Dynamic Vector Research Articles

Application of Machine Learning Combined with Wireless Network in Design of Online Translation System

The importance of translation services has become increasingly prominent with the acceleration of economic globalization. Compared with human translation, machine translation is cheaper and faster, and therefore more suitable for the current era. The current mainstream machine translation method is neural machine translation, which employs machine methods to train on parallel corpora and create translation models. Research into neural machine translation has yielded a wealth of information. Learning and generalization abilities of neural networks have substantially enhanced the effectiveness of neural machine translation. This work applies machine learning and wireless network technology to build an online translation system for real-time translation. First, this work proposes a multigranularity feature fusion method based on a directed acyclic graph, which uses a directed acyclic graph to fuse different granularities as input and obtain a position representation. Secondly, this paper improves the Transformer model and proposes multigranularity position encoding and multigranularity self-attention. Then, on the basis of multigranularity features as input, this work introduces dynamic word vectors to improve the word embedding module, and uses the ELMo model to obtain dynamic word vector embeddings. Finally, this work builds a multigranularity feature-dynamic word vector machine translation model with above strategy, deploys it on server. Users can upload the content to be translated and download the translated content through the wireless network and realize an online translation system based on machine learning and wireless network.

Effects of Dynamic IMU-to-Segment Misalignment Error on 3-DOF Knee Angle Estimation in Walking and Running.

The inertial measurement unit (IMU)-to-segment (I2S) alignment is an important part of IMU-based joint angle estimation, and the accurate estimation of the three degree of freedom (3-DOF) knee angle can provide practical support for the evaluation of motions. In this paper, we introduce a dynamic weight particle swarm optimization (DPSO) algorithm with crossover factor based on the joint constraint to obtain the dynamic alignment vectors of I2S, and use them to perform the quaternion-based 3-DOF knee angle estimation algorithm. The optimization algorithm and the joint angle estimation algorithm were evaluated by comparing with the optical motion capture system. The range of 3-DOF knee angle root mean square errors (RMSEs) is 1.6°-5.9° during different motions. Furthermore, we also set up experiments of human walking (3 km/h), jogging (6 km/h) and ordinary running (9 km/h) to investigate the effects of dynamic I2S misalignment errors on 3-DOF knee angle estimation during different motions by artificially adding errors to I2S alignment parameters. The results showed differences in the effects of I2S misalignment errors on the estimation of knee abduction, internal rotation and flexion, which indicate the differences in knee joint kinematics among different motions. The IMU to thigh misalignment error has the greatest effect on the estimation of knee internal rotation. The effect of IMU to thigh misalignment error on the estimation of knee abduction angle becomes smaller and then larger during the two processes of switching from walking to jogging and then speeding up to ordinary running. The effect of IMU to shank misalignment error on the estimation of knee flexion angle is numerically the largest, while the standard deviation (SD) is the smallest. This study can provide support for future research on the accuracy of 3-DOF knee angle estimation during different motions.

Integrated optimization of a high-lift low-pressure turbine cascade based on dynamic support vector regression

With the increase of the lift of turbine components, the performance of the turbine is significantly affected by the secondary flow. The combined optimization of the root modification and the profiled endwall (PEW) is investigated for more efficient secondary flow control in the endwall region of a high-lift low-pressure turbine cascade. The NURBS surface is used in this paper to perform parametric modelling. Support vector machines (SVM) and particle swarm optimization (PSO) are used to complete the optimal design of the combination of cascade root and endwall. To ensure the accuracy of the regression model, dynamic parameter adjustment and dynamic point addition strategies are introduced in the optimization process to update the regression model. The study found that when the separate profiled endwall was optimized for the cascade, the total pressure loss of the cascade decreased by 10.39%; when the cascade was optimized by the combined shape of the cascade root and the endwall, the total pressure loss of the cascade decreased by 12.88%. The detailed flow field analysis shows that both the profiled endwall and integrated optimization can effectively weaken the cross-passage pressure gradient in the endwall region of the cascade passage. Compared with only profiled endwall, the integrated optimization restrains the passage vortex and the suction side leg of horseshoe vortex from developing towards the middle of the cascade, and the secondary flow loss in the endwall region is obviously reduced.

