SQL Server与性能监控工具：优化与调整关键

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Design And Analysis Of Experiments

Design And Analysis Of Experiments, 5Th Edition (Douglas C Montgomery)-Keep 试验设计与分析 5ed

Design and Analysis of Experiments vol.2.pdf

从以上内容可以推断出，本书《Design and Analysis of Experiments vol.2.pdf》是一本专注于高级实验设计和分析的学术书籍，主要受众为已经具备一定实验设计基础的读者，特别是在生物学领域的研究者。书中不仅介绍了...

path: pretrain_network_g: ./experiments/pretrained_models/HAT_SRx2_ImageNet-pretrain.pth strict_load_g: true param_key_g: 'params_ema'是什么意思

`param_key_g`表示预训练模型中的参数名称，这个参数名称通常是在预训练过程中指定的，用于区分预训练模型中的参数和模型本身中的参数。在这个例子中，`params_ema`指的是预训练模型中的指数移动平均参数。

[~, ~, ~, ~, f] = dacefit([x, y], z, @regpoly0, @corrgauss, theta, lob, upb);意义是什么

这行代码是使用DACE（Design and Analysis of Computer Experiments）工具箱中的dacefit函数进行高维数据拟合的过程。具体来说，dacefit函数将输入的自变量x、y和因变量z作为训练数据，使用高斯相关函数...

f = dacefit([x, y], z, @regpoly0, @corrgauss, theta, lob, upb);意义

这行代码是使用DACE (Design and Analysis of Computer Experiments)工具箱中的函数dacefit来拟合一个高维输入和单一输出的非线性函数。具体来说，它使用了多项式回归模型(regpoly0)和高斯相关函数(corrgauss)来建立...

home_dir = os.getcwd() parser.add_argument('--experiment_description', default='Exp4', type=str, help='Experiment Description') parser.add_argument('--run_description', default='run4', type=str, help='Experiment Description') parser.add_argument('--seed', default=3, type=int, help='seed value') parser.add_argument('--training_mode', default=' fine_tune', type=str, help='Modes of choice: random_init, supervised, self_supervised, fine_tune, train_linear') parser.add_argument('--selected_dataset', default='Epilepsy', type=str, help='Dataset of choice: sleepEDF, HAR, Epilepsy, pFD') parser.add_argument('--logs_save_dir', default='experiments_logs', type=str, help='saving directory')解释这段代码

parser.add_argument() 函数则是用来添加命令行选项和参数的，它们分别为实验描述 (--experiment_description)、运行描述 (--run_description)、种子值 (--seed)、训练模式 (--training_mode)、数据集 (--selected_...

matlab dace工具箱说明书

DACE代表Design and Analysis of Computer Experiments，该工具箱提供了一种可靠的方式来优化、模拟和预测实验数据。它是一种基于统计学的方法，可以帮助我们理解和优化复杂系统的行为。 DACE工具箱的主要特点包括...

pyschtoolbox

It is designed to provide a set of tools and functions for the creation and presentation of visual and auditory stimuli, response collection and analysis, and data visualization. Psychtoolbox is ...

Experimental and numerical study on detection of sleeve grouting defect with impact-echo method原文

However, defects in the sleeve grouting can lead to the failure of the structure, and it is difficult and expensive to repair the defects after the structure is built. Therefore, it is important to ...

use python to finish this task.please show me the code 1) Replicate the same numerical experiments as the examples for pricing barrier option in the PPTs.

Sure, here is the Python code to replicate the numerical experiments for pricing barrier options: ```python import numpy as np from scipy.stats import norm # Parameters S0 = 100.0 # initial stock ...

4 Experiments This section examines the effectiveness of the proposed IFCS-MOEA framework. First, Section 4.1 presents the experimental settings. Second, Section 4.2 examines the effect of IFCS on MOEA/D-DE. Then, Section 4.3 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on 19 test problems. Finally, Section 4.4 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on four real-world application problems. 4.1 Experimental Settings MOEA/D-DE [23] is integrated with the proposed framework for experiments, and the resulting algorithm is named IFCS-MOEA/D-DE. Five surrogate-based MOEAs, i.e., FCS-MOEA/D-DE [39], CPS-MOEA [41], CSEA [29], MOEA/DEGO [43] and EDN-ARM-OEA [12] are used for comparison. UF1–10, LZ1–9 test problems [44, 23] with complicated PSs are used for experiments. Among them, UF1–7, LZ1–5, and LZ7–9 have 2 objectives, UF8–10, and LZ6 have 3 objectives. UF1–10, LZ1–5, and LZ9 are with 30 decision variables, and LZ6–8 are with 10 decision variables. The population size N is set to 45 for all compared algorithms. The maximum number of FEs is set as 500 since the problems are viewed as expensive MOPs [39]. For each test problem, each algorithm is executed 21 times independently. For IFCS-MOEA/D-DE, wmax is set to 30 and η is set to 5. For the other algorithms, we use the settings suggested in their papers. The IGD [6] metric is used to evaluate the performance of each algorithm. All algorithms are examined on PlatEMO [34] platform.

