Best Buy Case Study Write Up Essay Example for Free

Best Buy Case Study Write Up Essay 1. How does Best Buy define customer centricity? The idea behind customer centricity was to be the customer’s smart friend and give a full solution. The sales people stand by the customers and try to find what they really need and what they want. The opposite of this would be product centricity. At this time the electronics were getting easier to use, their interaction required specific knowledge that only a fraction of the client base possessed. In this situation, the used to be advantage of best buy disappeared, and the company tested the first version of customer-centricity by setting up 12 laboratory stores and then rolling out tested concepts in 32 pilot stores. The test were successful very successful. Best buy changed its segments from products such as MP3, TV, or PC to customers like Barry. All these make customers more convenient to shop in the store. Compared to the other stores, Best Buy did not focus on brands but usage. The sales person never asked what do you want to buy, but rather ask what you want to do. In launching customer centricity, Anderson used an autocratic set of power tools and expected swift support from his top team to execute his vision. 2. Is Customer-Centricity the same as customer services? No, the customer-Centricity is not the same as customer service. The difference is that they can contour their sales and service pitch to each individual after they know which category that they fall into. They know from past company stastistics and knowledge about the different types and what their shopping style is like. Customer service is about trying to sell your existing products. The case states also that customer service may be in response to its competitors, and not its customers. Customer centricity mainly focuses on research of customers buying power, purchase preference and customer behavior. It is based on the research that Best Buy has gathered over time. From here, the company can redesign its products, and develop a new marketing strategy and give more suitable service. 3. How does it relate to Consumer behavior? Chapter 10 touches upon things that could be related to consumer behavior. In store decision making talks about spontaneous shopping which is unplanned buying and impulse buying. Point of purchase stimuli is product display or demonstration that draws attention. The salesperson also can create exchange process. This involves commercial friendships. This is basically what customer centricity is about, forming commercial friendships. Chapter 10 also talks post purchase satisfaction which is the overall feeling about a product after someone has purchased it. Chapter 13 discusses income and social class and this is directly correlated to the customer centricity model. The model puts people in different classes. They were Jill, Barry, angles and devils. Chapter 13 goes into detail and talks about how people can be put in classes based on income, education, age, religion, gender, just like Best Buy put these four people into classes to segment them. Best Buy, in essence, used consumer behavior to classify these people. 4. Finally, do you agree with this new strategy for Best Buy? What is its impact on the financial performance? We think that it was a good idea for Best Buy to implement this. It has worked financially and it seemed to have made the company more profitable. From looking at the financials from 2002 to 2004, it appears as though the strategy seems to be working. Best Buy remains at the top of the list for Consumer electronics companies in the United States. Its revenue was 49.7 billion dollars in 2010, which is 18% of North American market share. In the last 5 years, it keeps 25% of gross profit growth. It seemed like it was known that Wal Mart would catch Best Buy as the number one store at that time, but I think this was because of different reasons, like its building of so many new stores and supply chain capability.

Are Imf Loans Good Poor Countires Essay Example for Free

Are Imf Loans Good Poor Countires Essay A poor country with a weak government is suffering from shortages in terms of financial resources. Most of its population lives below poverty levels, there is high unemployment, low literacy rate, food shortages, no clean water and due to a combination of drought and lack of technology, no crops to export. As if it didn’t have enough problems, the country has debts to pay back to foreign governments, investors and agencies. This is where the IMF, which Easterly calls ‘the world’s most powerful creditor’, steps into the picture. [1] It was originally set up by the West in order to prevent large trade imbalances and unstable currencies. However, it shifted focus and started bailing out poor countries in financial crises. It has had success in helping countries out on a short-term basis. Most of the countries that have benefited from IMF loans are countries that need temporary assistance, do not qualify as ‘emerging markets’ and face difficulties in attracting foreign investors and lenders. For example, the IMF successfully helped South Korea and Thailand during their financial squeezes in the 1980’s[2] . However, there are problems in terms of the long-term development of countries which rely on the IMF. Easterly begins his article by describing a meeting between the IMF and the minister of finance and economic development of Ethiopia. At the meeting, the IMF set out several conditions that the government of Ethiopia would have to satisfy in order to receive assistance and most importantly, pay back their loans. The problem with the conditions was that they were at times contradictory and unrealistic. For example, while stating that it supported the government’s food security program, the IMF also told the finance minister that he would have to be careful that the program did not endanger ‘macroeconomic stability’.[3] How macroeconomic stability could be achieved in a country where most of the people are starving is a mystery. Other conditions that the IMF places on countries include getting them to agree to financial programs which reduce government spending and inflation, limit excessive money printing, increase taxes and put in place austerity measures. Through such strict conditions, the IMF has therefore accomplished very little when it comes to promoting long-term development. The conditions have been too intrusive into government policies. Easterly argues therefore that there is an association between â€Å"IMF involvement and the most extreme political event: state collapse†.