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Journal articles on the topic 'Electronic data processing – Batch processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

Akanbi, Adeyinka, and Muthoni Masinde. "A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring." Sensors 20, no. 11 (2020): 3166. http://dx.doi.org/10.3390/s20113166.

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In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the envir

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Demirezen, Mustafa Umut, and Tuğba Selcen Navruz. "Performance Analysis of Lambda Architecture-Based Big-Data Systems on Air/Ground Surveillance Application with ADS-B Data." Sensors 23, no. 17 (2023): 7580. http://dx.doi.org/10.3390/s23177580.

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This study introduces a novel methodology designed to assess the accuracy of data processing in the Lambda Architecture (LA), an advanced big-data framework qualified for processing streaming (data in motion) and batch (data at rest) data. Distinct from prior studies that have focused on hardware performance and scalability evaluations, our research uniquely targets the intricate aspects of data-processing accuracy within the various layers of LA. The salient contribution of this study lies in its empirical approach. For the first time, we provide empirical evidence that validates previously t

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Liu, Wei, Yu Yue Du, You Fu, and Chun Yan. "A Formal Representation of Collaborative Electronic Commerce Systems by Workflow Nets." Applied Mechanics and Materials 170-173 (May 2012): 3570–74. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3570.

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Combining the advantages of logical workflow nets and colored Petri nets, logical and colored workflow nets and collaborative logical and colored workflow nets are presented to facilitate the modeling of cooperative electronic commerce systems. The formal representation presented in this paper can not only describe the data objects, but also represent passing value indeterminacy and batch processing function of cooperative electronic commerce systems.

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4

Szmajduch, Magdalena. "Data and Task Scheduling in Distributed Computing Environments." Journal of Telecommunications and Information Technology, no. 4 (December 30, 2014): 71–78. http://dx.doi.org/10.26636/jtit.2014.4.1049.

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Data-aware scheduling in today’s large-scale heterogeneous environments has become a major research and engineering issue. Data Grids (DGs), Data Clouds (DCs) and Data Centers are designed for supporting the processing and analysis of massive data, which can be generated by distributed users, devices and computing centers. Data scheduling must be considered jointly with the application scheduling process. It generates a wide family of global optimization problems with the new scheduling criteria including data transmission time, data access and processing times, reliability of the data servers

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BĂJENESCU, Titu-Marius. "Quality and Reliability Evolution of Electronic Components." Electrotehnica, Electronica, Automatica 70, no. 2 (2022): 1–8. http://dx.doi.org/10.46904/eea.22.70.2.1108001.

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Today, most of the companies understand that the reliability must be built in at the design phase and then, monitored during the whole manufacturing process. Reliability building means the totality of techniques and procedures having the goal to ensure a foreseen reliability level for a given product. This concept is linked with reliability assessing, which cover the whole evaluation system aiming to find out and record, during and after the manufacturing process, the reliability level of the batch of products. This evaluation system contains tests, electrical measurements, failure analysis an

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Ahn, Sang Un, and Jin Kim. "A Conceptual Design of Job Pre-processing Flow for Heterogeneous Batch Systems in Data Center." Wireless Personal Communications 89, no. 3 (2016): 847–61. http://dx.doi.org/10.1007/s11277-016-3224-x.

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7

Zhang, Weikang, Desheng Wen, Zongxi Song, Xin Wei, Gang Liu, and Zhixin Li. "High Resolution and Fast Processing of Spectral Reconstruction in Fourier Transform Imaging Spectroscopy." Sensors 18, no. 12 (2018): 4159. http://dx.doi.org/10.3390/s18124159.

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High-resolution spectrum estimation has continually attracted great attention in spectrum reconstruction based on Fourier transform imaging spectroscopy (FTIS). In this paper, a parallel solution for interference data processing using high-resolution spectrum estimation is proposed to reconstruct the spectrum in a fast high-resolution way. In batch processing, we use high-performance parallel-computing on the graphics processing unit (GPU) for higher efficiency and lower operation time. In addition, a parallel processing mechanism is designed for our parallel algorithm to obtain higher perform

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Chen, Lipei, Cheng Xu, Shuai Lin, Siqi Li, and Xiaohan Tu. "A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR." Sensors 20, no. 8 (2020): 2224. http://dx.doi.org/10.3390/s20082224.

