Prompt Description
Please generate an image: The core of combining clustering algorithms with NIPALS (linear) and KPLS (nonlinear) is to construct a three-stage architecture of "clustering stratification - model fitting - fusion prediction" to adapt to the modeling needs of heterogeneous data (including multiple subgroups, mixed linear and nonlinear features). The specific process is as follows: 1) Clustering stratification principle: Hierarchical clustering algorithm is used to group the preprocessed data set. The silhouette coefficient is used as the evaluation index, and the optimal number of clusters is automatically determined within the range of the "maximum number of clusters" set by the user. At the same time, clusters with sample sizes lower than the "minimum sample number threshold" are merged to ensure that each cluster has enough modeling samples (20-50 are recommended). 2) Intra-cluster nonlinearity detection: For each independent cluster, the model performance of linear NIPALS and nonlinear KPLS is compared through internal cross-validation (using the increase of R² as the criterion). If the R² increase of KPLS compared to NIPALS exceeds the "nonlinearity detection threshold" (default 5.0%), the cluster is determined to be a nonlinear feature and KPLS is selected for modeling; otherwise, it is determined to be a linear feature and NIPALS is selected for modeling. 3) Model fusion and prediction: When a new sample is input, it is assigned to the corresponding cluster model through the nearest neighbor matching algorithm. The system automatically calculates the weighted R² of each cluster (weighted by the cluster sample size), outputs the overall prediction result, and retains the independent modeling parameters and performance indicators of each cluster to support interactive analysis. Please draw the algorithm flow chart of CPLS with reference to the given picture and export it in mermaid form.
![Please provide a diagram of the improved YOLO11s-seg architecture, showing as many layers of the network structure as possible with accurate structural details. The overall structure should be divided into left and right sections. The left side should illustrate the improved backbone structure, while the right side should display the improved neck and head sections. Each structural component should be clear and complete. The overall color scheme should be warm-toned, and the style should resemble network structure diagrams found in top-tier computer science conference or journal papers. The improved network structure is based on the following specifications:
Input: [3,640,640]
│
└─Backbone: MobileNetV4HybridMedium (factor=0.25)
│
├─Conv0: Conv2d(3→8, k=3, s=2) + BN + ReLU6 → [8,320,320]
│
├─Layer1: FusedIB(8→12, s=2) → [12,160,160] (P1)
│
├─Layer2:
│ ├─UIB(12→20, s=2) → [20,80,80]
│ └─UIB(20→20, s=1) → [20,80,80] (P2)
│
├─Layer3:
│ ├─UIB(20→40, s=2) → [40,40,40]
│ ├─UIB(40→40, s=1) → [40,40,40]
│ ├─UIB(40→40, s=1) → [40,40,40]
│ ├─4× (UIB+MHSA) → [40,40,40]
│ └─UIB(40→40, s=1) → [40,40,40] (P3)
│
└─Layer4:
├─UIB(40→64, s=2) → [64,20,20]
├─5× UIB → [64,20,20]
├─4× (UIB+MHSA) → [64,20,20]
└─2× UIB → [64,20,20] (P4)
│
└─Neck:
│
├─SPPF:
│ Conv(64→32) → 3×MaxPool → Concat → Conv(128→1024) → [1024,20,20] (P5)
│
└─C2PSA_mona ×2:
Split into two 512-channel branches
Branch A: Direct connection
Branch...](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2FQDBc1BIBGbvje1FqHJjm2d7CaYOob7cn%2Fcd190b7e-4df0-4d77-9c86-6989f592119d%2F9fa32595-8168-4390-8896-0d36eafa5a9f.png&w=3840&q=75)
![Please generate a detailed architectural diagram of the improved YOLO11s-seg model. The diagram should illustrate each layer of the network structure as comprehensively as possible, potentially using modules to represent groups of layers, while ensuring structural accuracy. The overall layout should be divided into left and right sections. The left side should depict the enhanced backbone structure, and the right side should display the improved neck and head components. Each structural block should be clear and complete. The color scheme should be warm-toned, and the style should emulate the figures found in top-tier computer science conference or journal papers. The specific improvements are based on the following: This improved version employs a hierarchical optimization strategy and a dual-branch attention mechanism to enhance YOLO11s-seg. In the Backbone, MobileNetV4HybridMedium replaces the original backbone network to improve lightweight performance. The neck incorporates the C2PSA_mona module, an innovative dual-branch attention mechanism that integrates PSA (Position Sensitive Attention) and Mona (Multi-scale Operator) in parallel branches. Feature fusion is achieved through learnable weights, significantly enhancing multi-scale feature representation and robustness in complex scenarios. In the Head, a hierarchical feature enhancement strategy derived from the DWRSeg paper is adopted: shallow features (P3/8) retain the standard C3k2 module to maintain lightweight characteristics; intermediate features (P4/16) use the C3k2_SIR module, which combines depthwise convolution and channel attention through a Spatial Information Refinement mechanism to sharpen features and enhance edge localization, particularly suitable for segmenting irregular objects; deep features (P5/32) employ the C3k2_DWR module, which achieves large-scale context aggregation through regional residualization (global context extraction) and semantic residualization (multi-branch atrous convolution with dilation rates of [1,3,5]), effectively compensating for the limited receptive field of the lightweight backbone network.](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2Fdd1ff8ydE0r2YmRRutlgTwqrekhr45kr%2F11eefaa9-7ae8-41c4-8b1f-ac4138029b28%2F4e817789-1df2-48dc-828d-6c88ffd781f9.png&w=3840&q=75)