4 实验本节将研究所提出的IFCS-MOEA框架的有效性。首先，在第4.1节中介绍实验设置。其次，在第4.2节中研究IFCS对MOEA/D-DE的影响。...使用IGD [6]指标评估每个算法的性能。所有算法都在PlatEMO [34]平台上进行检验。

Dynamic Light-Scattering Probe, cataract, equipment, project

To perform DLS experiments on biological tissues, specialized equipment is needed, such as a laser source, a detector, and a computer for data analysis. The equipment should also be calibrated and ...

帮我润色论文。In this paper, we propose a balancing training method to address problems in imbalanced data learning. To this end, we derive a new loss used in the balancing training phase that alleviates the influence of samples that cause an overfitted decision boundary. The proposed loss efficiently improves the performance of any type of imbalance learning methods. In experiments on multiple benchmark data sets, we demonstrate the validity of our method and reveal that the proposed loss outperforms the state-of-the-art cost-sensitive loss methods. Furthermore, since our loss is not restricted to a specific task, model, or training method, it can be easily used in combination with other recent resampling, meta-learning, and cost-sensitive learning methods for class-imbalance problems. Our code is made available at https://github.com/pseulki/IB-Loss.

所提出的损失有效提高了任何类型的不平衡学习方法的性能。在多个基准数据集的实验中，我们证明了我们的方法的有效性，并发现所提出的损失优于最先进的成本敏感损失方法。此外，由于我们的损失不受特定任务、模型或...

翻译这段英文，并解释： Deploying convolutional neural networks (CNNs) on em-bedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture de-sign. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost Feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convo-lutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight Ghost-Net can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-I accuracy) than MobileNetV3 with similar computational cost on the ImaseNet ILSVRC2012 classification dataset.

部署卷积神经网络（CNN）到嵌入式设备上是困难的，因为这些设备的内存和计算资源有限。特征图中的冗余是成功的CNN的一个重要特征，但在神经结构设计中很少被研究。本文提出了一种新颖的 Ghost 模块，可以通过廉价的...

翻译：Since we know the position of the previous and current frames, we project into the dynamic segments of the current frame the RGB and depth channels from a set of all the previ- ous keyframes (the last 20 in our experiments). Some gaps have no correspondences and are left blank: some areas cannot be inpainted because their correspondent part of the scene has not appeared so far in the keyframes, or, if it has appeared, it has no valid depth information. These gaps cannot be reconstructed with geometrical methods and would need a more elaborate in- painting technique. Fig. 5 shows the resulting synthetic images for three input frames from different sequences of the TUM benchmark. Notice how the dynamic content has been success- fully segmented and removed.

由于我们知道先前帧和当前帧的位置，因此我们可以将之前所有关键帧（在我们的实验中为最后20帧）中的RGB和深度通道投影到当前帧的动态区域中。一些缺口没有对应关系，因此被留空：一些区域由于其所对应的场景部分...

The conventional convolution neural network (CNN) adopts softmax function as classifier, which has problems of overflow and underflow. This paper proposes a rolling bearing intelligent fault diagnosis method based on multi-scale convolution neural network, bi-directional long short term memory and support vector machine (MCNN-BiLSTM-SVM). The wavelet threshold denoising algorithm is adopted for signal preprocessing. The multi-scale convolution neural network (MCNN) and the bidirectional long short-term memory network (BiLSTM) are combined as the feature extractor to improve feature extraction capability. The support vector machine (SVM) is adopted as the classifier to improve classification performance. Transfer learning is used in MCNN-BiLSTM-SVM for different conditions. According to the experiments, the proposed MCNN-BiLSTM-SVM fault diagnosis method has higher diagnostic accuracy, stronger anti-noise performance and better stability under different conditions than other diagnostic methods.给出以上内容审稿意见

本文提出了一种基于多尺度卷积神经网络、双向长短时记忆网络和支持向量机的轴承智能故障诊断方法（MCNN-BiLSTM-SVM）。该方法采用小波阈值去噪算法进行信号预处理，将多尺度卷积神经网络（MCNN）和双向长短时记忆...

Small but Mighty: Enhancing 3D Point Clouds Semantic Segmentation with U-Next Framework

To evaluate the effectiveness of the U-Next framework, the researchers conducted experiments on two benchmark datasets: S3DIS and SemanticKITTI. The results showed that U-Next outperformed several ...

