A Novel Multiple DBC-staked units Package to Parallel More Chips for SiC Power Module

Silicon carbide(SiC) power modules play an essential role in the electric vehicle drive system. To improve their performance, reduce their size, and increase production efficiency, this paper proposes a multiple staked direct bonded copper(DBC) unit based power module packaging method to parallel more chips. This method utilizes mutual inductance cancellation effect to reduce parasitic inductance. Because the conduction area in the new package is doubled, the overall area of power module can be reduced. Entire power module is divided into smaller units to enhance manufacture yield, and improve design freedom. This paper provides a detailed design, analysis and fabrication procedure for the proposed package structure. Additionally, this paper offers several feasible solutions for the connection between power terminals and DBC untis. With the structure, 18dies were paralleled for each phase-leg in a econodual size power module. Both simulation and double pulse test results demonstrate that, compared to conventional layouts, the proposed package method has 74.8% smaller parasitic inductance and 34.9% lower footprint.

Vehicle-oriented ridesharing package delivery in blockchain system

Package delivery via ridesharing provides appealing benefits of lower delivery cost and efficient vehicle usage.Most existing ridesharing systems operate the matching of ridesharing in a centralized manner,which may result in the single point of failure once the controller breaks down or is under attack.To tackle such problems,our goal in this paper is to develop a blockchain-based package delivery ridesharing system,where decentralization is adopted to remove intermediaries and direct transactions between the providers and the requestors are allowed.To complete the matching process under decentralized structure,an Event-Triggered Distributed Deep Reinforcement Learning(ETDDRL)algorithm is proposed to generate/update the real-time ridesharing orders for the new coming ridesharing requests from a local view.Simulation results reveal the vast potential of the ETDDRL matching algorithm under the blockchain framework for the promotion of the ridesharing profits.Finally,we develop an application for Android-based terminals to verify the ETDDRL matching algorithm.

Multi-Component Resource Recycling from Waste Light-Emitting Diode Under Hydrothermal Condition:Plastic Package Degradation,Speciation of Nano-TiO_(2),and Environmental Impact Assessment

Light emitting diodes(LEDs)have accounted for most of the lighting market as the technology matures and costs continue to reduce.As a new type of e-waste,LED is a double-edged sword,as it contains not only precious and rare metals but also organic packaging materials.In previous studies,LED recycling focused on recovering precious and strategic metals while ignoring harmful substances such as organic packaging materials.Unlike crushing and other traditional methods,hydrothermal treatment can provide an environment-friendly process for decomposing packaging materials.This work developed a closed reaction vessel,where the degradation rate of plastic polyphthalamide(PPA)was close to 100%,with nano-TiO_(2)encapsulated in plastic PPA being efficiently recovered,while metals contained in LED were also recycled efficiently.Besides,the role of water in plastic PPA degradation that has been overlooked in current studies was explored and speculated in detail in this work.Environmental impact assessment revealed that the proposed recycling route for waste LED could significantly reduce the overall environmental impact compared to the currently published processes.Especially the developed method could reduce more than half the impact of global warming.Furthermore,this research provides a theoretical basis and a promising method for recycling other plastic-packaged e-waste devices,such as integrated circuits.

DSN-BR-Based Online Inspection Method and Application for Surface Defects of Pharmaceutical Products in Aluminum-Plastic Blister Packages

