Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Internal Resonances in Power Transformers by Using Instrumental Variable Vector Fitting

Procedures for modeling potential/ power transformers and reactors in order to support electromagnetic transients analysis have been addressed by many recent researchers. Among all methods for obtaining black-box models, the Frequency Domain Vector Fitting (FD-VF) or, simply VF, has several successful application reports in electrical power systems. This article is two purpose: first to describe a procedure that combines instrumental variables to the VF method, called the Instrumental Variable VF (IV-VF). The main advantage of the IV-VF is the fact that if the iterative procedure converged, an optimal local solution is obtained. Also to present a case study based on actual single-phase transformer measurements (Benchmark JWG A2/C4.52 for Power Transformers) and a GIS installation, where IV-VF is used for evaluating internal resonances in power transformers due to high frequency transients. The knowledge of external and internal winding response to these transients contribute to insulation design and reliability in field operation.
Diagnosis and management of a case of retroperitoneal eosinophilic sclerosing fibroplasia in a cat

Case summary A 4-year-old neutered male cat was presented with a 2-month history of intermittent constipation that progressed to obstipation. Primary clinical findings included a large, multi lobulated mass in the caudodorsal abdomen, peripheral eosinophilia and hyperglobulinemia. Abdominal imaging revealed a multilobulated, cavitated mass in the sublumbar region. Exploratory celiotomy revealed multiple firm masses in the sublumbar retroperitoneal space causing ventral displacement and compression of the descending colon with extension of the masses into the pelvic canal. Histopathology was consistent with feline gastrointestinal eosinophilic sclerosing fibroplasia (FGESF). Aerobic culture was positive for Staphylococcus aureus. The cat was treated with prednisolone (2 mg/kg PO q24h), lactulose (0.5 g/kg PO q8h), amoxicillin/clavulanic acid (62.5 mg/cat PO q12h for 1 month) and fenbendazole (50 mg/kg PO q24h for 5 days). Six months postoperatively, the cat had no recurrence of clinical signs. Repeat evaluation and imaging at day 732 postoperatively revealed marked improvement of the abdominal mass, resolution of peripheral eosinophilia and no clinical signs with continued prednisolone therapy (0.5 mg/kg PO q24h). Relevance and novel information This is a report of a primary extramural FGESF lesion, and the first description of characteristics of FGESF on CT. Previous evidence suggests that the most favorable outcomes require immunosuppressive therapy and complete surgical excision; however, this case demonstrates a favorable outcome with medical management alone.
Tropistic RRT*: An Efficient Planning Algorithm via Adaptive Restricted Sampling Space

As a sampling based path planning method, Rapidly-exploring Random Tree (RRT) is powerful for its high efficiency in finding feasible solutions. Optimal RRT (RRT*) improves RRT by asymptotically searching the optimal solution, however, at the cost of time consuming. This paper proposes an efficient optimal planning algorithm based on RRT* with adaptive restricted sampling space, which is inspired by the tropism of plants and named as tropistic RRT*. Tropistic RRT* improves the efficiency of the path planning by restricting the sampling space to approach the goal position just like the phototropism of plants. To validate our proposed algorithm, we carry out both path planning and motion planning experiments in 2D environments. In the path planning experiments, we compare the proposed algorithm with RRT* and Informed RRT* and experimental results show that our method outperforms the other two in the convergence rate, final solution cost and robustness. Furthermore, we also implement our algorithm in solving motion planning problem for a nonholonomic mobile robot and compare the time cost in the robot navigation process. Compared results exhibit that the time cost of our algorithm is much less than that of Real-Time RRT* in both static and dynamic environments.
Numerical Analysis of the Influence of Polymeric Materials on a MEMS Package Performance Under Humidity and Temperature Loads

