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Готові списки джерел за темами / Automatic dynamic memory management / Статті в журналах

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Статті в журналах з теми "Automatic dynamic memory management"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

APPEL, ANDREW W. "Garbage Collection: Algorithms for Automatic Dynamic Memory Management by Richard Jones and Rafael Lins, John Wiley & Sons, 1996." Journal of Functional Programming 7, no. 2 (March 1997): 227–29. http://dx.doi.org/10.1017/s0956796897212682.

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2

Qu, Yanyu, Fangling Pu, Jianguo Yin, Lingzi Liu, and Xin Xu. "Dynamic Traffic Detection and Modeling for Beidou Satellite Networks." Journal of Sensors 2020 (January 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/4575721.

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Beidou navigation system (BDS) has been developed as an integrated system. The third BDS, BSD-3, will be capable of providing not only global positioning and navigation but also data communication. When the volume of data transmitted through BDS-3 continues to increase, BDS-3 will encounter network traffic congestion, unbalanced resource usage, or security attacks as terrestrial networks. The network traffic monitoring is essential for automatic management and safety assurance of BDS-3. A dynamic traffic detection method including traffic prediction by Long Short-Term Memory (LSTM) and a dynamically adjusting polling strategy is proposed to unevenly sample the traffic of each link. A distributed traffic detection architecture is designed for collection of the detected traffic and its related temporal and spatial information with low delay. A time-varying graph (TVG) model is introduced to represent the dynamic topology, the time-varying link, and its traffic. The BDS-3 network is simulated by STK. The WIDE dataset is used to simulate the traffic between the satellite and ground station. Simulation results show that the dynamic traffic detection method can follow the variation of the traffic of each link with uneven sampling. The detected traffic can be transmitted to the ground station in near real time through the distributed traffic detection architecture. The traffic and its related information are stored by using Neo4j in terms of the TVG model. The nodes, edges, and traffic of BDS-3 can be quickly queried through Neo4j. The presented dynamic traffic detection and representation schemes will support BDS-3 to establish automatic management and security system and develop business.

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3

Li, A. Xiaochang, B. Jichen Chen, C. Zhengjun Zhai, D. Mingchen Feng, and E. Xin Ye. "URM: A Unified RAM Management Scheme for NAND Flash Storage Devices." Discrete Dynamics in Nature and Society 2022 (May 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/3376904.

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In NAND flash storage devices, the random access memory (RAM) is composed of a data buffer and mapping cache that play critical roles in storage performance. Furthermore, as the capacity growth rate of RAM chips lags far behind that of flash memory chips, determining how to take advantage of precious RAM is still a crucial issue. However, most existing buffer management studies on storage devices report performance degradation since these devices cannot refine reference regularities such as sequential, hot, or looping data patterns. In addition, most of these studies focus only on separately managing the data buffer or mapping cache. Compared with the existing buffer/cache management schemes (BMSs), we propose a unified RAM management (URM) scheme for not only the mapping cache but also the data buffer in NAND flash storage devices. URM compresses the mapping table to save memory space, and the remaining dynamic RAM space is used for the data buffer. For the data buffer part, we utilize the program counter-technique in the host layer that provides automatic pattern recognition for different applications, in contrast to existing BMSs. The program counter-technique in our design is able to distinguish four patterns. According to these patterns, the data buffer is divided into four size-adjustable zones. Therefore, our approach is linked to multimodal data and used in a data-intensive system. In particular, in URM, we use a multivariate classification to predict prefetching length in mapping buffer management. Our multivariate classification is transformed into multiple binary classifications (logistic regressions). Finally, we extensively evaluate URM using various realistic workloads, and the experimental results show that, compared with three data buffer management schemes, CFLRU, BPLRU, and VBBMS, URM can improve the hit ratio of data buffer and save response time by an average to 32% and 18%, respectively.

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4

Chen, Zai Ping, and Ya Jing Yang. "The Application and Implementation of ADO Technology in Industrial Management System Based on MFC." Advanced Materials Research 791-793 (September 2013): 1562–65. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1562.

