Miracle-dz/UniFAR-FTUs huggingface dataset:
Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results

Recent technology reviews have identified the need for objective assessments of aircraft engine health management (EHM) technologies. To help address this issue, a gas path diagnostic benchmark problem has been created and made publicly available. This software tool, referred to as the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES), has been constructed based on feedback provided by the aircraft EHM community. It provides a standard benchmark problem enabling users to develop, evaluate and compare diagnostic methods. This paper will present an overview of ProDiMES along with a description of four gas path diagnostic methods developed and applied to the problem. These methods, which include analytical and empirical diagnostic techniques, will be described and associated blind-test-case metric results will be presented and compared. Lessons learned along with recommendations for improving the public benchmarking processes will also be presented and discussed.Copyright © 2013 by ASME
RPL Enhancement for a Parent Selection Mechanism and an Efficient Objective Function

In routing protocol (RPL), a child node exclusively makes the decision in selecting a parent based on the Rank value. In this paper, we make the parent selection mechanism, a mutual decision between the potential parent node and the child node, which significantly improves the network throughput under a heavy and highly dynamic load. To demonstrate the effectiveness of our parent selection mechanism, we used it to balance the load to avoid congestion by preventing the child node from using the congested parent node. The proposed parent selection mechanism is implemented as an add-on feature in RPL and hence is called enhanced RPL (EN-RPL). Furthermore, we introduce a new objective function for efficient routing (OF-ER) that is based on the newly defined composite efficient routing (CER) metric. CER metric takes important metrics, namely, the link quality, queue utilization, lifetime of node, delay, and number of bottleneck nodes, into account for optimal parent selection. The simulation results demonstrate that EN-RPL lowers the packet loss by up to 37% and reduces energy consumption by up to 46.5% compared with the standard RPL. The evaluation results of OF-ER outperform the state-of-the-art load balancing-based OF with respect to the network stability, reliability, and energy efficiency.
Condition Monitoring of Machines Using Fused Features From EMD-Based Local Energy With DNN

Several data-driven methods such as signal processing and machine learning exist separately to analyze non-linear and non-stationary data but their performance degrades due to insufficient information in the real-time application. In order to improve the performance, this paper proposes a novel feature extraction method using fusion of hand-crafted (low-level) features and high-level features, followed by feature extraction/selection on fused features. Local energy-based hand-crafted features have been derived from empirical mode decomposition, and high-level features have been extracted from the deep neural network. A method is also proposed for reduction of massive data points in the samples. The proposed scheme has studied the effect of variation in the number of extracted/selected features. The effectiveness of the proposed scheme is validated through three case studies: a) on acoustic dataset collected from the reciprocating type air compressor, b) on vibration dataset collected from deep groove ball bearing, and c) on steel plate faults dataset. The classification accuracy on acoustic dataset are obtained as high as 100.0%, 99.78%, and 99.78% using the random forest, linear support vector machine, and radial basis function support vector machine, respectively, with 5-fold cross-validation. Similarly, on vibration dataset obtained accuracies are 100.0%. The proposed scheme has been compared with ten conventional methods on five-fold cross-validation. These experimental results show considerable improvement in the prediction performance of machine conditions using the proposed scheme.
PMU Assisted Integrated Impedance Angle-Based Microgrid Protection Scheme

Development of protection scheme for microgrid is a challenging issue due to the inclusion of distributed generations (DGs). The research work for developing the primary protection scheme for microgrid is progressing continuously. This paper proposes an integrated impedance angle (IIA) based protection scheme using wide-area positive sequence components of voltages and currents. The proposed scheme uses data retrieved from the phasor measurement units (PMUs) of IEEE C37.118. 1 complied standards. Furthermore, the IIA for the line is computed considering both ends PMU information, which is the key indicator for identifying the faults in the microgrid. The proposed protection scheme is extensively tested considering the different operating conditions of microgrid, variation in DG penetration and variation in fault parameters such as fault resistance, fault types, and fault location. Furthermore, cross validation on no-fault situations including section cutoff, disconnection of high penetration DG, grid-islanding resynchronization, motor starting, capacitor switching, and load encroachment are carried out. The proposed scheme is also tested for the insensitivity towards the external faults. The test results on both 15-bus and 34-bus systems clearly indicate that the proposed IIA-based wide-area scheme can be a potential protection measure for microgrid under varied operating circumstances.
Long-term scale adaptive tracking with kernel correlation filters