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The overhead contact system (OCS) is a critical railway infrastructure for train power supply. Periodic inspections, aiming at acquiring the operational condition of the OCS and detecting problems, are necessary to guarantee the safety of railway operations. One of the OCS inspection means is to analyze data of point clouds collected by mobile 2D LiDAR. Recognizing OCS components from the collected point clouds is a critical task of the data analysis. However, the complex composition of OCS makes the task difficult. To solve the problem of recognizing multiple OCS components, we propose a new

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Yang, Sen, Xiaobao Wang, Qijuan Yang, Enzeng Dong, and Shengzhi Du. "Instance Segmentation Based on Improved Self-Adaptive Normalization." Sensors 22, no. 12 (2022): 4396. http://dx.doi.org/10.3390/s22124396.

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The single batch normalization (BN) method is commonly used in the instance segmentation algorithms. The batch size is concerned with some drawbacks. A too small sample batch size leads to a sharp drop in accuracy, but a too large batch may result in the memory overflow of graphic processing units (GPU). These problems make BN not feasible to some instance segmentation tasks with inappropriate batch sizes. The self-adaptive normalization (SN) method, with an adaptive weight loss layer, shows good performance in instance segmentation algorithms, such as the YOLACT. However, the parameter averag

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Mao, Ning, Haigang Yang, and Zhihong Huang. "An Instruction-Driven Batch-Based High-Performance Resource-Efficient LSTM Accelerator on FPGA." Electronics 12, no. 7 (2023): 1731. http://dx.doi.org/10.3390/electronics12071731.

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In recent years, long short-term memory (LSTM) has been used in many speech recognition tasks, due to its excellent performance. Due to a large amount of calculation and complex data dependencies of LSTM, it is often not so efficient to deploy on the field-programmable gate array (FPGA) platform. This paper proposes an LSTM accelerator, driven by a specific instruction set. The accelerator consists of a matrix multiplication unit and a post-processing unit. The matrix multiplication unit uses staggered timing of read data to reduce register usage. The post-processing unit can complete various

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Lee, Ah-Hyoung, Jihun Lee, Farah Laiwalla, et al. "A Scalable and Low Stress Post-CMOS Processing Technique for Implantable Microsensors." Micromachines 11, no. 10 (2020): 925. http://dx.doi.org/10.3390/mi11100925.

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Implantable active electronic microchips are being developed as multinode in-body sensors and actuators. There is a need to develop high throughput microfabrication techniques applicable to complementary metal–oxide–semiconductor (CMOS)-based silicon electronics in order to process bare dies from a foundry to physiologically compatible implant ensembles. Post-processing of a miniature CMOS chip by usual methods is challenging as the typically sub-mm size small dies are hard to handle and not readily compatible with the standard microfabrication, e.g., photolithography. Here, we present a soft

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Elkhoukhi, Hamza, Mohamed Bakhouya, Driss El Ouadghiri, and Majdoulayne Hanifi. "Using Stream Data Processing for Real-Time Occupancy Detection in Smart Buildings." Sensors 22, no. 6 (2022): 2371. http://dx.doi.org/10.3390/s22062371.

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Controlling active and passive systems in buildings with the aim of optimizing energy efficiency and maintaining occupants’ comfort is the major task of building management systems. However, most of these systems use a predefined configuration, which usually do not match the occupants’ preferences. Therefore, occupancy detection is imperative for energy use management mainly in residential and industrial buildings. Most works related to data-driven-based occupancy detection have used batch learning techniques, which need to store first and then train the data. It is not appropriate for a non-s

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Chien, Chen-Fu, and Shih-Chung Chuang. "A Framework for Root Cause Detection of Sub-Batch Processing System for Semiconductor Manufacturing Big Data Analytics." IEEE Transactions on Semiconductor Manufacturing 27, no. 4 (2014): 475–88. http://dx.doi.org/10.1109/tsm.2014.2356555.

