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Статті в журналах з теми "Memory optimisation"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Weinhardt, M., and W. Luk. "Memory access optimisation for reconfigurable systems." IEE Proceedings - Computers and Digital Techniques 148, no. 3 (May 1, 2001): 105–12. http://dx.doi.org/10.1049/ip-cdt:20010514.

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2

Kulhavý, Rudolf, and Petya Ivanova. "Memory-based prediction in control and optimisation." IFAC Proceedings Volumes 32, no. 2 (July 1999): 4058–63. http://dx.doi.org/10.1016/s1474-6670(17)56692-6.

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3

Sayadi, Fatma Elzahra, Marwa Chouchene, Haithem Bahri, Randa Khemiri, and Mohamed Atri. "CUDA memory optimisation strategies for motion estimation." IET Computers & Digital Techniques 13, no. 1 (September 6, 2018): 20–27. http://dx.doi.org/10.1049/iet-cdt.2017.0149.

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4

Zhu, Zongwei, Xi Li, Chao Wang, and Xuehai Zhou. "Memory power optimisation on low-bit multi-access cross memory address mapping schema." International Journal of Embedded Systems 6, no. 2/3 (2014): 240. http://dx.doi.org/10.1504/ijes.2014.063822.

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5

Martin, Jose L. Risco, Oscar Garnica, Juan Lanchares, J. Ignacio Hidalgo, and David Atienza. "Particle swarm optimisation of memory usage in embedded systems." International Journal of High Performance Systems Architecture 1, no. 4 (2008): 209. http://dx.doi.org/10.1504/ijhpsa.2008.024205.

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6

Bossard, Antoine. "Memory Optimisation on AVR Microcontrollers for IoT Devices’ Minimalistic Displays." Chips 1, no. 1 (April 21, 2022): 2–13. http://dx.doi.org/10.3390/chips1010002.

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Анотація:

The minimalistic hardware of most Internet of things (IoT) devices and sensors, especially those based on microcontrollers (MCU), imposes severe limitations on the memory capacity and interfacing capabilities of the device. Nevertheless, many applications prescribe not only textual but also graphical display features as output interface. Due to the aforementioned limitations, the storage of graphical data is however highly problematic and existing solutions have even resorted to requiring external storage (e.g., a microSD card) for that purpose. In this paper, we present, evaluate and discuss two solutions that enable loading fullscreen, optimal 18-bit colour image data directly from the MCU, that is, without having to rely on additional hardware. Importantly, these solutions retain a very low footprint to suit the microcontroller architecture; the AVR architecture has been selected given its popularity. The obtained results show the feasibility and practicability of the proposal: in the worst case, 21 Kbytes of memory are required, in other words approximately 33% of the flash memory of a 32-Kbyte MCU remain available.

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7

Tirelli, D., and S. Mascelloni. "Characterisation and optimisation of shape memory alloys for seismic applications." Le Journal de Physique IV 10, PR9 (September 2000): Pr9–665—Pr9–670. http://dx.doi.org/10.1051/jp4:20009111.

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8

Cao, Zhengnan, Xiaoqing Han, William Lyons, and Fergal O'Rourke. "Energy management optimisation using a combined Long Short-Term Memory recurrent neural network – Particle Swarm Optimisation model." Journal of Cleaner Production 326 (December 2021): 129246. http://dx.doi.org/10.1016/j.jclepro.2021.129246.

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9

Ding, Weijie, Jin Huang, Guanyu Shang, Xuexuan Wang, Baoqiang Li, Yunfei Li, and Hourong Liu. "Short-Term Trajectory Prediction Based on Hyperparametric Optimisation and a Dual Attention Mechanism." Aerospace 9, no. 8 (August 20, 2022): 464. http://dx.doi.org/10.3390/aerospace9080464.

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Highly accurate trajectory prediction models can achieve route optimisation and save airspace resources, which is a crucial technology and research focus for the new generation of intelligent air traffic control. Aiming at the problems of inadequate extraction of trajectory features and difficulty in overcoming the short-term memory of time series in existing trajectory prediction, a trajectory prediction model based on a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) network combined with dual attention and genetic algorithm (GA) optimisation is proposed. First, to autonomously mine the data association between input features and trajectory features as well as highlight the influence of important features, an attention mechanism was added to a conventional CNN architecture to develop a feature attention module. An attention mechanism was introduced at the output of the BiLSTM network to form a temporal attention module to enhance the influence of important historical information, and GA was used to optimise the hyperparameters of the model to achieve the best performance. Finally, a multifaceted comparison with other typical time-series prediction models based on real flight data verifies that the prediction model based on hyperparameter optimisation and a dual attention mechanism has significant advantages in terms of prediction accuracy and applicability.

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10

Thiruvady, Dhananjay, Asef Nazari, and Aldeida Aleti. "Multi-objective Beam-ACO for Maximising Reliability and Minimising Communication Overhead in the Component Deployment Problem." Algorithms 13, no. 10 (October 3, 2020): 252. http://dx.doi.org/10.3390/a13100252.

