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4 results for “algorithm engineering”
Acquired data necessary to perform the control algorithm introduced in the scientific paper: "Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention" (Advances in Mechanical Engineering, 2016, vol. 8, pp. 1-13)
<p>Acquired data necessary to perform the control algorithm introduced in this paper.</p> <p>a) Figure 6: Calibration data for the three FSRs placed on the prosthetic hand and covered with silicon caps.<br> b) Figure 9: Data for the cost during the learning of two grasping tasks of an egg: bi-digital grasp and tri-digital grasp.<br> c) Figure 10 and Figure 11: Data for the experimental results with the plastic cup and with the highlighter shown in the paper.<br> </p> <p> </p>
Performance results of different scheduling algorithms used in the simulation of a modern game engine
<p><strong>Performance results of different scheduling algorithms used in the simulation of a modern game engine</strong></p> <p>These results are a companion to the paper entitled "<em>Exploring scheduling algorithms for parallel task graphs: a modern game engine case study</em>" by M. Regragui et al.</p> <p><strong>General information</strong></p> <p>This dataset contains raw outputs and scripts to visualize and analyze the scheduling results from our game engine simulator.<br> The result analysis can be directly reproduced using the script run_analysis.sh. A series of Jupyter Notebook files are also available to help visualize the results.</p> <p><strong>File information</strong></p> <p>- All Scenario*.ipynb files contain python scripts to visualize and analyze the simulation results.<br> - The Scenario*.py files contain python scripts that can be run directly with Jupyter Notebook.<br> - The requirements.txt file contains the names and versions of python packages necessary to reproduce the analysis.<br> - The run_analysis.sh file contains a bash script to install the required python packages and run the Scenario*.py scripts.</p> <p>The results are organized in five folders:</p> <p>1. Result_1 contains the results for Scenario 1 generated using file input_scenario_1.txt.<br> 2. Result_2 contains the results for Scenario 2 generated using file input_scenario_2.txt.<br> 3. Result_3 contains the results for Scenario 3 generated using file input_scenario_3.txt.<br> 4. Result_CP_1 contains the results for the critical path of Scenarios 1 and 2 generated using file input_CP_scenario_1.txt.<br> 5. Result_CP_3 contains the results for the critical path of Scenario 3 generated using file input_CP_scenario_3.txt.</p> <p>Each result file (e.g., HLF_NonSorted_Random_1_200_10.txt) contains 200 lines representing information of the 200 frames that were simulated. Each line contains four values: the frame number, the duration of the frame (in microseconds), a critical path estimation for the previous frame (in microseconds), and the load parameter (value between 0 and 1).</p> <p>The outputs of this analysis include some PDF files representing the figures in the paper (in order) and some CSV files representing the values shown in tables. The standard output shows the p-values computed in parts of the statistical analysis.</p> <p><strong>Software and hardware information</strong></p> <p>The simulation results were generated on an Intel Core i7-1185G7 processor, with 32 GB of LPDDR4 RAM (3200 MHz). The machine ran on Ubuntu 20.04.3 LTS (5.14.0-1034-oem), and g++ 9.4.0 was used for the simulator's compilation (-O3 flag).</p> <p>The results were analyzed using Python 3.8.10, pip 20.0.2 and jupyter-notebook 6.0.3. The following packages and their respective versions were used:</p> <p>- pandas 1.3.2<br> - numpy 1.21.2<br> - matplotlib 3.4.3<br> - seaborn 0.11.2<br> - scipy 1.7.1<br> - pytz 2019.3<br> - python-dateutil 2.7.3<br> - kiwisolver 1.3.2 <br> - pyparsing 2.4.7 <br> - cycler 0.10.0 <br> - Pillow 7.0.0<br> - six 1.14.0 </p> <p><strong>Simulation information</strong></p> <p>Simulation results were generated from 4 to 20 resources. Each configuration was run with 50 different RNG seeds (1 up to 50).</p> <p>Each simulation is composed of 200 frames. The load parameter (lag) starts at zero and increases by 0.01 with each frame up to a value equal to 100% in frame 101. After that, the load parameter starts to decrease in the same rhythm down to 0.01 in frame 200.</p> <p><strong>Algorithms abbreviation in presentation order</strong></p> <p>FIFO serves as the baseline for comparisons.</p> <p>1. FIFO: First In First Out.<br> 2. LPT: Longest Processing Time First.<br> 3. SPT: Shortest Processing Time First.<br> 4. SLPT: LPT at a subtask level.<br> 5. SSPT: SPT at a subtask level.<br> 6. HRRN: Highest Response Ratio Next. <br> 7. WT: Longest Waiting Time First.<br> 8. HLF: Hu's Level First with unitary processing time of each task.<br> 9. HLFET: HLF with estimated times.<br> 10. CG: Coffman-Graham's Algorithm.<br> 11. DCP: Dynamic Critical Path Priority.</p> <p><strong>Metrics</strong></p> <p>* SF: slowest frame (maximum frame execution time)<br> * DF: number of delayed frames (with 16.667 ms as the due date)<br> * CS: cumulative slowdown (with 16.667 ms as the due date)<br> </p>
The optimization of a jet turbojet engine by PSO and searching algorithms