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Morcillo-Jimenez, Roberto, Karel Gutiérrez-Batista, and Juan Gómez-Romero. "TSxtend: A Tool for Batch Analysis of Temporal Sensor Data." Energies 16, no. 4 (2023): 1581. http://dx.doi.org/10.3390/en16041581.

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Pre-processing and analysis of sensor data present several challenges due to their increasingly complex structure and lack of consistency. In this paper, we present TSxtend, a software tool that allows non-programmers to transform, clean, and analyze temporal sensor data by defining and executing process workflows in a declarative language. TSxtend integrates several existing techniques for temporal data partitioning, cleaning, and imputation, along with state-of-the-art machine learning algorithms for prediction and tools for experiment definition and tracking. Moreover, the modular architect

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Jawarneh, Isam Mashhour Al, Luca Foschini, and Paolo Bellavista. "Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data." Sensors 23, no. 19 (2023): 8178. http://dx.doi.org/10.3390/s23198178.

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The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus countera

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Rasyid, Irfan, Muhammad Resa Arif Yudianto, Maimunah Maimunah, and Tuessi Ari Purnomo. "Electronic Product Recommendation System Using the Cosine Similarity Algorithm and VGG-16." sinkron 8, no. 4 (2023): 2120–29. http://dx.doi.org/10.33395/sinkron.v8i4.12936.

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The recommendation system is a mechanism for filtering a batch of data into numerous data sets based on what the user wants. Cosine similarity is one of the algorithms used in creating recommendation model. This algorithm employs a calculation approach between two things by measuring the cosine between the two objects to be compared. Image-based recommendation systems were recently introduced since word processing to generate recommendations had the issue of duplicating product descriptions for different types of items. Before processing with cosine similarity, image feature extraction require

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Adewole, Kayode S., Taofeekat T. Salau-Ibrahim, Agbotiname Lucky Imoize, et al. "Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection." Electronics 11, no. 19 (2022): 3109. http://dx.doi.org/10.3390/electronics11193109.

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Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant

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Yao, Yuman, Yiyang Dai, and Wenjia Luo. "Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis." Sensors 21, no. 23 (2021): 8075. http://dx.doi.org/10.3390/s21238075.

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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

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19

Sujatha, K., and S. Jagannatha. "Performance Analysis of Batch Jobs in Cloud Environment." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4150–55. http://dx.doi.org/10.1166/jctn.2020.9036.

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The complexity of running batch jobs in cloud environment is increasing day by day due to huge volume of back end data increasing dynamically in different formats, growing number of online or offline requests from various environments or availability of system resources. This paper explores the various behaviors of batch jobs from production cluster and the following observations are made: (i) The number of user jobs submitted at offline are high thereby increasing the wait time and processing time. (ii) Each job submitted with huge volume of data causing increase in queue size, job failure an

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Pereira, Fabio Henrique, Francisco Elânio Bezerra, Diego Oliva, et al. "Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator." Sensors 20, no. 24 (2020): 7242. http://dx.doi.org/10.3390/s20247242.

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The prediction of partial discharges in hydrogenerators depends on data collected by sensors and prediction models based on artificial intelligence. However, forecasting models are trained with a set of historical data that is not automatically updated due to the high cost to collect sensors’ data and insufficient real-time data analysis. This article proposes a method to update the forecasting model, aiming to improve its accuracy. The method is based on a distributed data platform with the lambda architecture, which combines real-time and batch processing techniques. The results show that th

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21

Chang, Tsung-Hui, Mingyi Hong, Hoi-To Wai, Xinwei Zhang, and Songtao Lu. "Distributed Learning in the Nonconvex World: From batch data to streaming and beyond." IEEE Signal Processing Magazine 37, no. 3 (2020): 26–38. http://dx.doi.org/10.1109/msp.2020.2970170.

