A fundamental question of data analysis is how to distinguish noise corrupted deterministic chaotic dynamics from time-(un)correlated stochastic fluctuations when just short length data is available. Despite its importance, direct tests of chaos vs stochasticity in finite time series still lack of a definitive quantification. Here we present a novel approach based on recurrence analysis, a nonlinear approach to deal with data. The main idea is the identification of how recurrence microstates and permutation patterns are affected by time reversibility of data, and how its behavior can be used to distinguish stochastic and deterministic data. We demonstrate the efficiency of the method for a bunch of paradigmatic systems under strong noise influence, as well as for real-world data, covering electronic circuit, sound vocalization and human speeches, neuronal activity, heart beat data, and geomagnetic indexes. Our results support the conclusion that the method distinguishes well deterministic from stochastic fluctuations in simulated and empirical data even under strong noise corruption, finding applications involving various areas of science and technology. In particular, for deterministic signals, the quantification of chaotic behavior may be of fundamental importance because it is believed that chaotic properties of some systems play important functional roles, opening doors to a better understanding and/or control of the physical mechanisms behind the generation of the signals

时间: 2023-03-14 09:12:47 浏览: 62

Recently, the renowned actor Zhang Songwen has sparked a fascinating phenomenon known as "two-way rejection", which has captured the attention of many and inspired the masses. The roots of this phenomenon are complex, with one of the fundamental causes being the fear of failure that plagues most of us. Rejection can instill a sense of inadequacy and a fear of being perceived as a failure, which can be challenging to overcome. However, the concept of "two-way rejection" teaches us that rejection is a natural part of life, and it's acceptable to reject and be rejected in return. This empowers us to recognize that life is not just about failures, but also about perseverance, and striving to achieve our aspirations, which may include fame and fortune. Despite the distractions we may encounter, the concept of "two-way rejection" reminds us to turn away from erroneous opportunities and remain steadfast in our principles and moral compass. While there are both advantages and drawbacks to this approach, "two-way rejection" ultimately inspires us to embrace rejection, learn from it, and emerge stronger and more self-assured. However, it is essential to distinguish between a sound and an unsound opportunity to avoid blindly rejecting the right ones. In conclusion, the concept of "two-way rejection" should be approached with discretion, but it can prove to be a valuable tool in enabling us to adhere to our goals and persevere through rejection. It teaches us to embrace rejection, learn from it, and move forward with confidence, ultimately empowering us to achieve our dreams and aspirations.结合双向拒绝进行内容补充

The concept of "two-way rejection" is not only about rejecting others but also about being willing to accept rejection ourselves. It encourages us to be open to constructive criticism and feedback, which can help us improve ourselves and our skills. By embracing rejection, we can learn from our mistakes, understand our weaknesses, and work on improving them. Moreover, "two-way rejection" also emphasizes the importance of communication. It is essential to communicate effectively and honestly with others, whether we are rejecting them or being rejected. We must be respectful and considerate of other people's feelings while also being true to ourselves and our goals. Another aspect of "two-way rejection" is the idea of resilience. It is about bouncing back from rejection and not letting it define us. When we face rejection, it can be tempting to give up and lose hope, but by embracing "two-way rejection," we can develop resilience and determination to keep pushing forward. In summary, "two-way rejection" is a powerful concept that can help us overcome the fear of failure and embrace rejection. It encourages us to communicate effectively, be open to feedback, and develop resilience in the face of rejection. By adopting this approach, we can become more self-assured and empowered to achieve our dreams and aspirations.

