Search results for: Xiangfang Xu

Authors: Bintao Wu, Xiangfang Xu, Yugang Miao, Duanfeng Han

Abstract:

Joining of 1mm thick aluminum 6061 to titanium TC4 was conducted using Bypass-current MIG welding-brazed, and stable welding process and good bead appearance were obtained. The Joint profile and microstructure of Ti/Al joints were observed by optical microscopy and SEM and then the structure of the interfacial reaction layers were analyzed in details. It was found that the intermetallic compound layer at the interfacial top is in the form of columnar crystal, which is in short and dense state. A mount of AlTi were observed at the interfacial layer near the Ti base metal while intermetallic compound like Al3Ti, TiSi3 were formed near the Al base metal, and the Al11Ti5 transition phase was found in the center of the interface layer due to the uneven distribution inside the weld pool during the welding process. Tensile test results show that the average tensile strength of joints is up to 182.6 MPa, which reaches about 97.6% of aluminum base metal. Fracture is prone to occur in the base metal with a certain amount of necking.

Keywords: Bypass-current MIG welding-brazed, Al alloy, Ti alloy, joint characteristics, mechanical properties.

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Authors: Hossein Jafari, Xiangfang Li, Lijun Qian, Alexander Aved, Timothy Kroecker

Abstract:

Traditionally in sensor networks and recently in the Internet of Things, numerous heterogeneous sensors are deployed in distributed manner to monitor a phenomenon that often can be model by an underlying stochastic process. The big time-series data collected by the sensors must be analyzed to detect change in the stochastic process as quickly as possible with tolerable false alarm rate. However, sensors may have different accuracy and sensitivity range, and they decay along time. As a result, the big time-series data collected by the sensors will contain uncertainties and sometimes they are conflicting. In this study, we present a framework to take advantage of Evidence Theory (a.k.a. Dempster-Shafer and Dezert-Smarandache Theories) capabilities of representing and managing uncertainty and conflict to fast change detection and effectively deal with complementary hypotheses. Specifically, Kullback-Leibler divergence is used as the similarity metric to calculate the distances between the estimated current distribution with the pre- and post-change distributions. Then mass functions are calculated and related combination rules are applied to combine the mass values among all sensors. Furthermore, we applied the method to estimate the minimum number of sensors needed to combine, so computational efficiency could be improved. Cumulative sum test is then applied on the ratio of pignistic probability to detect and declare the change for decision making purpose. Simulation results using both synthetic data and real data from experimental setup demonstrate the effectiveness of the presented schemes.

Keywords: CUSUM, evidence theory, KL divergence, quickest change detection, time series data.

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