Clustering adaptive canonical correlations for high-dimensional multi-modal data

Multi-modal canonical correlation analysis (MCCA) is an important joint dimension reduction method and has been widely applied to clustering tasks of multi-modal data. MCCA-based clustering is usually dimension reduction of high-dimensional data followed by clustering of low-dimensional data. However, the two-stage clustering is difficult to ensure the adaptability of dimension reduction and clustering, which will affect the final clustering performance. To solve the issue, we propose a novel clustering adaptive multi-modal canonical correlations (CAMCCs) method, which constructs a unified optimization model of multi-modal correlation learning and clustering. The method not only realizes discriminant learning of correlation projection directions under unsupervised cases, but also is able to directly obtain class labels of multi-modal data. Additionally, the method also realizes out-of-sample extension in class labels. Solutions of CAMCCs are optimized by an iterative way, and we analyze its convergence. Extensive experimental results on various datasets have demonstrated the effectiveness of the method.     

Adaptive retrieval practice with multiple-choice questions in the university classroom

Retrieval practice promotes retention more than restudying (i.e., the testing effect) and is applied to many educational settings. However, little research has investigated means to enhance this effect in educational settings. Theoretical accounts assume retrieval practice to be the most effective whenever retrieval is difficult but successful. Therefore, we developed a novel retrieval practice procedure, which adapts to learners' abilities and can be applied irrespective of learning content. This adaptive procedure aims to make retrieval gradually easier whenever students provide an incorrect answer. In a field experiment, students read book chapters as part of a weekly university course. In three consecutive weeks, they then practiced reading assignments by (a) adaptive testing, (b) non-adaptive testing and (c) restudy. In Week 4, a surprise criterial test took place. Restudy outperformed both testing conditions, whereas adaptive testing performed equally well as non-adaptive testing. However, exploratory analyses revealed that with increasing retention intervals, the superiority of restudy disappeared. Furthermore, whenever participants fully read the assignments and retention intervals increased, adaptive testing outperformed non-adaptive testing. In sum, adaptive retrieval practice did not prove to be generally superior, but retention interval and students' preparation for class might be conditions rendering adaptive retrieval useful in educational settings.     

Adaptive quaternion control of a 3-DOF inertial stabilised platforms

ABSTRACT

Inertial stabilised platforms are increasingly popular with a large range of products available mainstream. Most items are controlled using popular algorithms that sometimes do not offer best achievable performances. Present paper proposes an advanced control which aims at improving these latter. The exposed solution is based on quaternion representation and self-adapts to the characteristics of the payload it tries to stabilise. Proposed control law ensures the stability of the system whatever the required orientation path is. Although only simulation has been performed to check the performances of such control, results look very promising compared to non-adaptive controls and may help to construct more polyvalent and efficient gimbals which would further facilitate their expansion. Proposed control law can also be applied, as is, to every system that shares the same quaternion-based rotational dynamics.     

A stiffness parameter and truncation error criterion for adaptive path following in structural mechanics

We explore a truncation error criterion to steer adaptive step length refinement and coarsening in incremental-iterative path following procedures, applied to problems in large-deformation structural mechanics. Elaborating on ideas proposed by Bergan and collaborators in the 1970s, we first describe an easily computable scalar stiffness parameter whose sign and rate of change provide reliable information on the local behavior and complexity of the equilibrium path. We then derive a simple scaling law that adaptively adjusts the length of the next step based on the rate of change of the stiffness parameter at previous points on the path. We show that this scaling is equivalent to keeping a local truncation error constant in each step. We demonstrate with numerical examples that our adaptive method follows a path with a significantly reduced number of points compared to an analysis with uniform step length of the same fidelity level. A comparison with Abaqus illustrates that the truncation error criterion effectively concentrates points around the smallest-scale features of the path, which is generally not possible with automatic incrementation solely based on local convergence properties.     

