This paper focus on further developing the filter theory on residuated lattices (RL). The notion of (∈, ∈ Vq)-IVIF filter on residuated lattices(RLs) is studied; the properties and equivalent characterizations of (∈, ∈ Vq)-IVIF filter are investigated; the relation between (∈, ∈ Vq)-IVIF filter and filter is studied.
A novel nonlinear control scheme based on backstepping and nonlinear dynamic inversion methods for the hypersonic vehicles is presented here. The MIMO strongly coupled system is accurately linearized using the dynamic inversion method. The system global stability is guaranteed by the baskstepping control method, which tracks the command signals accurately. The effects of the uncertain parameters is attenuated using the fuzzy observer. The simulation results show that the scheme solves the uncertainties brought by the actual flight of the vehicles.
Fuzzy cognitive maps (FCMs) are a model for causal modeling and causal inference. It represents the real-world concepts and the causal relations between the concepts by using fuzzy variables. The major benefit of the fuzzy variables is that the model is more robust to the errors in the observed data. Although FCMs have been widely used in different research areas, it is still an open problem to efficiently construct large scale FCM models. To further improve the efficiency of the existing FCM learning algorithms, we propose a new algorithm that combines ant colony optimization algorithm, gradient descent local search and a decomposed parallel computing framework to build large scale FCMs from observational data. A set of network inference problem is used to evaluate the performance of the proposed algorithm and the results are compared to other algorithms including traditional ant colony optimization, and real coded genetic algorithms. Experimental results suggest that our algorithm outperforms the other algorithms in terms of model accuracy. We also compared the computation time required by the non-parallel ant colony optimization algorithm and the proposed parallel algorithm. When the number of nodes is appropriate, the speedup could be very close to linear speedup.
In order to improve the cabin safety of civil aviation and reduce the accident or incident result from cabin crew fatigue, a cabin crew fatigue risk index system is formed through the deep study on the factors affecting cabin crew fatigue and the cabin crew questionnaires from an airline. Based on the order relation analysis method and specialists questionnaires, the weight of each factor is determined. Combination the 1490 cabin crew questionnaires with each self-assessment fatigue result by the cabin crew themselves, the fatigue risk value scope related to every fatigue rating is obtained. Finally, the cabin crew fatigue risk comprehensive evaluation model is established. The results show that this model can be used to evaluate the cabin crew fatigue risk for the airlines, and it is a scientific and objective way to measure and reflect fatigue state.
During selection of expansion terms from both clicked documents and queries in log, current methods never consider the large amount of irrelevant feedbacks in records of new queries, which leads to the inconsistency between users' intents and selected expansion terms. To solve this problem, this paper integrated the ranking model with the recognition of irrelevant document content according to users' study processes, so it can reduce the weights of irrelevant expansion terms. The experimental results show that this method can measure relevancy between query and document terms better, especially in the situation when records have more irrelevant feedbacks, and can select expansion terms accorded with users' intent better.
Based on the definitions of pseudo-operations, the pseudo-Henstock integral is present in this article, some elementary properties and its transformation theorem are obtained. Finally, the properties of pseudo-bounded variation are discussed.
Graphical password authentication techniques have been opening new doors in the world of Password Security. This technique acquires graphical password authentication in such a way that it prevents Shoulder Surfing Attack when an adversary is watching a user while user enters a password. Graphical password authentication is more venerable to shoulder attack. The Servers Voice Graphical Authentication having graphical passwords prevents them along with the users ease. This technique involves a server voice coming to a user which the user only listens. Processes and then enters it into a graphical symbol pad. So this technique comprises of a server voice, then a factor of authentication Something You Process and lastly graphical password entrance.
In this paper, we research the relationship between the probabilistic products and customer preferences, which has not been studied before. In order to find out products which can meet a customer's maximum demand, we present an uncertain dynamic skyline (UDS) query, and propose effective pruning strategies to reduce the search space of the UDS query processing. In addition, effective algorithms are presented by integrating the proposed pruning strategies. Extensive experiments illustrate the efficiency and effectiveness of our proposed algorithms with a variety of experiments settings.