[4] This is caused by the involvement of the fund in domestic politics. By ‘forcing’ governments to carry out social cuts such as reducing subsidies on basic goods, the proposed IMF measures create riots and political and social instability. The article demonstrates that out of 8 countries that collapsed or failed, 7 had spent a high share of time ranging from 46 to 74 % of the decade before the collapse on IMF programs. This shows that the IMF measures are often too difficult to comply with and their ultimate success is limited. The author therefore suggests that the countries that ultimately collapsed would have probably been better off without IMF involvement.[5] This is because such countries have far greater problems than the IMF can fix. However, despite this, the IMF never turns a country down even if it fails its programs several times. The author gives the example of Sierra Leone which went into civil war after participating in an IMF program and then returned into the program and failed again, this time requiring UN intervention to protect its population from genocide. He suggests therefore that the IMF should have left it alone in the first place and not intervened. Trying to help was according to Easterly, clear evidence of the ’Planner’s mentality’.[6] However, one might ask what would happen if the IMF did not intervene in such a case? Easterly ´s suggestion of leaving the country alone would lead to the struggles of the people being ignored, genocide would occur and the country would sink into further poverty. The result would be over-reliance on aid, more refugees escaping to struggling neighbouring countries and a low literacy rate which would affect future generations. Perhaps therefore the solution is not for the IMF to turn a blind eye to countries that fail despite decades of following IMF programs. Perhaps the solution is for it to change its strategy and program in order to tailor it to the unique needs of each country. Easterly mentions this as well. He states that not only do the staff at the IMF operate a  ´one size fits all ´ model to all countries, their accounting relies on shaky numbers as evidenced in page 22 of the article. Thus Easterly argues that it is better for a country’s balance to bounce than for it to rely on shaky statistics by the IMF which do not reflect reality. Very little can be achieved if unrealistic goals are set for countries and if their achievements or failings are not measured accurately. Thus IMF loans do not work in the majority of cases. They may only work where a country has some form of reliable government and does not already have many loans to pay back. Getting an IMF loan in such a case is just a temporary measure and the country can pay back without great consequences. In relation to the most poor however, their problems persist so they renew their loans from one change of government to another with little or no prospect of being able to pay back. The IMF stipulates in all its agreements with countries that they need to pay it back before they pay other creditors. However, Easterly argues that by making such a condition, the IMF is actually bailing itself out.[7] It ends up in a situation where it provides new loans to countries so that they can pay it back for old loans. It also drafts the World Bank in to make an adjustment loan as part of the bailout package. This is to no benefit to the country which sinks deeper into the debt to the IMF and still has other loans from other investors to pay back. The IMF calls countries that are dependent on its loans ‘prolonged users’. The definition of a prolonged user is a country which spent 7 out of a 10 year period under an IMF program. The addiction to IMF loans is evidenced by the fact that 44 countries qualify for the definition of prolonged user and half of IMF lending goes to such countries.[8] However, repeated debts do nothing to solve the problem. In1996, the IMF and World Bank decided to forgive part of their loans to the poorest nations. These nations had accumulated loans from not only these organisations but also loans from western countries and other agencies. There was very little chance of them being able to repay the loans and the interest that had accumulated. Such countries were named heavily indebted poor countries (HIPCs).[9] 17 out of 18 of the HIPCs were among the countries receiving above average amounts of IMF and World Bank loans. They had no growth of income or resources. They continued to sink into debt with interest still growing. The forgiving of the debts over a period according to Easterly, only encouraged borrowers to keep borrowing. For example Bolivia and other countries got 100 percent debt relief, but they still made no recovery.[10] Another example is offered by the Argentina disaster set out in the article. Argentina was a star pupil from 1991 to 1999.[11] It had gone through several IMF programs and in 1991, it achieved financial stability. After almost a decade of financial stability, the president who was faced with elections led the wave of public spending and loans from private foreign investors. Financial crisis ensued and the IMF put together a rescue loan plan that included loans from the World Bank, Inter-American development Bank and Spain. In 2001 lenders demanded interest rates from Argentina that were 10 percent higher than elsewhere. The IMF continued to give loans worth several billions to support Argentina so that it could pay its private creditors. However, despite this, Argentina failed to pay any of its creditors back their full amount. Its debt reached 81 billion dollars and it eventually had to make ’take it or leave it offers’ to its creditors who had to accept not getting most of their money back. This supports the argument that loans on their own are not the solution to the poor countries’ problem. They need help to resolve their unique political and social problems. Putting them in debt is not going to assist them as whatever progress they make, they will have to give the money back. Easterly concludes therefore that the world bank which is an aid agency should give countries grants not loans. And the IMF should get out of the business of bailing countries out. It has inadequate knowledge of what is happening at ground level and it was not designed to offer the kind of assistance that poor countries need and the long-term planning their needs require. Thus it would be better for aid agencies to continue their work at grass-root level and to contribute to long-term change.

