among all nodes, the trusted routing table consisting of vectors of all nodes is built in each DTNs node. The method for detecting malicious nodes and selfish nodes is proposed, which exploits the global summary information to analyze the history forwarding behavior of node and judge whether it is a malicious node or selfish node. The routing decision method is proposed based on trust degree of forwarding messages between nodes, which adopts trust degree as relay node selection strategy. Simulation results show that compared with existing schemes SER scheme could detect the attacks behavior of malicious nodes and selfish nodes, at the same time, with higher delivery rate and lower average delivery delay.