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Journal articles on the topic 'CNC machine; Distributed processor'
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1
Fountas, Nikolaos A., Constantinos I. Stergiou, Nikolaos M. Vaxevanidis, and Redha Benhadj-Djilali. "A Generic Multi-Axis Post-Processor Engine for Optimal CNC Data Creation and Intelligent Surface Machining." Solid State Phenomena 261 (August 2017): 463–69. http://dx.doi.org/10.4028/www.scientific.net/ssp.261.463.
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This paper focuses on the development of a multi-axis post-processor engine with a curvature-based feed adaptation module, capable of extracting generic CNC data for high precision machining. The motivation of this work stems from the drawback of standard and commercial post-processors to modify their internal source codes so as to be implemented to newly-developed functions which integrate modern CNC units. The multi-axis post-processor proposed in this work operates as a stand-alone function of an artificial intelligent module that optimizes machining parameters for standard swept cut multi-axis surface tool-paths. The post-processor developed receives APT source files previously been optimized by means of a genetic algorithm that handles cutting tool selection; radial cut engagement; maximum discretization step; lead and tilt angles. The algorithm optimizes the aforementioned machining parameters towards the minimization of the number of cutter locations found in a specific APT source file as well as the surface machining error as a combined effect of chordal deviation and scallop height. The final APT output is then properly handled by the post-processor engine so as to extract the final ISO code for a double-pivoted head 5-axis CNC machine and compute optimal values for feed rate in each NC block considering the interpolation error and curvature analysis given the surface properties. To simulate and verify our proposals, the MAZAK Vortex 1000 gantry-type 5-axis CNC machine tool equipped with a Fanuc 15i CNC unit has been selected as the manufacturing resource corresponding to the final CNC output that the proposed post-processor computes. A benchmark sculptured part is created and used for the virtual material removal simulation in CATIA® V5 R18. For that part, both the proposed post-processor engine and a commercially available post-processor were employed to extract G-code data whilst it was shown that identical outputs were obtained.
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Xiang, Si Tong, Mu Wen Shen, and Jian Guo Yang. "Distributed Numerical Control Strategy for Error Compensation on CNC Machine Tools." Applied Mechanics and Materials 415 (September 2013): 188–91. http://dx.doi.org/10.4028/www.scientific.net/amm.415.188.
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A distributed numerical control (DNC) strategy for error compensation on Fanuc and Siemens CNC machine tools is proposed. A DNC network is built in multi-Fanuc CNC machine tools and the error compensation of all the machine tools is realized simultaneously. A human machine interface (HMI) is developed for Siemens 840D CNC machine tools, error components are decoupled in the X, Y and Z directions and they are compensated by 840Ds own function of thermal error compensation. Experimental verification is conducted and it proves that the proposed DNC strategy for error compensation is an effective and precision manner to improve the accuracy of machine tools.
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My, Chu Anh, and Vuong Xuan Hai. "Generalized pseudo inverse kinematics at singularities for developing five-axes CNC machine tool postprocessor." Vietnam Journal of Mechanics 35, no. 2 (2013): 147–55. http://dx.doi.org/10.15625/0866-7136/35/2/3074.
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This paper presents an analytical scheme for analyzing the singular configuration problem of general five-axes CNC machine tool. A computational technique based on the generalized pseudo inverse kinematics for finding out feasible solution of the problem is proposed. The technique is then used for developing post processor algorithm of five-axes CNC machine tool. Typical examples of singularity analysis are carried out, and real cutting parts are implemented for verifying the research result.
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Liu, Yadong, Xingui Guo, Wei Li, Kazuo Yamazaki, Keizo Kashihara, and Makoto Fujishima. "An intelligent NC program processor for CNC system of machine tool." Robotics and Computer-Integrated Manufacturing 23, no. 2 (2007): 160–69. http://dx.doi.org/10.1016/j.rcim.2006.04.003.
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Djurica, Danijel, Milenko Sekulić, Davorin Kramar, Pavel Kovač, and Marin Gostimirović. "Using Advanced CAM System in Modern Machining." Key Engineering Materials 686 (February 2016): 27–32. http://dx.doi.org/10.4028/www.scientific.net/kem.686.27.
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Goal of this paper highlight characteristics and spectrum of machining cutting sequences that programming system SolidCAM support. The practical goal of this paper is defining post-processor and machine simulation for 3-axis CNC machine like a tool for verification modern tool-path and generation G-code that will be used for cutting real part.
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Yudianto, Yudianto. "AN OVERVIEW OF DIRECT OR DISTRIBUTED NUMERICAL CONTROL IN COMPUTER NUMERICAL CONTROL APPLICATIONS." Journal of Mechanical Science and Engineering 7, no. 2 (2021): 025–29. http://dx.doi.org/10.36706/jmse.v7i2.38.
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Direct numerical control is also known as distributed numerical control (also abbreviated DNC), is a common manufacturing term for networking CNC machine tools. On some CNC machine controllers, the available memory is too small to contain the machining program (for example machining complex surfaces), so in this case the program is stored in a separate computer and sent directly to the machine, one block at a time. If the computer is connected to a number of machines it can distribute programs to different machines as required. Usually, the manufacturer of the control provides suitable DNC software. However, if this provision is not possible, some software companies provide DNC applications that fulfill the purpose. DNC networking or DNC communication is always required when Computer-Aided Manufacturing (CAM) programs are to run on some CNC machine control. Wireless DNC is also used in place of hard-wired versions. Controls of this type are very widely used in industries with significant sheet metal fabrication, such as the automotive, appliance, and aerospace industries. A DNC system has to fulfill two basic tasks such as first guaranteeing secure, timely data transfer from and to the CNC controllers, secondly administering many thousands of NC programs.
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Saputra, Roni Permana, Tinton Dwi Atmaja, and Budi Prawara. "Distributed Control System Design for Portable PC Based CNC Machine." Journal of Mechatronics, Electrical Power, and Vehicular Technology 5, no. 1 (2014): 37. http://dx.doi.org/10.14203/j.mev.2014.v5.37-44.
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Wang, Chao, Yuan Yao, Qian Sheng Zhao, and Qing Xi Hu. "Research about Numerical Control Machining of STL Files Based on Fagor CNC System." Advanced Materials Research 314-316 (August 2011): 1597–602. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.1597.