<p>The turbojet engine operates on the ideal Brayton cycle (gas turbine) and consists of six main parts: diffusers, compressors, combustion chambers, turbines, afterburners and nozzles. Using computer code writing in MATLAB software environment, exergy analysis on all selected turbojet engine components, exergy analysis on J85-GE-21 turbojet engine for selective height of 10008000 meters above sea level at speeds of 200 m/s and temperatures of 10, 20 and 40 ° C have been provided and then, according to the system functions, the system is optimized based on the PSO method. For the purpose of optimization, variables of Mach number, efficiency of the compressor, turbine, nozzle and compressor pressure ratio are considered in the range of 0.6 to 1.4, 0.8 to 0.95, 0.8 to 0.95 and 7 to 10, respectively. The highest exergy efficiency of different parts of the engine at sea level with an inlet air velocity of 200 m/s corresponds to a diffuser with 73.1%. Then, the nozzle and combustion chamber are respectively 68.6% and 51.5%. The lowest exergy efficiency is related to compressor with 4%. After that, the afterburner is ranked second with 11.6%. Also, the values of entropy produced and the efficiency of the second law before optimization were 1176.99 and 479 w/k respectively and the same values after optimization were 1129 and 51.4 w/k respectively which is identified. After the optimization process, the amount of entropy produced is reduced and the efficiency of the second law of thermodynamics has increased.<br> </p>
A Novel Algorithm for Estimating Web Page Ranking in Search Engine Results Pages
<p><em><strong>Abstract:</strong> </em>Search engine optimization (SEO) can make a big improvement in the traffic to a web page. Because search engines keep their main rules of ranking undeclared, it’s important to develop models that can estimate the ranking of a web page in the search engine to be able to optimize web pages to rank higher in the search engine. The available research methodologies used machine learning algorithms to provide solutions for this target with the help of generated datasets by scraping the search engine results pages (SERP) and crawling web pages. Their proposed models suffered from the inability to be updated dynamically if the search engine updated its ranking algorithm, and their input data did not include the diversity of web pages and languages. This research will propose a novel original rank estimation algorithm that’s able to overcome other research challenges, with a set of comparative experiments and complexity analysis. Results will show that the proposed algorithm could achieve higher values of accuracy, precision, and recall.</p> <p><strong><em>Dataset: </em></strong></p> <p>For research purpose, the dataset will play two roles, first, it will act the role of search engine result pages (SERP), and second, it will be used to test algorithms and calculate performance measurements. Dataset is consisting of 9930 web pages, aimed to identify search results pages, focusing on the top 3 pages of SERP, with 31 extracted attributes that's related to search engine optimization (SEO). The distribution of examples between class labels was balanced, with changes due to scraping operation issues, but not significantly different, with fractions of 39.9%, 34.6%, and 25.5% for the class labels page1, page2, and page 3. Feature names are: 'Title 1 Length', 'Title 2 Length', 'Meta Description 1 Length', 'Meta Description 2 Length', 'Meta Keywords 1 Length', 'H1-1 Length', 'H1-2 Length', 'H2-1 Length', 'H2-2 Length', 'Size (bytes)', 'Word Count', 'Text Ratio', 'Inlinks', 'Unique Inlinks', 'Unique JS Inlinks', '% of Total', 'Outlinks', 'Unique Outlinks', 'Unique JS Outlinks', 'External Outlinks', 'Unique External Outlinks', 'Unique External JS Outlinks', 'Response Time', 'Status Code', 'Keyword in MetaDescription1', 'Keyword in Title1', 'Keyword in MetaKeywords1', 'Keyword in URL', 'Has LastModified', 'Keyword in Headers', and 'Keyword in Emphasized Text'.</p> <p>The process of dataset generation involved scraping the search engine, extracting URLs for selected keywords, focusing on feature extraction, cleaning and preprocessing, and generating new attributes related to keywords in web pages. It involved also removing missing values, duplicates, and data type conversions to obtain a comprehensive dataset.<br> Keyword selection involves selecting keywords from various categories and considering diversity, including high and low traffic, long-term and short-term keywords, and generic and branded keywords. Apify online tool was used for search engine scraping with default language and US country, resulting in 388 selected keywords with 30 results per keyword. Dataset included extracted SEO features from 9991 web pages using screamingFrog desktop software and Rapidminer desktop software, determining page SEO-friendliness and comparing it to SERP rankings. Dataset cleaning involved removing redundant attributes, removing paid SERP results, replacing missing values, and converting data types. Rapidminer was used for data cleaning and preprocessing, generating new attributes related to keyword usage in web pages.<br> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.