Max-min Distance Research Articles

Study of Hybrid Strategy for Ambulatory ECG Waveform Clustering

A hybrid strategy has been proposed to reduce the wrong clustering on Ambulatory ECG (electrocardiogram). Since Ambulatory ECG is usually composed by 24 hours data, the number of individual ECG waveform can reach to 100,000, the request for accurate clustering result is highly required. The proposed strategy adopted some intelligent algorithms to solve the above problem. It clusters ECG waveform sample (selected from Ambulatory ECG) synchronously by Max-Min distance clustering algorithm, K-means algorithm and Simulated annealing algorithm first. And then, it adopted all three outputs from the above three algorithms as input on Back Propagation Artificial Neural Network (BP ANN). In the end, we got more accurate clustering result from the output of ANN. For testing the results, data of MIT/BIH arrhythmia database were used for experiments. After the controlled trial on MIT/BIH data, it can be safely concluded that the clustering result achieved by improved strategy can got more accurate than that by the traditional clustering algorithm. An average accuracy ratio is about 94.6%, 1.6% higher than k-means algorithm averagely and 1.3% higher than Simulated Annealing algorithm averagely.

A New Item Recommendation Procedure Based on Min-Max Distance

When new items are released, it is necessary to promote these items. In this situation, a recommender system specializing in new items help item providers find potential customers. This study aims to suggest a Min-Max distance-based preference boundary, and to develop a preference boundary-based recommender procedure applied for recommending new items. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. The new item recommendation procedure is organized in the following two phases. The first phase defines each customer's preference boundary based on min-max distance, and the second phase decides the target customer set for recommending new items. In this research, customer' preferences and item characteristics including new items are represented in a feature space. And the scope of boundary of the target customer's preference is extended to those of neighbors'. Diverse algorithms are suggested for the procedure, and their effectiveness scores are measured and compared through a series of experiments with a real mobile image transaction data set. The experiment results are compared, and discussions about the results are also given with a further research opportunity.

Improved NSGA-II Multi-objective Genetic Algorithm Based on Hybridization-encouraged Mechanism

To improve performances of multi-objective optimization algorithms, such as convergence and diversity, a hybridization- encouraged mechanism is proposed and realized in elitist nondominated sorting genetic algorithm (NSGA-II). This mechanism uses the normalized distance to evaluate the difference among genes in a population. Three possible modes of crossover operators—“Max Distance”, “Min-Max Distance”, and “Neighboring-Max”—are suggested and analyzed. The mode of “Neighboring-Max”, which not only takes advantage of hybridization but also improves the distribution of the population near Pareto optimal front, is chosen and used in NSGA-II on the basis of hybridization-encouraged mechanism (short for HEM-based NSGA-II). To prove the HEM-based algorithm, several problems are studied by using standard NSGA-II and the presented method. Different evaluation criteria are also used to judge these algorithms in terms of distribution of solutions, convergence, diversity, and quality of solutions. The numerical results indicate that the application of hybridization-encouraged mechanism could effectively improve the performances of genetic algorithm. Finally, as an example in engineering practices, the presented method is used to design a longitudinal flight control system, which demonstrates the obtainability of a reasonable and correct Pareto front.

A Robust and Computationally Efficient Optimal Design Algorithm of Electromagnetic Devices Using Adaptive Response Surface Method

This paper presents a robust and computationally efficient optimal design algorithm for electromagnetic devices by combining an adaptive response surface approximation of the objective function and(<TEX>$1+{\lambda}$</TEX>) evolution strategy. In the adaptive response surface approximation, the design space is successively reduced with the iteration, and Pareto-optimal sampling points are generated by using Latin hypercube design with the Max Distance and Min Distance criteria. The proposed algorithm is applied to an analytic example and TEAM problem 22, and its robustness and computational efficiency are investigated.

A Multi-Objective Differential Evolution Algorithm Based on Max-Min Distance Density

A Multi-Objective Differential Evolution Algorithm Based on Max-Min Distance Density

Information Retrieval by Semantic Analysis and Visualization of the Concept Space of D-Lib® Magazine

In this article we present a method for retrieving documents from a digital library through a visual interf based on automatically generated concepts. We used a vocabulary generation algorithm to generate a se concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks.

Determination of some structural features of procyanidins and related compounds by photodiode-array detection

In this work we have examined the use of a photodiode-array detector to identify unknown peaks correspoding to procyanidins and related compounds. The type of flavan-3-ol unit can be recognised from the absorption maximum (278.9) nm for catechin and epicatechin, and 270.6 nm for epigallocatechin), whereas the number of flavan-3-ol units does not affect this parameter. The min-max distance (distance between the minimum and the maximum in the original spectrum) decreases by 1.3 nm for each catechin or epicatechin unit. Gallates of flavan-3-ol can be detected by reference to the value of the convexity interval (distance between the inflection points before and after the maximum in the original spectrum).

Fuzzy vs. Minmax Weighted Multiobjective Linear Programming Illustrative Comparisons

ABSTRACTThis paper compares two approaches to the solution of weighted multiobjective linear programming problems: the fuzzy linear programming method and the minmax distance metric. The two models produce an identical solution for equally weighted objectives, but the solutions differ when the objectives are unequally weighted. This is due to the underlying meaning of the weights attached to each solution method. The paper illustrates the graphical meaning of the weights and the implications to the decision maker.