Introduction: Improving adverse drug event (ADE) detection is important for post-marketing drug safety surveillance. Existing statistical approaches can be further optimized owing to their high efficiency and low cost.Objective: The objective of this study was to evaluate the proposed approach for use in pharmacovigilance, the early detection of potential ADEs, and the improvement of drug safety.Methods: We developed a novel integrated approach, the Bayesian signal detection algorithm, based on the pharmacological network model (IC_PNM) using the FDA Adverse Event Reporting System (FAERS) data published from 2004 to 2009 and from 2014 to 2019Q2, PubChem, and DrugBank database. First, we used a pharmacological network model to generate the probabilities for drug-ADE associations, which comprised the proper prior information component (IC). We then defined the probability of the propensity score adjustment based on a logistic regression model to control for the confounding bias. Finally, we chose the Side Effect Resource (SIDER) and the Observational Medical Outcomes Partnership (OMOP) data to evaluate the detection performance and robustness of the IC_PNM compared with the statistical approaches [disproportionality analysis (DPA)] by using the area under the receiver operator characteristics curve (AUC) and Youden’s index.Results: Of the statistical approaches implemented, the IC_PNM showed the best performance (AUC, 0.8291; Youden’s index, 0.5836). Meanwhile, the AUCs of the IC, EBGM, ROR, and PRR were 0.7343, 0.7231, 0.6828, and 0.6721, respectively.Conclusion: The proposed IC_PNM combined the strengths of the pharmacological network model and the Bayesian signal detection algorithm and performed better in detecting true drug-ADE associations. It also detected newer ADE signals than a DPA and may be complementary to the existing statistical approaches.

High-performance control of inertial stabilization imaging sensors (ISISs) is always challenging because of the complex nonlinearities induced by friction, mass imbalance, and external disturbances. To overcome this problem, a terminal sliding mode controller (TSMC) based on a novel exponential reaching law (NERL) method with a high-order terminal sliding mode observer (HOTSMO) is suggested. First, the TSMC based on NERL is adopted to improve system performance. The NERL incorporates the power term and switching gain term of the system state variables into the conventional exponential reaching law, and the convergent speed of the TSMC is accelerated. Then, an HOTSMO is designed, which considers the speed and lumped disturbances of the system as the observation object. The estimated disturbance is then provided as a compensation for the controller, which enhances the disturbance rejection ability of the system. Comparative simulation and experimental results show that the proposed method achieves the best tracking performance and the strongest robustness than PID and the traditional TSMC methods.

Disturbance compensation is an important criteria to evaluate the performance of a photoelectric tracking servo control system. In order to effectively compensated disturbance in high frequency, a novel sliding mode disturbance observer (NSMDOB) is proposed in this paper. The dynamic performance of the system is improved by the design of the compound control scheme with the NSMDOB and additive decomposition theory. According to the additive decomposition theory, the photoelectric tracking platform servo system was decomposed into the main system and the auxiliary system. The tracking control and disturbance compensation were considered respectively in two systems. A rapid switching technique of the sliding mode was used to estimate the high frequency disturbance in the NSMDOB and the compensator of the auxiliary system. Finite-time control (FTC) was used to design the sliding mode compensator, which could maintain the state of auxiliary system asymptotically stable. Experiment results prove that the proposed method may offer the fast convergence rate, the high tracking accuracy, and the strong robustness of the control system.

A structure mode of virtual compound-axis servo system is proposed to improve the tracking accuracy of the ordinary optoelectric tracking platform. It is based on the structure and principles of compound-axis servo system. A hybrid position control scheme combining the PD controller and feed-forward controller is used in subsystem to track the tracking error of the main system. This paper analyzes the influences of the equivalent disturbance in main system and proposes an adaptive sliding mode robust control method based on the improved disturbance observer. The sliding mode technique helps this disturbance observer to deal with the uncompensated disturbance in high frequency by making use of the rapid switching control value, which is based on the subtle error of disturbance estimation. Besides, the high-frequency chattering is alleviated effectively in this proposal. The effectiveness of the proposal is confirmed by experiments on optoelectric tracking platform.