fundamental of computer graph.pdf

《计算机图形基础》是一本关于计算机图形学的基础教材。计算机图形学是研究如何利用计算机生成和显示图像的学科。本书主要介绍了计算机图形学的基本原理和算法。 本书首先介绍了计算机图形学的发展和应用领域,对图形学的基本概念进行了定义和解释。接着介绍了计算机图形的表示方法,包括点、线、多边形等基本图元的表示和存储方式。然后详细讲解了图形变换和坐标变换的基本概念和算法,包括平移、旋转、缩放等变换操作。 本书还介绍了光栅化算法和图像渲染技术。光栅化是将连续的图形转化为离散的像素点或线段的过程,是图形显示的基础。本书详细讲解了光栅化算法的原理和实现,并介绍了常用的光栅化算法,如Bresenham算法、扫描线算法等。图像渲染是将模型转化为能够在计算机上显示的图像的过程,本书介绍了图像渲染的基本原理和算法,如光照模型、阴影算法等。 此外,本书还介绍了计算机图形学中的几何建模和图形处理技术。几何建模是描述物体形状和结构的方法,本书介绍了几何建模的基本原理和方法,如Bezier曲线和曲面、多边形几何等。图形处理是对图像进行编辑和修饰的技术,本书介绍了图形处理的基本方法和常用技术,如图像压缩、图像融合等。 总之,《计算机图形基础》是一本全面介绍计算机图形学基础知识的教材,适合计算机图形学的初学者学习和参考。通过学习本书,读者可以了解计算机图形学的基本原理和算法,掌握图形渲染和图像处理的基本技术,为进一步研究和应用计算机图形学奠定基础。


