Dynamic Analysis of UAV’s Motor Support Bar Length Control System

UAV (Unmanned Aerial Vehicle) can be described as aircraft that do not need any presence of pilots inside it. Basically, UAV is come out in a small aircraft sothat the aircraft can be easily controlled by the people from afar[1]. The UAV’s motor support bar length control systems are the UAV’s control systems that move according to the variable arm length movement and also a constant revolution of the propeller speeds. The purpose of the study is to run the dynamic analysis at the UAV’s motor support bar length control systems and also to enhance the UAV’s mathematical modellingby using the SOLIDWORKS®software which involved in using both CAD and CAE systems[2]. The detaileddesign is used SOLIDWORKS®software to conduct the static and dynamic analysis of UAV’s motor support bar length control systems. The design is restricted to the arm due to the critical part that has the highest vibration at the UAV’s motor support bar length control systems. The results that obtain from the study from the static and dynamic analysis are the displacement of the motor, Von Misses stress of the arm, and also the resonance frequency that will give the modes shape to the systems

Non-unique factorization of polynomials over residue class rings of the integers

We investigate non-unique factorization of polynomials in Z_{p^n}[x] into irreducibles. As a Noetherian ring whose zero-divisors are contained in the Jacobson radical, Z_{p^n}[x] is atomic. We reduce the question of factoring arbitrary non-zero polynomials into irreducibles to the problem of factoring monic polynomials into monic irreducibles. The multiplicative monoid of monic polynomials of Z_{p^n}[x] is a direct sum of monoids corresponding to irreducible polynomials in Z_p[x], and we show that each of these monoids has infinite elasticity. Moreover, for every positive integer m, there exists in each of these monoids a product of 2 irreducibles that can also be represented as a product of m irreducibles.Comment: 11 page

A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape

Calculation of some properties of the vacuum

In this article, we calculate the dressed quark propagator with the flat bottom potential in the framework of the rain-bow Schwinger-Dyson equation, which is determined by mean field approximation of the global colour model lagrangian. The dressed quark propagator exhibits a dynamical symmetry breaking phenomenon and gives a constituent quark mass about 392 MeV, which is close to the value of commonly used constituent quark mass in the chiral quark model. Then based on the dressed quark propagator, we calculate some properties of the vacuum, such as quark condensate, mixed quark condensate $g_{s}< 0|\bar{q}G_{\mu\nu}\sigma^{\mu\nu}q|0>$, four quark condensate $<0|\bar{q} \Gamma q\bar{q} \Gamma q |0>$, tensor, $\pi$ vacuum susceptibilities. The numerical results are compatible with the values of other theoretical approaches.Comment: 10 pages, 2 figures, 3 tables, some writing errors are correcte