An Intelligent Auxiliary Vacuum Brake System

The purpose of this paper focuses on designing an intelligent, compact, reliable, and robust auxiliary vacuum brake system (VBS) with Kalman filter and self-diagnosis scheme. All of the circuit elements in the designed system are integrated into one programmable system-on-chip (PSoC) with entire computational algorithms implemented by software. In this system, three main goals are achieved: (a) Kalman filter and hysteresis controller algorithms are employed within PSoC chip by software to surpass the noises and disturbances from hostile surrounding in a vehicle. (b) Self-diagnosis scheme is employed to identify any breakdown element of the auxiliary vacuum brake system. (c) Power MOSFET is utilized to implement PWM pump control and compared with relay control. More accurate vacuum pressure control has been accomplished as well as power energy saving. In the end, a prototype has been built and tested to confirm all of the performances claimed above

Class numbers of multinorm-one tori

We present a formula for the class number of a multinorm one torus $T_{L/k}$ associated to any \'etale algebra $L$ over a global field $k$. This is deduced from a formula for analogues of invariants introduced by T.~Ono, which are interpreted as a generalization of Gauss genus theory. This paper includes the variants of Ono's invariant for arbitrary $S$-ideal class numbers and the narrow version, generalizing results of Katayama, Morishita, Sasaki and Ono.Comment: 21 pages; comments welcom

Investigation of Parameters That Affect the Acquired Near Infrared Diffuse Reflected Signals in Non-Destructive Soluble Solids Content Prediction

Near infrared spectroscopy is a susceptible technique which can be affected by various factors including the surface of samples. According to the Lambertian reflection, the uneven and matte surface of fruits will provide Lambertian light or diffuse reflectance where the light enters the sample tissues and that uniformly reflects out in all orientations. Bunch of researches were carried out using near infrared diffuse reflection mode in non-destructive soluble solids content (SSC) prediction whereas fewer of them studying about the geometrical effects of uneven surface of samples. Thus, this study aims to investigate the parameters that affect the near infrared diffuse reflection signals in non-destructive SSC prediction using intact pineapples. The relationship among the reflectance intensity, measurement positions, and the SSC value was studied. Next, three independent artificial neural networks were separately trained to investigate the geometrical effects on three different measurement positions. Results show that the concave surface of top and bottom parts of pineapples would affect the reflectance of light and consequently deteriorate the predictive model performance. The predictive model of middle part of pineapples achieved the best performance, i.e. root mean square error of prediction (RMSEP) and correlation coefficient of prediction (Rp) of 1.2104 °Brix and 0.7301 respectively

The Method of Approximate Particular Solutions for Solving Elliptic Problems with Variable Coefficients

A new version of the method of approximate particular solutions (MAPSs) using radial basis functions (RBFs) has been proposed for solving a general class of elliptic partial differential equations. In the solution process, the Laplacian is kept on the left-hand side as a main differential operator. The other terms are moved to the right-hand side and treated as part of the forcing term. In this way, the close-form particular solution is easy to obtain using various RBFs. The numerical scheme of the new MAPSs is simple to implement and yet very accurate. Three numerical examples are given and the results are compared to Kansa\u27s method and the method of fundamental solutions