### 回答1: 《计算机图形学(第三版)》是计算机图形学领域的一本经典教材,主要介绍了计算机图形学的基础知识和相关技术。本书内容丰富全面,适合计算机科学与技术专业的学生以及从事计算机图形学相关研究的人员阅读。 这本书主要包括了以下几个方面的内容: 首先,本书介绍了计算机图形学的发展历史以及其基础概念。读者可以了解到图形学的起源和发展,以及它在现代计算机科学领域的重要性。同时,本书还详细解释了计算机图形学中的一些基本概念,如几何学、投影、颜色模型等,帮助读者建立起对计算机图形学基础知识的基本认知。 其次,本书介绍了计算机图形学中的图像处理技术。这包括了图像的获取和表示、图像的变换和处理、图像的压缩和传输等方面。通过学习这些内容,读者可以了解到计算机图形学中图像处理的基本原理和方法,从而能够熟练地进行图像处理和编辑。 另外,本书还介绍了计算机图形学中的三维建模和可视化技术。这包括了三维物体的建模、渲染和动画等方面。通过学习这些内容,读者可以了解到如何通过计算机生成逼真的三维图形,并将其应用于模拟、设计和可视化等领域。 最后,本书还介绍了计算机图形学中的一些高级技术和应用。这包括了虚拟现实、计算机游戏、计算机辅助设计等方面。通过学习这些内容,读者可以了解到计算机图形学在现实生活中的具体应用,如虚拟现实技术在游戏、医学和建筑等领域的广泛应用。 总之,《计算机图形学(第三版)》是一本介绍计算机图形学基础知识和相关技术的经典教材。通过学习本书,读者可以全面了解计算机图形学的发展和应用,掌握其基础原理和技术方法,从而能够应用于实际工作和研究中。 ### 回答2: 《计算机图形学基础(第三版)》是一本经典的计算机图形学教材,它系统地介绍了计算机图形学的基本概念、算法和技术。本书主要包括以下几个方面的内容: 首先,本书从图形系统的构成和基本原理入手,介绍了图形系统的硬件和软件组成部分,以及图形处理的基本原理和流程。读者可以了解计算机图形系统的基本结构和工作原理。 其次,本书详细介绍了计算机图形学的数学基础,包括坐标系、矩阵变换、曲线和曲面等数学工具的运用。这些数学基础对于理解图形的数学表示和变换非常重要。 然后,本书介绍了常见的图形数据结构和算法,包括向量和点的表示、线段和多边形的绘制、二维和三维图形的剪裁和光栅化等。这些算法是计算机图形学的核心,能够帮助读者实现基本的图形绘制和处理功能。 此外,本书还介绍了计算机图形学中的光照和颜色处理、纹理映射和三维变换等高级技术。这些技术可以使图形更加真实、细腻,提高图形的质量和逼真度。 最后,本书还涉及了计算机动画、虚拟现实和图形学应用等方面的知识。读者可以了解到计算机图形学在其他领域的广泛应用,如电影、游戏、工程设计等。 总之,《计算机图形学基础(第三版)》系统全面地介绍了计算机图形学的基本概念、算法和技术,并提供了丰富的数学基础和实践案例,对于计算机图形学的学习和应用非常有帮助。如果你对计算机图形学感兴趣,这本书是值得推荐的。 ### 回答3: 《计算机图形学基础(第三版)》是一本关于计算机图形学的教材。该书的第三版主要是基于前两个版本的基础上进行了更新和完善。这本书的内容涵盖了计算机图形学的基本原理、算法和应用等方面。 首先,本书从图形学的基本概念入手,介绍了图像的表示和处理方法,包括像素、颜色模型、图像压缩等。然后,着重介绍了计算机图形学的三个基本要素:图形输入、图形输出和图形处理。其中,图形输入主要包括设备和技术,如鼠标、键盘、触摸屏等;图形输出则涉及到显示器、打印机等设备;而图形处理则涵盖了图像处理的各种算法和技术,如平移、旋转、缩放、裁剪、光照、阴影等。 此外,本书还介绍了三维图形学的基本概念和算法,包括三维坐标系统、投影、曲面建模、三维变换等。同时,还深入讲解了光线追踪、视景体裁剪、隐藏面消除、颜色和着色、纹理映射等高级图形学算法。 《计算机图形学基础(第三版)》的特点是理论与实践相结合,内容丰富而实用。在阅读过程中,读者不仅能够了解计算机图形学的基本原理和算法,还能够学习到如何在实际应用中灵活运用图形学的相关知识和技术。 总之,本书是一本系统全面介绍计算机图形学基础的教材。对于计算机图形学的学习者和从事相关领域工作的人员来说,该书可以作为了解基本概念和算法的入门书籍,并为进一步深入学习和研究提供了扎实的基础。
### 回答1: 《功率电子基础》是一本介绍功率电子学原理和应用的教材。根据习题的要求,下面列举了一些《功率电子基础》的习题答案供参考。 1. 电源和负载分别为V_s和R_L,求电感L的大小: 首先根据电源方程可知 V_s = L di/dt + V_R,其中 V_R = R_L i。考虑到 V_R = R_L i,我们可以得到 V_s = L di/dt + R_L i。这是一个一阶可分离变量的微分方程,可以通过分类讨论和积分求解得到L的值。 2. 若三相全桥整流器的输入电压为V_in,求输出电压V_out与输入电压之间的关系: 三相全桥整流器是一种常见的功率电子装置,通过将三相交流电转换为直流电输出。根据电路原理可知,输出电压V_out是输入电压V_in经过整流后得到的。整流是将交流电信号转换为一个方向的直流电信号的过程,因此输出电压V_out的幅值一般是输入电压V_in的幅值的一部分。 3. 对于开关电源,在开关管关断后,电感L上的电流会发生怎样的变化? 在开关电源中,开关管是一个重要的元件。开关管的开闭状态决定了电路的导通和截止。当开关管关断后,相当于电感L断开,此时电流无法通过电感L。因此,电感L上的电流会迅速下降。 4. 功率晶体管的工作原理是什么? 功率晶体管是一种常见的功率电子开关元件。功率晶体管的工作原理是根据控制输入信号来控制电流的流通。通过输入信号的控制,可以将功率晶体管从关断状态转变为导通状态,实现电流的流通,或者从导通状态转变为关断状态,切断电流的流通。这种开关操作使得功率晶体管可以对电能进行有效的控制与调节。 以上是对《功率电子基础》习题的一些回答,希望对您有所帮助。 ### 回答2: 《功率电子基础》习题答案如下: 1. 功率电子是一门研究利用电力电子设备控制和转换电能的技术学科。功率电子技术在现代电气系统中具有重要的应用价值。 2. 功率电子器件是实现功率电子转换的关键部件,包括晶闸管、IGBT、MOSFET等。晶闸管可用作交流电源的控制开关,IGBT可用作高频开关,MOSFET适用于低功率、高速的开关应用。 3. 功率电子电路中常用的元件有二极管、变压器、电感器和电容器等。二极管用于整流和保护电路,变压器用于电压变换和隔离,电感器用于稳流和滤波,电容器用于储能和滤波。 4. 功率电子转换器主要包括变压器、整流器、逆变器和直流/直流变换器等。变压器用于电压变换和隔离,整流器用于将交流电转换为直流电,逆变器用于将直流电转换为交流电,直流/直流变换器用于不同电压级别的直流电转换。 5. 电源电路是提供功率电子系统所需电能的关键部分,常见的电源电路包括开关电源、线性电源和电池等。开关电源具有高效率和小体积等优点,广泛应用于电子设备中。 6. 控制与调节是功率电子系统中必不可少的环节,常用的控制方法有PWM调制、失谐控制、电流控制和电压控制等。控制技术的选择取决于具体应用需求和性能要求。 7. 功率电子技术在可再生能源利用、电动汽车、工业自动化和通信等领域具有广阔的应用前景。随着科技进步和能源需求的增长,功率电子技术的研究和发展将呈现更加重要的意义。

