Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Chatter Mitigation Control for Low Radial Immersion Machining Processes

Chatter is a typical kind of unstable dynamics often encountered in machining processes, which often results in overcut and rapid tool wear. Hence, chatter phenomenon worsens the surface quality and reduces productivity in milling systems as well. Recent years have witnessed a surging industrial demand of high quality and high efficiency machining. Specifically, for low radial immersion milling situation, large depth of cuts is inevitably needed so as to increase the machining efficiency. To fulfill such a task, this paper develops a robust active control method to mitigate the chatter dynamics of low radial immersion milling processes. The present approach increases the axial depth of cuts, and the improvement is inversely proportional to the radial immersion ratio. Finally, case studies are conducted to show the substantially enlarged stable region in the stability lobe diagram (SLD) spanned by spindle rotational speed and axial depth of cut. Thus, the method can be expected to improve the efficiency of milling processes. Note to Practitioners—This paper seeks to mitigate machining chatters that worsen the workpiece surface quality in high-speed machining processes. To overcome such unexpected effects, traditional passive schemes simply adjust the rotational speed and cutting depth, which do not seek assistance from external power sources. Hence, neither the damping nor stiffness of the system can be changed, and a high maximal material removal rate (MMRR) is arduous to obtain. By contrast, this paper develops a robust active control method to mitigate the chatter dynamics for low radial immersion milling. In the design stage of the milling spindle, two active magnetic bearings (AMBs) are embedded in the tool house as control actuators, which produce extra force to counteract the chatters. In real milling processes, the chatters are detected by the associated inductive displacement sensors around the AMBs, and an uncertainty model is accordingly established with low radial immersion. With the detected vibration displacements, robust active control signals are calculated online, and then fed into the power amplifiers to drive the two AMBs. By this means, a closed-loop active control system is thus established for milling processes, which substantially enlarges the stable region of the SLD spanned by rotational speed and cutting depth. As a result, the proposed active control scheme can be expected to increase the MMRR and, hence, to enhance the productivity of milling processes.
A CMOS Temperature Sensor With Versatile Readout Scheme and High Accuracy for Multi-Sensor Systems

In this paper, a smart CMOS temperature sensor with a versatile readout scheme is presented. A digital-assisted readout solution is proposed to improve the compatibility of the circuit and maintain the performance of a traditional smart temperature sensor. In addition, the potential multi-operating points of the analog front-end are analyzed, and a compact start-up circuit is designed to make the analog front-end work properly. Fabricated in a standard 0.13 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process, the proposed temperature sensor occupies a silicon area of 0.29 mm<sup>2</sup> and draws a current of <inline-formula> <tex-math notation="LaTeX">$95~\mu \text{A}$ </tex-math></inline-formula> from a 2 to 3.6 V supply voltage at the room temperature. Measured results show that the proposed design has the inaccuracy of ± 0.47 °C (<inline-formula> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula>) from −40 °C to 125 °C after a single-point temperature calibration. It achieves a resolution of 16 mK at a conversion time of 5.12 ms and a supply sensitivity of less than 0.05 °C/V. Thanks to the versatile readout scheme, the proposed temperature sensor is compatible with multi-sensor systems where the readout-circuitry can be shared between different input signals.
A Method of Feature Selection Based on Word2Vec in Text Categorization

In text categorization, the performance of classifier decreases with the increase of feature dimension. The main purpose of feature selection is to remove irrelevant features and redundant features in features and reduce feature dimension. Traditional methods of feature selection, such as CHI, IG, DF and so on, take into account only the number of appearances of features and ignore the feature semantics and part-of-speech features. The vector representations of words learned by word2vec models have been shown to carry semantic meanings and are useful in various NLP tasks. Based on the word vectors generated by Word2Vec, the paper proposes the algorithm Word2Vec-SM to reduce the dimensionality of the features. Experimental proof word2vec-SM algorithm.
An Imaging Method for Automated SkinLesion Segmentation using Statistical Analysis and Bit Plane Slicing

Melanoma can prove fatal if not diagnosed at early stage. Computer aided identification of diseases can equip normal people in performing a screening test of diseases. The accurate lesion segmentation plays a crucial role in correct diagnosis of skin diseases. This work proposes a skin lesion segmentation method using statistical analysis and bit plane slicing. Hole filling operation is competent enough to sufficiently reject the noisy pixels from the finally segmented image. The results of skin lesion segmentation obtained from the proposed algorithm has been compared with the annotated images available with the database. The results arepresented in form of overlapping score and correlation coefficient. An average overlapping score and correlation coefficient of 91.59% and 92.07%, respectively, is obtained from the proposed algorithm. Also, an image based performance analysis of segmented lesion has been done and an average sensitivity of 94.33% has been achieved. The results are convincing and suggests that the proposed work can be used for some real-time application.
Feedback linearization control for HRV attitude control based on high order disturbance observer