The rapid expansion of the Internet of Things (IoT) and consumer electronics is driving the demand for microelectromechanical systems (MEMS) in the area of wearables, smartphones, and home and building applications. MEMS sensor packages feature a variety of polymeric materials which can significantly affect their behavior under environmental loads as humidity or temperature. A broad range of different polymeric materials can be applied in the packages, but to get a good MEMS sensor performance, an optimized application tailored material combination should be applied. To analyze the effect of the applied material types an advanced simulation approach has to be considered. A hygro-thermo-mechanical simulation based on measured temperature and moisture dependent material properties is presented. As MEMS sensor packages are complex multimaterial systems, the main challenge is the modeling of the moisture discontinuities in the interfaces. The discontinuity of moisture concentration has been solved by using the solubility approach.
Design of Lower Extremity Rehabilitation Robots with Magnetorheological Dampers and Wire-Driven System*

Lower extremity rehabilitation robots play significantly important role in rehabilitation medicine, which aims to bring back the capabilities of patients’ lower limbs because of illness, injury and disease. However, controllability, energy consumption and control precision of the robot motion remain difficult to solve out. In many cases, electric motors are required to have high output torque to move the entire machine legs along with human legs, while extravagant motor torque may lead to excessive bending, fracture or other secondary damages. And the motors would generate the braking torque to prevent this at the cost of consuming large armature current. Controllable braking torque also counts while patients’ active anti-resistance training. Smart materials have provided inspiration and magneto-rheological (MR) fluids vary in rheological behavior with responding to applied magnetic field. Based on the characteristics, magnetorheological dampers (MR dampers) were developed and have realized variable damping resistance according to different magnetic field. Besides, wire-driven system is another guarantee of safety and controllability due to its flexible structure and compliant control. So, with combination of MR dampers and wire-driven system, the lower extremity rehabilitation robot would perform better, more efficiently and more safely. Actually, such robot can hardly be seen in previous related research or publication. In this paper, three kinds of rehabilitation robots with MR dampers and wire-driven system are introduced. They are standing/walking rehabilitation robot with cylindrical dampers, standing/walking rehabilitation robot with discal dampers and sitting/lying rehabilitation robot with discal dampers. Their advantages and drawbacks are also explained.
Finding Unstable Operating Points via One-Dimensional Manifolds

Renewable energy sources reinforce the energy supplied, but their sudden power changes can lead the system to unstable operating points. Therefore, finding the unstable points by varying the power injected into generation buses can help determine the robustness of a system with respect to the high penetration of renewable generation. In this paper, unstable solutions and bifurcation points of full QV and PV curves of one-dimensional manifolds are focused. The continuation power flow method is used to trace both curves. The simulations carried out in IEEE 14-bus system indicate that in the presence of generators injecting a large amount of active power, the system could become unstable without major disturbances since one may identify bifurcations and unstable solutions around the nominal voltage.
CAS-YNU Multi-modal Cross-view Human Action Dataset

Human action recognition is a hot research topic in computer vision, which has extensive applications including human-computer interaction, robot and surveillance. Some outstanding human action datasets have been published. However, they still have some defects, such as the limited modality types, camera views, complexity of scene and action categories. Furthermore, most of them are devised for a subset of the action recognition, such as single-modal recognition and single-view recognition. In this paper, we propose a multi-modal cross-view human action dataset (named CAS-YNU MCHAD). It consists of five synchronous data modalities, including RGB image, depth maps, human segment maps, skeleton data and inertial data. Our dataset contains 14782 action samples from 50 subjects and 10 actions in two perspectives. Extensive benchmark experiments on this dataset are conducted with the state-of-the-art recognition approaches. Experimental results illustrate that it is challenging on cross-view action recognition. We also provide the baseline for the assessment of existing recognition methods.
Fault Diagnosis Method Based on MFCC Fusion and SVM*

