Yangyi Xia

Chengjun Yu, Yixin Ran, Yangyi Xia et al.

Wireless Sensor Networks (WSNs) are a cutting-edge domain in the field of intelligent sensing. Due to sensor failures and energy-saving strategies, the collected data often have massive missing data, hindering subsequent analysis and decision-making. Although Latent Factor Learning (LFL) has been proven effective in recovering missing data, it fails to sufficiently consider data privacy protection. To address this issue, this paper innovatively proposes a federated latent factor learning (FLFL) based spatial signal recovery (SSR) model, named FLFL-SSR. Its main idea is two-fold: 1) it designs a sensor-level federated learning framework, where each sensor uploads only gradient updates instead of raw data to optimize the global model, and 2) it proposes a local spatial sharing strategy, allowing sensors within the same spatial region to share their latent feature vectors, capturing spatial correlations and enhancing recovery accuracy. Experimental results on two real-world WSNs datasets demonstrate that the proposed model outperforms existing federated methods in terms of recovery performance.

Lujia Lv, Di Wu, Yangyi Xia et al.

With the continuous improvement of people's living standards and fast-paced working conditions, pre-made dishes are becoming increasingly popular among families and restaurants due to their advantages of time-saving, convenience, variety, cost-effectiveness, standard quality, etc. Object detection is a key technology for selecting ingredients and evaluating the quality of dishes in the pre-made dishes industry. To date, many object detection approaches have been proposed. However, accurate object detection of pre-made dishes is extremely difficult because of overlapping occlusion of ingredients, similarity of ingredients, and insufficient light in the processing environment. As a result, the recognition scene is relatively complex and thus leads to poor object detection by a single model. To address this issue, this paper proposes a Differential Evolution Integrated Hybrid Deep Learning (DEIHDL) model. The main idea of DEIHDL is three-fold: 1) three YOLO-based and transformer-based base models are developed respectively to increase diversity for detecting objects of pre-made dishes, 2) the three base models are integrated by differential evolution optimized self-adjusting weights, and 3) weighted boxes fusion strategy is employed to score the confidence of the three base models during the integration. As such, DEIHDL possesses the multi-performance originating from the three base models to achieve accurate object detection in complex pre-made dish scenes. Extensive experiments on real datasets demonstrate that the proposed DEIHDL model significantly outperforms the base models in detecting objects of pre-made dishes.