High response speed, coupling dynamics and nonlinearities of hypersonic re-entry vehicles (HRVs) makes the attitude control a significant technology. This paper proposes a feedback linearization control scheme based on backstepping technology design for attitude tracking control of HRV in the presence of disturbances. In the proposed control design scheme, the higher order disturbance observer (HODO) is adopted to estimate and compensate for the disturbances. Then the convergence of HODO and stability of closed-loop system are presented. Finally, simulation results are given to illustrate the effectiveness of the proposed method.
Robust Adaptive Fault Estimation for a Class of Over-actuated Systems with Loss of Effectiveness Actuator Faults

Focusing on the fault estimation problem of a class of over-actuated systems in the presence of loss of effectiveness actuator faults, we propose a robust adaptive fault estimation method. In this method, the persistence of excitation condition required by parameter estimation methods is relaxed via concurrent learning technique. Bounded external disturbance is eliminated by constructing the estimation error information with a series of filtered system variables. Simulation results demonstrate the effectiveness and robustness of the proposed method.
Rubbinput: An Interaction Technique for Wet Environments Utilizing Squeak Sounds Caused by Finger-Rubbing

Rubbinput, a user interface for wet environments, utilizes the squeaky sounds made when rubbing a smooth, wet surface with fingers. Rubbinput analyzes squeaks, interpreting “duration time”, “reciprocating motions”, and “number of rubbing fingers”, converting them to user interface events. Harmonic structures of squeak caused by each finger are utilized to detect events with low latency. In this paper, we describe Rubbinput use cases and analyze user interface detection processes and performance. Specific applications and uses accompanied by user evaluations, are also described.
Log Posterior Approach in Learning Rules Generated using N-Gram based Edit distance for Keyword Search

Abstract Challenging searching mechanisms are required to cater to the needs of search engine users in probing the voluminous web database. Searching the query matching keyword based on a probabilistic approach is attractive in most of the application areas, viz. spell checking and data cleaning, because it allows approximate search. A probabilistic approach with maximum likelihood estimation is used to handle real-world problems; however, it suffers from overfitting data. In this paper, a rule-based approach is presented for keyword searching. The process consists of two phases called the rule generation phase and the learning phase. The rule generation phase uses a new technique called N-Gram based Edit distance (NGE) to generate the rule dictionary. The Turing machine model is implemented to describe the rule generation using the NGE technique. In the learning phase, a log model with maximum-a-posterior estimation is used to select the best rule. When evaluated in real time, our system produces the best result in terms of efficiency and accuracy.
Dual Active Bridge Bidirectional DC-DC Converter Modeling for Battery Energy Storage System

This paper studies the mathematical model of dual active bridge (DAB) DC-DC converter under the variations of bus voltage and battery voltage. In view of the fact that the DAB converter has a bounded change of the power supply voltage on the high-voltage side and a controllable change of the electromotive force (EMF) load on the low-voltage side, the power supply voltage on high-voltage side and the EMF load voltage on low-voltage side are taken as control variables. The states of DAB converter during a switching cycle are analyzed, and the averaged model (HL model) of the DAB converter under the condition of continuous conduction mode (CCM) is established as well as the small-signal model. The comparisons are made between the output voltage of the HL model and the topology circuit with simulation. And according to the small-signal model, the dynamic performance of the DAB converter is analyzed under the variations of the power supply voltage, the EMF load and phase-shift radio. The result shows that the proposed HL model has the same steady and dynamic performance as the circuit topology model. The relative error of the model at steady state is only 0.35%. Therefore, the applicability and the accuracy of the proposed model in battery energy storage system can be verified.
Fast Image Dehazing Using Color Attributes Prior

In this paper, we propose a color attributes prior, which establishes a relationship between the RGB observations and the linguistic color labels, for fast haze removal from a single input hazy image. Based on the novel prior, the transmission map and the RGB observations can be connected by a linear function through the physically-based analytical models of the image formation. Using the linear function, the transmission map as well as the haze-free image can be recovered. Experiments on synthetic and real datasets demonstrate that the proposed algorithm is able to achieve significantly better results as compared to the recent state-of-the-art methods. Moreover, it can efficiently restore a hazy image at a very low computational complexity, which has a huge potential for real-time application.
Investigations of Active Antenna Doherty Power Amplifier Modules Under Beam-Steering Mismatch