润色:In the recent years, the world is constantly stricken by various terrifying natural or man-made disasters, all of which calls on our attention to the global sustainable development, defined as the overall coordinating development of nature, society and economy, to meet the current needs without at the cost of the future.\\ Back in 2015, UN Assembly has stipulated the 2030 Agenda for Sustainable Development to address the problem including 17 goals which can be further classified into 5 categories: \textbf{fundamental necessities pursuit} (GOAL 1, 2), \textbf{sustainable social development} (GOAL 3, 4, 5, 6, 7), \textbf{sustainable economic development} (GOAL 8, 9, 10, 11, 12),\textbf{ sustainable ecological development} (GOAL 13, 14, 15) and \textbf{human symbiont pursuit} (GOAL 16, 17). In the article, we mainly discuss the relationships between the SDGs, the priority ranking of SDGs, base on which future implications are given including predicted blue print, ideal achievements and potential goals. Finally the influence of external factors on the structure is researched. In consideration of the priority of each SDG, the point weight is given to each SDG itself, and the edge weight is given to the degree of correlation between SDGs (that is, the degree of influence), to represent the degree of contribution of each SDG to the human-wellbeing. By final calculation, Goal 5,2,1 rank the top3.\\ In future implication, the sliding window model is employed. The predicted blue-print in 10 years is given qualitively and quantitively, and for the ideal achievements we find that fundamental necessities pursuit has a huge impact on the other goals, while human symbiont pursuit influence gradually and sustainable economic development has a general impact too.

