Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPREHENSIVE ANALYSIS OF A NOVEL HYBRID EXCITED PERMANENT MAGNET VERNIER MOTOR

This paper proposes a hybrid excited permanent magnet vernier motor for low-speed and high-torque applications in electrical drive. Traditional PM vernier motors are with PM excitation field, and the air-gap magnetic field density is hard to adjust, which limit the wide speed range of PM motor. The hybrid excitation method is proposed in the PM vernier with excitation windings set in the region between modulation pole pieces. With the finite analysis method, the basic structure and working principle of the proposed motor are introduced, and the low-speed and high-torque characteristics with wide speed range are revealed. Then, the drive control system of the motor is designed and applied with the prototype motor. Finally, the experimental results verify the reliability and effectiveness of the design theory and simulation results.
Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization

Many bridge structures, one of the most critical components in transportation infrastructure systems, exhibit signs of deteriorations and are approaching or beyond the initial design service life. Therefore, structural health inspections of these bridges are becoming critically important, especially after extreme events. To enhance the efficiency of such an inspection, in recent years, autonomous damage detection based on computer vision has become a research hotspot. This article proposes a three‐level image‐based approach for post‐disaster inspection of the reinforced concrete bridge using deep learning with novel training strategies. The convolutional neural network for image classification, object detection, and semantic segmentation are, respectively, proposed to conduct system‐level failure classification, component‐level bridge column detection, and local damage‐level damage localization. To enable efficient training and prediction using a small data set, the model robustness is a crucial aspect to be taken into account, generally through its hyperparameters’ selection. This article, based on Bayesian optimization, proposes a principled manner of such selection, with which very promising results (well over 90% accuracies) and robustness are observed on all three‐level deep learning models.
LSU Digital Commons LSU Digital

Manufacturing robotics have been used for decades to perform repetitive tasks, or tasks that require increased levels of speed, strength, or precision to meet production and specification requirements. Determining the appropriate degree of automation for both the human and robot collaborative team members is critical to optimize production as well as user experience. If the degree of automation is too high, the human will be out of the loop which can result in the loss of situational awareness and be detrimental to intervention time and accuracy. If the degree of automation is too low, then the human may experience greater than necessary cognitive workloads resulting in fatigue. In this experiment, the human team member performed a typing task while the robot team member performed a pick and place task. The human remained in a supervisory role to the robot’s actions. As the degree of automation increased, objective and subjective measurements of the cognitive effect were collected. 30 participants aged 18-44 years were divided between two levels of automation. Group one (n=15) participated in the decision support variation of the experiment. The human team member typed from provided literature while they supervised the Universal Robot performing a pick and place task. The robot then initiated movement to pick up the object when the human pressed a button. Group two participated in the automatic execution variation of the experiment in which the robot executed all movements automatically, without input from the human operator. The robot performed the pick task when an object was detected by a photelectric switch. In each scenario, an error was presented on the 5 th , 9 th , 15 th , and 19 th production cycle requiring the human to intervene. Time measurements were collected at this juncture to determine how the human reacted to an unexpected situation at each level of automation. The Subjective Workload Assessment Technique (SWAT) (Luximon & Goonetilleke, 2001) was used to measure the perceived cognitive workload for each participant. Time data were collected for the production cycle and intervention. Typed words per minute were collected and compared to each participant’s control. T -tests were used to analyze the mean time within groups comparing the control words per minute to the treatment words per minute. Additionally, t -tests were used to analyze mean time between groups in the following areas, time to complete twenty cycles, intervention time, and words per minute
Conceptualization and Design of a Workflow Management System Front End for Augmented Reality Headsets

A currently discussed approach to increase efficiency during task execution, inter alia to reduce error rates and execution times, is the utilization of headset-based augmented reality systems (HARS). Additional to direct task support, HARSes can offer workflow management and control functions. However, these are only covered very limitedly by existing design-oriented approaches. Thus, users have to resort to additional devices, decreasing efficiency, and usability. Based on a three-step systematic literature analysis and two focus groups, we present a novel tentative design theory for HARSes supporting the full range of workflow management and control functions. Our design theory consists of four design requirements and nine design principles and is the basis for a software artifact prototype. Both our tentative design theory and software artifact are formatively evaluated by a third focus group. Our contributions add to the prescriptive knowledge base of the community and may be adapted by researchers and practitioners.
VoxCommunis: A Corpus for Cross-linguistic Phonetic Analysis

