A Method of Gene-Function Annotation Based on Variable Precision Rough Sets

It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotation of new biological sequences is presented by using the variable-precision rough set theory. The proposed method is applied to the real data in GO database to examine its effectiveness. Numerical results show that the proposed method has better precision, recall-rate and harmonic mean value compared with existing methods.

Establishment and Optimization of Status Assessment Variables for Heavy Haul Railway Line Service Performance

In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status evaluation variable set construction and reduction optimization method is proposed. Firstly, the status of heavy haul railway line is analyzed, and an initial set of evaluation variables affecting the line status is constructed. Then, based on the association rule and the principal component analysis method, key variables are extracted from the initial variable set to establish the evaluation system. Finally, this method is verified with actual data of a line. The results show that the service performance of heavy haul railway line can still be evaluated accurately when the evaluation variables are reduced by 60% in the proposed method.

Variable precision rough set for multiple decision attribute analysis

A variable precision rough set (VPRS) model is used to solve the multi-attribute decision analysis (MADA) problem with multiple conflicting decision attributes and multiple condition attributes. By introducing confidence measures and a β-reduct, the VPRS model can rationally solve the conflicting decision analysis problem with multiple decision attributes and multiple condition attributes. For illustration, a medical diagnosis example is utilized to show the feasibility of the VPRS model in solving the MADA problem with multiple decision attributes and multiple condition attributes. Empirical results show that the decision rule with the highest confidence measures will be used as the final decision rules in the MADA problem with multiple conflicting decision attributes and multiple condition attributes if there are some conflicts among decision rules resulting from multiple decision attributes. The confidence-measure-based VPRS model can effectively solve the conflicts of decision rules from multiple decision attributes and thus a class of MADA problem with multiple conflicting decision attributes and multiple condition attributes are solved.

Product Approximation of Grade and Precision

The normal graded approximation and variable precision approximation are defined in approximate space. The relationship between graded approximation and variable precision approximation is studied, and an important formula of conversion between them is achieved The product approximation of grade and precision is defined and its basic properties are studied.

Multi-Span and Multiple Relevant Time Series Prediction Based on Neighborhood Rough Set

Rough set theory has been widely researched for time series prediction problems such as rainfall runoff.Accurate forecasting of rainfall runoff is a long standing but still mostly signicant problem for water resource planning and management,reservoir and river regulation.Most research is focused on constructing the better model for improving prediction accuracy.In this paper,a rainfall runoff forecast model based on the variable-precision fuzzy neighborhood rough set(VPFNRS)is constructed to predict Watershed runoff value.Fuzzy neighborhood rough set dene the fuzzy decision of a sample by using the concept of fuzzy neighborhood.The fuzzy neighborhood rough set model with variable-precision can reduce the redundant attributes,and the essential equivalent data can improve the predictive capabilities of model.Meanwhile VFPFNRS can handle the numerical data,while it also deals well with the noise data.In the discussed approach,VPFNRS is used to reduce superuous attributes of the original data,the compact data are employed for predicting the rainfall runoff.The proposed method is examined utilizing data in the Luo River Basin located in Guangdong,China.The prediction accuracy is compared with that of support vector machines and long shortterm memory(LSTM).The experiments show that the method put forward achieves a higher predictive performance.

A Low Complexity VCS Method for PAPR Reduction in Multicarrier Code Division Multiple Access

This paper investigates a peak to average power ratio （PAPR） reduction method in multicarrier code division multiple access （MC-CDMA） system. Variable code sets （VCS）, a spreading codes selection scheme, can improve the PAPR property of the MC-CDMA signals, but this technique requires an exhaustive search over the combinations of spreading code sets. It is observed that when the number of active users increases, the search complexity will increase exponentially. Based on this fact, we propose a low complexity VCS （LC-VCS） method to reduce the computational complexity. The basic idea of LC-VCS is to derive new signals using the relationship between candidature signals. Simulation results show that the proposed approach can reduce PAPR with lower comtational pucomplexity. In addition, it can be blindly received without any side information.

Architecture Design of a Variable Length Instruction Set VLIW DSP

The cost of the central register file and the size of the program code limit the scalability of very long instruction word（VLIW） processors with increasing numbers of functional units.This paper presents the architectural design of a six-way VLIW digital signal processor（DSP） with clustered register files.The architecture uses a variable length instruction set and supports dynamic instruction dispatching.The one-level memory system architecture of the processor includes 16-KB instruction and data caches and 16-KB instruction and data on-chip RAM.A compiler based on the Open64 was developed for the system.Evaluations show that the processor is suitable for high performance applications with a high code density and small program code size.

Text Classification Using Sentential Frequent Itemsets

Text classification techniques mostly rely on single term analysis of the document data set, while more concepts, especially the specific ones, are usually conveyed by set of terms. To achieve more accurate text classifier, more informative feature including frequent co-occurring words in the same sentence and their weights are particularly important in such scenarios. In this paper, we propose a novel approach using sentential frequent itemset, a concept comes from association rule mining, for text classification, which views a sentence rather than a document as a transaction, and uses a variable precision rough set based method to evaluate each sentential frequent itemset＇s contribution to the classification. Experiments over the Reuters and newsgroup corpus are carried out, which validate the practicability of the proposed system.

THE IMPLICIT CONVEX FEASIBILITY PROBLEM AND ITS APPLICATION TO ADAPTIVE IMAGE DENOISING

The implicit convex feasibility problem attempts to find a point in the intersection of a finite family of convex sets, some of which are not explicitly determined but may vary. We develop simultaneous and sequential projection methods capable of handling such problems and demonstrate their applicability to image denoising in a specific medical imaging situation. By allowing the variable sets to undergo scaling, shifting and rotation, this work generalizes pre^ious results -vherein the implicit convex feasibility problem was used for cooperative wireless sensor network positioning where sets are balls and their centers were implicit.