Symbolic Aggregation approXimation (SAX) has been the de facto standard representation methods for knowledge discovery in time series on a number of tasks and applications. So far, very little work has been done in empirically investigating the intrinsic properties and statistical mechanics in SAX words. In this paper, we applied several statistical measurements and proposed a new statistical measurement, i.e. information embedding cost (IEC) to analyze the statistical behaviors of the symbolic dynamics. Our experiments on the benchmark datasets and the clinical signals demonstrate that SAX can always reduce the complexity while preserving the core information embedded in the original time series with significant embedding efficiency. Our proposed IEC score provide a priori to determine if SAX is adequate for specific dataset, which can be generalized to evaluate other symbolic representations. Our work provides an analytical framework with several statistical tools to analyze, evaluate and further improve the symbolic dynamics for knowledge discovery in time series.
Word order differences between source and target languages pose a serious challenge to statistical machine translation (SMT). Pre-ordering, an approach that reorders source words into a target-word-like order as a preprocessing step, has been shown effective in handling word order between different languages and improving translation performance of SMT. In this paper, we propose a novel word reordering method based on the pre-ordering framework. Instead of using a supervised parser trained on a monolingual treebank, our method extracts bilingual structural information for reordering from automatically wordaligned sentence pairs into dependency-tree-like structures, then learns a reordering model by training a dependency parser on this extracted pseudo-treebank. Experiment results show that our pre-ordering method is effective in permuting source words to resemble word order of the target language, and improving translation quality.
A coupled adaptive PI tuning method for rapid thermal chemical vapor deposition systems (RTCVD) is proposed in this paper. RTCVD, as one of rapid thermal processing (RTP) craft, is the main method to produce polycrystalline silicon thin films. The temperature in the furnace is crucial to the quality of polycrystalline silicon thin films, which has great influence on semiconductor industries. To improve the quality of the films, a coupled adaptive PI tuning method based on simultaneous perturbation stochastic approximation (SPSA) algorithm is applied to the RTCVD systems. Simulation results show that satisfactory performances can be obtained using the proposed tuning method.
Image steganalysis based on supervised distance metric learning is to find an appropriate measure of similarity between image features where the distribution discrepancy between cover-images and stego-images are analyzed in the reduced dimensional space. Our approach is novel in that it combines the merits of weight metric learning and image distribution analysis in reduced dimension space. By this learning metrics, we exploit a new steganalysis metric to discriminate stego-images from clean images. The experiment results show the effectiveness of the propose approach for some data hiding method.
Existing sentiment identification technologies for Chinese texts are mostly based on Chinese words/phrases. However, we notice that Chinese characters are ideogram and many of them contain rich sentiment information. Hence, a Chinese text sentiment orientation identification model based on Chinese characters but not words or phrases is proposed in this paper. Contrasting to existent models, our model has many advantages such as no necessary of dictionaries, words segmentation and stop-words removal. Moreover, its dimension of feature vector is lower and its processing efficiency is higher. Experiments show that our model greatly simplifies the identification process and improves the computational efficiency at very little loss of accuracy.
To produce speech synchronized articulatory animation, Electro-Magnetic Articulography (EMA) data is one type of important training data for establishing the relationship between speech and articulatory movements. Because the EMA data is easily contaminated by the head motion during the capturing process, this paper proposes a real-time robust stabilization system for EMA noisy data. Firstly, global motion parameters are obtained by fitting the EMA noisy data between the reference frame and current frame with random sample consensus algorithm. Secondly, multiple evaluation criteria, i.e., global motion parameters and location errors of corresponding EMA noisy data matches, are fused by an adaptive low-pass filter to smooth global motion for obtaining correction vector. Finally, motion compensation is applied to the current frame by using correction vector, and stabilized EMA data is obtained. By comparing between the EMA noisy data and stabilized EMA data, the experimental results demonstrate the system can increase the average peak signal-to-noise ratio around 5.92 dB, the perceptive comfort on the speech synchronized articulatory animation driven by the stabilized EMA data.
