Additive Manufacturing of Porous Structures for Unmanned Aerial Vehicles Applications

Unmanned aerial vehicles (UAVs) have shown promising benefits in many applications. This has been enabled by the emergence of additive manufacturing (AM), which give the designers a large amount of geometrical freedom. In this paper, a novel design process of fused deposition modeling (FDM) combining both topology and infill optimization is introduced for AM of high performance porous structures. Tensile testing of FDM printed samples is first carried out to study the effect of the build orientation on the mechanical properties of acrylonitrile butadiene styrene (ABS) samples. It is found that samples built perpendicular to the load axis are the weakest with a tensile strength of 29 MPa and Young's modulus of 1960 MPa. The materials properties are fed to the finite elements analysis (FEA) for geometrical topology optimization, aiming to maximize stiffness and reduce weight of those parts. Afterwards, an infill optimization is carried out on the topology optimized parts using different mesostructures such as honeycomb, triangular, and rectangular to achieve high structural performance. The results showed that triangular pattern with 50% infill density had the lowest developed stresses, less mass, and strain energy when compared to other structures. Optimum UAVs parts of a quadcopter are successfully manufactured, assembled, and tested.

     

以第一性原理研究立方相CeO2的弹性、电子结构和光学性质

运用基于密度泛函理论(DFT)框架下的超软赝势平面波(USPP)方法,计算了立方相CeO2的几何结构、弹性性质、电子结构和光学性质,并采用HSE06杂化泛函矫正了带隙.所得晶格参数及体模量与先前文献报道数据基本吻合.计算出了二阶弹性常数及德拜温度值,并给出了能带结构、态密度、差分电荷密度的分布情况.最后,为了阐明CeO2的光学跃迁机制,计算并分析了其复介电常数、折射率、吸附光谱、反射光谱等光学性质.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition

The automatic recognition of dialogue act is a task of crucial importance for the processing of natural language dialogue at discourse level. It is also one of the most challenging problems as most often the dialogue act is not expressed directly in speaker’s utterance. In this paper, a new cue-based model for dialogue act recognition is presented. The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network’s random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. To evaluate the proposed approaches of design, three stages of experiments have been conducted. In the initial stage, the dynamic Bayesian network model is constructed using sets of lexical cues selected manually from the dialogue corpus. The model is evaluated against two previously proposed models and the results confirm the potentiality of dynamic Bayesian networks for dialogue act recognition. In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks’ random variables. The developed approach is evaluated against some of the previously used ranking approaches and the results provide experimental evidences on its ability to avoid the drawbacks of the ranking approaches. In the third stage, the dynamic Bayesian networks model is constructed using random variables constituted from the sets of lexical cues generated in the second stage and the results confirm the effectiveness of the proposed approaches for designing dialogue act recognition model.     

基于3D-CNN的暴力行为检测

大量的研究行为识别方法集中在检测简单的动作,如：步行,慢跑或者跳跃等;针对于打斗或者动作复杂的攻击性行为则研究较少;而这些研究在某些监控场景下非常有用,如：监狱,自助银行,商场等. 传统的暴力行为识别研究方法主要利用先验知识来手动设计特征,而本文提出了一种基于3D-CNN结构的暴力检测方法,通过三维深度神经网络直接对输入进行操作,能够很好的提取暴力行为的时空特征信息,从而进行检测. 从实验结果可以看出,本文方法能较好地识别出暴力行为,准确率要高于人工设计特征的方法.     

Identification schemes for unmanned excavator arm parameters

Parameter identification is a key requirement in the field of automated control of unmanned excavators （UEs）. Furthermore, the UE operates in unstructured, often hazardous environments, and requires a robust parameter identification scheme for field applications. This paper presents the results of a research study on parameter identification for UE. Three identification methods, the Newton-Raphson method, the generalized Newton method, and the least squares method are used and compared for prediction accuracy, robustness to noise and computational speed. The techniques are used to identify the link parameters （mass, inertia, and length） and friction coefficients of the full-scale UE. Using experimental data from a full-scale field UE, the values of link parameters and the friction coefficient are identified. Some of the identified parameters are compared with measured physical values. Furthermore, the joint torques and positions computed by the proposed model using the identified parameters are validated against measured data. The comparison shows that both the Newton-Raphson method and the generalized Newton method are better in terms of prediction accuracy. The Newton-Raphson method is computationally efficient and has potential for real time application, but the generalized Newton method is slightly more robust to measurement noise. The experimental data were obtained in collaboration with QinetiQ Ltd.     