Give a brief introduction to a novel including the following aspects: - genre - characters - plot - setting - theme

However, they soon realize that their experiments have caused a dangerous rift in the fabric of space-time, and they must race against time to fix it before it destroys everything they know. ...

翻译This SiO2 shell is a key component in the mechanism for reversible actuation, as illustrated by finite element analysis (FEA) in Fig. 1C. An increase in temperature transforms the SMA (nitinol) from the martensitic to the austenitic phase, causing the 3D structure to flatten into a 2D shape. The responses of the SMA elements at the joints act as driving forces to deform the PI skeleton. This process also elastically deforms the SiO2 shell, resulting in a counter force that limits the magnitude of the deformation. The change in shape ceases when the forces from the shell balance those from the joints (right frame in Fig. 1C). Upon a reduction in temperature, the SMA changes from the austenitic back to the martensitic phase, thereby reducing the force produced by the SMA at the joints to zero. The elastic forces associated with the shell then push the entire system back to the original 3D geometry (left frame in Fig. 1C). Figure S3A simulates the moments generated by the SMA and the SiO2 shell. In the FEA model, the SiO2 shell appears on both the outer and inner surfaces of the 3D robot, consistent with experiments (fig. S3B). Although a single layer of the SiO2 shell at the outer or inner surface can also provide restoring force, the double-layer shell structure follows naturally from the conformal deposition process. This actuation scheme allows for reversible shape transformations using a one-way shape memory material. Without the shell, the structure only supports a single change in shape, from 3D to 2D, as illustrated in fig. S3C. Figure 1D shows optical images of a freestanding 3D peekytoe crab on the edge of a coin, highlighting the preserved 3D geometry enabled by the SiO2 shell after release from the elastomer substrate. Other 3D structures in geometries that resemble baskets, circular helices, and double-floor helices also exhibit high shape storage ratios (>85%) after cycles of heating and cooling (fig. S4). This ratio (s) is defined as s = 1 − |L1 − L0|/L0 × 100%, where L0 and L1 are the distances between the bonding sites at both ends at the initial stage and subsequent stages, respectively

这个SiO2壳是可逆作用机制的关键组成部分，如图1C所示的有限元分析所示。温度的升高将SMA（尼钛）从...这个比率（εs）定义为εs = 1-|L1-L0|/L0×100%，其中L0和L1分别是初始阶段和随后阶段两端结合点之间的距离。

请将下列英文翻译成中文： Soil moisture is critical to agricultural business, ecosystem health, and certain hydrologically driven natural disasters. Monitoring data, though, is prone to instrumental noise, wide ranging extrema, and nonstationary response to rainfall where ground conditions change. Furthermore, existing soil moisture models generally forecast poorly for time periods greater than a few hours. To improve such forecasts, we introduce two data-driven models, the Naive Accumulative Representation (NAR) and the Additive Exponential Accumulative Representation (AEAR). Both of these models are rooted in deterministic, physically based hydrology, and we study their capabilities in forecasting soil moisture over time periods longer than a few hours. Learned model parameters represent the physically based unsaturated hydrological redistribution processes of gravity and suction. We validate our models using soil moisture and rainfall time series data collected from a steep gradient, post-wildfire site in southern California. Data analysis is complicated by rapid landscape change observed in steep, burned hillslopes in response to even small to moderate rain events. The proposed NAR and AEAR models are, in forecasting experiments, shown to be competitive with several established and state-of-the-art baselines.

土壤水分对农业业务、生态系统健康和某些水文驱动的自然灾害至关重要。然而，监测数据容易受到仪器噪声、极端宽泛和非平稳的降雨反应的影响，在地面条件发生变化时。此外，现有的土壤水分模型一般在几个小时以上的...

韩大人的指尖记录

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SQL Server与性能监控工具：优化与调整关键

Design And Analysis Of Experiments

Design and Analysis of Experiments vol.2.pdf

path: pretrain_network_g: ./experiments/pretrained_models/HAT_SRx2_ImageNet-pretrain.pth strict_load_g: true param_key_g: 'params_ema'是什么意思

[~, ~, ~, ~, f] = dacefit([x, y], z, @regpoly0, @corrgauss, theta, lob, upb);意义是什么

f = dacefit([x, y], z, @regpoly0, @corrgauss, theta, lob, upb);意义

matlab dace工具箱说明书

pyschtoolbox

Experimental and numerical study on detection of sleeve grouting defect with impact-echo method原文

use python to finish this task.please show me the code 1) Replicate the same numerical experiments as the examples for pricing barrier option in the PPTs.

Dynamic Light-Scattering Probe, cataract, equipment, project

Small but Mighty: Enhancing 3D Point Clouds Semantic Segmentation with U-Next Framework

Give a brief introduction to a novel including the following aspects: - genre - characters - plot - setting - theme

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