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22

Song, Libo, Chang Liu, Haibo Shi, and Jun Zhu. "An Improved Immune Genetic Algorithm for Solving the Flexible Job Shop Scheduling Problem with Batch Processing." Wireless Communications and Mobile Computing 2022 (June 26, 2022): 1–17. http://dx.doi.org/10.1155/2022/2856056.

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This paper presents a mathematical model for the flexible job shop scheduling problem (FJSP) with batch processing for manufacturing enterprises with both the flexible job shop scheduling problem and a batch process (BP) problem in actual production. An improved immune genetic algorithm (IGA) based on greedy thought combined with local scheduling rules is used to solve this scheduling problem. In the flexible job shop part, the greedy optimal solution is obtained through the greedy thought. The concept of cross-entropy is then introduced to improve the standard IGA. Calculating the cross-entro

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23

Talaie, A., Z. Boger, J. A. Romagnoli, S. B. Adeloju, and Y. J. Yuan. "Data acquisition, signal processing and modelling: a study of a conducting polypyrrole formate biosensor Part 1: Batch experiment." Synthetic Metals 83, no. 1 (1996): 21–26. http://dx.doi.org/10.1016/s0379-6779(97)80048-3.

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Marvellini, Richard Yan. "Medical Students’ Knowledge Level about X-Ray as a Diagnostic Support Tool at Universitas Kristen Indonesia, Jakarta Batch 2016." International Journal of Health Sciences and Research 12, no. 2 (2022): 318–28. http://dx.doi.org/10.52403/ijhsr.20220242.

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The study is aimed at investigating the medical students’ knowledge level about x-ray as a diagnostic support tool. It was done at Universitas Kristen Indonesia. The type of research is in the form of quantitative descriptive, which aims to systematically, factually, and accurately regarding the level of knowledge. Research data collection was carried out using a cross-sectional research design. The population in this study were all medical students of the Universitas Kristen Indonesia, batch 2016. The respondents were medical students, batch 2016 semester 1, who had just graduated from high s

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Wang, Jing, ShuXia Yu, and YuYue Du. "The Equivalency between Logic Petri Workflow Nets and Workflow Nets." Scientific World Journal 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/121492.

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Logic Petri nets (LPNs) can describe and analyze batch processing functions and passing value indeterminacy in cooperative systems. Logic Petri workflow nets (LPWNs) are proposed based on LPNs in this paper. Process mining is regarded as an important bridge between modeling and analysis of data mining and business process. Workflow nets (WF-nets) are the extension to Petri nets (PNs), and have successfully been used to process mining. Some shortcomings cannot be avoided in process mining, such as duplicate tasks, invisible tasks, and the noise of logs. The online shop in electronic commerce in

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Łuczak, Dominik, Stefan Brock, and Krzysztof Siembab. "Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity." Sensors 23, no. 7 (2023): 3755. http://dx.doi.org/10.3390/s23073755.

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The human-centric and resilient European industry called Industry 5.0 requires a long lifetime of machines to reduce electronic waste. The appropriate way to handle this problem is to apply a diagnostic system capable of remotely detecting, isolating, and identifying faults. The authors present usage of HTTP/1.1 protocol for batch processing as a fault diagnosis server. Data are sent by microcontroller HTTP client in JSON format to the diagnosis server. Moreover, the MQTT protocol was used for stream (micro batch) processing from microcontroller client to two fault diagnosis clients. The first

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Lu, Xiuhua, Wei Yin, Qiaoyan Wen, Kaitai Liang, Liqun Chen, and Jiageng Chen. "Message Integration Authentication in the Internet-of-Things via Lattice-Based Batch Signatures." Sensors 18, no. 11 (2018): 4056. http://dx.doi.org/10.3390/s18114056.

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The internet-of-things (also known as IoT) connects a large number of information-sensing devices to the Internet to collect all kinds of information needed in real time. The reliability of the source of a large number of accessed information tests the processing speed of signatures. Batch signature allows a signer to sign a group of messages at one time, and signatures’ verification can be completed individually and independently. Therefore, batch signature is suitable for data integration authentication in IoT. An outstanding advantage of batch signature is that a signer is able to sign as m

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Dutra, Odilon, Luís H. C. Ferreira, Gustavo D. Colletta, and Leonardo B. Zoccal. "A Low Power R-peak Detector Clocked at Signal Sampling Rate." Journal of Integrated Circuits and Systems 19, no. 1 (2024): 1–10. http://dx.doi.org/10.29292/jics.v19i1.798.