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Automated deployment of software components into hardware resources is a highly constrained optimisation problem. Hardware memory limits which components can be deployed into the particular hardware unit. Interacting software components have to be deployed either into the same hardware unit, or connected units. Safety concerns could restrict the deployment of two software components into the same unit. All these constraints hinder the search for high quality solutions that optimise quality attributes, such as reliability and communication overhead. When the optimisation problem is multi-objective, as it is the case when considering reliability and communication overhead, existing methods often fail to produce feasible results. Moreover, this problem can be modelled by bipartite graphs with complicating constraints, but known methods do not scale well under the additional restrictions. In this paper, we develop a novel multi-objective Beam search and ant colony optimisation (Beam-ACO) hybrid method, which uses problem specific bounds derived from communication, co-localisation and memory constraints, to guide the search towards feasibility. We conduct an experimental evaluation on a range of component deployment problem instances with varying levels of difficulty. We find that Beam-ACO guided by the co-localisation constraint is most effective in finding high quality feasible solutions.

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11

Kiliç, Sadik Engin, Mirsadegh Seyedzavvar, Hassan Ali M. Meshri, and Samet Akar. "Micro-WEDM of Ni55.8Ti shape memory superalloy: experimental investigation and optimisation." International Journal of Mechatronics and Manufacturing Systems 14, no. 1 (2021): 18. http://dx.doi.org/10.1504/ijmms.2021.10038217.

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12

Meshri, Hassan Ali M., Samet Akar, Mirsadegh Seyedzavvar, and Sadik Engin Kiliç. "Micro-WEDM of Ni55.8Ti shape memory superalloy: experimental investigation and optimisation." International Journal of Mechatronics and Manufacturing Systems 14, no. 1 (2021): 18. http://dx.doi.org/10.1504/ijmms.2021.115461.

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13

Fairhurst, M. C., and H. M. S. Abdel Wahab. "Approach to performance optimisation in frequency-weighted memory network pattern classifiers." Electronics Letters 23, no. 21 (1987): 1116. http://dx.doi.org/10.1049/el:19870779.

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14

Muir, Luke, Paul F. McKay, Velislava N. Petrova, Oleksiy V. Klymenko, Sven Kratochvil, Christopher L. Pinder, Paul Kellam, and Robin J. Shattock. "Optimisation of ex vivo memory B cell expansion/differentiation for interrogation of rare peripheral memory B cell subset responses." Wellcome Open Research 2 (October 5, 2017): 97. http://dx.doi.org/10.12688/wellcomeopenres.11386.1.

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Анотація:

Background:Human memory B cells play a vital role in the long-term protection of the host from pathogenic re-challenge. In recent years the importance of a number of different memory B cell subsets that can be formed in response to vaccination or infection has started to become clear. To study memory B cell responses, cells can be culturedex vivo,allowing for an increase in cell number and activation of these quiescent cells, providing sufficient quantities of each memory subset to enable full investigation of functionality. However, despite numerous papers being published demonstrating bulk memory B cell culture, we could find no literature on optimised conditions for the study of memory B cell subsets, such as IgM+memory B cells.Methods:Following a literature review, we carried out a large screen of memory B cell expansion conditions to identify the combination that induced the highest levels of memory B cell expansion. We subsequently used a novel Design of Experiments approach to finely tune the optimal memory B cell expansion and differentiation conditions for human memory B cell subsets. Finally, we characterised the resultant memory B cell subpopulations by IgH sequencing and flow cytometry.Results:The application of specific optimised conditions induce multiple rounds of memory B cell proliferation equally across Ig isotypes, differentiation of memory B cells to antibody secreting cells, and importantly do not alter the Ig genotype of the stimulated cells. Conclusions:Overall, our data identify a memory B cell culture system that offers a robust platform for investigating the functionality of rare memory B cell subsets to infection and/or vaccination.

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15

Muir, Luke, Paul F. McKay, Velislava N. Petrova, Oleksiy V. Klymenko, Sven Kratochvil, Christopher L. Pinder, Paul Kellam, and Robin J. Shattock. "Optimisation of ex vivo memory B cell expansion/differentiation for interrogation of rare peripheral memory B cell subset responses." Wellcome Open Research 2 (January 24, 2018): 97. http://dx.doi.org/10.12688/wellcomeopenres.11386.2.

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Анотація:

Background:Human memory B cells play a vital role in the long-term protection of the host from pathogenic re-challenge. In recent years the importance of a number of different memory B cell subsets that can be formed in response to vaccination or infection has started to become clear. To study memory B cell responses, cells can be culturedex vivo,allowing for an increase in cell number and activation of these quiescent cells, providing sufficient quantities of each memory subset to enable full investigation of functionality. However, despite numerous papers being published demonstrating bulk memory B cell culture, we could find no literature on optimised conditions for the study of memory B cell subsets, such as IgM+memory B cells.Methods:Following a literature review, we carried out a large screen of memory B cell expansion conditions to identify the combination that induced the highest levels of memory B cell expansion. We subsequently used a novel Design of Experiments approach to finely tune the optimal memory B cell expansion and differentiation conditions for human memory B cell subsets. Finally, we characterised the resultant memory B cell subpopulations by IgH sequencing and flow cytometry.Results:The application of specific optimised conditions induce multiple rounds of memory B cell proliferation equally across Ig isotypes, differentiation of memory B cells to antibody secreting cells, and importantly do not alter the Ig genotype of the stimulated cells. Conclusions:Overall, our data identify a memory B cell culture system that offers a robust platform for investigating the functionality of rare memory B cell subsets to infection and/or vaccination.

