No abstract

Multi-output Gaussian process (MGP) has been attracting increasing attention as a transfer learning method to model multiple outputs. Despite its high flexibility and generality, MGP still faces two critical challenges when applied to transfer learning. The first one is negative transfer, which occurs when there exists no shared information among the outputs. The second challenge is the input domain inconsistency, which is commonly studied in transfer learning yet not explored in MGP. In this paper, we propose a regularized MGP modeling framework with domain adaptation to overcome these challenges. More specifically, a sparse covariance matrix of MGP is proposed by using convolution process, where penalization terms are added to adaptively select the most informative outputs for knowledge transfer. To deal with the domain inconsistency, a domain adaptation method is proposed by marginalizing inconsistent features and expanding missing features to align the input domains among different outputs. Statistical properties of the proposed method are provided to guarantee the performance practically and asymptotically. The proposed framework outperforms state-of-the-art benchmarks in comprehensive simulation studies and one real case study of a ceramic manufacturing process. The results demonstrate the effectiveness of our method in dealing with both the negative transfer and the domain inconsistency.

With more unmanned aircraft (UA) becoming airborne each day, an already high manned aircraft to UA exposure rate continues to grow. Pilots and rulemaking authorities realize that UA visibility is a real, but unquantified, threat to operations under the see-and-avoid concept. To finally quantify the threat, a novel contrast-based UA visibility model is constructed here using collected empirical data as well as previous work on the factors affecting visibility. This work showed that UA visibility less than 1300 m makes a mid-air collision a serious threat if a manned aircraft and a UA are on a collision course while operating under the see-and-avoid concept. Similarly, this work also showed that a mid-air collision may be unavoidable when UA visibility is less than 400 m. Validating pilot and rulemaking authority concerns, this work demonstrated that UA visibility distances less than 1300 m and 400 m occur often in the real world. Finally, the model produced UA visibility lookup tables that may prove useful to rulemaking authorities such as the FAA and ICAO for future work in the proof of equivalency of detect and avoid operations. Until then, pilots flying at slower airspeeds in the vicinity of UA may improve safety margins.

No abstract

Hydrothermal-assisted jet fusion (HJF) process is a new additive manufacturing (AM) method for ceramics, which is capable of fabricating ceramic 3D structures without the need for organic binders. The HJF process selectively deposits a water-based solution into a ceramic powder bed in a layer-by-layer manner and fuses particles together through a hydrothermal mechanism. Since no organic binder is used in the HJF process, such a binder-free AM method can produce ceramic parts with less energy consumed for binder removal and has the potential to reach the material’s theoretical properties such as full density and exceptional mechanical strength. Nevertheless, the fabricated parts by the HJF process still suffers from issues, such as over fusion base-layer damage, pattern shifting, etc. In this paper, the effects of process parameters (e.g., stroke, droplet size, press number, final press, etc.) on the fabrication quality (e.g., diffusion behavior of deposited inks and shape fidelity of the fabricated parts, etc.) of the HJF process are studied. Optimum process settings are identified to alleviate those quality issues, and 3D structures with high shape fidelity were successfully fabricated to highlight the capability of the HJF process in achieving ceramic 3D structures with high accuracy and high performance.