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With the rapid development of computer network, automation and electronic commerce, more and more industrial production are in urgent need of the convenient and efficient integration of production and management system to adapt to the change and solve the condition of the onerous task, low efficiency, high cost, personnel tension. This article focuses on a typical industrial management system which is based on C/S architecture and uses MFC application framework. And it introduces ADO technology as the middle bridge which is used by the client to access databases. What is important is that the dynamic link library is used in the system to help ADO access database. The ADO technology has the characteristics of easily-using, high speed, low memory expenditure and less disk space, which optimize the system, make the system realize the high performance, high compatible data access.

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5

Corker, Kevin M. "Cognitive Performance for Multiple Operators in Complex Dynamic Airspace Systems: Computational Representation and Empirical Analyses." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 3 (October 1998): 341–45. http://dx.doi.org/10.1177/154193129804200334.

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This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in enroute and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation through knowledge of the intention and action of remote operators. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.

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6

Du, Wenjun, Bo Sun, Jiating Kuai, Jiemin Xie, Jie Yu, and Tuo Sun. "Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion." Journal of Advanced Transportation 2021 (July 9, 2021): 1–16. http://dx.doi.org/10.1155/2021/9512501.

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Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).

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7

Laqtib, Safaa, Khalid El Yassini, and Moulay Lahcen Hasnaoui. "A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 2701. http://dx.doi.org/10.11591/ijece.v10i3.pp2701-2709.

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Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network Inception-CNN, Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning methods in MANET.

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8

Gunawan, Chichi Rizka, Nurdin Nurdin, and Fajriana Fajriana. "Design of A Real-Time Object Detection Prototype System with YOLOv3 (You Only Look Once)." International Journal of Engineering, Science and Information Technology 2, no. 3 (October 17, 2022): 96–99. http://dx.doi.org/10.52088/ijesty.v2i3.309.

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Object detection is an activity that aims to gain an understanding of the classification, concept estimation, and location of objects in an image. As one of the fundamental computer vision problems, object detection can provide valuable information for the semantic understanding of images and videos and is associated with many applications, including image classification. Object detection has recently become one of the most exciting fields in computer vision. Detection of objects on this system using YOLOv3. The You Only Look Once (YOLO) method is one of the fastest and most accurate methods for object detection and is even capable of exceeding two times the capabilities of other algorithms. You Only Look Once, an object detection method, is very fast because a single neural network predicts bounded box and class probabilities directly from the whole image in an evaluation. In this study, the object under study is an object that is around the researcher (a random thing). System design using Unified Modeling Language (UML) diagrams, including use case diagrams, activity diagrams, and class diagrams. This system will be built using the python language. Python is a high-level programming language that can execute some multi-use instructions directly (interpretively) with the Object Oriented Programming method and also uses dynamic semantics to provide a level of syntax readability. As a high-level programming language, python can be learned easily because it has been equipped with automatic memory management, where the user must run through the Anaconda prompt and then continue using Jupyter Notebook. The purpose of this study was to determine the accuracy and performance of detecting random objects on YOLOv3. The result of object detection will display the name and bounding box with the percentage of accuracy. In this study, the system is also able to recognize objects when they object is stationary or moving.

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9

Wu, Hong, Jiatong Ji, Haimei Tian, Yao Chen, Weihong Ge, Haixia Zhang, Feng Yu, Jianjun Zou, Mitsuhiro Nakamura, and Jun Liao. "Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model." JMIR Medical Informatics 9, no. 12 (December 1, 2021): e26407. http://dx.doi.org/10.2196/26407.

