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a class of optimal minimum odd-weight-column SEC-DED Codes
a class of optimal minimum odd-weight-column SEC-DED Codes
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更新于2023-05-27
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单bit错误校正，多bit错误检测的ECC算法论文；比传统的Hamming更加简洁、高效。
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Optimal Sampled-Data Control Systems
Chen Tongwen,Francis Bruce. December 1994
Optimal Control Theory - An Introduction.(Kirk D. Dover, 2004
Optimal Control Theory - An Introduction.(Kirk D. Dover, 2004
Write a paper about Deep-learning based analysis of metal-transfer images in GMAW process
Abstract: Gas metal arc welding (GMAW) is a widely used welding process in various industries. One of the significant challenges in GMAW is to achieve optimal welding parameters and minimize defects
Spread Spectrum and CDMA -- Valery P. Ipatov
1 Spread spectrum signals and systems 1 1.1 Basic definition 1 1.2 Historical sketch 5 2 Classical reception problems and signal design 7 2.1 Gaussian channel, general reception problem and optimal de
Optimal State Estimation(最优状态估计)2
Optimal State Estimation - Kalman, Hoo and Nonlinear Approaches SOLUTION MANUAL 2 程序练习部分，习题部分请参考http://download.csdn.net/detail/mchen_6431/7848011
maxz= 1.15x4A+1.40x2c+1.25x3в +1.06x5d 满足约束条件 X1A+X1D =100000 -1.06x1D+X2A+X2C+X2D =0 -1.15x1A-1.06 X2D +X3A +X3b +X3D =0 - 1.15x2A -1.06 X3D +X4A +X4D =0 -1.15x3A-1.06 x4D +X5D =0 X2c ≤30000 ≤40000 X3B xiA，XiB， xic， XiD ≥0 (i=1,2,3,4,5)
这是一个线性规划问题，可以使用线性规划算法求解。您可以使用Python中的PuLP、SciPy或CVXpy等库来解决这个问题。以下是PuLP库的一个示例代码： ```python from pulp import * # Create the problem prob = LpProblem("Maximize Profit", LpMaximize) # Create the variab
微软内部资料-SQL性能优化3
Contents Overview 1 Lesson 1: Concepts – Locks and Lock Manager 3 Lesson 2: Concepts – Batch and Transaction 31 Lesson 3: Concepts – Locks and Applications 51 Lesson 4: Information Collection and Ana
用C语言解决下列问题：Kirill wants to weave the very beautiful blanket consisting of n×m of the same size square patches of some colors. He matched some non-negative integer to each color. Thus, in our problem, the blanket can be considered a B matrix of size n×m consisting of non-negative integers. Kirill considers that the blanket is very beautiful, if for each submatrix A of size 4×4 of the matrix B is true: A11⊕A12⊕A21⊕A22=A33⊕A34⊕A43⊕A44, A13⊕A14⊕A23⊕A24=A31⊕A32⊕A41⊕A42, where ⊕ means bitwise exclusive OR Kirill asks you to help her weave a very beautiful blanket, and as colorful as possible! He gives you two integers n and m . Your task is to generate a matrix B of size n×m , which corresponds to a very beautiful blanket and in which the number of different numbers maximized. Input The first line of input data contains one integer number t (1≤t≤1000 ) — the number of test cases. The single line of each test case contains two integers n and m (4≤n,m≤200) — the size of matrix B . It is guaranteed that the sum of n⋅m does not exceed 2⋅105 . Output For each test case, in first line output one integer cnt (1≤cnt≤n⋅m) — the maximum number of different numbers in the matrix. Then output the matrix B (0≤Bij<263) of size n×m . If there are several correct matrices, it is allowed to output any one. It can be shown that if there exists a matrix with an optimal number of distinct numbers, then there exists among suitable matrices such a B that (0≤Bij<263) .
我可以回答这个问题。这是一个算法问题，需要用C语言编写程序来解决。题目要求生成一个n×m的矩阵B，使得对于B的任意一个4×4的子矩阵A，都满足A11⊕A12⊕A21⊕A22=A33⊕A34⊕A43⊕A44, A13⊕A14⊕A23⊕A24=A31⊕A32⊕A41⊕A42，并且B中不同数字的数量最大。可以使用循环和位运算符来实现这个算法。
Please revise the paper：Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.
回答：本文提出了一种基于底部类型自适应的卫星深度测量（BA-SDB）方法，以解决传统卫星深度测量模型中假设底部均匀的局限性以及不能充分考虑影响反射率的干扰因素，如浊度等。通过基于聚类的底部反射率自适应分割，并采用PSO-LightGBM算法考虑多种因素，实现了对不同沉积物和水深的准确深度估算，与空中激光测深和多波束回声深度声纳联合实测结果相比，均方根误差值达到0.85m，是传统比值法、多波段法和
Kalman Filtering with Real Time Applications Springer出版社
高清PDF电子书， 关于卡尔曼滤波和小波的，经典书籍，第四版了 Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear
a bisection algorithm for time-optimal trajectory planning along fully speci
Bisection算法是一种常用的时间最优轨迹规划算法。这种算法可以在已知车辆的起点、终点、速度、加速度等参数的情况下，直接计算出车辆的最优轨迹。其具体思想是：在时间上二分，不断缩小搜索空间，直到找到最短时间的最优轨迹。 在实际应用中，Bisection算法可以分为两种：一种是基于匀加速度模型的轨迹规划；另一种则是基于动力学模型的轨迹规划。两种算法均可用于车辆的行驶控制，但基于动力学模型的算法可
翻译下面一段话到中文： Real-time control of the reactor is achieved by P and PI controller established in 3.2. The adaptation law in 3.3 provides the optimal set-point of the controller under fluctuating solar radiation. In this subsection, three different control strategies are purposed and tested to obtain the optimal control effect. 4.1 Establishment of adaptative PI control strategies In case 1, a PI controller is used to manipulate "Q" _"in" to maintain a stable "χ" _(〖"CH" 〗_"4" ), S/C remains constant during the calculation. There is no real-time control in case 2, "Q" _"in" and S/C are set as constants, and the value of "Q" _"in" is the same as the average value during 2 hours in case 1. In case 3, a PI controller is used to manipulate "Q" _"in" to control "χ" _(〖"CH" 〗_"4" ), and a P controller is used to manipulate S/C to control "Q" _("H" _"2" ). The inlet "Q" _(CH_4 ) and "Q" _(H_2 O) are dynamically regulated through double controllers. The set-point of P and PI controller has been predicted in 3.3, and the specific parameters are shown in table 2.
