Batch Processing Method Research Articles

A Batch Processing Technique for Wearable Health Crowd-Sensing in the Internet of Things

The influx of wearable sensor devices has influenced a new paradigm termed wearable health crowd-sensing (WHCS). WHCS enables wearable data collection through active sensing to provide health monitoring to users. Wearable sensing devices capture data and transmit it to the cloud for data processing and analytics. However, data sent to the cloud is vulnerable to on-path attacks. The bandwidth limitation issue is also another major problem during large data transfers. Moreover, the WHCS faces several anonymization issues. In light of this, this article presents a batch processing method to solve the identified issues in WHCS. The proposed batch processing method provides an aggregate authentication and verification approach to resolve bandwidth limitation issues in WHCS. The security of our scheme shows its resistance to forgery and replay attacks, as proved in the random oracle (ROM), while offering anonymity to users. Our performance analysis shows that the proposed scheme achieves a lower computational and communication cost with a reduction in the storage overhead compared to other existing schemes. Finally, the proposed method is more energy-efficient, demonstrating that it is suitable for the WHCS system.

Analysis of the interaction between A1R and A2AR proteins in living cells based on FRET imaging and batch processing method

The quantitative FRET analysis of living cells is a tedious and time-consuming task for freshman lacks technical training. In this study, FRET imaging and batch processing method were combined to analyze reagents-induced interactions of A1 R and A2A R on cell membranes. Results showed that the method had taken less time than if cell-by-cell was analyzed. The accuracy and repeatability of FRET efficiency values were likewise improved by removing the interference from anthropogenic factors. Then this method was applied to rapidly analyze acetaldehyde-induced interactions, which analyzed hundreds of single-cell trends by one operation, and the results revealed that interactions were consistently attenuated in LX-2 cells, and statistical differences appeared after 30 min. Combined with batch processing method, procedures of cells FRET analysis have been greatly simplified without additional technical work, which has broad prospects in large-scale analysis of cellar protein interaction.

STOCK PRICE PREDICTION

Machine learning has many important applications in the stock price prediction. Here, we will discuss about predicting the returns on stocks. This has uncertainties and it is a very complex task. This project will be developed into two parts: First, we will learn how to predict stock price using the Long Short-Term Memory neural networks. Predicting stock market prices involves human-computer interaction. For stock market analysis, conventional batch processing methods cannot be utilized efficiently due to the correlated nature of stock prices. We suggest an algorithm that utilizes a kind of recurrent neural network (RNN) called Long Short-Term Memory (LSTM), where using stochastic gradient descent the weights are adjusted for individual data points

Dynamic community detection based on the Matthew effect

The identification of community structures plays a crucial role in analyzing network topology, exploring network functions, and mining potential patterns in complex networks. Many algorithms have been proposed for identifying community structures in static networks from different perspectives. However, most networks in the real world are not static and their structures constantly evolve over time. Identifying community structures in dynamic networks remains a challenging task because of the variability, complexity, and large scale of dynamic networks. In this study, we propose a framework and Matthew effect model for community detection in dynamic networks. Based on this architecture and model, we design a dynamic community detection algorithm called, Dynamic Community Detection based on the Matthew effect (DCDME), which employs a batch processing method to reveal communities incrementally in each network snapshot. DCDME has several desirable benefits: high-quality community detection, parameter-free operation, and good scalability. Extensive experiments on synthetic and real-world dynamic networks have demonstrated that DCDME has many advantages and outperforms several state-of-the-art algorithms.

Urban Heat Island Effect and its Driving Mechanism for All the Cities in China: Based on a New Batch Processing Method

Urban Heat Island Effect and its Driving Mechanism for All the Cities in China: Based on a New Batch Processing Method

Automated Stock Price Prediction using LSTM Recurrent Neural Network

In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume. A correct prediction of stocks can lead to huge profits for the seller and the broker. Frequently, it is brought out that prediction is chaotic rather than random, which means it can be predicted by carefully analyzing the history of the respective stock market. Machine learning is an efficient way to represent such processes. It predicts a market value close to the tangible value, thereby increasing the accuracy. The introduction of machine learning to the area of stock prediction has appealed to many researchers because of its efficient and accurate measurements. In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and more authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high, and volume. Stock market prediction is a major exertion in the field of finance and establishing businesses. The stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g.national elections, natural calamities etc. Investors in the stock market seek to maximize their profits for which they require tools to analyze the prices and trends of various stocks. Machine learning algorithms have been used to devise new techniques to build prediction models that can forecast the prices of stock and tell about the market trend with good accuracy. Many prediction models have been proposed to incorporate all the major factors affecting the price of stocks. Due to the correlated nature of stock prices, conventional batch processing methods cannot be utilized efficiently for stock market analysis. We propose an online learning algorithm that utilizes a kind of recurrent neural network (RNN) called Long Short Term Memory (LSTM), where the weights are adjusted for individual data points using stochastic gradient descent. This will provide more accurate results when compared to existing stock price prediction algorithms. The network is trained and evaluated for accuracy with various sizes of data, and the results are tabulated. A comparison with respect to accuracy is then performed against an Artificial Neural Network.

Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis.

The products of a batch process have high economic value. Meanwhile, a batch process involves complex chemicals and equipment. The variability of its operation leads to a high failure rate. Therefore, early fault diagnosis of batch processes is of great significance. Usually, the available information of the sensor data in batch processing is obscured by its noise. The multistage variation of data results in poor diagnostic performance. This paper constructed a standardized method to enlarge fault information as well as a batch fault diagnosis method based on trend analysis. First, an adaptive standardization based on the time window was created; second, utilizing quadratic fitting, we extracted a data trend under the window; third, a new trend recognition method based on the Euclidean distance calculation principle was composed. The method was verified in penicillin fermentation. We constructed two test datasets: one based on an existing batch, and one based on an unknown batch. The average diagnostic rate of each group was 100% and 87.5%; the mean diagnosis time was the same; 0.2083 h. Compared with traditional fault diagnosis methods, this algorithm has better fault diagnosis ability and feature extraction ability.

Modelling of a continuous veneer drying unit of industrial scale and model-based ANOVA of the energy efficiency

Drying, a crucial step in process engineering aimed at producing optimal product moisture content, has evolved over time from batch processing methods to continuous processing alternatives. Continuous drying methods offer uniform moisture content of the product at lower operational cost. In this study, a continuous veneer drying model was developed based on mass and energy balances. The simulated veneer dryer is a semiautomatic machine designed to maximise the drying process efficiency via control mechanisms such as the veneer transport rate, fan speed, opening angle of the inlet and outlet dampers, and radiator temperature. In the dryer, veneer plates are conveyed horizontally through the number of connected chambers where hot air is blown transversely. The optimal drying process is dynamically maintained via the Proportional–integral–derivative controllers, manipulating the rate of the damper lids opening, that are connected to the sensors monitoring the air properties in the chambers of the drying unit. The model-based sensitivity analysis ANOVA was carried out for energy optimisation purposes. The analysis outcomes indicated that radiator temperature, initial moisture content of veneer sheets and conveyor speed are the most influential parameters affecting the drying rate. Automatic control of damper lids provides optimal temperature and moisture content of drying environment at lower energy costs.

Forecasting the momentum using customised loss function for financial series

PurposeWe propose a Machine Learning (ML) approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms. The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities. The network will be trained and evaluated for accuracy with various sizes of data sets, i.e. weekly historical data of MCX, GOLD, COPPER and the results will be calculated.Design/methodology/approachDesirable LSTM model for script price forecasting from the perspective of minimizing MSE. The approach which we have followed is shown below. (1) Acquire the Dataset. (2) Define your training and testing columns in the dataset. (3) Transform the input value using scalar. (4) Define the custom loss function. (5) Build and Compile the model. (6) Visualise the improvements in results.FindingsFinancial series is one of the very aged techniques where a commerce person would commerce financial scripts, make business and earn some wealth from these companies that vend a part of their business on trading manifesto. Forecasting financial script prices is complex tasks that consider extensive human–computer interaction. Due to the correlated nature of financial series prices, conventional batch processing methods like an artificial neural network, convolutional neural network, cannot be utilised efficiently for financial market analysis. We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic (LSTM). The LSTM Classic is quite different from normal LSTM as it has customised loss function in it. This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure, and it helps forecast financial time series. Financial Series Index is the combination of various commodities (time series). This makes Financial Index more reliable than the financial time series as it does not show a drastic change in its value even some of its commodities are affected. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.Originality/valueWe had built the customised loss function model by using LSTM scheme and have experimented on MCX index and as well as on its commodities and improvements in results are calculated for every epoch that we run for the whole rows present in the dataset. For every epoch we can visualise the improvements in loss. One more improvement that can be done to our model that the relationship between price difference and directional loss is specific to other financial scripts. Deep evaluations can be done to identify the best combination of these for a particular stock to obtain better results.

