Intermetalic Growth Rate in Transient Liquid Phase Sintering of Pb-Free Solder Interconnects

Following the electron devices are widely used in daily life, Pb-free solder alloy, as the replacement of Pb solder joint material, needs extensive researches to observe the properties for using and simulation purpose. Solder are used as the joint to connect two work pieces in printed wiring board of electronics. Most lead-free solders comprise tin (Sn) as the majority component, and nominally pure b-Sn is the majority phase in the microstructure of these solders. The most important thing for solder joint that researchers care about is its life cycle. Due to the incomplete of the mechanical profile of Pb-free solder joint for now, this research worked on obtain data of life cycles. At the boundary of the Sn phase and Ag phase, the intermetallic would grow during the heating process, which affects the life cycle of the solder. This study incorporates mechanical testing and measurement of the intermetallic in scanning electron microscope (SEM) images in image J, to get enough data of life cycle to form a profile and the effects of the intermetallic. The measurement on images shows that the intermetalic layer grows in scallop-shape and the thickness increases with the temperature and sintering time. The growth rate can be modeled as a linear equation of the power of one half of the sintering time. This measurement will contribute to the ongoing research about the transit liquid phase sintering and the ball-grid array (BGA) reliability

Efficient VOLE based Multi-Party PSI with Lower Communication Cost

We present a new method for doing multi-party private set intersection against a malicious adversary, which reduces the total communication cost to $O(nl\kappa)$. Additionally, our method can also be used to build a multi-party Circuit-PSI without payload. Our protocol is based on Vector-OLE(VOLE) and oblivious key-value store(OKVS). To meet the requirements of the protocol, we first promote the definition of VOLE to a multi-party version. After that, we use the new primitive to construct our protocol and prove that it can tolerate all-but-two malicious corruptions. Our protocol follows the idea of [RS21], where each party encodes the respective set as a vector, uses VOLE to encrypt the vector, and finally construct an OPRF to get the result. When it comes to multi-party situation, we have to encrypt several vectors at one time. As a result, the VOLE used in [RS21] and follow-up papers is not enough, that brings our idea of an multi-party VOLE

Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the Random Forest algorithm

Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the Random Forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in structural characteristics between most crops were the largest. At the same time, the images in spring (April and May) and autumn (October) also contributed to the crop classification since they respectively provided unique information for discriminating fruit crops (almond and walnut) as well as summer crops (corn, sunflower, and tomato). As a result, the combined use of only four acquisitions (dated May, July, August, and October for 2011 and April, June, August, and October for 2014) was adequate to achieve a nearly-optimal overall accuracy. In light of the promising classification accuracies demonstrated in this research, it becomes increasingly viable to provide accurate and up-to-date crops inventories over large areas based solely on multitemporal polarimetric SAR

Adaptive neural network cascade control system with entropy-based design

A neural network (NN) based cascade control system is developed, in which the primary PID controller is constructed by NN. A new entropy-based measure, named the centred error entropy (CEE) index, which is a weighted combination of the error cross correntropy (ECC) criterion and the error entropy criterion (EEC), is proposed to tune the NN-PID controller. The purpose of introducing CEE in controller design is to ensure that the uncertainty in the tracking error is minimised and also the peak value of the error probability density function (PDF) being controlled towards zero. The NN-controller design based on this new performance function is developed and the convergent conditions are. During the control process, the CEE index is estimated by a Gaussian kernel function. Adaptive rules are developed to update the kernel size in order to achieve more accurate estimation of the CEE index. This NN cascade control approach is applied to superheated steam temperature control of a simulated power plant system, from which the effectiveness and strength of the proposed strategy are discussed by comparison with NN-PID controllers tuned with EEC and ECC criterions