In order to effectively improve the expressiveness and classification accuracy of fault signal characteristics of equipment, this paper proposed a fault diagnosis method based on Mel-Frequency Cepstrum Coefficient (MFCC) fusion and Support Vector Machines (SVM). First of all, the MFCC features, the Wavelet Packet Decomposition Energy features and the Zero-crossing rate (ZCR) features of the signal are separately extracted. Then, linearly combining the three features based on the MFCC features to obtain the MFCC fusion features. And the SVM classifier is used to classify the faults. Experiments show that compared with the traditional single MFCC features and Wavelet Packet Decomposition Energy features, the MFCC fusion features and SVM method have higher classification accuracy under the same noise environment.
A Mixed Integer SDP Method for Optimal Meter Placement in Power Transmission Systems

This paper proposes an optimization method to optimally place meters in transmission systems aiming at enhanced state estimation accuracy and minimized number of installed meters. Two optimization criteria are considered for the meter placement formulation, both yielding objective functions with integer decision variables subject to linear matrix inequality constraints. An efficient mixed integer semidefinite programming algorithm is used for solving the problem. The validity of the proposed method is evaluated via numerical simulations on the IEEE 14- and 30- bus benchmark systems.
An Application of Machine Learning for a Smart Grid Resource Allocation Problem

The ability to predict aggregator profits is important in the design of an efficient aggregator-based residential demand response (DR) system. In this paper, supervised machine learning models are designed based on historical data to investigate the influence of customer schedulable loads and the forecasted daily electricity price profile on aggregator profits. The k-nearest neighbors (ICNN) and Gaussian process regression (GPR) are chosen because of their consistent performance and high accuracy compared to other machine learning (ML) classification and regression algorithms. Our study demonstrates the classification model is an effective approach to identify the set of schedulable loads that may yield high aggregator profits and the regression model may enable awareness of day-ahead aggregator profits.
Kingsoft’s Neural Machine Translation System for WMT19

This paper describes the Kingsoft AI Lab’s submission to the WMT2019 news translation shared task. We participated in two language directions: English-Chinese and Chinese-English. For both language directions, we trained several variants of Transformer models using the provided parallel data enlarged with a large quantity of back-translated monolingual data. The best translation result was obtained with ensemble and reranking techniques. According to automatic metrics (BLEU) our Chinese-English system reached the second highest score, and our English-Chinese system reached the second highest score for this subtask.
Preparation and Characterization of Electroplated Cu/Graphene Composite

Cu and unoxidized graphene composite films were prepared by electroplating under room temperature. Graphene was added to the electrolyte of Cu to form composite materials to improve the performance of Cu for interconnection. Composite electrolyte containing high concentration of graphene (up to 0.5 g/L) was prepared by adding CTAB as surfactant. And ultrasound was also used to increase dispersion degree and reduce graphene agglomeration in electrolyte. Average coefficient of thermal expansion (CTE) of the composite films determined by thermo-mechanical analysis (TMA) shows a decrease from 17.3 ppm/K to 14.2 ppm/K from 260 K to 320 K, which reduce CTE by 18 % compared with Cu. CTE of the composite materials can be reduced to 10 ppm/K at 243 K, which is only 60 % of CTE of Cu. The thermal conductivity of the composite materials measured by phase sensitive transient thermo-reflectance (PSTTR) technique shows an improvement from 385 W/m.K to 468 W/m.K. CTE and thermal conductivity of the composite materials both decrease with the increase of current density. And they also both increase with the increase of graphene concentration. The composite materials also show good mechanical properties. The average hardness is 2.5 GPa, which is about 2 times of Cu. And the average elastic modulus is 145 GPa, which is 38 % higher than that of Cu. The resistivity of the composite materials is basically equivalent to that of pure Cu. Result of the experiment proved that the composite materials have better performance under low temperature conditions. The improvement of material properties makes composite materials have good application prospects for 3D interconnection in the near future.
Reliable Data Communications Device Configuration Using IEC61850

