# Concatenated Attention: A Novel Method for Regulating Information Structure Based on Sensors
## 1 Introduction  
With the continuous development of Artificial Intelligence (AI), research in many fields has been significantly optimized by leveraging this technology [1, 2, 3]. For example, AI can be employed to analyze real-time sensor data, such as temperature, humidity, and gas concentration, to assess air quality and predict weather changes [4, 5, 6]. Furthermore, AI has the ability to predict environmental trends based on long-term data [7, 8]. Many inefficient and unstable monitoring systems have been replaced by AI-based automation systems, improving the efficiency and accuracy of monitoring or forecasting, which is critical for applications such as disaster management and pollution control [9, 10]. In smart transportation planning, AI analyzes urban traffic conditions to dynamically allocate road resources, thus alleviating traffic congestion [11, 12]. Similarly, in wireless sensor networks, AI evaluates the status and access requirements of communication nodes, enabling the dynamic adjustment of resource allocation to minimize power consumption. Additionally, neural networks are adept at analyzing object image data, thereby completing tasks such as predictive maintenance and anomaly detection [13, 14]. For example, in the application of chest X-ray images, neural networks can effectively improve diagnostic efficiency [15, 16]. These advantages in efficiency and stability also demonstrate AI's potential in enabling smarter, more adaptable systems across various industries. However, the environments in which sensors acquire and analyze images in real time are often less than ideal [17, 18]. For instance, underwater monitoring systems lack sufficient lighting, resulting in reduced clarity of images captured by sensors. Even under consistent lighting conditions, factors such as depth and suspended particles can attenuatethe light reflected from an object's surface to the sensor [19; 20]. Moreover, water flow can induce object motion or microbial adhesion, further leading to blurred or distorted images. Under these conditions, real-time monitoring systems become unstable, failing to meet the demands for accurate detection and recognition. These limitations not only reduce the clarity of the collected data but also hinder the backpropagation process in neural networks during training. Therefore, developing more robust solutions is crucial. To address these challenges, many improvements have been made in both hardware and software. From a hardware perspective, low-light cameras, specialized underwater imaging sensors, and enhanced lighting conditions can improve image acquisition [21; 22]. On the software side, techniques such as noise reduction, image enhancement, and illumination compensation can significantly improve image clarity. However, while these methods meet practical requirements, they are often unable to adapt to specific tasks or perform effectively in more general scenarios [23; 24]. For example, optimization methods may fail when devices operate in highly dynamic environments. At the same time, neural network-based algorithms for feature extraction and detailed classification have become the mainstream approach in image processing [25; 26]. Convolutional Neural Networks (CNNs), such as AlexNet, were among the earliest methods proposed for handling image-related tasks [27]. AlexNet pioneered the extraction of image features from the lowest layers of the network, enabling higher-level layers to monitor targets from a global perspective. However, the rigid structure of AlexNet hinders adaptation to task-specific requirements, and issues such as shallow network depth and susceptibility to overfitting remain. These limitations have exceeded the capabilities of existing system architectures, highlighting the need to develop more advanced neural networks to meet the requirements of a broader range of applications.