A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

An airlight estimation method for image dehazing based on gray projection

Multimedia Tools and Applications - As the key parameter of dehazing algorithms, airlight value directly affect the calculation accuracy of sky region, and any deviation will lead to the chromatic...     

An efficient video summarization for surveillance system using normalized k-means and quick sort method

There has been a vast augmentation in quantity of Video Content (VC) generated amid the last some years. The Video Summarization (VS) approach is introduced for managing the VC. Prevailing VS techniques have endeavored to render the VS but the systems have Execution Time (ET) as well as condensing the video's content in domain specific manner. To triumph over such disadvantages, this paper proposed efficient VS for surveillance system using normalized k-means along with quick sort method. The proposed technique comprises eight stages, like split video into frames, pre-sampling, provide ID number, feature extraction, Feature Selection (FS), clustering, extract frames, video summary. Initially, the video frames are pre-sampled utilizing the proposed Three Step Cross Searching Algorithm (TSCS) technique. Then, give the ID number for every frame. Next, the features are extracted as of the frames. Then, the necessary features are selected using Entropy based Spider Monkey Algorithms (ESMA). In next stage, the features are grouped using Normalized K-Means (N-Kmeans) algorithm for identifying best candidate frames. Then select the minimum distance value based cluster set is the Key Frame (KF) selection. At last, the video is orderly summarized using quick sort method. Finally, in experimental evaluation the proposed work is compared with the prevailing methods. The proposed VS gave better outcome than the existing approaches.     

A design of a robust discrete-time controller

In this paper, we proposed a robust discrete-time controller. This control system, which is derived from the idea of the normalized plant, does not include plant parameters. Thus, we obtain a control system independent of plant parameters and that has the same structure as a conventional optimal servo control system. Simulation and experimental results show that the proposed method is fairly robust to plant parameter variations and external disturbances.     

A dynamic SVR–ARMA model with improved fruit fly algorithm for the nonlinear fiber stretching process

Natural Computing - The fiber stretching process plays the key role in the process of fiber production and its effects is measured by the stretching ratio. The stretching ratio is determined by the...     

High-efficiency Silicon Solar Cells: A Review

Over the past few decades, crystalline silicon solar cells have been extensively studied due to their high efficiency, high reliability, and low cost. In addition, these types of cells lead the industry and account for more than half of the market. For the foreseeable future, Si will still be a critical material for photovoltaic devices in the solar cell industry. In this paper, we discuss key issues, cell concepts, and the status of recent high-efficiency crystalline silicon solar cells.     

Compiler compatible 5.66 Mb/mm2 8T 1R1W register file in 14 nm FinFET technology

1-read/1-write (1R1W) register file (RF) is a popular memory configuration in modern feature rich SoCs requiring significant amount of embedded memory. A memory compiler is constructed using the 8T RF bitcell spanning a range of instances from 32 b to 72 Kb. An 8T low-leakage bitcell of 0.106 μm² is used in a 14 nm FinFET technology with a 70 nm contacted gate pitch for high-density (HD) two-port (TP) RF memory compiler which achieves 5.66 Mb/mm² array density for a 72 Kb array which is the highest reported density in 14 nm FinFET technology. The density improvement is achieved by using techniques such as leaf-cell optimization (eliminating transistors), better architectural planning, top level connectivity through leaf-cell abutment and minimizing the number of unique leaf-cells. These techniques are fully compatible with memory compiler usage over the required span. Leakage power is minimized by using power-switches without degrading the density mentioned above. Self-induced supply voltage collapse technique is applied for write and a four stack static keeper is used for read Vmin improvement. Fabricated test chips using 14 nm process have demonstrated 2.33 GHz performance at 1.1 V/25 °C operation. Overall V_min of 550 mV is achieved with this design at 25 °C. The inbuilt power-switch improves leakage power by 12x in simulation. Approximately 8% die area of a leading 14 nm SoC in commercialization is occupied by these compiled RF instances.     

Model-based detection of critical driving situations with fuzzy logic decision making

A precise knowledge about the current driving condition is getting increasingly important for future driver assistance systems like global chassis control or collision avoidance systems for avoiding any critical driving situation. Moreover a precise knowledge about the driving situation can be used in testing, in evaluation, and for comparison of new passenger cars. A two degree of freedom model of vehicle lateral dynamics is used to derive a characteristic velocity stability indicator (CVSI). The CVSI is used to distinguish between different driving and stability conditions (i.e. understeering, oversteering, and neutralsteering). This forms the basis for a driving condition detection system with fixed thresholds. It is then extended to a detection system with fuzzy logic thresholds. The CVSI and the fuzzy systems are compared experimentally using (i) a slalom test drive on an icy road and (ii) a stationary circular test drive on a dry asphalt road.