Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization and Comparison of Two Hybrid Permanent Magnet Synchronous Motors with Contrary Saliency Characteristic

In this paper, the hybrid permanent magnet synchronous motor (HPMSM) with the special external flux barriers design of q-axis is investigated. Two HPMSMs with contrary saliency characteristic are proposed by the rational design of flux barriers. The equivalent magnetic circuits of the d- axis and q-axis are given, and the saliency characteristics are analyzed. In addition, based on the multi-objective optimization method, the two motors are optimized simultaneously. The inductance, torque, anti-demagnetization characteristic of positive saliency (PS) characteristic motor and inverse saliency (IS) characteristic motor are compared in detail. The comparison results indicate that the HPMSM with IS characteristic is a competitive candidate for overload operation.
"Kapow!": Augmenting Contacts with Real and Virtual Objects Using Stylized Visual Effects

We propose a set of stylized visual effects (VFX) meant to improve the sensation of contact with objects in Augmented Reality (AR). Various graphical effects have been conceived, such as virtual cracks, virtual wrinkles, or even virtual onomatopoeias inspired by comics. The VFX are meant to augment the perception of contact, with either real or virtual objects, in terms of material properties or contact location for instance. These VFX can be combined with a pseudohaptics approach to further increase the range of simulated physical properties of the touched materials. An illustrative setup based on a HoloLens headset was designed, in which our proposed VFX could be explored. The VFX appear each time a contact is detected between the user’s finger and one object of the scene. Such VFX- based approach could be introduced in AR applications for which the perception and display of contact information are important.
Methyl Cellulose Stabilized Polyaniline Dispersions

Methyl cellulose has been used as a stabilizer in the oxidative dispersion polymerization of aniline using ammonium persulfate oxidant. The dispersion polymerization is unsuccessful in water but successfully effected in aqueous alcohol media. Evidence has been presented that the stabilizer incorporation in polyaniline is increasingly facilitated as the alcohol concentration of the medium is increased. Particle morphology changes from long needle-shaped to oblong (doublets of spheres) or spherical as the alcohol concentration is increased from 30% to 70%.
Structural Rationalization of the Phase Transition Behavior in a Solid Organic Inclusion Compound: Bromocyclohexane/Thiourea

Structural properties of the bromocyclohexane/thiourea inclusion compound have been determined using both single-crystal and powder X-ray diffraction over a range of temperatures above and below a first-order phase transition at 233 K in this material. The results demonstrate marked contrasts to the phase transition behavior in the prototypical cyclohexane/thiourea inclusion compound, demonstrating that relatively small changes in molecular geometry (in this case bromine substitution) can have a profound influence on the structural properties of the low-temperature phase in such materials.
Study on the Generation Characteristics Of Oil-Paper Insulation Bubble Inside High-Voltage Bushing

During the operation of the transformer bushing, due to the long-term superposition of the electric field and conductor heating, many bubbles will precipitate on the surface of the oil-immersed cardboard inside the bushing. When the bubbles migrate to the strong electric field inside the casing, it will cause partial discharge. Therefore, the initial temperature of bubbles over the oil-paper was defined based on liquid physical analysis in this paper. The test platform for bubble observation inside the bushing oil-paper insulation was established, and prepared samples with different moisture content. The rapid heating method was used to study the law of bubble generation, and the law of the initial temperature change of bubble generation was obtained. The results showed that the faster the temperature rise, the lower the bubble formation starting temperature. When the temperature rise rate increases by 4°C / min, the bubble initial temperature drops by about 20 □; While when the temperature rise rate is slower, the bubble generation initial temperature is more dispersive but the results are normally distributed. And as the water content increases, the initial temperature of bubble generation reduced. When the moisture content increased from 2.7% to 6.8%, the bubble initial temperature dropped by 44 □.
Optimal Bound Based Ensemble Approach for Probabilistic Wind Power Forecasting

Due to the intermittency and fluctuation of wind power, a prediction interval (PI) with high stability, reliability and low sharpness is utmost important in system economic dispatch. This paper proposes a novel optimal bound based ensemble approach (OBE) where the weights for upper and lower prediction bounds are optimized by heuristic method respectively. Three base probabilistic model, K-means based conditional error Distribution (KCED), Quantile regression neural network (QRNN), and Pretraining-NN based direct interval forecast (PNNDI) are introduced where the KCED and PNNDI have been modified to obtain higher performance. A numerical case of a wind farm in Northeast China proves the superior PI-quality by the proposed ensemble method and the effectiveness of the modification of the base probabilistic models.
Two Dimensional Forward-Looking Missile-Borne Radar Imaging Based on Vortex Electromagnetic Waves