Cross-linguistic phonetic analysis has long been limited by data scarcity and insufficient computational resources. In the past few years, the availability of large-scale cross-linguistic spoken corpora has increased dramatically, but the data still require considerable computational power and processing for downstream phonetic analysis. To facilitate large-scale cross-linguistic phonetic research in the field, we release the VoxCommunis Corpus, which contains acoustic models, pronunciation lexicons, and word- and phone-level alignments, derived from the publicly available Mozilla Common Voice Corpus. The current release includes data from 36 languages. The corpus also contains acoustic-phonetic measurements, which currently consist of formant frequencies (F1–F4) from all vowel quartiles. Major advantages of this corpus for phonetic analysis include the number of available languages, the large amount of speech per language, as well as the fact that most language datasets have dozens to hundreds of contributing speakers. We demonstrate the utility of this corpus for downstream phonetic research in a descriptive analysis of language-specific vowel systems, as well as an analysis of “uniformity” in vowel realization across languages. The VoxCommunis Corpus is free to download and use under a CC0 license.
Adding a Zoom to Linear Temporal Logic

Temporal logic. One of the great success stories of non-classical logics in mainstream computer science is that of linear temporal logic and its applications to systems specification and verification [2]. The simple formalism of a sequence of states and a valuation function mapping atomic propositions to sets of states, combined with propositional and modal operators to construct complex formulas, is surprisingly powerful when describing the behaviour of a given system over time. However, this logic does not support the concept of modularisation in a natural manner. Each time we want to add additional detail about a subsystem associated with one of the states, we have to revise the specification of the entire system.
New flexibility drivers for manufacturing, supply chain and service operations

Increasing product proliferation, customisation, competition and customer expectations, as well as supply side disruptions, pose significant challenges to firm operations. Such challenges require improved efficiency and resilience in manufacturing, service and supply chain systems. New and innovative flexibility concepts and models offer a prospective route to such operational improvements. Several emerging issues in flexibility, such as risk and uncertainty management, environmental sustainability, optimal strategies under competition, optimal operations with strategic consumer behaviours are being examined in this regard. This overview provides a concise review of these critical research issues, and discusses related papers featured in this special issue. Four major flexibility drivers are classified: disruption risks, resilience and the ripple effect in the supply chain; digitalisation, smart operations and e-supply chains; sustainability and closed-loop supply chains; and supplier integration and behavioural flexibility.
Retrievals of Ozone in the Troposphere and Lower Stratosphere Using FTIR Observations Over Greenland

When retrieving geophysical parameters, it is advantageous to have an estimate of prior information that is based on observations with associated uncertainties, but this is often not possible. Long-term ground-based remote sensing measurements and the ozonesonde program at Summit Station, Greenland, provide an opportunity to create a unique framework to retrieve atmospheric ozone using observationally based prior information in the Arctic. This study investigates the potential of using the ground-based polar atmospheric emitted radiance interferometer (P-AERI) to estimate ozone below 10 km. Downlooking or limb-viewing sensors, such as those from satellites, have limited sensitivity to the lower atmosphere; however, uplooking, ground-based instruments provide complementary information to satellite observations to improve trace gas estimates at lower atmospheric levels. Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) is a reanalysis product that integrates satellite information but also inherits their higher uncertainties at lower atmospheric levels. An observation-based climatology of the uncertainty in the MERRA-2 ozone dataset is estimated using ozonesondes launched at Summit Station. MERRA-2 shows high accuracy in the middle stratosphere but larger uncertainties below 10 km. Retrievals that use spectral radiance measurements from the P-AERI improve the estimates of ozone concentrations in the troposphere and lower stratosphere by using prior information from MERRA-2 and our climatology of MERRA-2 uncertainties as the covariance of the prior. Using ozonesonde observations from 2012 to 2017 at Summit Station, Greenland, the quality of the retrieved results is assessed. Comparisons show that retrieved partial columns reduce the bias of MERRA-2 ozone estimation below 10 km, and the average tropospheric ozone concentration is improved significantly.
Leveraging Code-Test Co-evolution Patterns for Automated Test Case Recommendation