Effect of news and noise shocks of US monetary policy on economic fluctuations in emerging market economies

AbstractThis study investigates the effect of news and noise shocks of US monetary policy on economic fluctuations in emerging market economies. In the first part of our two‐step estimation method, the news and noise shocks of US monetary policy are estimated using dynamic structural vector autoregression identification. In the second step, the impact of the news and noise shocks on macro variables reflecting the business cycle (e.g., production, consumption, investment and trade balance) is examined using local projection. Our empirical results show no significant differences in the responses to both shocks at the early stage, when news and noise are not separable. However, when the monetary policy becomes known, emerging market economies enter a full‐scale recession with respect to a news shock of raised US interest rates. Meanwhile, emerging market economies enter an economic boom phase of the business cycle when the shock turns out to be noise. Fluctuations driven by noise are likely to incur greater costs than normal economic fluctuations because the former are out of sync with the fundamentals.

A Multimodel-Based Deep Learning Framework for Short Text Multiclass Classification with the Imbalanced and Extremely Small Data Set.

Text classification plays an important role in many practical applications. In the real world, there are extremely small datasets. Most existing methods adopt pretrained neural network models to handle this kind of dataset. However, these methods are either difficult to deploy on mobile devices because of their large output size or cannot fully extract the deep semantic information between phrases and clauses. This paper proposes a multimodel-based deep learning framework for short-text multiclass classification with an imbalanced and extremely small dataset. Our framework mainly includes five layers: the encoder layer, the word-level LSTM network layer, the sentence-level LSTM network layer, the max-pooling layer, and the SoftMax layer. The encoder layer uses DistilBERT to obtain context-sensitive dynamic word vectors that are difficult to represent in traditional feature engineering methods. Since the transformer part of this layer is distilled, our framework is compressed. Then, we use the next two layers to extract deep semantic information. The output of the encoder layer is sent to a bidirectional LSTM network, and the feature matrix is extracted hierarchically through the LSTM at the word and sentence level to obtain the fine-grained semantic representation. After that, the max-pooling layer converts the feature matrix into a lower-dimensional matrix, preserving only the obvious features. Finally, the feature matrix is taken as the input of a fully connected SoftMax layer, which contains a function that can convert the predicted linear vector into the output value as the probability of the text in each classification. Extensive experiments on two public benchmarks demonstrate the effectiveness of our proposed approach on an extremely small dataset. It retains the state-of-the-art baseline performance in terms of precision, recall, accuracy, and F1 score, and through the model size, training time, and convergence epoch, we can conclude that our method can be deployed faster and lighter on mobile devices.

HandGest: Hierarchical Sensing for Robust-in-the-Air Handwriting Recognition With Commodity WiFi Devices

Recent advances in wireless sensing techniques have made it possible to recognize hand gestures using channel state information (CSI) in commodity WiFi devices. Existing WiFi-based gesture recognition systems mainly use learning-based pattern recognition methods to recognize different gestures, however, these methods fail to work well when the locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and/or the hand gesturing size change, leading to inconsistent signal patterns caused by those factors. Although some recent efforts have been made to address the so-called “domain-dependent” gesture recognition problem, they either require prior knowledge on initial locations of the hand and WiFi devices or need to train several classifiers for the specific domains. Different from the state-of-the-art methods, we construct two distinct features from a hand-oriented view (rather than from a transceiver’s view), namely, the dynamic phase vector (DPV) and motion rotation variable (MRV), which are quite consistent in characterizing a big set of handwriting gestures, despite significant change in locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and the drawing sizes. We further incorporate a hierarchical sensing framework and develop HandGest—a real-time handwriting gesture recognition system using commodity WiFi devices, to precisely recognize a great number of “in-the-air” handwritings based on the aforementioned two domain-independent features and a pipeline of specific features. Extensive experiments have been done in practical settings with 20 volunteers, evaluation results demonstrate that HandGest outperforms state-of-the-art methods on a large number of handwritings with different transceivers’ location, different initial hand locations and orientations, as well as different drawing sizes. Given its superior performance, we believe that HandGest paves a new way to enhance the real-world practicality of WiFi-based gesture recognition.