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This paper presents a real-time, low-power R-peak detector implemented in an FPGA. It is different from other implementations as it runs at the same signal sampling rate, rather than utilizing a high clock frequency as utilized in batch processing with high throughput systems. Such implementation relies on a Savitzky-Golay filter for power line noise filtering, and on an adapted version of the Difference Operation Method (DOM) algorithm. The modification in DOM is needed in order to be able to process the data either without increasing the clock to process data in a batch fashion or unsustaina

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Garcia, Ausberta Jesus Cabezas, and Boaventura F. Reis. "Instrumentation and Automated Photometric Titration Procedure for Total Acidity Determination in Red Wine Employing a Multicommuted Flow System." Journal of Automated Methods and Management in Chemistry 2006 (2006): 1–8. http://dx.doi.org/10.1155/jammc/2006/83247.

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An automated procedure for photometric titration of red wine and associated instrumentation is described. The procedure was based on the flow-batch approach implemented employing multicommutation. The photometric detection was carried out using a homemade LED-based photometer. The mixing device, LED, and photodetector were attached to the titration chamber in order to form a compact and small-sized unit. The flow system comprised an automatic injector and three-way solenoid valves, which were controlled by a microcomputer through an electronic interface card. The software, written in Quick BAS

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Huang, Qingrong, Xiaocong Lai, Qianxiang Su, and Ying Pan. "RDF Subgraph Query Based on Common Subgraph in Distributed Environment." Wireless Communications and Mobile Computing 2023 (January 13, 2023): 1–15. http://dx.doi.org/10.1155/2023/7148071.

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With the gradual development of the network, RDF graphs have become more and more complex as the scale of data increases; how to perform more effective query for massive RDF graphs is a hot topic of continuous research. The traditional methods of graph query and graph traversal produce great redundancy of intermediate results, and processing subgraph collection queries in stand-alone mode cannot perform efficient matching when the amount of data is extremely large. Moreover, when processing subgraph collection queries, it is necessary to iterate the query graph multiple times in the query of t

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Tronci, Stefania, Paul Van Neer, Erwin Giling, et al. "In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors." Sensors 19, no. 22 (2019): 5009. http://dx.doi.org/10.3390/s19225009.

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The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this wor

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Jeon, Wang-Su, and Sang-Yong Rhee. "Tool Wear Monitoring System Using Seq2Seq." Machines 12, no. 3 (2024): 169. http://dx.doi.org/10.3390/machines12030169.

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The advancement of smart factories has brought about small quantity batch production. In multi-variety production, both materials and processing methods change constantly, resulting in irregular changes in the progression of tool wear, which is often affected by processing methods. This leads to changes in the timing of tool replacement, and failure to correctly determine this timing may result in substantial damage and financial loss. In this study, we sought to address the issue of incorrect timing for tool replacement by using a Seq2Seq model to predict tool wear. We also trained LSTM and G

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Hidayatika, Nur Khairina, Mustafa Mustafa, and Tarmizi Tarmizi. "The Influence of Gadget Use on Academic Achievement of 2016 Unsyiah Physics Batch." Asian Journal of Science Education 1, no. 1 (2019): 40–44. http://dx.doi.org/10.24815/ajse.v1i1.14735.

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In this globalization era, communication tools are very fast examples. Gadgets are a communication tool that has many functions. Where the function is already using the different features. Gadgets considered more complete than other electronics because of its functionality and different. Gadgets examined included laptops and mobile phones. The approach used in this research is the quantitative study. In the approach comes from the students who actively use the gadget in the context lectured. Collection of activity data using questionnaires and student evidence. As for the CPI data analysis and

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Zhang, Xiangbo, Ji Guo, Yonghui Hu, Dangli Zhao, and Zaimin He. "Research of Eliminating the Day-Boundary Discontinuities in GNSS Carrier Phase Time Transfer through Network Processing." Sensors 20, no. 9 (2020): 2622. http://dx.doi.org/10.3390/s20092622.