This letter investigates the performance of a Doherty power amplifier (DPA) in an <inline-formula> <tex-math notation="LaTeX">$8\times 1$ </tex-math></inline-formula> active antenna transmitter module under an average voltage-standing-wave ratio of 4:1 induced by the array 1-D beam steering and mutual coupling. The GaN-based DPA and a <inline-formula> <tex-math notation="LaTeX">$2\times 1$ </tex-math></inline-formula> segment of the array are verified via measurements at the operating frequency of 3.5 GHz. The co-design and co-simulation of both the antenna array and the behavioral model of the DPA show that the inherent back-off efficiency benefit—when compared to traditionally used class-AB and -B amplifiers in base stations—can be maintained when restricting the beam-steering angle, in this case <inline-formula> <tex-math notation="LaTeX">$\theta \in $ </tex-math></inline-formula> [−45°, 45°], and operational bandwidth of the combined system.
Development and Implementation of Human Face Alignment and Tracking in Video Streams

The paper presents a method that allows detection, alignment and tracking of a human face in a real time in video streams. To detect and to align face on an image a face shape regression approach is used. The developed method uses scanning window, a cascade of ensembles of regression and classification trees, and adaptive boosting. The same trees are used for classification whether the given window contains a face and for regression of a face shape. For face tracking a starting position for face search is taken from the found shape on the previous frame. Conducted analysis of the proposed method implementation gave good performance results but revealed shortcomings and directions for future work. Sensitivity of face detection is 78% and accuracy of face alignment is 95%. The implementation can track faces in real time with a speed of at least 20 frames per second.
Microstructure and properties of AuSn20 solder ribbons prepared by rapid solidification process

The AuSn20 solder ribbons were prepared by single roll rapid solidification device and the effect of rapid solidification process parameters on the forming performance of the ribbons were studied. Microstructure, composition distribution, phase composition, melting characteristics and welding strength of the rapidly solidified AuSn20 ribbons were studied through OM, EPMA, SEM, XRD, DSC and other testing methods. AuSn20 solder ribbons prepared by single roll rapid solidification device have fine formability. The width of the strip is 6 mm and the thickness is 100 μm. The rapid solidification AuSn20 solder are composed of ζ′-Au5Sn and δ-AuSn. The quantity of primary phase ζ′-Au5Sn is reduced and the morphology is significantly refined. The eutectic (ζ′-Au5Sn+δ-AuSn) structure is small and the composition is uniformly distributed. The rapid solidification solder has a lower melting temperature and narrow melting range compared to the common solidification solder.
Study of the interaction energy at the composite interface between PDMS and functionalized Graphene using molecular dynamics simulations

In this study, the interfacial interaction energy between polydimethylsiloxane (PDMS) and functionalized graphene (fG) composite system were investigated using molecular dynamics simulations. Two functional groups including amino, carboxy were used to cover the graphene surface with surface densities of 4.84 groups per nm2. The tensile properties of the composite have also been analyzed using uniaxial tension simulation. Analyzes the changing tendency of interfacial bonding energy, system shape and energy distribution in interfacial tensile failure process, and studies the influence of PDMS cross-linking degree on interfacial tensile failure rule. The results show that the interfacial interaction energy between the PDMS and the functionalized graphene was larger than that between the PDMS and the pure single layer graphene. Additionally, the increase in the degree of cross-linking of PDMS makes the interaction energy of the PDMS/fG composite system miraculous. Under the same PDMS cross-linking degree, the interaction energy of PDMS/fGNGH2 composite system is superior to that of PDMS/fGCOOH composite system for the same number of functional groups.
Study on preparation process of Au80Sn20 eutectic solder on thin film substrate

In this work, the Au80Sn20 thin film solder was successfully prepared by mixed thermal evaporation in Au. The main properties of AuSn, AuSn2, and AuSn4 intermetallic compounds produced by Au/Sn interdiffusion at room temperature were analyzed from the diffusion mechanism. The manual eutectic welding machine was used for welding and the solder composition was measured. Under the welding temperature of 380°C for 10s, the area of the hole was effectively controlled to be less than 10%, and the shear strength of the sample meets the related technical requirements.
Pick Me Up and I Will Tell You Who You Are: Analyzing Pick-Up Motions to Authenticate Users

Authentication is a cumbersome task for users, as it needs to be repeated multiple times a day. While the introduction of fingerprint authentication on mobile devices reduces users’ memorization and typing efforts, users still need to perform a specific action to unlock, e.g., their smartphone. In order to further reduce the associated overheads, we analyze in this paper the feasibility of identifying users based on the way they pick up their device located on, e.g., a table. By doing so, no dedicated actions would be required from the users, as the authentication motion is already done when they want to access different services available on their devices. We have therefore designed and conducted a lab study involving 24 participants to collect the sensor data associated to these pick-up motions. The obtained classification results are promising and suggest that motion sensors could be integrated in a future implicit authentication scheme.
Surface Tracking Along the Interphase Barrier of a Large Transformer: Effect of Moisture within Barrier Board

