Using a recent convex formulation of IBM Model 2, we propose a new initialization scheme which has some favorable comparisons to the standard method of initializing IBM Model 2 with IBM Model 1. Additionally, we derive the Viterbi alignment for the convex relaxation of IBM Model 2 and show that it leads to better F-Measure scores than those of IBM Model 2.

Redundancy and fault tolerance are two of the most important considerations in automation control systems, as they provide stability and reliability for the whole system. This paper presents the types of redundancy that can be found in a Programmable Logic Controller (PLC) system at component-level, connection-level, and system-level, and is a review of various techniques and implementations of PLC redundancy, accompanied by a summary of redundancy options offered by PLC manufacturers in the global market.

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Abstrucf -This paper analyses the output voltage nonlinearity of current switching relaxntion oscillator. An analytical modeltaking into account the current sources finite output resistanceis developed, in order (1) to get more precise design equation for transistor lengtfi and (2) to evaluate the oscillation amplitude influence on the oscillation frequency. The results obtained for an oscillntor realized using B standard 0.6 pn CMOS process were checked by SPICE simulation.Inder term -relaxation oscillator, output voltage linearity, oscillation frequency.

IBM Model 1 is a classical alignment model. Of the first generation word-based SMT models, it was the only such model with a concave objective function. For concave optimization problems like IBM Model 1, we have guarantees on the convergence of optimization algorithms such as Expectation Maximization (EM). However, as was pointed out recently, the objective of IBM Model 1 is not strictly concave and there is quite a bit of alignment quality variance within the optimal solution set. In this work we detail a strictly concave version of IBM Model 1 whose EM algorithm is a simple modification of the original EM algorithm of Model 1 and does not require the tuning of a learning rate or the insertion of an l 2 penalty. Moreover, by addressing Model 1's shortcomings, we achieve AER and F-Measure improvements over the classical Model 1 by over 30%.