In this article, a novel two-dimensional (2D) imaging scheme for forward-looking missile-borne radar systems is proposed, where the vortex electromagnetic (EM) waves with different orbital angular momentum (OAM) modes are transmitted. First, the vortex EM waves’s radar imaging model is established. Then, two processing algorithms, i.e. the FFT and the sparse recovery algorithms, are proposed. Finally, the simulation results confirm the proposed algorithms. Compared with the traditional missile-borne radar, the OAM-based system can not only achieve the slant range resolution, but also the azimuth resolution. Besides, be different from the traditional 2D synthetic aperture radar (SAR), the azimuth resolution of the novel system is independent from the Doppler bandwidth and can be obtained even at the forward-looking mode.
Optimization of Sitting and Sizing of Microgrid in Distributed Generation Considering Reliability Costs

To reasonably optimize entrance location and capacity of microgrid so as to improve the reliability and economy is a vital target for allocation of microgrid in distribution system. Taking into the influence of outage costs into account, an optimization model of sitting and sizing of microgrid is established with an objective of the sum of investments, operation & maintenance costs, fuel costs, environment costs and outage costs. In order to considerate the impacts of entrance location of microgrid, access point and capacity of microsource on outage costs of distribution system, the outage costs are calculated by a reliability evaluation method for distribution system with microgrid. A case study is carried out based on RBTS-BUS 6. Using the genetic algorithm to solve this optimization model of sitting and sizing of microgrid, the results demonstrated that this model proposed can obtain the optimal scheme of sitting and sizing of microgrid to enhance the reliability and reduce the total costs.
Learning Image From Projection: A Full-Automatic Reconstruction (FAR) Net for Computed Tomography

The x-ray computed tomography (CT) is essential for medical diagnosis and industrial nondestructive testing. The aim of CT is to recover or reconstruct image from projection data. However, in particular, the reconstructed image usually suffers from complex artifacts and noise, such as the sampling is insufficient or low-dose CT. In order to deal with such issues and achieve reconstruction, a full automatic reconstruction (FAR) net is proposed for CT reconstruction via deep learning technique. Different with the usual network in deep learning reconstruction, the proposed neural network is an end-to-end network by which the image is predicted directly from projection data. The main challenge for such a FAR-net is the space complexity of the CT reconstruction in full-connected (FC) network. For a CT image with the size <inline-formula> <tex-math notation="LaTeX">$N\times N$ </tex-math></inline-formula>, a typical requirement of memory space for the image reconstruction is <inline-formula> <tex-math notation="LaTeX">$O(N^{4})$ </tex-math></inline-formula>, for which is unacceptable by conventional calculation device, e.g. GPU workstation. In this paper, we utilize a series of smaller fully connected layers (FCL) to replace the huge Radon transform matrix based on the sparse nonnegative matrix factorization (SNMF) theory. By applying such an approach, the FAR-net is able to reconstruct images with the size <inline-formula> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula> on only single workstation. The results of numerical experiments show that the projection matrix and the FAR-net is able to reconstruct the CT image from projection data with a superior quality to conventional methods such as optimization based approach. Meanwhile, the factorization for the inverse projection matrix is validated in simulation and real experiments.
Interactive rule correction, imputation and execution in rule-driven database completion system

This paper solves the problem of correcting database completion rules, imputing missing rule conditions, and executing them interactively and efficiently. Often in real-world scenarios, database completion rules have syntactic errors. Even after correcting these rules, the process of correction can introduce spurious rule conditions that can result in inefficient rule execution. We solve this problem by leveraging programming-by-example data transformations, sketching data structures such as bloom filters, and leveraging entity resolution rules. The paper proposes to use programming-by-example data transformations along with reducing the size of examples generated for it. We build upon current work to involve the user in the process of making PBE examples. The paper also proposes to use sketching data structures for the database completion process under dynamic rules, along with reducing their numbers and sizes of them. We again build upon existing work to involve the user in the process of making a summary on an incomplete database, which also increases efficiency. The paper also proposes to efficiently leverage entity resolution rules to find missing rule conditions in database completion rules.
A Study of Educational Adjustment of Secondary School Students

The present study aims to investigate whether there is any significant relationship between adjustment and academic achievement of higher secondary school students. All the higher secondary schools must give importance to providing counseling service to the students who are having emotional and adjustment problems. A well trained counselor should be appointed for giving guidance and counseling services. The schools must not give importance only to high achievement of their students, but must give importance to teaching to adjust with oneself and with their environment. The schools must often conduct meetings with the parents of the students and make them to know about their child's status. In this survey study, the investigators used stratified random sampling technique for selecting the sample from the population. The stratification was done on the basis of gender and locality of students. The tools used for the present study were Adjustment Inventory developed by A.K. P. Sinha and R.P. Singh (2007) and academic achievement constructed by the investigator. The statistical techniques used for analyzing the data for the present study was Karl Pearson's product moment co-efficient of correlation. The finding shows that, there is a significant relationship between emotional, social, educational adjustment of higher secondary school students in relation to academic achievement.
Dynamic Equivalence Method of DDPMSG Wind Farm for Sub-Synchronous Oscillation Analysis

