Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers' objectives and nurses' preferences as much as possible by generating fair shift schedules. This paper presents...
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Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers' objectives and nurses' preferences as much as possible by generating fair shift schedules. This paper presents a nurse scheduling problem based on a real case study, and proposes two meta-heuristics - a differential evolution algorithm (DE) and a greedy randomised adaptive search procedure (GRASP) - to solve it. To investigate the efficiency of the proposed algorithms, two problems are solved. Furthermore, some comparison metrics are applied to examine the reliability of the proposed algorithms. The computational results in this paper show that the proposed DE outperforms the GRASP.
It is difficult to well distinguish the dimensionless indexes between normal petrochemical rotating machinery equipment and those with complex faults. When the conflict of evidence is too big, it will result in uncert...
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It is difficult to well distinguish the dimensionless indexes between normal petrochemical rotating machinery equipment and those with complex faults. When the conflict of evidence is too big, it will result in uncertainty of diagnosis. This paper presents a diagnosis method for rotation machinery fault based on dimensionless indexes combined with K-nearest neighbor (KNN) algorithm. This method uses a KNN algorithm and an evidence fusion theoretical formula to process fuzzy data, incomplete data, and accurate data. This method can transfer the signals from the petrochemical rotating machinery sensors to the reliability manners using dimensionless indexes and KNN algorithm. The input information is further integrated by an evidence synthesis formula to get the final data. The type of fault will be decided based on these data. The experimental results show that the proposed method can integrate data to provide a more reliable and reasonable result, thereby reducing the decision risk.
As global online access to information becomes more common, the technology ofmultilingual optical character recognition (OCR) increases in importance as a way to convert paperdocuments into electronic, searchable, tex...
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As global online access to information becomes more common, the technology ofmultilingual optical character recognition (OCR) increases in importance as a way to convert paperdocuments into electronic, searchable, text. In OCR, as in any evolving technology, carefulevaluation is an integral part of research and development. OCR evaluation is done by comparing asystem's output for a dataset of document test images with the corresponding correct symbolic text,known as ground truth. Unfortunately, the usual way of obtaining ground truth is by manual dataentry by humans, which is labor-intensive, time-consuming, expensive, and prone to errors. Worse,because no single set of ground truth evaluation data can be used in more than one language, therehas until now been no way to conduct carefully controlled OCR experiments in a multilingual setting.
The self-organizing hidden Markov model map (SOHMMM) introduces a hybrid integration of the self-organizing map (SOM) and the hidden Markov model (HMM). Its scaled, online gradient descent unsupervised learning algori...
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The self-organizing hidden Markov model map (SOHMMM) introduces a hybrid integration of the self-organizing map (SOM) and the hidden Markov model (HMM). Its scaled, online gradient descent unsupervised learning algorithm is an amalgam of the SOM unsupervised training and the HMM reparameterized forward-backward techniques. In essence, with each neuron of the SOHMMM lattice, an HMM is associated. The image of an input sequence on the SOHMMM mesh is defined as the location of the best matching reference HMM. Model tuning and adaptation can take place directly from raw data, within an automated context. The SOHMMM can accommodate and analyze deoxyribonucleic acid, ribonucleic acid, protein chain molecules, and generic sequences of high dimensionality and variable lengths encoded directly in nonnumerical/symbolic alphabets. Furthermore, the SOHMMM is capable of integrating and exploiting latent information hidden in the spatiotemporal dependencies/correlations of sequences' elements.
Wireless sensor network consists of hundreds or thousands of low cost, low power, and self-organizing tiny sensor nodes that are deployed within the sensor network. Sensor network is susceptible to physical attacks du...
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Wireless sensor network consists of hundreds or thousands of low cost, low power, and self-organizing tiny sensor nodes that are deployed within the sensor network. Sensor network is susceptible to physical attacks due to deprived power and restricted resource capability and is exposed to external environment for transmitting and receiving data. Node capture attack is one of the most menacing attack in the wireless sensor network and may be physically captured by an adversary for extracting confidential information regarding cryptographic keys, node's unique id, and so forth, from its memory to eliminate the confidentiality and integrity of the wireless links. Node capture attack suffers from severe security breach and tremendous network cost. We propose an empirically designed multiple objectives node capture attack algorithm based on optimization functions as an effective solution against the attacking efficiency of node capture attack. Finding robust assailant optimization-particle swarm optimization and genetic algorithm (FiRAO-PG) consists of multiple objectives: maximum node participation, maximum key participation, and minimum resource expenditure to find optimal nodes using PSO and GA. It will leverage a comprehensive tool to destroy maximum portion of the network realizing cost-effectiveness and higher attacking efficiency. The simulation results manifest that FiRAO-PG can provide higher fraction of compromised traffic than matrix algorithm (MA) so the attacking efficiency of FiRAO-PG is higher.