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According to the situation that surface grid models are widely used, this article summarizes the research status of numerical control machining based on Triangle facets models, introduces the STL files in detail, expounds the process of machining STL files by using Pro/Toolmaker system, designs an independent post processor for generating NC code for Fagor machine, simulates the virtual machining process through Vericut software, verifies the actual machining process on Fagor three axis CNC milling machine. Proves the method of numerical control machining based on STL files is actually feasible for planar milling、cavity milling、profile milling, Realizes the machining of STL files based on Fagor CNC system.
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Chen, Guo Jin, Jing Ni, Ting Ting Liu, Ming Xu, and Lu Gao Lin. "Study on the Key Technology for Large-Scale High-Performance CNC Vertical Broaching Machine." Advanced Materials Research 705 (June 2013): 483–86. http://dx.doi.org/10.4028/www.scientific.net/amr.705.483.
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For the general broaching machines problems of structure bulkiness, poor cylinder synchronization, lack of optimization design, low-grade control system, low efficiency and operational reliability, the paper studied the structural optimization technology of the large-scale high-performance CNC vertical broaching machine, designed the new 360-degree high-load lateral rotary table, the axial-rotary multi-pass joint of the anti-leakage and the elastic-contact, and the modular slider rails, so as to improve the mechanical performance, the working stability and reliability. The remote network intelligent monitoring system and the multi-function data acquisition processor for the developed CNC broaching machine, realize the enterprise management information, the enterprise resource optimization, and the automation and intelligence of the manufacturing process.
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Altintas¸, Y., N. Newell, and M. Ito. "Modular CNC Design for Intelligent Machining, Part 1: Design of a Hierarchical Motion Control Module for CNC Machine Tools." Journal of Manufacturing Science and Engineering 118, no. 4 (1996): 506–13. http://dx.doi.org/10.1115/1.2831060.
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An hierarchical motion-control design strategy is presented to integrate various intelligent machining modules to CNC systems. There are two independent buses in the CNC system. Modular and independent processor boards reside in the primary bus which is global for manufacturing process monitoring, control and machine tool position commands. A digital signal processing board provides a secondary CNC bus, which has one imbedded microprocessor for the position control of each axis. The system is open to integration of new hardware and software modules. Any process control and monitoring task in the system can manipulate the feed, acceleration, control law parameters and position commands in real time. New process control, monitoring and position control algorithms can be integrated to the system by using globally available CNC parameters. The process control and monitoring functions are executed in the real-time multi-tasking environment within 1 ms time intervals without disturbing the position control system. The proposed design provides an open development platform for parallel testing of intelligent manufacturing algorithms on machine tools and robots.
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Newman, Daniel, Mahmoud Parto, Kyle Saleeby, Thomas Kurfess, and Andrew Dugenske. "Development of a Digital Architecture for Distributed CNC Machine Health Monitoring." Smart and Sustainable Manufacturing Systems 3, no. 2 (2019): 20190038. http://dx.doi.org/10.1520/ssms20190038.
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Slavov, Stoyan D., and Diyan M. Dimitrov. "A study for determining the most significant parameters of the ball-burnishing process over some roughness parameters of planar surfaces carried out on CNC milling machine." MATEC Web of Conferences 178 (2018): 02005. http://dx.doi.org/10.1051/matecconf/201817802005.
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Ball burnishing process often applies for obtaining specifically characteristics in the surface layer of the functional surfaces of the machine parts. Using CNC machines for carrying out this process significantly increases the possibility of control the characteristics of the toolpath of the ball tool and thus to produce regularly distributed roughness with specific size and direction of cells alignment. The current study presents obtained results from L8 Taguchi experimental design for determining the significantly influenced parameters of the ball burnishing process carried out on CNC milling machine on the size and arrangement direction of the cells form regular distributed roughness on planar surfaces.
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Castillo, Enrique, Diego Peteiro-Barral, Bertha Guijarro Berdiñas, and Oscar Fontenla-Romero. "Distributed One-Class Support Vector Machine." International Journal of Neural Systems 25, no. 07 (2015): 1550029. http://dx.doi.org/10.1142/s012906571550029x.
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This paper presents a novel distributed one-class classification approach based on an extension of the ν-SVM method, thus permitting its application to Big Data data sets. In our method we will consider several one-class classifiers, each one determined using a given local data partition on a processor, and the goal is to find a global model. The cornerstone of this method is the novel mathematical formulation that makes the optimization problem separable whilst avoiding some data points considered as outliers in the final solution. This is particularly interesting and important because the decision region generated by the method will be unaffected by the position of the outliers and the form of the data will fit more precisely. Another interesting property is that, although built in parallel, the classifiers exchange data during learning in order to improve their individual specialization. Experimental results using different datasets demonstrate the good performance in accuracy of the decision regions of the proposed method in comparison with other well-known classifiers while saving training time due to its distributed nature.
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Wu, Chao, Yun Wang Ge, Gang Zhang, and Ting Bu. "S-Curve Algorithm Based on SCM." Applied Mechanics and Materials 416-417 (September 2013): 626–30. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.626.
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Control of the process of ACC/DEC (Acceleration/Deceleration) is one of most complicated problems in CNC system development. With the increase of acceleration and velocity in high-speed, high-precision CNC system, discontinuousness of acceleration will result in vibration, impact and debasing of position accuracy. The excellent ACC/ DEC control can solve these problems and improve the positioning precision. In this paper, S curve ACC/DEC algorithm is deeply studied, through comparison with other algorithms; its proved to be an efficient way in advanced CNC system. Usually, such kind of algorithm requires high-speed processor. However realization based on SCM (Single Chip Machine) is proposed in this paper. Through simulation, the result shows that the algorithm works well. Finally practical tests are carried out in Panasonic Minas A5 servo motor which indicates the control system is effective and accuracy.
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Zhang, Q., S. J. Liu, C. Y. Duan, and Y. M. Zhu. "The Architecture of Reliability Information System for CNC Machine Tool in Concurrent Engineering." Materials Science Forum 471-472 (December 2004): 603–7. http://dx.doi.org/10.4028/www.scientific.net/msf.471-472.603.
