Cyber–physical–social systems (CPSSs) emerged as a novel paradigm addressing the growing interactions among physical, social, and cyber domains. Many CPSS challenges were mathematically modeled as optimization problems, with a significant portion requiring multiobjective optimization approaches. To address inefficient learning caused by large-scale distributed sensor data in CPSS, a transformer-based anomaly detection framework for end-edge-cloud systems was proposed. For specific anomaly signals, an improved anomaly detection matrix was developed through systematic calculation of attention weights and corresponding scores. A distributed intelligence architecture integrating transformer-based anomaly detection was implemented in edge nodes for fiber-optic sensors. The proposed artificial Intelligence of Things (AIoT) based distributed edge computing model dynamically allocated tasks among idle, partially available, and fully utilized nodes through optimized resource management in task transmission. A high-efficiency allocation strategy was designed to optimize task processing rates and enable edge nodes to dynamically balance task execution and transmission without compromising detection accuracy. Experimental results demonstrated the effectiveness of both the enhanced transformer method and the computational pipeline across end-edge-cloud architectures. Compared with the vision transformer baseline, our method achieved 3.62% higher accuracy and 3.07% improved recall. Computational time was reduced by approximately 50% while memory consumption decreased by 40%. The proposed approach showed significant potential for anomaly detection in AIoT applications.

Industrial Internet of Things (IIoTs) are significantly changing informative and manufacturing pattern in smart factories while it also brings security and trustworthiness issue. Concerning about trustworthiness issues and private preservation of tracking systems, a hierarchical framework with federated control theory is designed, which consists of a federated control center, network layer, and a federated control node. The framework combines a collaborative Cloud-Edge-End structure and machine learning-oriented localization, which further forms the EM-based federated scheme. On this basis, a trustworthy localization model is built with the untrustworthiness probability as a latent variable. By exploring expectation maximization (EM) of trustworthy localization, the local messages and aggression equations are derived in an iterative way of federated learning. The EM-based federated control scheme with machine learning-oriented localization is finally given. Experiments have been conducted to prove the localization accuracy and convergence of the proposed method with trustworthiness issue. The results show that trustworthy localization outperforms traditional methods without considering the security threats.

Various attacks in communication network threaten the security of vehicular system. For cooperative location-sensing system, man-in-the-middle attack and eavesdropping attack would lead to catastrophic collapse of vehicular network localization. In this paper, we propose a federated cryptosystem localization based on optimized constraints. In the localization phase, a message passing algorithm combining belief propagation (BP) and variational message passing (VMP) is derived by defining penalty function, which can detect the distance outliers. The messages generated by each vehicular node or base station are encrypted with Paillier cryptosystem. Because Paillier cryptosystem is featured by homomorphic addition, message aggregation reduces large amount of decryption. The localization messages finally work in a federated transmission scheme for privacy preserving against man-in-the-middle attack and eavesdropping attack. In terms of localization accuracy, algorithm parameters, convergence analysis and efficiency, simulation results show that cybersecurity of cooperative localization is proved to be effective in various scenarios of vehicular network.

The power-spectrum sub-band energy ratio (PSER) has been applied in a variety of fields, but reports on its statistical properties and application in signal detection have been limited. Therefore, the statistical characteristics of the PSER were investigated and a signal detection method based on the PSER was created in this paper. By analyzing the probability and independence of power spectrum bins, as well as the relationship between F and beta distributions, we developed a probability distribution for the PSER. Our results showed that in a case of pure noise, the PSER follows beta distribution. In addition, the probability density function exhibited no relationship with the noise variance—only with the number of bins in the power spectrum. When Gaussian white noise was mixed with the signal, the resulting PSER followed a doubly non-central beta distribution. In this case, the probability density and cumulative distribution functions were represented by infinite double series. Under the constant false alarm strategy, we established a signal detector based on the PSER and derived the false alarm probability and detection probability of the PSER. The main advantage of this detector is that it did not need to estimate noise variance. Compared with time-domain energy detection and local spectral energy detection, we found that the PSER had better robustness under noise uncertainty. Finally, the results in the simulation and real signal showed that this detection method was valid.

Clinical management and accurate disease diagnosis are evolving from qualitative stage to the quantitative stage, particularly at the cellular level. However, the manual process of histopathological analysis is lab-intensive and time-consuming. Meanwhile, the accuracy is limited by the experience of the pathologist. Therefore, deep learning-empowered computer-aided diagnosis (CAD) is emerging as an important topic in digital pathology to streamline the standard process of automatic tissue analysis. Automated accurate nucleus segmentation can not only help pathologists make more accurate diagnosis, save time and labor, but also achieve consistent and efficient diagnosis results. However, nucleus segmentation is susceptible to staining variation, uneven nucleus intensity, background noises, and nucleus tissue differences in biopsy specimens. To solve these problems, we propose Deep Attention Integrated Networks (DAINets), which mainly built on self-attention based spatial attention module and channel attention module. In addition, we also introduce a feature fusion branch to fuse high-level representations with low-level features for multi-scale perception, and employ the mark-based watershed algorithm to refine the predicted segmentation maps. Furthermore, in the testing phase, we design Individual Color Normalization (ICN) to settle the dyeing variation problem in specimens. Quantitative evaluations on the multi-organ nucleus dataset indicate the priority of our automated nucleus segmentation framework.

The existing semi-active suspensions are very expensive and difficult to be popularized. Therefore, the search for the low-cost semi-active suspension has become a hotspot. To reduce the product cost of semi-active suspensions, this study proposed a new hydraulic semi-active suspension system based on road statistical properties and creates an identification method of road conditions. Firstly, the working process of the proposed semi-active suspension was given. Then, the formula for calculating the mean square value of the body acceleration under the running road condition was derived. Based on the formula, the road identification method was created. Finally, the road test was carried out using a test vehicle. The identified road PSD (power spectral density) values are all consistent with the real values. The test results show that the road identification method can effectively perceive the actual road grades. The identification interval with 20 s can meet the requirements of practical applications. The slight fluctuation of the running speed v has little effect on the identification results because of the statistical characteristics of road conditions.