In response to the challenge that traditional camouflage design methods struggle to evade detection by modern unmanned aerial reconnaissance, we propose a camouflage pattern generation adversarial network model using color-semantic constraints. A reference image generation model using color-semantic constraints has been established, which through sentence encoding model, generates reference images with fundamental texture and color features. This is achieved by incorporating adversarial loss, texture loss, and pixel-level loss. We design a color standardization processing strategy based on the SimCLR framework. This model generates semantic camouflage images in batches by designing data augmentation strategies, positive-negative sample similarity measurement strategies, and sample structural similarity algorithms, regard to reference images. Qualitative and quantitative experimental results demonstrate that our proposed method exhibits strong camouflage performance in different environmental settings.
Interactive web interfaces are widely used, yet users often struggle to discover important and useful information. Psychologists have pointed out that in the process of human visual perception, the global topological characteristics of objects will be perceived first. In this study, we propose a cognitive experiment system to explore what object properties affect visual efficiency in interactive web environments. Our experimental results showed that, first, if a small number of colors are allocated to different objects, these objects could be more effectively memorized than no color or too many colors. Second, in static single-page, different topology structures will not cause apparently different visual attraction, however, topological structural differences could be stronger memorized. Third, in dynamic HTML, the motion changes will induce stronger visual attention and memory intensity, than their own topology changes. Subsequently, we verified the feasibility of the experiment system in real webpages and demonstrated that the above results can theoretically guide better web interaction design works with less cognitive load of users, higher operation efficiency and more natural user experience. In addition, some design reference suggestions are also provided in our study.
Financial time series usually consist of multiple time series, and financial time series data forecasting models use the historical data plays of multiple driving series to predict the future values of the target series. In recent years, attention-based Long and Short-Term Memory (LSTM) neural networks and Temporal Convolutional Networks (TCN) have been widely used in time series forecasting. In this paper, we propose a two-stage attention-based TCN and LSTM hybrid forecasting model, in order to better obtain the spatial correlation of driving sequences, we used causal self-attention to obtain the spatial attention weights of driving sequences, then use TCN to extract the short-term features of the series in the first stage, in the second stage, adding the temporal attention module computes the sequence adaptively assigning weights to the input sequence for the current and historical moments, and finally use LSTM to capture the long-term dependence of the time-series data. We used the NASDAQ 100 stock dataset and the financial time series of CSI 300 companies to measure the performance of the proposed model in financial data forecasting.
KEYWORDS: Camouflage, 3D modeling, Image processing, 3D acquisition, Image segmentation, 3D image processing, Virtual reality, Target detection, Digital imaging, Digital image processing
A camouflage design strategy based on battlefield environment twinning is proposed to address the camouflage concealment of military equipment in a realistic battlefield environment. The strategy is based on the construction of a three-dimensional battlefield environment digital twin model, and by synthesizing the digital camouflage based on the background primary color and natural camouflage decoration, a camouflage design scheme with a higher camouflage success rate can be generated to fit the realistic battlefield environment. A neural network-based image segmentation network and a target detection network are used to evaluate the performance of the camouflage design scheme. The experiment results show that the camouflage design strategy proposed in this paper has better interference and confrontation with detection technology, and it can provide the most adaptable camouflage design scheme for the target object in the real battlefield environment, which has strong practical value and strategic significance.
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