Plume Localization Using Fuzzy Hidden Markov Model: An Efficient Decoding Method

The capabilities of smart meters and the potential vulnerabilities they introduce make the Advanced Metering Infrastructure (AMI) a critical components of the Smart Grid. A virus propagating among meters and massively issuing remote disconnect commands could have catastrophic consequences. To understand and assess the risk posed by a given set of meter devices getting compromised, we introduce a novel modeling approach based on fuzzy Markov chain. Markov modeling is one of the main approaches used to analyze the reliability of critical systems. Markov models are usually analyzed using mathematical approaches or Monte-Carlo Simulation. For large and complex systems, traditional Markov models suffer from the state explosion problem and quickly become intractable. The key idea of our approach to solve this issue in the context of the smart grid is to leverage the hierarchical topology of AMI to build a high-level model where components represent Neighborhood Area Networks (NAN) instead of individual meters. The fuzzy Markov formalism enables our solution to take into account gradual component degradation. We propose a Fuzzy Markov Model (FMM) for reliability analysis of a Smart Grid. The reliability of each NAN is represented by a number in [0, 1] which denotes the state of that component in the FMM.We define the transition probabilities between states of the fuzzy Markov model based on the fuzzy time-to-failure value for each NAN. We utilize the Fuzzy Monte Carlo Simulation (FMCS) approach to calculate the fuzzy time-to-failure value of each NAN. Our proposed method enables us to assess the reliability of the entire AMI at the meter level while keeping the model manageable.

Humans are immersed in a high-tech computing environment. Dependence on the pervasiveness of modern devices encompasses even simple everyday tasks. Unfortunately, the rapidly increasing expectations on the intelligence of these devices often exceeds their abilities. Devices are expected to perceive their environment, understand our intent, and autonomously carry out appropriate tasks. This paper presents an approach to bring device perception one step closer to meeting the high user expectations. A novel application of fuzzy hidden Markov models to automatically identify dynamic facial expressions is proposed in a human-device interaction context. A low-complexity vision based facial feature tracker is integrated with a hidden Markov model adapted to take advantage of fuzzy measures. Benefits over traditional hidden Markov mode Is for facial expression recognition include a reduction in training time, and improved flexibility for use as a valuable part of a larger multimodal system for natural human-device interaction

Measuring effectiveness of movement-based three-way decision using fuzzy Markov model

The fuzzy HMM algorithm is regarded as an application of the fuzzy expectation-maximization (EM) algorithm to the Baum-Welch algorithm in the HMM. The Texas Instruments p4 used speech and speaker recognition experiments and show better results for fuzzy HMMs compared with conventional HMMs. Equation and how estimation of discrete and continuous HMM parameters on based this two algorithm is explained and performance of two speech recognition method for one hundred is surveyed. This paper show better results for the fuzzy HMM, compared with the conventional HMM. After of that work we use Fuzzy-Neural Network system was proposed for Farsi speech recognition. Instead of using the fuzzy membership input with class membership desired-output during training procedure as proposed by several researches. we used the fuzzy membership input with fundamental binary desired-output. This can reduce the misunderstood training, decrease the training time and also improve the recognition ability.

Performance Evaluation of Link Prediction Techniques Based on Fuzzy Soft Set and Markov Model

Hidden Markov model (HMM) has been widely applied in human action recognition. In this paper an extension of HMM called fuzzy hidden Markov model (fuzzy HMM) is used for action recognition. It tries to increase the classification performance and decrease the information loss due to feature vector quantisation. Using fuzzy concepts with HMM leads to better recognition of similar actions such as walking, jogging and running. Two feature extraction methods including skeleton and space-time approaches are used for action representation. Actions could be represented efficiently using skeleton features where scene background is plain. Space-time features are extracted directly from video, and therefore avoid possible failures of other pre-processing methods. We propose space-time-based features by considering temporal relation between them. Experimental results show the effectiveness of fuzzy HMM in human action recognition. Moreover, it is shown that fuzzy HMM leads to significant improvement in recognition of similar actions. The accuracy rates of fuzzy HMM in comparison to HMM are incremented 3.33% and 5.59% in Weizmann and KTH datasets respectively.

Reports some early results of our study on continuous Korean sign language (KSL) recognition using color vision. In recognizing gesture words such as sign language, it is very difficult to segment a continuous sign into individual sign words since the patterns are very complicated and diverse. To solve this problem, we disassemble the KSL into 18 hand motion classes according to their patterns and represent the sign words as some combination of hand motions. Observing the speed and the change of speed of hand motion and using fuzzy partitioning and state automata, we reject unintentional gesture motions such as preparatory motion and meaningless movement between sign words. To recognize 18 hand motion classes we adopt the hidden Markov model. Using these methods, we recognize 15 KSL sentences and obtain 94% recognition ratio.