Context: Prior research revealed that code components with similar structures tend to require structurally similar test cases and they often co-evolve over time. Objective: Leveraging this pattern, we implemented a prototype tool, Test Suite Evolver (TSE), to support the generation of test cases for structurally similar methods. Our prototype tool is applicable to both, incomplete test suites with the purpose of test case augmentation, as well as evolving test suites when a newer version of a software becomes available. Method: Making use of the aforementioned code-test co-evolution pattern, TSE leverages source code structural similarity and a set of existing test cases to synthesise and recommend new unit tests. For this, TSE initially identifies groups of structurally similar methods in the code, determines changes at the token-level, and integrates the retrieved information to synthesise updated test cases. Results: Our evaluations indicated that 38% of the source code of five large open-source applications consist of methods which are structurally similar to each other and therefore for which, the co-evolution pattern holds. Applying TSE to these methods, TSE generated 72% of the minimum required test cases with only 11% token-based edit distance between the generated test cases and those written by developers. To evaluate the test cases’ validity, we used code2vec, a neural network to measure the structural and semantic similarity between the generated and developed test cases. The results indicated that TSE successfully generated test cases with a similarity measure of more than 80% in more than 95.6% of test cases and similarity measure of 100% for 88.35% of the test cases. Conclusion: The evaluations revealed that TSE is able to generate the majority of the necessary test cases with an insignificant amount of modifications required from the developers. ACM Reference Format: Samiha Shimmi, Mona Rahimi. 2022. Leveraging Code-Test Co-evolution Patterns for Automated Test Case Recommendation. In IEEE/ACM 3rd International Conference on Automation of Software Test (AST '22), May 17-18, 2022, Pittsburgh, PA, USA. ACM, Pittsburgh, PA, USA, 12 pages. https://doi.org/10.1145/3524481.3527222
KOOPI: Keypoint-Oriented Object Positioning in Industry

In manufacturing, object detection via industrial images enables many typical applications such as positioning of screw holes, electronic components, and other devices. The conventional method based on classical image processing generally consists of two steps: image feature extraction and object classification, so that it usually results in a low detection speed and poor accuracy due to its complicated procedure. In recent years, thanks to the rapid development of deep learning networks, higher classification accuracy and less computing performance requirement can be achieved in many typical applications, compared to using the conventional schemes. In this paper, by investigating object detection based on deep learning, a new idea utilizing some keypoint-oriented deep learning networks to the workpiece positioning area is proposed and verified by collecting dataset from practical workpieces. Our novel method performs competitively with existing schemes and runs in real-time. As for the simulation of screw hole positioning, the proposed network can effectively detect the screw hole in the image and accurately locate it. By comparing with commercial software such as Halcon® or VisionPro®, the feasibility of applying keypoint-oriented deep learning networks to intelligent manufacturing is validated.
UDAVA: An Unsupervised Learning Pipeline for Sensor Data Validation in Manufacturing

Manufacturing has enabled the mechanized mass production of the same (or similar) products by replacing craftsmen with assembly lines of machines. The quality of each product in an assembly line greatly hinges on continual observation and error compensation during machining using sensors that measure quantities such as position and torque of a cutting tool and vibrations due to possible imperfections in the cutting tool and raw material. Patterns observed in sensor data from a (near-)optimal production cycle should ideally recur in subsequent production cycles with minimal deviation. Manually labeling and comparing such patterns is an insurmountable task due to the massive amount of streaming data that can be generated from a production process. We present UDAVA, an unsupervised machine learning pipeline that automatically discovers process behavior patterns in sensor data for a reference production cycle. UDAVA performs clustering of reduced dimensionality summary statistics of raw sensor data to enable high-speed clustering of dense time-series data. It deploys the model as a service to verify batch data from subsequent production cycles to detect recurring behavior patterns and quantify deviation from the reference behavior. We have evaluated UDAVA from an AI Engineering perspective using two industrial case studies.
Effect of Sintering Temperature for Die-shear Strength of Non-pressure Sintering Copper Paste on Metal Plating

Die-bonding technology using Cu sinter pastes is an attractive and important joint method for power devices operating at higher temperatures. This paper describes the effect of sintering temperatures on die-shear strengths of non-pressure sintered copper (sCu) onto six different metal platings (Cu, Ni, Electroless Ni, Pd, Ag, and Au). The die-shear strengths of sCu on the metal plating under sintering temperatures (225-350 °C) in H2 gas were measured. As the sintering temperatures increased, the shear strengths on the Cu, Ni, and ENi plating increased, whereas those on the Pd, Ag, and Au plating decreased. By analyzing the element distribution on the bonding interfaces using SEM/EDX, it was found that the self- and non-diffusion couples (sCu:Cu, sCu:Ni, sCu:ENi) can keep the high shear strengths and the diffused couples (sCu:Pd, sCu:Ag, sCu:Au) can deteriorate the shear strengths.
A Method and Experiment to evaluate Deep Neural Networks as Test Oracles for Scientific Software

