Dynamic Parking Fee Model Based on System Equilibrium Theory

根据贝兹理论和空气动力学，风力机从风能中捕获并输出的功率Pw为：Pw=πρR²Cpv³/2。式中，ρ为空气密度，常取1.225kg/m³，R为风轮半径，单位为m；λ为风机叶尖速比；v为风速，单位为m/s；Cp为风机的风能利用系数，反映风力机吸收和利用风能的效率，由桨距角β和叶尖速比λ决定。叶尖速比λ是一个与风速v和机械角速度相关的函数，其公式为：λ=ωmR/v。将不同风速下的最大功率点连接，可以得到一条风力机的最大输出功率曲线，在该曲线上的功率均为风力机在不同风速下的最大输出功率，且该输出功率只与风力机的机械转速有关，其公式为：Pw=0.5πρR⁵Cpωm³/λ³。对于不同桨距角β，当桨距角β越小，Cp-λ特性曲线的峰值越大。当桨距角β为0°时，风能转换率Cp达到最大值0.48，该值被称为最大风能转换率Cp_max，其对应的叶尖速比λ成为最佳叶尖速比λ_opt*，值为8.1。模型中包含了完整的风力机模型并对模型进行了仿真验证了其准确性。最后欢迎进行风电相关方向的讨论。

Model-based value iteration algorithm for deterministic cleaning robots. This simple implementation of the value iteration algorithm serves as a helpful starting point for beginners in reinforcement learning and dynamic programming. The deterministic cleaning robot MDP involves the robot collecting used cans and recharging its battery. The state represents the robot's position, and the action defines the movement direction, either left or right. The first (1) and last (6) states are terminal states. The goal is to find the optimal policy to maximize the reward from any initial state. This is an example of Q-iteration (model-based value iteration DP). Reference: Algorithm 2-1, from: @book{busoniu2010reinforcement, title={Reinforcement Learning and Dynamic Programming Using Function Approximation}, authors={Busoniu, Lucian and Babuska, Robert and De Schutter, Bart and Ernst, Damien}, year={2010}, publisher={CRC Press}}.

粗糙集是一种针对不确定性数据的数据挖掘理论，邻域粗糙集是处理混合型数据的常用模型。为了提高对混合型数据的抗噪能力，提出一种混合信息系统的变精度粗糙集模型；由于现实环境下信息系统的动态性，进一步提出对象增加和减少时的动态变精度粗糙集模型。首先研究混合信息系统中条件概率随对象增加和减少时的变化关系，然后在该变化关系的基础上提出混合信息系统变精度粗糙集上下近似的增量式更新机制，最后根据这一更新机制提出相应的增量式近似更新算法。实验结果表明，所提出的增量式更新算法比非增量的算法具有更高的计算效率，从而验证了所提出模型的有效性，同时也表明所提出模型更加适用于复杂的数据环境。

《基于Hadoop的商品推荐系统详解》在大数据时代，如何有效地利用海量用户行为数据，为用户提供个性化推荐，已经成为电商行业的重要课题。将深入探讨一个基于Hadoop的商品推荐算法，该算法利用MapReduce进行分布式计算，实现高效的数据处理，为用户推荐最符合其兴趣的商品。 Hadoop核心组件 我们要理解Hadoop的核心组件MapReduce。MapReduce是一种编程模型，用于大规模数据集的并行计算。在商品推荐系统中，Map阶段主要负责数据的拆分和映射，将原始的用户购买记录转化为键值对；Reduce阶段则负责聚合这些键值对，对数据进行整合和计算。在这个过程中，YARN（Yet Another Resource Negotiator）作为Hadoop的资源管理器，负责任务调度和集群资源分配，确保整个计算过程在分布式环境下高效运行。 推荐算法流程 信息采集：收集用户的购买历史、浏览行为、评价等多维度数据。这些信息存储在HDFS（Hadoop Distributed File System）中，提供高可靠性和可扩展性的数据存储。 构建用户购买向量：在Map阶段，通过解析用户购买记录，形成用户-商品的购买矩阵，每个用户对应一列，每个商品对应一行，矩阵中的元素表示用户购买商品的次数或权重。 生成商品推荐矩阵：基于用户的购买行为，计算每件商品与其他商品的相关性，形成商品推荐矩阵。常用策略包括协同过滤、基于内容的推荐或混合推荐策略。 矩阵运算：将用户购买向量与商品推荐矩阵相乘，得到每个用户的推荐结果。此过程可能需进行矩阵稀疏化处理，减少计算复杂度和存储需求。 去重处理：通过去重算法确保推荐的唯一性，例如使用哈希表或排序去重。 数据提交到数据库：将推荐结果导入数据库，如HBase或MySQL，便于实时查询和展示。 性能优化 在实际应用中，还需注意关键问题，例如数据倾斜、性能优化以及推荐结果的多样性和新颖性平衡。通过分区策略可以解决数据倾斜问题，通过优化Shuffle阶段提升计算效率，并引入时间衰减机制增加推荐的新颖性。 总结 基于Hadoop的商品推荐系统通过MapReduce进行分布式计算，有效提升了推荐系统在大数据环境下的处理能力。