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16

Mohamad Basir, Muhammad Sufyan Safwan. "Window Optimisation of Power Quality Signal Detection using Gabor Transform." ASM Science Journal 14 (April 2, 2021): 1–10. http://dx.doi.org/10.32802/asmscj.2020.596.

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This paper presents power quality analysis on different signal characteristics, namely instantaneous sag, momentary sag, temporary sag, instantaneous swell, momentary swell, and temporary swell. Power quality signals were analyzed using linear time-frequency distribution (TFD) namely short-time Fourier transform (STFT) and proposed Gabor transform (GT), and the best technique for power quality detection was determined based on the performance analysis of varied window length. Optimum window length for different signal characteristics which are effective and reliable for developing real-time monitoring system was employed using MATLAB. From a time-frequency representation (TFR) results based on STFT and GT, parameters such as instantaneous RMS voltage, VRMS(t) and instantaneous total waveform distortion, VTWD(t) had been extracted. In finding the best technique for power quality, the TFDs had been compared in terms of accuracy, memory and computational complexity of the analysis. Based on the performance analysis conducted, GT was able to compute with high accuracy with 94% averagely as well as low memory size by 6% compared to STFT. Hence, GT is considered as the best TFD, and recommended for low cost PQ monitoring system.

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17

Dumont, Georges, and Christofer Kühl. "Finite element simulation for design optimisation of shape memory alloy spring actuators." Engineering Computations 22, no. 7 (October 2005): 835–48. http://dx.doi.org/10.1108/02644400510619549.

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18

Murdoch, Heather A., and R. Edwin Garcia. "Crystallographic texture optimisation in polycrystalline ferroelectric films for Random Access Memory applications." International Journal of Materials and Product Technology 35, no. 3/4 (2009): 293. http://dx.doi.org/10.1504/ijmpt.2009.025683.

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19

Matsumoto, Yuki, and Shota Nakamura. "qualign: solving sequence alignment based on quadratic unconstrained binary optimisation." EMBnet.journal 28 (March 8, 2023): e1020. http://dx.doi.org/10.14806/ej.28.0.1020.

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Bioinformatics has, among others, the issue of solving complex computational problems with vast amounts of sequencing data. Recently, a new computing architecture, the annealing machine, has emerged that applies to actual problems and is available for practical use. This novel architecture can solve discrete optimisation problems by replacing algorithms designed under the von Neumann architecture. To perform computations on the annealing machine, quadratic unconstrained binary optimisation (QUBO) formulations should be constructed and optimised according to the application. In this study, we developed an algorithm under the annealing machine architecture to solve sequence alignment problems, a known fundamental process widely used in genetic analysis, such as mutation detection and genome assembly. We constructed a QUBO formulation based on dynamic programming to solve a pairwise sequence alignment and derived its general form. We compared with conventional methods to solve 40 bp of pairwise alignment problem. Our implementation, named qualign, solved sequence alignment problems with accuracy comparable to that of conventional methods. Although a small pairwise alignment was solved owing to the limited memory size of this method, this is the first step of the application of annealing machines. We showed that our QUBO formulation solved the sequencing alignment problem. In the future, increasing the memory size of annealing machine will allow annealing machines to impact a wide range of bioinformatics applications positively.Availability: the source code of qualign is available at https://github.com/ymatsumoto/qualign

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20

Alvisi, S., M. Franchini, M. Gavanelli, and M. Nonato. "Near-optimal scheduling of device activation in water distribution systems to reduce the impact of a contamination event." Journal of Hydroinformatics 14, no. 2 (August 4, 2011): 345–65. http://dx.doi.org/10.2166/hydro.2011.147.

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This paper proposes an innovative procedure for identifying, in the event of accidental or intentional contamination of a water distribution system, the optimal scheduling of activation of a pre-selected set of flow control devices which will serve to minimise the volume of contaminated water consumed by users after the detection of the contaminant in the system. The constraints are represented by the number of available response teams and the maximum speed at which these teams can travel along the roadway. The optimal scheduling of device activation is sought by means of an optimisation process based on a genetic algorithm (GA) which interacts with a mixed integer linear programming (MILP) solver in order to ensure the feasibility of the scheduling identified. The optimisation procedure is coupled to a hydraulic and quality simulator, which enables a calculation of the volumes of contaminated water consumed by users, and a dynamic cache memory, which, by storing information on the system's behaviour as the optimisation process progresses, serves to limit the computational times. The application of the procedure to a highly complex real water distribution system shows that the optimisation process is robust and efficacious and produces a smaller volume of contaminated water consumed by the users than when the activation of all the devices was completed in the shortest amount of time.

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21

Wang, Hui, Huansheng Chen, Qizhong Wu, Junmin Lin, Xueshun Chen, Xinwei Xie, Rongrong Wang, Xiao Tang, and Zifa Wang. "GNAQPMS v1.1: accelerating the Global Nested Air Quality Prediction Modeling System (GNAQPMS) on Intel Xeon Phi processors." Geoscientific Model Development 10, no. 8 (August 1, 2017): 2891–904. http://dx.doi.org/10.5194/gmd-10-2891-2017.