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Background With the increasing variety of drugs, the incidence of adverse drug events (ADEs) is increasing year by year. Massive numbers of ADEs are recorded in electronic medical records and adverse drug reaction (ADR) reports, which are important sources of potential ADR information. Meanwhile, it is essential to make latent ADR information automatically available for better postmarketing drug safety reevaluation and pharmacovigilance. Objective This study describes how to identify ADR-related information from Chinese ADE reports. Methods Our study established an efficient automated tool, named BBC-Radical. BBC-Radical is a model that consists of 3 components: Bidirectional Encoder Representations from Transformers (BERT), bidirectional long short-term memory (bi-LSTM), and conditional random field (CRF). The model identifies ADR-related information from Chinese ADR reports. Token features and radical features of Chinese characters were used to represent the common meaning of a group of words. BERT and Bi-LSTM-CRF were novel models that combined these features to conduct named entity recognition (NER) tasks in the free-text section of 24,890 ADR reports from the Jiangsu Province Adverse Drug Reaction Monitoring Center from 2010 to 2016. Moreover, the man-machine comparison experiment on the ADE records from Drum Tower Hospital was designed to compare the NER performance between the BBC-Radical model and a manual method. Results The NER model achieved relatively high performance, with a precision of 96.4%, recall of 96.0%, and F1 score of 96.2%. This indicates that the performance of the BBC-Radical model (precision 87.2%, recall 85.7%, and F1 score 86.4%) is much better than that of the manual method (precision 86.1%, recall 73.8%, and F1 score 79.5%) in the recognition task of each kind of entity. Conclusions The proposed model was competitive in extracting ADR-related information from ADE reports, and the results suggest that the application of our method to extract ADR-related information is of great significance in improving the quality of ADR reports and postmarketing drug safety evaluation.

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10

Chang, J. Morris, Witawas Srisa-an, Chia-Tien Dan Lo, and Edward F. Gehringer. "DMMX: Dynamic memory management extensions." Journal of Systems and Software 63, no. 3 (September 2002): 187–99. http://dx.doi.org/10.1016/s0164-1212(02)00014-6.

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11

Umetalieva, Ch T., N. Y. Temirbaeva, and B. N. Nurtaev. "THEORY OF BUILDING AUTOMATED CONTROL SYSTEMS." Herald of KSUCTA, №2, Part 1, 2022, no. 2-1-2022 (April 30, 2022): 295–99. http://dx.doi.org/10.35803/1694-5298.2022.2.295-299.

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The task of collecting, processing and disseminating (exchanging) information stood at all stages of human development. For a long time, the main tools for solving it were the human brain, hearing and language. Storing information in computer memory gives a fundamentally new quality of dynamics, that is, the ability to quickly recover and use it directly in problems solved on a computer. Modern printing equipment, equipped with modern computers, allows, if necessary, to quickly present any selected information on paper. In the process of development of the administrative data management system, the need arose for an automated facility management system (ACS).

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12

Henderson, Scott, and Sidney C. Bailin. "A dynamic memory of software designs." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8, no. 2 (1994): 163–76. http://dx.doi.org/10.1017/s0890060400000743.

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AbstractThis paper describes an application of artificial intelligence to support software reuse. We begin by discussing the characteristics of software engineering that establish dynamic reorganization as a requirement for a repository of software artifacts. We then present an experimental system that uses incremental concept formation as the basis for dynamic reorganization, and the conceptual hierarchy that was generated by the system for a set of 67 artifacts. The hierarchy is compared to a hierarchy produced manually by independent investigators, and the automatic hierarchy is evaluated in terms of retrieval efficiency and retrieval reliability. The paper ends with a discussion of three projects that share similar objectives with our work.

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13

Kumar, Sanjeev, and Kai Li. "Dynamic memory management for programmable devices." ACM SIGPLAN Notices 38, no. 2 supplement (February 15, 2003): 139–49. http://dx.doi.org/10.1145/773039.512447.

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14

Ungar, David, David Grove, and Hubertus Franke. "Dynamic atomicity: optimizing swift memory management." ACM SIGPLAN Notices 52, no. 11 (December 2017): 15–26. http://dx.doi.org/10.1145/3170472.3133843.

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15

Simões, Hugo, Pedro Vasconcelos, Mário Florido, Steffen Jost, and Kevin Hammond. "Automatic amortised analysis of dynamic memory allocation for lazy functional programs." ACM SIGPLAN Notices 47, no. 9 (October 15, 2012): 165–76. http://dx.doi.org/10.1145/2398856.2364575.

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16

Artusi, Alessandro, Tania Pouli, Francesco Banterle, and Ahmet Oğuz Akyüz. "Automatic saturation correction for dynamic range management algorithms." Signal Processing: Image Communication 63 (April 2018): 100–112. http://dx.doi.org/10.1016/j.image.2018.01.011.

