Non-iterative Conditional Pairwise Estimation for the Rating Scale Model

We investigate two non-iterative estimation procedures for Rasch models, the pair-wise estimation procedure (PAIR) and the Eigenvector method (EVM), and identify theoretical issues with EVM for rating scale model (RSM) threshold estimation. We develop a new procedure to resolve these issues—the conditional pairwise adjacent thresholds procedure (CPAT)—and test the methods using a large number of simulated datasets to compare the estimates against known generating parameters. We find support for our hypotheses, in particular that EVM threshold estimates suffer from theoretical issues which lead to biased estimates and that CPAT represents a means of resolving these issues. These findings are both statistically significant ( p < .001) and of a large effect size. We conclude that CPAT deserves serious consideration as a conditional, computationally efficient approach to Rasch parameter estimation for the RSM. CPAT has particular potential for use in contexts where computational load may be an issue, such as systems with multiple online algorithms and large test banks with sparse data designs.

Efficient Iterative Algorithms for a Class of Nonlinear Systems using the Block Matrix Inversion and Hierarchical Principle

This paper is concerned with the identification of the bilinear systems in the state-space form. The parameters to be identified of the considered system are coupled with the unknown states, which makes the identification problem difficult. To deal with the trouble, the iterative estimation theory is considered to derive the joint parameter and state estimation algorithm. Specifically, a moving data window least squares-based iterative (MDW-LSI) algorithm is derived to estimate the parameters by using the window data. Then, the unknown states are estimated by a bilinear state estimator. Moreover, for the purpose of improving the computational efficiency, a matrix decomposition-based MDW-LSI algorithm and a hierarchical MDW-LSI algorithm are developed according to the block matrix and the hierarchical identification principle. Finally, the computational efficiency is discussed and the numerical simulation is employed to test the proposed approaches.

Hasty Fault Diagnosis of a Rotating Machinery Hinge on Stalwart Trippy Classifier with Robust Harmonized Swan Machine

Monitoring with fault diagnosis of machineries are critically important for production efficiency and plant safety in modern enterprises. Along the process of fault diagnosis due to the addition of faulty signals, it is not an easy task to extract the exact representative features from the original signal. Accordingly, for making the vibration signal analysis more effective, there is a need to have the proper faulty feature extraction and moreover to have the proper estimation of spectral density for eminently producing stable decomposition results even if the signal contains missing values. Moreover, there is a difficulty to measure the correlation between the features with the existing fault diagnosis researches and also it considers more learning time as well as memory constraints which makes the learned concept difficult to understand for classifying the faulty features prominently. Thus to commensurate a perfect diagnosis, in this research a “Robust Harmonised Swan Machine (RHSM) with Stalwart Trippy classifier” is formulated in which the iterative estimation of each mode satisfying a self-consistency nature in decomposition method of RHSM which in turn resolves the missing sample problem eminently and aids reinforcement learning precisely which measures the correlation between the features to classify the faulty features extremely thereby it takes only less memory constraint with less learning time.

MAV quaternion attitude determination for accelerometer-magnetometer combination: Internal analysis

This paper proposes a novel fast deterministic quaternion attitude determination method for accelerometer and magnetometer combination (AMC). After taking insight to the attitude determination theory, an important relationship between the sensor outputs and the magnetometer’s reference vector is successfully derived. Based on the relationship, the optimal quaternion associated with the attitude of a certain object is easily calculated. The main breakthrough of this paper is that it significantly simplifies the determination of the magnetometer’s reference vector which always needs systematic calibration or iterative estimation in existing methods. We name the proposed method the Fast Accelerometer-Magnetometer Fusion (FAMF). Our proposed method has the advantages of better computation accuracy and less time consumption. Several experiments are carried out to illustrate the attitude determination results. Besides, comparisons with existing representative methods are also presented in the experimental section of this paper, which verify the effectiveness of the proposed FAMF. Finally, we experimentally show that the FAMF’s roll and pitch angles are immune to magnetic distortion, which ensures the robustness under complex environments for micro air vehicles (MAV).

An Improved Triangle Centroid Localization Algorithm Based on PIT Criterion

Considering that the traditional triangle centroid localization algorithm based on RSSI is susceptible to surrounding environment, this paper improves the algorithm from two aspects of positioning accuracy and response speed also proposes an improved triangle centroid localization algorithm based on PIT criterion. Combined with actual positioning situation, the algorithm treats the calculated coordinates of the intersection points as the new beacon nodes. Thus, the area of triangle in the intersection region is reduced. Repeat positioning process until the predicted position of node is outside the triangle according to the PIT criterion. Compared with traditional triangle centroid localization algorithm, it showed from the simulation results that the improved triangle centroid localization algorithm can increase the localization accuracy up to 5 times based on the guaranteed response time when communication distance is 15 ~ 30 m, and this algorithm has higher localization accuracy and faster response speed than centroid iterative estimation algorithm in larger communication range. In additions, the experimental platform is built to verify that the proposed algorithm can effectively reduce the positioning error.