Single or multiple wind turbine generators (WTGs) model have already been used in sub-synchronous oscillation (SSO) analysis. However, there is little researches on the equivalence method of wind farm (WF) for the SSO study. The general equivalence method may lose the weak damping SSO mode. In this paper, a DDPMSG WF multi-machine equivalence method is proposed for SSO analysis. According to their SSO modes, the DDPMSGs are grouped in two steps. The oscillation frequency or damping is only considered in each step. The parameters of equivalent DDPMSG model are calculated by aggregation in each group. Furthermore, an online application processing method is proposed. The simulation verification is carried out based on small signal model and electromagnetic transient model of WF. The results show that the weak damping SSO mode of WF is well preserved in the proposed method.
Antlion Optimized Robust Control Approach for Micropositioning Trajectory Tracking Tasks

Planar 3-Degree of Freedom (3-DOF) micropositioning stages are widely adopted in many precision applications for their ability to provide in-plane rotation. However, their motion accuracy is adversely affected by cross-couplings, model uncertainty, and external disturbances. This work proposes an optimized robust control methodology based on disturbance estimation to address these issues. Systematic modeling of a 3-DOF precision micropositioning stage was utilized to develop a nonlinear disturbance observer-based sliding mode control methodology. This methodology can estimate and compensate the unavoidable cross-couplings between the major axes of motion. A stability analysis is conducted to prove the stability of the feedback system when the control approach is combined with the disturbance observer. A maximum bound on the tracking error is derived through finite-time analysis, and the parameters that affect this bound are identified. The antlion optimizer algorithm is used to optimize the control parameter based on the mean square error cost function to eliminate the requirement for manual tuning and to achieve the best attainable performance. The proposed control method is experimentally demonstrated to track complex trajectory with less tracking error than the classical sliding mode approach. The main contribution of this study is the improvement of the trajectory tracking accuracy in multi-DOF micropositioners using an optimized disturbance observer-based robust control technique.
Improving Mobile Network Performance with Mobile Edge Computing

Most cloud-based applications utilize a cloud server to handle data produced by various terminal devices, including smart devices and wearable devices. The above model has big negative effect on user experience. Furthermore, the emergence of some new computing applications, including automatic drive and virtual reality, has become possible because of availability of abundant cloud resources. Nonetheless, the delay-sensitive applications have strict response time requirements. The existing computing model does not have capability to meet the above demand. Based on the above phenomenon, Mobile Edge Computing (MEC) has been suggested to bring cloud resources, such as computation and storage resources, to the edge network. Now, MEC is considered as a good method to reduce the workload on the cloud server and to reduce response time for mobile users. In this paper, the definition of the MEC is presented. Then, we discuss several typical applications of MEC. Also, we describe some key research challenges in MEC scenario. In the end, we highlight the critical points and conclude this paper.
Automatic Breast Tissue Segmentation in MRI Scans

The performance of many methods of MRI-based computer-aided diagnosis of breast disease rely on accurate delineation of the breast boundary. This problem has been known to be a challenging one on the account of the complex composition of breast tissue and its extensive inter-subject variability. To address this problem, this paper introduces a new approach to whole-breast segmentation which, as opposed to many existing solutions, can operate in the absence of any prior information on the patient-specific breast anatomy. The proposed algorithm takes advantage of Dijkstra’s procedure which allows accurately tracking the boundary between the pectoralis muscle and breast tissue as well as between the breast and its background. The performance of the proposed method has been tested on in vivo MRI volumes and quantified in terms of several performance metrics. The experimental results demonstrate consistent and stable performance of the proposed algorithm in terms of its accuracy and robustness to imaging artefacts.
Modeling of Cavity Discharges under AC and DC Voltage — Part II: Opportunities of the Dipole-based PD Model

To explain the occurrence of partial discharges (PD) in gaseous cavities embedded in the bulk dielectric of HV equipment, as well as to analyze the associated PD transients, two fundamental approaches have been proposed in the past, which are commonly referred to as network-based and dipole-based PD model. The first one has been reviewed critically in Part I of this publication, while this Part II is addressed to the dipole-based PD model. This alternative approach, which was promoted by Pedersen in the 1980, is based on the fundamentals of the gas discharge physics and adopts the electromagnetic field theory to treat the electromagnetic transients, which are associated with the establishment of an electrical dipole as consequence of a cavity discharge. However, understanding this more sophisticated concept needs a deep knowledge of the electromagnetic field theory based on Maxwell's equations. Presumably, this may be the reason why the dipole-based PD model is rarely adopted in the relevant literature. However, introducing some simplifications, this approach becomes easy understandable and can simply be handled, as highlighted in this paper based on some chosen case studies of practical interest.
Towards Fast and Efficient VVC Encoding