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Based on the functional requirement analysis of the compute-aided reliability information system, some concepts are proposed, such as the distributed progressive reliability analysis and the functional decomposition and reconstitution for CNC machine tool. At the same time, the architecture of the CARNC-CE (Computer Aided Reliability Numerical Control for Current Engineering) including physical layer, foundational layer, executive layer, applied layer and illustration layer, is introduced, which provides the general guidance and the frame to realize CNC machine tool reliability analysis system in concurrent engineering.
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REN, FUJI, and HONGCHI SHI. "A MULTI-ENGINE TRANSLATION APPROACH TO MACHINE TRANSLATION." International Journal of Information Technology & Decision Making 01, no. 02 (2002): 349–66. http://dx.doi.org/10.1142/s0219622002000191.
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One of the most difficult problems in dialogue machine translation is to correctly translate irregular expressions in natural conversations such as ungrammatical, incomplete, or ill-formed sentences. However, most existing machine translation systems reject utterances including irregular expressions. In this paper, we present a dialogue machine translation approach based on a cooperative distributed natural language processing model to attack the complex machine translation problem. In this approach, different types of translation processors are used in the analysis of the original language and the generation of the target language. The idea of combining multiple machine translation engines provides a new effective way to increase the success rate and quality of dialogue machine translation. A dialogue machine translation using multiple processors (DMTMP) system has been built using the following machine translation processors: (i) Robust Parser based Translation Processor, (ii) Example based Translation Processor, (iii) Family Modal based Translation Processor, and (iv) Super Function based Translation Processor. DMTMP is used in a practical machine translation environment called SWKJC. Experiments show that the approach presented in this paper is effective in implementation of robust dialogue machine translation systems.
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Zheng, Dong, Hui Xing Zhou, Can Huang, and Ming Hou. "Development of Derived CNC System for Ship Engine Piston Turning." Applied Mechanics and Materials 336-338 (July 2013): 1076–79. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1076.
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Based on the principle of derived CNC, a NC lathe system is redesigned for cutting ship engine pistons which have a non-circular middle-convex and varying oval (NMVO) profile in the skirt. With an attached high-speed linear motor servo unit, a new derived CNC lathe system based on a normal NC lathe system is developed to machine non-circular section. Using this method, the development cost is reduced. A moving magnet voice coil motor is designed as the actuator to provide sufficient stroke and driving force for the cutting. The controller of the derived unit is designed based on digital signal processor (DSP) TMS320F28335, and a method of pre-interpolating data on main computer is employed in the software development. By doing this, the real-time performance of the system is satisfied.
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Feng, Zhan Rong, Li Xia Wang, and Jie Wang. "Study on Post Processing for MIKRON Five-Axis Machining Center Based on Catia V5." Advanced Materials Research 650 (January 2013): 523–28. http://dx.doi.org/10.4028/www.scientific.net/amr.650.523.
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To solve the NC post processing problems of generated tool path file APT generated by Catia V5 in the application of MIKRON UCP600 Vario five-axis machining center, on the basis of the theoretical analysis of tool path parameter and data calculation, the post processor programmed with VC++ that is accordance with machine tool was used. It verified with Simulation and material test: the NC post possessing program can be directly used in CNC machining without manual modification. Furthermore, there was no mark by tool path and the accuracy of the size meets the requirements.
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Wang, Tai Yong, H. W. Li, Cheng Zu Ren, Wen Jin Wang, and W. Zhao. "Research on Intelligent CNC Platform Based on Hierarchical and Distributed Architecture for Grinding Machine." Key Engineering Materials 259-260 (March 2004): 715–19. http://dx.doi.org/10.4028/www.scientific.net/kem.259-260.715.
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Draghici, George. "Infrastructure for Integrated Collaborative Product Development in Distributed Environment." Applied Mechanics and Materials 760 (May 2015): 9–14. http://dx.doi.org/10.4028/www.scientific.net/amm.760.9.
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The tools used for collaborative product design is based on PLM solutions, integrating BOM, CAD, CAE, CAPP, CAM and PDM software. The PLM platforms are distributed in virtual project team of partners. Currently, the integrated collaborative and distributed product design and manufacture can be supported by the cloud technology, given advances in Cloud Computing. The objective of our research is to develop an infrastructure for integrated collaborative and distributed product design and manufacturing, including software applications, CNC machine tools, additive manufacturing machines etc., dispersed in different universities labs for students’ education.
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Kabaldin, Yu G., D. A. Shatagin, M. S. Anosov, and A. M. Kuzmishina. "Development of digital twin of CNC unit based on machine learning methods." Vestnik of Don State Technical University 19, no. 1 (2019): 45–55. http://dx.doi.org/10.23947/1992-5980-2019-19-1-45-55.
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Introduction. It is shown that the digital twin (electronic passport) of a CNC machine is developed as a cyber-physical system. The work objective is to create neural network models to determine the operation of a CNC machine, its performance and dynamic stability under cutting.Materials and Methods. The development of mathematical models of machining processes using a sensor system and the Industrial Internet of Things is considered. Machine learning methods valid for the implementation of the above tasks are evaluated. A neural network model of dynamic stability of the cutting process is proposed, which enables to optimize the machining process at the stage of work preparation. On the basis of nonlinear dynamics approaches, the attractors of the dynamic cutting system are reconstructed, and their fractal dimensions are determined. Optimal characteristics of the equipment are selected by input parameters and debugging of the planned process based on digital twins.Research Results. Using machine learning methods allowed us to create and explore neural network models of technological systems for cutting, and the software for their implementation. The possibility of applying decision trees for the problem of diagnosing and classifying malfunctions of CNC machines is shown.Discussion and Conclusions. In real production, the technology of digital twins enables to optimize processing conditions considering the technical and dynamic state of CNC machines. This provides a highly accurate assessment of the production capacity of the enterprise under the development of the production program. In addition, equipment failures can be identified in real time on the basis of the intelligent analysis of the distributed sensor system data.
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Kaldashev, Tsvetan. "DEVELOPMENT OF A POSTPROCESSOR FOR TURNING CENTER AND MULTI-TASK MACHINE WITH MULTI-CHANNEL CNC SYSTEMS." Proceedings of CBU in Natural Sciences and ICT 1 (November 16, 2020): 28–32. http://dx.doi.org/10.12955/pns.v1.117.
