Scanning Speed Effect on Mechanical Properties of Ti-6Al-4V Alloy Processed by Electron Beam Additive Manufacturing

AbstractIn this study, mechanical properties in Ti-6Al-4V samples built with the powder-bed electron beam additive manufacturing (EBAM) process over a range of beam scanning speeds were experimentally investigated. Four levels of speed functions were applied to build samples, which were used to prepare the specimens for the nanoindentation test to obtain their mechanical properties. The measured averaged Young's modulus and hardness are about 111.7∼119.0GPa and 5.24∼6.52GPa, respectively. It has been found that the Young's modulus and hardness increase with the increase of scanning speed in EBAM. The scanning surface presents more superior mechanical properties than those from side surface. The mechanical properties are also correlated to the microstructure characterization of EBAM components

GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation

Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving for its powerful spatial representation ability. It is challenging to estimate the BEV semantic maps from monocular images due to the spatial gap, since it is implicitly required to realize both the perspective-to-BEV transformation and segmentation. We present a novel two-stage Geometry Prior-based Transformation framework named GitNet, consisting of (i) the geometry-guided pre-alignment and (ii) ray-based transformer. In the first stage, we decouple the BEV segmentation into the perspective image segmentation and geometric prior-based mapping, with explicit supervision by projecting the BEV semantic labels onto the image plane to learn visibility-aware features and learnable geometry to translate into BEV space. Second, the pre-aligned coarse BEV features are further deformed by ray-based transformers to take visibility knowledge into account. GitNet achieves the leading performance on the challenging nuScenes and Argoverse Datasets. The code will be publicly available

Exploiting wireless received signal strength indicators to detect evil-twin attacks in smart homes

Evil-twin is becoming a common attack in Smart Home environments where an attacker can set up a fake AP to compromise the security of the connected devices. To identify the fake APs, The current approaches of detecting Evil-twin attacks all rely on information such as SSIDs, the MAC address of the genuine AP or network traffic patterns. However, such information can be faked by the attacker, often leading to low detection rates and weak protection. This paper presents a novel evil-twin attack detection method based on the received signal strength indicator (RSSI). Our key insight is that the location of the genuine AP rarely moves in a home environment and as a result the RSSI of the genuine AP is relatively stable. Our approach considers the RSSI as a fingerprint of APs and uses the fingerprint of the genuine AP to identify fake ones. We provide two schemes to detect a fake AP in two different scenarios where the genuine AP can be located at either a single or multiple locations in the property, by exploiting the multipath effect of the WIFI signal. As a departure from prior work, our approach does not rely on any professional measurement devices. Experimental results show that our approach can successfully detect 90% of the fake APs, at the cost of an one-off, modest connection delay

Exploiting dynamic scheduling for VM-based code obfuscation

Code virtualization built upon virtual machine (VM) technologies is emerging as a viable method for implementing code obfuscation to protect programs against unauthorized analysis. State-of-the-art VM-based protection approaches use a fixed scheduling structure where the program follows a single, static execution path for the same input. Such approaches, however, are vulnerable to certain scenarios where the attacker can reuse knowledge extracted from previously seen software to crack applications using similar protection schemes. This paper presents DSVMP, a novel VM-based code obfuscation approach for software protection. DSVMP brings together two techniques to provide stronger code protection than prior VM-based schemes. Firstly, it uses a dynamic instruction scheduler to randomly direct the program to execute different paths without violating the correctness across different runs. By randomly choosing the program execution paths, the application exposes diverse behavior, making it much more difficult for an attacker to reuse the knowledge collected from previous runs or similar applications to perform attacks. Secondly, it employs multiple VMs to further obfuscate the relationship between VM bytecode and their interpreters, making code analysis even harder. We have implemented DSVMP in a prototype system and evaluated it using a set of widely used applications. Experimental results show that DSVMP provides stronger protection with comparable runtime overhead and code size when compared to two commercial VMbased code obfuscation tools

Enhance virtual-machine-based code obfuscation security through dynamic bytecode scheduling