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Abstract. The Global Nested Air Quality Prediction Modeling System (GNAQPMS) is the global version of the Nested Air Quality Prediction Modeling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present the porting and optimisation of GNAQPMS on a second-generation Intel Xeon Phi processor, codenamed Knights Landing (KNL). Compared with the first-generation Xeon Phi coprocessor (codenamed Knights Corner, KNC), KNL has many new hardware features such as a bootable processor, high-performance in-package memory and ISA compatibility with Intel Xeon processors. In particular, we describe the five optimisations we applied to the key modules of GNAQPMS, including the CBM-Z gas-phase chemistry, advection, convection and wet deposition modules. These optimisations work well on both the KNL 7250 processor and the Intel Xeon E5-2697 V4 processor. They include (1) updating the pure Message Passing Interface (MPI) parallel mode to the hybrid parallel mode with MPI and OpenMP in the emission, advection, convection and gas-phase chemistry modules; (2) fully employing the 512 bit wide vector processing units (VPUs) on the KNL platform; (3) reducing unnecessary memory access to improve cache efficiency; (4) reducing the thread local storage (TLS) in the CBM-Z gas-phase chemistry module to improve its OpenMP performance; and (5) changing the global communication from writing/reading interface files to MPI functions to improve the performance and the parallel scalability. These optimisations greatly improved the GNAQPMS performance. The same optimisations also work well for the Intel Xeon Broadwell processor, specifically E5-2697 v4. Compared with the baseline version of GNAQPMS, the optimised version was 3.51 × faster on KNL and 2.77 × faster on the CPU. Moreover, the optimised version ran at 26 % lower average power on KNL than on the CPU. With the combined performance and energy improvement, the KNL platform was 37.5 % more efficient on power consumption compared with the CPU platform. The optimisations also enabled much further parallel scalability on both the CPU cluster and the KNL cluster scaled to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 and 42.2 %, respectively.

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22

Osei, Richard Sakyi, and Daphne Lopez. "Experience Replay Optimisation via ATSC and TSC for Performance Stability in Deep RL." Applied Sciences 13, no. 4 (February 4, 2023): 2034. http://dx.doi.org/10.3390/app13042034.

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Catastrophic forgetting is a significant challenge in deep reinforcement learning (RL). To address this problem, researchers introduce the experience replay (ER) concept to complement the training of a deep RL agent. However, the buffer size, experience selection, and experience retention strategies adopted for the ER can negatively affect the agent’s performance stability, especially for complex continuous state action problems. This paper investigates how to address the stability problem using an enhanced ER method that combines a replay policy network, a dual memory, and an alternating transition selection control (ATSC) mechanism. Two frameworks were designed: an experience replay optimisation via alternating transition selection control (ERO-ATSC) without a transition storage control (TSC) and an ERO-ATSC with a TSC. The first is a hybrid of experience replay optimisation (ERO) and dual-memory experience replay (DER) and the second, which has two versions of its kind, integrates a transition storage control (TSC) into the first framework. After comprehensive experimental evaluations of the frameworks on the pendulum-v0 environment and across multiple buffer sizes, retention strategies, and sampling ratios, the reward version of ERO-ATSC with a TSC exhibits superior performance over the first framework and other novel methods, such as the deep deterministic policy gradient (DDPG) and ERO.

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23

Wu, Xinzhao, Peiqing Li, Qipeng Li, and Zhuoran Li. "Two-dimensional-simultaneous Localisation and Mapping Study Based on Factor Graph Elimination Optimisation." Sustainability 15, no. 2 (January 8, 2023): 1172. http://dx.doi.org/10.3390/su15021172.

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A robust multi-sensor fusion simultaneous localization and mapping (SLAM) algorithm for complex road surfaces is proposed to improve recognition accuracy and reduce system memory occupation, aiming to enhance the computational efficiency of light detection and ranging in complex environments. First, a weighted signed distance function (W-SDF) map-based SLAM method is proposed. It uses a W-SDF map to capture the environment with less accuracy than the raster size but with high localization accuracy. The Levenberg–Marquardt method is used to solve the scan-matching problem in laser SLAM; it effectively alleviates the limitations of the Gaussian–Newton method that may lead to insufficient local accuracy, and reduces localisation errors. Second, ground constraint factors are added to the factor graph, and a multi-sensor fusion localisation algorithm is proposed based on factor graph elimination optimisation. A sliding window is added to the chain factor graph model to retain the historical state information within the window and avoid high-dimensional matrix operations. An elimination algorithm is introduced to transform the factor graph into a Bayesian network to marginalize the historical states and reduce the matrix dimensionality, thereby improving the algorithm localisation accuracy and reducing the memory occupation. Finally, the proposed algorithm is compared and validated with two traditional algorithms based on an unmanned cart. Experiments show that the proposed algorithm reduces memory consumption and improves localisation accuracy compared to the Hector algorithm and Cartographer algorithm, has good performance in terms of accuracy, reliability and computational efficiency in complex pavement environments, and is better utilised in practical environments.

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24

Jeewantha, L. H. J., K. D. C. Emmanuel, H. M. C. M. Herath, M. M. Islam, L. Fang, and J. A. Epaarachchi. "Multi-attribute parametric optimisation of shape memory polymer properties for adaptive orthopaedic plasters." Materialia 21 (March 2022): 101325. http://dx.doi.org/10.1016/j.mtla.2022.101325.