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This article discusses the possibility of developing a postprocessor for turning center with 2 turrets and a multi-channel CNC system with NC program format characteristic of machine Mazak Integrex I series. Typical for these CNCs is the usage of two support programs - one for each turret. The CAD / CAM system PTC Creo is used to solve the problem, where tool transitions are developed for machining the workpieces. The postprocessor is software that translates the CL Data file i.e. turns it into a NC program with preparatory, technological, and supplementary commands to control the machine. G-POST, which is integrated into the PTC Creo CAD / CAM system, is used to develop the post-processor. To solve the problem, a specialized programming language FIL (Factory Interface Language) is used which, with its features and capabilities to work with files in ASCII code, achieves the ultimate goal - NC program in format characteristics suitable for machines Mazak Integrex I series.
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Guo, Bo, Fu-Shin Lee, Chen-I. Lin, and Yuan-Jun Lin. "An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network." Concurrent Engineering 29, no. 1 (2021): 35–48. http://dx.doi.org/10.1177/1063293x21998083.
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This paper suggests an optimization strategy to train a CNN deep-learning network, which successfully recognizing working status on the HMI panels of CNC machines. To verify the developed strategy, the research experiments using a prototype that consists of a CNC milling machine and an industrial robot. In the optimization strategy, the research first defines a length-varying hyperparameter list for the deep-learning network, and the entities in the list adjust themselves to optimize the model scales. During the optimization process, this paper adopts a two-stage training scheme that gradually augments image datasets to improve HMI control-panel recognition performances, such as recognition accuracy and recognition speed to identify the CNC machine working status. Using an open-source PyTorch platform, this research establishes a cloud-based distributed architecture to build training codes for the deep-learning network, in which an applicable optimization model is deployed to recognize the CNC control-panel working status. The optimization strategy employs minimal codes to rebuild the architecture and the least efforts to reform the manufacturing system. The optimally trained model provides up to a 99.34% CNC panel-message recognition accuracy and a high-speed recognition of 100 images in 0.6 s. Moreover, the developed optimization strategy enables the prediction of necessitated dataset augmentation to training a practically implemented CNN network.
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Greenberg, Albert G., Otmar Schlunk, and Ward Whitt. "Using Distributed-Event Parallel Simulation to Study Departures from Many Queues in Series." Probability in the Engineering and Informational Sciences 7, no. 2 (1993): 159–86. http://dx.doi.org/10.1017/s0269964800002850.
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In this paper we describe an application of distributed-event parallel simulation to study the transient behavior of a large non-Markovian network of queues. In particular, we implemented the parallel-prefix-based algorithm of Greenberg, Lubachevsky, and Mitrani [13,14] on the 8,192-processor CM-2 Connection machine and the 16,384-processor MasPar computer to simulate the departure times D(k, n) of the kth customer from the nth queue in a long series of single-server queues. Each queue has unlimited waiting space and uses the first-in first-out discipline. The service times of all the customers at all the queues are i.i.d. with a general distribution, and the system starts out with k customers in the first queue and all other queues empty. Glynn and Whitt [11] established limit theorems for this model, but very little could be said about the limits themselves. The simulation results presented here describe the limits and the quality of the approximations resulting from using the limits for finite k and n. Indeed, the simulations suggest interesting conjectures. For this model speeding up a single long run is far superior to independent replications, because very long runs are required to obtain unbiased estimates of the desired quantities and the variance of the estimator at the end of the run is small. The achieved simulation rate was about 17 billion service completions per hour, which is a speedup by about a factor of 100 compared to simulation on a conventional single-processor machine. This speedup contributed greatly to performing the desired experiments.
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Zhao, Jiafei, Rongkun Jiang, Xuetian Wang, and Hongmin Gao. "Robust CFAR Detection for Multiple Targets in K-Distributed Sea Clutter Based on Machine Learning." Symmetry 11, no. 12 (2019): 1482. http://dx.doi.org/10.3390/sym11121482.
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For K-distributed sea clutter, a constant false alarm rate (CFAR) is crucial as a desired property for automatic target detection in an unknown and non-stationary background. In multiple-target scenarios, the target masking effect reduces the detection performance of CFAR detectors evidently. A machine learning based processor, associating the artificial neural network (ANN) and a clustering algorithm of density-based spatial clustering of applications with noise (DBSCAN), namely, DBSCAN-CFAR, is proposed herein to address this issue. ANN is trained with a symmetrical structure to estimate the shape parameter of background clutter, whereas DBSCAN is devoted to excluding interference targets and sea spikes as outliers in the leading and lagging windows that are symmetrical about the cell under test (CUT). Simulation results verified that the ANN-based method provides the optimal parameter estimation results in the range of 0.1 to 30, which facilitates the control of actual false alarm probability. The effectiveness and robustness of DBSCAN-CFAR are also confirmed by the comparisons of conventional CFAR processors in different clutter conditions, comprised of varying target numbers, shape parameters, and false alarm probabilities. Although the proposed ANN-based DBSCAN-CFAR processor incurs more elapsed time, it achieves superior CFAR performance without a prior knowledge on the number and distribution of interference targets.
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Wu, Yu Hou, Qiang Gao, and De Hong Zhao. "UG and VERICUT-Based Processing of Special-Shaped Stone Samples." Advanced Materials Research 415-417 (December 2011): 924–28. http://dx.doi.org/10.4028/www.scientific.net/amr.415-417.924.
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In order to verify the processability of a special-shaped stone machining center’s horizontal working portion, to process complex stone products. On the basis of analysis of the physical characteristics of diamond cutting tool and stone, create a model and use UG software to program, making this machine center’s dedicated post processor. Simulate NC code that is generated with VERICUT software, optimize the cutting parameters and analyse its surface quality. Avoid interference in the program, gouge, collisions and other phenomena by adjusting the tool axis’s attitude and the tool paths. Actual process verified the reliability of the program, technological parameter is rational, quality of the parts’ surface is well. Make precisely outline and tool wear is small, overcome the drawbacks of CNC system, get a higher efficiency.