Performance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasets

The number of clusters (i.e., the number of classes) for unsupervised classification has been recognized as an important part of remote sensing image clustering analysis. The number of classes is usually determined by cluster validity indices (CVIs). Although many CVIs have been proposed, few studies have compared and evaluated their effectiveness on remote sensing datasets. In this paper, the performance of 16 representative and commonly-used CVIs was comprehensively tested by applying the fuzzy c-means (FCM) algorithm to cluster nine types of remote sensing datasets, including multispectral (QuickBird, Landsat TM, Landsat ETM+, FLC1, and GaoFen-1) and hyperspectral datasets (Hyperion, HYDICE, ROSIS, and AVIRIS). The preliminary experimental results showed that most CVIs, including the commonly used DBI (Davies-Bouldin index) and XBI (Xie-Beni index), were not suitable for remote sensing images (especially for hyperspectral images) due to significant between-cluster overlaps; the only effective index for both multispectral and hyperspectral data sets was the WSJ index (WSJI). Such important conclusions can serve as a guideline for future remote sensing image clustering applications

Division Managers’ Private Information and Capital Investment: Exploiting External Social Connections as an Information Source

We examine the relationship between division-level information and capital investment in conglomerates, exploiting the external social connections of division managers (DMs) as an information source. We find that DMs who are socially connected with the CEOs of industry peers undertake more investment than those without such connections. The documented effect is stronger when (i) the DM’s information source is more useful, proxied by connected external firms having superior performance, high growth, large market shares, or experienced CEOs; (ii) the industry environment is more uncertain and less transparent; and (iii) the DM is more influential within the conglomerate. Along with increased investment, connected divisions display greater responsiveness to investment opportunities and subsequently realize higher profitability. Overall, division-level information helps improve capital investment decisions despite exacerbated information asymmetries

Exploring achievement gamification on online medical quality based on machine learning and empirical analysis

How to improve online medical quality is an important challenge for practitioners of digital health platforms. Gamification creates new opportunities to deal with the problem persistent in online health services. To better understand the role of gamification in online health services context, this study intends to use the research method of machine learning and natural experiment to explore the impact of achievement gamification on online medical quality in online health services, as well as the moderating effects of doctors’ personality and image. Theoretically, this study will expand the application of game strategy in the field of healthcare, and make up for the deficiency of the effects of gamification on online medical quality. Practically, it provides guidance for promoting doctors\u27 online participation behavior, improves the quality of online health services, and suggests ways for optimizing the rational allocation of online health resources

A hybrid OSVM-OCNN Method for Crop Classification from Fine Spatial Resolution Remotely Sensed Imagery

Accurate information on crop distribution is of great importance for a range of applications including crop yield estimation, greenhouse gas emission measurement and management policy formulation. Fine spatial resolution (FSR) remotely sensed imagery provides new opportunities for crop mapping at a detailed level. However, crop classification from FSR imagery is known to be challenging due to the great intra-class variability and low inter-class disparity in the data. In this research, a novel hybrid method (OSVM-OCNN) was proposed for crop classification from FSR imagery, which combines a shallow-structured object-based support vector machine (OSVM) with a deep-structured object-based convolutional neural network (OCNN). Unlike pixel-wise classification methods, the OSVM-OCNN method operates on objects as the basic units of analysis and, thus, classifies remotely sensed images at the object level. The proposed OSVM-OCNN harvests the complementary characteristics of the two sub-models, the OSVM with effective extraction of low-level within-object features and the OCNN with capture and utilization of high-level between-object information. By using a rule-based fusion strategy based primarily on the OCNN’s prediction probability, the two sub-models were fused in a concise and effective manner. We investigated the effectiveness of the proposed method over two test sites (i.e., S1 and S2) that have distinctive and heterogeneous patterns of different crops in the Sacramento Valley, California, using FSR Synthetic Aperture Radar (SAR) and FSR multispectral data, respectively. Experimental results illustrated that the new proposed OSVM-OCNN approach increased markedly the classification accuracy for most of crop types in S1 and all crop types in S2, and it consistently achieved the most accurate accuracy in comparison with its two object-based sub-models (OSVM and OCNN) as well as the pixel-wise SVM (PSVM) and CNN (PCNN) methods. Our findings, thus, suggest that the proposed method is as an effective and efficient approach to solve the challenging problem of crop classification using FSR imagery (including from different remotely sensed platforms). More importantly, the OSVM-OCNN method is readily generalisable to other landscape classes and, thus, should provide a general solution to solve the complex FSR image classification problem