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Time and frequency transfer through global navigation satellite system (GNSS) precise point positioning (PPP) based on carrier-phase measurements has been widely used for clock comparisons in national timing laboratories. However, the time jumps up to one nanosecond at the day boundary epochs of adjacent daily batches lead to discontinuities in the time transfer results. Therefore, it is a major obstacle to achieve continuous carrier phase time transfer. The day-boundary discontinuities have been studied for many years, and they are believed to be caused by the long-term pseudorange noise duri

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He, Chuan, Bo Zhang, Liang Zhang, Zesheng Xi, Yuan Fang, and Yunfan Wang. "Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification." Electronics 12, no. 22 (2023): 4700. http://dx.doi.org/10.3390/electronics12224700.

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Mobile sensors enjoy the advantages of easy installation and low consumption, which have been widely adopted in many information systems. In those systems where data are generated rapidly, the throughput of the sensors is one of the most fundamental factors that determine the system functionality. For example, to guarantee data integrity, digital signature techniques can be applied. In many practical scenarios, such as the smart grid system, data are generated rapidly and, hence, the signature together with the data must also be transmitted and verified in time. This requires the mobile sensor

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Zhang, Yizhong. "Deep Learning Distributed Architecture Design Implementation for Computer Vision." Wireless Communications and Mobile Computing 2022 (July 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/9726286.

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In the era of big data, to achieve an efficient deep learning and computer vision system for big data, developers need to build a computerized deep learning and computer vision system, and the system can simultaneously complete the tasks of deep learning and computer vision and large-scale data processing. The existing training dataset is reused, and the scene information is small, which cannot meet the needs of large-scale machine training, so it is necessary to include large-scale data, distributed computer system to complete the training. How to meet the training accuracy requirements of de

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Sun, Shuwen, Yunfei Qiao, Zhentao Gao, and Chao Huang. "Research on Machining Error Analysis and Traceability Method of Globoidal Indexing Cam Profile." Machines 10, no. 3 (2022): 219. http://dx.doi.org/10.3390/machines10030219.

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The profile of the globoidal indexing cam is a spatially undevelopable surface. It needs a special computer numerical control (CNC) machine tool to finish batch production, and its machining quality will be affected by the motion error of each part of the machine tool and the clamped positioning error of the workpiece. Firstly, the mathematical model of the error of the machine tool for machining the globoidal cam surface is derived, and the influence of the error of the machine tool for machining the globoidal cam surface is given. Secondly, an error tracking method for globoidal cam profile

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Cardoen, Thorsten, Sam Leroux, and Pieter Simoens. "Iterative Online 3D Reconstruction from RGB Images." Sensors 22, no. 24 (2022): 9782. http://dx.doi.org/10.3390/s22249782.

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3D reconstruction is the computer vision task of reconstructing the 3D shape of an object from multiple 2D images. Most existing algorithms for this task are designed for offline settings, producing a single reconstruction from a batch of images taken from diverse viewpoints. Alongside reconstruction accuracy, additional considerations arise when 3D reconstructions are used in real-time processing pipelines for applications such as robot navigation or manipulation. In these cases, an accurate 3D reconstruction is already required while the data gathering is still in progress. In this paper, we

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NAKAMURA, SYOUJI, KEIKO NAKAYAMA, and TOSHIO NAKAGAWA. "OPTIMAL REORGANIZATION SCHEDULES OF STRUCTURAL DATABASE DETERIORATION." International Journal of Reliability, Quality and Safety Engineering 17, no. 02 (2010): 157–65. http://dx.doi.org/10.1142/s0218539310003718.