Despite its innovative logical architecture, Multitenant licensing in versions beyond 12.1 aligns with previous models. Software licenses, encompassing database and additional options, are determined by the number of CPU cores. This holds true for Named User Plus (NUP) licensing as well. Let's illustrate: an 8-core server (two Intel processors with four cores each) necessitates the procurement of licenses for four cores (8 * 0.5, where 0.5 is the Intel core licensing factor). This encompasses database, Multitenant, and other options like partitioning. Consider a scenario with one Container Database (CDB) and three Pluggable Databases (PDBs). PDB1 utilizes partitioning, PDB2 utilizes only Automatic Storage Optimization (ASO), and PDB3 uses no additional options. Regardless of individual PDB usage, licenses must be acquired for the database, Multitenant feature, ASO feature, and partitioning for four cores. In the case of NUP licensing, a minimum of 100 NUPs (4 cores * 25 NUPs per core) or the actual number of users, whichever is greater, is required. Essentially, license calculation hinges on the total number of server cores and the utilization of any option within the CDB, irrespective of specific PDB usage. All purchased options are accessible to every PDB within the CDB.

为了对高校教师课堂教学质量进行客观评价，使用多元统计分析中的主成分分析方法对原有的复杂评价指标体系进行降维处理，确定了对教师教学质量评价起核心作用的主成分，建立教学质量评价模型，构建主成分综合评价函数，实现对教师教学质量的综合评价排名。以某高校8位外语教师的课堂教学质量评价为例对模型进行验证，并与其原始排名进行对比。结果表明：该模型优化了原有教学质量评价指标体系，与传统评价模式相比，所建立的评价模型的评价结果更客观，且准确率高，验证了模型的有效性。

PowerBuilder-Based Student Grade Management System Design and Implementation I. System Overview With the advancement of information technology, the digitization of the education sector has gained increasing attention. The Student Grade Management System is a typical MIS (Management Information System) aimed at efficiently managing student grades using computer technology. This paper presents the development process of a PowerBuilder-based student grade management system. The system uses PowerBuilder 9.0 as the frontend development tool and Microsoft SQL Server 2000 as the backend database, enabling efficient management of student, course, teacher, and grade data. II. System Functionality Requirements The development of the Student Grade Management System must meet the following functional requirements:1. Database setup and maintenance: Establish a database with high consistency, integrity, and security to ensure accurate data.2. Frontend application development: Provide a feature-rich, user-friendly interface that allows users to easily perform various operations. III. System Architecture and Design The key functional modules in system design are as follows:1. User Login Module: Supports different user levels—students, teachers, and administrators. Students and teachers can query grades, while administrators have more comprehensive access, such as database maintenance.2. System Maintenance Module: Provides system initialization features to recover the system in case of issues.3. Basic Information Maintenance Module: Allows administrators to manage basic data such as classes, students, and courses.4. Database Management Module: Supports database backup and restoration.5. Grade Management Module: Designed for administrators to enter, delete, and modify grades.6. Student Grade Query Module: Supports personal, class, and departmental grade queries.7. Teacher Grade Query Module: Includes functions for single-subject and parallel class grade analysis.8. Report and Statistics Module: Provides functionality for generating and printing grade reports, as well as displaying statistical charts. IV. Database Design During the database design phase, the main entities and their relationships are clearly defined:1. Entity Design: The system involves entities such as students, teachers, courses, classes, departments, and users.2. Entity Relationships: The system defines many-to-many relationships between students and grades, teachers and grades, as well as one-to-many relationships between students and classes, and classes and departments. V. System Implementation PowerBuilder 9.0: PowerBuilder is used as the frontend development tool, providing powerful GUI design capabilities and rich features for building the user interface.

该课题为基于MATLAB眼部检测的疲劳驾驶系统。我们可以假设有一部摄像头对着大巴司机或者普通司机，对司机进行实时的监测，每隔数秒进行一次疲劳的判别。如果判定为疲劳驾驶，则会进行报警或者提示司机。检测方法为： 人脸定位：首先通过算法定位司机的面部特征。 眼睛检测：在定位到人脸后，进一步识别眼睛的部位。 睁眼闭眼状态判别：根据眼睛的开闭情况进行判断，统计闭眼的频率，若频繁闭眼则判定为疲劳。 该系统能够有效提高司机的安全驾驶意识，避免因疲劳驾驶引发的交通事故。

Start the project with LOGIN.m. The system is based on MySQL as the backend, allowing users to add, delete, and search for members. Member details, including photo uploads, are supported. For book issues/returns, if the return date exceeds 15 days, a fine is applied. Users can also add or delete books, with options for searching books under various conditions. The library allows a maximum of 3 books per user at any given time. The software is password-protected, with the default password set as 'tilak' (without quotes). Before running, ensure to copy all commands from mysql.txt in the zip file into your MySQL terminal. Also, ensure that port 3306 is open.