Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

Due to the independent sleep/wake scheduling of nodes in asynchronous wireless sensor networks (WSNs), a sender should send the same packet several times to make sure all the neighbor nodes can receive the packet in a broadcast process. Moreover, the order of the neighbor nodes receiving the packet has effect on the performance of broadcast. In this letter, we focus on the dynamics in the process of broadcast in WSNs and jointly consider the coverage knowledge, link quality, and sleep/wake scheduling information of neighbor nodes. We first choose a receiver with large newly added coverage area to accelerate the broadcast process. Then, we select the sender and its receiver or the coverage order for the nodes not being accommodated in the overlap coverage area to efficiently improve the broadcast performance. Simulation results have shown that our proposed protocol can reduce energy consumption and number of transmissions.

Phase measuring deflectometry (PMD) shows excellent performance in measuring the shape of specular surfaces. However, the existing height-slope ambiguity affects the measurement of PMD. In this paper, we propose a deep learning-based method to solve the ambiguity and reconstruct the specular surface. A neural network was built to reconstruct the surface. A set of simulation surfaces were generated using two-dimensional Legendre polynomials to train and test the network. A spherical-shaped surface was reconstructed in a simulation to evaluate the validity of the proposed method and compare our method to the state-of-the-art method. Experimental results demonstrate a 35.88% improvement in this method’s accuracy. Our method provides a great improvement in measurement accuracy.

Collecting environmental information of crop growth and dynamically adjusting agricultural production has been proved an effective way to improve the total agricultural yield. Agricultural IoT technology, which integrates the information sensing equipment, communication network, and information processing systems, can support such an intelligent manner in the agricultural environment. Traditional agricultural IoT could meet the service demand of small‐scale agricultural production scenarios to a certain extent. However, the emerging application scenario of the agricultural environment is becoming more and more complicated, and the data nodes of the underlying access to IoT backend system are increasing in large number, while the upper‐layer applications are requiring high quality of data service. Hence, the traditional architecture‐based (i.e., centralised cloud computing) IoT systems suffer from the problems such as small network coverage, data security issue, and limited power supply time while attempting to provide high‐quality services at the edge of the network. Emerging edge computing offers the opportunity to solve these issues. This paper builds an intelligent IoT system for agricultural environment monitoring by integrating edge computing and artificial intelligence. We conducted an experiment to validate the proposed system considering the reliability and usability. The experimental results prove the system’s reliability (e.g., data packet loss rate is less than 0.1%). The proposed system achieves the concurrency of 500TPS and the average response time of 300 ms, which meet the practical requirements in agricultural environment monitoring.

Under the digital revolution that spawned in recent years, AI support is raised in the context of urban design and governance as it aims to match the operation of the urban developing process. It offers more chances for ensuring equality in public participation and empowerment, with the possibility of projection and computation of integrated social, cultural, and physical spaces. Therefore, this research explored how scenario simulation of social attributes and social interaction dimensions can be incorporated into digital twin city research and development, which is seen as a problem to be addressed in the refinement and planning of future digital platforms and management in terms of decision-making. To achieve the research aim, this paper examined the evolution of social governance state and strain decision models, built a simulation method for the evolution of complex systems of social governance driven by the fusion of data and knowledge, and proposed a system response to residents’ ubiquitous perception and ubiquitous participation. The findings can help inspire the application of computational decision-making support in urban governance, and enhance the internal drive for comprehensive and sustainable urban regeneration. Moreover, they imply the role of the updated iterations of physical space and social interaction on social attributes.