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25

Wang, Lei, Jing Wen, CiHui Yang, Shan Gai, and XiangShui Miao. "Optimisation of Write Performance of Phase-Change Probe Memory for Future Storage Applications." Nanoscience and Nanotechnology Letters 7, no. 11 (November 1, 2015): 870–78. http://dx.doi.org/10.1166/nnl.2015.2048.

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26

Deepan Bharathi Kannan, T., T. Pavani priya, P. Sathiya, and T. Ramesh. "Metallurgical Aspects and Optimisation of Yb: YAG Laser Welded NiTinol Shape Memory Alloy." Materials Today: Proceedings 4, no. 2 (2017): 1268–76. http://dx.doi.org/10.1016/j.matpr.2017.01.147.

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27

Chagwiza, Godfrey. "A New Plant Intelligent Behaviour Optimisation Algorithm for Solving Vehicle Routing Problem." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/9874356.

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Анотація:

A new plant intelligent behaviour optimisation algorithm is developed. The algorithm is motivated by intelligent behaviour of plants and is implemented to solve benchmark vehicle routing problems of all sizes, and results were compared to those in literature. The results show that the new algorithm outperforms most of algorithms it was compared to for very large and large vehicle routing problem instances. This is attributed to the ability of the plant to use previously stored memory to respond to new problems. Future research may focus on improving input parameters so as to achieve better results.

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28

HARNSOONGNOEN, SANCHAI, CHIRANUT SA-NGIAMSAK, and APIRAT SIRITARATIWAT. "OPTIMIZATION OF PHASE CHANGE MEMORY WITH THIN METAL INSERTED LAYER ON MATERIAL PROPERTIES." International Journal of Modern Physics B 23, no. 17 (July 10, 2009): 3625–30. http://dx.doi.org/10.1142/s0217979209063080.

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Анотація:

This works reports, for the first time, the thorough study and optimisation of Phase Change Memory (PCM) structure with thin metal inserted chalcogenide via electrical resistivity (ρ) using finite element modeling. PCM is one of the best candidates for next generation non-volatile memory. It has received much attention recently due to its fast write speed, non-destructive readout, superb scalability, and great compatibility with current silicon-based mass fabrication. The setback of PCM is a high reset current typically higher than 1mA based on 180nm lithography. To reduce the reset current and to solve the over-programming failure, PCM with thin metal inserted chalcogenide (bottom chalcogenide/metal inserted/top chalcogenide) structure has been proposed. Nevertheless, reports on optimisation of the electrical resistivity using the finite element method for this new PCM structure have never been published. This work aims to minimize the reset current of this PCM structure by optimizing the level of the electrical resistivity of the PCM profile using the finite element approach. This work clearly shows that PCM characteristics are strongly affected by the electrical resistivity. The 2-D simulation results reveal clearly that the best thermal transfer of and self-joule-heating at the bottom chalcogenide layer can be achieved under conditions; ρ_bottom chalcogenide > ρ_metal inserted > ρ_top chalcogenide More specifically, the optimized electrical resistivity of PCMTMI is attained with ρ_top chalcogenide: ρ_metal inserted: ρ_bottom chalcogenide ratio of 1:6:16 when ρ_top chalcogenide is 10-3 Ωm. In conclusion, high energy efficiency can be obtained with the reset current as low as 0.3mA and with high speed operation of less than 30ns.

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29

Hanifi, Shahram, Saeid Lotfian, Hossein Zare-Behtash, and Andrea Cammarano. "Offshore Wind Power Forecasting—A New Hyperparameter Optimisation Algorithm for Deep Learning Models." Energies 15, no. 19 (September 21, 2022): 6919. http://dx.doi.org/10.3390/en15196919.

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Анотація:

The main obstacle against the penetration of wind power into the power grid is its high variability in terms of wind speed fluctuations. Accurate power forecasting, while making maintenance more efficient, leads to the profit maximisation of power traders, whether for a wind turbine or a wind farm. Machine learning (ML) models are recognised as an accurate and fast method of wind power prediction, but their accuracy depends on the selection of the correct hyperparameters. The incorrect choice of hyperparameters will make it impossible to extract the maximum performance of the ML models, which is attributed to the weakness of the forecasting models. This paper uses a novel optimisation algorithm to tune the long short-term memory (LSTM) model for short-term wind power forecasting. The proposed method improves the power prediction accuracy and accelerates the optimisation process. Historical power data of an offshore wind turbine in Scotland is utilised to validate the proposed method and compare its outcome with regular ML models tuned by grid search. The results revealed the significant effect of the optimisation algorithm on the forecasting models’ performance, with improvements of the RMSE of 7.89, 5.9, and 2.65 percent, compared to the persistence and conventional grid search-tuned Auto-Regressive Integrated Moving Average (ARIMA) and LSTM models.

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30

Rosenman, Stephen J. "Electroconvulsive therapy stimulus titration: Not all it seems." Australian & New Zealand Journal of Psychiatry 52, no. 5 (December 5, 2017): 410–14. http://dx.doi.org/10.1177/0004867417743793.