Deposits in Thermal Power Plant Condensers

Deposits in Thermal Power Plant Condensers Abstract: Unexpected fouling in condensers has always been one of the main operational concerns in thermal power plants. This paper describes an approach to predict fouling deposits in thermal power plant condensers by means of support vector machines (SVMs). The periodic fouling formation process and residual fouling phenomenon are analyzed. To improve the generalization performance of SVMs, an improved differential evolution algorithm is introduced to optimize the SVMs parameters. The prediction model based on optimized SVMs is used in a case study of 300MW thermal power station. The experiment result shows that the proposed approach has more accurate prediction results and better dynamic self-adaptive ability to the condenser operating conditions change than asymptotic model and T-S fuzzy model. Keywords: Fouling prediction; Condensers; Support vector machines; Differential evolution 1. Introduction Condenser is one of key equipments in thermal power plant thermodynamic cycle, and its thermal performance directly impacts the economic and safe operation of the overall plant [1]. Fouling of steam condenser tubes is one of the most important factors affecting their thermal performance, which reduces effectiveness and heat transfer capability with time [2, 3]. It is found that the maximum decrease in effectiveness due to fouling is about 55 and 78% for the evaporative coolers and condensers, respectively [2]. As a consequence, the formation of fouling in condenser of thermal power plants has special economic significance [4-6]. Furthermore, it represents the concerns of modem society in respect of conservation of limited resources, for the environment and the natural world, and for the improvement of industrial working conditions [6, 7]. The fouling of heat exchangers is a wide ranging topic coveting many aspects of technology, the designing and operating of condenser must contemplate and estimate the fouling resistance to the heat transfer. The knowledge of the progression and mechanisms of formation of fouling will allow a design of * Manuscript an appropriate fouling mitigation strategy such as optimal cleaning schedule to be made. The most common used models for fouling estimation are the thermal resistance method and heat transfer coefficient method [6-10]. However, the residual fouling of periodic fouling deposition process and the dynamic changes of heat exchanger operating condition are not considered in these models. Consequently, the estimation error of those methods is very large. Artificial Neural Networks (ANNs) are capable of efficiently dealing with many industrial problems that cannot be handled with the same accuracy by other techniques. To eliminate most of the difficulties of traditional methods, ANNs are used to estimate and control the fouling of heat exchanger in recent years. Prieto et al [11] presented a model that uses non-fully connected feedforward artificial neural networks for the forecasting of a seawater-refrigerated power plant condenser performance. Radhakrishnan et al [12] developed a neural network based fouling model using historical plant operating data. Teruela et al [13] described a systematic approach to predict ash deposits in coal-fired boilers by means of artificial neural networks. To minimize the boiler energy and efficiency losses, Romeo and Gareta illustrated a hybrid system that combines neural networks and fuzzy logic expert systems to control boiler fouling and optimize boiler performance in [14]. Fan and Wang proposed diagonal recurrent neural network [15] and multiple RBF neural network [16] based models for measuring fouling in thermal power plant condenser. Although the technique of ANNs is able to estimate the fouling evolution of heat exchanger with satisfaction, there are some problems. The selection of structures and types of ANNs dependents on experience greatly, and the training of ANNs are based on empirical risk minimization (ERM) principle [18], which aims at minimizing the training errors. ANNs therefore face some disadvantages such as over-fitting, local optimal and bad generalization ability. Support vector machines (SVMs) are a new machine learning method deriving from statistical learning theory [18, 19]. Since later 1990s, SVMs are becoming more and more popular and have been successfully applied to many areas such as handwritten digit recognition, speaker identification, function approximation, chaotic time series forecasting, nonlinear control and so on [20-24]. Established on the theory of structural risk minimization (SRM) [19] principle, compared with ANNs, SVMs have some distinct advantages such as globally optimal, small sample-size, good generalization ability and resistant to the over-fitting