Object tracking in video sequences has broad applications in both military and civilian domains. However, as the length of input video sequence increases, a number of problems arise, such as severe object occlusion, object appearance variation, and object out-of-view (some portion or the entire object leaves the image space). To deal with these problems and identify the object being tracked from cluttered background, we present a robust appearance model using Speeded Up Robust Features (SURF) and advanced integrated features consisting of the Felzenszwalb's Histogram of Oriented Gradients (FHOG) and color attributes. Since re-detection is essential in long-term tracking, we develop an effective object re-detection strategy based on moving area detection. We employ the popular kernel correlation filters in our algorithm design, which facilitates high-speed object tracking. Our evaluation using the CVPR2013 Object Tracking Benchmark (OTB2013) dataset illustrates that the proposed algorithm outperforms reference state-of-the-art trackers in various challenging scenarios.
Towards Automated Aerial Refueling: Real Time Position Estimation with Stereo Vision

Aerial refueling is essential to the United States Air Force (USAF) core mission of rapid global mobility. However, in-flight refueling is not available to remotely piloted aircraft (RPA) or unmanned aerial systems (UAS). As reliance on drones for intelligence, surveillance, and reconnaissance (ISR) and other USAF core missions grows, the ability to automate aerial refueling for such systems becomes increasingly critical. New refueling platforms include sensors that could be used to estimate the relative position of an approaching aircraft. Relative position estimation is a key component to solving the automated aerial refueling (AAR) problem. Analysis of data from a one-seventh scale, real world refueling scenario demonstrates that the relative position of an approaching aircraft can be estimated at rates between 10 Hz and 30 Hz using stereo vision. Linear regression models on position estimate accuracies predict results reported by other research in the simulation domain, suggesting that real world accuracies are comparable to simulation domain accuracies reported by others. Further, by seeding the position estimation algorithm with previous position estimates, subsequent errors in position estimation are reduced.
Benchmarking Teaming Instruction Across a Curriculum

Cornerstone and capstone design courses rely heavily on effective student team functioning. Student teaming experiences in these courses is influenced by students’ prior experiences on teams in other courses across the curriculum. In the various team experiences students have in a curriculum, they may or may not develop effective team behaviors. As an initial step towards designing scaffolds for student teaming experiences, this paper seeks to document instructional characteristics of student teaming experiences in required courses throughout the current Mechanical Engineering curriculum at Rose-Hulman Institute of Technology via faculty surveys. In particular, the survey asked about size of the team, duration of the project, instruction on teaming, feedback on teaming, methods of dealing with team dysfunction, and impact on individual grades. In the data it is apparent that there is a large lapse of time between consecutive experiences, gaps in teamwork instruction, and lack of scaffolding of teaming. The results of this benchmarking process will be used to focus departmental deliberations and cast a shared vision of how to effectively scaffold instruction and development of each student’s teamwork skills. As we focus on the intentional design of a coordinated plan for teaming across our curriculum, we also share our process for this curriculum revision through building shared vision so that others could leverage beneficial elements for their contexts. Our work on teaming is part of a larger curriculum vision effort in our department.
RF-Based Noncontact Respiratory Rate Monitoring With Parametric Spectral Estimation

Respiratory rate (RR) monitoring of an adult or an infant during sleep or in steady position can be lifesaving, especially in home care systems. In this paper, we examine the potentials of wireless radio frequency (RF) signals to monitor the RR. A new noncontact RR monitoring system is proposed. The system includes a simple motion detection algorithm based on adaptive thresholding to eliminate the effects of the large-scale body movements on the RR estimation. In the proposed system, the high resolution subspace-based parametric spectral estimation approaches, estimation of signal parameters by rotational invariance technique (ESPRIT) and multiple signal classification (MUSIC), are presented as the RR estimation algorithms. According to our knowledge, the ESPRIT algorithm, which estimates the spectrum without searching, is used for the first time in this paper for RR estimation. It is shown that ESPRIT is computationally efficient and works approximately 49 times faster than the MUSIC algorithm. It is also shown with various experiments conducted with ten volunteers that the proposed noncontact RR monitoring system attains 0.13 breath per minute (bpm) error rate with the limited number of data and outperforms the periodogram method commonly used as the benchmark.
Evaluation of sound quality features on environmental noise effects – a case study applied to road traffic noise