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Objective: To examine the provenance and implications of seizure threshold titration in electroconvulsive therapy. Background: Titration of seizure threshold has become a virtual standard for electroconvulsive therapy. It is justified as individualisation and optimisation of the balance between efficacy and unwanted effects. Result: Present day threshold estimation is significantly different from the 1960 studies of Cronholm and Ottosson that are its usual justification. The present form of threshold estimation is unstable and too uncertain for valid optimisation or individualisation of dose. Threshold stimulation (lowest dose that produces a seizure) has proven therapeutically ineffective, and the multiples applied to threshold to attain efficacy have never been properly investigated or standardised. The therapeutic outcomes of threshold estimation (or its multiples) have not been separated from simple dose effects. Threshold estimation does not optimise dose due to its own uncertainties and the different short-term and long-term cognitive and memory effects. Potential harms of titration have not been examined. Conclusion: Seizure threshold titration in electroconvulsive therapy is not a proven technique of dose optimisation. It is widely held and practiced; its benefit and harmlessness assumed but unproven. It is a prematurely settled answer to an unsettled question that discourages further enquiry. It is an example of how practices, assumed scientific, enter medicine by obscure paths.

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31

Mattausch, H. J., and K. Yamada. "Application of port-access-rejection probability theory for integrated N-port memory architecture optimisation." Electronics Letters 34, no. 9 (1998): 861. http://dx.doi.org/10.1049/el:19980649.

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32

Huang, S., M. Leary, T. Ataalla, K. Probst, and A. Subic. "Optimisation of Ni–Ti shape memory alloy response time by transient heat transfer analysis." Materials & Design 35 (March 2012): 655–63. http://dx.doi.org/10.1016/j.matdes.2011.09.043.

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33

Santi, Micol, Jakob Seitz, Rossana Cicala, Tomas Hardwick, Nisar Ahmed, and Thomas Wirth. "Memory of Chirality in Flow Electrochemistry: Fast Optimisation with DoE and Online 2D‐HPLC." Chemistry – A European Journal 25, no. 71 (November 21, 2019): 16230–35. http://dx.doi.org/10.1002/chem.201904711.

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34

Sayyafzadeh, Mohammad, Manouchehr Haghighi, Keivan Bolouri, and Elaheh Arjomand. "Reservoir characterisation using artificial bee colony optimisation." APPEA Journal 52, no. 1 (2012): 115. http://dx.doi.org/10.1071/aj11009.

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To obtain an accurate estimation of reservoir performance, the reservoir should be properly characterised. One of the main stages of reservoir characterisation is the calibration of rock property distributions with flow performance observation, which is known as history matching. The history matching procedure consists of three distinct steps: parameterisation, regularisation and optimisation. In this study, a Bayesian framework and a pilot-point approach for regularisation and parameterisation are used. The major focus of this paper is optimisation, which plays a crucial role in the reliability and quality of history matching. Several optimisation methods have been studied for history matching, including genetic algorithm (GA), ant colony, particle swarm (PS), Gauss-Newton, Levenberg-Marquardt and Limited-memory, Broyden-Fletcher-Goldfarb-Shanno. One of the most recent optimisation algorithms used in different fields is artificial bee colony (ABC). In this study, the application of ABC in history matching is investigated for the first time. ABC is derived from the intelligent foraging behaviour of honey bees. A colony of honey bees is comprised of employed bees, onlookers and scouts. Employed bees look for food sources based on their knowledge, onlookers make decisions for foraging using employed bees’ observations, and scouts search for food randomly. To investigate the application of ABC in history matching, its results for two different synthetic cases are compared with the outcomes of three different optimisation methods: real-valued GA, simulated annealing (SA), and pre-conditioned steepest descent. In the first case, history matching using ABC afforded a better result than GA and SA. ABC reached a lower fitness value in a reasonable number of evaluations, which indicates the performance and execution-time capability of the method. ABC did not appear as efficient as PSD in the first case. In the second case, SA and PDS did not perform acceptably. GA achieved a better result in comparison to SA and PSD, but its results were not as superior as ABC’s. ABC is not concerned with the shape of the landscape; that is, whether it is smooth or rugged. Since there is no precise information about the landscape shape of the history matching function, it can be concluded that by using ABC, there is a high chance of providing high-quality history matching and reservoir characterisation.

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35

Klawohn, Sascha, James R. Kermode, and Albert P. Bartók. "Massively parallel fitting of Gaussian approximation potentials." Machine Learning: Science and Technology 4, no. 1 (February 16, 2023): 015020. http://dx.doi.org/10.1088/2632-2153/aca743.

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Abstract We present a data-parallel software package for fitting Gaussian approximation potentials (GAPs) on multiple nodes using the ScaLAPACK library with MPI and OpenMP. Until now the maximum training set size for GAP models has been limited by the available memory on a single compute node. In our new implementation, descriptor evaluation is carried out in parallel with no communication requirement. The subsequent linear solve required to determine the model coefficients is parallelised with ScaLAPACK. Our approach scales to thousands of cores, lifting the memory limitation and also delivering substantial speedups. This development expands the applicability of the GAP approach to more complex systems as well as opening up opportunities for efficiently embedding GAP model fitting within higher-level workflows such as committee models or hyperparameter optimisation.