problem [18-20]. In this paper, the use of SVMs model is developed for the predicting of a thermal power plant condenser. The prediction model was used in a case study of 300MW thermal power station. The experiment result shows that the prediction model based on SVMs is more precise than thermal resistance model and other methods, such as T-S fuzzy model [17]. Moreover, to improve the generalization performance of SVMs, an improved differential evolution algorithm is introduced to optimize the parameters of SVMs. 2. Periodic fouling process in condenser The accumulation of unwanted deposits on the surfaces of heat exchangers is usually referred to as fouling. In thermal power station condensers, fouling is mainly formed inside the condenser tubes, reducing heat transfer between the hot fluid (steam that condenses in the external surface of the tubes) and the cold water flowing through the tubes. The presence of the fouling represents a resistance to the transfer of heat and therefore reduces the efficiency of the condenser. In order to maintain or restore efficiency it is often necessary to clean condensers. The Taprogge system has found wide application in the power industry for the maintenance of condenser efficiency, which is one of on-line cleaning systems [6]. When the fouling accumulation in condensers reached a threshold, the sponge rubber balls cleaning system is activated, slightly oversized sponge rubber balls continuously passed through the tubes of the condenser by the water flow, and the fouling in the condenser is reduced or eliminated. The progresses of fouling accumulating and cleaning continue alternatively with time. Therefore, the fouling evolution in power plant condensers is periodic. However, the sponge rubber ball system is only effective of preventing the accumulation of waterborne mud, biofilm formation, scale and corrosion product deposition [6]. As for some of inorganic materials strongly attached on the inside surface of tubes, e.g. calcium and magnesium salts, can not be effectively reduced by this technique. As a result, at the end of every sponge rubber ball cleaning period, there still exist a lot of residual fouling in the condensers, and the residual fouling will be accumulated continuously with the time. Where, the fouling can be cleaned by the Taprogge system is called soft fouling, and those can not be cleaned residual fouling is called hard fouling. When the residual fouling accumulated to some degree, the cleaning techniques that can eliminate them, such as chemistry cleaning method, should be used. Generally, the foul degree of heat exchanger is expressed as fouling thermal resistance, defined as the difference between rates of deposition and removal [6]. In this paper, the corresponding fouling thermal resistance of soft fouling and hard fouling expressed as Rfs and Rfh, respectively. Then, the condenser fouling thermal resistance Rf in any time is the sum of soft fouling thermal resistance and hard fouling thermal resistance, expressed as Eq. (1). ( ) ( ) ( ) ( ) ( ) ( ) 0 0 0 R t R t R t R t R t t R t t f fs fh f fs fh ? ? ? ? ? ? ? (1) where ( ) 0 R t f is the initial fouling. Fig. 1 periodic fouling evolution in power plant condensers Fig. 1 demonstrates the periodic evolution process of fouling in power plant condensers. In fact, the evolution process of fouling in a condenser is very complex, which is related to a great number of variables, such as condenser pressure, cooling water hardness, the velocity of the circulating water and the corresponding inlet and outlet temperatures, the non-condensing gases present in the condenser, and so on. The Rfs(t) and Rfh(t) expressed a very complex physical and chemical process, their accurate mathematic models are very hard to be obtained. Hence, measurement and prediction of fouling development is a very difficult task. Since the fouling evolution process is a very complex nonlinear dynamic system, the traditional techniques based on mathematic analysis, i.e. asymptotic fouling model, are not efficient to describe it [11]. SVMs, as a small sample method to deal with the highly nonlinear classification and regression problems based on statistic learning theory, is expected to be able to reproduce the nonlinear behavior of the system. 3. SVMs regression and parameters 3.1 SVMs regression SVMs are a group of supervised learning methods that can be applied to classification or regression. SVMs represent an extension to nonlinear models of the generalized portrait algorithm developed by Vladimir Vapnik [18]. The SVMs algorithm is based on the statistical learning theory and the Vapnik-Chervonenkis (VC) dimension introduced by Vladimir Vapnik and Alexey Chervonenkis [19]. Here, the SVMs regression is applied to forecast the fouling in power plant condensers. Let the given training data sets represented as ?