Versatile Video Coding (VVC) is a new international video coding standard to be finalized in July 2020. It is designed to provide around 50% bit-rate saving at the same subjective visual quality over its predecessor, High Efficiency Video Coding (H.265/HEVC). During the standard development, objective bit-rate savings of around 40% have been reported for the VVC reference software (VTM) compared to the HEVC reference software (HM). The unoptimized VTM encoder is around 9x, and the decoder around 2x, slower than HM. This paper discusses the VVC encoder complexity in terms of soft-ware runtime. The modular design of the standard allows a VVC encoder to trade off bit-rate savings and encoder runtime. Based on a detailed tradeoff analysis, results for different operating points are reported. Additionally, initial work on software and algorithm optimization is presented. With the optimized software algorithms, an operating point with an over 22x faster single-threaded encoder runtime than VTM can be achieved, i.e. around 2.5x faster than HM, while still providing more than 30% bit-rate savings over HM. Finally, our experiments demonstrate the flexibility of VVC and its potential for optimized soft-ware encoder implementations.
Simulation on Motion Characteristics of Free Metal Particles in GIS / GIL under Power Frequency Voltage and Vibration Superposition

Free metal particles are an important cause of insulation failure to the gas-insulated switchgear (GIS) or the gas-insulated transmission lines (GIL), and there are few research reports that free metal particles in GIS/GIL may move and induce insulation breakdown, affected by the high voltage bus voltage and multiple frequency or occasional vibration. In this paper, a simulation model of metal particle movement in the 1100 kV GIS or GIL system is established. The charge, force and movement of free spherical metal particles under the power frequency voltage and mechanical vibration superposition was analyzed. The movement law of spherical metal particles under power frequency voltage was studied. And the influence of different vibration conditions on the movement of metal particles was obtained. Based on the movement law of metal particles, the collision momentum-flight time spectrum (CMFT) of metal particles under the superposition of power frequency voltage and vibration were extracted. The results of this study may provide a new idea for the detection and identification of free spherical metal particles in GIS/GIL. The results show that the threshold voltage of particle take-off is affected by the density and size of metal particles, the amplitude and frequency of vibration. The multiple frequency vibration amplitude can effectively increase the width of the CMFT, the maximum flight time and the maximum collision momentum. And the particle movement of materials with high collision recovery coefficients is more obvious and easier to detect. But occasional vibration has little effect on the movement of spherical metal particles. It can only affect the initial state of the particles and generate a few points to extend the spectrum.
Distributed Dynamic State Estimation for Microgrids

Conventionally, the dynamic state estimation of variables in power networks is performed based on the forecasting-aided model of bus voltages. This approach is effective in the stiff grids at the transmission level, where the bus voltages are less sensitive to variations of the load. However, in microgrids, bus voltages can fluctuate significantly under load changes, the forecasting-aided model may not sufficiently accurate. To resolve this problem, this paper proposes a dynamic state estimation scheme for microgrids using the state-space model derived from differential equations of power networks. In the proposed scheme, the branch currents are the state variables, whereas the bus voltages become the inputs which can vary freely with loads. As a result, the entire microgrids system can be partitioned into local areas, where neighbor areas share the common inputs. The proposed estimation scheme then can be implemented in a distributed manner. A novel Kalman-based filtering method is derived to estimate both states and inputs simultaneously. Only information of common inputs is exchanged between neighboring estimators. Simulation results of the 13-bus Potsdam microgrid (New York State) are provided to prove the feasibility and performances of the proposed scheme.
An N-ary Tree-based Model for Similarity Evaluation on Mathematical Formulae

Accurate and efficient measurements for evaluating the similarity between mathematical formulae play an important role in mathematical information retrieval. Most previous studies have focused on representing formulae in different types to catch their features and combining the traditional structure matching algorithms. This paper presents a new unsupervised model called N-ary Tree-based Formula Embedding Model (NTFEM) for the task of mathematical similarity evaluation. Using an n-ary tree structure to represent the formula, we convert the formula into a linear sequence that can be viewed as the input sentence and then embed the formula by using a word embedding model. Based on the characteristics of mathematical formulae, a weighting function is also used to get the final weighted average embedding vector. Through some experiments on NTCIR-12 Wikipedia Formula Browsing Task, our model can outperform previous formula search engines in Bpref prediction metrics. In addition, compared with traditional tree-based models, NTFEM not only improves the retrieval effect, but also greatly reduces the training time and improves training efficiency.