Adaptive Neuro-Fuzzy Inference System (ANFIS) has been popular among other fuzzy inference systems. It has been widely applied in the field of business and economics. Many have trained ANFIS parameters using metaheuristic algorithms but very few have tried optimizing its fuzzy rule-base. The auto-generated rules, using grid partitioning, comprise of both the potential and weak rules. This increases the complexity of ANFIS architecture as well as the cost of computation. Therefore, pruning less or non-contributing rules would serve as optimizing ANFIS rule-base. However, reducing complexity and increasing accuracy of ANFIS network needs effective training and optimization mechanism. This paper proposes an efficient technique for optimizing ANFIS rule-base without compromising on accuracy. The proposed technique uses a newly developed optimization algorithm called Mine Blast Algorithm (MBA) for the first time for ANFIS learning. The ANFIS optimized by MBA is employed to model strength prediction for Malaysian small medium enterprises (SMEs). The results prove that MBA optimized ANFIS rule-base and trained its parameters more efficiently than Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
Change Data Capture from source system is the first step in the incremental maintenance of data warehouses and business intelligence and is a key component of ETL (Extract, Transform and Load) technique. Methods of CDC are currently available, namely, time stamps, differential snapshots, triggers, and archive log. Differential snapshots do not rely on the implementation mechanism of the information sources, and therefore demonstrates better universality and adaptability. Due to the lack of computing resources, the differential snapshots based on sort merge and hash partition are sometimes error and not effective. This paper proposes the differential snapshot of low cost and high efficiency which combines open source database and Hadoop MapReduce. The differential snapshot based data summary which is generated by the MD5 algorithm is very effective but I/O cost is very heavy. So the paper proposes the SQL statement which queries the database while generating the tuples summary only once I/O. We implement the SQL statement on the open source database MySQL. In addition the parallel programming of MapReduce is used to find difference of database files which improves the efficiency and avoids the error. Experiment verifies the different performances among differential snapshot algorithms difference algorithm.
Link prediction is an important tool for many social media sites to find the missing and future links among users. Understanding users' sentiment and their social relationships are potentially valuable. In this paper, two new sentiment similarity measures have been proposed and an algorithm has been designed to do link prediction by incorporating the structure and sentiment attribute of nodes. In order to evaluate the proposed algorithm, links and tweets with regard to some hot topics of 2014 FIFA World Cup Brazil are crawled from Tencent Weibo, and the sentiment distributions of crowds are analyzed for each topic. The experimental results show that the number of users with the same emotion and the sentiment distributions of crowds will influence a user to link with another user, so the sentiment attribute of nodes in social network can help to improve the performance of link prediction.
Because of constant noise estimations, the speech enhancement of standard Wiener filters is poor under varied noise environments. In the present study, we propose an improved Wiener filter method for speech enhancement based on wavelet entropy. Wavelet entropy (WE) point detection can discriminate between speech activity segments and noise segments. This discrimination provides a basis through which noise can be estimated and updated accurately, leading to accurate a priori signal-to-noise ratios obtained from updated noise estimations, reduction of residual musical noise, and enhancement of speech signals degraded by non-uniform noise. Spectrogram comparisons of enhanced speech signals between the proposed WE Wiener filter and a standard Wiener filter show that the former is better at suppressing non-uniform noise than the later. Their perceptual evaluation of speech quality measures also show that the WE Wiener filter yields better enhanced speech quality than the standard Wiener filter.
Handwritten digit recognition is an important research topic in computer vision and pattern recognition. This paper proposes an effective handwritten digit recognition approach based on specific multi-feature extraction and deep analysis. First, we normalize images of various sizes and stroke thickness in preprocessing to eliminate negative information and keep relevant features. Secondly, considering that handwritten digit image recognition is different from traditional image semantics recognition, we propose specific feature definitions, including structure features, distribution features and projection features. Moreover, we fuse multiple features into the deep neural networks for semantics recognition. Experiments results on benchmark database of MNIST handwritten digit images show that the performance of our algorithm is remarkable and demonstrate its superiority over several existing algorithms.
Target tracking software that running on airborne radar is usually developed by hard coding in C language. This method is not efficient, large amount of time and money is spent during the development process. In this paper, we present an integrated development environment for target tracking software, which is Target tracking software Development Environment (TDE). TDE supports the whole process of target tracking software development including design, implementation, testing and assessment. It provides most of the algorithms and function blocks widely used in target tracking software, and enables developers to construct the software in a short time, and then test and verify it in simulation. The example shows that TDE is effective and highly improved the development efficiency.