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Both response time and processing time become recently big problems in the processing time of various online and batch systems. Especially, the access efficiency of the database should be greatly controlled as for the speed of the processing time of accesses to the database. Because records are inserted far from their logical position, the storages of deleted records occupy some diverse spaces in the database. Then, to cover the weak point of the database, we execute the database reorganization at suitable times to achieve a good performance requirement for the application. There are two types

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Mahdi, Shakir Mahmood, Sabreen Ali Hussein, Hayder Mahmood Salman, Alyaa Hamel Sfayyih, and Nasri Bin Sulaiman. "Developing microphone-based audio commands classifier using convolutional neural network." Eastern-European Journal of Enterprise Technologies 1, no. 9 (121) (2023): 72–81. http://dx.doi.org/10.15587/1729-4061.2023.273492.

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Audio command recognition methods are essential to be recognized for performing user instructions, especially for people with disabilities. Previous studies couldn’t examine and classify the performance optimization of up to twelve audio commands categories. This work develops a microphone-based audio commands classifier using a convolutional neural network (CNN) with performance optimization to categorize twelve classes including background noise and unknown words. The methodology mainly includes preparing the input audio commands for training, extracting features, and visualizing auditory sp

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Amin, Abdullah Al, and K. Siddique E. Rabbani. "Development of Low Cost Miniature USB Based Data Acquisition System for Biomedical Instrumentation." Bangladesh Journal of Medical Physics 10, no. 1 (2018): 1–11. http://dx.doi.org/10.3329/bjmp.v10i1.39146.

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In biomedical instrumentation, computer based data acquisition system is required for recording of physiological parameters and bioelectric signals, which allows signal processing, display, analysis and storage in digital media. However, Most of the commercially available PC based Data Acquisition systems are of very high cost and requires specific commercial software, again at a very high cost. Moreover, if the data is not stored in raw binary or known format, it is not possible for the user to use the data in other system or software of their own choice. Therefore, a low cost, simple and ope

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Lifshin, Eric. "Minimizing Errors in Electron Microprobe Analysis." Microscopy and Microanalysis 5, S2 (1999): 568–69. http://dx.doi.org/10.1017/s1431927600016160.

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Back in the sixties our ability to do both qualitative and quantitative microprobe analysis was often restricted by many factors we now take for granted. Limitations of power supplies, electron optical systems and the mechanical aspects of specimen stages and crystal spectrometers often led to unacceptable drift in both beam position and x-ray intensity measurements. Detector electronics were relatively primitive and phenomena such as pulse distribution shifts with count rate could cause serious errors when pulse height analysis was incorrectly used for noise reduction. The ZAF method, the pri

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Hu, Zhang, and Wei Qin. "Fuzzy Method and Neural Network Model Parallel Implementation of Multi-Layer Neural Network Based on Cloud Computing for Real Time Data Transmission in Large Offshore Platform." Polish Maritime Research 24, s2 (2017): 39–44. http://dx.doi.org/10.1515/pomr-2017-0062.

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Abstract With the rapid development of electronic technology, network technology and cloud computing technology, the current data is increasing in the way of mass, has entered the era of big data. Based on cloud computing clusters, this paper proposes a novel method of parallel implementation of multilayered neural networks based on Map-Reduce. Namely in order to meet the requirements of big data processing, this paper presents an efficient mapping scheme for a fully connected multi-layered neural network, which is trained by using error back propagation (BP) algorithm based on Map-Reduce on c

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Sun, Xuemin, Shimin Liu, Jinsong Bao, Jie Li, and Zengkun Liu. "A Performance Prediction Method for a High-Precision Servo Valve Supported by Digital Twin Assembly-Commissioning." Machines 10, no. 1 (2021): 11. http://dx.doi.org/10.3390/machines10010011.

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The manufacturing of a high-precision servo valve belongs to multi-variety, small-batch, and customized production modes. In the process of assembly and commissioning, various characteristic parameters are critical indicators to measure product performance. To meet the performance requirements of a high-precision servo valve, the traditional method usually relies on the test bench and manual experience for continuous trial and error commissioning, which significantly prolongs the whole assembly-commissioning cycle. Therefore, this paper proposed a performance prediction method for a high-preci

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Ha, Donghee, Mooseop Kim, KyeongDeok Moon, and Chi Yoon Jeong. "Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory." Sensors 21, no. 7 (2021): 2364. http://dx.doi.org/10.3390/s21072364.