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17

Atienza, David, Jose M. Mendias, Stylianos Mamagkakis, Dimitrios Soudris, and Francky Catthoor. "Systematic dynamic memory management design methodology for reduced memory footprint." ACM Transactions on Design Automation of Electronic Systems 11, no. 2 (April 2006): 465–89. http://dx.doi.org/10.1145/1142155.1142165.

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18

Huang, Ning, Shuguang Huang, and Chao Chang. "Automatic Generation of ROP Through Static Instructions Assignment and Dynamic Memory Analysis." International Journal of Digital Crime and Forensics 13, no. 2 (March 2021): 57–76. http://dx.doi.org/10.4018/ijdcf.2021030104.

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W⊕X is a protection mechanism against control-flow hijacking attacks. Return-oriented programming (ROP) can perform a specific function by searching for appropriate assembly instruction fragments (gadgets) in a code segment and bypass the W⊕X. However, manual search for gadgets that match the conditions is inefficient, with high error and missing rates. In order to improve the efficiency of ROP generation, the authors propose an automatic generation method based on a fragmented layout called automatic generation of ROP. This method designs new intermediate instruction construction rules based on an automatic ROP generation framework Q, uses symbolic execution to analyze program memory states and construct data constraints for multi-modules ROP, and solves ROP data constraints to generate test cases of an ROP chain. Experiments show that this method can effectively improve the space efficiency of the ROP chain and lower the requirements of the ROP layout on memory conditions.

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19

Heirman, Wim, Stijn Eyerman, Kristof Du Bois, and Ibrahim Hur. "Automatic Sublining for Efficient Sparse Memory Accesses." ACM Transactions on Architecture and Code Optimization 18, no. 3 (June 2021): 1–23. http://dx.doi.org/10.1145/3452141.

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Sparse memory accesses, which are scattered accesses to single elements of a large data structure, are a challenge for current processor architectures. Their lack of spatial and temporal locality and their irregularity makes caches and traditional stream prefetchers useless. Furthermore, performing standard caching and prefetching on sparse accesses wastes precious memory bandwidth and thrashes caches, deteriorating performance for regular accesses. Bypassing prefetchers and caches for sparse accesses, and fetching only a single element (e.g., 8 B) from main memory (subline access), can solve these issues. Deciding which accesses to handle as sparse accesses and which as regular cached accesses, is a challenging task, with a large potential impact on performance. Not only is performance reduced by treating sparse accesses as regular accesses, not caching accesses that do have locality also negatively impacts performance by significantly increasing their latency and bandwidth consumption. Furthermore, this decision depends on the dynamic environment, such as input set characteristics and system load, making a static decision by the programmer or compiler suboptimal. We propose the Instruction Spatial Locality Estimator ( ISLE ), a hardware detector that finds instructions that access isolated words in a sea of unused data. These sparse accesses are dynamically converted into uncached subline accesses, while keeping regular accesses cached. ISLE does not require modifying source code or binaries, and adapts automatically to a changing environment (input data, available bandwidth, etc.). We apply ISLE to a graph analytics processor running sparse graph workloads, and show that ISLE outperforms the performance of no subline accesses, manual sublining, and prior work on detecting sparse accesses.

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20

Anagnostopoulos, Iraklis, Sotirios Xydis, Alexandros Bartzas, Zhonghai Lu, Dimitrios Soudris, and Axel Jantsch. "Custom Microcoded Dynamic Memory Management for Distributed On-Chip Memory Organizations." IEEE Embedded Systems Letters 3, no. 2 (June 2011): 66–69. http://dx.doi.org/10.1109/les.2011.2146228.

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21

Paul, Olivier, and Maryline Laurent. "Improving packet filters management through automatic and dynamic schemes." Annales Des Télécommunications 56, no. 9-10 (September 2001): 595–608. http://dx.doi.org/10.1007/bf03008836.

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22

Ferguson, E. E., D. S. Cook, and D. H. Bartley. "Designing a Hard Real-Time System with Automatic Memory Management." IFAC Proceedings Volumes 24, no. 2 (May 1991): 71–72. http://dx.doi.org/10.1016/s1474-6670(17)51272-0.