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36

SHAMSHIRI, SAEED, and S. MEHDI FAKHRAIE. "GENETIC-ALGORITHM MEMORY MINIMISATION FOR DESIGNING RECONFIGURABLE IP ADDRESS LOOKUP ENGINE." International Journal of Computational Intelligence and Applications 05, no. 01 (March 2005): 69–90. http://dx.doi.org/10.1142/s1469026805001507.

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IP address lookup engine is the beating heart of a router. For meeting the requirements of a desirable high-speed router, speed, memory consumption, scalability, and reconfigurability of its IP lookup engine are critical. This paper uses a genetic-algorithm approach to optimise the structure of fixed-stride multibit-trie IP lookup methods. In this work, the genetic algorithm is used first in the design phase as an offline optimisation mechanism. This nature-inspired simulation finds the most memory-efficient configuration of IP address segmentation for a fixed number of address segments. Then, for adapting to network variations, the proposed method dynamically changes the number of address segments to compromise between speed and memory consumption. Each time the number of segments changes, an online genetic program is run to optimise the segmentation. Therefore, the lookup engine reconfigures itself to cover more prefixes during the time. The reconfigurability in response to the network variations, and scalability to the number of prefixes improves the life time of a router that uses this method.

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37

Boguslawski, P., P. Balak, and C. Gold. "DATABASE STORAGE AND TRANSPARENT MEMORY LOADING OF BIG SPATIAL DATASETS IMPLEMENTED WITH THE DUAL HALF-EDGE DATA STRUCTURE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 1, 2022): 9–15. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-9-2022.

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Abstract. 3D spatial models covering big areas, such as cities, are widely developed in recent years. Loading of a whole model from a hard drive into a computer memory is often not possible due to big amount of data and memory size limitations. Optimisation techniques based on spatial indexing, such as tiling, are applied in order to load at least a part of a model as soon as possible, while the remaining parts are collected in the background. It is especially useful in visualisation of cities. A similar idea is proposed for a transparent loading of a model implemented with the dual half-edge (DHE) data structure and stored in a database. The existing DHE-based solutions require the whole model to be present in the memory, which is a considerable limitation in case of models covering big areas and including detailed representations of city objects, such as buildings and their interiors. The prototype mechanism developed in this work includes loading and unloading of model parts at a level of single edges as well as model tiling. This allows for spatial analysis without complete loading a big amount of data into memory.

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38

Tomczak, Tadeusz. "Data-Oriented Language Implementation of the Lattice–Boltzmann Method for Dense and Sparse Geometries." Applied Sciences 11, no. 20 (October 13, 2021): 9495. http://dx.doi.org/10.3390/app11209495.

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The performance of lattice–Boltzmann solver implementations usually depends mainly on memory access patterns. Achieving high performance requires then complex code which handles careful data placement and ordering of memory transactions. In this work, we analyse the performance of an implementation based on a new approach called the data-oriented language, which allows the combination of complex memory access patterns with simple source code. As a use case, we present and provide the source code of a solver for D2Q9 lattice and show its performance on GTX Titan Xp GPU for dense and sparse geometries up to 40962 nodes. The obtained results are promising, around 1000 lines of code allowed us to achieve performance in the range of 0.6 to 0.7 of maximum theoretical memory bandwidth (over 2.5 and 5.0 GLUPS for double and single precision, respectively) for meshes of sizes above 10242 nodes, which is close to the current state-of-the-art. However, we also observed relatively high and sometimes difficult to predict overheads, especially for sparse data structures. The additional issue was also a rather long compilation, which extended the time of short simulations, and a lack of access to low-level optimisation mechanisms.

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39

Jaurigue, Lina, Elizabeth Robertson, Janik Wolters, and Kathy Lüdge. "Reservoir Computing with Delayed Input for Fast and Easy Optimisation." Entropy 23, no. 12 (November 23, 2021): 1560. http://dx.doi.org/10.3390/e23121560.

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Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

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40

Ghorashi, Seyed, Tanveer Zia, Yinhao Jiang, and Michael Bewong. "Software Optimisation of Lightweight Klein Encryption in the Internet of Things." Journal of Information Security and Cybercrimes Research 4, no. 2 (December 30, 2021): 159–72. http://dx.doi.org/10.26735/pxae9280.

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The Internet of Things (IoT) and Wireless Sensor Network (WSN) devices are prone to security vulnerabilities, especially when they are resource constrained. Lightweight cryptography is a promising encryption concept for IoT and WSN devices, that can mitigate these vulnerabilities. For example, Klein encryption is a lightweight block cipher, which has achieved popularity for the trade-off between performance and security. In this paper, we propose one novel method to enhance the efficiency of the Klein block cipher and the effects on the Central Processing Unit (CPU), memory usage, and processing time. Furthermore, we evaluated another approach on the performance of the Klein encryption iterations. These approaches were implemented in the Python language and ran on the Raspberry PI 3. We evaluated and analysed the results of two modified encryption algorithms and confirm that two enhancing techniques lead to significantly improved performance compared to the original algorithm.

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41

Khalfi, Souheila, Amer Draa, and Giovanni Iacca. "A compact compound sinusoidal differential evolution algorithm for solving optimisation problems in memory-constrained environments." Expert Systems with Applications 186 (December 2021): 115705. http://dx.doi.org/10.1016/j.eswa.2021.115705.