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LOULERGUE, F. "DISTRIBUTED EVALUATION OF FUNCTIONAL BSP PROGRAMS." Parallel Processing Letters 11, no. 04 (2001): 423–37. http://dx.doi.org/10.1142/s0129626401000701.
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The BS λp-calculus is a calculus of functional bulk synchronous parallel (BSP) programs. It is the basis for the design of a bulk synchronous parallel ML language. For data-parallel languages, there are two points of view: the programming model where a program is seen as a sequence of operations on parallel vectors, and the execution model where the program is a parallel composition of programs run on each processor of the parallel machine. BSP algorithms are defined by data-parallel algorithms with explicit (physical) processes in order to allow their parallel execution time to be estimated. We present here a distributed evaluation minimally synchronous for BSP execution (which corresponds to the execution model). This distributed evaluation is correct w.r.t. the call-by-value strategy of the BS λp-calculus (which corresponds to the programming model).
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Sattar, Abdul, Qadir Bakhsh, and Muhammad Sharif. "Industrial Automation and Manufacturing Systems: Concepts and Applications." Advanced Materials Research 903 (February 2014): 291–96. http://dx.doi.org/10.4028/www.scientific.net/amr.903.291.
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Manufacturing comprises an effective and efficient integration of automation tools and advanced technologies for the industrial production. Automation is an advanced technique used in the manufacturing industry for integrating the machine tools to automatically perform different tasks. This paper presents the study about industrial automation and manufacturing system. The research and development in the area of automation includes programmable logic control (PLC), robotics, distributed control system (DCS), computerized numerical control machine (CNC), radio frequency identification (RFID). The intelligent systems for scheduling and manufacturing the product such as flexible manufacturing systems (FMS), computer aided manufacturing (CAM), computer integrated manufacturing (CIM), lean manufacturing and green manufacturing.
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Wang, Jun, Bo Wu, You Min Hu, Er Hua Wang, and Yao Cheng. "Modeling and Modal Analysis of Tool Holder-Spindle Assembly on CNC Milling Machine Using FEA." Applied Mechanics and Materials 157-158 (February 2012): 220–26. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.220.
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In machine dynamics the modal analysis of tool holder-spindle assembly is carried out to verify the reasonability of spindle speed, recognize the impaction of vibration modes on machining accuracy and optimize the design of spindle. This paper presents modeling and modal analysis of tool holder-spindle assembly utilizing FEA. Bearing supports is simulated by four uniformly distributed translational spring-damper elements and the radial stiffness of bearings is calculated based on Hertz contact theory. The connection at the tool holder-spindle interface is assumed to be the rigid-connection. This study also proposes two numerical methods in the finite element analysis software ANSYS to simulate the rigid-connection. Consequently, the present modeling and analysis approach by use of FEA is feasible for analyzing tool holder-spindle assembly dynamics.
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Shaikh, M. Bilal, M. Abdul Rehman, and Attaullah Sahito. "Optimizing Distributed Machine Learning for Large Scale EEG Data Set." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 114. http://dx.doi.org/10.30537/sjcms.v1i1.14.
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Distributed Machine Learning (DML) has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential) which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.
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Fricke, J. Robert. "Reverse-time migration in parallel: A tutorial." GEOPHYSICS 53, no. 9 (1988): 1143–50. http://dx.doi.org/10.1190/1.1442553.
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A reverse-time migration is implemented on a fine-grain or massively parallel computer. With fine-grain architectures many processors are distributed throughout the memory space and can operate on data “in place.” In addition, via a general communication system, any processor can access data from anywhere in the entire memory-processor space. Thus, operations on both local and global data elements are possible. These capabilities are controlled by parallel language constructs which allow parallel variable declaration, parallel arithmetic operation, and parallel random memory access. Reverse-time migration was programmed on a fine-grain machine with these hardware and software features. The reverse-time migration process had a speed improvement of two orders of magnitude relative to a state-of-the-art serial machine. At least another order of magnitude performance can be achieved with currently available floating-point processors. Similar increases in performance are expected for other seismic processes such as velocity estimation, data interpolation, 2-D filtering, and others.
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Gao, Wei Qiang, Qiu Sheng Yan, Bo Xie, Jia Bin Lu, and Hong Jin. "Automatic Programming and Simulation for NC Machining of Mold Core of Luminaire Reflector with Axisymmetric Uniform-Distributed Reflecting Cells." Materials Science Forum 626-627 (August 2009): 675–80. http://dx.doi.org/10.4028/www.scientific.net/msf.626-627.675.
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The design and machining quality of mold core of luminaire reflector is the key to luminaire manufacturing. According to the structural characteristics of the mold core with axisymmetric uniform-distribution reflecting cells, the mathematical model with five-axis NC programming of this mold core was established. Based on five-axis CNC machine tools with dual rotary tables, then, the NC automatic programming calculation for this mold core was researched. Besides, a simple three-dimensional entity simulation was discussed. In this way, the three-dimensional entity simulation of automatic programming and processing in 5-axis NC machining of this mold core was realized.
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Subramanian, K., and M. Zargham. "Distributed and Parallel Demand Driven Logic Simulation Algorithms." VLSI Design 1, no. 2 (1994): 169–79. http://dx.doi.org/10.1155/1994/12503.
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Based on the demand-driven approach, distributed and parallel simulation algorithms are proposed. Demand-driven simulation ties to minimize the number of component evaluations by restricting to only those component computations required for the watched output requests. For a specific output value request, the input line values that are required are requested to the respective component. The process continues until known signal values are needed (system input signal values). We present a distributed demand-driven algorithm with infinite memory requirement (but still the memory required at each process is no greater than the sequential demand-driven simulation), and a parallel demand-driven simulation with finite memory requirement. In our algorithms, each component is assigned a logical process.The algorithms have been implemented on the Sequent Balance 8000 Multi-processor machine. Several sample circuits were simulated. The algorithms were compared with the distributed discrete-event simulation. Our distributed algorithm performed many times faster than the discrete-event simulation for cases when few results were needed. Parallel algorithm performed 2 to 4 times faster than the distributed discrete-event simulation.