宽带数字接收机IQ不平衡估计与自适应补偿算法

针对宽带调制解调技术中广泛存在的IQ不平衡问题,在IQ不平衡模型及补偿原理的分析基础上提出了一种宽带数字接收机IQ不平衡估计与自适应补偿算法。首先利用解调数据对IQ不平衡参数进行实时估计,然后利用估计参数对接收信号进行自适应补偿。实验结果表明,所提算法可以有效解决宽带调制解调系统中普遍存在的IQ不平衡问题,提升系统误码性能。     

Stabilisation of stochastic differential equations driven by G-Brownian motion via aperiodically intermittent control

ABSTRACT

As an emerging field, the G-Itô stochastic calculus plays an important role in describing the model uncertainty. Many interesting works have been done on stochastic differential equations driven by G-Brownian motion (G-SDEs). Among the theories and applications of G-SDEs, the stability is the vital important one. In this paper, we investigate the stabilisation for G-SDEs based on G-Lyapunov function and aperiodically adaptive intermittent controller. As an application, the sufficient conditions are established for the stabilisation of stochastic Cohen–Grossberg neural networks driven by G-Brownian motion (G-SCGNNs). Finally, an example is provided to illustrate the obtained results.     

The effectiveness of adaptive versus non-adaptive learning with digital educational games

For the training of academic skills, digital educational games with integrated adaptivity are promising. Adaptive games are considered superior to non-adaptive games, because they constantly assess children's performance, and accordingly adapt the difficulty of the tasks corresponding to the children's individual level. However, empirical evidence with regard to the effectivity of adaptive compared to non-adaptive games is limited. A study was conducted with 191 children from the third year of Kinder garten who were enrolled in one of three conditions, that is, playing an adaptive version of the reading game (RG), a non-adaptive version of the RG or training with pen-and-paper exercises. In all three conditions, children trained emergent reading (phonological awareness and letter knowledge) once a week for 30 min over a period of 5 weeks. Children's performance on cognitive (phonological awareness, letter knowledge, reading fluency) and non-cognitive (motivation, self-concept) factors was assessed. Results revealed a significant improvement in phonological awareness and letter knowledge in all conditions. However, no differences between the conditions were observed with respect to children's improvement on phonological awareness and letter knowledge or on their post-test scores for reading fluency. With regard to motivation and self-concept, again, no differences in these non-cognitive factors were observed across conditions.     

Leader-following consensus of fractional-order multi-agent systems via adaptive control

This article focuses on the consensus problem of leader-following fractional-order multi-agent systems (MASs) with general linear and Lipschitz nonlinear dynamics. First, the distributed adaptive protocols for linear and nonlinear fractional-order MASs are constructed, respectively. We allow the control coupling gains to be time varying for each agent. Moreover, the adaptive modification schemes for the control gain are designed, which renders smaller control gains and thus requires smaller amplitude on the control input without sacrificing consensus convergence. Second, based on fractional-order Lyapunov stability theorem and Barbalat's lemma, two novel sufficient conditions in terms of linear matrix inequalities are provided to ensure that the leader-following consensus can be obtained in the case for any undirected connected communication graph. Furthermore, we show that the proposed algorithm also works for consensus of agents with intrinsic Lipschitz nonlinear dynamics. As a result, the proposed framework requires no global information and thus can be implemented in a fully distributed manner. Finally, the numerical simulations are given to demonstrate the effectiveness of obtained the theoretical results.     

Carbon,nitrogen and phosphorus recovery from liquid swine wastes: a review

Due to the pollution characteristics of the wastewater generated in piggeries, these waste streams have to be treated before discharging into the environment. These wastes have a high content of organic matter, nitrogen and phosphorus that can be converted or recovered into valuable products. The valuation of swine waste by anaerobic digestion has already been assessed, but no systematic review on the technologies used for recovering nutrients from this waste has been reported to date. Therefore, the present work has the goal of presenting the most studied technologies to different scales of recovery of carbon (i.e. organic matter), nitrogen and phosphorus from swine waste. The main characteristics and parameters of the processes involved in these technologies (mainly air stripping, membrane, enhance biological phosphorus removal and struvite formation) are also highlighted, pointing out its advantages and disadvantages and posing some final feasibility considerations on the subject addressed. Experiments reported in the literature proved that technologies used for nutrient recovery from swine waste are mostly applied to the effluents previously treated by anaerobic digestion as a primary treatment, and greater effort is required for the future implementation of these technologies in large scale. © 2020 Society of Chemical Industry