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23

Chang, J. Morris, and Charles H. Daugherty. "An efficient data structure for dynamic memory management." Journal of Systems and Software 54, no. 3 (November 2000): 219–26. http://dx.doi.org/10.1016/s0164-1212(00)00039-x.

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24

Charreteur, Florence, Bernard Botella, and Arnaud Gotlieb. "Modelling dynamic memory management in constraint-based testing." Journal of Systems and Software 82, no. 11 (November 2009): 1755–66. http://dx.doi.org/10.1016/j.jss.2009.06.029.

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25

Adams, Marilyn Jager, Yvette J. Tenney, and Richard W. Pew. "Situation Awareness and the Cognitive Management of Complex Systems." Human Factors: The Journal of the Human Factors and Ergonomics Society 37, no. 1 (March 1995): 85–104. http://dx.doi.org/10.1518/001872095779049462.

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The issue of how to support situation awareness among operators of complex systems or vehicles is a growing concern in a number of industries, especially when automation takes the operators partly "out of the loop." Cognitive theory suggests that comprehension of the flow of events is an active process, constrained by the dynamics and modularity of attention and memory. Focusing on issues of commercial aviation, we review the meaning of the term situation awareness both definition ally and by way of examples. We then discuss the cognitive factors involved in achieving and maintaining situation awareness, drawing on recent cognitive theory to clarify their interactions. Finally, having emphasized the need for a more analytic and precise understanding of situation awareness, we discuss strengths and weaknesses of various empirical approaches that might be used toward that end.

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26

JOHNSON, THEODORE, and TIMOTHY A. DAVIS. "PARALLEL BUDDY MEMORY MANAGEMENT." Parallel Processing Letters 02, no. 04 (December 1992): 391–98. http://dx.doi.org/10.1142/s0129626492000544.

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Shared memory multiprocessor systems need efficient dynamic storage allocators, both for system purposes and to support parallel programs. Memory managers are often based on the buddy system, which provides fast allocation and release. Previous parallel buddy memory managers made no attempt to coordinate the allocation, splitting and release of blocks, and as a result needlessly fragment memory. We present a fast and simple parallel buddy memory manager that is also as space efficient as a serial buddy memory manager. We test our algorithms using memory allocation/deallocation traces collected from a parallel sparse matrix algorithm.

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27

Cho, Hyungmin, Bernhard Egger, Jaejin Lee, and Heonshik Shin. "Dynamic data scratchpad memory management for a memory subsystem with an MMU." ACM SIGPLAN Notices 42, no. 7 (July 13, 2007): 195–206. http://dx.doi.org/10.1145/1273444.1254804.

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28

Moltó, Germán, Miguel Caballer, Eloy Romero, and Carlos de Alfonso. "Elastic Memory Management of Virtualized Infrastructures for Applications with Dynamic Memory Requirements." Procedia Computer Science 18 (2013): 159–68. http://dx.doi.org/10.1016/j.procs.2013.05.179.

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29

Brockwell, A. E., and N. H. Chan. "Long-memory dynamic Tobit models." Journal of Forecasting 25, no. 5 (August 2006): 351–67. http://dx.doi.org/10.1002/for.992.

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30

Sharafinejad, Reza, Bijan Alizadeh, and Zainalabedin Navabi. "Automatic Correction of Dynamic Power Management Architecture in Modern Processors." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 26, no. 2 (February 2018): 308–18. http://dx.doi.org/10.1109/tvlsi.2017.2762006.

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31

Hu, Guang, Zhilei Chai, and Shiliang Tu. "Automatic Memory Management for Embedded Real-Time Java Processor Jpor-32." Intelligent Automation & Soft Computing 17, no. 8 (January 2011): 1193–205. http://dx.doi.org/10.1080/10798587.2011.10643221.

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32

Willnecker, Felix, and Helmut Krcmar. "Model-based prediction of automatic memory management and garbage collection behavior." Simulation Modelling Practice and Theory 93 (May 2019): 164–91. http://dx.doi.org/10.1016/j.simpat.2018.09.014.

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33

Russell, M. J., R. K. Moore, and M. J. Tomlinson. "Dynamic Programming and Statistical Modelling in Automatic Speech Recognition." Journal of the Operational Research Society 37, no. 1 (January 1986): 21. http://dx.doi.org/10.2307/2582543.

