29468800
SceBoost Learning Algorithm for Feature Selection
This paper proposes an improved boost learning algorithm, the SceBoost algorithm, and its application in developing fast and robust features for citrus canker detection by machine vision. The algorithm use symmetric cross entropy to eliminate redundancy among selected features using AdaBoost algorithm. Selected features are subjected to recognize citrus canker symptoms on given pictures of citrus foliage. Compared with related feature selection algorithm our method can get improvements in classification accuracy and significantly reduce computation time when reach the same requirements.
11948620
The Inconsistency in Rough Set Based Rule Generation
As the amount of information in the world is steadily increasing, there is a growing demand for tools for analyzing the information. The problem of data mining is investigated in this paper. It is very important and useful to generate decision rules and reason under inconsistency. Propositional default rules are generated in this paper. Based on analysis of inconsistency, Skowron's default rule generation algorithm is improved. A corresponding reasoning method with a rule-choosing stratagem of lower frequency first under inconsistency is also developed. A suitable decision can be generated for any yet unseen object including one with unknown attribute values and one that is even inconsistent (conflicting) with objects of the training decision table. The rule-choosing stratagem is shown to be valid by our experiments.
7806615
A Novel Ordering-Based Greedy Bayesian Network Learning Algorithm on Limited Data
Existing algorithms for learning Bayesian network (BN) require a lot of computation on high dimensional itemsets, which affects accuracy especially on limited datasets and takes up a large amount of time. To alleviate the above problem, we propose a novel BN learning algorithm OM- RMRG, Ordering-based Max Relevance and Min Redun- dancy Greedy algorithm. OMRMRG presents an ordering- based greedy search method with a greedy pruning proce- dure, applies Max-Relevance and Min-Redundancy feature selection method, and proposes Local Bayesian Increment function according to Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Exper- imental results show that OMRMRG algorithm has much better efficiency and accuracy than most of existing BN learning algorithms on limited datasets.
21928964
Energy-aware optimal workload allocation among the battery-powered devices to maximize the co-operation life time
Traditional low power design minimizes the total power consumption. However for systems that want to maximize co-operation life time among the battery-powered devices, the total power minimization strategy may not be the best. The workload allocation among these devices has a dramatic impact on the co-operation life time. In this paper, we address this problem by formulating it as a linear programming (LP) problem. A simplified energy-aware algorithm is proposed to provide the optimal solution according to the energy capacity ratio of the devices. This algorithm is simple enough to be embedded into real-time system and we illustrate this by an example using a configurable transmitter-receiver pair. Experimental results show that the proposed algorithm significantly outperforms the other workload allocation strategies by 400.66% in the randomly-generated systems and 23.20% in a point-to-point (P2P) communication system, respectively
856762
Research on System Integrative Evaluation Method Based on Survivability
Survivability is the essential ability of a system to fulfill its mission, in a timely manner, in the presence of attacks, failures, or accidents. Survivability should be considered beyond security for information system, and quantitative analysis can assess system survivability accurately for improvement. The paper presents a new type of analysis method for computer system survivability which considers both exterior environment and interior components and their correlations. The method can describe the survivability more precisely and exactly. The paper also elaborates on the process of using this method to evaluate system survivability and its advantages.
6688376
Frequency sensitivity in Hodgkin–Huxley systems
Abstract. The frequency sensitivity of weak periodic signal detection has been studied via numerical simulations for both a single neuron and a neuronal network. The dependence of the critical amplitude of the signal upon its frequency and a resonance between the intrinsic oscillations of a neuron and the signal could account for the frequency sensitivity. In the presence of both a subthreshold periodic signal and noise, the signal-to-noise ratio (SNR) of the output of either a single neuron or a neuronal network present the typical characteristics of stochastic resonance. In particular, there exists a frequency-sensitive range of 30–100 Hz, and for signals with frequencies within this range the SNRs have large values. This implies that the system under consideration (a single neuron or a neuronal network) is more sensitive to the detection of periodic signals, and the frequency sensitivity may be of a functional significance to signal processing.
9882029
The Group Search Optimizer and its Application on Truss Structure Design
This paper introduces a novel optimization algorithm, group search optimization (GSO) algorithm and its implementation method is presented in detail. The GSO is used to investigate the planar and space truss structures with continuous variables and is tested by two truss optimization problems. The optimization results are compared with that of the particle swarm optimization (PSO) algorithm, the particle swarm optimization with passive congregation (PSOPC) and the heuristic particle swarm optimizer (HPSO) algorithm. Results from the two tested cases illustrate the ability of the GSO algorithm to find the optimal results, which are better than that of the PSO and PSOPC, while are at the same level of that of HPSO optimization method. Meanwhile, the results also show that the GSO algorithm maintains a preferable convergence accuracy among these four algorithms.