Showing papers by "Harbin Institute of Technology published in 2009"

miR2Disease: a manually curated database for microRNA deregulation in human disease

Abstract: 'miR2Disease', a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA-disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA-disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA-disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA-disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org.

Sentiment classification of online reviews to travel destinations by supervised machine learning approaches

Abstract: The rapid growth in Internet applications in tourism has lead to an enormous amount of personal reviews for travel-related information on the Web. These reviews can appear in different forms like BBS, blogs, Wiki or forum websites. More importantly, the information in these reviews is valuable to both travelers and practitioners for various understanding and planning processes. An intrinsic problem of the overwhelming information on the Internet, however, is information overloading as users are simply unable to read all the available information. Query functions in search engines like Yahoo and Google can help users find some of the reviews that they needed about specific destinations. The returned pages from these search engines are still beyond the visual capacity of humans. In this research, sentiment classification techniques were incorporated into the domain of mining reviews from travel blogs. Specifically, we compared three supervised machine learning algorithms of Naive Bayes, SVM and the character based N-gram model for sentiment classification of the reviews on travel blogs for seven popular travel destinations in the US and Europe. Empirical findings indicated that the SVM and N-gram approaches outperformed the Naive Bayes approach, and that when training datasets had a large number of reviews, all three approaches reached accuracies of at least 80%.

Tag ranking

Abstract: Social media sharing web sites like Flickr allow users to annotate images with free tags, which significantly facilitate Web image search and organization. However, the tags associated with an image generally are in a random order without any importance or relevance information, which limits the effectiveness of these tags in search and other applications. In this paper, we propose a tag ranking scheme, aiming to automatically rank the tags associated with a given image according to their relevance to the image content. We first estimate initial relevance scores for the tags based on probability density estimation, and then perform a random walk over a tag similarity graph to refine the relevance scores. Experimental results on a 50, 000 Flickr photo collectionshow that the proposed tag ranking method is both effective and efficient. We also apply tag ranking into three applications: (1) tag-based image search, (2) tag recommendation, and (3) group recommendation, which demonstrates that the proposed tag ranking approach really boosts the performances of social-tagging related applications.

Stability, ${l}_{2}$ -Gain and Asynchronous ${H}_{{\infty}}$ Control of Discrete-Time Switched Systems With Average Dwell Time

Abstract: This paper first investigates the stability and l 2-gain problems for a class of discrete-time switched systems with average dwell time (ADT) switching by allowing the Lyapunov-like functions to increase during the running time of subsystems. The obtained results then facilitate the studies on the issue of asynchronous control, where "asynchronous" means the switching of the controllers has a lag to the switching of system modes. In light of the proposed Lyapunov-like functions, the desired mode-dependent controllers can be designed since the unmatched controllers are allowed to perform in the interval of asynchronous switching before the matched ones are applied. The problem of asynchronous H infin control for the underlying systems in linear cases is then formulated. The conditions of the existence of admissible asynchronous H infin controllers are derived, and a numerical example is provided to show the potential of the developed results.

Fiber reinforced shape-memory polymer composite and its application in a deployable hinge

Abstract: This paper investigates the shape recovery behavior of thermoset styrene-based shape-memory polymer composite (SMPC) reinforced by carbon fiber fabrics, and demonstrates the feasibility of using an SMPC hinge as a deployable structure. The major advantages of shape-memory polymers (SMPs) are their extremely high recovery strain, low density and low cost. However, relatively low modulus and low strength are their intrinsic drawbacks. A fiber reinforced SMPC which may overcome the above-mentioned disadvantages is studied here. The investigation was conducted by three types of test, namely dynamic mechanical analysis (DMA), a shape recovery test, and optical microscopic observations of the deformation mechanism for an SMPC specimen. Results reveal that the SMPC exhibits a higher storage modulus than that of a pure SMP. At/above Tg, the shape recovery ratio of the SMPC upon bending is above 90%. The shape recovery properties of the SMPC become relatively stable after some packaging/deployment cycles. Additionally, fiber microbuckling is the primary mechanism for obtaining a large strain in the bending of the SMPC. Moreover, an SMPC hinge has been fabricated, and a prototype of a solar array actuated by the SMPC hinge has been successfully deployed.

