Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects

One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.

Simulations of Lunar Dust interaction with exploration systems and instruments

<p>Lunar Dust is representing both an engineering challenge for future exploration missions due to systems potential contamination (due to regolith mobilization during e.g. traverse phases, landings, scooping, astronauts EVAs..) and a scientific target for e.g. mineralogical and compositional analysis of the Lunar surface. Therefore predicting not only interactions with systems but also payloads landed at the lunar surface is an important part of future missions design. Strong partnerships and synergies between agencies and space industries are now allowing the preparation of new missions with challenging timescales, for a return to the Moon in the next couple of years. In this context, the analysis of re-analysis of some of the Apollo era data and other landed assets is of high interest to perform the calibration of predictive algorithms and simulations tools of regolith transport and interactions with systems.</p> <p>The present work is organized in two parts: in the first part, we present a modelling study of two experiments included in the Apollo Lunar Surface Experiment Package (ALSEP): the Lunar Ejecta and Meteoroids Experiment (LEAM), which experienced failures linked to thermal control and the Dust Detector Experiment (DDE) which could measure solar cells performance variations due to dust coverage.</p> <p>In the second part, we present simulation results for the contamination of the Imaging System accommodated on the PROSPECT experiment that will be embarked on the Luna 27 lander, due to land on the Moon in the next couple of years.</p> <p>We will discuss the quality of our predictions, the uncertainties inherent to the measurements, and the way forward in terms of better representation of lunar dust transport and interactions processes through models.</p>

Electric Vehicle Charger Placement Optimization in Michigan Considering Monthly Traffic Demand and Battery Performance Variations

Limited charging infrastructure for electric vehicles (EVs) is one of the main barriers to adoption of these vehicles. In conjunction with limited battery range, the lack of charging infrastructure leads to range-anxiety, which may discourage many potential users. This problem is especially important for long-distance or intercity trips. Monthly traffic patterns and battery performance variations are two main contributing factors in defining the infrastructure needs of EV users, particularly in states with adverse weather conditions. Knowing this, the current study focuses on Michigan and its future needs to support the intercity trips of EVs across the state in two target years of 2020 and 2030, considering monthly traffic demand and battery performance variations. This study incorporates a recently developed modeling framework to suggest the optimal locations of fast EV chargers to be implemented in Michigan. Considering demand and battery performance variations is the major contribution of the current study to the proposed modeling framework by the same authors in the literature. Furthermore, many stakeholders in Michigan are engaged to estimate the input parameters. Therefore, the research study can be used by authorities as an applied model for optimal allocation of resources to place EV fast chargers. The results show that for charger placement, the reduced battery performance in cold weather is a more critical factor than the increased demand in warm seasons. To support foreseeable annual EV trips in Michigan in 2030, this study suggests 36 charging stations with 490 chargers and an investment cost of $23 million.

Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

A crucial performance concern in distributed decentralized environments, like clouds, is how to guarantee that jobs complete their execution within the estimated completion times using the available resources’ bandwidth fairly and efficiently while considering the resource performance variations. Formerly, several models including reservation, migration, and replication heuristics have been implemented to solve this concern under a variety of scheduling techniques; however, they have some undetermined obstacles. This paper proposes a dynamic job scheduling model (DTSCA) that uses job characteristics to map them to resources with minimum execution time taking into account utilizing the available resources bandwidth fairly to satisfy the cloud users quality of service (QoS) requirements and utilize the providers’ resources efficiently. The scheduling algorithm makes use of job characteristics (length, expected execution time, expected bandwidth) with regards to available symmetrical and non-symmetrical resources characteristics (CPU, memory, and available bandwidth). This scheduling strategy is based on generating an expectation value for each job that is proportional to how these job’s characteristics are related to all other jobs in total. That should make their virtual machine choice closer to their expectation, thus fairer. It also builds a feedback method which deals with reallocation of failed jobs that do not meet the mapping criteria.

Modeling of Advanced Ultrasonic Testing Methods for Improved Characterization of Pipeline Damage

Characterizing the performance of nondestructive evaluation (NDE) methods for the in-ditch detection and characterization of stress corrosion cracking (SCC) and other damage types has proven challenging for several reasons. Firstly, the availability of pipeline samples with real damage is limited. Compounding this issue, real samples often need to be destructively evaluated to establish the true damage characteristics such that the performance of the NDE methods can be properly understood. Destructive testing is both costly and results in the loss of valuable test samples. Secondly, the actual application of NDE methods to real samples has been shown to have significant human performance variations, which are difficult to separate from technology performance. To address these challenges, this work reviews advanced ultrasonic NDE modeling tools that have been developed to assist in benchmark studies for characterizing crack-like defects, such as SCC, and for establishing best practices. Specifically, explicit/dynamic finite element modeling techniques are utilized to model and compare traditional phasedarray ultrasonic testing (PAUT) and the newer full-matrix capture (FMC) PAUT techniques, such as the total focusing method (TFM). Insights into performance levels of the different modeled approaches are reviewed and guidance on the optimal application of these approaches is discussed.