Quality-Analysis-Based Process Monitoring for Multi-Phase Multi-Mode Batch Processes

In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance between the historical modes and the new mode. Further, online quality prediction is achieved applying the selected model based on multi-mode quality analysis, and an according process-monitoring strategy is developed. The simulation results show the availability of this method for multi-phase multi-mode batch processes.

Estimate the Unknown Environment with Biosonar Echoes-A Simulation Study.

Unmanned aerial vehicles (UAVs) have shown great potential in various applications such as surveillance, search and rescue. To perform safe and efficient navigation, it is vitally important for a UAV to evaluate the environment accurately and promptly. In this work, we present a simulation study for the estimation of foliage distribution as a UAV equipped with biosonar navigates through a forest. Based on a simulated forest environment, foliage echoes are generated by using a bat-inspired bisonar simulator. These biosonar echoes are then used to estimate the spatial distribution of both sparsely and densely distributed tree leaves. While a simple batch processing method is able to estimate sparsely distributed leaf locations well, a wavelet scattering technique coupled with a support vector machine (SVM) classifier is shown to be effective to estimate densely distributed leaves. Our approach is validated by using multiple setups of leaf distributions in the simulated forest environment. Ninety-seven percent accuracy is obtained while estimating thickly distributed foliage.

Proactive Fragmentation Management Scheme Based on Crosstalk-Avoided Batch Processing for Spectrally-Spatially Elastic Optical Networks

Fragmentation with crosstalks is the major obstacle in spectrally-spatially elastic optical networks, which suppresses resource utilization while degrading the quality-of-transmission. To overcome this issue, this article proposes, for the first time, a proactive fragmentation management scheme based on batch processing while satisfying both inter-core and inter-mode crosstalks to enhance resource utilization. The proposed scheme adopts a batch processing method to create batches of lightpath requests received within a time threshold to utilize spectrum resources effectively. In batch processing, lightpath requests are prioritized based on the number of links in their routes and required slots. To maintain fairness in batch processing, when any request is rejected, the proposed scheme triggers a procedure that gives an equal opportunity to all arriving requests within the threshold, irrespective of numbers of hops and requested capacities, for allocation. We formulate the static batch processing of lightpath requests (SBPLR) as an integer linear programming (ILP) problem. We prove that SBPLR is an NP-Complete problem. We introduce a heuristic solution when ILP is intractable. To serve lightpath requests in each batch while avoiding inter-core and inter-mode crosstalks, we develop a core-mode-spectrum allocation algorithm. We present a dynamic batch processing based fragmentation management approach. Numerical results indicate that the proposed scheme outperforms the benchmark schemes.

Nonparametric Identification Based on Multi-inherited Gaussian Process Regression for Batch Process

This study presents a multi-inherited Gaussian process regression (GPR)-based nonparametric identification method for batch process. In the GPR framework, the impulse response of each time point co...

Intelligent batch process method for analyzing the effect of the suspension parameters on the vibration of the suspended monorail

As for the batch process, it is needed to carry out a unique simulation for each group of parameters. The simplest method to achieve this goal is to uses the Simulink as the integration space. It cannot solve some complicated dynamic model set up in dynamic software. This paper introduces an intelligent batch program to enable the model solved in the dynamic software during the simulation. This paper also put forward the road equation for the suspended monorail, which has not been introduced before. The results of multi rigid body dynamics and vehicle bridge coupling dynamics are compared in this paper. It proves that the bridge vibration has little effect on the vehicle system. Through the batch process method, the effect of different parameters on the vehicle system could be obtained.

Assessing the standardisation of Egyptian shabti manufacture via morphology and elemental analyses

Ancient Egyptian shabtis have been described as mass-produced, mould-made objects. We analyse the morphology and elemental composition of three sets of seven shabtis using 3D scanning, X-ray fluorescence spectrometry and principal component analysis clustering. The morphology and elemental composition of the shabtis allow us to conclude that these object types were made with different degrees of morphological and compositional standardisation indicating different manufacturing methods. We suggest that mass-production is an oversimplified label to describe shabti manufacturing as our findings demonstrate evidence of batch-processing methods.