Bioactive ceramic coatings containing carbon nanotubes on metallic substrates by electrophoretic deposition

A range of potentially bioactive ceramic coatings, based on combinations of either hydroxyapatite (HA) or titanium oxide nanoparticles with carbon nanotubes (CNTs), have been deposited on metallic substrates, using electrophoretic deposition (EPD). Sol–gel derived, ultrafine HA powders (10–70 nm) were dispersed in multi-wall nanotube-containing ethanol suspensions maintained at pH = ∼3.5 and successfully coated onto Ti alloy wires at 20 V for 1–3 min For TiO₂/CNT coatings, commercially available titania nanopowders and surface-treated CNTs in aqueous suspensions were co-deposited on stainless steel planar substrates. A field strength of 20 V/cm and deposition time of 4 min were used working at pH = 5. Although the co-deposition mechanism was not investigated in detail, the evidence suggests that co-deposition occurs due to the opposite signs of the surface charges (zeta potentials) of the particles, at the working pH. Electrostatic attraction between CNTs and TiO₂ particles leads to the creation of composite particles in suspension, consisting of TiO₂ particles homogenously attached onto the surface of individual CNTs. Under the applied electric field, these net negatively charged “composite TiO₂/CNT” elements migrate to and deposit on the anode (working electrode). The process of EPD at constant voltage conditions was optimised in both systems to achieve homogeneous and reasonably adhered deposits of varying thicknesses on the metallic substrates_.     

Power·delay product in COSMOS logic circuits

In this simulation work, we use COSMOS logic devices—a novel single gate CMOS architecture recently announced [1]—in multi-input logic gates, assessing its performance in terms of power·delay product. We consider three different multi-input logic circuits: a two-input NOR gate, a three-input NOR gate, and a three-input composite NOR/NAND (NORAND) gate. For this power·delay analysis, the transient TCAD simulations are employed in a mixed-mode approach where circuit and device simulations are coupled together, culminating in the delay response of the circuits as well as the static/dynamic current components. The analysis shows that all circuits, except the 3-input NOR gate, has acceptable characteristics at low-power applications and static leakage limits all COSMOS circuits at high-bias conditions.     

SATCHMOREBID: SATCHMO(RE) with BIDirectional relevancy

SATCHMORE was introduced as a mechanism to integrate relevancy testing with the model-generation theorem prover SATCHMO. This made it possible to avoid invoking some clauses that appear in no refutation, which was a major drawback of the SATCHMO approach. SATCHMORE relevancy, however, is driven by the entire set of negative clauses and no distinction is accorded to the query negation. Under unfavorable circumstances, such as in the presence of large amounts of negative data, this can reduce the efficiency of SATCHMORE. In this paper we introduce a further refinement of SATCHMO called SATCHMOREBID: SATCHMORE with BIDirectional relevancy. SATCHMOREBID uses only the negation of the query for relevancy determination at the start. Other negative clauses are introduced on demand and only if a refutation is not possible using the current set of negative clauses. The search for the relevant negative clauses is performed in a forward chaining mode as opposed to relevancy propagation in SATCHMORE which is based on backward chaining. SATCHMOREBID is shown to be refutationally sound and complete. Experiments on a prototype SATCHMOREBID implementation point to its potential to enhance the efficiency of the query answering process in disjunctive databases. Donald Loveland, Ph.D.: He is Emeritus Professor of Computer Science at Duke University. He received his Ph.D. in mathematics from New York University and taught at NYU and CMU prior to joining Duke in 1973. His research in automated deduction includes defining the model elimination proof procedure and the notion of linear resolution. He is author of one book and editor/co-editor of two other books on automated theorem proving. He has done research in the areas of algorithms, complexity, expert systems and logic programming. He is an AAAI Fellow, ACM Fellow and winner of the Herbrand Award in Automated Reasoning. Adnan H. Yahya, Ph.D.: He is an associate professor at the Department of Electrical Engineering, Birzeit University, Palestine. He received his Diploma and PhD degrees from St. Petersburg Electrotechnical University and Nothwestern University in 1979 and 1984 respectively. His research interests are in Artificial Intelligence in general and in the areas of Deductive Databases, Logic Programming and Nonmonotonic Reasoning in particular. He had several visiting appointments at universities and research labs in the US, Germany, France and the UK. Adnan Yahya is a member of the ACM, IEEE and IEEE Computer Society.     

Characterization of Clay Particle Surfaces for Contaminant Sorption in Soil Barriers Using Flow Microcalorimetry

Clays such as kaolinite and bentonite are widely used in various industries as sorbents. The sorptive characteristics of clays are exploited when they are used in contaminant barrier systems. To use clays effectively, their surface characteristics need to be known; especially, when they are used for contaminant sorption. Available surface area of clay minerals and the characteristics that depend on it are very sensitive to environmental changes such as those that can be induced by changes in the composition of pore fluid. Flow microcalorimetry with a down-stream concentration was used to determine the heats and amounts of adsorption of acids and bases on the clays. Test results presented herein revealed that both kaolinite and bentonite exhibit significantly different adsorption isotherms and heat of wetting under high pH and low pH conditions. Kaolinite has the capacity to adsorb both acidic and basic molecules almost equally. However, it has a tendency to adsorb more base than acid because of its stronger complexation capacity with acids than with bases. On the other hand, bentonite has a tendency to adsorb more acidic than basic molecules per gram. These results also indicate that both kaolinite and bentonite have different heats of wetting characteristics. As the concentrations of the acids and bases increase, the heat of wetting of kaolinite decreases while that of bentonite increases.