Solving the bi-objective personnel assignment problem using particle swarm optimization

A particle swarm optimization (PSO) algorithm combined with the random-key (RK) encoding scheme (named as PSORK) for solving a bi-objective personnel assignment problem (BOPAP) is presented. The main contribution of this work is to improve the f₁_f₂ heuristic algorithm which was proposed by Huang et al. [3]. The objective of the f₁_f₂ heuristic algorithm is to get a satisfaction level (SL) value which is satisfied to the bi-objective values f₁, and f₂ for the personnel assignment problem. In this paper, PSORK algorithm searches the solution of BOPAP space thoroughly. The experimental results show that the solution quality of BOPAP based on the proposed method is far better than that of the f₁_f₂ heuristic algorithm.     

3D block-based medial axis transform and chessboard distance transform based on dominance

Traditionally, the block-based medial axis transform (BB-MAT) and the chessboard distance transform (CDT) were usually viewed as two completely different image computation problems, especially for three dimensional (3D) space. In fact, there exist some equivalent properties between them. The relationship between both of them is first derived and proved in this paper. One of the significant properties is that CDT for 3D binary image V is equal to BB-MAT for image V' where it denotes the inverse image of V. In a parallel algorithm, a cost is defined as the product of the time complexity and the number of processors used. The main contribution of this work is to reduce the costs of 3D BB-MAT and 3D CDT problems proposed by Wang [65]. Based on the reverse-dominance technique which is redefined from dominance concept, we achieve the computation of the 3D CDT problem by implementing the 3D BB-MAT algorithm first. For a 3D binary image of size N³, our parallel algorithm can be run in O(logN) time using N³ processors on the concurrent read exclusive write (CREW) parallel random access machine (PRAM) model to solve both 3D BB-MAT and 3D CDT problems, respectively. The presented results for the cost are reduced in comparison with those of Wang's. To the best of our knowledge, this work is the lowest costs for the 3D BB-MAT and 3D CDT algorithms known. In parallel algorithms, the running time can be divided into computation time and communication time. The experimental results of the running, communication and computation times for the different problem sizes are implemented in an HP Superdome with SMP/CC-NUMA (symmetric multiprocessor/cache coherent non-uniform memory access) architecture. We conclude that the parallel computer (i.e., SMP/CC-NUMA architecture or cluster system) is more suitable for solving problems with a large amount of input size.     

A multi-stage learning framework for intelligent system

As information technologies advance and user-friendly interfaces develop, the interaction between humans and computers, information devices, and new consumer electronics is increasingly gaining attention. One example that most people can relate to is Apple’s innovation in human–computer interaction which has been used on many products such as iPod and iPhone. Siri, the intelligent personal assistant, is a typical application of machine-learning human–computer interaction.Algorithms in machine learning have been employed in many disciplines, including gesture recognition, speaker recognition, and product recommendation systems. While the existing learning algorithms compute and learn from a large quantity of data, this study proposes an improved learning to rank algorithm named MultiStageBoost. In addition to ranking data through multiple stages, the MultiStageBoost algorithm significantly improves the existing algorithms in two ways. Firstly, it classifies and filters data to small quantities and applies the Boosting algorithm to achieve faster ranking performance. Secondly, it enhances the original binary classification by using the reciprocal of fuzzily weighted membership as the ranking distance.The importance of data is revealed in their ranked positions. Usually data ranked in the front are given more attention than those ranked in the middle. For example, after ranking 10,000 pieces of data, the top 10, or at most 100, are the most important and relevant. Whether the data after the top ones are ranked precisely does not really matter. Due to this reason, this study has made improvement on the conventional methods of the pair-wise ranking approach. Not only are data classified and ranked binarily, they are also given different weights depending on whether they are concordant or discordant. Incorporating the concept of weighting into the ranking distance allows us to increase the precision of ranking. Results from experiments demonstrate that our proposed algorithm outperforms the conventional methods in three evaluation measures: P@n, MAP, and NDCG. MultiStageBoost was then applied to speech recognition. However, we do not aim to improve the technology of speech recognition, but simply hope to provide evidences that MultiStageBoost can be used in the classification and ranking in speech recognition. Experiments show that the recognition optimization procedures established by this study are able to increase the recognition rate to over 95% in the personal computing device and industrial personal computer. It is expected that in the future this voice management system will accurately and effectively identify speakers answering the voice response questionnaire and will successfully carry out the functions in the choice of answers, paying the way for the formation of a virtual customer service person.     

