Construction of LDPC Codes for the Layered Decoding Algorithm

Abstract： The layered decoding algorithm has been widely used in the implementation of Low Density Parity Check （LDPC） decoders, due to its high convergence speed. However, the pipeline operation of the layered decoder may introduce memory access conflicts, which heavily deteriorates the decoder throughput. To essentially deal with the issue of memory access conflicts,

New algorithm for variable-rate linear broadcast network coding

To adjust the variance of source rate in linear broadcast networks, global encoding kernels should have corresponding dimensions to instruct the decoding process. The algorithm of constructing such global encoding kernels is to adjust heterogeneous network to possible link failures. Linear algebra, graph theory and group theory are applied to construct one series of global encoding kernels which are applicable to all source rates. The effectiveness and existence of such global encoding kernels are proved. Based on 2 information flow, the algorithm of construction is explicitly given within polynomial time O（｜E｜ ｜T｜.ω^2max）, and the memory complexity of algorithm is O（｜E｜）. Both time and memory complexity of this algorithm proposed can be O（ωmax） less than those of algorithms in related works.

Freights loading optimization with balanced and unconcentrated loading constraints

The optimization of high density and concentrated-weight freights loading requires an even distribution of the freight's weight and unconcentrated loading on the floor of the car.Based on the characteristics of concentrated-weight category freights,an improvement method is put forward to build freight towers and a greedy-construction algorithm is utilized based on heuristic information for the initial layout.Then a feasibility analysis is performed to judge if the balanced and unconcentrated loading constrains are reached.Through introducing optimization or adjustment methods,an overall optimal solution can be obtained.Experiments are conducted using data generated from real cases showing the effectiveness of our approach: volume utility ratio of 90.4% and load capacity utility ratio of 86.7% which is comparably even to the packing of the general freights.

HAPE3D—a new constructive algorithm for the 3D irregular packing problem

We propose a new constructive algorithm, called HAPE3 D, which is a heuristic algorithm based on the principle of minimum total potential energy for the 3D irregular packing problem, involving packing a set of irregularly shaped polyhedrons into a box-shaped container with fixed width and length but unconstrained height. The objective is to allocate all the polyhedrons in the container, and thus minimize the waste or maximize profit. HAPE3 D can deal with arbitrarily shaped polyhedrons, which can be rotated around each coordinate axis at different angles. The most outstanding merit is that HAPE3 D does not need to calculate no-fit polyhedron(NFP), which is a huge obstacle for the 3D packing problem. HAPE3 D can also be hybridized with a meta-heuristic algorithm such as simulated annealing. Two groups of computational experiments demonstrate the good performance of HAPE3 D and prove that it can be hybridized quite well with a meta-heuristic algorithm to further improve the packing quality.

Multiple-Instance Learning with Instance Selection via Constructive Covering Algorithm

Multiple-Instance Learning （MIL） is used to predict the unlabeled bags＇ label by learning the labeled positive training bags and negative training bags.Each bag is made up of several unlabeled instances.A bag is labeled positive if at least one of its instances is positive,otherwise negative.Existing multiple-instance learning methods with instance selection ignore the representative degree of the selected instances.For example,if an instance has many similar instances with the same label around it,the instance should be more representative than others.Based on this idea,in this paper,a multiple-instance learning with instance selection via constructive covering algorithm （MilCa） is proposed.In MilCa,we firstly use maximal Hausdorff to select some initial positive instances from positive bags,then use a Constructive Covering Algorithm （CCA） to restructure the structure of the original instances of negative bags.Then an inverse testing process is employed to exclude the false positive instances from positive bags and to select the high representative degree instances ordered by the number of covered instances from training bags.Finally,a similarity measure function is used to convert the training bag into a single sample and CCA is again used to classification for the converted samples.Experimental results on synthetic data and standard benchmark datasets demonstrate that MilCa can decrease the number of the selected instances and it is competitive with the state-of-the-art MIL algorithms.

MICkNN:Multi-Instance Covering kNN Algorithm

Mining from ambiguous data is very important in data mining. This paper discusses one of the tasks for mining from ambiguous data known as multi-instance problem. In multi-instance problem, each pattern is a labeled bag that consists of a number of unlabeled instances. A bag is negative if all instances in it are negative. A bag is positive if it has at least one positive instance. Because the instances in the positive bag are not labeled, each positive bag is an ambiguous. The mining aim is to classify unseen bags. The main idea of existing multi-instance algorithms is to find true positive instances in positive bags and convert the multi-instance problem to the supervised problem, and get the labels of test bags according to predict the labels of unknown instances. In this paper, we aim at mining the multi-instance data from another point of view, i.e., excluding the false positive instances in positive bags and predicting the label of an entire unknown bag. We propose an algorithm called Multi-Instance Covering kNN （MICkNN） for mining from multi-instance data. Briefly, constructive covering algorithm is utilized to restructure the structure of the original multi-instance data at first. Then, the kNN algorithm is applied to discriminate the false positive instances. In the test stage, we label the tested bag directly according to the similarity between the unseen bag and sphere neighbors obtained from last two steps. Experimental results demonstrate the proposed algorithm is competitive with most of the state-of-the-art multi-instance methods both in classification accuracy and running time.

Construction of High Dependable Distributed Access Control Architecture Based on Quorum System

Quorum system is a preferable model to construct distributed access control architecture, but not all quorum system can satisfy the requirements of distributed access control architecture. Aiming at the dependable problem of authorization server in distributed system and combining the requirements of access control, a set of criterions to select and evaluate quorum system is presented. The scheme and algorithm of constructing an authorization server system based on Paths quorum system are designed, and the integrated sys- tem performance under some servers attacked is fully analyzed. Role-based access control on the Web implemented by this scheme is introduced. Analysis shows that with certain node failure probability, the scheme not only has high dependability but also can satisfy the special requirements of distributed access control such as real-time, parallelism, and consistency of security policy.

Research on the Optimization Approach for Cargo Oil Tank Design Based on the Improved Particle Swarm Optimization Algorithm

Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach.

A New Representation and Algorithm for Constructing Convex Hulls in Higher Dim ensional Spaces

This paper presents a new and simple scheme to describe the convex hull in R^d,which only uses three kinds of the faces of the convex hull,i.e.,the d-1-faces,d-2-faces and 0-faces.Thus,we develop an efficient new algorithm for constructing the convex hull of a finite set of points incrementally. This algorithm employs much less storage and time than that of the previously-existing approaches.The analysis of the running time as well as the storage for the new algorithm is also theoretically made.The algorithm is optimal in the worst case for even d.

Dual-stage constructed random graph algorithm to generate random graphs featuring the same topological characteristics with power grids

It is a common practice to simulate some historical or test systems to validate the efficiency of new methods or concepts. However, there are only a small number of existing power system test cases, and validation and evaluation results, obtained using such a limited number of test cases, may not be deemed sufficient or convincing. In order to provide more available test cases, a new random graph generation algorithm, named ‘‘dualstage constructed random graph’’ algorithm, is proposed to effectively model the power grid topology. The algorithm generates a spanning tree to guarantee the connectivity of random graphs and is capable of controlling the number of lines precisely. No matter how much the average degree is,whether sparse or not, random graphs can be quickly formed to satisfy the requirements. An approach is developed to generate random graphs with prescribed numbers of connected components, in order to simulate the power grid topology under fault conditions. Our experimental study on several realistic power grid topologies proves that the proposed algorithm can quickly generate a large number of random graphs with the topology characteristics of real-world power grid.