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Recent studies have applied the superior performance of deep learning to mobile devices, and these studies have enabled the running of the deep learning model on a mobile device with limited computing power. However, there is performance degradation of the deep learning model when it is deployed in mobile devices, due to the different sensors of each device. To solve this issue, it is necessary to train a network model specific to each mobile device. Therefore, herein, we propose an acceleration method for on-device learning to mitigate the device heterogeneity. The proposed method efficiently

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Ferilli, Stefano. "Integration Strategy and Tool between Formal Ontology and Graph Database Technology." Electronics 10, no. 21 (2021): 2616. http://dx.doi.org/10.3390/electronics10212616.

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Ontologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as a Knowledge Graph. On the other hand, database technology has often focused on the optimal organization of data so as to boost efficiency in their storage, management and retrieval. Graph databases are a recent technology specifically focusing on element-driven data browsing rather than on batch processing. While the complemen

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Fernández-Plazaola, Unai, Jesús López-Fernández, Eduardo Martos-Naya, José F. Paris, and Francisco Javier Cañete. "HW/SW Platform for Measurement and Evaluation of Ultrasonic Underwater Communications." Sensors 22, no. 17 (2022): 6514. http://dx.doi.org/10.3390/s22176514.

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The purpose of this work is to present a flexible system that supports the study of wideband underwater acoustic communications (UAC). It has been developed both to measure channels and to test transmission techniques under realistic conditions in the ultrasonic band. This platform consists of a hardware (HW) part that includes multiple hydrophones, projectors, analog front-ends, acquisition boards, and computers, and a software (SW) part for the generation, reception, and management of acoustic sounding signals and noise. UAC channels are among the most hostile ones and exhibit an important a

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Radi, Muhammad Rivai, and Mauridhi Hery Purnomo. "Study on Electronic-Nose-Based Quality Monitoring System for Coffee Under Roasting." Journal of Circuits, Systems and Computers 25, no. 10 (2016): 1650116. http://dx.doi.org/10.1142/s0218126616501164.

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Roasting process needs to be monitored and carefully controlled because it plays as the most important stage for determining flavor quality on the final product in the secondary coffee processing. Common quality monitoring method by applying parameters namely roasting time, roasting temperature and grain color may have disadvantages especially for nonuniform quality of green beans and stirring mechanism of regular roasters; therefore, an alternative quality monitoring model is necessary. Because emitted vapor during roasting may represent the occurred reaction stage, it is possible to indicate

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Shokeen, Vivek, and Lalit Malik. "Cloud Computing – Data Center – Access from Anywhere." International Journal of Computer Science and Mobile Computing 11, no. 6 (2022): 212–19. http://dx.doi.org/10.47760/ijcsmc.2022.v11i06.015.

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Now days we are attached with our electronic gadgets either offline or online in such a way as we breathe, as without breathing we can’t alive, same like these electronics have become a vital part of our daily life. As of now by growing demand of these gadgets either in office work, or in research work or in various technical fields. As the use of computers increases as in day in and day out, the relevant resources that we need also go up. For companies like Oracle, IBM and Microsoft, utilizing the resources and establishing a huge infrastructure or network is not a big issue for these big gia

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Liu, Guangjun, Xiaoping Xu, Xiangjia Yu, and Feng Wang. "A New Method of Identifying Graphite Based on Neural Network." Wireless Communications and Mobile Computing 2021 (December 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/4716430.

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The unique physical properties of graphite enable it to be applied in various fields of the national economy and people’s livelihood, which has very important industrial value. Many countries have listed graphite as a key mineral. To promote the transformation of the mining industry to informatization and intelligence, the realization of the intelligent recognition of graphite is particularly critical. Aiming at the problems of long time and low efficiency in manually identifying graphite, an improved AlexNet convolution neural network is proposed for graphite image recognition. First, we perf

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