Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Permanent Magnet Aging on the Performance of Synchronous Motors for Traction Applications

With the continuous electrification of the industrial and transportation systems, higher demanding specifications are being imposed on electrical motors, leading to higher degradation due to the aging of their components. This work focuses on the aging impact of permanent magnets in synchronous machines for traction applications. To characterize the aging effects on permanent magnets under typical thermal and magnetic profiles of electrical machines, two different NdFeB samples are aged using experimental procedures. Different from previous research, where only the magnetic operation point is evaluated along the aging stage, a partial BH curve of the permanent magnet is obtained. With this partial BH curve, the evolution of the remanent magnetic flux density and relative magnetic permeability is obtained for different aging stages, and for different temperatures and magnetic points. The results are used to evaluate the impact of PM aging on the nominal performance of a permanent magnet synchronous machine through finite element analysis.
LRPRNet: Lightweight Deep Network by Low-Rank Pointwise Residual Convolution

Deep learning has become popular in recent years primarily due to powerful computing devices such as graphics processing units (GPUs). However, it is challenging to deploy these deep models to multimedia devices, smartphones, or embedded systems with limited resources. To reduce the computation and memory costs, we propose a novel lightweight deep learning module by low-rank pointwise residual (LRPR) convolution, called LRPRNet. Essentially, LRPR aims at using a low-rank approximation in pointwise convolution to further reduce the module size while keeping depthwise convolutions as the residual module to rectify the LRPR module. This is critical when the low-rankness undermines the convolution process. Moreover, our LRPR is quite general and can be directly applied to many existing network architectures such as MobileNetv1, ShuffleNetv2, MixNet, and so on. Experiments on visual recognition tasks, including image classification and face alignment on popular benchmarks, show that our LRPRNet achieves competitive performance but with a significant reduction of Flops and memory cost compared to the state-of-the-art deep lightweight models.
Predicting Voltage Changes in Low-Voltage Secondary Networks using Deep Neural Networks

High penetrations of residential solar PV can cause voltage issues on low-voltage (LV) secondary networks. Distribution utility planners often utilize model-based power flow solvers to address these voltage issues and accommodate more PV installations without disrupting the customers already connected to the system. These model-based results are computationally expensive and often prone to errors. In this paper, two novel deep learning-based model-free algorithms are proposed that can predict the change in voltages for PV installations without any inherent network information of the system. These algorithms will only use the real power (P), reactive power (Q), and voltage (V) data from Advanced Metering Infrastructure (AMI) to calculate the change in voltages for an additional PV installation for any customer location in the LV secondary network. Both algorithms are tested on three datasets of two feeders and compared to the conventional model-based methods and existing model-free methods. The proposed methods are also applied to estimate the locational PV hosting capacity for both feeders and have shown better accuracies compared to an existing model-free method. Results show that data filtering or pre-processing can improve the model performance if the testing data point exists in the training dataset used for that model.
Record Framework Utilization for the Collection of Wireless Device Static Users

The cell of today is a typical instrument. It has become a significant "social item," as opposed to only a minor "specialized object," in the regular daily existences of its users. In this article, we look at a few ongoing improvements in the examination of mobile phone data. With the developing openness of enormous, anonymised datasets, this field of concentrate previously seemed decade prior and has since formed into an independent subject. The estimating investigation of record framework utilization for a gathering of mobile gadget users in different spots is introduced in this work. The purpose of this research is to refute the notion that mobile system behaviour can be modelled as being uniform across different environments. The research demonstrates that when participants travel between places, they obtain statistically substantially diverse sets of data. These findings call into question how mobile computing systems are normally evaluated, which often models a user's behaviour as being comparable to her behaviour at a single place over a longer length of time.
Intracellular diversity of WNV within circulating avian peripheral blood mononuclear cells reveals host-dependent patterns of polyinfection

