Comparison of Cloud Base Height Derived from a Ground-Based Infrared Cloud Measurement and Two Ceilometers

The cloud base height (CBH) derived from the whole-sky infrared cloud-measuring system (WSIRCMS) and two ceilometers (Vaisala CL31 and CL51) from November 1, 2011, to June 12, 2012, at the Chinese Meteorological Administration (CMA) Beijing Observatory Station are analysed. Significant differences can be found by comparing the measurements of different instruments. More exactly, the cloud occurrence retrieved from CL31 is 3.8% higher than that from CL51, while WSIRCMS data shows 3.6% higher than ceilometers. More than 75.5% of the two ceilometers’ differences are within ±200 m and about 89.5% within ±500 m, while only 30.7% of the differences between WSIRCMS and ceilometers are within ±500 m and about 55.2% within ±1000 m. These differences may be caused by the measurement principles and CBH retrieval algorithm. A combination of a laser ceilometer and an infrared cloud instrument is recommended to improve the capability for determining cloud occurrence and retrieving CBHs

Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform

We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment. It transforms a tensor program written for a single device into an equivalent distributed program that is capable of scaling up to thousands of devices with no user configuration. Rhino firstly works on a semantically independent intermediate representation of tensor programs, which facilitates its generalization to unprecedented applications. Additionally, it implements a task-oriented controller and a distributed runtime for optimal performance. Rhino explores on a complete and systematic parallelization strategy space that comprises all the paradigms commonly employed in deep learning (DL), in addition to strided partitioning and pipeline parallelism on non-linear models. Aiming to efficiently search for a near-optimal parallel execution plan, our analysis of production clusters reveals general heuristics to speed up the strategy search. On top of it, two optimization levels are designed to offer users flexible trade-offs between the search time and strategy quality. Our experiments demonstrate that Rhino can not only re-discover the expert-crafted strategies of classic, research and production DL models, but also identify novel parallelization strategies which surpass existing systems for novel models

The role of lithium in the aging precipitation process of al-zn-mg-cu alloys and its effect on the properties

It is well known that the development of lightweight alloys with improved comprehensive performance and application value are the future development directions for the ultra-high-strength 7xxx series Al-Zn-Mg-Cu alloys used in the aircraft field. As the lightest metal element in nature, lithium (Li) has outstanding advantages in reducing the density and increasing the elastic modulus in aluminum alloys, so Al-Zn-Mg-Cu alloys containing Li have gained widespread attention. Furthermore, since the Al-Zn-Mg-Cu alloy is usually strengthened by aging treatment, it is crucial to understand how Li addition affects its aging precipitation process. As such, in this article, the effects and mechanism of Li on the aging precipitation behavior and the impact of Li content on the aging precipitation phase of Al-Zn-Mg-Cu alloys are briefly reviewed, and the influence of Li on the service properties, including mechanical properties, wear resistance, and fatigue resistance, of Al-Zn-Mg-Cu alloys are explained. In addition, the corresponding development prospects and challenges of the Al-Zn-Mg-Cu-Li alloy are also proposed. This review is helpful to further understand the role of Li in Al-Zn-Mg-Cu alloys and provides a reference for the development of high-strength aluminum alloys containing Li with good comprehensive properties

Ceramic particles reinforced copper matrix composites manufactured by advanced powder metallurgy: Preparation, performance, and mechanisms

Copper matrix composites doped with ceramic particles are known to effectively enhance the mechanical properties, thermal expansion behavior and high-temperature stability of copper while maintaining high thermal and electrical conductivity. This greatly expands the applications of copper as a functional material in thermal and conductive components, including electronic packaging materials and heat sinks, brushes, integrated circuit lead frames. So far, endeavors have been focusing on how to choose suitable ceramic components and fully exert strengthening effect of ceramic particles in the copper matrix. This article reviews and analyzes the effects of preparation techniques and the characteristics of ceramic particles, including ceramic particle content, size, morphology and interfacial bonding, on the diathermancy, electrical conductivity and mechanical behavior of copper matrix composites. The corresponding models and influencing mechanisms are also elaborated in depth. This review contributes to a deep understanding of the strengthening mechanisms and microstructural regulation of ceramic particle reinforced copper matrix composites. By more precise design and manipulation of composite microstructure, the comprehensive properties could be further improved to meet the growing demands of copper matrix composites in a wide range of application fields