( , ), ( , ), , ( , )? 1 1 2 2 n n D ? x y x y ? ? ? x y , where d i x ? R is an input vector, y R i ? is its corresponding desired output, and n is the number of training data. In SVMs, the original input space is mapped into a high dimensional space called feature space by a nonlinear mapping x ? g(x) . Let f (x) be the SVM outputs corresponding to input vector x. In the feature space, a linear function is constructed: f (x) ? wT g(x) ? b (2) where w is a coefficient vector, b is a threshold. The learning of SVMs can be obtained by minimization of the empirical risk on the training data. Where, ? -intensive loss function is used to minimize the empirical risk. The loss function is defined as L? (x, y, f ) ? y ? f (x) ? max(0, y ? f (x) ) e (3) where ? is a positive parameter to allow approximation errors smaller than ? , the empirical risk is ? n i emp i i L x y f n R w 1 ( , , ) 1 ( ) ? (4) Besides using ? -intensive loss, SVMs tries to reduce model complexity by minimizing 2 w , which can be described by slack variables. Introduce variables i ? and i , then SVMs regression is obtained as the following optimization problem: min ? ? ? ? n i i i w C 1 2 ( ? ) 2 1 ? ? (5) s.t. i i i y ? f (x ) ? ? , i i i f (x ) ? y ? ? , i ? , i ? 0 where C is a positive constant to be regulated. By using the Lagrange multiplier method [18], the minimization of (5) becomes the problem of maximizing the following dual optimization problem max ( ? )( ? ) ( , ) 2 1 ( ? ) ( ? ) 1 1 , 1 j j i j n i j i i n i i i i i n i i ? y ? ? ? ? ? ? ? ? ? K x x ? ? ? (6) s.t. ( ? ) 0 1 ? ? ? ? n i i i ? ? ,C = i , i ? =0 where i and i ? are Lagrange multipliers, and kernel ( , ) i j K x x is a symmetric function which is equivalent to the dot product in the feature space. The kernel ( , ) i j K x x is defined as the following. ( , ) ( ) ( ) j T i j i K x x ? g x g x (7) There are some kernels, i.e. polynomial kernel K(x, y) ? (x ? y ? 1) d and hyperbolic tangent kernel ( , ) tanh( ( ) ) 1 2 K x y ? c x ? y ? c can be used. Where the Gaussian function is used as the kernel. ) 2 ( , ) exp( 2 2 ? x y K x y ? ? ? (8) Replacing i i i ? ? ? ? ? ? and relation 0 ? ? ? ? i , then the optimization of (6) is rewritten as max ( , ) 2 1 1 1 , 1 j i j n i j i n i i i n i i ? y ? ? ? ? ? K X X ? ? ? ? ? (9) s.t. 0 1 ? ? ? n i i ? ,C ? i ? ? ? C The learning results for training data set D can be derived from equation (9). Note that only some of coefficients i ? are not zeros and the corresponding vectors x are called support vectors (SV). That is, only those vectors whose corresponding coefficients i i are not zero are SV. Then the regression function is expressed as equation (10). f x K x x b i j p i i i ? ? ? ( ) ( ? ) ( , ) 1 ? ? (10) It should be noted that p is the numbers of SV, and the constant b is expressed as ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? p i i i i i p i i i i i b y K x x y K x x 1 1 min ( ? ) ( , ) max ( ? ) ( , ) 2 1 ? ? ? ? (11) 3.2 SVM parameters The quality of SVMs models strongly depends on a proper setting of parameters and SVMs approximation performance is sensitive to parameters [25, 26]. Parameters to be regulated include hyper-parameters C, ? and kernel parameter? , if the Gaussian kernel is used [25]. The values of C, ? and ? are relate to the actual object model and there are not fixed for different data set. So the problem of parameter selection is complicated. The values of parameter C, ? and ? affect model complexity in a different way. The parameter C determines the trade-off between model complexity and the tolerance degree of deviations larger than ? . The parameter? controls the width of the ? -insensitive zone and can affect the numbers of SV in optimization problem. The kernel parameter? determines the kernel width and relates to the input range of the training data set. Here, parameters selection is regarded as compound optimization problem and an improved differential evolution algorithm is proposed to select suitable parameters value. 4. Improved Differential Evolution Differential evolution (DE) algorithm is a simple but powerful population-based stochastic search technique for solving global optimization problems [27]. DE has three operations: mutation, crossover and selection. The crucial idea behind DE is a scheme for generating trial vectors. Mutation and crossover are used to generate trial vectors, and selection then determines which of the vectors will survive into the next generation. The original DE algorithm is described in the following briefly. 