Error-prone replication of RNA viruses generates the genetic diversity required for adaptation within rapidly changing environments. Thus, arthropod-borne virus (arbovirus) populations exist in nature as mutant swarms that are maintained between arthropods and vertebrates. Previous studies have demonstrated that West Nile virus (WNV) population dynamics are host dependent: In American crows, which experience extremely high viremia, purifying selection is weak and population diversity is high compared to American robins, which have 100 to 1000-fold lower viremia. WNV passed in robins experiences fitness gains, whereas that passed in crows does not. Therefore, we tested the hypothesis that high crow viremia allows higher genetic diversity within individual avian peripheral-blood mononuclear cells (PBMCs), reasoning that this could have produced the previously observed host-specific differences in genetic diversity and fitness. Specifically, we infected cells and birds with a novel, barcoded version of WNV and sequenced viral RNA from single cells to quantify the number of WNV barcodes that each contained. Our results demonstrate that the richness of WNV populations within crows far exceeds that in robins. Similarly, rare WNV variants were maintained by crows more frequently than by robins. Our results suggest that increased viremia in crows relative to robins leads to maintenance of defective genomes and less prevalent variants, presumably through complementation. Our findings further suggest that weaker purifying selection in highly susceptible crows is attributable to this higher viremia, polyinfections and complementation. These studies further document the role of particular, ecologically relevant hosts in shaping virus population structure. Author Summary WNV mutational diversity in vertebrates is species-dependent. In crows, low frequency variants are common, and viral populations are more diverse. In robins, fewer mutations become permanent fixtures of the overall viral population. We infected crows, robins and a chicken cell line with a genetically marked (barcoded) WNV. Higher levels of virus led to multiple unique WNV genomes infecting individual cells, even when a genotype was present at low levels in the input viral stock. Our findings suggest that higher levels of circulating virus in natural hosts allow less fit viruses to survive in RNA virus populations through complementation by more fit viruses. This is significant as it allows less represented and less fit viruses to be maintained at low levels until they potentially emerge when virus environments change. Overall our data reveal new insights on the relationships between host susceptibility to high viremia and virus evolution.
Recent Progress in the Development of High-Density TSV for 3-Layers CMOS Image Sensors

Copper TSV in a via last approach were developed for advanced imaging applications. An aspect ratio of 10:1 was targeted with a $\boldsymbol{diameter/space}$ of $1\mu m/1\mu m$ (pitch $2\mu \boldsymbol{m}$) associated to a depth of $10\mu m$. A test vehicle made of a single metal level on each side of a thinned Si substrate bonded onto a thick carrier was used to develop and optimize a process flow compatible with the constraints of a subsequent hybrid bonding process. An electrical resistance of 0. 7 $\Omega$ (median value) was measured on Kelvin patterns and a yield of 100% was obtained on daisy chains made of 10 000 TSV. No failure was observed after 2000 h of HTS at 150°C nor after 2000 cycles of TCT between $-55^{\circ}\boldsymbol{Cand}+150^{\circ}C$.
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition <Supplementary Material>

The Supplementary Material is structured as follows. In Section 1, we show results obtained on the RobotCar Seasons v2 and Extended CMU Seasons datasets split by query condition. Section 2 contains additional quantitative results on the Pittsburgh 30k and Tokyo 24/7 datasets, as well as additional results on the computation time split across the feature extraction and feature matching processes. Section 3 contains a variety of additional ablation studies, some of them further demonstrating the robustness of our method, while others detail experiments that might logically be expected and were conducted, but that were not fruitful. Section 4 contains various qualitative results, showcasing both challenging success cases of Patch-NetVLAD and some failure cases. This section also contains examples of incorrect dataset annotations, where Patch-NetVLAD actually found the correct match but this match was not within the ground-truth matches due to errors in the ground-truth. Finally, in Section 5 we describe in detail the six key benchmark datasets on which we evaluate Patch-NetVLAD.
Warpage Estimation of Panel-Level Package from Panel to Strip by Using Multi-Scaling Sub-modeling Technique

The portable devices have involved the human life for decades. The high input/output (I/O) counts, and high frequency has been essential demands for the packaging of devices. The novel panel-level fan-out package (FO-PLP) is the solution for the higher demands. The warpage is always a difficult issue for the large area and thin film structure during the manufacturing process. The finite element analysis (FEA) is regarded as an efficient method to estimate the warpage. The difficulties of FEA for FO-PLP is the size changing from panel to strip. The warpage will change tremendously with different size of manufacturing process with a non-continues stress transmission. This research proposed a FEA with multiple point constraint (MPC) method for warpage estimation from panel to strip for FO-PLP. The chemical shrinkage of epoxy material is considered. The FE model is divided into multiple regions based on the ratio of copper contents for FEA. The element is non-continuous on the edge between the panel and strip. The stress between panel and strip is transmit with the MPC method. The complicated fine RDL, copper pillar, solder, and chip in packaging are modeling with equivalent layers to decrease the element numbers. The material test of FEA is utilized to obtain the material properties of equivalent layers, such as coefficient of thermal expansion, Young's modulus, shear modulus, and Poisson’ ratio. The reference temperature for equivalent layers are set with modified Timoshenko bimaterial approach in FEA. The estimated warpage of panel with complete RDL is 0.435 mm from FEA, which is close to the measured warpage of 0.419 mm from experiment. The variation between the simulation and experiment is 3.81 % only. Then the process of reflow, molding and carrier removing are finished on the separated strip. The warpage of strip from simulation and experiment are -2.847 mm and -2.76 mm, respectively. The variation of 3.15 % between the simulation and experiment prove the proposed method can predict the warpage behavior of the FO-PLP.
Ultra High Density Low Temperature SoIC with Sub-0.5 μm Bond Pitch

