Study on PMF Rotary Compressor for Unitary Air Conditioners in N.A. Market

Unitary air conditioner in N.A. market takes the non-bleed TXV as its throttle valve. When the system turns off, the TXV automatically shut down to maintain the pressure in heat exchangers. This is benefit for SEER and comfort in use. However, pressure maintaining resulted in a fast-restart issue to fix-speed rotary compressor applying in this kind of system. To solve this problem, this paper firstly gave two concepts and principles of passive pressure balance and active pressure balance to conclude the means to achieve pressure balance in rotary compressor. Then, designed the technical scheme of PMF (Pressure Maintaining and Fast-restart) solution and analyzed the working process based on the two pressure balance principles. Furthermore, verified the PMF solution by compressor and system performance tests, PMF function test and reliability test. The results show that rotary compressor with PMF solution has good performance, stable PMF function and high reliability, which is a suitable product for unitary air conditioners

Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios

Federated Learning (FL) is a promising technology that enables multiple actors to build a joint model without sharing their raw data. The distributed nature makes FL vulnerable to various poisoning attacks, including model poisoning attacks and data poisoning attacks. Today, many byzantine-resilient FL methods have been introduced to mitigate the model poisoning attack, while the effectiveness when defending against data poisoning attacks still remains unclear. In this paper, we focus on the most representative data poisoning attack - "label flipping attack" and monitor its effectiveness when attacking the existing FL methods. The results show that the existing FL methods perform similarly in Independent and identically distributed (IID) settings but fail to maintain the model robustness in Non-IID settings. To mitigate the weaknesses of existing FL methods in Non-IID scenarios, we introduce the Honest Score Client Selection (HSCS) scheme and the corresponding HSCSFL framework. In the HSCSFL, The server collects a clean dataset for evaluation. Under each iteration, the server collects the gradients from clients and then perform HSCS to select aggregation candidates. The server first evaluates the performance of each class of the global model and generates the corresponding risk vector to indicate which class could be potentially attacked. Similarly, the server evaluates the client's model and records the performance of each class as the accuracy vector. The dot product of each client's accuracy vector and global risk vector is generated as the client's host score; only the top p\% host score clients are included in the following aggregation. Finally, server aggregates the gradients and uses the outcome to update the global model. The comprehensive experimental results show our HSCSFL effectively enhances the FL robustness and defends against the "label flipping attack.

The noise control of minicar body in white based on acoustic panel participation method

It is very important to predict the acoustic radiation of vehicle body for the control of interior noise. Firstly, the kinetic equations of coupled acoustic-structural finite element method are explained and the numerical analytical methods of noise transfer function and acoustic panel participation are further obtained. Then the coupled acoustic-structural finite element model of body in white and passenger compartment cavity of a minicar is established and verified by modal test. The passive side of engine mounting points are chosen as the excitation points, and driver’s right ear is the output point of sound pressure response. The noise transfer function is calculated and the critical frequency of vehicle interior noise is obtained. The acoustic panel participation analysis of vehicle roof and floor are conducted, and the key acoustic panels are identified. In order to reduce the noise of critical frequency, the measures, pasting damping material and welding beam, are adopted. The results indicate that, compared with the results of structure improvement of modal method, the vehicle interior noise is controlled more effectively by using the acoustic panel participation analytical method

Controlled Assembly of Sb₂S₃ Nanoparticles on Silica/Polymer Nanotubes:Insights into the Nature of Hybrid Interfaces

Silica nanotubes can serve as high aspect ratio templates for the deposition of inorganic nanoparticles to form novel hybrids. However, the nature of the interfacial binding is still an unresolved challenge when considered at the atomic level. In this work, novel nanocomposites have been successfully fabricated by the controlled nucleation and assembly of Sb(2)S(3) nanoparticles on the surface of mercaptopropyl-functionalized silica/polymer hybrid nanotubes (HNTs). The Sb(2)S(3) nanoparticles were strongly attached to the HNTs surface by interactions between the pendent thiol groups and inorganic sulfur atoms. Detailed analysis of the geometric and electronic structure using first–principle density functional theory demonstrates charge transfer from the nanoparticles to the underlying HNTs at the Sb(2)S(3)/HNTs interfaces. Formation of a packed array of Sb(2)S(3) nanoparticles on the HNTs results in mixing of the electronic states of the components, and is mediated by the mercaptopropyl bridges between Sb(2)S(3) and the outer layer of the HNTs

CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction

Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and content is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling models. While rule-based approaches often lead to low recalls, sequence labeling models cannot well handle quotations with complicated structures. In this paper, we propose the Context and Former-Label Enhanced Net (CofeNet) for quotation extraction. CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets (i.e., PolNeAR and Riqua) and one proprietary dataset (i.e., PoliticsZH), we show that our CofeNet achieves state-of-the-art performance on complicated quotation extraction.Comment: Accepted by COLING 202

Eliminating micro-porous layer from gas diffusion electrode for use in high temperature polymer electrolyte membrane fuel cell

© 2016 Elsevier B.V. In this work, we report a simple strategy to improve the performance of high temperature polymer electrolyte membrane fuel cell (HT-PEMFC) by eliminating the micro-porous layer (MPL) from its gas diffusion electrodes (GDEs). Due to the absence of liquid water and the general use of high amount of catalyst, the MPL in a HT-PEMFC system works limitedly. Contrarily, the elimination of the MPL leads to an interlaced micropore/macropore composited structure in the catalyst layer (CL), which favors gas transport and catalyst utilization, resulting in a greatly improved single cell performance. At the normal working voltage (0.6 V), the current density of the GDE eliminated MPL reaches 0.29 A cm−2, and a maximum power density of 0.54 W cm−2 at 0.36 V is obtained, which are comparable to the best results yet reported for the HT-PEMFCs with similar Pt loading and operated using air. Furthermore, the MPL-free GDE maintains an excellent durability during a preliminary 1400 h HT-PEMFC operation, owing to its structure advantages, indicating the feasibility of this electrode for practical applications