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Fischer-Tropsch (F-T) diesel fuel is characterized by a high cetane number, a nearzero sulphur content, and a very low aromatic level. In the present paper, the performance and emissions of an unmodified single-cylinder direct injection diesel engine operating on F-T diesel fuel are studied and compared with those of conventional diesel fuel operation. The results show that F-T diesel fuel exhibits a slightly longer injection delay and injection duration, displays an 18.7 per cent average shorter ignition delay, and has a lower peak value of premixed burning rate and a higher peak value of diffusion burning rate than conventional diesel fuel. The engine with F-T diesel fuel has a slightly lower peak combustion pressure and a far lower rate of pressure rise, and consequently a lower mechanical load and combustion noise compared with conventional diesel fuel. In addition, the brake specific fuel consumption is lower and the brake fuel conversion efficiency is higher for F-T diesel fuel operation. The CO 2 , HC, CO, NO x , and smoke emissions with F-T diesel fuel are reduced at all operating conditions when compared with conventional diesel fuel. On average, NO x and smoke emissions are reduced by 16.7 and 40.3 per cent respectively with F-T diesel fuel. The study demonstrates that F-T diesel fuel is an excellent clean alternative fuel for diesel engines.

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen classes. Since semantic knowledge is built on attributes shared between different classes, which are highly local, strong prior for localization of object attribute is beneficial for visual-semantic embedding. Interestingly, when recognizing unseen images, human would also automatically gaze at regions with certain semantic clue. Therefore, we introduce a novel goal-oriented gaze estimation module (GEM) to improve the discriminative attribute localization based on the class-level attributes for ZSL. We aim to predict the actual human gaze location to get the visual attention regions for recognizing a novel object guided by attribute description. Specifically, the task-dependent attention is learned with the goal-oriented GEM, and the global image features are simultaneously optimized with the regression of local attribute features. Experiments on three ZSL benchmarks, i.e., CUB, SUN and AWA2, show the superiority or competitiveness of our proposed method against the state-of-the-art ZSL methods. The ablation analysis on real gaze data CUB-VWSW also validates the benefits and accuracy of our gaze estimation module. This work implies the promising benefits of collecting human gaze dataset and automatic gaze estimation algorithms on high-level computer vision tasks. The code is available at https://github.com/osierboy/GEM-ZSL.

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This paper presents the combustion characteristics of a light-duty direct-injection diesel engine operating on dimethyl ether (DME). The indicated pressure diagrams and injector needle lifts are recorded and the combustion characteristics are demonstrated and compared with those of an engine operated on diesel fuel. The experimental and calculated results show that the DME engine has a longer delay of injection and duration of injection, a lower maximum cylinder pressure and rate of pressure rise, as well as a shorter ignition delay compared with those of a diesel engine. The DME engine has a low mechanical load and combustion noise, a fast rate of diffusion combustion and a shorter combustion duration than that of a diesel engine. It has the ideal pattern of compression ignition engine heat release.

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is smaller than the ambient dimension. Traditional subspace clustering methods often rely on the self-expressiveness property, which has proven effective for linear subspace clustering. However, they perform unsatisfactorily on real data with complex nonlinear subspaces. More recently, deep autoencoder based subspace clustering methods have achieved success owning to the more powerful representation extracted by the autoencoder network. Unfortunately, these methods only considering the reconstruction of original input data can hardly guarantee the latent representation for the data distributed in subspaces, which inevitably limits the performance in practice. In this paper, we propose a novel deep subspace clustering method based on a latent distribution-preserving autoencoder, which introduces a distribution consistency loss to guide the learning of distribution-preserving latent representation, and consequently enables strong capacity of characterizing the real-world data for subspace clustering. Experimental results on several public databases show that our method achieves significant improvement compared with the state-of-the-art subspace clustering methods.