Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems

This paper is dedicated to control theoretically explainable application of autoencoders to optimal fault detection in nonlinear dynamic systems. Autoencoder-based learning is a standard method of machine learning technique and widely applied for fault (anomaly) detection and classification. In the context of representation learning, the so-called latent (hidden) variable plays an important role towards an optimal fault detection. In ideal case, the latent variable should be a minimal sufficient statistic. The existing autoencoder-based fault detection schemes are mainly application-oriented, and few efforts have been devoted to optimal autoencoder-based fault detection and explainable applications. The main objective of our work is to establish a framework for learning autoencoder-based optimal fault detection in nonlinear dynamic systems. To this aim, a process model form for dynamic systems is firstly introduced with the aid of control and system theory, which also leads to a clear system interpretation of the latent variable. The major efforts are devoted to the development of a control theoretical solution to the optimal fault detection problem, in which an analog concept to minimal sufficient statistic, the so-called lossless information compression, is introduced for dynamic systems and fault detection specifications. In particular, the existence conditions for such a latent variable are derived, based on which a loss function and further a learning algorithm are developed. This learning algorithm enables optimally training of autoencoders to achieve an optimal fault detection in nonlinear dynamic systems. A case study on three-tank system is given at the end of this paper to illustrate the capability of the proposed autoencoder-based fault detection and to explain the essential role of the latent variable in the proposed fault detection system

Effects of a dietary supplement on inflammatory marker expression in middle-aged and elderly hypertensive patients

OBJECTIVES: We aimed to explore the effects of diet on the inflammatory response in middle-aged and elderly people with hypertension. METHODS: Thirty overweight or obese patients with stage one hypertension (age range, 45-75 years) were allocated to either the intervention or control group (n=15 per group; age- and sex-matched). Patients in the intervention group consumed a food powder supplement (100 g) instead of a regular meal. The control group maintained their normal dietary habits. This study lasted for six weeks. Blood pressure, inflammatory marker levels, and energy intake were measured before and after the study. RESULTS: After 6 weeks, the diet composition of the intervention group changed significantly (po0.05). The intake of proteins, dietary fibre, monounsaturated fat, and polyunsaturated fat increased significantly (po0.05), while the total energy intake trended towards an increase (p40.05). In the control group, the total energy intake decreased significantly (po0.05). The levels of nuclear factor-kB (NF-kB), soluble intercellular adhesion molecule-1 (sICAM-1) and high sensitivity C-reactive protein (hs-CRP) decreased, and adiponectin increased significantly in the intervention group (po0.05); however, no significant changes were observed in the inflammatory marker levels of the control group. In the intervention group, systolic blood pressure decreased significantly (po0.05), and diastolic blood pressure also exhibited a decreasing trend. No significant change in blood pressure was observed in the control group. CONCLUSION: The consumption of a food powder supplement can improve diet composition, decrease blood pressure and reduce inflammation in middle-aged and elderly overweight or obese hypertensive patients. The food powder supplement may also have an anti-atherosclerotic effect in hypertensive patients