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34

Russell, M. J., R. K. Moore, and M. J. Tomlinson. "Dynamic Programming and Statistical Modelling in Automatic Speech Recognition." Journal of the Operational Research Society 37, no. 1 (January 1986): 21–30. http://dx.doi.org/10.1057/jors.1986.4.

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35

Pan, D., and R. S. Sharp. "Automatic Formulation of Dynamic Equations of Motion of Robot Manipulators." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 202, no. 6 (November 1988): 397–404. http://dx.doi.org/10.1243/pime_proc_1988_202_141_02.

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Анотація:

Based on the use of homogeneous transformation matrices with Denavit-Hartenberg notation and the Lagrangian formulation method, a general computer program ROBDYN.RED for the symbolic derivation of dynamic equations of motion for robot manipulators has been developed and is discussed in this paper. The program is developed by using REDUCE, an algebraic manipulation language, and is versatile for open-chain structure robot manipulators with any number of degrees of freedom and with any combination of types of joint. Considerations are also given to saving computer memory space required for execution and to minimizing the runtime. Several examples are included to demonstrate the use of the program. Equations of motion in scalar form can be automatically transferred to FORTRAN format for later numerical simulations. The efficiency of the resulting equations in terms of numerical integration is also discussed and some further developments to improve the efficiency are suggested.

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36

Efremova, Tatiana, and Sergey Shchegolev. "Boiler drum automatic power management system." E3S Web of Conferences 222 (2020): 01009. http://dx.doi.org/10.1051/e3sconf/202022201009.

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Dynamic properties of boiler sections as control objects are studied. The requirements for mathematical models and the main adjustable parameters in drum boilers are determined, on the basis of which the requirements for the designed model are made. The construction of a block diagram and calculation of transfer functions of the automatic control system of the control object. The main types of disturbances are determined: feed water and steam consumption, consumer load, fuel consumption, and feed water temperature. Each perturbation is represented as a control action. Models of the object using perturbation channels are obtained. The article considers the construction of an effective model of the system of automatic control of the drum boiler power supply based on the system’s working out of disturbing influences. The paper uses the Matlab application software and the Simulink graphical programming environment.

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37

Ahmadinia, Ali. "Efficient Dynamic Memory Management for Multiprocessor Cyber-Physical Systems." International Journal of Cyber-Physical Systems 1, no. 1 (January 2019): 35–44. http://dx.doi.org/10.4018/ijcps.2019010103.

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Dynamic data management for multiprocessor systems in the absence of an operating system (OS) is a challenging area of research. OSs are typically used to abstract developers from the process of managing dynamic data at runtime. However, due to the many different types of multiprocessor available, an OS is not always available, making the management of dynamic data a difficult task. In this article, we present a hardware and software co-design methodology for the management of dynamic data in multiprocessor system on chips (MPSoC) development environments without an OS. We compare and contrast the method of sharing dynamic data between cores with standard methods and also to static data management methods and find that the proposed methodology can improve the performance of dynamic memory operations by up to 72.94% with negligible power and resource consumption.

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38

Herlihy, Maurice, Victor Luchangco, Paul Martin, and Mark Moir. "Nonblocking memory management support for dynamic-sized data structures." ACM Transactions on Computer Systems 23, no. 2 (May 2005): 146–96. http://dx.doi.org/10.1145/1062247.1062249.

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39

Marchal, P., J. I. Gomez, D. Atienza, S. Mamagkakis, and F. Catthoor. "Power aware data and memory management for dynamic applications." IEE Proceedings - Computers and Digital Techniques 152, no. 2 (2005): 224. http://dx.doi.org/10.1049/ip-cdt:20045077.

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40

Lee, Woo Hyong, and Morris Chang. "A study of dynamic memory management in C++ programs." Computer Languages, Systems & Structures 28, no. 3 (October 2002): 237–72. http://dx.doi.org/10.1016/s0096-0551(02)00015-2.