The future continuation of Moore's Law will require a combination of transistor scaling as well as system scaling through the utilization of 3D chip stacking. Transistor scaling in advanced process nodes has become increasingly challenging and is projected to face difficulties in maintaining historical trends in Moore's Law scaling. System scaling utilizing 3D chip stacking in conjunction with transistor scaling offers an enticing avenue to continue density and power/performance scaling along historical trends. 3D chip stacking utilizing system on integrated chip (SoIC) has demonstrated high density interconnects and energy efficient performance. SoIC has enabled new architectures such as memory-on-logic stacking and logic-on-deep trench capacitor (DTC) with exceptional energy efficient performance (EEP). Further scaling of the 3D interconnect pitch into the sub-micron regime will lead to increasing EEP and enable new 3D chip stacking architectures such as partitioning 2D functional blocks into 3 dimensions. These new architectures are anticipated to provide significant gains in computing performance, bandwidth, and transistors per unit area. In this paper we present a bond pitch of 400 nm in a wafer-on-wafer low temperature SoIC configuration. At the chip-to-chip interface I/O interconnect level, the EEP is over 2000 times greater than SoIC currently in mass production. Process challenges and associated mitigation strategies resulting in excellent electrical and reliability performance will be addressed. In particular, we have achieved the required tight bonding overlay control for highly scaled bond pads.
Forecasting Consumer Price Index (CPI) Using Deep Learning and Hybrid Ensemble Technique

In today’s day and age, economic crises are all over the world due to high inflation. Inflation is a rise in price of the goods and services produced in a country. As a result of rising prices, a given amount of money can now buy fewer goods and services. The general public’s cost of living is affected by this loss of purchasing power, which ultimately slows economic growth. Thus, it has a negative impact on the purchasing power of the people. Various sorts of baskets of commodities are generated and tracked as price indices to calculate inflation or deflation, depending on the chosen set of goods and services used. One type of price index proposed in this project is Consumer Price Index (CPI), which looks at the weighted average of costs for a variety of products and services like transportation, food, and healthcare. This paper proposes different deep learning time series models such as LSTM, BiLSTM and hybrid ensemble learning to forecast the Indian consumer price index (CPI). These two single RNN models (LSTMs and BiLSTMs) are compared with the hybrid ensemble learning model to see which gives better forecasting results for the consumer price index.
Adaptive Model Predictive Control for Large-scale Coordinated PEV Recharging

This paper presents a centralised model predictive control (MPC) approach for the control of the recharging process of a fleet of plug-in electric vehicles (PEVs), The first control goal is to recharge the PEVs so that the user preferences on the final desired state of charge, and on the time available for recharging are respected. A second goal is from the perspective of the grid operator. The charging processes should be coordinated in such a way that the aggregated power profile tracks a proper power reference established by the grid operator to ensure the safe operation of the grid. We propose an adaptive MPC formulation, in which the weights of the objective function are changed based on the feedback on the current progress of the charging sessions. This allows to better adapt the control effort to the current PEVs' status, and to reduce the computation burden by allowing to decrease the MPC prediction horizon. We validate the proposed approach via simulations on a large scale realistic scenario randomly generated.
Quasi-Z-Source Cascaded H-Bridge Multilevel Inverters for PV Applications with Fault

Nowadays, the use of quasi-Z-source cascaded H-bridge multilevel inverters (qZS-CHBMLI) in photovoltaic (PV) has gained their attention. They permit the single-stage power transformation of a PV generator adjusting the DC voltage independently in each module to operate each PV generator at maximum power. This paper develops a new control strategy that allows the system to continue operating after a fault in a module, by increasing the DC voltage in the operating modules. The control strategy is employed to a grid-connected qZS-CHBMLI, which consists of three cascaded qZSI powered by independent 4.8 kW PV cells. Under several operating circumstances, the proposed control system is evaluated. The results demonstrate how the control strategy applied to the system successfully reacts to the failure of a module.
Design, Analysis and Implementation of Microstrip UWB Antenna to monitor the Fetus in Uterus

With the growth of the healthcare and biomedical industries, biomedical telemetry antenna has attracted a lot of interest. With the help of conventional hospital visits and follow-up routine check-ups, it is now possible to remotely monitor a patient's physiological indications. This is made possible by the Use of integrated Hilbert curve expandable antenna technology. The integrated Hilbert curve expandable biomedical antenna devices (IHEBAD) that have been proposed play a significant part in the monitoring of the fetus found in the patient using wireless telemetry such as an ISM (2.45 GHz) and UWB (4.25 GHz) band. For the extendable antenna, a number of elements need to be taken into account, including miniaturization, patient safety, biocompatibility, low power consumption, reduced frequency band of operation, and dual band specifications. Choosing an antenna is a difficult task for Hilbert to design. This paper presents the key finding in the integrated Hilbert curve expandable biomedical antenna proposal with wearable substrate. To confirm the use of the suggested antenna design, the proposed antenna's essential characteristics are analyzed. They also demonstrate a good level of simulation agreement.
Low-stress TSVs for high-density 3D integration