Sentiment analysis based on Chinese BERT and fused deep neural networks for sentence-level Chinese e-commerce product reviews

Driven by the rapid development of Internet, more e-commerce product reviews are available on e-commerce platforms, which can help enterprises make business decisions. Currently, bidirectional encoder representations from transformers (BERT) applied in the embedding layer contributes to achieve promising results in English text sentiment analysis (SA). This paper proposes a novel model Chinese BERT with fused deep neural networks (CBERT-FDNN), extracting richer and more accurate semantic and grammatical information in Chinese text. First, Chinese BERT with whole word masking (Chinese-BERT-wwm) is used in the embedding layer to generate dynamic sentence representation vectors. It is a Chinese pre-training model based on the whole word masking (WWM) technology, which is more effective for Chinese text contextual embedding. Second, multi-channel and multi-scale convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are designed to capture further crucial features in the feature extraction layer. To obtain more comprehensive sentence attributes, these features are concatenated together. Last, the model is evaluated on 100,000 sentence-level Chinese e-commerce product reviews for sentiment binary classification. The accuracy and F1 score can achieve 94.37% and 94.34%, respectively. Compared with the baseline models, the experiments show that our proposed model has higher accuracy and better prediction performance.

HPC-enabling technologies for high-fidelity combustion simulations

With the increase in computational power in the last decade and the forthcoming Exascale supercomputers, a new horizon in computational modelling and simulation is envisioned in combustion science. Considering the multiscale and multiphysics characteristics of turbulent reacting flows, combustion simulations are considered as one of the most computationally demanding applications running on cutting-edge supercomputers. Exascale computing opens new frontiers for the simulation of combustion systems as more realistic conditions can be achieved with high-fidelity methods. However, an efficient use of these computing architectures requires methodologies that can exploit all levels of parallelism. The efficient utilization of the next generation of supercomputers needs to be considered from a global perspective, that is, involving physical modelling and numerical methods with methodologies based on High-Performance Computing (HPC) and hardware architectures. This review introduces recent developments in numerical methods for large-eddy simulations (LES) and direct-numerical simulations (DNS) to simulate combustion systems, with focus on the computational performance and algorithmic capabilities. Due to the broad scope, a first section is devoted to describe the fundamentals of turbulent combustion, which is followed by a general description of state-of-the-art computational strategies for solving these problems. These applications require advanced HPC approaches to exploit modern supercomputers, which is addressed in the third section. The increasing complexity of new computing architectures, with tightly coupled CPUs and GPUs, as well as high levels of parallelism, requires new parallel models and algorithms exposing the required level of concurrency. Advances in terms of dynamic load balancing, vectorization, GPU acceleration and mesh adaptation have permitted to achieve highly-efficient combustion simulations with data-driven methods in HPC environments. Therefore, dedicated sections covering the use of high-order methods for reacting flows, integration of detailed chemistry and two-phase flows are addressed. Final remarks and directions of future work are given at the end.

Soft sensing of SO2 emission for ultra-low emission coal-fired power plant with dynamic model and segmentation model

Static models, segmentation models and dynamic models for soft sensing of SO2 emission were developed based on the Ordinary Least Squares, Support Vector Regression, eXtreme Gradient Boosting, Random Forest and Neural Network algorithms with real operational data from a 1000 MW coal-fired power plant with ultra-low emission systems. The prediction results of test set show that static models are not suitable for modeling the desulfurization systems with complex condition and time-delay process. Thus, segmentation models and dynamic models were used to optimize the prediction accuracy. All the prediction performance of the nonlinear models was improved significantly with dynamic model, while the prediction performance of the linear model was improved with segmentation model. The dynamic Neural Network model with one hidden layer achieves the most accurate regression result (R2 = 70.4 %, RMSE = 1.95 mg/m3, MAE = 1.53 mg/m3). The results show the superiority of the dynamic Neural Network model over the other and the dynamic Support Vector Regression model perform second-best.

