Fault diagnosis of chemical process data becomes one of the most important directions in research and practice. Conventional fault diagnosis and classification methods firstly extract features from the raw process data. Then certain classifiers are adopted to make diagnosis. However, these conventional methods suffer from expertise of feature extraction and classifier design. They also lack the adaptive processing of the dynamic information in raw data. This paper proposes a fault diagnosis method based on long short-term memory (LSTM) neural network. The novel method can directly classify the raw process data without specific feature extraction and classifier design. It is also able to adaptively learn the dynamic information in raw data. Firstly, raw process data are used to train the LSTM neural network until the cost function of LSTM converges below certain predefined small positive value. In this step, the dynamic information of raw process data is adaptively learned by LSTM. Then testing data are used to obtain the diagnosis results of the trained LSTM neural network. The application of LSTM to fault identification and analysis is evaluated in the Tennessee Eastman (TE) benchmark process. Extensive experimental results show LSTM can better separate different faults and provide more promising fault diagnosis performance.

The nonconvex optimization problems have recently attracted significant attention. However, both efficient algorithm and solid theory are still very limited. The difficulty is even pronounced for structured large-scale problems in many real-world applications. This article proposes an application-driven algorithmic framework for structured nonconvex optimization problems with distributed and parallel techniques, which jointly handles the high dimensionality of model parameters and distributed training data. The theoretical convergence of our algorithm is established under moderate assumptions. We apply the proposed method to popular multitask applications, including a multitask reinforcement learning problem. The promising performance demonstrates our framework is effective and efficient.

In online sequential applications, a machine learning model needs to have a self-updating ability to handle the situation, that the training set is changing. Conventional incremental extreme learning machine (ELM) and online sequential ELM are usually achieved in two approaches: directly updating the output weight and recursively computing the left pseudo inverse of the hidden layer output matrix. In this paper, we develop a novel solution for incremental and decremental ELM, via recursively updating and downdating the generalized inverse of the hidden layer output matrix. By preserving the global optimality and best generalization performance, our approach implements node incremental ELM (N-IELM) and sample incremental ELM (S-IELM) in a universal form, and overcomes the problem of self-starting and numerical instability in the conventional online sequential ELM. We also propose sample decremental ELM (S-DELM), which is the first decremental version of ELM. The experiments on regression and classification problems with real-world datasets demonstrate the feasibility and effectiveness of the proposed algorithms with encouraging performances.

Embodied multi-agent systems (EMAS) have attracted growing attention for their potential to address complex, real-world challenges in areas such as logistics and robotics. Recent advances in foundation models pave the way for generative agents capable of richer communication and adaptive problem-solving. This survey provides a systematic examination of how EMAS can benefit from these generative capabilities. We propose a taxonomy that categorizes EMAS by system architectures and embodiment modalities, emphasizing how collaboration spans both physical and virtual contexts. Central building blocks, perception, planning, communication, and feedback, are then analyzed to illustrate how generative techniques bolster system robustness and flexibility. Through concrete examples, we demonstrate the transformative effects of integrating foundation models into embodied, multi-agent frameworks. Finally, we discuss challenges and future directions, underlining the significant promise of EMAS to reshape the landscape of AI-driven collaboration.