Partial Discharge (PD) measurements are an important tool for assessment of the condition of the insulation systems of power equipment such as high voltage transformers. One of the most dangerous failure modes in a large autotransformer is due to surface discharge along the interphase barrier board. Previous work has shown the measurement of Phase Resolved Partial Discharge, PRPD, patterns of surface tracking using either a local PD source only or for a local PD source in the presence of an additional single phase electric field. More recently, results obtained for a barrier board with 9% moisture content in the presence of a two phase applied field as well as a PD source have been reported. In this paper, experiments are described using two phase applied fields for samples with different moisture content of 3% and 6%. The experimental results and discussion highlight the effect of different moisture content within pressboards under the effect of two phase energization, on surface charge behavior. The results should assist in determining the effect of moisture content on PRPD patterns and will help to improve diagnostics of field data captured in real-time. The experiments have been undertaken at the Tony Davies High Voltage Laboratory, University of Southampton.
Analysis of the Complexity of the Automatic Pipeline Generation Problem

Strategies to automatize the selection of Machine Learning algorithms and their parameters have gained popularity in recent years, to the point of coining the term Automated Machine Learning. The most general version of this problem is pipeline optimization, which seeks an optimal combination of preprocessors and classifiers, along with their respective parameters. In this paper we address the pipeline generation problem from a broader perspective, that of problem complexity understanding as a previous step before proposing a solution, a comprehension we consider critical. The main contribution of this work is the analysis of the characteristics of the fitness landscape. Furthermore, a recently introduced tool for pipeline generation is used to investigate how an automatic method behaves in the previously studied landscape. Results show the high complexity of the pipeline optimization problem, as it can contain several disperse optima, and suffers from a severe lack of generality. Results also suggest that, depending on the dimensions of the search, the model quality target, and the data being modeled, basic search methods can produce results that match the user's expectations.
Vision-Assisted Human Motion Analysis for Bed Exit Prediction Model Construction

This paper presents a vision-assisted human motion analysis method by which to classify unknown preparatory motions for bed exit. A convolutional neural network was applied to identify the existence of essential postures between two meaningful sequential motions from low-resolution depth images. The preliminary experimental results showed that sequential images of bed exit preparatory motions can be classified as essential motion states, which is useful for construction of the states of a hidden Markov model for bed exit prediction.
Efficient and Exact Query of Large Process Model Repositories in Cloud Workflow Systems

As cloud computing platforms are widely accepted by more and more enterprises and individuals, the underlying cloud workflow systems accumulate large numbers of business process models. Retrieving and recommending the most similar process models according to the tenant's requirements become extremely important, for it is not only beneficial to promote the reuse of the existing model assets, but also helpful to reduce the error rate of the modeling process. Since the scales of cloud workflow repositories become bigger and bigger, developing efficient and exact query approaches is urgent. To this end, an improved two-stage exact query approach based on graph structure is proposed. In the filtering stage, the composite task index, which consists of the label, join-attribute and split-attribute of a task, is adopted to acquire candidate models, which can greatly reduce the number of process models needed to be tested by a time-consuming verification algorithm. In the verification stage, a novel subgraph isomorphism test based on task code is proposed to refine the candidate model set. Experiments are conducted on six synthetic model sets and two real model sets. The results demonstrate that the presented approach can significantly improve the query efficiency and reduce the query response time.
Metabotropic Glutamate Receptor 5 in the Medial Prefrontal Cortex as a Molecular Determinant of Pain and Ensuing Depression

Pain and depression affect one another, and this bidirectional interaction implies the existence of common or interacting neural pathways. Among the neural circuits relevant to negative affection, the medial prefrontal cortex (mPFC) is known to be involved in both pain and depression. Persistent stress from physical pain and mental distress can evoke maladaptive changes in mPFC circuits to induce depression. Conversely, the unpleasant mood condition alters mPFC circuits to distort the appraisal of aversion and make individuals vulnerable to pain. In this article, recent findings regarding mPFC in chronic pain and/or depression are reviewed, with particular focus on the metabotropic glutamate receptor 5 (mGluR5). Although the involvement of mGluR5 within the mPFC in both pain and depressive disorders has been extensively studied, there are controversies regarding changes in the activity of the mPFC during chronic pain and depression, and the functional roles of mGluR5 on altered mPFC activity. We discuss alterations in the availability of mGluR5 in the mPFC in these disorders, its role in behavioral manifestations, and its possible influence on cellular subpopulations that mediate dysfunction in the mPFC. We also propose molecular mechanisms that may cause expressional changes in mGluR5 within the mPFC circuitry.