Clonal optimization-based negative selection algorithm with applications in motor fault detection

The Negative Selection Algorithm (NSA) and clonal selection method are two typical kinds of artificial immune systems. In this paper, we first introduce their underlying inspirations and working principles. It is well known that the regular NSA detectors are not guaranteed to always occupy the maximal coverage of the nonself space. Therefore, we next employ the clonal optimization method to optimize these detectors so that the best anomaly detection performance can be achieved. A new motor fault detection scheme using the proposed NSA is also presented and discussed. We demonstrate the efficiency of our approach with an interesting example of motor bearings fault detection, in which the detection rates of three bearings faults are significantly improved.     

A new document representation using term frequency and vectorized graph connectionists with application to document retrieval

This paper presents a new document representation with vectorized multiple features including term frequency and term-connection-frequency. A document is represented by undirected and directed graph, respectively. Then terms and vectorized graph connectionists are extracted from the graphs by employing several feature extraction methods. This hybrid document feature representation more accurately reflects the underlying semantics that are difficult to achieve from the currently used term histograms, and it facilitates the matching of complex graph. In application level, we develop a document retrieval system based on self-organizing map (SOM) to speed up the retrieval process. We perform extensive experimental verification, and the results suggest that the proposed method is computationally efficient and accurate for document retrieval.     

Secretome analysis of novel IgE-binding proteins from Penicillium citrinum

The Penicillium genus of fungi is a frequently reported cause of allergic reactions. However, only a limited number of allergens have been reported. In Penicillium spp., many allergens show higher IgE-binding activity in culture filtrate extracts than in cellular extracts. In order to investigate the IgE-reactive profile of mold-sensitized patients, secreted IgE-reactive proteins from Penicillium citrinum were identified by 2-DE, serum immunoblotting, and nanoLC-MS/MS. Among the IgE-reactive spots, one known allergen, Pen c 13, and four novel allergens were identified. The cDNAs coding for Pen c 32 and Pen c 30 were cloned using designed primers based on nanoLC-MS/MS analysis. The amino acid sequences of Pen c 32 and Pen c 30 were, respectively, found to have extensive similarity with those of pectate lyases and catalases from various fungi. Native Pen c 30 was shown to have catalase activity and to bind to serum IgE from 48% of mold-allergic patients and induced immediate type skin reactions in a sensitized patient. Here, we present a proteome approach which resulted in the identification of four novel secreted allergens. These novel allergens might be useful in allergy diagnosis and in the treatment of mold-allergic disorders.     

Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction

Dealing with high-dimensional data has always been a major problem in many pattern recognition and machine learning applications. Trace ratio criterion is a criterion that can be applicable to many dimensionality reduction methods as it directly reflects Euclidean distance between data points of within or between classes. In this paper, we analyze the trace ratio problem and propose a new efficient algorithm to find the optimal solution. Based on the proposed algorithm, we are able to derive an orthogonal constrained semi-supervised learning framework. The new algorithm incorporates unlabeled data into training procedure so that it is able to preserve the discriminative structure as well as geometrical structure embedded in the original dataset. Under such a framework, many existing semi-supervised dimensionality reduction methods such as SDA, Lap-LDA, SSDR, SSMMC, can be improved using our proposed framework, which can also be used to formulate a corresponding kernel framework for handling nonlinear problems. Theoretical analysis indicates that there are certain relationships between linear and nonlinear methods. Finally, extensive simulations on synthetic dataset and real world dataset are presented to show the effectiveness of our algorithms. The results demonstrate that our proposed algorithm can achieve great superiority to other state-of-art algorithms.     

Magnetic properties and microstructure of FePt–SiO2 nanocomposite films fabricated by nanocluster beam technology

Nanocluster beam technology combined with conventional sputtering was used to fabricate FePt–SiO₂ nanocomposite films in this present work. The post-deposition annealing affected the final particle size and size distribution of FePt nanoclusters. The effects of both volume fraction of SiO₂ matrix and annealing temperature on magnetic and microstructural properties were studied. Partial ordering, grain growth and agglomeration of FePt particles in FePt–SiO₂ nanocomposite films occurred during annealing. A higher volume fraction of SiO₂ matrix was effective in suppressing diffusion of atoms and magnetic exchange coupling of FePt grains. Excessive SiO₂ however resulted in a lower degree of FePt ordering and thus lower coercivity.     

Odyssey: a high‐performance clustered video server

Video servers are essential in video‐on‐demand and other multimedia applications. In this paper, we present our high‐performance clustered CBR video server, Odyssey. Odyssey is a server connecting PCs with switched Ethernet. It provides efficient support for normal play and interactive browsing functions such as fast‐forward and fast‐backward. We designed a set of algorithms for scheduling, synchronization and admission control, which results in a high utilization of resources. Odyssey is able to deliver a large number of video streams. Copyright © 2003 John Wiley & Sons, Ltd.     

Developing an Automated Enzyme Immunoassay for High-Throughput Screening of Bovine Serum Antibodies to Neospora Caninum

An enzyme-linked immunosorbent assay (ELISA) for Neospora caninum antibodies was automated with a robotic workstation, the Beckman Coulter Biomek 2000, to screen 200 bovine sera. Comparing these results with manually run ELISA data, a 95.92% agreement (K = 0.9592) between the two assays was obtained. The automated assay was specific and sensitive with excellent positive and negative predictive values. The results were repeatable and reproducible. The automation flexibility was high and the operation complexity was minimal. High-throughput screening (HTS) for bovine antibodies to Neospora caninum was achieved. The assay was developed according to the internationally recognized ISO17025 standard requirements.     

Identifying managers who need ethics training in using IT at work

This paper identifies managers who require ethics training in using IT at work. The identification of these managers is mainly based on studying the impact of their individual characteristics on a set of seven ethical issues and attitudes, which translated into IT ethical scenarios. This paper proposes a canonical model by treating individual characteristics of managers as predictor variables and considering information describing their beliefs on those seven ethical issues and attitudes as explained variables. A questionnaire survey was used for data collection and 249 managers participated in this study. The results show that the significant explained variables are based on a combination of six ethical events of accountability, conflict of interest, disclosure, personal conduct, protection of privacy, and social responsibility. The significant predictor variables are based on a combination of four individual characteristics of age, educational level, organizational level, and working experience. Discussion on the implication of the results and recommendations for remedial action are also provided in this paper.     

Design and Analysis of Internet-Based Tele-Coordinated Multi-Robot Systems

The coordination of multi-robots is required in many scenarios for efficiency and task completion. Combined with teleoperation capabilities, coordinating robots provide a powerful tool. Add to this the Internet and now it is possible for multi-experts at multi-remote sites to control multi-robots in a coordinated fashion. For this to be feasible there are several hurdles to be crossed including Internet type delays, uncertainties in the environment and uncertainties in the object manipulated. In addition, there is a need to measure and control the quality of tele-coordination. To this end, the measure of force sensed by each robot is suggested and justified as a coordination index. It was proven that if n robots are event-transparent and event-synchronous then they can be teleoperated under random delay conditions to coordinate to any index value, which is feasible under no delay conditions. The design procedure that ensures a system can satisfy a small coordination index was presented and analyzed.In addition, the design and analysis of event-synchronous systems using Petri Nets is detailed. The Petri Net design methodology is presented for both event-synchronous single operator single robot teleoperation systems and event-synchronous multi-operator multi-robot teleoperation systems.The theory developed was tested by bilaterally tele-coordinating two mobile manipulators via the Internet. The experimental results confirmed the theoretical results presented.