4.1 Basic differential evolution Let S ? Rn be the search space of the problem under consideration. Then, the DE algorithm utilizes NP, n-dimensional vectors X x x xt S i NP in t i t i t i ( , , , ) , 1,2, , 1 2 ? ? ? ? ? as a population for each generation of the algorithm. t denotes one generation. The initial population is generated randomly and should cover the whole parameter space. In each population, two operators, namely mutation and crossover, are applied on each individual to yield a trial vector for each target vector. Then, a selection phase takes place to determine the trial vector enters the population of the next generation or not. For each target individual t i X , a mutant vector { 1 , , 1} 1 ?1 ? ? t ? n t t i V v ? v is determined by the following equation. ( ) 1 2 3 1 t r t r t r t i V ? ? X ? F ? X ? X (12) Where F ? 0 is a real parameter, called mutation constant, which controls the amplification of the difference vector ( ) 2 3 t r t r X ? X to avoid search stagnation. According to Storn and Price [27], the F is set in (0, 2]. 1 r , 2 r , 3 r are indexes, randomly selected from the set {1,2,, NP} . Note that indexes must be different from each other and from the running index i so that NP must be a least four. Following the mutation phase, the crossover (recombination) operator is applied on the population. For each mutant vector t ?1 i V , a trial vector { 1 , , 1} 1 ?1 ? ? t ? n t t i U u ? u is generated, using the following scheme. ? ? ? ? ? ? ? ? , ( ) ( ) 1 , ( ) ( ) 1 x rand j CR and j randn i v rand j CR or j randn i u t ij t t ij ij (13) Where j=1, 2, ?, n. rand( j) is the jth evaluation of a uniform random number generator within [0, 1]. CR is a crossover probability constant in the range [0, 1], which has to be determined previously by the user. randn(i) ? (1,2,,n) is a randomly chosen index which ensures that t ?1 i U gets at least one element from t ?1 i V . Otherwise, no new parent vector would be produced and the population would not alter. To decide whether the trial vector t ?1 i U should be a member of the population comprising the next generation, it is compared to the corresponding target vector t i X , and the greedy selection strategy is adopted in DE. The selection operator is as following. ? ? ? ? ? ? , otherwise 1 , ( 1 ) ( ) 1 t i t i t i t t i i X U f U f X X . (14) 4.2 Modification of Mutation From the mutation Eq. (12) we can see that in the original DE three vectors are chosen at random for mutation and the base vector is then chosen at random within the three, which has an exploratory effect but it slows down the convergence of DE. In order to accelerate the convergence speed, a modified mutation scheme is adopted. The randomly selected three vectors for mutation are sorted by ascending in terms of the fitness function value. The tournament best vector is t tb x , the better vector is t tm x and the worst vector is t tw x . For speeding up convergence, the base vector in the mutation equation should select t tb x , and the direction of difference vector should direct to t tm x , that is to choose ( t ) tw t tm x ? x as the difference vector. Then the new modified mutation strategy is as following Eq. (15). 1 ( t ) tw t tm t tb t i v ? ? x ? F ? x ? x . (15) After such modification, this process explores the region around each t tb x in the direction of ( t ) tw t tm x ? x for each mutated point. The mutation operator is not random search any more, but a determinate search. However, the vectors for mutation are selected randomly in the population space, so in the whole evolutionary process it is still a random search, which can ensure the global optimization performance of the algorithm [28]. 5 Optimization procedures of IDE for SVMs 5.1 Objective function The objective of SVMs parameters optimization is to minimize deviations between the outputs of training data and the outputs of SVMs. Where, the mean square error (MSE) is used as the performance criterion. 2 1 1 ( ( , ))2 1 ? ? ? ? ? ? ? ? K k k k y f x w K Obj (16) Where K is the number of training data, k y is the output of the kth training data, and f (x ,w) k is the output of SVMs correspond to input k x . Then the objective of the IDE is to search optimal parameter C, ? and ? to minimize Obj: min F(C,? ,? ) ? minObj (17) Generally, the search range of these parameters is C? [1, 1000], ? ? (0, 1], ? ? (0, 0.5]. For special problem, the search range is changeable. 5.2 Optimization procedures The searching procedures of the improved differential evolution (IDE) for optimization of SVMs parameters are shown as below. Step1: Input the training data and test data, select the Gaussian kernel function. Step2: Specify the number of population NP, the difference vector scale factor F, the crossover probability constant CR, and the maximum number of generations T. Initialize randomly the individuals, i.e. C, ? and ? , of the population and the trial vector in the given searching space. Set the current generation t=0. Step3: Use each individual as the control parameters of SVMs, train the SVMs using training data. Step4: Calculate the fitness value of each individual in the population using the objective function given by equation (17). Step5: Compare each individual?s fitness value and get the best fitness and best individual. Step6: Generate a mutant vector according to equation (15) for each individual. Step7: According to equation (13), do the crossover operation and yield a trial vector. Step8: Execute the selection operation in terms of equation (14) and generate a new population. Step9: t=t+1, return to Step3 until to the maximum number of generations. 