Semi-supervised model adaptation for statistical machine translation

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a significant improvement in translation quality on both tasks.     

Data-Driven Texture Rendering on an Electrostatic Tactile Display

In this article, we present a data-driven texture rendering method applied to a tactile display based on electrostatic attraction. The proposed method was examined in two steps. First, accelerations occurring due to sliding a tool on three different surfaces were measured, and then the collected data were replayed on an electrostatic tactile display. The proposed data-driven texture rendering method was evaluated against a conventional method in which a standard input such as a square wave was used for texture representation. Second, data from the Penn Haptic Texture Toolkit were used to generate virtual textures on the same tactile display. Psychophysical experiments were carried out for both steps, during which subjects rated similarities among the rendered virtual textures and the real samples. Confusion matrices were created, and multidimensional scaling (MDS) analysis was performed to create a perceptual space for further examination and to extract underlying dimensions of the textures. The results show that the virtual textures generated using the data-driven method were similar to the real textures. Roughness and stickiness were the primary dimensions of texture perception. Together with the supporting results from the MDS analysis, this study showed that the data-driven method is a viable solution for realistic texture rendering with electrostatic attraction.     

Seismic geomorphology and stratigraphic trap analyses of the Lower Cretaceous siliciclastic reservoir in the Kopeh Dagh-Amu Darya Basin

Lower Cretaceous Shurijeh–Shatlyk Formations host some of the main reservoirs in the Kopeh Dagh-Amu Darya Basin.Exploration in this area so far has focused on the development of structural traps, but recognition of stratigraphic traps in this area is of increasing importance. Integration of 3D seismic data with borehole data from thirteen wells and five outcrop sections was used to identify potential reservoir intervals and survey the hydrocarbon trap types in the East Kopeh Dagh Foldbelt(NE Iran). Analyses of horizontal slices indicated that the lower Shurijeh was deposited in a braided fluvial system.Generally, three types of channel were identified in the lower Shurijeh Formation: type 1, which is low-sinuosity channels interpreted to be filled with non-reservoir fine-grained facies; type 2, which is a moderately sinuous sand-filled channel with good prospectively; and type 3, which is narrow, high sinuosity channel filled with fine-grained sediments. Results indicate that upper Shurijeh–Shatlyk Formations were deposited in fluvial to delta and shallow marine environments. The identified delta forms the second reservoir zone in the Khangiran Field. Study of the stratigraphic aspects of the Shurijeh succession indicates that both lower and upper Shurijeh reservoirs are stratigraphic reservoir traps that improved during folding.     

Dielectric Structure with Periodic Strips for Increasing Radiation Power of Photoconductive Antennas: Theoretical Analysis

Low terahertz (THz) radiation power and low efficiency are the well-known drawbacks of photoconductive antennas (PCAs). To increase THz-radiation power of PCAs, a dielectric structure with periodic low-temperature-grown GaAs strips is proposed. Transmitted power of the proposed structure is obtained from a theoretical model, and further confirmed by finite element simulations. Results show that the structure is capable to transmit into the substrate 90 % of the power of transverse magnetic wave with wavelength as wide as from 0.7 to 1.0 μm. Favorability of this property gets amplified when power transmission in a wide range of frequency bandwidth is desired, e.g., for optical pulse with short duration time incident to PCA, which generates carriers in the semiconductor that create THz emission. Furthermore, the proposed dielectric structure with periodic strips, the whole structure placed in between electrodes of PCA is considered, and analyzed by the existing photoconductive antenna equivalent circuit model, to see how power radiation changes. Interestingly, THz-radiation power enhancements of 70 and 20 % are evinced for, respectively, 20 and 150 mW incident optical powers as instances, as compared to PCA without strips in the gap area.     

A Novel 2.5–3.1 GHz Wide-Band Low-Noise Amplifier in 0.18 \upmu \hbox {m} CMOS

Aiming for the simultaneous realization of constant gain, accurate input and output impedance matching and minimum noise figure (NF) over a wide frequency range, the circuit topology and detailed design of wide broadband low noise amplifier (LNA) are presented in this paper. A novel 2.5–3.1 GHz wide-band LNA with unique characteristics has been presented. Its design and layout are done by TSMC 0.18  \(\upmu \hbox {m}\) technology. Common gate stage has been used to improve input matching. In order to enhance output matching and reduce the noise as well, a buffer stage is utilized. Mid-stages which tend to improve the gain and reverse isolation are exploited. The proposed LNA achieves a power gain of 15.9 dB, a NF of 3.5 dB with an input return loss less than \(-\) 11.6, output return loss of \(-\) 19.2 to \(-\) 19 and reverse isolation of \(-\) 38 dB. The LNA consumes 54.6 mW under a supply voltage of 2 V while having some acceptable characteristics.     

Simulation of SiGe:C HBTs using neural network and adaptive neuro-fuzzy inference system for RF applications

In this article, the small-signal equivalent circuit model of SiGe:C heterojunction bipolar transistors (HBTs) has directly been extracted from S-parameter data. Moreover, in this article, we present a new modelling approach using ANFIS (adaptive neuro-fuzzy inference system), which in general has a high degree of accuracy, simplicity and novelty (independent approach). Then measured and model-calculated data show an excellent agreement with less than 1.68?×?10^?5% discrepancy in the frequency range of higher than 300 GHz over a wide range of bias points in ANFIS. The results show ANFIS model is better than ANN (artificial neural network) for redeveloping the model and increasing the input parameters.     

Implementation of supervisory control using extended finite-state machines

This article aims at bridging the gap between traditional designs to discrete-event control problems and supervisory control theory of Ramadge and Wonham. We propose to implement supervisory control by extending the plant's finite state machine with Boolean variables, guard formulas and updating functions. Boolean variables are used to encode the supervisor's states, event observation is captured by a set of Boolean functions that update the value of variables, and control is introduced by guarding events with Boolean formulas. The framework developed in this work is fundamental in our ongoing research on communication between supervisors in a distributed discrete-event system.     

Mixed Biogeography-Based Optimization for GENCOs’ Maintenance Scheduling in Restructured Power Systems

Power industry restructuring has brought new challenges to the generation unit maintenance scheduling problem. Maintenance scheduling establishes the outage time scheduling of units in a particular time horizon. In the restructured power systems, the decision-making process is decentralized where each generating company (GENCO) tries to maximize its own benefit. Therefore, the principle to draw up the unit maintenance scheduling is different from the traditional centralized power systems. The objective function for GENCOs is to minimize his maintenance investment loss. Therefore, he hopes to put its maintenance on the weeks when the market-clearing price is lowest so that maintenance investment loss descends. This paper addresses the unit maintenance scheduling problem of GENCOs in restructured power systems. The problem is formulated as a mixed integer programming problem, and it is solved by using an optimization method known as biogeography-based optimization (BBO). BBO is simple to implement in practice and requires a reasonably small amount of computing time and a small amount of data communication. BBO has been tested by applying it to a GENCO with three generating units. This model consists of an objective function and related constraints, e.g., maintenance window, generation capacity, load and network flow. The simulation result of this method is compared with a classic method. The outcome is very encouraging and proves that BBO is powerful for minimizing GENCOs’ objective function.