"Fundamental fluent mechanics" 是一个术语,可以理解为基础流畅力学。在物理学和工程学中,力学指的是研究物体的运动和受力的学科。而流畅力学则强调了力学的基础原理和基本概念在解决实际问题中的应用。 基础流畅力学涵盖了多个重要的概念和原理。其中包括牛顿力学,即力等于物体质量乘以加速度;惯性,即物体保持原来状态的趋势;动量守恒,即在没有外部力的情况下,物体总动量保持不变;以及能量守恒,即能量在一个系统内的总量不会改变。 通过理解和应用这些基本概念,我们可以解释并预测物体的运动和受力情况。例如,我们可以用流畅力学的原理解释为什么一个投掷的物体会沿着抛物线轨迹运动,为什么物体在没有外力作用下会一直保持匀速直线运动,为什么不同形状的物体受到相同力的作用时会有不同的运动情况等等。 基础流畅力学也是工程学中设计和分析机械系统的重要工具。例如,当设计一辆汽车时,我们需要了解流畅力学的原理以预测和优化车辆的运动性能、操控性和燃油效率。当分析一个机械系统的运动和力学性质时,我们可以利用流畅力学的知识来解决问题,从而改进系统的效率和性能。 总之,基础流畅力学是物理学和工程学中一门重要的学科,它通过研究物体的运动和受力,运用基本的力学原理和概念,解释和预测物体的行为。它不仅在理论研究中具有重要意义,也在工程应用中发挥着关键作用。
Title: Image Recognition Based on Convolutional Neural Networks Abstract: Image recognition has been a popular research topic in the field of computer vision. With the development of deep learning, convolutional neural networks (CNNs) have shown excellent performance in this area. In this paper, we introduce the basic structure and principles of CNNs, and then discuss the application of CNNs in image recognition. Specifically, we focus on the training process of CNNs, including data preprocessing, network initialization, and optimization algorithms. We also compare different CNN architectures and evaluate their performance on benchmark datasets. Finally, we summarize the advantages and limitations of CNNs in image recognition, and suggest some potential directions for future research. Keywords: Convolutional neural networks, image recognition, deep learning, data preprocessing, network initialization, optimization algorithms 1. Introduction Image recognition, also known as image classification, is a fundamental task in computer vision. The goal is to assign a label to an input image from a predefined set of categories. Image recognition has a wide range of applications, such as object detection, face recognition, and scene understanding. Traditional image recognition methods usually rely on handcrafted features and machine learning algorithms, which require domain expertise and extensive manual effort. In recent years, deep learning has emerged as a powerful tool for image recognition, and convolutional neural networks (CNNs) have become the state-of-the-art approach in this area. CNNs are a class of neural networks that are specifically designed for image analysis. They employ convolutional layers to extract local features from the input image, and use pooling layers to reduce the spatial dimensionality. The output of the convolutional layers is then fed into fully connected layers, which perform high-level reasoning and produce the final classification result. CNNs have several advantages over traditional methods. First, they can automatically learn hierarchical representations of the input data, without the need for manual feature engineering. Second, they are able to capture spatial correlations and translation invariance, which are important characteristics of natural images. Third, they can handle large-scale datasets and are computationally efficient. In this paper, we provide a comprehensive overview of CNNs for image recognition. We begin by introducing the basic structure and principles of CNNs, including convolutional layers, pooling layers, and fully connected layers. We then discuss the training process of CNNs, which includes data preprocessing, network initialization, and optimization algorithms. We also compare different CNN architectures, such as LeNet, AlexNet, VGG, GoogLeNet, and ResNet, and evaluate their performance on benchmark datasets, such as MNIST, CIFAR-10, and ImageNet. Finally, we summarize the advantages and limitations of CNNs in image recognition, and suggest some potential directions for future research. 2. Convolutional Neural Networks 2.1 Basic Structure and Principles CNNs are composed of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The input to a CNN is an image, represented as a matrix of pixel values. The output is a predicted label, which is one of the predefined categories. Convolutional layers are the core components of a CNN. They consist of a set of learnable filters, each of which is a small matrix of weights. The filters are convolved with the input image, producing a feature map that highlights the presence of certain patterns or structures. The convolution operation is defined as follows: \begin{equation} y_{i,j}=\sum_{m=1}^{M}\sum_{n=1}^{N}w_{m,n}x_{i+m-1,j+n-1}+b \end{equation} where y_{i,j} is the output at position (i,j) of the feature map, x_{i+m-1,j+n-1} is the input at position (i+m-1,j+n-1), w_{m,n} is the weight at position (m,n) of the filter, b is a bias term, and M and N are the dimensions of the filter. Pooling layers are used to reduce the spatial dimensionality of the feature map. They operate on small regions of the map, such as 2x2 or 3x3 patches, and perform a simple operation, such as taking the maximum or average value. Pooling helps to improve the robustness of the network to small translations and distortions in the input image. Fully connected layers are used to perform high-level reasoning and produce the final classification result. They take the output of the convolutional and pooling layers, flatten it into a vector, and pass it through a set of nonlinear activation functions. The output of the last fully connected layer is a probability distribution over the predefined categories, which is obtained by applying the softmax function: \begin{equation} p_{i}=\frac{e^{z_{i}}}{\sum_{j=1}^{K}e^{z_{j}}} \end{equation} where p_{i} is the predicted probability of category i, z_{i} is the unnormalized score of category i, and K is the total number of categories. 