An efficient approach to acoustic emission source identification based on harmonic wavelet packet and hierarchy support vector machine

A new approach for acoustic emission (AE) source type identification based on harmonic wavelet packet (HWPT) feature extraction and hierarchy support vector machine (H-SVM) classifier is proposed for solving the fatigue damage identification problem of helicopter moving component. In this approach, HWPT is employed to extract the energy feature of AE signals on different frequency bands, as well as to reduce the dimensionality of original data features. We trained the H-SVM classifier on a subset of the experimental data for known AE source type, and then tested on the remaining set of data. Also, the pressure off experiment on specimen of carbon fiber materials is investigated. The experimental results indicate that the proposed approach can implement AE source type identification effectively, and achieves better performance on computational efficiency and identification accuracy than wavelet packet (WPT) feature extraction and RBF neural network classification

Outstanding supercapacitive properties of Mn-doped TiO2 micro/nanostructure porous film prepared by anodization method.

Mn-doped TiO2 micro/nanostructure porous film was prepared by anodizing a Ti-Mn alloy. The film annealed at 300 °C yields the highest areal capacitance of 1451.3 mF/cm(2) at a current density of 3 mA/cm(2) when used as a high-performance supercapacitor electrode. Areal capacitance retention is 63.7% when the current density increases from 3 to 20 mA/cm(2), and the capacitance retention is 88.1% after 5,000 cycles. The superior areal capacitance of the porous film is derived from the brush-like metal substrate, which could greatly increase the contact area, improve the charge transport ability at the oxide layer/metal substrate interface, and thereby significantly enhance the electrochemical activities toward high performance energy storage. Additionally, the effects of manganese content and specific surface area of the porous film on the supercapacitive performance were also investigated in this work

Lithium Recovery from Brines Including Seawater, Salt Lake Brine, Underground Water and Geothermal Water

Demand to lithium rising swiftly as increasing due to its diverse applications such as rechargeable batteries, light aircraft alloys, air purification, medicine and nuclear fusion. Lithium demand is expected to triple by 2025 through the use of batteries, particularly electric vehicles. The lithium market is expected to grow from 184,000 TPA of lithium carbonate to 534,000 TPA by 2025. To ensure the growing consumption of lithium, it is necessary to increase the production of lithium from different resources. Natural lithium resources mainly associate within granite pegmatite type deposit (spodumene and petalite ores), salt lake brines, seawater and geothermal water. Among them, the reserves of lithium resource in salt lake brine, seawater and geothermal water are in 70–80% of the total, which are excellent raw materials for lithium extraction. Compared with the minerals, the extraction of lithium from water resources is promising because this aqueous lithium recovery is more abundant, more environmentally friendly and cost-effective

Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning

Deep cooperative multi-agent reinforcement learning has demonstrated its remarkable success over a wide spectrum of complex control tasks. However, recent advances in multi-agent learning mainly focus on value decomposition while leaving entity interactions still intertwined, which easily leads to over-fitting on noisy interactions between entities. In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle not only the joint value function into agent-wise value functions for decentralized execution, but also the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities. OPT facilitates filtering the noisy interactions between irrelevant entities and thus significantly improves generalizability as well as interpretability. Specifically, OPT introduces a sparse disagreement mechanism to encourage sparsity and diversity among discovered interaction prototypes. Then the model selectively restructures these prototypes into a compact interaction pattern by an aggregator with learnable weights. To alleviate the training instability issue caused by partial observability, we propose to maximize the mutual information between the aggregation weights and the history behaviors of each agent. Experiments on both single-task and multi-task benchmarks demonstrate that the proposed method yields results superior to the state-of-the-art counterparts. Our code is available at https://github.com/liushunyu/OPT

Securing Cloud File Systems using Shielded Execution

Cloud file systems offer organizations a scalable and reliable file storage solution. However, cloud file systems have become prime targets for adversaries, and traditional designs are not equipped to protect organizations against the myriad of attacks that may be initiated by a malicious cloud provider, co-tenant, or end-client. Recently proposed designs leveraging cryptographic techniques and trusted execution environments (TEEs) still force organizations to make undesirable trade-offs, consequently leading to either security, functional, or performance limitations. In this paper, we introduce TFS, a cloud file system that leverages the security capabilities provided by TEEs to bootstrap new security protocols that meet real-world security, functional, and performance requirements. Through extensive security and performance analyses, we show that TFS can ensure stronger security guarantees while still providing practical utility and performance w.r.t. state-of-the-art systems; compared to the widely-used NFS, TFS achieves up to 2.1X speedups across micro-benchmarks and incurs <1X overhead for most macro-benchmark workloads. TFS demonstrates that organizations need not sacrifice file system security to embrace the functional and performance advantages of outsourcing