Technion – Israel Institute of Technology

About: Technion – Israel Institute of Technology is a education organization based out in Haifa, Israel. It is known for research contribution in the topics: Population & Upper and lower bounds. The organization has 31714 authors who have published 79377 publications receiving 2603976 citations. The organization is also known as: Technion Israel Institute of Technology & Ṭekhniyon, Makhon ṭekhnologi le-Yiśraʼel.

Friction-Reducing Surface-Texturing in Reciprocating Automotive Components

Abstract: A model is presented to study the potential use of micro-surface structure in the form of micro pores to improve tribological properties of reciprocating automotive components. The Reynolds equation and the equation of motion are solved simultaneously for a simplified “piston/cylinder” system with surface texturing. The solution provides the time behavior of both the clearance and the friction force between the “piston ring” and “cylinder liner” surfaces. It is shown that surface texturing can efficiently be used to maintain hydrodynamic effects even with nominally parallel surfaces. It is also shown that optimum surface texturing may substantially reduce the friction losses in reciprocating automotive components. Presented at the 56th Annual Meeting Orlando, Florida May 20–24, 2001

Distributed network protocols

Abstract: A unified approach to the formal description and validation of several distributed protocols is presented. After introducing two basic protocols, a series of known and new protocols for connectivity test, shortest path, and path updating are described and validated. All protocols are extended to networks with changing topology.

Exploiting Task Relatedness for Multiple Task Learning

Abstract: The approach of learning of multiple “related” tasks simultaneously has proven quite successful in practice; however, theoretical justification for this success has remained elusive. The starting point for previous work on multiple task learning has been that the tasks to be learned jointly are somehow “algorithmically related”, in the sense that the results of applying a specific learning algorithm to these tasks are assumed to be similar. We offer an alternative approach, defining relatedness of tasks on the basis of similarity between the example generating distributions that underline these task.