The paper shows a study on the relationship between noise measures and sound quality (SQ) features that are related to annoyance caused by the traffic noise. First, a methodology to perform analyses related to the traffic noise annoyance is described including references to parameters of the assessment of road noise sources. Next, the measurement setup, location and results are presented along with the derived sound quality features. Then, statistical analyses are performed to compare the measurement results and sound quality features. The included conclusions are focused on showing that the obtained loudness values, regardless of the used system, are similar in a statistical sense. Contrarily, sharpness, roughness and fluctuation strength values differ for the tools employed.
A Real-Time Real-Power Emulator of a Medium-Voltage High-Speed Induction Motor Loaded With a Centrifugal Compressor

This paper provides an experimental discussion on a real-time real-power emulator that plays an important role in achieving a total test of medium-voltage, high-power, high-speed induction motor drives. The use of the emulator brings cost and time savings to the test. The test bench including the emulator is characterized by connecting two modular multilevel double-star chopper-cell (DSCC) converters connected in a front-to-front way. This paper designs, builds, and tests a 400-${\rm V}_{\rm dc}$, 10-kW downscaled test bench. It mainly consists of the following two identical DSCC converters: 1) one DSCC converter is used as an inverter under test; and 2) the other as a real-time real-power high-fidelity emulator. Experimental waveforms confirm that the emulator can reproduce electrical and mechanical operating performance of the three-phase 200-V, 10-kW, four-pole, 9000-r/min induction motor loaded with a centrifugal compressor.
A Survey on Deep Learning in Image Polarity Detection: Balancing Generalization Performances and Computational Costs

Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost.
An Integrated Wildlife Recognition Model Based on Multi-Branch Aggregation and Squeeze-And-Excitation Network

Infrared camera trapping, which helps capture large volumes of wildlife images, is a widely-used, non-intrusive monitoring method in wildlife surveillance. This method can greatly reduce the workload of zoologists through automatic image identification. To achieve higher accuracy in wildlife recognition, the integrated model based on multi-branch aggregation and Squeeze-and-Excitation network is introduced. This model adopts multi-branch aggregation transformation to extract features, and uses Squeeze-and-Excitation block to adaptively recalibrate channel-wise feature responses based on explicit self-mapped interdependencies between channels. The efficacy of the integrated model is tested on two datasets: the Snapshot Serengeti dataset and our own dataset. From experimental results on the Snapshot Serengeti dataset, the integrated model applies to the recognition of 26 wildlife species, with the highest accuracies in Top-1 (when the correct class is the most probable class) and Top-5 (when the correct class is within the five most probable classes) at 95.3% and 98.8%, respectively. Compared with the ROI-CNN algorithm and ResNet (Deep Residual Network), on our own dataset, the integrated model, shows a maximum improvement of 4.4% in recognition accuracy.
Learning in architecture: Students’ perceptions of the architecture studio

In architecture education, the design studio has long been regarded as the centre of teaching and learning. As a learning environment, the studio is the physical site for learning and teaching, where active interaction between students as well as with faculty takes place. The studio is where the enculturation of students into the profession occurs, and where students undergo a transformation that influences the way they relate to the built environment, to their peers, and to their tutors.
Zero Current Switching Switched-Capacitors Balancing Circuit for Energy Storage Cell Equalization and Its Associated Hybrid Circuit with Classical Buck-Boost

To overcome the problem of switching loss during the balancing process, a novel cell balancing circuit is proposed with the integration of a zero current switching technique. Moreover, the balancing circuit proposed can change between a classical buck-boost pattern and a resonant switched-capacitor pattern with flexible control to cater to the balancing requirements under different driving scenarios. The results of the simulation of field experiments demonstrate successful balancing, various balancing speed, and low energy loss. The proposed balancing circuit proves to be effective for a wide range of application and is the first attempt to integrate a dual balancing function in a single balancing circuit for cells.
Segmentation and Analysis of Diagnostic Images for Lesion Detection: Current Status and Future Potential