Code virtualization built upon virtual machine (VM) technologies is emerging as a viable method for implementing code obfuscation to protect programs against unauthorized analysis. State-of-the-art VM-based protection approaches use a fixed scheduling structure where the program always follows a single, deterministic execution path for the same input. Such approaches, however, are vulnerable in certain scenarios where the attacker can reuse knowledge extracted from previously seen software to crack applications protected with the same obfuscation scheme. This paper presents Dsvmp, a novel VM-based code obfuscation approach for software protection. Dsvmp brings together two techniques to provide stronger code protection than prior VM-based approaches. Firstly, it uses a dynamic instruction scheduler to randomly direct the program to execute different paths without violating the correctness across different runs. By randomly choosing the program execution path, the application exposes diverse behavior, making it much more difficult for an attacker to reuse the knowledge collected from previous runs or similar applications to launch an attack. Secondly, it employs multiple VMs to further obfuscate the mapping from VM opcode to native machine instructions, so that the same opcode could be mapped to different native instructions at runtime, making code analysis even harder. We have implemented Dsvmp in a prototype system and evaluated it using a set of widely used applications. Experimental results show that Dsvmp provides stronger protection with comparable runtime overhead and code size, when it is compared to two commercial VM-based code obfuscation tools

Research on quantitative inversion of ion adsorption type rare earth ore based on convolutional neural network

Rare earth resource is a national strategic resource, which plays an essential role in the field of high technology research and development. In this paper, we aim to use remote sensing quantitative inversion prospecting technology, use surface-to-surface mode, and model inversion and evaluation through convolutional neural network model to achieve a new research method for large-scale, low-cost, rapid and efficient exploration of ion-adsorbed rare earth ore. The results show that the RE2O3 content of samples has significant negative correlation with the second, third and fourth band of GF-2 image, but has no significant correlation with the first band of GF-2 image; the convolution neural network model can be used to reconstruct the RE2O3 content. The content distribution map of RE2O3 obtained by inversion is similar to that of geochemical map, which indicates that the convolution neural network model can be used to invert the RE2O3 content in the sampling area. The quantitative inversion results show that the content distribution characteristics of ion adsorption rare earth ore in the study area are basically consistent with the actual situation; there are two main high anomaly areas in the study area. The high anomaly area I is a known mining area, and the high anomaly area II can be a prospective area of ion adsorption type rare earth deposit. It shows that the remote sensing quantitative inversion prospecting method of ion adsorption type rare earth deposit based on Convolutional Neural Networks (CNN) model is feasible

Targeted Methotrexate Prodrug Conjugated With Heptamethine Cyanine Dye Improving Chemotherapy and Monitoring Itself Activating by Dual-Modal Imaging

Theranostic prodrug plays a vital role in reducing the side effects and evaluating the therapeutic efficiency of prodrug in vivo. In particular, small conjugate-based theranostic prodrugs have attracted much attention because of their clear and simple structures. In this work, we synthesized a novel tumor-targeting and glutathione-activated conjugate-based theranostic prodrug (Cy-SS-MTX). The prodrug was constructed by conjugating Cy (IR780) to methotrexate (MTX) via a disulfide bond. The Cy dye as targeting molecule bring prodrug to cancer cells and then the prodrug was activated by the high levels of glutathione in tumor. In cell experiments, the results showed the excellent ability of prodrug to target tumor. Meanwhile, the prodrug apparently improved the anti-tumor ability and hugely reduced toxicity of free MTX on normal cells. Furthermore, owing to intramolecular charge transfer between Cy and MTX, the Cy structure in the prodrug showed an absorption peak at 654 nm in UV-Vis spectroscopy. However, when the disulfide bond of prodrug was broken by glutathione, a new UV-Vis absorption peak at 802 nm of Cy structure in prodrug was arised. At the same time, the fluorescence (FL) emission peak at 750 nm (excitation at 640 nm) would turn into 808 nm (excitation at 745 nm). What's more, the photoacoustic (PA) signal with excitation at 680 and 808 nm also changed. The experimental results in vivo showed that the prodrug has been successfully utilized for real-timely tracking MTX activation by FL and PA imaging upon near infrared laser excitation and cancer targeting therapy. Our studies further encourage application of small conjugate-based prodrug based on tumor-targeted heptamethine cyanine dye as reporter group for targeted therapy and real-timely tracking activation of drug