这段话的翻译为：3.2 中建立的 P 和 PI 控制器实现了反应堆的实时控制。3.3 中的自适应法则提供了在太阳辐射波动下控制器的最佳设定点。在本小节中，提出并测试了三种不同的控制策略，以获得最佳控制效果。4.1 建立自适应 PI 控制策略。在情况 1 中，使用 PI 控制器操纵 "Q"_"in"，以维护稳定的 "χ"_("CH"_4)，S/C 在计算期间保持不变。在情况 2 中，没有实时控制，"
用matlab写代码maxz=1.15x4A+1.40 x2c +1.25 x3B +1.06 x5D (xiA,XiB,Xic,XiD ≥ 0( i = 1, 2, 3, 4, 5) X3B ≤ 40000 X2c ≤ 30000 s,ti -1.15x3A-1.06 x4D + X5D=0 -1.15x2a-1.06 xD +X4A + X4D=0 -1.15x1A-1.06 x2D+X3A+X3B+X3D=0 -1.06x1D+X2A+X2c+X2D=0(X1A+X1D=100000
x = optimvar('x',5,'LowerBound',0); maxz = 1.15*x(1) + 1.40*x(2) + 1.25*x(3) + 1.06*x(5); s = optimproblem('Objective',maxz); s.constraints.c1 = x(3) <= 40000; s.constraints.c2 = x(2) <= 30000; s.cons
I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by typing text inside curly brackets { like this }. My first command is fdisk -l.
# fdisk -l Disk /dev/sda: 465.8 GiB, 500107862016 bytes, 976773168 sectors Disk model: WDC WD5000AZRX-00A Units: sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 4096 bytes
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分享能源储存站对于智能电网的可持续发展至关重要。在优化经济调度方面，需要考虑多方面因素，如能源供应和需求之间的平衡、储能站的投资和运营成本、储能站的容量和维护等。首先，通过分析能源市场的价格变化和能源供应的波动性，可以制定出一套有效的经济调度策略，以充分利用储能站的储能容量，最大化能源供应收益。其次，需要降低储能站的投资和运营成本，选择合适的设备和技术，以提高储能站的效率和稳定性，降低生产成本。最
Develop and analyze a mathematical model that will assist negotiators to respond to a fixed set of water supply and demand conditions. Use the model to inform dam operations: When the water level in Lake Mead is M and the water level in Lake Powell is P, how much water should be drawn from each lake to meet stated demands? If no additional water is supplied (from rainfall, etc.), and considering the demands as fixed, how long will it take before the demands are not met? How much additional water must be supplied over time to ensure that these fixed demands are met?
Mathematical models can be useful tools in analyzing water supply and demand conditions and making decisions about dam operations. One approach to modeling this situation is to use a linear programmin
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In the Q-learning algorithm, the corresponding update equation is as follows: Q s t, at αt st, at r t ���������������� c max a Q s t+ 1, at+1 Q s t, a t ⟶ Qt+1 st, at . (4) )e Q-table implements a mapping strategy from the state s to the optimal action. When the number of states of the environment is large, the storage space required by the Q table will become large. Neural networks can fit large nonlinear functions and have generalization capabilities. Instead of bulky Q tables, neural networks can be used to fit mapping functions from states to actions.
我们可以使用神经网络来替代Q-table，它可以适用于大规模的非线性函数，并具有泛化能力。Q-学习算法的更新方程如下：Qt+1 (s t, at ) = Qt (s t, at ) + αt (r t + cmax a Qt (s t+1, at+1 ) - Qt (s t, at ))，其中αt为学习率。它实现了从状态s到最优动作的映射策略。
请用c++完善下列代码：void Knapsack_DP(Item items[], int n, int C) { int i, j, dp[n + 1][C + 1]; bool t[n + 1][C + 1]; // direction of recursion, True means selecting the item int solution[n + 1]; // the collection of items in the solution } bool cmp_value(Item a, Item b){ if(a.val != b.val) return a.val > b.val; return a.size < b.size; } bool cmp_size(Item a, Item b){ if() } bool cmp_density(Item a, Item b){ }
void Knapsack_DP(Item items[], int n, int C) { int i, j, dp[n+1][C+1]; bool t[n+1][C+1]; // direction of recursion, True means selecting the item int solution[n+1]; // the collection of
基于Android的高校学习交友分享平台（android毕业设计，包括源码、数据库）.zip
Android 毕业设计，前后端分离，含有代码注释，新手也可看懂。毕业设计、期末大作业、课程设计、高分必看，下载下来简单部署就可以使用。 包含：项目源码、数据库脚本、软件工具等，该项目可以作为毕设、课程设计使用。 该项目经过严格调试，确保代码可以运行！ 1. 技术组成 后台框架：SpringBoot/SSM （如果有的话） 前端：Android 数据库：MySQL （建议用 5.7 版本）
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