2.2 Training Process The training process of a CNN involves several steps, including data preprocessing, network initialization, and optimization algorithms. Data preprocessing is a crucial step in CNN training, as it can significantly affect the performance of the network. Common preprocessing techniques include normalization, data augmentation, and whitening. Normalization scales the pixel values to have zero mean and unit variance, which helps to stabilize the training process and improve convergence. Data augmentation generates new training examples by applying random transformations to the original images, such as rotations, translations, and flips. This helps to increase the size and diversity of the training set, and reduces overfitting. Whitening removes the linear dependencies between the pixel values, which decorrelates the input features and improves the discriminative power of the network. Network initialization is another important aspect of CNN training, as it can affect the convergence and generalization of the network. There are several methods for initializing the weights, such as random initialization, Gaussian initialization, and Xavier initialization. Random initialization initializes the weights with small random values, which can lead to slow convergence and poor performance. Gaussian initialization initializes the weights with random values drawn from a Gaussian distribution, which can improve convergence and performance. Xavier initialization initializes the weights with values that are scaled according to the number of input and output neurons, which helps to balance the variance of the activations and gradients. Optimization algorithms are used to update the weights of the network during training, in order to minimize the objective function. Common optimization algorithms include stochastic gradient descent (SGD), Adam, and Adagrad. SGD updates the weights using the gradient of the objective function with respect to the weights, multiplied by a learning rate. Adam adapts the learning rate dynamically based on the first and second moments of the gradient. Adagrad adapts the learning rate for each weight based on its past gradients, which helps to converge faster for sparse data. 3. CNN Architectures There have been many CNN architectures proposed in the literature, each with its own strengths and weaknesses. In this section, we briefly introduce some of the most popular architectures, and evaluate their performance on benchmark datasets. LeNet is one of the earliest CNN architectures, proposed by Yann LeCun in 1998 for handwritten digit recognition. It consists of two convolutional layers, followed by two fully connected layers, and uses the sigmoid activation function. LeNet achieved state-of-the-art performance on the MNIST dataset, with an error rate of 0.8%. AlexNet is a landmark CNN architecture, proposed by Alex Krizhevsky et al. in 2012 for the ImageNet challenge. It consists of five convolutional layers, followed by three fully connected layers, and uses the rectified linear unit (ReLU) activation function. AlexNet achieved a top-5 error rate of 15.3% on the ImageNet dataset, which was a significant improvement over the previous state-of-the-art method. VGG is another CNN architecture, proposed by Karen Simonyan and Andrew Zisserman in 2014. It consists of up to 19 convolutional layers, followed by two fully connected layers, and uses the ReLU activation function. VGG achieved a top-5 error rate of 7.3% on the ImageNet dataset, which was the best performance at the time. GoogLeNet is a CNN architecture, proposed by Christian Szegedy et al. in 2014. It consists of 22 layers, including multiple inception modules, which are composed of parallel convolutional and pooling layers at different scales. GoogLeNet achieved a top-5 error rate of 6.7% on the ImageNet dataset, with much fewer parameters than VGG. ResNet is a CNN architecture, proposed by Kaiming He et al. in 2015. It consists of residual blocks, which allow the network to learn residual connections between layers, and avoid the vanishing gradient problem. ResNet achieved a top-5 error rate of 3.57% on the ImageNet dataset, which was the best performance at the time. 4. Conclusion and Future Work In this paper, we provided a comprehensive overview of CNNs for image recognition, including the basic structure and principles, the training process, and the comparison of different architectures on benchmark datasets. CNNs have shown remarkable performance in image recognition, and have become the state-of-the-art approach in this area. However, there are still some challenges that need to be addressed, such as improving the robustness and interpretability of the network, handling noisy and incomplete data, and scaling up the training process to larger datasets and more complex tasks. In the future, we expect to see more research on these topics, and more applications of CNNs in various domains.