Model-driven development (MDD) and model driven engineering (MDE) are techniques where abstract models are used to represent the important components of any infrastructure. IEC61850 is used as a model driven architecture on which to base substation design, the configuration of instrumentation and the final implementation. The IEEE are standardizing YANG for Ethernet and the data communications industry are beginning to use YANG models as the basis for network configuration, with NETCONF as a configuration and state assessment protocol. Information may be extracted from IEC61850 SCL files which can be used to create YANG models to dynamically build an abstract representation of the required network. This creates a vendor-neutral generalized solution to automate the specification and configuration of data communications network devices in electrical substations.
A Blocker-Tolerant Two-Stage Harmonic-Rejection RF Front-End

SAW-less wideband receivers need to operate linearly in the presence of strong out-of-band blockers. In this paper, we introduce a blocker tolerant harmonic rejection RF front-end which is able to suppress blockers present at the local oscillator harmonics. The suppression is achieved by applying harmonic rejection in two stages, such that the first harmonic rejection already occurs at the output of LNA. The proposed front-end achieves this harmonic rejection with simpler 6-phase LO clocking and reduced number of base-band signal paths compared to 8-phase HR architectures. Further, the proposed design does not require any precise gain coefficients and implementing the harmonic rejection in two stages makes it more mismatch tolerant. In addition, near-band blocker linearity is improved by implementing a third order base-band feedback response which acts in conjunction with N-path filtering. Implemented in a 28nm FDSOI process, the front-end demonstrate 18-37dB harmonic rejection from the first stage and around 46-53dB of harmonic rejection from the second stage with a state-of-the-art blocker compression point of 2.5dBm for a third harmonic blocker and a near-band blocker compression point of -6.5dBm.
A MEMS Microphone in a FOWLP

This work presents a fully functional miniaturized MEMS microphone demonstrator assembled within a modified Fan Out Wafer Level Package (FOWLP) process chain. Core of the development is the adaption of the FOWLP-process to MEMS-microphones, which have not yet been fully processed in the wafer fab. Instead, these microphone chips contain non-released membranes making them sufficiently robust to withstand backend processes, such as laminating and molding. The membrane release itself is performed on the reconstituted mold-wafer and postponed to a later process step. Mechanical stress effects induced by the package onto the MEMS are limited to a minimum by the implementation of stress decoupling suspension structures on the MEMS die.
Automatic Transient Thermal Impedance Tester for Quality Inspection of Soldered and Sintered Power Electronic Devices on Panel and Tile Level

Transient thermal analysis (TTA) is an established method to evaluate thermal integrity of interconnects in power semiconductor modules. In this paper the suitability to detect bad interconnects is investigated. In especially for sintered interconnects TTA has potential advantages compared to standard methods like X-Ray where voids but no bad wetted contact can be observed or SAM (scanning acoustic microscopy) where the device has to be immerged in water. An automated TTA equipment prototype was developed to enable fast automatic mass testing for soldered and sintered MOSFETs. The automatic TTA tester was benchmarked to X-Ray, SAM and shear strength tests on sintered MOSFETs. Thereby different packages, surface metallization and sinter profiles were tested. The measurements showed that the evaluation of X-Ray and SAM data is strongly limited for sintered interconnects. With TTA and the destructive shear strength test, small variation in quality were detectable.
Energy transfer for lower limb rehabilitation robot: A Pilot Analysis *

The form and the mechanism of energy transfer for lower limb rehabilitation robot are very important, which not only affect the structure of rehabilitation robot, but also involve patient’s safety. This study aimed to analyze different energy transfer methods for lower limb rehabilitation robot. Based on the analysis of lower extremity anatomy and gait, pros and cons of common-used energy transfer form and mechanism are introduced, respectively, such as rotary motor type, ball screws type, and pneumatic muscle type. Moreover, efficiency of energy transfer which is convenient to practical application for lower limb rehabilitation robot is emphasized as well as safety. Finally, future development trend of energy transfer for lower limb rehabilitation robot is discussed combining with the biggest demands in china. This pilot analysis would give some helps for the future design and practical application of lower limb rehabilitation robot.
DOES SHILAJIT HAVE AN EFFECT ON NEW BONE REMODELLING IN THE RAPID MAXILLARY EXPANSION TREATMENT ? A BIOCHEMICAL , HISTOPATHOLOGICAL AND IMMUNOHISTOCHEMICAL STUDY

