Experimental Study on Working Performance of Rice Rope Direct Seeding Machine

Structure and sowing principles of rice rope direct seeding machine are introduced. In order to test the machine＇ s working performance, such as compacting effect, sowing depth, influence of sowing device to rice rope, etc., field experiments were conducted. It is concluded that mean slip ratio of compacting wheel 1 is 4.44%, wheel 2 is 5.58%, wheel 3 is 7.81%, and wheel 4 is 6.96%; mean depth of planting is 29.72 mm, and mean variability coefficient of planting depth is 6.39%. Maximum variability coefficient of planting depth is 8.40%. Rice rope＇s snapping is closely related with the machine＇s speed and guide thread wheel by sowing device orthogonal experiments. Test results show that the machine has a rational design, safe work and meets to the requirements of planting. This study has laid the foundation for further studying the project.

Surface texture formation in precision machining of direct laser deposited tungsten carbide

This paper focuses on an analysis of the surface texture formed during precision machining of tungsten carbide. The work material was fabricated using direct laser deposition （DLD） technology. The experiment included precision milling of tungsten carbide samples with a monolithic torus cubic boron nitride tool and grinding with diamond and alumina cup wheels. An optical surface profiler was applied to the measurements of surface textures and roughness profiles. In addition, the micro-geometry of the milling cutter was measured with the appli- cation of an optical device. The surface roughness height was also estimated with the application of a model, which included kinematic-geometric parameters and minimum uncut chip thickness. The research revealed the occurrence of micro-grooves on the machined surface. The surface roughness height calculated on the basis of the traditional kinematic-geometric model was incompatible with the measurements. However, better agreement between the theoretical and experimental values was observed for the minimum uncut chip thickness model.

Electromagnetic side-channel attack based on PSO directed acyclic graph SVM

Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyclic graph support vector machine(DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K ? 1)/2 binary support vector machine(SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph(DAG) testing model. Further, particle swarm optimization(PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one(OVO)SVM, one-versus-all(OVA)SVM, Probabilistic neural networks(PNN), K-means clustering and fuzzy neural network(FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.