Energy management comprises of planning and operation of energy production and energy consumption units. Home energy management system is sometimes called as smart grids. The energy management system optimizes the ene...
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(纸本)9781467395458
Energy management comprises of planning and operation of energy production and energy consumption units. Home energy management system is sometimes called as smart grids. The energy management system optimizes the energy usage by informing the consumer on a live basis of their corresponding consumption units. Home appliances are generally scheduled to operate on various power ratings. But none have turned to solve the problems associated with the power demand. This paper proposes an effective solution by making a modification in the already existing electric meter by implementing markov chain algorithm. This algorithm makes use of the information particularly the power generated and the power available at that time. Based on the obtained information there are three modes of operation namely custom mode, limited mode and the full power mode. The software implementation has been done by using Proteus software. The hardware details ant their implementation are presented in this paper. The fuzzy rules are used to determine how the power gets distributed. A Matlab software is used to demonstrate it. The prototype hardware is demonstrated by using PIC16f877a microcontroller.
The research topic of this article is the English personalized learning recommendation module based on markov chain algorithm and adaptive learning algorithm. Personalized learning recommendation systems have been wid...
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The research topic of this article is the English personalized learning recommendation module based on markov chain algorithm and adaptive learning algorithm. Personalized learning recommendation systems have been widely applied in the field of education. Firstly, this article uses markov chain algorithm to analyze user learning behavior data, abstract user learning behavior as a process of state transition, predict their future behavior based on their past behavior, better understand their learning needs and interests, and provide more personalized learning recommendations for them. Subsequently, combined with adaptive learning algorithms, recommendation strategies and content are dynamically adjusted based on the user's learning goals and level, providing more accurate and effective learning recommendations according to their personalized needs and learning progress. By comparing with traditional recommendation systems, evaluate the accuracy and personalization of the recommendation module to better meet the learning needs of users.
Despite their legal protection status, protected areas (PAs) can benefit from priority ranks when ongoing threats to their biodiversity and habitats outpace the financial resources available for their conservation. It...
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Despite their legal protection status, protected areas (PAs) can benefit from priority ranks when ongoing threats to their biodiversity and habitats outpace the financial resources available for their conservation. It is essential to develop methods to prioritize PAs that are not computationally demanding in order to suit stakeholders in developing countries where technical and financial resources are limited. We used expert knowledge-derived biodiversity measures to generate individual and aggregate priority ranks of 98 mostly terrestrial PAs on Madagascar. The five variables used were state of knowledge (SoK), forest loss, forest loss acceleration, PA size and relative species diversity, estimated by using standardized residuals from negative binomial models of SoK regressed onto species diversity. We compared our aggregate ranks generated using unweighted averages and principal component analysis (PCA) applied to each individual variable with those generated via markovchain (MC) and PageRank algorithms. SoK significantly affected the measure of species diversity and highlighted areas where more research effort was needed. The unweighted- and PCA-derived ranks were strongly correlated, as were the MC and PageRank ranks. However, the former two were weakly correlated with the latter two. We recommend using these methods simultaneously in order to provide decision-makers with the flexibility to prioritize those PAs in need of additional research and conservation efforts.
Core-shell semiconductor quantum dots (QDs) are one of the biggest nanotechnology successes so far. In particular, type-I QDs with straddling band offset possess the ability to enhance the charge carriers capturing wh...
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Core-shell semiconductor quantum dots (QDs) are one of the biggest nanotechnology successes so far. In particular, type-I QDs with straddling band offset possess the ability to enhance the charge carriers capturing which is useful for memory application. Here, the type-I core-shell QD-based bipolar resistive switching (RS) memory with anomalous multiple SET and RESET processes was demonstrated. The synergy and competition between space charge limited current conduction (arising from charge trapping in potential well of type-I QDs) and electrochemical metallization (ECM, originating from redox reaction of Ag electrode) process were employed for modulating the RS behavior. Through utilizing stochastic RS mechanisms in QD-based devices, four situations of RS behaviors can be classified into three states in markovchain for implementing the application of a true random number generator. Furthermore, a 6 x 6 crossbar array was demonstrated to realize the generation of random letters with case distinction.
The predicted Bridge Condition Index (BCI) is a guiding indicator for formulating preventive maintenance strategies for bridges and is hardly available even if using the most advanced neural network prediction model, ...
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The predicted Bridge Condition Index (BCI) is a guiding indicator for formulating preventive maintenance strategies for bridges and is hardly available even if using the most advanced neural network prediction model, owing to the few measured BCIs and neural network model defects. In this paper, an improved Back-Propagation Neural Network (BPNN) model named CPSO-BP-MC is proposed to predict BCI, which has a BPNN as its core, combined with the Coordinated Particle Swarm Optimization (CPSO) algorithm aims to faster convergence and avoid local optimal solution even with less training sample, and the markovchain (MC) algorithm aims to further revise the fluctuation of CPSO-BP prediction output due to maintenance and reinforcement. A case study is presented to demonstrate the efficiency of CPSO-BP-MC predicting BCI with fewer training samples. Comparing the predicted BCI of CPSO-BP-MC with the measured BCI and other models, the results show that CPSO-BP-MC converges faster than other models, and can predict the BCI more stably and accurately, and the error between prediction and measured BCI is less than 3 %.
This paper considers the Monte Carlo dynamics of random dimer coverings of the square lattice, which can be mapped to a rough interface model. Two kinds of slow modes are identified, associated respectively with long-...
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This paper considers the Monte Carlo dynamics of random dimer coverings of the square lattice, which can be mapped to a rough interface model. Two kinds of slow modes are identified, associated respectively with long-wavelength fluctuations of the interface height, and with slow drift (in time) of the system-dde mean height. Within a continuum theory, the longest relaxation time for either kind of mode scales as the system size N. For the real, discrete model, an exact lower bound of O(N) is placed on the relaxation time, using variational eigenfunctions corresponding to the two kinds of continuum modes.
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