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41

Moniruzzaman, A. B. M. "Analysis of Memory Ballooning Technique for Dynamic Memory Management of Virtual Machines (VMs)." International Journal of Grid and Distributed Computing 7, no. 6 (December 31, 2014): 81–90. http://dx.doi.org/10.14257/ijgdc.2014.7.6.07.

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42

Mabu, Shingo, Kotaro Hirasawa, Yuko Matsuya, and Jinglu Hu. "Genetic Network Programming for Automatic Program Generation." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 4 (July 20, 2005): 430–36. http://dx.doi.org/10.20965/jaciii.2005.p0430.

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In this paper, a recently proposed Evolutionary Computation method called Genetic Network Programming (GNP) is applied to generate programs such as Boolean functions. GNP is an extension of Genetic Algorithm (GA) and Genetic Programming (GP). It has a directed graph structure as gene and can search for solutions effectively. GNP has been mainly applied to dynamic problems and has shown better performances compared to GP. However, its application to static problems has not yet been studied well. Thus in this paper, GNP is applied to generate programs as its extension to solving static problems. In order to apply GNP to generating static problems, we introduced a new element, memory. In the proposed method, a GNP individual consists of a directed graph and a memory, while one in conventional GNP consists only of a directed graph. In the simulations, GNP succeeded in solving Even-n-Parity problem and Mirror Symmetry problem.

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43

Petrak, Slavenka, and Maja Mahnič Naglic. "Dynamic Anthropometry – Defining Protocols for Automatic Body Measurement." TEKSTILEC 60, no. 4 (December 5, 2017): 254–62. http://dx.doi.org/10.14502/tekstilec2017.60.254-262.

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44

Liu, Cai Xia, Xiao Qing Tian, and Zhi Bin Zhang. "Optimal Space Management Mechanism of Sharable Multi-Channel On-Chip Memory." Advanced Materials Research 629 (December 2012): 542–47. http://dx.doi.org/10.4028/www.scientific.net/amr.629.542.

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Анотація:

A kind of shared multi-channel on-chip memory CMP architecture is proposed in this article to efficiently support embedded applications. For the multi-channel on-chip memory being scarce resource, optimal space management mechanism of multi-channel on-chip memory is proposed including automatic space allocation strategy based on application parallelization mapping pattern and optimal space utilization scheme. ILP-model-based analysis of system performance verifies that the proposed optimal space management mechanism can deeply exploit the efficiency of multi-channel on-chip memory to improve system performance.

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45

Ykman-Couvreur, C., J. Lambrecht, D. Verkest, F. Catthoor, B. Svantesson, A. Hemani, and F. Wolf. "Dynamic memory management methodology applied to embedded telecom network systems." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 10, no. 5 (October 2002): 650–67. http://dx.doi.org/10.1109/tvlsi.2002.801602.

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46

Kim, Yooseong, David Broman, and Aviral Shrivastava. "WCET-Aware Function-Level Dynamic Code Management on Scratchpad Memory." ACM Transactions on Embedded Computing Systems 16, no. 4 (September 15, 2017): 1–26. http://dx.doi.org/10.1145/3063383.

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47

Bendersky, Anna, and Erez Petrank. "Space overhead bounds for dynamic memory management with partial compaction." ACM SIGPLAN Notices 46, no. 1 (January 26, 2011): 475–86. http://dx.doi.org/10.1145/1925844.1926441.

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48

Sakamoto, Masaki, and Saneyasu Yamaguchi. "Dynamic Memory Resource Management in Virtual Machines with Different Applications." Advanced Science Letters 22, no. 10 (October 1, 2016): 2745–49. http://dx.doi.org/10.1166/asl.2016.7077.

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49

Sivaraman, Vijay, Arun Vishwanath, Diethelm Ostry, and Marina Thottan. "Greening Router Line-Cards via Dynamic Management of Packet Memory." IEEE Journal on Selected Areas in Communications 34, no. 12 (December 2016): 3843–53. http://dx.doi.org/10.1109/jsac.2016.2600412.

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50

Wang, XiaoLin, and YingWei Luo. "Dynamic memory mapping delivers additional flexibility to virtual resource management." Chinese Science Bulletin 55, no. 26 (September 2010): 3071. http://dx.doi.org/10.1007/s11434-010-9985-9.

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