Zonotopic fault detection observer design for Takagi–Sugeno fuzzy systems

This paper considers zonotopic fault detection observer design in the finite-frequency domain for discrete-time Takagi–Sugeno fuzzy systems with unknown but bounded disturbances and measurement noise. We present a novel fault detection observer structure, which is more general than the commonly used Luenberger form. To make the generated residual sensitive to faults and robust against disturbances, we develop a finite-frequency fault detection observer based on generalised Kalman–Yakubovich–Popov lemma and P-radius criterion. The design conditions are expressed in terms of linear matrix inequalities. The major merit of the proposed method is that residual evaluation can be easily implemented via zonotopic approach. Numerical examples are conducted to demonstrate the proposed methodPeer ReviewedPostprint (author's final draft

Terahertz (THz) Generator and Detection

In the whole research process of electromagnetic wave, the research of terahertz wave belongs to a blank for a long time, which is the least known and least developed by far. But now, people are trying to make up the blank and develop terahertz better and better. The charm of terahertz wave originates from its multiple attributes, including electromagnetic field attribute,photon attribute and thermal attribute, which also attracts the attention of researchers in different fields and different countries, and also terahertz technology have been rated as one of the top ten technologies to change the future world by the United States. The multiple attributes of terahertz make it have broad application prospects in military and civil fields, such as medical imaging,astronomical observation, 6G communication, environmental monitoring and material analysis. It is no exaggeration to say that mastering terahertz technology means mastering the future. However, it is because of the multiple attributes of terahertz that the terahertz wave is difficult to be mastered. Although terahertz has been applied in some fields,controlling terahertz (such as generation and detection) is still an important issue. Nowadays, a variety of terahertz generation and detection technologies have been developed and continuously improved. In this paper, the main terahertz generation and detection technologies (including already practical and developing) are reviewed in terms of scientific and engineering principles,in order to provide a systematic and up-to-date reference for researchers in terahertz field

Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem

The 0-1 multidimensional knapsack problem(MKP) is a classical NP-hard combinatorial optimization problem. In this paper, we propose a novel heuristic algorithm simulating evolutionary computation and large neighbourhood search for the MKP. It maintains a set of solutions and abstracts information from the solution set to generate good partial assignments. To find high-quality solutions, integer programming is employed to explore the promising search space specified by the good partial assignments. Extensive experimentation with commonly used benchmark sets shows that our approach outperforms the state of the art heuristic algorithms, TPTEA and DQPSO, in solution quality. It finds new lower bound for 8 large and hard instance

Rail-induced Traffic in China

The rapid development of China’s railway has exerted an enormous influence on the intercity passenger transport structure in recent years. However, it has not satisfied the passengers’ travel demand due to induced traffic. This paper is committed to solving such issue, with the aim of satisfying the current travel demand, and of anticipating the demand of the predicted traffic growth over the next 20 to 30 years. The paper has considered the increase in rail passenger kilometres caused by the growth of rail kilometres as rail-induced traffic. Based on the concept and former research of induced traffic, the panel data of 26 provinces and 3 municipalities of China between the year 2000 and 2014 were collected, and the elasticity models (including elasticity-based model, distributed lag model, high-speed rail (HSR) elasticity model and rail efficiency model) have been constructed. The results show the importance of model formation incorporation of rail-induced traffic. It is better to get the correct value in divided zones with different train frequencies or incorporation rail efficiency in cities or provinces. The lag time and rail types also need to be considered. In summary, the results analysis not only confirms the existence of rail-induced traffic, but also provides substantial recommendations to train operation planning.</p

Multivariate Dynamic Mediation Analysis under a Reinforcement Learning Framework

Mediation analysis is an important analytic tool commonly used in a broad range of scientific applications. In this article, we study the problem of mediation analysis when there are multivariate and conditionally dependent mediators, and when the variables are observed over multiple time points. The problem is challenging, because the effect of a mediator involves not only the path from the treatment to this mediator itself at the current time point, but also all possible paths pointed to this mediator from its upstream mediators, as well as the carryover effects from all previous time points. We propose a novel multivariate dynamic mediation analysis approach. Drawing inspiration from the Markov decision process model that is frequently employed in reinforcement learning, we introduce a Markov mediation process paired with a system of time-varying linear structural equation models to formulate the problem. We then formally define the individual mediation effect, built upon the idea of simultaneous interventions and intervention calculus. We next derive the closed-form expression and propose an iterative estimation procedure under the Markov mediation process model. We study both the asymptotic property and the empirical performance of the proposed estimator, and further illustrate our method with a mobile health application

Profiles of cyclin B and cdc2 during ovarian and embryonic development in <em>Exopalaemon carinicauda</em>

Mitosis-promoting factor (MPF) is a complex formed by cyclin B (cyclin B) and cyclin-dependent kinase (cdc2). To investigate the role of MPF in the reproduction of Exopalaemon carinicauda, we cloned its full-length cDNA of the Ec-cyclin B and Ec-cdc2 genes. We analyzed their molecular characteristics and expression profiles during ovarian and embryonic development. The results showed that the Ec-cyclin B gene was 1194 bp long and encoded a 397 amino acid (aa) long protein. However, Ec-cdc2 was 900 bp long, which encoded 299 aa with a conserved cyclin binding motif PSTAIRE. The phylogenetic tree analysis showed that Ec-cyclin B had the highest homology with the cyclin B of Macrobrachium rosenbergii (81.06%). In comparison, Ec-cdc2 had the highest homology with the cdc2 of E. modestus (96.80%). Ec-cyclin B showed the highest expression in the ovary, whereas Ec-cdc2 was the highest in the hepatopancreas, followed by the ovary. In the five stages of ovarian development, Ec-cyclin B and Ec-cdc2 expression levels reach the highest at stage Ⅴ(p < 0.05). Overall, the expression of these two genes first increased and then decreased at different embryonic developmental stages. Therefore, these findings suggested that cyclin B and cdc2 played an essential role in the ovarian and embryonic development of E. carinicauda

Lagrange tracking-based long-term drift trajectory prediction method for Autonomous Underwater Vehicle

Autonomous Underwater Vehicle (AUV) works autonomously in complex marine environments. After a severe accident, an AUV will lose its power and rely on its small buoyancy to ascend at a slow speed. If the reserved buoyancy is insufficient, when reaching the thermocline, the buoyancy will rapidly decrease to zero. Consequently, the AUV will experience prolonged lateral drift within the thermocline. This study focuses on developing a prediction method for the drift trajectory of an AUV after a long-term power loss accident. The aim is to forecast the potential resurfacing location, providing technical support for surface search and salvage operations of the disabled AUV. To the best of our knowledge, currently, there is no mature and effective method for predicting long-term AUV underwater drift trajectories. In response to this issue, based on real AUV catastrophes, this paper studies the prediction of long-term AUV underwater drift trajectories in the cases of power loss. We propose a three-dimensional trajectory prediction method based on the Lagrange tracking approach. This method takes the AUV's longitudinal velocity, the time taken to reach different depths, and ocean current data at various depths into account. The reason for the AUV's failure to ascend to sea surface lies that the remaining buoyancy is too small to overcome the thermocline. As a result, AUV drifts long time within the thermocline. To address this issue, a method for estimating thermocline currents is proposed, which can be used to predict the lateral drift trajectory of the AUV within the thermocline. Simulation is conducted to compare the results obtained by the proposed method and that in a real accident. The results demonstrate that the proposed approach exhibits small directional and positional errors. This validates the effectiveness of the proposed method