The effect of chronic L-dopa administration on supersensitive pre- and postsynaptic dopaminergic receptors in rat brain

Chronic administration of haloperidol induced supersensitivity of the pre- and postsynaptic dopaminergic receptors in rat brain. The response of the presynaptic receptors was determined by an enhanced inhibitory effect of apomorphine on dopamine synthesis after gamma-butyrolactone injection. This change in the receptor function was detected both in the nigrostriatal and mesolimbic pathways. Haloperidol also increased the 3H-spiperone binding sites in striatal membranes, indicating supersensitivity of the postsynaptic receptors. Subsequent prolonged treatment with high doses of L-DOPA/carbidopa resulted in a decrease in 3H-spiperone binding sites, but had no effect on the supersensitive presynaptic receptors. It is suggested that tardive dyskinesia may be a state of both pre- and postsynaptic dopamine receptor supersensitivity and that chronic L-DOPA treatment may have a differential effect on these sites.     

SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events. As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly. Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy in abnormal detection. As the trajectory feature is the key to both goals, in this paper, a new method for feature extraction of moving object trajectory is proposed, and we use the SOINN (Self-Organizing Incremental Neural Network) method to accomplish a high accuracy abnormal detection. In the results, our method is able to shirk the video size to 10% storage size of the original video, and achieves 95% accuracy of abnormal event detection, which shows our method is useful and applicable to the surveillance industry.     

Fuzzy rules generation using new evolutionary algorithms combinedwith multilayer perceptrons

Based on evolutionary algorithms (EAs) and multilayer perceptrons (MLPs), a fuzzy rules generation method inclusive of two main learning stages is presented in this paper. In the primary stage, a new EA is developed to generate numerical control rules from input-output data without the help of experts, which increases the diversity of individuals to reduce the opportunities of falling into local optima. Every generated numerical rule is accumulated in a lookup table called a numerical-rule-based controller (NRC). In the secondary stage, both antecedent and consequent variables of the numerical rules are fuzzified by training MLPs with the backpropagation algorithm. All training data are directly derived from the NRC with simple manipulations. Consequently, a linguistic-rule-based controller (LRC) consisting of the generated fuzzy rules is completed. Two illustrative experiments are successfully made on the computer simulation and hardware implementation of the NRCs and LRCs of different types using the new EA combined with the MLPs. The experimental results reveal that the proposed EA-MLP MLP approach is efficient and effective to generate fuzzy rules which control nonlinearly dynamical systems exceedingly well     

An adaptive reduction procedure for the piecewise linearapproximation of digitized curves

A new algorithm is presented for the piecewise linear approximation of two-dimensional digitized curves against a square grid. The algorithm utilizes an adaptive reduction procedure in two approximation phases to select the critical points of a digitized curve such that the deviation, from the digitized curve to its final approximated curve, is bounded by a uniform error tolerance. The time complexity of this algorithm is O(m²) rather than O( n²) on the theoretical plane. In the experiments of fixing the initial and the final processing points, the performance of the algorithm has been compared to those of three prominent other algorithms regarding the required number of critical points and the total execution time of the program. Of the four algorithms compared, the present algorithm consistently has the shortest execution time of the program, and it tends most to require as few critical points as the optimum algorithm that was tested     

Preparation and characterization of conductive poly(vinyl alcohol)/polyaniline doped by dodecyl benzene sulfonic acid (PVA/PANDB) blend films

In this study a solution‐blend method is adopted to prepare conductive poly(vinyl alcohol)/polyaniline doped by dodecyl benzene sulfonic acid (PVA/PANDB) blend films. Emeraldine base (EB)‐type polyaniline (PANI) is dissolved in N‐methyl‐2‐pyrrolidinone (NMP) and then blended with PVA/dodecyl benzene sulfonic acid (DBSA) solution by various amounts. It is found that the electrical conductivity and the thermal degradation onset temperature of the PVA/PANDB blend film are increased as the amount of EB‐type PANI solution is increased. Fourier transform infrared (FTIR) spectra show that the intensity of the characteristic peak of the functional groups in the blend film is significantly changed as the amount of EB‐type PANI is changed. From optical microscopy examination, it indicates that the amount and size of green particles are increased with increasing the amount of EB‐type PANI solution. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 3415–3422, 2007     

Einfluß der Struktur und der Morphologie von Bleicherden auf die Bleichwirkung bei Ölen und Fetten

Influence of the Structure and Morphology of Bleaching Earths on Their Bleaching Action on Oils and Fats In the manufacture of highly active bleaching earths from bentonite, the acid activation causes alterations in the chemical composition and structural as well as morphological properties of bentonite, depending on the concentration of acid, temperature, time etc. This has been demonstrated by changes in specific surface, volume of the micropores, particle size distribution and proportion of soluble silicic acid, and the impact of these alterations on the bleaching of vegetable oils is discussed. The results are supported by electron optical and X-ray investigations. Studies on repeated removal of silicic acid formed by acid treatment of bentonite as well as repeated acid activation indicate that the bleaching action of these earths depends not only on specific surface, but also, to a considerable extent, on the volume of micropores.