6 Case study 6.1 Fouling prediction scheme The formation and development of fouling in condensers is influenced not only by cooling water hardness and turbidity but also by working conditions of condensers, such as velocity of the cooling water and the corresponding inlet and outlet temperatures, the saturation temperature of steam under entrance pressure of condenser, the non-condensing gases present in the condenser, and so on. According to the previous analysis of periodic fouling process of power plant condensers, the fouling can be classified as soft fouling and hard fouling. Therefore, two SVMs models are developed to forecast thermal resistance of soft and hard fouling, respectively. Then, the whole prediction fouling thermal resistance ( f R? ) in condenser is the sum of output of soft fouling prediction model ( fs R? ) and output of hard fouling prediction model ( fh R? ). Generally, the evolution of soft fouling is determined by the velocity (v), turbidity (d), inlet (Ti) and outlet temperatures (To) of cooling water, saturation temperature of steam under entrance pressure of condenser (Ts), and prediction time range (Tp) (the running time in a sponge rubber ball cleaning period). Therefore, these variables are chosen as inputs of the soft fouling thermal resistance predictive model. As for hard fouling of the class of calcium and magnesium salts, it is related to the residual fouling at the beginning and the end of previous sponge rubber ball cleaning period (corresponding thermal resistance is Rfb,n-1, Rfe,n-1, respectively), hardness of cooling water (s), saturation temperature of steam under entrance pressure of condenser (Ts), and the accumulating running time of condenser (Ta). Hence, those variables are chosen as the inputs of hard fouling thermal resistance prediction model. The soft and hard fouling prediction model based on SVMs illustrated in Fig. 2 and Fig. 3, respectively. ( , ) 1 K x x ( , ) 2 K x x ( , ) p?1 K x x ( , ) p K x x 1 1 2 2 1 1 ? ? ? ? p p ? ? p p ( , ) 1 K x x ( , ) 2 K x x ( , ) p?1 K x x ( , ) p K x x S b Ts 1 1 2 2 1 1 ? ? ? ? p p ? ? p p Ta Rfh Rfb,n-1 R fe,n-1 ? ^ Fig. 2 Soft fouling prediction model Fig. 3 Hard fouling prediction model The parameters of the two prediction models are optimized by the IDE algorithm. Fig. 4 illustrates the fouling prediction model using SVMs optimized by IDE. ? Fig. 4 fouling prediction model based on SVMs optimized by IDE 6.2 Experiment results In this section, experiments on N-3500-2 condenser (300MW) in Xiangtan thermal power plant are carried out to prove the effectiveness of the proposed approach. The cooling water of this plant is river water that pumped from the Xiangjiang river. The Taprogge systems are installed in the plant to on-line clean the condensers. At present, the condenser is cleaned every two days using the Taprogge system, and every cleaning time is about 6 hours. Obviously, the fitted cleaning period is not optimal, because the fouling accumulating process is dynamic changing with the operating conditions changing. The experiment system consists of sensors for operating condition parameters measuring, data acquisition system, PC-type computer, etc. A set of 1362 real-time running data in different operating conditions in 84 cleaning periods is collected to train and optimize the SVMs model for fouling prediction, another set of 300 data is chosen for model verification. The proposed IDE is used to optimize the SVMs parameters. The control parameters of IDE are the following. The number of population is 30, the crossover probability constant CR is 0.5, the mutation factor F is 0.5, and the maximum number of generations is 100. The selection of above parameters is based on the literature [27] and [28]. After application of IDE, the optimal SVMs parameters of soft fouling prediction model are C=848, ? =0.513, ? =0.0117, the optimal SVMs parameters of hard fouling prediction model are C=509, ? =0.732, ? =0.0075. The velocity, turbidity, and inlet temperature of cooling water is different in summer and winter, the evolution of fouling in condensers is also different in the two seasons. In the experiments, four sponge rubber ball cleaning periods in different seasons are investigated. Among them, three periods, i.e. the first, 18th and 40th period, are in summer, and the other period is in winter. The hardness and turbidity of cooling water is 56mg/L and 17mg/L in summer, and is 56mg/L and 29mg/L in winter. To demonstrate the effectiveness of the proposed approach, the comparison between the SVMs model, T-S fuzzy logic model [17] and asymptotic model is considered. The asymptotic model is obtained by probability analysis method, and the corresponding expression is the following [17]. ( ) ? 