Review of electro-active shape-memory polymer composite

Abstract: Shape-memory polymers (SMPs) have been one of the most popular subjects under intensive investigation in recent years, due to their many novel properties and great potential. These so-called SMPs by far surpass shape-memory alloys and shape-memory ceramics in many properties, e.g., easy manufacture, programming, high shape recovery ratio and low cost, and so on. However, they have not fully reached their technological potential, largely due to that the actuation of shape recovery in thermal-responsive SMPs is normally only driven by external heat. Thus, electro-activate SMP has been figured out and its significance is increasing in years to come. This review focuses on the progress of electro-activate SMP composites. Special emphases are given on the filler types that affect the conductive properties of these composites. Then, the mechanisms of electric conduction are addressed.

Hierarchical Ensemble of Global and Local Classifiers for Face Recognition

Abstract: In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.

A self-sensing carbon nanotube/cement composite for traffic monitoring.

Abstract: In this paper, a self-sensing carbon nanotube (CNT)/cement composite is investigated for traffic monitoring. The cement composite is filled with multi-walled carbon nanotubes whose piezoresistive properties enable the detection of mechanical stresses induced by traffic flow. The sensing capability of the self-sensing CNT/cement composite is explored in laboratory tests and road tests. Experimental results show that the fabricated self-sensing CNT/cement composite presents sensitive and stable responses to repeated compressive loadings and impulsive loadings, and has remarkable responses to vehicular loadings. These findings indicate that the self-sensing CNT/cement composite has great potential for traffic monitoring use, such as in traffic flow detection, weigh-in-motion measurement and vehicle speed detection.

Giant magnetic-field-induced strains in polycrystalline Ni–Mn–Ga foams

Abstract: The magnetic shape-memory alloy Ni-Mn-Ga shows, in monocrystalline form, a reversible magnetic-field-induced strain (MFIS) up to 10%. This strain, which is produced by twin boundaries moving solely by internal stresses generated by magnetic anisotropy energy, can be used in actuators, sensors and energy-harvesting devices. Compared with monocrystalline Ni-Mn-Ga, fine-grained Ni-Mn-Ga is much easier to process but shows near-zero MFIS because twin boundary motion is inhibited by constraints imposed by grain boundaries. Recently, we showed that partial removal of these constraints, by introducing pores with sizes similar to grains, resulted in MFIS values of 0.12% in polycrystalline Ni-Mn-Ga foams, close to those of the best commercial magnetostrictive materials. Here, we demonstrate that introducing pores smaller than the grain size further reduces constraints and markedly increases MFIS to 2.0-8.7%. These strains, which remain stable over >200,000 cycles, are much larger than those of any polycrystalline, active material.

Fuzzy Filter Design for ItÔ Stochastic Systems With Application to Sensor Fault Detection

Abstract: The paper deals with the robust fault detection problem for Takagi-Sugeno (T-S) fuzzy Ito stochastic systems. Our aim is to develop a robust fault detection approach to the T-S fuzzy systems with Brownian motion. By using a general observer-based fault detection filter as a residual generator, the robust fault detection is formulated as a filtering problem. Attention is focused on the design of both the fuzzy-rule-independent and the fuzzy-rule-dependent fault detection filters guaranteeing a prescribed noise attenuation level in an Hinfin sense. Sufficient conditions are proposed to guarantee the mean-square asymptotic stability with an Hinfin performance for the fault detection system. The corresponding solvability conditions for the desired fuzzy-rule-independent and fuzzy-rule-dependent fault detection filters are also established. Finally, a numerical example is provided to illustrate the effectiveness of the proposed theory.

