Theory and characteristics of novel variable reluctance 1X resolver

Although brushless resolvers have been used widely as angular position transducers, they are expensive due to their intricate construction, involving a rotary transformer to supply the exciting coils wound on the rotor poles with the current. It is shown theoretically in this paper that the resolver without rotary transformer or brushes can be realized by simple construction, which consists of the stator core with both 4-poles exciting windings and 2-poles output ones and the rotor core carrying no windings. In this resolver the rotor core has minimum gap at one side and a maximum gap at another side of the diameter. It is characterized by an outer surface form that makes the fluctuation part of gap permeance very in proportion to cos θ, where θ represents the angular position of a point in the air gap with respect to the origin on the rotor, the point of minimum air gap. The method determining the rotor form to embody the aforementioned gap permeance variation is also shown. It has been confirmed not only by simulation but also experiment that the 2-phase output voltages of a model designed based on the theory have sinusoidal waveforms with very small harmonic contents. Moreover, the rotor position detected by processing the output voltages through the conventional resolver/digital converter was within acceptable engineering accuracy.     

用于氢化的铜铬氧化物催化剂的研究

通过铜铬氧化物催化剂用于豆油的选择氢化,研究了制备方法及氢化条件对催化剂活性的影响.在所用的五种催化剂中,以Adkins法制备的催化剂活性最好,碳酸氢铵法次之.文中还对催化剂焙烧气氛、焙烧温度、焙烧时间、铜铬原子比对催化剂活性的影响以及氢化温度、催化剂用量、搅拌速度、氢流速等对反应的影响进行了较详细的讨论.     

入侵检测在存储区域网中的应用

从SAN(存储区域网)的安全隐患出发,分析了SAN在应用环境中受到的三种典型威胁。结合现行的入侵检测技术给出一个可行的解决方案。     

Data-mining based SQL injection attack detection using internal query trees

Detecting SQL injection attacks (SQLIAs) is becoming increasingly important in database-driven web sites. Until now, most of the studies on SQLIA detection have focused on the structured query language (SQL) structure at the application level. Unfortunately, this approach inevitably fails to detect those attacks that use already stored procedure and data within the database system. In this paper, we propose a framework to detect SQLIAs at database level by using SVM classification and various kernel functions. The key issue of SQLIA detection framework is how to represent the internal query tree collected from database log suitable for SVM classification algorithm in order to acquire good performance in detecting SQLIAs. To solve the issue, we first propose a novel method to convert the query tree into an n-dimensional feature vector by using a multi-dimensional sequence as an intermediate representation. The reason that it is difficult to directly convert the query tree into an n-dimensional feature vector is the complexity and variability of the query tree structure. Second, we propose a method to extract the syntactic features, as well as the semantic features when generating feature vector. Third, we propose a method to transform string feature values into numeric feature values, combining multiple statistical models. The combined model maps one string value to one numeric value by containing the multiple characteristic of each string value. In order to demonstrate the feasibility of our proposals in practical environments, we implement the SQLIA detection system based on PostgreSQL, a popular open source database system, and we perform experiments. The experimental results using the internal query trees of PostgreSQL validate that our proposal is effective in detecting SQLIAs, with at least 99.6% of the probability that the probability for malicious queries to be correctly predicted as SQLIA is greater than the probability for normal queries to be incorrectly predicted as SQLIA. Finally, we perform additional experiments to compare our proposal with syntax-focused feature extraction and single statistical model based on feature transformation. The experimental results show that our proposal significantly increases the probability of correctly detecting SQLIAs for various SQL statements, when compared to the previous methods.     

Discourse cues to deception in the case of multiple receivers

Behavioral cues to deception are instrumental in detecting deception. As one of the primary sources of deception behavior, text has been analyzed at the level of sub-sentence or message but not the discourse of interaction. Additionally, empirical studies on cues to deception in the case of multiple receivers remain nonexistent. To fill these voids, we propose a discourse framework and six hypotheses about deception behaviors in a multi-receiver environment. The deception behaviors are operationalized by discourse features based on an analysis of real-world data. The results of statistical analysis validate the efficacy of discourse features in discriminating deceivers from truth-tellers.     

Quality characteristics of edible vegetable oil blends

Two edible oil blends, namely groundnut oil:rice-bran oil and mustard oil:rice-bran oil, were prepared in different proportions and stored for a period of three years. Their physicochemical characteristics were determined. The results agreed with expected values except for free fatty acid percents and butyrorefrac-tometer readings, presumably due to rancidity. Fatty acid compositions of the blends were determined and ratios of characteristic fatty acids, like lignoceric to palmitic for groundnut oil:rice-bran oil blends, and erucic to palmitic for mustard oil:rice-bran oil blends, were calculated to identify individual oils in the blend.     

南阳直馏柴油碱洗乳化的原因及对策

分析了南阳石蜡精细化工厂直馏柴油碱洗乳化的原因，介绍了该厂直馏柴油碱洗抗乳化措施，这些措施包括：优化操作参数，加强生产管理，深度碱洗柴油轻组分然后轻、重组分混合出厂。这些措施的综合应用彻底解决了南阳石蜡精细化工厂直馏柴油碱洗乳化的问题，又保证了柴油收率。     

Face detection based on skin color likelihood

We propose a face detection method based on skin color likelihood via a boosting algorithm which emphasizes skin color information while deemphasizing non-skin color information. A stochastic model is adapted to compute the similarity between a color region and the skin color. Both Haar-like features and Local Binary Pattern (LBP) features are utilized to build a cascaded classifier. The boosted classifier is implemented based on skin color emphasis to localize the face region from a color image. Based on our experiments, the proposed method shows good tolerance to face pose variation and complex background with significant improvements over classical boosting-based classifiers in terms of total error rate performance.