A hybrid distributed batch-stream processing approach for anomaly detection

Batch and stream processing are separately and efficiently applied in many applications. However, some newer data-driven applications such as the Internet of Things and cloud computing call for hybrid processing approaches in order to handle the speed and accuracy required for processing such complex data. In this paper, we propose a Hybrid Distributed Batch-Stream (HDBS) architecture for anomaly detection in real-time data. The hybrid architecture, while benefiting from the accuracy provided by batch processing, also enjoys the speed and real-time features of stream processing. In the proposed architecture, our focus is on the algorithmic aspects of hybrid processing including the interaction models between batch and stream processing units, the characteristics of batch and stream machine learning algorithms and the principles of merging the results of different processing units. The driving idea of such combination is that the results of batch and stream processing units are complementary with each other, as one of them constructs accurate models based on previous data, and the other one is capable of processing new stream data in real-time. Furthermore, we propose a generalized version of the HDBS with respect to its algorithms and communication policy levels. In the generalized HDBS architecture, we address the various aspects of the interaction between the batch and stream processing units, and the merging operations to produce the final results. the evaluations of the proposed architecture using various criteria (accuracy, space complexity, and time complexity) demonstrate that the accuracy of the proposed method is higher than the accuracy of the batch processing methods, its time complexity is also similar to one of the stream processing methods and much less than the batch processing methods, which makes our proposed architecture an efficient and practical solution for real-time anomaly detection.

Fine-Scale Modeling and Monitoring of Wide-Range Nonstationary Batch Processes With Dynamic Analytics

Batch manufacturing has attracted extensive attention as a crucial way in process industry. It has typical wide-range nonstationary characteristics subject to time variant conditions, not only within each batch but also for different batches. The presence of closed-loop control complicates the task of batch process monitoring which shows that closed-loop control can significantly alter the process characteristics with both static deviations and varying temporal correlations. It is of a considerable challenge for batch process analysis and monitoring under closed-loop control which may work quite unlike that under open-loop control. In this article, a fine-scale process modeling and monitoring method are developed with dynamics analytics for wide-range nonstationary batch manufacturing with duration uncertainty. Both static and dynamic monitoring statistics are designed with clear physical interpretation which can work together to achieve fine-scale batch process status identification and monitoring. On one hand, it can identify frequent normal switching of operation status at different time by simultaneously checking both static and dynamic deviations regulated by closed-loop control. On the other hand, it can detect small disturbances in early stage where the impact may be overshadowed by the regulation of closed-loop controllers, making faults difficult to detect in time by conventional methods. Experiments have shown the validity of the proposed fine-scale monitoring method for wide-range batch process.

Impact of feed injection and batch processing methods in hydrothermal liquefaction

Starch hydrothermal liquefaction (HTL) using batch and feed injection methods was studied at 300 °C and 10, 30, 60 min residence times with 2% feedstock concentration. The authors found much higher solid yield in batch mode compared to feed injection mode and that the solid yield increased linearly with feedstock injection rate. The HTL oil formation pathway was established that light water-soluble oil (ethyl acetate extracted liquid phase oil) forms first, followed by heavy water-soluble oil (dichloromethane extracted liquid phase oil), and then heavy acetone-soluble oil (dichloromethane extracted acetone wash phase oil). Solid hydrochar was formed mainly through heavy acetone-soluble oil repolymerization as the reaction residence time increased. The oil compositions and HPLC analysis of the aqueous phase indicated that at an injection rate of 20 ml/min, the reaction followed the alkaline pathway while at higher injection rates of 50 and 100 ml/min, the reaction shifted to the acidic pathway.

Multi-Frame Track-Before-Detect Algorithm for Maneuvering Target Tracking

Multi-frame track-before-detect (MF-TBD) is a model-based batch processing method. Assuming a particular model for the evolution of target states (e.g. a constant velocity model) within a batch processing time, MF-TBD integrates the target energy by taking advantage of the space-time correlations between a number of consecutive frames. Its performance is known to be heavily dependent on the accuracy of the motion model, and to substantially degrade when target maneuvers occur and motions do not follow the presumed model. We make two contributions towards addressing this problem. Firstly, we analyze and summarize the direct strategies to incorporate the effect of target maneuvers into MF-TBD. Our analysis shows that although these strategies are straightforward to implement, they either suffer from considerable performance loss or are computationally expensive. Motivated by the analysis, we propose a general measurement-directed (MD) strategy to address target maneuvers. It carries out an on-line study of target dynamics from the observations, and is capable of achieving both low computational complexity and high adaptability to different target maneuvers. Secondly, as the proposed MD strategy is a general framework without any particular model assumptions, we further derive its detailed implementation equations for linear motion and measurement models. Simulation results for various tracking scenarios are presented to demonstrate the effectiveness of the proposed MD strategy.

Compound dimensionality reduction based multi-dynamic kernel principal component analysis monitoring method for batch process with large-scale data sets

Compound dimensionality reduction based multi-dynamic kernel principal component analysis monitoring method for batch process with large-scale data sets