Stability and Stabilization of Delayed T--S Fuzzy Systems: A Delay Partitioning Approach

Abstract: This paper proposes a new approach, namely, the delay partitioning approach, to solving the problems of stability analysis and stabilization for continuous time-delay Takagi-Sugeno fuzzy systems. Based on the idea of delay fractioning, a new method is proposed for the delay-dependent stability analysis of fuzzy time-delay systems. Due to the instrumental idea of delay partitioning, the proposed stability condition is much less conservative than most of the existing results. The conservatism reduction becomes more obvious with the partitioning getting thinner. Based on this, the problem of stabilization via the so-called parallel distributed compensation scheme is also solved. Both the stability and stabilization results are further extended to time-delay fuzzy systems with time-varying parameter uncertainties. All the results are formulated in the form of linear matrix inequalities (LMIs), which can be readily solved via standard numerical software. The advantage of the results proposed in this paper lies in their reduced conservatism, as shown via detailed illustrative examples. The idea of delay partitioning is well demonstrated to be efficient for conservatism reduction and could be extended to solving other problems related to fuzzy delay systems.

Distributed Data Aggregation Scheduling in Wireless Sensor Networks

Abstract: Data aggregation is an essential operation in wireless sensor network applications. This paper focuses on the data aggregation scheduling problem. Based on maximal independent sets, a distributed algorithm to generate a collision-free schedule for data aggregation in wireless sensor networks is proposed. The time latency of the aggregation schedule generated by the proposed algorithm is minimized using a greedy strategy. The latency bound of the schedule is 24D + 6 Delta + 16, where D is the network diameter and Delta is the maximum node degree. The previous data aggregation algorithm with least latency has the latency bound (Delta- Delta 1)R, where R is the network radius. Thus in our algorithm Delta contributes to an additive factor instead of a multiplicative factor, which is a significant improvement. To the best of our knowledge, the proposed algorithm is the first distributed algorithm for data aggregation scheduling. This paper also proposes an adaptive strategy for updating the schedule when nodes fail or new nodes join in a network. The analysis and simulation results show that the proposed algorithm outperforms other aggregation scheduling algorithms.

Detection of Melamine in Milk Products by Surface Desorption Atmospheric Pressure Chemical Ionization Mass Spectrometry

Abstract: Without any sample pretreatment, trace amounts of melamine in various milk products were rapidly detected noting the characteristic fragments (i.e., m/z 110, 85, and 60) in the MS/MS spectrum of protonated melamine molecules (m/z 127) recorded by using surface desorption atmospheric pressure chemical ionization mass spectrometry. Signal responses of the most abundant ionic fragment (m/z 85) of protonated melamine were well correlated with the amounts of melaime in milk products, showing a dynamic range about 5 orders of magnitude. The limit of detection (LOD) was found to be 3.4 × 10−15 g/mm2 (S/N = 3) for the detection of pure melamine deposited on the paper surface, which was much lower than that for detection of melamine in powdered milk (1.6 × 10−11 g/mm2, S/N = 3) or liquid milk (1.3 × 10−12 g/mm2, S/N = 3). The significant difference in LOD was ascribed to the relatively strong molecular interactions between melamine and the matrix such as proteins in the milk products. As demonstrated using desorpt...

$H_{\bm \infty}$ Fuzzy Filtering of Nonlinear Systems With Intermittent Measurements

Abstract: This paper is concerned with the problem of H infin fuzzy filtering of nonlinear systems with intermittent measurements. The nonlinear plant is represented by a Takagi-Sugeno (T-S) fuzzy model. The measurements transmission from the plant to the filter is assumed to be imperfect, and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the phenomenon of the missing measurements. Attention is focused on the design of an H infin filter such that the filter error system is stochastically stable and preserves a guaranteed H infin performance. A basis-dependent Lyapunov function approach is developed to design the H infin filter. By introducing some slack matrix variables, the coupling between the Lyapunov matrix and the system matrices is eliminated, which greatly facilitates the filter-design procedure. The developed theoretical results are in the form of linear matrix inequalities (LMIs). Finally, an illustrative example is provided to show the effectiveness of the proposed approach.