Improving Heterogeneous Model Reuse by Density Estimation

This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been developed. However, although pre-trained local classifiers are utilized in these approaches, the characteristics of the local data are not well exploited. This motivates us to estimate the density of local data and design an auxiliary model together with the local classifiers for reuse. To address the scenarios where some local models are not well pre-trained, we further design a multiparty cross-entropy loss for calibration. Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method.Comment: 9 pages, 5 figues. Accepted by IJCAI 202

Ultra-high pressure balloon angioplasty for pulmonary artery stenosis in children with congenital heart defects: Short- to mid-term follow-up results from a retrospective cohort in a single tertiary center

ObjectiveBalloon angioplasty (BA) has been the treatment of choice for pulmonary artery stenosis (PAS) in children. There remains, however, a significant proportion of resistant lesions. The ultra-high pressure (UHP) balloons might be effective in a subset of these lesions. In this study, we analyzed the safety and efficacy with short- to mid-term follow-up results of UHP BA for PAS in children with congenital heart defects (CHD) in our center.MethodsThis is a retrospective cohort study in a single tertiary heart center. Children diagnosed with PAS associated with CHD were referred for UHP BA. All data with these children were collected for analysis with updated follow-up.ResultsA total of 37 UHP BAs were performed consecutively in 28 children. The success rate was 78.4%. A significantly (P = 0.005) larger ratio of the balloon to the minimal luminal diameter at the stenotic waist (balloon/waist ratio) was present in the success group (median 3.00, 1.64–8.33) compared to that in the failure group (median 1.94, 1.41 ± 4.00). Stepwise logistic regression analysis further identified that the balloon/waist ratio and the presence of therapeutic tears were two independent predictors of procedural success. The receiver operating characteristic curve revealed a cut-off value of 2.57 for the balloon/waist ratio to best differentiate success from failure cases. Signs of therapeutic tears were present in eight cases, all of whom were in the success group. Perioperative acute adverse events were recorded in 16 patients, including 11 pulmonary artery injuries, three pulmonary hemorrhages, and two pulmonary artery aneurysms. During a median follow-up period of 10.4 (0.1–21.0) months, nine cases experienced restenosis at a median time of 40 (4–325) days after angioplasty.ConclusionsThe UHP BA is safe and effective for the primary treatment of PAS in infants and children with CHD. The success rate is high with a low incidence of severe complications. The predictors of success are a larger balloon/waist ratio and the presence of therapeutic tears. The occurrence of restenosis during follow-up, however, remains a problem. A larger number of cases and longer periods of follow-up are needed for further study

Intramyocardial Transplantation of Undifferentiated Rat Induced Pluripotent Stem Cells Causes Tumorigenesis in the Heart

BACKGROUND: Induced pluripotent stem cells (iPSCs) are a novel candidate for use in cardiac stem cell therapy. However, their intrinsic tumorigenicity requires further investigation prior to use in a clinical setting. In this study we investigated whether undifferentiated iPSCs are tumorigenic after intramyocardial transplantation into immunocompetent allogeneic recipients. METHODOLOGY/PRINCIPAL FINDINGS: We transplanted 2 × 10(4), 2 × 10(5), or 2 × 10(6) cells from the established rat iPSC line M13 intramyocardially into intact or infarcted hearts of immunocompetent allogeneic rats. Transplant duration was 2, 4, or 6 weeks. Histological examination with hematoxylin-eosin staining confirmed that undifferentiated rat iPSCs could generate heterogeneous tumors in both intracardiac and extracardiac sites. Furthermore, tumor incidence was independent of cell dose, transplant duration, and the presence or absence of myocardial infarction. CONCLUSIONS/SIGNIFICANCE: Our study demonstrates that allogeneic iPSC transplantation in the heart will likely result in in situ tumorigenesis, and that cells leaked from the beating heart are a potential source of tumor spread, underscoring the importance of evaluating the safety of future iPSC therapy for cardiac disease

A Novel Method of Fault Diagnosis for Injection Molding Systems Based on Improved VGG16 and Machine Vision

Artificial intelligence technology has enabled the manufacturing industry and actively guided its transformation and promotion for the past few decades. Injection molding technology is a crucial procedure in mechanical engineering and manufacturing due to its adaptability and dimensional stability. An essential step in the injection molding process is quality inspection and manual visual inspection is still used in conventional quality control, but this open-loop working method has issues with subjectivity and real-time monitoring capacity. This paper proposes an integrated “processing–matching–classification–diagnosis” concept based on machine vision and deep learning that allows for efficient and intelligent diagnosis of injection molding in complex scenarios. Based on eight categories of failure images of plastic components, this paper summarizes the theoretical method of processing fault categorization and identifies the various causes of defects from injection machines and molds. A template matching mechanism based on a new concept—arbitration function Jψij—provided in this paper, matches the edge features to achieve the initial classification of plastic components images. A conventional VGG16 network is innovatively upgraded in this work in order to further classify the unqualified plastic components. The classification accuracy of this improved VGG16 reaches 96.67%, which is better than the 53.33% of the traditional network. The accuracy, responsiveness, and resilience of the quality inspection are all improved in this paper. This work enhances production safety while promoting automation and intelligence of fault diagnosis in injection molding systems. Similar technical routes can be generalized to other industrial scenarios for quality inspection problems