VHDL (VHSIC Hardware Description Language) is a hardware description language used in digital circuit design and simulation. A VHDL process is a block of sequential statements that execute concurrently in a specific order. It is the basic building block for designing digital systems using VHDL. In VHDL, a process is defined using the process statement and is enclosed within the architecture of a module. It can contain variables, signals, and sequential statements such as if-else, case, loops, etc. A process can also be sensitive to events like rising or falling edges of signals, changes in signal values, or a combination of both. Processes in VHDL are used to model the behavior of digital circuits. They allow designers to describe complex control logic, data flow, and timing requirements. The statements within a process are executed sequentially within a clock cycle or in response to specific events. This helps in creating synchronous and asynchronous designs. Here's an example of a simple VHDL process: vhdl architecture Behavioral of MyModule is begin process (clk) begin if rising_edge(clk) then -- sequential statements here -- ... end if; end process; end architecture; In this example, the process is sensitive to the rising edge of the 'clk' signal. The sequential statements inside the process will execute whenever there is a rising edge on the 'clk' signal. Processes in VHDL are a fundamental concept for describing the behavior of digital systems and are widely used in FPGA and ASIC designs. They allow for the creation of complex and efficient hardware designs.
I recently completed a course on "Object-Oriented Programming in Java". Through this course, I learned about the fundamental concepts of object-oriented programming and how to apply them using the Java programming language. One of the key skills I acquired is the ability to design and implement classes and objects in Java. I learned how to define class properties and methods, as well as how to use inheritance and polymorphism to create more complex and flexible object models. I also learned about exception handling and how to handle errors in a Java program. In addition to the theoretical concepts, I also had the opportunity to apply my newfound knowledge by developing several small Java programs and projects. For example, I built a simple text-based adventure game and a student management system. These projects allowed me to put into practice the concepts I learned and helped me better understand how to apply them in real-world scenarios. As for the future of Java, I believe it will continue to be a widely-used programming language, especially in the development of enterprise-level applications and mobile applications. With its strong performance, ease of use, and compatibility with a wide range of platforms, Java has proven to be a highly effective and versatile programming language. In conclusion, the course on "Object-Oriented Programming in Java" was a valuable learning experience for me. Through this course, I was able to deepen my understanding of object-oriented programming concepts and gain hands-on experience in Java programming. I am confident that the skills and knowledge I acquired will be useful in my future software development endeavors.
As an AI language model, I cannot experience emotions, but I can provide information on how to boost threads in programming. Boost.Thread is a C++ library that provides a wide range of synchronization primitives, thread-safe containers, and other useful tools for multithreaded programming. Here are some tips for boosting threads in Boost.Thread: 1. Use mutexes: Mutexes are fundamental synchronization primitives that allow threads to safely access shared resources. Boost.Thread provides a variety of mutex types, including recursive and timed mutexes, that can be used to protect critical sections of code. 2. Use condition variables: Condition variables are a powerful tool for synchronizing threads. They allow threads to wait for a specific condition to be met before proceeding. Boost.Thread provides several types of condition variables, including timed condition variables, that can be used to implement complex synchronization patterns. 3. Use atomics: Atomic types provide a way to perform atomic operations on shared variables without the need for locks. Boost.Thread provides a variety of atomic types, including integers, booleans, and pointers, that can be used to implement lock-free algorithms. 4. Use thread-safe containers: Boost.Thread provides several thread-safe container types, including queues, stacks, and maps, that can be used to safely share data between threads. 5. Use thread pools: Thread pools provide a way to manage a pool of worker threads that can be used to execute tasks in parallel. Boost.Thread provides a thread pool implementation that can be used to boost performance in multithreaded applications. By using these tools and techniques, you can safely and efficiently boost threads in your C++ applications using Boost.Thread.


