Test–Retest Reliability of Social Cognition Screening Questionnaire–Taiwan Version in People With Schizophrenia

Date Presented 04/19/21 The Social Cognition Screening Questionnaire–Taiwan version (SCSQT) was designed to assess multiple domains of social cognition in people with schizophrenia in Taiwan. The SCSQT contains five subscales and provides estimates of the core domains of mentalizing and social perception and an overall social cognition score. Our validation of SCSQT indicated that the SCSQT had good test–retest reliability, acceptable random measurement error, and negligible practice effects. Primary Author and Speaker: Shu-Chun Lee Additional Authors and Speakers: Trudy Mallinson Contributing Authors: Alison M. Cogan, Ann Guernon, Katherine O'Brien, and Piper Hansen

Universal Measurement Matrix Design for Sparse and Co-Sparse Signal Recovery

Compressed Sensing (CS) avails mutual coherence metric to choose the measurement matrix that is incoherent with dictionary matrix. Random measurement matrices are incoherent with any dictionary, but their highly uncertain elements necessitate large storage and make hardware realization difficult. In this paper deterministic matrices are employed which greatly reduce memory space and computational complexity. To avoid the randomness completely, deterministic sub-sampling is done by choosing rows deterministically rather than randomly, so that matrix can be regenerated during reconstruction without storing it. Also matrices are generated by orthonormalization, which makes them highly incoherent with any dictionary basis. Random matrices like Gaussian, Bernoulli, semi-deterministic matrices like Toeplitz, Circulant and full-deterministic matrices like DFT, DCT, FZC-Circulant are compared. DFT matrix is found to be effective in terms of recovery error and recovery time for all the cases of signal sparsity and is applicable for signals that are sparse in any basis, hence universal.

PD-Type Iterative Learning Control With Adaptive Learning Gains for High-Performance Load Torque Tracking of Electric Dynamic Load Simulator

To realize the high-performance load torque tracking of an electric dynamic load simulator system with random measurement noises and strong position disturbances, a PD-type iterative learning control (ILC) algorithm with adaptive learning gains is proposed in this paper. With the principle of system analyzing, a nonlinear discrete state-space model is established. The adaptive learning gains is used to suppress the effects of periodic disturbances and random measurement noises on the load torque tracking performance. A traditional PD feedback controller in parallel with the proposed ILC is designed to stabilize the system and render the ILC converge quickly. The convergence analysis of the proposed control method ensures the stability of the system. Compared with the fixed learning gains, the experiment results show that the proposed control method has better load torque tracking performance and can effectively suppress the adverse effects of periodic and aperiodic disturbances on tracking accuracy.

Analysis of the random measurement error of areal 3D coordinate measurements exclusively based on measurement repetitions

The measurement uncertainty characteristics of a measurement system are an important parameter when evaluating the suitability of a certain measurement system for a specific measurement task. The measurement uncertainty can be calculated from observed measurement errors, which consist of both systematic and random components. While the unfavourable influence of systematic components can be compensated by calibration, random components are inherently not correctable. There are various measurement principles which are affected by different measurement error characteristics depending on specific properties of the measurement task, e. g. the optical surface properties of the measurement object when using fringe projection or the material properties when using industrial X-ray computed tomography. Thus, it can be helpful in certain scenarios if the spatial distribution of the acquisition quality as well as uncertainty characteristics on the captured surface of a certain measurement task can be found out. This article demonstrates a methodology to determine the random measurement error solely from a series of measurement repetitions without the need of additional information, e. g. a reference measurement or the nominal geometry of the examined part.