In this study, we have presented an elaborate overview of various segmentation techniques found in the current medical literature for computer-aided detection of lesions from different diagnostic imaging modalities. Lesions can be broadly classified as benign or malignant. Computer-aided detection of lesions solely depends on the diagnostic modality. Analysis of medical images for detection of lesions requires a high degree of accuracy and precision. Diagnostic imaging modalities can be broadly classified as invasive, non-invasive and minimally invasive. Each of the modalities has its own challenges. From each of the modalities, we have considered some challenging hazardous diseases. Amongst invasive techniques, microscopic images of the breast and cervical cancers were considered for the study. In non-invasive technique ultrasound images of breast cancer were considered and in minimally invasive technique colorectal cancer detection using colonoscopy has been considered.
Frequency-Reconfigurable Antenna Inspired by Origami Flasher

This letter proposes a frequency-reconfigurable dipole antenna using an origami flasher. The dipole antenna is designed on flat paper in the unfolded state, and the paper can then be folded into a cube inspired by the origami flasher. Dipole length is extended in the folded state due to the patch pole, which acts as a dummy in the unfolded state. The proposed origami antenna can switch resonance frequency from 1.23 to 0.77 GHz by folding the paper. Measured return losses are 11.6 and 13.9 dB, peak gains are −0.48 and −3.41 dBi, and efficiencies are 90% and 20% at each resonance frequency in the unfolded and folded states, respectively.
EEA-Aware For Large-Scale Scientific Applications On Heterogeneous Architectures

Heterogeneous parallel programming has two main problems on large computation systems: the first is the increase of power consumption on supercomputers in proportion to the number of computational resources used to obtain high performance, the second one is the underuse of these resources by scientific applications with improper distribution of tasks. Select the optimal computational resources and make a good mapping of task granularity is the fundamental challenge for the next generation of Exascale Systems. This research proposes an integrated energy-aware scheme called efficiently energetic acceleration (EEA) for large-scale scientific applications running on heterogeneous architectures. The EEA scheme uses statistical techniques to get GPU power levels to create a GPU power cost function and obtains the computational resource set that maximizes energy efficiency for a provided workload. The programmer or load balancing framework can use the computational resources obtained to schedule the map parallel task granularity in static time.
Isolation Enhancement in Closely Coupled Dual-Band MIMO Patch Antennas

This letter presents a closely coupled dual-band multiple-input–multiple-output (MIMO) patch antenna that resonates at 3.7 and 4.1 GHz. The MIMO antenna is composed of two mirror-symmetrical single-feed patch antennas that are closely placed with approximately 0.034 λ0 (where λ0 is the wavelength at 3.7 GHz). The decoupling structure consists of the modified array antenna decoupling surface (MADS) and H-shaped defect ground structures for the lower band and upper band, respectively. Through simulation and measurement, the isolation is determined to be greater than 30 dB in both frequency bands, showing a noticeable improvement compared to the original antenna array. Under the effect of the MADS, the measured gain increases by 2.2 and 0.8 dB at the resonance frequencies of 3.7 and 4.1 GHz, respectively. The measured results indicate that the proposed decoupling structure is quite suitable for closely spaced dual-band MIMO antennas.
Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.
Human-Like Trajectory Planning on Curved Road: Learning From Human Drivers

The ultimate goal of self-driving technologies is to offer a safe and human-like driving experience. As one of the most important enabling functionalities, trajectory planning has been extensively studied from the perspective of safety. However, human-like trajectory planning on curved roads has rarely been studied. In this paper, we characterize and model human driving using extensive experimental driving collected on an urban curved road with 30 participants (10 experienced and 20 novice drivers) and five vehicles of different types. Differential global positioning system (GPS) is used to measure vehicle positions in high precision. We study factors that affect the driving trajectory, including vehicle speed, road curvature, and sight distance. We find that the human drivers typically do not follow lane centerline and the human-driven trajectories are very different from planners like rapidly exploring random tree (RRT). To generate human-like driving trajectory, we develop a data-driven trajectory model using general regression neural network (GRNN). The model was validated in various cases with promising performance.