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Du, Juan, Xian Guo Yan, and Zhi Chen. "A Multi-Agent Based Tool Path Planning Method for STEP-NC Compliant Milling." Advanced Materials Research 97-101 (March 2010): 3382–86. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3382.
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Today a new model of data transfer between CAD/CAM systems and CNC machines, called STEP-NC, is being developed by the ISO technical Committees. It overcomes the shortcomings of ISO 6983 by specifying machining processes rather than machine motion, using the object-oriented concept of working step. Although the STEP-NC contains abundant geometry information of part and associated process parameters, the tool path is general not included in the STEP-NC program. So before the beginning of the STEP-NC-compliant CNC machining, the tool path should be firstly designed. By analyzing the characteristic of STEP-NC data model for milling, a working step-oriented tool path planning method for milling was proposed in this paper, and the distributed artificial intelligence methods, namely collaborative multi-agent was employed to accomplish tool path planning. Firstly a multi-agent architecture was constructed to allow multiple agents to work cooperatively to realize automation of tool path planning, and then, the function of every kind of agent and the communication between the agents were all described in detail. At last, one test component was designed and simulated to demonstrate the capabilities of this research in the paper.
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Rehage, G., F. Isenberg, R. Reisch, J. Weber, B. Jurke, and P. Pruschek. "Intelligente Arbeitsvorbereitung in der Cloud/Smarter operations planning and scheduling in the cloud - Cloud application reduces setup and non-productive times by the use of virtual machine tools." wt Werkstattstechnik online 106, no. 01-02 (2016): 77–82. http://dx.doi.org/10.37544/1436-4980-2016-01-02-79.
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Auf dem Weg zu Industrie 4.0 wird die Arbeitsvorbereitung zunehmend von kognitiver Informationstechnik unterstützt. Der Beitrag präsentiert die bisherigen Ergebnisse des Forschungsprojekts „Intelligente Arbeitsvorbereitung auf Basis virtueller Werkzeugmaschinen“. Projektziel ist eine Cloud-Dienstleistungsplattform zur Reduzierung der Rüst- und Nebenzeiten durch eine intelligente Planung. Hierzu zählen unter anderem die Auswahl und Validierung alternativer Maschinen sowie die automatische Optimierung der Einrichtungsparameter durch verteilte Simulationen.   On the way to industry 4.0, the operations planning and scheduling will be aided by cognitive information systems. This contribution presents the previous findings of a research project called “Smart operations planning and scheduling on the basis of virtual machine tools” (translated from German). The aim of the project is the development of a cloud service for the smart planning of manufacturing operations; that will reduce the setup and non-productive times of machine tools. This is achieved by the automatic selection of alternative CNC machines, as well as the optimization of setup parameters via distributed simulation.
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XIROUCHAKIS, PAUL C., PEARL Y. WANG, and OPHIR FRIEDER. "DATA PARALLEL VISUAL RECONSTRUCTION AND PARTITIONING ALGORITHMS." Parallel Processing Letters 03, no. 03 (1993): 267–77. http://dx.doi.org/10.1142/s0129626493000319.
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Data parallel visual reconstruction and partitioning algorithms and the associated code are developed for a vector random access machine (V-RAM). Finite element algorithms are constructed for solving the one-dimensional visual reconstruction problem with the input data consisting of a symmetrical top hat loading for the modeling of interacting step discontinuities. The advantage of the V-RAM implementation is the general code applicability to a variety of architectures. A specific implementation is performed on a Distributed Array Processor (DAP) simulator on the VAX 6000–420. Execution times on the DAP simulator are obtained and are found to be in agreement with the algorithmic complexities of the V-RAM code.
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Nishida, Isamu, Ryo Tsuyama, Keiichi Shirase, Masahiro Onishi, and Katsuyuki Koarashi. "Development of Innovative Intelligent Machine Tool Based on CAM-CNC Integration Concept – Adaptive Control Based on Predicted Cutting Force –." International Journal of Automation Technology 13, no. 3 (2019): 373–81. http://dx.doi.org/10.20965/ijat.2019.p0373.
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A new methodology to generate instruction commands for prompt machine control as a replacement for the previously prepared numerical control (NC) programs is developed to realize an innovative intelligent machine tool. This machine tool can eliminate NC program preparation, achieve cutting process control, reduce the production lead time, and realize an autonomous distributed factory. In this study, the innovative intelligent machine tool based on the computer-aided manufacturing-computer NC integrated concept is developed. The special feature of this system is to generate instruction commands in real time for prompt machine control instead of using NC programs. Digital Copy Milling, which is a digitized version of traditional copy milling, is realized by using only the computer-aided design model of the product. In this system, the cutting-force simulation is performed simultaneously with the real-time tool path generation. Then, the tool feed rate can be controlled according to the predicted cutting force. Therefore, both the improvement of the machining efficiency and the avoidance of machining problems can be achieved. The instantaneous cutting force model predicts the cutting force. In this system, the work material is represented by the voxel model, and the uncut chip thickness is calculated discretely from the number of voxels removed. Thus, it is possible to predict the cutting force in the case of non-uniform contact between the tool and the work material. In this study, a machining simulation is conducted to validate the proposed method. The results of the simulation show successful tool feed speed adaptation based on the predicted cutting force. The results also show the effective reduction of the machining time. A case study of a custom-made product for dental prosthetics is examined as a good application of both the proposed adaptive control and the Digital Copy Milling system. Through this method, it is possible to improve the machining efficiency and prevent tool breakage.
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Zaidi, Taskeen, and Rampratap Rampratap. "Virtual Machine Allocation Policy in Cloud Computing Environment using CloudSim." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (2018): 344. http://dx.doi.org/10.11591/ijece.v8i1.pp344-354.
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Cloud computing has been widely accepted by the researchers for the web applications. During the past years, distributed computing replaced the centralized computing and finally turned towards the cloud computing. One can see lots of applications of cloud computing like online sale and purchase, social networking web pages, country wide virtual classes, digital libraries, sharing of pathological research labs, supercomputing and many more. Creating and allocating VMs to applications use virtualization concept. Resource allocates policies and load balancing polices play an important role in managing and allocating resources as per application request in a cloud computing environment. Cloud analyst is a GUI tool that simulates the cloud-computing environment. In the present work, the cloud servers are arranged through step network and a UML model for a minimization of energy consumption by processor, dynamic random access memory, hard disk, electrical components and mother board is developed. A well Unified Modeling Language is used for design of a class diagram. Response time and internet characteristics have been demonstrated and computed results are depicted in the form of tables and graphs using the cloud analyst simulation tool.