The theory of unit memory repetitive processes is used to investigate local convergence and stability properties of algorithms fbr the solution of discrete optimal control problems. in particular, the properties are a...
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The theory of unit memory repetitive processes is used to investigate local convergence and stability properties of algorithms fbr the solution of discrete optimal control problems. in particular, the properties are addressed of a method for finding the correct solution of an optimal control problem where the model used for optimisation is different from reality. Limit profile and stability concepts of unit memory linear repetitive process theory are employed to demonstrate optimality and to obtain necessary and sufficient conditions for convergence. Two main stability theorems are obtained from different approaches and their equivalence is proved: The theoretical results are verified through simulation and numerical analysis, and it is demonstrated that repetitive process theory provides a useful tool for the analysis of iterative algorithms for the solution of dynamic optimal control problems.
Non-annotated video is more common than ever and this fact leads to an emerging field called video summarization. Key frame selection using motion analysis can greatly increase the understanding of the video content b...
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Non-annotated video is more common than ever and this fact leads to an emerging field called video summarization. Key frame selection using motion analysis can greatly increase the understanding of the video content by presenting a series of frames summarizing the intended video. In this paper, we present an automatic video summarization technique based on motion analysis. The proposed technique defines motion metrics estimated from two optical flow algorithms, each using two different key frame selection criteria. We conducted a subjective user study to evaluate the performance of the motion metrics. The summarization process is threshold free and experimental results have verified the effectiveness of the method. (C) 2012 Elsevier Ltd. All rights reserved.
This paper is devoted to the development of physically correct rendering model of scenes containing volume scattering objects. The solution of the rendering equation is based on the Monte-Carlo bidirectional ray traci...
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This paper is devoted to the development of physically correct rendering model of scenes containing volume scattering objects. The solution of the rendering equation is based on the Monte-Carlo bidirectional ray tracing. Two efficient approaches to the solution of the rendering equation for different parameters of the volume scattering medium (typically, concentration of scattering particles) are developed. Examples illustrate how the proposed models can be used for photorealistic visualization of scenes containing volume scattering objects and for the simulation of illuminators based on the volume scattering effect. Possible limitations of the application field of the proposed models are considered.
In order to improve the performance of a real-time system, asymmetric multiprocessors have been proposed. The benefits of improved system performance and reduced power consumption from such architectures cannot be ful...
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In order to improve the performance of a real-time system, asymmetric multiprocessors have been proposed. The benefits of improved system performance and reduced power consumption from such architectures cannot be fully exploited unless suitable task scheduling and task allocation approaches are implemented at the operating system level. Unfortunately, most of the previous research on scheduling algorithms for performance asymmetricmultiprocessors is focused on task priority assignment. They simply assign the highest priority task to the fastest processor. In this paper, we propose BSF-EDF (best speed fit for earliest deadline first) for performance asymmetric multiprocessor scheduling. This approach chooses a suitable processor rather than the fastest one, when allocating tasks. With this proposed BSF-EDF scheduling, we also derive an effective schedulability test.
We present an O(n(4))-time algorithm for the following problem: Given a set of items with known access frequencies, find the optimal binary search tree under the realistic assumption that each comparison can only resu...
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We present an O(n(4))-time algorithm for the following problem: Given a set of items with known access frequencies, find the optimal binary search tree under the realistic assumption that each comparison can only result in a two-way decision: either an equality comparison or a less-than comparisons. This improves the best known result of O(n(5)) time, which is based on split tree algorithms. Our algorithm relies on establishing thresholds on the frequency of an item that can occur as an equality comparison at the root of an optimal tree. (C) 2002 Elsevier Science (USA). All rights reserved.
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