Artificial Intelligence (AI) appears to be making important advances in the prediction and diagnosis of mental disorders. Researchers have used visual, acoustic, verbal, and physiological features to train models to predict or aid in the diagnosis, with some success. However, such systems are rarely applied in clinical practice, mainly because of the many challenges that currently exist. First, mental disorders such as depression are highly subjective, with complex symptoms, individual differences, and strong socio-cultural ties, meaning that their diagnosis requires comprehensive consideration. Second, there are many problems with the current samples, such as artificiality, poor ecological validity, small sample size, and mandatory category simplification. In addition, annotations may be too subjective to meet the requirements of professional clinicians. Moreover, multimodal information does not solve the current challenges, and within-group variations are greater than between-group characteristics, also posing significant challenges for recognition. In conclusion, current AI is still far from effectively recognizing mental disorders and cannot replace clinicians’ diagnoses in the near future. The real challenge for AI-based mental disorder diagnosis is not a technical one, nor is it wholly about data, but rather our overall understanding of mental disorders in general.

Accurate monitoring of abnormalities is of great significance to the stable operation of the blast furnace ironmaking process. This article proposes a data-driven model to accurately monitor the abnormal conditions of blast furnaces. Generally, data-driven models primarily rely on feature extraction from high-dimensional raw data. Recently, deep learning networks have been developed and considered a promising technology in extracting high-level abstract features. However, most of these networks cannot capture deep target-related features for abnormality monitoring. Thus, this article proposes a novel stacked dynamic target-driven denoising autoencoder for layer-by-layer hierarchical feature representation, and the dynamic relationship between samples and targets is described by dynamic factors. Then, we design a corresponding target-driven reconstruction loss function to pretrain the deep network successively. Experimental results in an ironmaking plant demonstrate the effectiveness and feasibility of the proposed method.

Molten iron quality (MIQ) indices prediction based on data-driven models is an important way to monitor product quality and smelting status in the blast furnace ironmaking process. However, some challenges still place in the MIQ prediction: 1) limited nonlinear and dynamic description capabilities and interpretability of data-driven models; 2) high demand on the number of the labeled samples; and 3) insufficient exploration of the underlying relationship between MIQ indices. In this case, we propose a novel data-driven deep model for the online prediction of MIQ indices. First, we design an attention-wise module to self-learn the nonlinear and dynamic relationship between process variables and prediction targets and enhance interpretability. Then, the minute-level molten iron temperature (MIT) data detected by our previously developed equipment is used to pretrain the attention-wise deep network to obtain the improved weights and reduce dependence on labeled samples. Finally, the pretrained model is extended to a structure with a weight-shared attention-wise module and task-separated prediction networks to explore the relationship between multiple prediction tasks. The effectiveness of the proposed attention-wise deep network is verified in an industrial ironmaking plant, which shows a significant improvement in performance, i.e., high accuracy and interpretability.

Online assessment of the molten iron quality of a blast furnace ironmaking process strongly depends on reliable measurement of silicon (Si) content. In this article, we propose a novel cooperative strategy to train the data-driven prediction model and estimate variable time delay (VTD) values. First, a dynamic deep network based on stacked denoising autoencoders (D-SDAE) with the ability to describe process nonlinearity and time-varying is designed for Si estimation. Then, time delay between the process variables and Si, which may disrupt the data distribution pattern (i.e., the real input–output relationship), is recovered in the modeling process. It is an overlooked data feature that occurs due to material transportation times, installation location, and the analysis period of sampling equipment. The VTD values are considered to be the parameters of the D-SDAE model and obtained in the training process by the proposed double-scale collaborative search particle swarm optimization algorithm. The effectiveness of the proposed VTD-based D-SDAE model is validated in a numerical simulation and an industrial ironmaking plant, and a marked improvement in the prediction performance is achieved when VTD information is considered.

Accurate temperature measurement of blast furnace molten iron is essential for regulating the furnace temperature. However, the dust interference at blast furnace cast field makes it difficult to measure molten iron temperature accurately by infrared temperature measurement method. To reduce the influence of dust on the results of infrared measurement method, a compensation method is proposed using the heterogeneous features of infrared images. First, the infrared image of molten iron flow is divided into subregions, from which the subregions containing only molten iron are selected. Then, the statistic features and texture features influenced by dust are extracted from the selected subregions. Finally, the heterogeneous features are used as the compensation model input to estimate the measurement error of each subregion and compensate for the molten iron temperature. Experimental results demonstrate that the compensation method can significantly reduce the infrared measurement errors caused by dust and obtain accurate molten iron temperature.