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Kumar M, Sudhir, P. Chakravarthi, B. Jagadeesh, and M. Sai Prakash. "Load balancing in cloud computing." International Journal of Engineering & Technology 7, no. 1.1 (2017): 306. http://dx.doi.org/10.14419/ijet.v7i1.1.9709.
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The expected take-up of Cloud, based on settled research in Web administrations, systems, utility figuring, disseminated processing and virtualization, will get many favorable circumstances cost, changeability and approachability for benefit clients. These advantages are foreseen to propel drive the requirements for Cloud organizations, growing both the Cloud's customer base and the extent of Cloud foundations. This has implications for many technical issues in Service Oriented Architectures and Internet of Services (IoS)-type applications; including fault tolerance, large accessibility and adaptability. Focal to these issues will be the stronghold for successful balancing load techniques. Balancing load includes allocating assignments to every processor and limiting the time for the execution program. Practically speaking, it will be conceivable even to execute the applications on any machine of overall distributed frameworks. We are going to talk the possible distributed solutions proposed for balancing load and pros and cons of load balancing.
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Scirè, Alessandro, Fabrizio Tropeano, Aris Anagnostopoulos, and Ioannis Chatzigiannakis. "Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning." Algorithms 12, no. 2 (2019): 32. http://dx.doi.org/10.3390/a12020032.
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Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network. The resulting solution enables the analysis and interpretation of sensor-data traces within the wearable device to provide actionable alerts without any dependence on cloud services. In this paper, we use a supervised-learning approach to detect heartbeats and classify arrhythmias. The system uses a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching. We evaluate the performance of the system in comparison with various existing approaches, in terms of achieved accuracy in the detection of abnormal events. The results show that the proposed embedded system achieves a high detection rate that in some cases matches the accuracy of the state-of-the-art algorithms executed in standard processors.
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Naz, Najia, Abdul Haseeb Malik, Abu Bakar Khurshid, et al. "Efficient Processing of Image Processing Applications on CPU/GPU." Mathematical Problems in Engineering 2020 (October 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/4839876.
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Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.
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BAHGAT, REEM, OSAMA MOSTAFA, and GEORGE A. PAPADOPOULOS. "CONCURRENT ABDUCTIVE LOGIC PROGRAMMING IN PANDORA." International Journal on Artificial Intelligence Tools 10, no. 03 (2001): 387–406. http://dx.doi.org/10.1142/s021821300100057x.
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The extension of logic programming with abduction (ALP) allows a form of hypothetical reasoning. The advantages of abduction lie in the ability to reason with incomplete information and the enhancement of the declarative representation of problems. On the other hand, concurrent logic programming is a framework which explores AND-parallelism and/or OR-parallelism in logic programs in order to efficiently execute them on multi-processor / distributed machines. The aim of our work is to study a way to model abduction within the framework of concurrent logic programming, thus taking advantage of the latter's potential for parallel and/or distributed execution. In particular, we describe Abductive Pandora, a syntactic sugar on top of the concurrent logic programming language Pandora, which provides the user with an abductive behavior for a concurrent logic program. Abductive Pandora programs are then transformed into Pandora programs which support the concurrent abductive behavior through a simple programming technique while at the same time taking advantage of the underlying Pandora machine infrastructure.
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Matlakhov, Vitaliy, and Viktor Khandozhko. "STAND OF SYSTEMS AND MEANS OF AUTOMATION OF EQUIPMENT REFUELING OF PETROLEUM RESERVOIRS." Bulletin of Bryansk state technical university 2021, no. 8 (2021): 38–45. http://dx.doi.org/10.30987/1999-8775-2021-8-38-45.
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The aim of the work is to develop a general structure of the stand that simulates the operation of systems and means of automation of equipment for tank farms of oil products using modern hardware and software, for researching the operation of algorithms for protection and interlocks, alarms and automatic control in accordance with the current regulatory documentation based on modern micro- processor systems. The method of system analysis, the theory of automatic control, structural solutions of human-machine systems intended for the automation of production and intelligent support of control processes and the necessary data processing in organizational, technological and distributed control systems were used. The concept of the stand, the block diagram of the control system and the hydraulic diagram of the stand have been developed. The novelty of the work lies in the creation of a compact model of a tank farm with a complex distributed control system for researching its operation.
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Renugadevi, T., K. Geetha, K. Muthukumar, and Zong Woo Geem. "Optimized Energy Cost and Carbon Emission-Aware Virtual Machine Allocation in Sustainable Data Centers." Sustainability 12, no. 16 (2020): 6383. http://dx.doi.org/10.3390/su12166383.
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Cloud data center’s total operating cost is conquered by electricity cost and carbon tax incurred due to energy consumption from the grid and its associated carbon emission. In this work, we consider geo-distributed sustainable datacenter’s with varying on-site green energy generation, electricity prices, carbon intensity and carbon tax. The objective function is devised to reduce the operating cost including electricity cost and carbon cost incurred on the power consumption of servers and cooling devices. We propose renewable-aware algorithms to schedule the workload to the data centers with an aim to maximize the green energy usage. Due to the uncertainty and time variant nature of renewable energy availability, an investigation is performed to identify the impact of carbon footprint, carbon tax and electricity cost in data center selection on total operating cost reduction. In addition, on-demand dynamic optimal frequency-based load distribution within the cluster nodes is performed to eliminate hot spots due to high processor utilization. The work suggests optimal virtual machine placement decision to maximize green energy usage with reduced operating cost and carbon emission.
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Nishida, Isamu, Ryo Tsuyama, Ryuta Sato, and Keiichi Shirase. "Customized End Milling Operation of Dental Artificial Crown Without CAM Operation." International Journal of Automation Technology 12, no. 6 (2018): 947–54. http://dx.doi.org/10.20965/ijat.2018.p0947.