Testing scientific software is challenging because usually such type of systems have non-deterministic behaviours and, in addition, they generate non-trivial outputs such as images. Artificial intelligence (AI) is now a reality which is also helping in the development of the software testing activity. In this article, we evaluate seven deep neural networks (DNNs), precisely deep convolutional neural networks (CNNs) with up to 161layers, playing the role of test oracle procedures for testing scientific models. Firstly, we propose a method, TOrC, which starts by generating training, validation, and test image datasets via combinatorial interaction testing applied to the original codes and second-order mutants. Within TOrC we also have classical steps such as transfer learning, a technique recommended for DNNs. Then, we verified the performance of the oracles (CNNs). The main conclusions of this research are: i) not necessarily a greater number of layers means that a CNN will present better performance; ii) transfer learning is a valuable technique but eventually we may need extended solutions to get better performances; iii) data-centric AI is an interesting path to follow; and iv) there is not a clear correlation between the software bugs, in the scientific models, and the errors (image misclassifications) presented by the CNNs. CCS CONCEPTS • Software and its engineering → Software testing and debugging;. Computing methodologies → Neural networks; Supervised learning by classification; Computer vision.
Structural Causal Models as Boundary Objects in AI System Development

Artificial Intelligence (AI), and especially machine learning can be used to find statistical patterns in datasets with thousands of variables with ease. But an understanding of causality is difficult to learn for a machine. For humans however, realising causal relations is often not a difficult process, as we can refer to experience or scientific knowledge. Here we propose the use of structural causal models, represented through direct acyclic graphs, to design, determine, and communicate causal relations hidden beyond the statistical models of an AI. The idea is to make human insight in causal relations explicit and use this knowledge during AI system development. In a joint-industry project we discovered that structural causal models can serve as living boundary objects that facilitate coordination of domain experts, data scientists, systems engineers, and AI experts in AI system development.
Nonlinear Analysis of Flexible Pile near Undrained Clay Slope under Lateral Loading

In this paper, a method is developed for nonlinear analysis of laterally loaded long-flexible pile near undrained clay slope. The ideal elastic-plastic p-y curve model is used as the basic calculation model to study the pile-soil interaction system. To consider the slope effect on the soil resistance along with pile length, related reduction function expressions of soil resistance are selected by assessing the existing methods. A finite difference iteration scheme is proposed to solve the pile’s deflection curve equations to obtain nonlinear response of pile. The developed method is validated by comparing its results with the existing method, which shows a good agreement. A number of parameters analyses are carried out, and effects of different influence factors on laterally loaded pile are further discussed.
Temperature-Insensitive Inclinometer Based on Transmission Line Fabry–Perot Resonators

This article reports a novel self-compensated inclinometer with a wide dynamic range and high measurement resolution based on two hollow coaxial cable Fabry–Perot resonators (HCC-FPRs). The HCC-FPR is formed between a metal post that shorts the inner conductor and the outer conductor of the HCC and the water level inside the HCC. Changes in tilt angles result in variations of the water level in the HCC, leading to changes in the cavity length of the HCC-FPR. By tracking the shift of the resonance wavelength of the HCC-FPR, a single HCC-FPR can be used as a sensor device for tilt measurements. Furthermore, two HCC-FPRs are used in the inclinometer design presented herein, which enables the inclinometer to achieve self-compensation for variations of environmental factors, e.g., temperature. The tilt measurement sensitivity of the double-HCC-FPR-based inclinometer is also increased. The proposed inclinometer holds several advantages, such as robustness, ease of fabrication, ease of signal interrogation, low cost, and high measurement sensitivity and resolution.
Resistive pressure sensor based on cylindrical micro structures in periodically ordered electrospun elastic fibers

We present a resistive pressure sensor based on the thin film printed via melt electrospinning of polyether block amide (PEBA). This thin film is created by arranging the electrospun cylindrical fibers periodically into a narrow vertical wall. We coat this film using poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) and place it between top and bottom counter electrodes for the pressure sensor. With the application of pressure, the variation of the contact area between the electrode and the cylindrical surface of the film produces a sensitive current response for detecting a wide range of pressure with tunable sensitivities. We present the optimized process conditions of the melt electrospinning for the construction of the micro structured thin film. We also develop theoretical model based on the classical contact theory to characterize and predict the sensor responses. Finally, a multi-touch interface for a mobile device is demonstrated based on the described methods. The reliable, cost effective, and scalable characteristics of the presented method offer great promise for mobile or wearable applications.