Ensuring high product quality is of paramount importance in pharmaceutical drug manufacturing,as it is subject to rigorous regulatory practices.This study presents a research focused on the development of an on-line detection method and system for identifying surface defects in pharmaceutical products packaged in aluminum-plastic blisters.Firstly,the aluminum-plastic blister packages exhibit multi-scale features and inter-class indistinction.To address this,the deep semantic network with boundary refinement(DSN-BR)model is proposed,which leverages semantic segmentation domain knowledge,to accurately segment the defects in pixel level.Additionally,a specialized image acquisition module that minimizes the impact of ambient light is established,ensuring high-quality image capture.Finally,the image acquisition module,image detection module,and data management module are designed to construct a comprehensive online surface defect detection system.To validate the effectiveness of our approach,we employ a real dataset for instance verification on the implemented system.The experimental results substantiate the outstanding performance of the DSN-BR,achieving the mean intersection over union(MIoU)of 90.5%.Furthermore,the proposed system achieves an inference speed of up to 14.12 f/s,while attaining an F1-Score of 98.25%.These results demonstrate that the system meets the actual needs of the enterprise and provides theoretical and methodological support for intelligent inspection of product surface quality.By standardizing the control process of pharmaceutical manufacturing and improving the management capability of the manufacturing process,our approach holds significant market application prospects.

Ex⁃situ Measurement of Internal Deformation in Ball Grid Array Package with Digital Volume Correlation

In spacecraft electronic devices,the deformation of solder balls within ball grid array(BGA)packages poses a significant risk of system failure.Therefore,accurately measuring the mechanical behavior of solder balls is crucial for ensuring the safety and reliability of spacecraft.Although finite element simulations have been extensively used to study solder ball deformation,there is a significant lack of experimental validation,particularly under thermal cycling conditions.This is due to the challenges in accurately measuring the internal deformations of solder balls and eliminating the rigid body displacement introduced during ex-situ thermal cycling tests.In this work,an ex-situ three-dimensional deformation measurement method using X-ray computed tomography(CT)and digital volume correlation(DVC)is proposed to overcome these obstacles.By incorporating the layer-wise reliability-guided displacement tracking(LW-RGDT)DVC with a singular value decomposition(SVD)method,this method enables accurate assessment of solder ball mechanical behavior in BGA packages without the influence of rigid body displacement.Experimental results reveal that BGA structures exhibit progressive convex deformation with increased thermal cycling,particularly in peripheral solder balls.This method provides a reliable and effective tool for assessing internal deformations in electronic packages under ex-situ conditions,which is crucial for their design optimization and lifespan predictions.

GatherTweet: A Python Package for Collecting Social Media Data on Online Events

Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, leads to a series of challenges for researchers who want to analyze dynamic public discourse and opinion in response to and in the creation of world events. In this paper we present gatherTweet, a Python package that helps researchers efficiently collect social media data for events that are composed of many decentralized actions (across both space and time). The package is useful for studies that require analysis of the organizational or baseline messaging before an action, the action itself, and the effects of the action on subsequent public discourse. By capturing these aspects of world events gatherTweet enables the study of events and actions like protests, natural disasters, and elections.

基于re3data的中英科学数据仓储平台对比研究

以re3data为数据获取源,选取中英两国406个科学数据仓储为研究对象,从分布特征、责任类型、仓储许可、技术标准及质量标准等5个方面、11个指标对两国科学数据仓储的建设情况进行对比分析,试图为我国数据仓储的可持续发展提出建议:广泛联结国内外异质机构,推进多学科领域的交流与合作,有效扩充仓储许可权限与类型,优化技术标准的应用现况,提高元数据使用的灵活性。

Multi-Objective Prediction and Optimization of Vehicle Acoustic Package Based on ResNet Neural Network

Vehicle interior noise has emerged as a crucial assessment criterion for automotive NVH(Noise,Vibration,and Harshness).When analyzing the NVH performance of the vehicle body,the traditional SEA(Statistical Energy Analysis)simulation technology is usually limited by the accuracy of the material parameters obtained during the acoustic package modeling and the limitations of the application conditions.In order to effectively solve these shortcomings,based on the analysis of the vehicle noise transmission path,a multi-level objective decomposition architecture of the interior noise at the driver’s right ear is established.Combined with the data-driven method,the ResNet neural network model is introduced.The stacked residual blocks avoid the problem of gradient dis-appearance caused by the increasing network level of the traditional CNN network,thus establishing a higher-precision prediction model.This method alleviates the inherent limitations of traditional SEA simulation design,and enhances the prediction performance of the ResNet model by dynamically adjusting the learning rate.Finally,the proposed method is applied to a specific vehicle model and verified.The results show that the proposed meth-od has significant advantages in prediction accuracy and robustness.