A novel dynamic radius support vector data description based fault diagnosis method for proton exchange membrane fuel cell systems

Timely fault detection is critical to improving the reliability and durability of the proton exchange membrane fuel cell (PEMFC) system. This paper proposes a novel fault diagnosis method, dynamic radius support vector data description (DR-SVDD), to efficiently identify the PEMFC system's faults. Compared to the classic support vector data description (SVDD) and improved SVDDs, this method considers both the SVDD hypersphere radius information and the distribution characteristics of the training set samples to obtain a more accurate and adequate description of the sample data. The cell voltages and the pressure drops at the cathode and anode obtained experimentally under various fault conditions are chosen as the feature variables for the PEMFC fault diagnosis. The comparative results show that the proposed DR-SVDD strategy performs well in fault class identification for a PEMFC system.

Research on the Chinese Named-Entity–Relation-Extraction Method for Crop Diseases Based on BERT

In order to integrate fragmented text data of crop disease knowledge to solve the current problems of disordered knowledge management, weak correlation and difficulty in knowledge sharing, a Chinese named-entity–relation-extraction model for crop diseases (BBCPF) was proposed in this paper by utilizing the advantage of knowledge graph in describing complex relations between disease entities in a structured form. This model was composed of two parts, i.e., named-entity recognition and relation extraction, in the form of an assembly line. To deal with the different meanings of Chinese crop disease terms in different contexts and to better obtain the contextual information, the BERT model was introduced for dynamic vector representations. Then, the BiLSTM layer was used to learn long-distance text information, and the CRF was applied to obtain the globally optimal labeling sequence, so as to output the crop disease entities. According to the entity category, the entities were divided as subjects and objects, which were then input into the disordered language model PERT to extract the contextual features of the relation data. At last, the fully connected layer was used to decode the information and output the crop disease entity-relation triples. The experiment results show that, on the self-built disease corpus dataset, the Precision, Recall, and F1-Score values of the established model reached 85.63%, 79.46% and 82.43%, respectively, for entity extraction, and reached 97.96%, 98.43% and 98.16%, respectively, for relation extraction. This paper provides an effective method for information extraction in the construction of Chinese crop disease domain knowledge graphs.

UAV Standoff Tracking for Narrow-Area Target in Complex Environment

This article studies the path planning for an unmanned aerial vehicle (UAV) standoff tracking a narrow-area target in complex environment. Since the target state estimation considerably influences the target tracking performance, the constrained interacting multiple model, which uses the geographic information of narrow-areas to enhance the estimation accuracy, is proposed. Then, based on the obtained estimation results, the dynamic Lyapunov guidance vector field (DLGVF) is proposed, which can guide UAVs to converge to not only the expected standoff distance in the horizontal plane but also the desired height in the vertical plane. Moreover, the DLGVF renders the UAV trajectories flexible and shortens the convergence time by skillfully introducing adjustment parameters. Finally, to simultaneously complete the missions of target tracking and obstacle avoidance, an integrated method based on the DLGVF and the interfered fluid dynamical system is presented. Simulations are implemented to demonstrate that the proposed framework is effective.

Multivariate Relevance Vector Regression Based Degradation Modeling and Remaining Useful Life Prediction

Relevance vector regression (RVR) is a useful tool for degradation modeling and remaining useful life (RUL) prediction. However, most RVR models are for 1-D degradation processes and can only handle univariate observations. This article proposes a degradation path-based RUL prediction framework using a dynamic multivariate relevance vector regression model. Specifically, a multistep regression model is established for describing the degradation dynamics and extending the classical RVR into a multivariate one with consideration of the multivariate environment. The article introduces a matrix Gaussian distribution-based RVR approach and then estimates the hyperparameters with Nesterov’s accelerated gradient method to avoid the exhausting re-estimation phenomenon in seeking analytical solutions. It further forecasts the degradation path for monitoring the degradation status. Based on the forecasted path, the RUL is predicted by the first hitting time method. Finally, the proposed methods are illustrated by two case studies, one is presented in this article and the other in the supplement, which investigate the capacitors’ and bearings’ performance degradations.

