We analyze HARQ transmission over a fading channel with the help of a relay node. In the absence of the channel state information (CSI) at the transmitters, we establish the problem of maximizing the overall throughpu...
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ISBN:
(纸本)9781479930838
We analyze HARQ transmission over a fading channel with the help of a relay node. In the absence of the channel state information (CSI) at the transmitters, we establish the problem of maximizing the overall throughput of HARQ transmission by optimizing the variable transmission rate. We present a closed form of the throughput with respect to the transmission rates and use the well-known dynamic programming technique along with the necessary simplifications on the problem to find the optimal set of transmission rates for both the Source node and the Relay node in source-relay-destination scenario. We show that such an approach yields a significant increase in throughput compared to lived-rate HARQ.
We define a class of discrete-time resource allocation problems where multiple renewable resources must be dynamically allocated to different types of jobs arriving randomly. Jobs have geometric service durations, dem...
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ISBN:
(纸本)9781424498642
We define a class of discrete-time resource allocation problems where multiple renewable resources must be dynamically allocated to different types of jobs arriving randomly. Jobs have geometric service durations, demand resources, incur a holding cost while waiting in queue, a penalty cost of rejection when the queue is filled to capacity, and generate a reward on completion. The goal is to select which jobs to service in each time-period so as to maximize total infinite-horizon discounted expected profit. We present Markov Decision Process (MDP) models of these problems and apply a Lagrangian relaxation-based method that exploits the structure of the MDP models to approximate their optimal value functions. We then develop a dynamic programming technique to efficiently recover resource allocation decisions from this approximate value function on the fly. Numerical experiments demonstrate that these decisions outperform well-known heuristics by at least 35% but as much as 220% on an average.
When developing video-based surveillance systems the developer faces a highly complex task due to the wide range of application domains where video-based surveillance systems are applied. As the number of domains of a...
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ISBN:
(纸本)9781457704345
When developing video-based surveillance systems the developer faces a highly complex task due to the wide range of application domains where video-based surveillance systems are applied. As the number of domains of application increases, so does the level of variability of non-functional and functional properties that needs to be managed by video-based surveillance systems. The traditional response to make the process of managing all system variations more flexible is to use modular architectures based on filters for video applications. These filters are implemented using dynamic programming techniques (i.e. inheritance and virtual functions) that induce an overhead on the system performance. As each filter is implemented as a plugin, the non-linearity produced by the use of plug-ins penalize the overall system performance due to a increase in the number of cache misses and page faults. In this paper, a novel approach is proposed. The use of Aspect-Oriented programming (AOP) is proposed to modularize system variability without compromising system performance. All the system variation points are implemented in aspects that are injected for a specific configuration on the common base code related to all configurations. The experimental results show that AOP improves the management of system heterogeneity without sacrificing system performance.
Background: Mining gene patterns that are common to multiple genomes is an important biological problem, which can lead us to novel biological insights. When family classification of genes is available, this problem i...
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Background: Mining gene patterns that are common to multiple genomes is an important biological problem, which can lead us to novel biological insights. When family classification of genes is available, this problem is similar to the pattern mining problem in the data mining community. However, when family classification information is not available, mining gene patterns is a challenging problem. There are several well developed algorithms for predicting gene patterns in a pair of genomes, such as FISH and DAGchainer. These algorithms use the optimization problem formulation which is solved using the dynamic programming technique. Unfortunately, extending these algorithms to multiple genome cases is not trivial due to the rapid increase in time and space complexity. Results: In this paper, we propose a novel algorithm for mining gene patterns in more than two prokaryote genomes using interchangeable sets. The basic idea is to extend the pattern mining technique from the data mining community to handle the situation where family classification information is not available using interchangeable sets. In an experiment with four newly sequenced genomes ( where the gene annotation is unavailable), we show that the gene pattern can capture important biological information. To examine the effectiveness of gene patterns further, we propose an ortholog prediction method based on our gene pattern mining algorithm and compare our method to the bi-directional best hit (BBH) technique in terms of COG orthologous gene classification information. The experiment show that our algorithm achieves a 3% increase in recall compared to BBH without sacrificing the precision of ortholog detection. Conclusion: The discovered gene patterns can be used for the detecting of ortholog and genes that collaborate for a common biological function.