To address the issue of thermomechanical reliability caused by Through Silicon Via (TSV), this paper proposes a rugby-shaped hollow W-TSV. Simulations and experiments are carried out to study its performance. The simulation results show that the proposed TSV can relieve the thermal stress caused by TSV and has the characteristics of low stress. The maximum thermal stress induced by TSV on the substrate is only 176 MPa, and the thermal stress is reduced by 62.4% and 60.5% along the TSV axial and radial directions, respectively. An ultra-high-density (1600TSVs/mm2) TSV array with a size of $\mathbf{640}\times \mathbf{512}$, a pitch of $\mathbf{25}\ \boldsymbol{\mu}\mathbf{m}$, and an aspect ratio of 20.4 was fabricated, and its electrical properties and thermomechanical reliability were tested. The maximum stress on the surface of the TSV array is 31.02 MPa, and the keep-out zone (KOZ) region is not required. The resistance change of TSV after the thermal cycling test is less than 2%. It is demonstrated that the proposed hollow W-TSV has good thermomechanical reliability and electrical properties and can be used for high-density three-dimensional integration.
A Robust Breast Cancer Classification Model using Extra-Trees Classifier for Histopathological Image

Thousands of people die every quarter from breast cancer. Diagnosis and treatment at an early stage can drastically lower mortality rates. Traditional manual diagnosis, on the other hand, necessitates a large amount of labor by pathologists and is prone to diagnostic mistakes the longer they work. Rapid and accurate diagnosis are greatly aided by automatic histopathological image recognition. The biomedical industry has been drawn to Artificial Intelligence and its innovative methodologies because of its familiarity with the field's successes. Recent research has shown that AI can grasp details better than humans, leading to more accurate findings that aid professionals in making more informed judgments. This study presents the Extra-Tree classifier (ETC) for breast cancer image categorization. These findings demonstrate that ETC outperformed the other algorithms we examined for this data in terms of accuracy. Future researchers in the field of breast cancer will be able to use the findings of this study to guide their investigations and inform their efforts to boost the efficiency of certain algorithms.
Dynamic Response of Bridge-Ship Collision Considering Pile-Soil Interaction

According to most countries’ norms, and to find the effect of the bridge collision the equivalent static method was designed for bridge-ship collision, ignoring the dynamic effects of shocks. It is sharply different from actual situation. So based on the theory of Winkler foundation, shearing strain theory of Timoshenko and potential energy variation functional principle of Hamilton, the simulation models of bridge piers was built considering the pile–soil interaction. Lateral transient vibration equation of bridge piers was concluded. Based on the theory of integral transform, the differential equation of the collision system and the boundary conditions were transformed with Laplace transformation; the analytical solution of the stress wave in frequency domain was concluded. And then the inversion of solution in frequency domain was carried out using Matlab based on the Crump inverse transformation. Finally the dynamic response law of displacement, normal stress and the shear stress of bridge piers were obtained.
Machine Learning Based PCB/Package Stack-up Optimization for Signal Integrity

PCB/package stack-up design optimization is time-consuming and requiring a great deal of experience. Although some iterative optimization algorithms are applied to implement automatic stack-up design, evaluating the results of each iteration is still time-intensive. This paper proposes a combined Bayesian optimization-artificial neural network (BO-ANN) algorithm, utilizing a trained ANN-based surrogate model to replace a 2D cross-section analysis tool for fast PCB/package stack-up design optimization. With the acceleration of ANN, the proposed BO-ANN algorithm can finish 100 iterations in 40 seconds while achieving the target characteristic impedance. To better generalize the BO-ANN algorithm, a strategy of effective dielectric calculation is applied to multiple-dielectric stack-up optimization. the BO-ANN algorithm will be able to output optimized stack-up designs with dielectric layers chosen from the pre-defined library and the obtained designs are verified by 2D solver.
A New Approach to Fuzzy Arithmetic

This work shows an application of a generalized approach for constructing dilation-erosion adjunctions on fuzzy sets. More precisely, operations on fuzzy quantities and fuzzy numbers are considered. By the generalized approach an analogy with the well known interval computations could be drawn and thus we can define outer and inner operations on fuzzy objects. These operations are found to be useful in the control of bioprocesses, ecology and other domains where data uncertainties exist.