谈谈硬盘数据丢失与恢复

硬盘是电脑中存储数据的主要场所,因此数据安全性不可忽略。由于人们对数据的重视程度不够,人为的错误操作、病毒的攻击、硬件的损坏,都可能导致数据丢失,给用户的工作和生活带来巨大的困扰和不便。本文将向大家介绍各种数据丢失的原因与解决方案。     

在线手写签名认证研究

手写签名认证是人工智能领域的一个重要课题,对它的深入研究具有重要的现实意义。文章采用基于概率统计的方法表示手写签名的速度特征,用vague集相似度表示书写压力特征,并通过细分的拐点表示笔画特征进行签名认证,最后,构造了一个在线手写签名认证系统。实验证明了该方法的有效性。     

Solving a real-time allocation problem with constraint programming

In this paper, we present an original approach (CPRTA for “Constraint Programming for solving Real-Time Allocation”) based on constraint programming to solve a static allocation problem of hard real-time tasks. This problem consists in assigning periodic tasks to distributed processors in the context of fixed priority preemptive scheduling. CPRTA is built on dynamic constraint programming together with a learning method to find a feasible processor allocation under constraints. Two efficient new approaches are proposed and validated with experimental results. Moreover, CPRTA exhibits very interesting properties. It is complete (if a problem has no solution, the algorithm is able to prove it); it is non-parametric (it does not require specific tuning) thus allowing a large diversity of models to be easily considered. Finally, thanks to its capacity to explain failures, it offers attractive perspectives for guiding the architectural design process.     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.     

基于FAT32的数据恢复系统的设计

恶意删除或数据误删现象时常发生,数据恢复的成败关系到信息的安全.根据基于FAT32系统的删除文件的四种情形,设计基于FAT32的数据恢复算法.系统依据被删除文件的创建时间与该分区或该分区下某一目录中现存文件的创建时间的比较结果来定位被删除文件,读取该文件信息,恢复被删除文件.系统具有简单、易用、灵活、高效等特点.     

涉密信息网络安全应用系统研究

针对网络化条件下涉密信息应用和管理中的保密需求,提出了一种涉密信息网络化集中应用模式,建立了相应的涉密信息网络安全应用系统框架,并对其体系结构进行了详细叙述,最后通过建立系统原型验证了该系统框架的可行性和实用性.     

Influence of cryogenic coolant on turning performance characteristics: A comparison with wet machining

Machining of 17-4 Precipitation Hardenable Stainless Steel (PH SS) is one of the difficult tasks because of its high cutting temperatures. Conventional cutting fluids are used to overcome the high cutting temperatures, but these are not acceptable from the health and environmental sustainable points of view. Cryogenic cooling is one of the potential techniques to overcome such problems. In the current work, comparison is made of cryogenic turning results, such as tool flank wear, cutting forces (feed force, main cutting force), cutting temperature, chip morphology and surface integrity characteristics with wet machining during machining of heat-treated 17-4 PH SS. The result showed that in cryogenic machining, a maximum of 53%, 78%, 35% and 16% reductions was observed in tool flank wear, cutting temperature, surface roughness and cutting force, respectively, when compared with wet machining. It was also evident from the experimental results that cryogenic machining significantly improved the machining performance and product quality even at high feed rates.     

High-feed turning of AISI D2 tool steel using multi-radii tool inserts: Tool life,material removed,and workpiece surface integrity evaluation

Multi-radii tool inserts offer novel configuration that comprises of multiple radii at tool nose. A review of the available literature indicates that there exists a need for experimental investigation on certain key machining characteristics of such tools. This paper reports on tool wear/life, material removed, and workpiece surface roughness when multi-radii mixed alumina TiN coated tool inserts are employed for turning D2 steel. Inserts of three different nose radii (0.40, 0.80, 1.20?mm) at six levels of feed rates (ranging from 0.157 to 0.562?mm/rev) are used. Results show that flank wear is the dominant wear mode with catastrophic tool failure occurring at highest nose radius (1.20?mm) and feed rate (0.562?mm/rev) combination. Also, there is ～59% reduction in tool life accompanied by ～62% increase in quantity of material removed as the feed rate increases from 0.157 to 0.562?mm/rev at maximum nose radius (1.20?mm). Feed rate is found to be statistically significant factor for all three responses considered herein at 95% confidence level. Surface integrity assessment at maximum feed rate reveals presence of a strain hardened layer extending to the depth of 150?µm below the machined surface without any observance of white layer for all the tool conditions and nose radius.     

Formulation and performance evaluation of a novel coconut oil–based metalworking fluid

Metalworking fluid (MWF) supplies a film of lubricant to abate friction, acts as a cooling media to rebate induced heat, and prevents metal pick-ups by flushing away the chips. Hence a liquid used as a cutting fluid reduces wear on the tool, reduces the energy consumption, and produces a better surface quality on the work piece. This paper describes the formulation of a novel water-soluble MWF and its performance evaluation during straight turning and end milling experiments carried out with AISI 304 stainless steel, mild steel, and cast iron as work piece materials. The MWF was prepared by mixing water with white coconut oil as the base oil and food-grade additives as surfactants. Viscosity, pH value, and biodegradability were measured and compared with a commercially available non-vegetable oil–based MWF. The surface roughness and tool surface temperature were measured throughout the machining experiments, and better performances were observed with the coconut oil–based MWF. Tool tip geometry and flank wear for straight turning machining operation were identified by observing scanning electron microscope (SEM) images.     

Machinability enhancement of stir cast Al-TiCp composites under cryogenic condition

The current research work makes an attempt to develop a methodology in order to produce high-quality Al-TiC_p composites by using systemized inert atmospheric stir casting facility. The quality of produced composites was characterized using microstructural, Energy-dispersive X-ray spectroscopy (EDX), and machinability studies. Atomized-Cryogenic-Liquid (ACL) spray machining technique was developed and used to enhance the machinability of composite. The results of microstructural analysis reveal that TiC_p refines Al grain structure to 50 times lesser than non-reinforced Al grains along the full length of the casting. Energy-dispersive X-ray analysis confirms that no metallic reaction takes place. Machining studies reveal that atomized cryogenic liquid-spray machining significantly improves the quality of machined surface and reduces the progressive wear occurring on tool during turning of Al-TiC_p composites in comparison with cryogenically chilled argon, wet, and dry machining conditions, respectively. The scientific methodology highlighted in this study helps to improve the productivity and reduces the manufacturing cost in various industrial applications.