Existing techniques to identify moving forces based on traditional finite element method (TFEM) is subject to a large number of elements with detailed description of a structure, which makes modeling complicated. A ne...
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Existing techniques to identify moving forces based on traditional finite element method (TFEM) is subject to a large number of elements with detailed description of a structure, which makes modeling complicated. A new modeling method for a vehicle-bridge system called wavelet finite element method (WFEM) is presented in this paper. It makes use of a multi-scale analysis whereby detailed description can be achieved to overcome this problem. The shape function of WFEM is formed by a scale function in a wavelet space and by a transformation matrix to connect the wavelet space to the physical one. To evaluate the properties of WFEM, simulations of two moving forces on a simply supported and a continuous bridge are conducted with subsequent comparison with TFEM. To smooth the noise and large fluctuations on the boundaries of the identified results in the time history, the first-order Tikhonov regularizations combined with the dynamic programming technique are adapted and compared with the zeroth-order Tikhonov regularization. White noise is added to the simulated dynamic responses. Some parameter effects, such as vehicle bridge parameters, measurement parameters are also considered. Numerical results demonstrate the WFEM method and the first-order Tikhonov regularization method to be effective for moving force identification. The first-order Tikhonov regularization has the property of smoothing noise and avoiding large fluctuations on the boundaries. Meanwhile, the parameters analyzed affect the identified results to some extent.
The free market forces energy-intensive industrial enterprises to continuously compete. A possible competitive advantage for such enterprises is reducing the finished products cost. This may be achieved by reducing th...
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The free market forces energy-intensive industrial enterprises to continuously compete. A possible competitive advantage for such enterprises is reducing the finished products cost. This may be achieved by reducing the share of energy in this cost, including by rationalizing the use of energy resources. This study develops a system for the automated analysis and calculation of feasible boiler unit loads, defined according to the criterion of the minimum cost of live steam in a separate steam plant pipeline. The calculations consider the balance limit on the steam, the boiler unit's wear and tear, performance specifications, and economic indicators of fuel consumption in the calculation. The software also defines the optimal fuel mix composition when forecasting the operating modes of the power plant boiler units in real-time mode. The calculation algorithm is based on the dynamic programming technique combined with the sequential equivalenting method, which ensures the convergence of calculations. When a steam plant model is developed, much attention is paid to the thermal scheme and technical and economic specifications of boiler units. In the system, the boiler models are set as a table containing the ratio between the boiler unit's steam capacity and energy consumption while considering the cost of a ton of live steam with the specified parameters. The key economic effect of implementing the system is determined by reducing the fuel cost due to its rational redistribution between the power plant boiler units. Implementing the system allows the reduction of energy costs by 1.4%.
Numerous optimization problems arise in survey designs. The problem of obtaining an optimal (or near optimal) sampling design can be formulated and solved as a mathematical programming problem. In multivariate stratif...
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Numerous optimization problems arise in survey designs. The problem of obtaining an optimal (or near optimal) sampling design can be formulated and solved as a mathematical programming problem. In multivariate stratified sample surveys usually it is not possible to use the individual optimum allocations for sample sizes to various strata for one reason or another. In such situations some criterion is needed to work out an allocation which is optimum for all characteristics in some sense. Such an allocation may be called an optimum compromise allocation. This paper examines the problem of determining an optimum compromise allocation in multivariate stratified random sampling, when the population means of several characteristics are to be estimated. Formulating the problem of allocation as an all integer nonlinear programming problem, the paper develops a solution procedure using a dynamic programming technique. The compromise allocation discussed is optimal in the sense that it minimizes a weighted sum of the sampling variances of the estimates of the population means of various characteristics under study. A numerical example illustrates the solution procedure and shows how it compares with Cochran's average allocation and proportional allocation.
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