Mine GEÇGELEN CESUR1, Gözde ÖĞRENİM1, Kanat GÜLLE2, Fevziye Burcu ŞİRİN3, Meryem AKPOLAT4, Gökhan CESUR5 1 Department of Orthodontics, Faculty of Dentistry, Adnan Menderes University, Aydın, Turkey. 2 Department of Histology and Embryology, Faculty of Medicine, Suleyman Demirel University, Isparta, Turkey. 3 Department of Biochemistry, Faculty of Medicine, Suleyman Demirel University, Isparta, Turkey. 4 Department of Histology and Embryology, Faculty of Medicine, Bulent Ecevit University, Zonguldak, Turkey. 5 Department of Physiology, Faculty of Medicine, Adnan Menderes University, Aydın, Turkey.
Automatic associations of breast cancer and heart disease with fruit and vegetables and physical activity

Objectives: This exploratory research examined if breast cancer or heart disease is automatically associated with physical activity compared to fruit and vegetable stimuli; if reading messages about reducing risk of breast cancer or heart disease through physical activity and fruit and vegetable consumption would affect automatic associations; and if automatic associations were related to intentions to be physically active or consume fruit and vegetables. Methods: Participants were 80 women who completed pretest measures of automatic associations, then read a breast cancer, heart disease, or control message, followed by posttest measures. Results: There was a significant association of breast cancer–related words with fruit and vegetables compared to physical activity. Heart disease was also more strongly associated with fruit and vegetables than physical activity at pretest but not at posttest. There were no other significant findings. Conclusion: This research highlights that fruit and vegetables rather than physical activity are more strongly associated with perceptions of breast cancer and heart disease. Automatic associations are an attitudinal construct, and the strength of association between fruit and vegetables, rather than physical activity, indicates how messages may be processed.
Distilling deep neural networks with reinforcement learning

Deep architecture can improve performance of neural networks whereas it increases the computational complexity. Compressing networks is the key to solve this problem. The framework Knowledge Distilling (KD) compresses cumbersome networks well. It improved mimic learning, enabling knowledge to be transferred from cumbersome networks to compressed networks without constraint of architectures. Inspired by AlphaGo Zero, this paper proposed an algorithm combining KD with reinforcement learning to compress networks on changing datasets. In this algorithm, the compressed networks interact with the environment made by KD to produce datasets that are appropriate w.r. t the model. Monte Carlo Tree Search (MCTS) of AlphaGo Zero is used to produce the datasets by making a trade-off between the prediction of compressed networks and the knowledge. In experiments, the algorithm proved to be effective in compressing networks by training ResNet on CIFAR datasets, with mean squared error as the object function.
Online Load Control in Medium Voltage Grid by Means of Reactive Power Modification of Fast Charging Station

Fast charging stations support the electrification of transportation sector by reducing the charging time of electric vehicles. However, this process requires considerable power availability from the power system. In order to meet increasing load demand, the system operator is forced to oversize the distribution grid components. In order to maintain the system within its ampacity, the operator can adopt innovative strategies to handle peak demand. In this paper, a peak-shaving strategy for fast charging stations, based on the on-line load control, is proposed by means of reactive power injection. Controlling the reactive power injection from fast charging stations, the online load control is able to reduce the voltage amplitude in the distribution grid, therefore shave the active power consumption of voltage-dependent loads. The investigation the proposed control scheme has been implemented in a modified 20kV radial feeder. The analysis has showed the effectiveness in shaving the peak load.