A Novel Method of Fault Diagnosis for Injection Molding Systems Based on Improved VGG16 and Machine Vision

Artificial intelligence technology has enabled the manufacturing industry and actively guided its transformation and promotion for the past few decades. Injection molding technology is a crucial procedure in mechanical engineering and manufacturing due to its adaptability and dimensional stability. An essential step in the injection molding process is quality inspection and manual visual inspection is still used in conventional quality control, but this open-loop working method has issues with subjectivity and real-time monitoring capacity. This paper proposes an integrated “processing–matching–classification–diagnosis” concept based on machine vision and deep learning that allows for efficient and intelligent diagnosis of injection molding in complex scenarios. Based on eight categories of failure images of plastic components, this paper summarizes the theoretical method of processing fault categorization and identifies the various causes of defects from injection machines and molds. A template matching mechanism based on a new concept—arbitration function Jψij—provided in this paper, matches the edge features to achieve the initial classification of plastic components images. A conventional VGG16 network is innovatively upgraded in this work in order to further classify the unqualified plastic components. The classification accuracy of this improved VGG16 reaches 96.67%, which is better than the 53.33% of the traditional network. The accuracy, responsiveness, and resilience of the quality inspection are all improved in this paper. This work enhances production safety while promoting automation and intelligence of fault diagnosis in injection molding systems. Similar technical routes can be generalized to other industrial scenarios for quality inspection problems

Microstructure and Corrosion Characterization of Cr Film on Carburized CSS-42L Aerospace Bearing Steel by Filtered Cathodic Vacuum Arc Deposition

For present and future advanced aerospace bearing applications, it is significant and necessary to improve the corrosion resistance of carburized CSS-42L steel. In this study, Cr films of about 1 μm in thickness were fabricated onto carburized CSS-42L bearing steel using a filtered cathodic vacuum arc deposition system. The corrosion behavior of carburized CSS-42L steel with Cr films was investigated. The Cr film was composed of nanocrystalline α–Cr. The electrochemical experimental results indicated that the current density had two orders of magnitude decrease and the corrosion potential evidently increased after Cr film deposition. The protective efficiency of this Cr film was as high as 99.7%. Nanocrystalline exhibits a higher corrosion resistance and enhances the modification effect of Cr film on carburized CSS-42L steel

Comparison and analysis of predictive control of induction motor without weighting factors

The research and promotion of electric drive systems for new energy transportation equipment is an important link in the realization of low-carbon transportation. Model predictive control is an effective new control strategy for electrical drive systems. Taking the three phase induction motor drive system as the research object, three kinds of model predictive torque control methods are reviewed and analyzed in depth, and a theoretical proof of Pareto optimality of MPC methods without weighting factors is established. Predictive torque control is a classical model predictive control method for induction motor drives. When using it, a trial-and-error method is usually used to set appropriate weighting factors for different control objectives, which strongly depends on the designer’s experience. By changing the structure of the cost function, the sequential model predictive control eliminates the weighting factors and improves the control effect of the stator current. The even-handed model predictive control utilizes the interaction error and considers all the control objectives at the same time, so that the optimization order of the control objectives changes with the working conditions, which further improves the control performance. Through theoretical analysis and experimental test, this paper illustrates the performance differences between traditional predictive torque control, sequential model predictive control and even-handed model predictive control

Development of an E-Health App for Lower Limb Postoperative Rehabilitation Based on Plantar Pressure Analysis

The traditional postoperative rehabilitation training mode of lower limbs is mostly confined to hospitals or nursing sites. With the increase of postoperative patients, the current shortage of medical resources is obviously not satisfactory, and the medical costs are high, thus it is difficult to apply widely. A new mobile phone application (app) based on plantar pressure analysis is developed to fulfill the requirements of remote postoperative rehabilitation. It is designed, implemented, tested, and used for pilot experiment in conjunction with the system design methodology of the waterfall model. Preliminary testing and a pilot experiment showed that the app has realized basic functions and can achieve patient rehabilitation out of hospitals. The development of the app can shorten the hospitalization time of patients, reduce medical costs, and make up for the current shortage of medical resources. In the future, more experiments will be done to verify the effectiveness of the app