Applied Mechanics and Materials Vols. 58-60

Abstract: This paper present a review on the research of sentiment analysis in computational linguistics (CL). This information can contribute to defining the reference point for appraisal in CL. Some different approaches to related problems in documents are also be discussed with the aim of formulating the research issues.

Abstract: Kalman filter algorithm is an effective self-adaptive filtering algorithm, but it has limited practical application due to its relatively higher requirements for hardware. In this paper, an improved Kalman filter algorithm is used in actual systems, which based on stepped data processing, greatly reducing the hardware resource.

Abstract: Enumeration of Boolean functions with maximum algebraic immunity (MAI) is investigated in this paper. The even-variable Boolean functions with maximum AI were divided into 3 classes. First, we can obtain the number of the first two classes, and then we give a construction which provides large number of Boolean functions with maximum AI belong to the third classes. As a result, the lower bound on the number of balanced even-variable Boolean functions with maximum AI was improved.

Abstract: To meet the needs of open system and specific requirements of CNC platform, the component Software-Bus-Adapter model (SBA model) is presented. In this model, functional modules and application modules are encapsulated into components and integrated through software bus. The software bus is bridge between components communication. And the adapters accomplished hybrid components interface and data format transformation, which can be added to and removed from the system at runtime. Based on The SBA model is proven to be a solid base for CNC developing system with high efficiency, interoperability, scalability and openness. Development Practice has proved that SBA - based model of CNC system has more efficient and better interoperability and flexibility.

Abstract: This paper based on several common wireless sensor node localization algorithms. According to the concentric localization algorithm principle, we proposed an annular localization algorithm and its improved algorithm .The algorithm uses the anchor node to do node ring through certain rules, narrows unknown nodes estimate area continually, and until finally gets the minimum area contains unknown nodes. Then taking the minimum area centroid position as unknown node’s estimate coordinates. Through the simulation of concentric localization algorithm and its improved algorithm, circular localization algorithm and its improved algorithm, can conclude that: When the proportion of anchor node increases from 5% to 10%, the positioning accuracy is obviously improved in the situation of low energy consumption.

Abstract: Formal concept analysis (FCA) is based on a formalization of the philosophical understanding of a concept as a unit of thought constituted by its extent and intent. The rough set philosophy is founded on the assumption that with every object of the universe of discourse we associate some information. This paper deals with approaches to knowledge reduction in generalized consistent decision formal context. Finally, a new system model of semantic web based on FCA and rough set is proposed, which preserve more structural and featural information of concept lattice. In order to obtain the concept lattices with relatively less attributes and objects, we study the reduction of the concept lattices based on FCA and rough set theory. The experimental results indicate that this method has great promise.

Abstract: Since the algorithms of constraint frequent neighboring class set mining based on Apriori has some redundancy candidate constraint frequent neighboring class set and some repeated computing, so its efficiency isn’t improved. Hence, this paper proposes an algorithm of constraint frequent neighboring class set mining based on interval mapping, which may efficiently extract short constraint frequent neighboring class set from large spatial database via up search. The algorithm uses binary weights to change neighboring class set into integer, which is looked on as a spatial transaction, and it uses interval mapping to generate constraint frequent neighboring class set via up search, i.e. the algorithm creates an interval to map a range of generating candidate, up search is mapping candidate from minimum to maximum of the interval. The method is different from traditional up search or down-up search. The experimental result indicates that the algorithm is more efficient than the constraint frequent neighboring class set mining algorithm based on Apriori when mining short constraint frequent neighboring class set.

Abstract: Since the algorithms of constraint frequent neighboring class set mining based on Apriori is unsuitable for mining any length constraint frequent neighboring class set and has some redundancy computing, this paper proposes an algorithm of constraint frequent neighboring class set mining based on filling class set, which may efficiently extract any length constraint frequent neighboring class set from large spatial database. The algorithm uses binary conversion to turn neighboring class set into integer, and regards these integers as mining spatial database, and it uses double search strategy to generate constraint frequent neighboring class set, namely, one is that the algorithm uses two k-constraint frequent neighboring class sets to connect (k+1)-candidate constraint frequent neighboring class set, the other is that it also uses filling virtual class set of (k+1)-candidate constraint frequent neighboring class set to generate another candidate. In whole mining course the algorithm need only scan database once. The experimental result indicates that the algorithm is more efficient than the constraint frequent neighboring class set mining algorithm based on Apriori when mining any length constraint frequent neighboring class set.

Abstract: In order to solving the problem that the mass samples of mixed gas spectra data samples being unable to obtain, characteristic absorption spectrum line of the component gas for mixed gas being overlap, and the problem of randomness of component concentration distribution for mixed gas and so on, support vector machine is introduced for the infrared spectra analysis for the mixed gas. Key technologies as feature selection of spectra data samples, data preprocessing, SVM calibration model parameters optimization and level structure for spectrum analysis of a mixed gas is proposed in the paper. The influence of above-mentioned four key technologies to the analysis results is discussed by using experimental means. The experimental result shows that with adoption of the key technologies, the maximum absolute error of component concentration analysis for the mixed gas is 2.93%, and the maximum average absolute error is of 0.73%. The method can also be used for infrared spectra analysis for other mixed gas, and it has practical application value.

Abstract: According to characteristics of order delivery problem under B2C Electronic Commerce, the paper constructed a mathematical model and designed improved Tabu Search algorithm to solve it. Relevant papers’ data was used to emulate experiments. Experiment results showed that the proposed algorithm could get better results than the GA-SA and TS algorithms given in relevant papers in shorter time. It also proved rationality of the given model and effectiveness of the algorithm.