应用背景 Resources

Showing items tagged with "应用背景"

Application Context: This simulation has been optimized with modified obstacles and exits, demonstrating effective evacuation scenarios. Built using MATLAB, the core simulation runs through xingrenshusan.m as the main program, with supporting functions: Dststspread.m for fire source propagation modeling, PopSn.m for evacuation logic, renyuanshusan.fig providing the GUI interface, and ShuSanDieDai.m implementing the evacuation iteration method as a called function. Key Features: Two additional exits have been implemented with modular code structure allowing easy addition of more exits through parameter modification. The codebase features comprehensive inline comments to assist researchers new to the simulation framework. Obstacle coordinates can be customized based on real-world layouts to create tailored evacuation environments.

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Application Background Traffic signs play a crucial role in road traffic systems by displaying current road conditions, alerting drivers to potential hazards, enforcing speed regulations, and prohibiting specific maneuvers like turning or parking in special zones. These functions significantly contribute to road safety. Therefore, traffic sign detection and recognition represent vital research areas for preventing accidents and ensuring driver safety. Among traffic signs, prohibition signs (43 types) hold particular importance by restricting specific behaviors. Speed limit signs and no-turn signs are especially critical for safe driving and remain focal points in current traffic sign recognition research.

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Application Background: This code is highly practical for load forecasting and related applications. It has been successfully implemented in wind power prediction projects. Beneficial for users seeking source code references, this solution is based on understandable logic with provided sample data - simply replace with your own datasets for immediate implementation. Key Technology: Primarily utilizing MATLAB's computational capabilities with standard programming constructs, the code generates graphical outputs for daily/monthly load forecasting. The prediction algorithm leverages historical power data patterns, employing time-series analysis techniques to model and project future load demands.

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Application Background hailangboxing.m is the source program for generating 2D ocean wave profiles, which takes wind scale and frequency number as inputs to produce wave forms under specified wind conditions; SDwave.m is the source program using fractal method, which employs a different approach compared to the linear addition method used in the main program. These were part of my graduation project. The generated waveforms are 2D ocean waves. Key Technologies The two documents mentioned contain waveform diagrams of ocean waves in 2D and 3D (generated by the provided MATLAB source programs). bopu.m implements the standard P-M spectrum, accepting wind scale and frequency number to generate P-M spectra for corresponding wind conditions. erweihailangboxing.m is the source program for generating 3D ocean wave waveforms, which takes wind scale, frequency number, and angle number as inputs to produce wave forms under specified wind conditions.

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Application Context: Dynamic programming is an optimization method for solving multi-stage decision-making processes, initially proposed by American mathematician R. Bellman in the early 1950s. This methodology established a new branch of operations research by successfully addressing practical challenges in production management and resource allocation. Key Technology: As a crucial decision-making tool in modern enterprise management, dynamic programming effectively solves problems including optimal path finding, resource allocation, production planning, inventory control, and investment optimization. Its unique problem-solving approach often outperforms linear programming for specific optimization scenarios.

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This project researches stock pair trading strategies using MATLAB, accompanied by comprehensive PDF documentation. The implementation employs statistical techniques including correlation process analysis and cointegration analysis to validate pair trading feasibility and execute trading strategies. Key functions involve time series processing, statistical testing, and automated trading signal generation.

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Application Context: Implementing a unified queuing system for bank branch service counters where multiple teller windows serve from a common queue. The bank can dynamically adjust the number of operational counters. Technical Implementation: A simulation program based on queuing theory, primarily modeling M/M/1 and M/D/1 systems with parameterized inputs and computational outputs.

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Application Context When employing Latin Hypercube Sampling (LHS) to sample from multiple variables, maintaining independence between variables is crucial. Values sampled for one variable must be independent of those sampled for other variables (unless intentional correlation is desired). Independence preservation is achieved by randomly selecting sampling intervals for each variable. In a given iteration, variable #1 may sample from stratum #4 while variable #2 samples from stratum #22, ensuring both randomness and independence while preventing unintended correlations. As a more efficient sampling technique, LHS offers significant benefits in sampling efficiency and runtime performance (due to fewer iterations). These advantages are particularly notable in PC-based simulation environments like @RISK.

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Application Background Optimization techniques are ubiquitous in our society, with applications ranging from scheduling aircraft and crew members to coordinating steel production and managing iron ore transportation from mines to ports. These techniques facilitate the clearing of day-ahead and real-time electricity markets, enabling power supply to millions. Additionally, optimization supports kidney transplant coordination, cancer treatment planning, and aids scientists in understanding fundamental life structures, controlling complex chemical reactions, and designing drugs with potential benefits for billions. This course introduces discrete optimization and presents fundamental concepts and algorithms in the field. It covers constraint programming, local search, and mixed-integer programming, progressing from basics to their application in solving complex real-world problems like scheduling, vehicle routing, supply chain optimization, and resource allocation. The curriculum includes extensive MATLAB implementations of core algorithms with detailed code explanations.

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Application Context: Programmable fractional calculus and fractional-order differential equations (also known as extraordinary differential equations) represent a generalization of ordinary differential equations through fractional calculus. Fractional-Order Control (FOC) is an emerging field in control theory that utilizes fractional-order integrators as part of the control system design toolkit. Key Technologies: Control system toolboxes now incorporate fractional calculus for modeling real-world dynamic systems with non-integer order dynamics, enabling more accurate representation of complex systems compared to traditional integer-order calculus. Fractional calculus concepts hold transformative potential for redefining modeling and control methodologies.

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