Potential seed dispersal mechanism of Kigelia africana (Lam.) Benth. in an urban habitat

ABSTRACT Kigelia africana is known to exhibit megafaunal syndrome. Many mega faunal taxa having become extinct with time, the species seems to have adapted to its changing environment by ensuring the dispersal of its seeds by alternative dispersers. The present investigation reports the mechanism of seed dispersal adopted by individuals of K. africana grown in India, outside the species natural environment. The massive – ~2.0 kg – indehiscent and unattractive fruits retain the wingless seeds. Of more than 300 seeds present in a fruit in average, approximately 92% remained viable at maturity, and the viability decreased over time after 1 year. We observed that numerous termites (Coptotermes formosanus) initiated the disintegration of the fallen fruits particularly during monsoon. Three-striped squirrels (Funambulus palmarum), classified as scatter hoarding rodents, then accessed these fruits and their seeds, and actively carried about 48% seeds with broken coats to a distance of 40 m away from the mother trees. The coordinated activity of these two dynamic vectors ensured successful seed dispersal in K. africana at the study site, suggesting that the species may have developed a way to adapt to its new environment.

Comparison of the Autoregressive Vector VAR with the Dynamic Error Correction Vector DVECM for Modeling COVID-19 Deaths

In this article, the Vector Auto-Regressive model and the Dynamic Error Correction Vector Model will be used in modeling data representing the number of deaths due to infection with the COVD-19 virus as a dependent variable and the variable platelet rate in the blood as an independent variable and finding the model equations that represent the relationship between the two variables using the two models and then estimating the equations that were obtained by estimating the two models using the least squares method, then choosing the best estimated equation from each model, and then, using the standard error of the regression and the coefficient of determination, selecting the best equations from the two models. The Dynamic Error Correction Vector Model is superior to the Vector Auto-Regressive model in assessing the link between corona virus mortality and the proportion of platelets in the blood, according to the analysis carried out using the E-Views application, and that there is a direct relationship through the equation for the Dynamic Error Correction Vector Model between the deaths of the corona virus and the proportion of platelets in the blood in the long term, which is logical as a result of the increase in the impact of the deaths of the Corona virus, the increase in the platelet rate, and thus the increase in the deaths of the corona virus.

Sentiment Analysis of Chinese E-Commerce Product Reviews Using ERNIE Word Embedding and Attention Mechanism

The development of e-commerce has ushered in a golden age. E-commerce product reviews are remarks initiated by online shopping users to evaluate the quality and service of the products they purchase; these reviews help consumers learn the reality of the product. The sentiment polarity of e-commerce product reviews is the best way to obtain customer feedback on products. Therefore, sentiment analysis of product reviews on e-commerce platforms is greatly significant. However, the challenges of sentiment analysis of Chinese e-commerce product reviews lie in dimension mapping, disambiguation of sentiment words, and polysemy of Chinese words. To solve the above problems, this paper proposes a sentiment analysis model ERNIE-BiLSTM-Att (EBLA). Here, the dynamic word vector generated using the Enhanced Representation through Knowledge Integration (ERNIE) word embedding model is input into the Bidirectional Long Short-term Memory (BiLSTM) to extract text features. Then, the Attention Mechanism (Att) is used to optimize the weight of the hidden layer. Finally, softmax is used as the output layer for sentiment classification. The experimental results on the JD.com Chinese e-commerce product review dataset show that the proposed model achieves more than 0.87 in precision, recall, and F1 values, which is superior to classic deep learning models proposed by other researchers; it has strong practicability in sentiment analysis of Chinese e-commerce product reviews.