Full textAbstract:
A new methodology to generate instruction commands for real-time machine control instead of preparing NC programs is developed under the CAM-CNC integration concept. A machine tool based on this methodology can eliminate NC program preparation, achieve cutting process control, reduce production lead time, and realize an autonomous distributed factory. The special feature of this methodology is the generation of instruction commands in real time for the prompt machine control instead of NC programs. Digital Copy Milling (DCM), which digitalizes copy milling, is realized by referring only to the CAD model of the product. Another special feature of this methodology is the control of the tool motion according to the information predicted by a cutting force simulator. This feature achieves both the improvement in the machining efficiency and the avoidance of machining trouble. In this study, the customized end milling operation of a dental artificial crown is realized as an application using the new methodology mentioned above. In this application, the CAM operation can be eliminated for the NC program generation, and tool breakage can be avoided based on the tool feed speed control from the predicted cutting force. The result shows that the new methodology has good potential to achieve customized manufacturing, and can realize both high productivity and reliable machining operation.
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Gruszczyński, Stanisław. "An Evaluation of Some Machine Learning Algorithms as Tools for Predicting Soil Characteristics Based on Their Spectral Response in the Vis‑NIR Range." Geomatics and Environmental Engineering 15, no. 1 (2021): 63–95. http://dx.doi.org/10.7494/geom.2021.15.1.63.
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Using the Land Use and Coverage Frame Survey (LUCAS) database of European soil surface layer properties, statistical and machine learning predictive models for several key soil characteristics (clay content, pH in CaCl2, concentration of organic carbon, calcium carbonates and nitrogen and exchange cations capacity) were compared on the basis of processing their spectral responses in the visible (Vis) and near‑infrared (NIR) parts. Standard methods of relationship modeling were used: stepwise regression, partial least squares regression and linear regression with input data obtained from principal components analysis. Using the inputs extracted by statistical algorithms various machine learning algorithms were used in the modeling. The usefulness of the models was analyzed by comparison with the values of the determination coefficients, the root mean square error and the distribution of residual values. The mean square error of estimation in the cross‑validation procedure for the stack model using the multilayer perceptron and the distributed random forest were as follows: for clay content – ca. 4.5%; for pH – ca. 0.35; for SOC – ca. 7.5 g/kg (0.75% by weight); for CaCO3 content – ca. 19 g/kg; for N content – ca. 0.50 g/kg; and for CEC – ca. 3.5 cmol(+)/kg.
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Bilal, Mohammed. "Attendance Logging In Webserver Using Multi Node Embedded System Connected Through Wi-Fi." International Journal of Reconfigurable and Embedded Systems (IJRES) 1, no. 3 (2012): 103. http://dx.doi.org/10.11591/ijres.v1.i3.pp103-107.
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In the present age, we are in need of fully automated attendance logging system. The design of Remote Attendance Logging System and its control is a challenging part. RFID reader reads the RFID tag, and the details of the tag is logged in the embedded system. The Web based distributed measurement and control is slowly replacing parallel architectures due to its non-crate architecture which reduces complexities. A new kind of expandable, distributed large attendance logging system based on ARM Cortex M3 boards has been investigated and developed in this paper, whose hardware boards use 32-bit RISC processor with wifi dongle attached to its USB port, and software platform use Keil MDK-ARM for firmware and HTML for man machine interface. This system can display date and time of log in and log out of a person. The data can be displayed on web pages at different geographical locations, and at the same time can be transmitted to a Remote Data Acquisition System by using HTTP protocol. The embedded board can act as a central CPU to communicate between web servers automatically.
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BOUTSINAS, B., and S. ATHANASIADIS. "ON MERGING CLASSIFICATION RULES." International Journal of Information Technology & Decision Making 07, no. 03 (2008): 431–50. http://dx.doi.org/10.1142/s0219622008003034.
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One of the main challenges of today's data mining systems is their ability to manage a huge volume of data generated possibly by different sources. On the other hand, inductive learning algorithms have been extensively researched in machine learning using small amounts of judiciously chosen laboratory examples. There is an increasing concern in classifiers handling data that are substantially larger than available main memory on a single processor. One approach to the problem is to combine the results of different classifiers supplied with different subsets of the data, in parallel. In this paper, we present an efficient algorithm for combining partial classification rules. Moreover, the proposed algorithm can be used to match classification rules in a distributed environment, where different subsets of data may have different domains. The latter is achieved by using given concept hierarchies for the identification of matching classification rules. We also present empirical tests that demonstrate that the proposed algorithm has a significant speedup with respect to the analog non-distributed classification algorithm, at a cost of a lower classification accuracy.
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Kao, Yung Chou, Mau Sheng Chen, and Tzu Hao Chiu. "Development of a Web-Based Virtual Five-Axis Milling Toolpath Simulation System." Applied Mechanics and Materials 284-287 (January 2013): 1816–20. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1816.
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Virtual machine tool has been used in the toolpath simulation of a real machine tool with very promising achievement in teaching assistance. However, a five-axis milling machine is generally very expensive in comparison with traditional three-axis milling machine in its configuration. This is mostly resulted from the addition of two more rotational axes in five-axis machine. This paper focuses on the development of a Web-based five-axis milling machining toolpath simulation system so as to facilitate the comprehensive understanding of students and to ease the explanation of operations. A user can access the developed system through the web browsers such as the Microsoft Internet Explorer and Google Chrome as long as the Java Runtime Environment and OpenGL are installed. Three general configurations of five-axis milling machine tool have been implemented including Head-Head, Head-Table, and Table-Table. Furthermore, five-axis post-processing is generally a bottleneck in the popularization of toolpath simulation. An intelligent distributed agent-based post-processor has been integrated in this system. For example, an APT file created by the usage of Catia CAD/CAM software according to a five-axis toolpath could be post-processed by the developed system for the generation of a five-axis NC program. This NC program could then be simulated in the proposed system. The developed system has been successfully implemented and shown very promising application in the understanding of five-axis milling operation.
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Shieh, Chin-Shiuh, Wan-Wei Lin, Thanh-Tuan Nguyen, Chi-Hong Chen, Mong-Fong Horng, and Denis Miu. "Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model." Applied Sciences 11, no. 11 (2021): 5213. http://dx.doi.org/10.3390/app11115213.
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DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks’ technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.