Intraprofessional Collaboration in Learning Evidence-Based Practice

Little is known about how collaborative curricular activities can help students learn about scientific evidence in occupational therapy (OT) and occupational therapy assistant (OTA) programs. We created and measured collaborative learning in evidence-based practice activities to enhance partnership building and intraprofessional collaboration among students in OT and OTA programs. Thirty-three OT students and 26 OTA students enrolled in a didactic course (Phase I) and an intraprofessional collaborative learning activity (Phase II) participated in this quasi-experimental repeated measure study. The students’ ability of how to read scholarly articles and their perceived importance and perceived ability to engage in intraprofessional collaboration were examined at three time points. Improvements were found in the OT students’ perceived importance of intraprofessional roles/responsibilities and the OTA students’ perceived ability to engage in intraprofessional communication after completing the Phase I didactic course. Both the OT and OTA students’ perceived ability to work as a team, identify their roles/responsibilities, communicate with peers, and their ability to read scholarly articles improved after the Phase II intraprofessional collaboration. Students valued the opportunity to gain collaboration experience and share different perspectives. They indicated they would have preferred to spend more time on the activities. The findings suggest that targeted learning activities can improve OT and OTA students’ ability to engage in evidence-based practice and their perceived importance and ability to engage in intraprofessional collaboration. Effective partnerships and intraprofessional collaboration are best introduced within academic programs

Reward prediction errors arising from switches between major and minor modes in music: An fMRI study

Evidence has accumulated that prediction error processing plays a role in the enjoyment of music listening. The present study examined listeners' neural responses to the signed reward prediction errors (RPEs) arising from switches between major and minor modes in music. We manipulated the final chord of J. S. Bach's keyboard pieces so that each major-mode passage ended with either the major (Major-Major) or minor (Major-Minor) tonic chord, and each minor-mode passage ended with either the minor (Minor-Minor) or major (Minor-Major) tonic chord. In Western music, the major and minor modes have positive and negative connotations, respectively. Therefore, the outcome of the final chord in Major-Minor stimuli was associated with negative RPE, whereas that in Minor-Major was associated with positive RPE. Twenty-three musically experienced adults underwent functional magnetic resonance imaging while listening to Major-Major, Major-Minor, Minor-Minor, and Minor-Major stimuli. We found that activity in the subgenual anterior cingulate cortex (extending into the ventromedial prefrontal cortex) during the final chord for Major-Major was significantly higher than that for Major-Minor. Conversely, a frontoparietal network for Major-Minor exhibited significantly increased activity compared to Major-Major. The contrasts between Minor-Minor and Minor-Major yielded regions implicated in interoception. We discuss our results in relation to executive functions and the emotional connotations of major versus minor mode.Comment: submitted to Psychophysiolog

A Comparison between the Post- and Pre-dispersive Near Infrared Spectroscopy in Non-Destructive Brix Prediction Using Artificial Neural Network

Even though near infrared (NIR) spectroscopy have been implemented in determining the Brix of pineapples, no traceable study compares the effects of different acquisition designs. Thus, this study aims to evaluate the prediction performance of both pre- and post-dispersive NIR sensing devices in non-destructive Brix prediction using artificial neural network (ANN). The pre-dispersive device has five narrowband light emitting diodes (LEDs) with different wavelengths and a photodiode detector, whereas the post-dispersive device has a bifurcated fiber optic, a broadband LED, and a spectral sensor. First, the NIR diffuse reflectance was non-destructively collected using both NIR devices. Then, the collected diffuse reflectance was calibrated with the white and dark references, and then pre-processed using normalization and standard normal variate methods. After that, ANNs were built for both devices using the pre-processed data. Results show both devices are suitable for sample screening application with range error ratio (RER) of more than seven. Nevertheless, the ANN that trained using the post-dispersive device outperformed that trained using the pre-dispersive device with an 8.1% improvement of correlation coefficient of prediction (i.e. from 0.6853 to 0.7408), and a 5.7% improvement of root mean square error of prediction (i.e. from 1.3918 to 1.313°Brix)

Low-rank matrix recovery with structural incoherence for robust face recognition

We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recog-nition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix ap-proximation algorithm with structural incoherence for ro-bust face recognition. Our method not only decomposes raw training data into a set of representative basis with corre-sponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are en-couraged to be as independent as possible due to the regu-larization on structural incoherence. We show that this pro-vides additional discriminating ability to the original low-rank models for improved performance. Experimental re-sults on public face databases verify the effectiveness and robustness of our method, which is also shown to outper-form state-of-the-art SRC based approaches. 1