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42

Mohamed, Omar Ahmed, Syed Hasan Masood, and Wei Xu. "Nickel-titanium shape memory alloys made by selective laser melting: a review on process optimisation." Advances in Manufacturing 10, no. 1 (January 5, 2022): 24–58. http://dx.doi.org/10.1007/s40436-021-00376-9.

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43

Leeman, Marc, David Atienza, Geert Deconinck, Vincenzo De Florio, José M. Mendías, Chantal Ykman-Couvreur, Francky Catthoor, and Rudy Lauwereins. "Methodology for Refinement and Optimisation of Dynamic Memory Management for Embedded Systems in Multimedia Applications." Journal of VLSI signal processing systems for signal, image and video technology 40, no. 3 (July 2005): 383–96. http://dx.doi.org/10.1007/s11265-005-5272-4.

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44

Ditmar, Johan, Steve McKeever, and Alex Wilson. "Area Optimisation for Field-Programmable Gate Arrays in SystemC Hardware Compilation." International Journal of Reconfigurable Computing 2008 (2008): 1–14. http://dx.doi.org/10.1155/2008/674340.

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This paper discusses a pair of synthesis algorithms that optimise a SystemC design to minimise area when targeting FPGAs. Each can significantly improve the synthesis of a high-level language construct, thus allowing a designer to concentrate more on an algorithm description and less on hardware-specific implementation details. The first algorithm is a source-level transformation implementing function exlining—where a separate block of hardware implements a function and is shared between multiple calls to the function. The second is a novel algorithm for mapping arrays to memories which involves assigning array accesses to memory ports such that no port is ever accessed more than once in a clock cycle. This algorithm assigns accesses to read/write only ports and read-write ports concurrently, solving the assignment problem more efficiently for a wider range of memories compared to existing methods. Both optimisations operate on a high-level program representation and have been implemented in a commercial SystemC compiler. Experiments show that in suitable circumstances these techniques result in significant reductions in logic utilisation for FPGAs.

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45

Thiruvady, Dhananjay, Christian Blum, and Andreas T. Ernst. "Solution Merging in Matheuristics for Resource Constrained Job Scheduling." Algorithms 13, no. 10 (October 9, 2020): 256. http://dx.doi.org/10.3390/a13100256.

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Matheuristics have been gaining in popularity for solving combinatorial optimisation problems in recent years. This new class of hybrid method combines elements of both mathematical programming for intensification and metaheuristic searches for diversification. A recent approach in this direction has been to build a neighbourhood for integer programs by merging information from several heuristic solutions, namely construct, solve, merge and adapt (CMSA). In this study, we investigate this method alongside a closely related novel approach—merge search (MS). Both methods rely on a population of solutions, and for the purposes of this study, we examine two options: (a) a constructive heuristic and (b) ant colony optimisation (ACO); that is, a method based on learning. These methods are also implemented in a parallel framework using multi-core shared memory, which leads to improving the overall efficiency. Using a resource constrained job scheduling problem as a test case, different aspects of the algorithms are investigated. We find that both methods, using ACO, are competitive with current state-of-the-art methods, outperforming them for a range of problems. Regarding MS and CMSA, the former seems more effective on medium-sized problems, whereas the latter performs better on large problems.

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46

Meelen, Marieke, Élie Roux, and Nathan Hill. "Optimisation of the Largest Annotated Tibetan Corpus Combining Rule-based, Memory-based, and Deep-learning Methods." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 1 (April 2021): 1–11. http://dx.doi.org/10.1145/3409488.

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This article presents a pipeline that converts collections of Tibetan documents in plain text or XML into a fully segmented and POS-tagged corpus. We apply the pipeline to the large extent collection of the Buddhist Digital Resource Center. The semi-supervised methods presented here not only result in a new and improved version of the largest annotated Tibetan corpus to date, the integration of rule-based, memory-based, and neural-network methods also serves as a good example of how to overcome challenges of under-researched languages. The end-to-end accuracy of our entire automatic pipeline of 91.99% is high enough to make the resulting corpus a useful resource for both linguists and scholars of Tibetan studies.

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47

Concilio, Antonio, and Salvatore Ameduri. "Influence of structural architecture on linear shape memory alloy actuator performance and morphing system layout optimisation." Journal of Intelligent Material Systems and Structures 25, no. 16 (December 31, 2013): 2037–51. http://dx.doi.org/10.1177/1045389x13517306.

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48

Pourahmad, Ali Akbar, Zahra Rohani, and Mehdi Neshat. "Improving the cooperation of fuzzy simplified memory A* search and particle swarm optimisation for path planning." International Journal of Swarm Intelligence 5, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijsi.2020.10027777.

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49

Neshat, Mehdi, Ali Akbar Pourahmad, and Zahra Rohani. "Improving the cooperation of fuzzy simplified memory A* search and particle swarm optimisation for path planning." International Journal of Swarm Intelligence 5, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijsi.2020.106388.

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50

Nieplocha, J., V. Tipparaju, M. Krishnan, G. Santhanaraman, and D. K. Panda. "Optimisation and performance evaluation of mechanisms for latency tolerance in remote memory access communication on clusters." International Journal of High Performance Computing and Networking 2, no. 2/3/4 (2004): 198. http://dx.doi.org/10.1504/ijhpcn.2004.008904.

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