Solar geyser’s standby element is controlled by a time-based controller, heating water to preset value if required. In this investigation the time-based controller will be replaced with a controller profiling weather conditions and warm water usage adjusting pre-set temperatures accordingly without human intervention. Multiple sensors recordings are used recording warm water usage and heating patterns. Official weather predictions replace physical sensors to record environmental conditions. Fuzzy logic and Hidden Markov Model are used, profiling and training the algorithm. Simulations with historical data are used evaluating and comparing the algorithm efficiency against time-based heating, but its functioning in such environment needs to be assessed.

This paper presents a mathematical framework for fuzzy discrete and continuous observable Markov models (OMMs) and their applications to written language, spam email and typist recognition. Experimental results show that the proposed OMMs are more effective than models such as vector quantization, Gaussian mixture model and hidden Markov model.

Purpose The purpose of this paper is to review the Fuzzy Markov development for assessing the structural integrity of buried transportation bridges. In doing so, the appropriateness of Fuzzy Markov will be assessed, leading to the subsequent model. Design/methodology/approach This research will utilize the Fuzzy Markov techniques as the conceptual framework. Such methodology is further supported via the utilization and evaluation of 30 buried transportation bridges using the developed Fuzzy Markov model. Findings Subsequently, through a developed Fuzzy Markov model, this research found that as the basis of structural resilience, specific matrices for age-dependent transition probability can be compiled using conditional survival probabilities in the various structural states; as the basis of structural integrity, specific environmental and economic schemes can also be established based on inspection intervals, intervention systems and failure phases; exact inspection and maintenance intervals can be scheduled to further prolong an asset’s life; and clear and early warning signs can also be formulated for immediate intervention when the structural integrity of the asset are indeed compromised. Originality/value The gap within the literature currently surrounds the limitation of computational analysis for some buried structures such as bridges. Specifically, to streamline such evaluation and regimes, a Fuzzy Markov is developed and reviewed.

With the advent of Industry 4.0, modern production systems have obvious polymorphic characteristics. However, due to unpredictable external working conditions, modern manufacturing systems have obvious fuzzy characteristics. Therefore, this paper proposes a health state evaluation method for fuzzy multi-state production systems based on a multi-state production network model (MPNM). First, the definition of the health state of the fuzzy multi-state production system is given, which comprehensively analyzes the machine performance state and the output product quality state. Second, the multi-state production network model of a multistate production system is established, which is based on the operational characteristics of the production system. Third, the fuzzy Markov model is used as a tool to propose a health state assessment method for multi-state production systems. Finally, the ferrite phase shifting unit production system is taken as an example to verify the proposed method.

Based on the framework of markov game theory, the attack engagement maneuvering strategy of 1 vs. 1 air combat is studied. First, the markov game model of air combat of two fighters is established with an introduction of its state space description and action set, and the maneuvering target set of both rivals is established. Second, the fuzzy theory is utilized and a fuzzy Markov model framework is introduced with its fuzzy state space and fuzzy reward function. Then, with the fuzzy markov game, the Nash-Q learning method is adopted to obtain the optimized strategy of the fighters. At last, a simulation shows that with the method in this paper, the 1 vs. 1 air combat decision making can be solved and the method is effective.

In this paper, a reconfiguration approach using fuzzy logic algorithm and neural network modelling is proposed. When failure has been detected, the state of the degraded system is evaluated by comparing the output of the system with the estimation provided by a neural model. Therefore, we propose to use the neural network in order to cover all the operating zone of the faulty system. By combining neural network capabilities and fuzzy logic for fault evaluation, a new control law is determined taking into account the impact of the failure on the system. Its potentialities are illustrated through simulation studies on a thermal plant presenting bilinear characteristics. >

Discrete fuzzy measures (capacities) are a powerful tool to model interacting inputs and aggregate them using Choquet, Sugeno, and other fuzzy integrals. Simulation studies and probabilistic optimization algorithms require efficient random sampling from the set of fuzzy measures. It is a challenging problem due to an extremely high complexity of the polytope of capacities. This article addresses random sampling from two special classes: supermodular and submodular fuzzy measures. A suitable marginal contributions representation converts the problem to sampling from an order polytope, which is dealt efficiently using the Markov chain random walk.

This paper presents a multiple criteria evaluation model for international multiple foundry partnership selection. In a multiple source foundry environment, the integrated circuit (IC) design house maintains partnership with several different foundries to diversify the risk of capacity shortage and natural disaster. To select a group of foundry partners, management culture, technology level and future growth are heavily interrelated. Therefore the fuzzy integral is used to evaluate the synthetic performance of the best partners. To show the practicability and usefulness of this method, a Taiwan consumer IC design house case was presented for evaluation. Results in this study not only confirm the practical nature of the proposed model, but also demonstrate the high flexibility in evaluation of a multiple criteria decision making (MCDM) problem.