279 research outputs found
Fault detection and fault-tolerant control for nonlinear systems
No full textLinlin Li addresses the analysis and design issues of observer-based FD and FTC for nonlinear systems. The author analyses the existence conditions for the nonlinear observer-based FD systems to gain a deeper insight into the construction of FD systems. Aided by the T-S fuzzy technique, she recommends different design schemes, among them the L_inf/L_2 type of FD systems. The derived FD and FTC approaches are verified by two benchmark processes. Contents Overview of FD and FTC Technology Configuration of Nonlinear Observer-Based FD Systems Design of L2 nonlinear Observer-Based FD Systems Design of Weighted Fuzzy Observer-Based FD Systems FTC Configurations for Nonlinear Systems< Application to Benchmark Processes Target Groups Researchers and students in the field of engineering with a focus on fault diagnosis and fault-tolerant control fields The Author Dr. Linlin Li completed her dissertation under the supervision of Prof. Steven X. Ding at the Faculty of Engineering, University of Duisburg-Essen, Germany
Development of butter cookies using seaweed (kappaphycus alvarezii) flour and overripe banana sweetener by response surface methodology and sensory evaluation
No full textThis study was conducted to develop a high dietary fibre and low glycaemic index healthy cookie for using seaweed flour (Kappaphycus alvarezii) to replace wheat flour, overripe banana sweetener (OBS) replacing table sugar, and butter replacing margarine. A total of three factors were set, each with a different percentage of seaweed flour (0%, 4%, 4%), OBS (0%, 50%,100%), and butter (50%, 50%, 100%) to replace flour, sugar and margarine. The ratios/levels of seaweed flour, OBS, and butter in the butter cookies’ formulation were optimized using Box Behnken design of response surface methodology (RSM). The physicochemical properties of cookies, such as texture profile analysis, nutrient analysis, total dietary fibre, and colour, were also assessed. Sensory evaluations involving 30 untrained panellists were conducted to determine the acceptability levels of the cookie. The results showed that the increment of seaweed flour in the formulations increased the weight (18.22 g), crispness (2.21 mm), and thickness (10 mm). Furthermore, the addition of seaweed flour reduced the diameter (53.8 mm), firmness (2.05 kg), and spread ratio (5.38). Two optimal formulations were obtained: experiment 12 (seaweed 4%, OBS 100%, butter 50%) and experiment 14 (seaweed 4%, OBS 50%, butter 50%). These two experiments were subjected to organoleptic evaluations and nutritional analyses in comparison with the control (seaweed 0%, OBS 0%, butter 50%). The addition of 4% seaweed flour, 50% OBS, and 50% butter to the formulation increased the composition of ash (2.53%), moisture (8.02%), and fat (20.92%), while the composition of protein (6.88%) and carbohydrate (62%) was slightly reduced. In addition, there was an increase in total dietary fibre (16.61%) for 4% seaweed-contained cookies as compared to the control sample. In the sensory evaluation, the overall scores for the control cookie (0%) and the 4% cookie combined with seaweed flour were not significantly different for all attributes. However, the addition of 4% seaweed flour and 50% OBS resulted in the highest scores for aroma, flavour, and overall acceptance attributes. In conclusion, substituting 4% seaweed flour to replace wheat flour, 50% OBS to replace table sugar, and 50% butter to replace margarine could be an effective combination to produce nutritious and tasty cookies. Therefore, Kappaphycus alvarezii able to improve the nutritional composition of cookie and has the potential to be used as an alternative ingredient for the development of high dietary fibre and nutritious bakery products
Prediction of the Jiatanchang Reservoir Bank Collapse in the Three Gorges Reservoir Area
No full textA novel hybrid technique for short-term electricity price forecasting in deregulated electricity markets
No full textThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively.
ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. In order to overcome the limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression algorithms is proposed to forecast the half-hour electricity prices in the UK electricity markets. According to the value of their power prices, thousands of the training data are classified by the unsupervised learning method of FCM clustering. SVM regression model is then applied to each cluster by taking advantage of the aggregated data information, which reduces the noise for each training program.
In order to demonstrate the predictive capability of the proposed model, ANNs and SVM models are presented and compared with the hybrid technique based on the same training and testing data sets in the case studies by using real electricity market data. The data was obtained upon request from APX Power UK for the year 2007.
Mean Absolute Percentage Error (MAPE) is used to analyze the forecasting errors of
different models and the results presented clearly show that the proposed hybrid
technique considerably improves the electricity price forecasting
A safety investment optimization model for power grid enterprises based on System Dynamics and Bayesian network theory
No full textIn recent years, frequent large-scale power grid accidents have caused serious economic losses and bad social impact, which has drawn great attention from power grid enterprises. As one of the key elements of production, safety investment plays an important role in improving the safety level and reducing accident loss. In this paper, System dynamics (SD) and Bayesian network (BN) are integrated to develop a novel safety investment optimization model for power grid enterprises, which takes into account the impact of safety investment factors on accidents and the interactions between them. Based on sensitivity analysis, critical safety investment factors are determined to form the subsystem of the SD model. Subsequently, the optimal safety investment strategy is determined by a three-step simulation. The simulation results show that there are barrel effects and a diminishing marginal utility in safety investment. The proposed safety investment optimization model is practical to provide technical supports and guidance for determining an effective safety investment strategy in power grid enterprises.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Safety and Security Scienc
Research and simulation on speech recognition by Matlab
No full textWith the development of multimedia technology, speech recognition technology has increasingly become a hotspot of research in recent years. It has a wide range of applications, which deals with recognizing the identity of the speakers that can be classified into speech identification and speech verification according to decision modes.The main work of this thesis is to study and research the techniques, algorithms of speech recognition, thus to create a feasible system to simulate the speech recognition. The research work and achievements are as following: First: The author has done a lot of investigation in the field of speech recognition with the adequate research and study. There are many algorithms about speech recognition, to sum up, the algorithms can divided into two categories, one of them is the direct speech recognition, which means the method can recognize the words directly, and another prefer the second method that recognition based on the training model. Second: find a useable and reasonable algorithm and make research about this algorithm. Besides, the author has studied algorithms, which are used to extract the word's characteristic parameters based on MFCC(Mel frequency Cepstrum Coefficients) , and training the Characteristic parameters based on the GMM(Gaussian mixture mode) . Third: The author has used the MATLAB software and written a program to implement the speech recognition algorithm and also used the speech process toolbox in this program. Generally speaking, whole system includes the module of the signal process, MFCC characteristic parameter and GMM training. Forth: Simulation and analysis the results. The MATLAB system will read the wav file, play it first, and then calculate the characteristic parameters automatically. All content of the speech signal have been distinguished in the last step. In this paper, the author has recorded speech from different people to test the systems and the simulation results shown that when the testing environment is quiet enough and the speaker is the same person to record for 20 times, the performance of the algorithm is approach to 100% for pair of words in different and same syllable. But the result will be influenced when the testing signal is surrounded with certain noise level. The simulation system won’t work with a good output, when the speaker is not the same one for recording both reference and testing signal
An Auxiliary System for Calculating Effective Connectivity from Co-Activation Network Data
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