Bisphenol A (BPA) Contamination in Whole Milk Marketed in Different Packages

Bisphenol A (BPA), an important endocrine disruptor, is used in the manufacturing of various materials, including food packaging. Ingestion of contaminated foodstuffs is, in fact, the most relevant form of exposure to this substance. However, scarce data on the presence of this contaminant in milk, or whether different types of food packaging influence food contamination are available in Brazil. This study, therefore, aimed to evaluate the BPA contamination of whole milk (fluid and powder) samples packaged in different types of packaging (Tetra Pak?;PET: Poly (ethylene terephthalate;Metallic can (epoxy resin);Polyethylene (PE) and poly (vinylidene chloride) (PVDC);Laminated Film - Metallized Polyester-Polyethylene and glass) and marketed metropolitan region of Rio de Janeiro, Brazil. An analytical method for the BPA determination in milk was optimized for both fluid (pasteurized and ultra-high temperature) and powdered milk samples. A modified QuEChERS method was applied, and BPA determinations were conducted by ultra-performance liquid chromatography coupled with sequential mass spectrometry (HPLC-MS/MS). The validated method was then applied to 51 milk samples, where BPA was detected in five samples (9.8%) and quantified in two (3.8%).

Data Secure Storage Mechanism for IIoT Based on Blockchain

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of IIoT.Blockchain technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the IIoT.In the traditional blockchain,data is stored in a Merkle tree.As data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based IIoT.Accordingly,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data.To solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC structure.Secondly,this paper uses PVC instead of the Merkle tree to store big data generated by IIoT.PVC can improve the efficiency of traditional VC in the process of commitment and opening.Finally,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments.This mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation

Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.

A new package:MySAS for small angle scattering data analysis

In this paper,A MySAS package,which is verified on Windows XP,can easily convert two-dimensional data in small angle neutron and X-ray scattering analysis,operate individually and execute one particular operation as numerical data reduction or analysis,and graphical visualization.This MySAS package can implement the input and output routines via scanning certain properties,thus recalling completely sets of repetition input and selecting the input files.On starting from the two-dimensional files,the MySAS package can correct the anisotropic or isotropic data for physical interpretation and select the relevant pixels.Over 50 model functions are fitted by the POWELL code using x^2 as the figure of merit function.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Benchmark experiment on slab^(238)U with D-T neutrons for validation of evaluated nuclear data

A benchmark experiment on^(238)U slab samples was conducted using a deuterium-tritium neutron source at the China Institute of Atomic Energy.The leakage neutron spectra within energy levels of 0.8-16 MeV at 60°and 120°were measured using the time-of-flight method.The samples were prepared as rectangular slabs with a 30 cm square base and thicknesses of 3,6,and 9 cm.The leakage neutron spectra were also calculated using the MCNP-4C program based on the latest evaluated files of^(238)U evaluated neutron data from CENDL-3.2,ENDF/B-Ⅷ.0,JENDL-5.0,and JEFF-3.3.Based on the comparison,the deficiencies and improvements in^(238)U evaluated nuclear data were analyzed.The results showed the following.(1)The calculated results for CENDL-3.2 significantly overestimated the measurements in the energy interval of elastic scattering at 60°and 120°.(2)The calculated results of CENDL-3.2 overestimated the measurements in the energy interval of inelastic scattering at 120°.(3)The calculated results for CENDL-3.2 significantly overestimated the measurements in the 3-8.5 MeV energy interval at 60°and 120°.(4)The calculated results with JENDL-5.0 were generally consistent with the measurement results.

An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the dataset.The FCSSVM is an improved version of the traditional CS-SVM.It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(RIME)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline.To verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets.The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significant roles.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

ST-Map:an Interactive Map for Discovering Spatial and Temporal Patterns in Bibliographic Data

Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.