Data Mining and Analysis_Fundamental Concepts and Algorithms_2014

Data Mining and Analysis_Fundamental Concepts and Algorithms_2014

The Scientist and Engineer Guide to Digital Signal Processing

The Scientist and Engineer Guide to Digital Signal Processing——Second Edition by Steven W. Smith This book was written for scientists and engineers in a wide variety of fields: physics, ...


android studio简易app实例 软件介绍: 1:软件使用Android stuido进行开发; 2:使用sqlite本地数据库进行数据的存储; 3:漫画数据来源于网页爬虫技术获取; 用户功能介绍: 1:注册模块,用户在使用软件前需要进行用户信息的注册 2:用户登录:用户通过自己的注册信息进行软件的登录, 3:首页信息:用户进入首页之后可以浏览漫画列表信息 4:查看漫画:点击一个漫画信息之后可以查看章节信息,以及点击章节进行详情的预览信息 5:我的收藏:用户可以对自己喜欢的漫画信息进行收藏 6:个人信息:用户可以浏览个人信息,以及对密码进行修改; ———————————————— 版权声明:本文为CSDN博主「Android毕业设计源码」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。 原文链接:https://blog.csdn.net/u014388322/article/details/131303773




这份PDF就是最强⼋股⽂! 1. C++ C++基础、C++ STL、C++泛型编程、C++11新特性、《Effective STL》 2. Java Java基础、Java内存模型、Java面向对象、Java集合体系、接口、Lambda表达式、类加载机制、内部类、代理类、Java并发、JVM、Java后端编译、Spring 3. Go defer底层原理、goroutine、select实现机制 4. 算法学习 数组、链表、回溯算法、贪心算法、动态规划、二叉树、排序算法、数据结构 5. 计算机基础 操作系统、数据库、计算机网络、设计模式、Linux、计算机系统 6. 前端学习 浏览器、JavaScript、CSS、HTML、React、VUE 7. 面经分享 字节、美团Java面、百度、京东、暑期实习...... 8. 编程常识 9. 问答精华 10.总结与经验分享 ......


1检索样式:无监督人脸特征传输与检索闽金虫1号mchong6@illinois.edu朱文生wschu@google.comAbhishek Kumar2abhishk@google.com大卫·福赛斯1daf@illinois.edu1伊利诺伊大学香槟分校2谷歌研究源源源参考输出参考输出参考输出查询检索到的图像(a) 眼睛/鼻子/嘴(b)毛发转移(c)姿势转移(d)面部特征检索图1:我们提出了一种无监督的方法来将局部面部外观从真实参考图像转移到真实源图像,例如,(a)眼睛、鼻子和嘴。与最先进的[10]相比,我们的方法能够实现照片般逼真的传输。(b) 头发和(c)姿势,并且可以根据不同的面部特征自然地扩展用于(d)语义检索摘要我们提出检索风格(RIS),一个无监督的框架,面部特征转移和检索的真实图像。最近的工作显示了通过利用StyleGAN潜在空间的解纠缠特性来转移局部面部特征的能力。RIS在以下方面改进了现有技术:1)引入


### 回答1: 要打散连通域,可以使用 HALCON 中的 `connection` 和 `disassemble_region` 函数。首先,使用 `connection` 函数将图像中的连通域连接起来,然后使用 `disassemble_region` 函数将连接后的连通域分离成单独的区域。下面是一个示例代码: ``` read_image(Image, 'example.png') Threshold := 128 Binary := (Image > Threshold) ConnectedRegions := connection(Binary) NumRegions :=




8526基于判别表示学习的无监督身份再识别Takashi Isobe1,2,Dong Li1,Lu Tian1,Weihua Chen3,Yi Shan1,ShengjinWang2*1 Xilinx Inc.,中国北京2清华大学3阿里巴巴集团{dongl,lutian,yishan}@xilinx.comjbj18@mails.tsinghua.edu.cnwgsg@tsinghua.edu.cnkugang. alibaba-inc.com摘要在这项工作中,我们解决的问题,无监督域适应的人重新ID注释可用于源域,但不为目标。以前的方法通常遵循两阶段优化管道,其中网络首先在源上进行预训练,然后使用通过特征聚类创建的伪标签在目标上进行微调。这种方法存在两个主要局限性。(1)标签噪声可能阻碍用于识别目标类别的区分特征的学习。(2)领域差距可能会阻碍知识从源到目标的转移。我们提出了三种技术方案来缓解(一)(b)第(1)款(c)第(1)款这些问题首先,我们提出了一个集群明智的对比学习算法(CCL)的特征学习和集群精炼的迭代优�


### 回答1: 开路电压、短路电流测等效内阻的缺点有以下几个: 1. 受环境条件影响较大:开路电压、短路电流测等效内阻需要在特定的环境条件下进行,如温度、湿度等,如果环境条件发生变化,测量结果可能会出现较大误差。 2. 测量精度较低:开路电压、短路电流测等效内阻的精度受到仪器精度、线路接触不良等因素的影响,误差较大。 3. 需要断开电池电路:开路电压、短路电流测等效内阻需要断开电池电路进行测量,这样会导致电池的使用受到影响,对于某些需要连续供电的设备来说不太适用。 4. 无法检测内部故障:开路电压、短路电流测等效内阻只能检测电池整体的性能,无法检测到电池内部的故障,如单体电池损坏等问