Predicting internal conditions of beehives using precision beekeeping

Precision beekeeping combines technology and statistics aimed at managing an apiary effectively and reducing the risk of situations that can lead to bee population losses. Databases of the we4bee project of three sensorised beehives were considered for analysis. They contain interior sensor data (temperature, relative humidity, and weight) and data of meteorological events. Static and dynamic vector autoregressive models and linear and nonlinear regression models were constructed to predict the hives' internal variables. They were compared by 100-fold cross-validation adapted for time series. In general, the dynamic vector autoregressive model provided the best predictions, with a feasible computational cost. Only in some specific cases did the static vector autoregressive version produces smaller errors, although the differences were not statistically significant. Generalised additive and dynamic linear models always provided less accurate results than the dynamic vector autoregressive model. There is a need of integrating accurate predictive models, such as the dynamic vector autoregressive one. This predictive model can be integrated into a decision support system to alert the beekeeper of out-of-the-ordinary situations in the hives, and thus aid in their efficient management. • Accurate models to predict temperature, relative humidity, and weight in a hive. • Predictions based on internal hive data and weather conditions. • Flexible models to include and use information affecting the hive. • Model comparison with different goodness-of-fit criteria and computing time. • Methodology for an early warning system to aid beekeepers in their decisions.

Early human capital: the driving force to economic growth in island economies

PurposeThe paper investigates into the human capital–economic growth nexus by arguing that investment in early education and health helps in achieving higher economic growth. Early investment in human capital matters most for economic growth than the increase in human capital over the years.Design/methodology/approachA dynamic vector error correction model (VECM) together with the impulse response function and variance decomposition are used on data for Mauritius from 1983 to 2019. The paper distinguishes between the short-run and the long-run effects of human capital measured by the pupil–teacher ratio in pre-primary education and life expectancy at birth.FindingsThis study’s findings reveal that investment in early education and health has contributed positively to growth performance. There is evidence for long-run growth effects arising from a positive shock in the education and health indicators.Originality/valueThis paper contributes to both the theoretical and empirical literature on the human capital–growth nexus. Mauritius as a natural resource poor small economy is an important case study as it has started early in investing in its people to promote economic growth.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2021-0674.

Dynamic Shocks of Crude Oil Price and Exchange Rate on Food Prices in Emerging Countries of Southeast Asia: A Panel Vector Autoregression Model

This paper empirically analyzes food price responses to shocks from crude oil price and exchange rate volatility in five emerging Southeast Asian nations between 2000 and 2020 using impulse response functions and variance decomposition analysis of the dynamic panel Vector Autoregression approach. Based on the findings of the impulse response functions analysis, food prices respond positively to both oil price and exchange rate shocks. Meanwhile, the results of variance decomposition analysis show that food prices account for a significant portion of volatility in its own shock. The contribution of oil price shock to food price volatility is found to be greater than the contribution of exchange rate shock. Hence, the study recommends that policymakers in these nations should be vigilant about the impacts of oil price and exchange rate shocks on food prices since these factors may undermine price stability and exacerbate food security.

Synchronization of Wave Flows of Arterial and Venous Blood with Phases of the Cardiac Cycle in Norm: Part 5

The muscular-fibrous frame of the heart (MFFH) synchronizes and compensates for the pressor effect of the myocardium between the high/low-energy regions (left/right) of the heart. The anatomical structures of MFFH (plastic muscular-fibrous formation with a phase change in contours, valves, valve rings) form the "cardiac mean integral pressure" (CMIP). MFFH, a variable spatial structure, forms the starting pressor levels of “opening/closing” of valves and hemodynamic vectors of the heart chambers, systemic and pulmonary circulation, and also compensates for excess pressor pressure (having a variable gradient) at the boundaries of the heart chambers. Throughout the cardiac cycle (CC), on the path "venous block-lung-arterial block-aorta," variable pressure values, compensated by the structures of MFFH, are formed between the blood flows of the right and left parts of the heart. A mutual adaptation of SVs of the ventricles is formed by phase-by-phase compensatory plastics of MFFH. CMIP of MFFH is an integral indicator, where each point reflects: 1) CC phase (time and place); 2) the average value of the range of values in which the equilibrium point of pressor compensation between the high/low-energy processes of this CC phase is located. CMIP is a vector of the MFFH phase dynamics, which compensates for the excess pressor effect with a changing gradient through CC.