41.3?[1? ?(t ?1.204) /14.57 ] f R t e (17) Table 1 and Table 2 show the fouling thermal resistance prediction results of the above three models in the first and the 18th cleaning periods, respectively. From the Table 1 and Table 2, we can see that compared with tradition asymptotic model and T-S fuzzy logic model, the SVMs based prediction model has higher prediction precision. Fig. 5 and Fig. 6 show the predicted fouling thermal resistance evolution based on the optimized SVMs model and asymptotic model. Fig.6 clearly shows that the asymptotic model is not able to forecast the fouling evolution process at the beginning stage of the 18th cleaning period, the reason is that the residual fouling in the periodic fouling formation process is not considered in the asymptotic models. Table 1 fouling thermal resistance prediction results in the first cleaning period Running time Tpa (hour) Operating conditions Measuring values Rf (K.m2/kW) Prediction values (K.m2/kW) Relative error v(m/s) Ti(?) Ts(?) SVMs model T-S model Asymptotic model SVMs model T-S model Asymptotic model 0 2.0 19.1 33.2 0.0258 0.0260 0.0258 0.62 0 5 2.0 18.5 33.3 0.0995 0.0992 0.1018 0.0947 0.26 2.31 4.82 10 2.0 15.6 31.9 0.2028 0.2037 0.2007 0.1872 0.45 1.04 7.69 15 2.0 14.3 31.6 0.2501 0.2494 0.2411 0.2528 0.27 3.6 1.08 20 2.0 15.5 33.5 0.2865 0.2864 0.2830 0.2993 0.03 1.22 4.48 25 2.0 15.5 34.0 0.3174 0.3172 0.3123 0.3323 0.06 1.61 4.69 30 2.0 16.1 34.8 0.3420 0.3393 0.3321 0.3558 0.79 2.89 4.04 35 2.0 14.4 34.6 0.3567 0.3562 0.3497 0.3724 0.14 1.96 4.40 40 2.0 14.2 34.9 0.3722 0.3736 0.3600 0.3842 0.37 3.28 3.22 Table 2 fouling thermal resistance prediction results of the 18th cleaning period Running time Ta (hour) Operating conditions Measuring values Rf (K.m2/kW) Prediction values (K.m2/kW) Relative error v(m/s) Ti(?) Ts(?) SVMs model T-S model Asymptotic model SVMs model T-S model Asymptotic model 632 2.0 14.0 29.8 0.0774 0.0791 0.074 2.26 0 637 2.0 14.2 30.9 0.1772 0.1773 0.1850 0.0947 0.06 4.40 46.56 642 2.0 12.5 30.4 0.2474 0.2479 0.2438 0.1872 0.21 1.46 24.33 647 2.0 11.9 30.4 0.2898 0.2908 0.2955 0.2528 0.36 1.97 12.77 652 2.0 10.6 30.1 0.3230 0.3222 0.3354 0.2993 0.25 3.84 7.34 657 2.0 11.4 31.5 0.3447 0.3437 0.3525 0.3323 0.28 2.26 3.60 662 2.0 10.2 31.2 0.3655 0.3652 0.3648 0.3558 0.08 0.19 2.65 667 2.0 10.7 32.0 0.3831 0.3815 0.3767 0.3724 0.42 1.67 2.79 672 2.0 11.8 33.5 0.3985 0.3978 0.3912 0.3842 0.18 1.83 3.59 To eliminate the influence of residual fouling and improve the prediction precision, an improved asymptotic models are i

The Stress of Childhood Gymnastics Essay -- Sports

The Stress of Childhood Gymnastics   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   For years gymnastics has been a sport that many children participate in. But as the years have gone by it has turned into something other than a place for kids to grow and learn. Its overwhelming commitment has continued to replace kids’ childhoods with stress, mental and physical pain and eating disorders. Many results have come from this change in the gymnastics society. Gymnasts have come to a point where they have been told and directed to understand that winning is the only important factor in gymnastics. â€Å" It’s about the elite child athlete and the American obsession with winning that has produced a training environment wherein results are bought in at any cost, no matter how devastating. It’s about how cultural fixation on beauty and weight on youth has shaped the sport and driven the athletes into a sphere beyond the quest for physical performance.† (Ryan 5)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   As a society we have the ability to change the ways in which our elite gymnasts are learning gymnastics. We need to redirect the teachings of the coaches and the parent involvement in order to achieve a atmosphere in which gymnasts can explore, learn and gain gymnastic abilities in which they feel they can handle. â€Å" Over the last 20 years there have been many publications on coaching as it relates to sport psychology or sport pedeology. No theoretical framework, however, exsits for explaining which factors are most important in the coaching process and which relationships among these factors are most significant.† (Cote pg.1) I propose that we create an environment with a stress on healthy dieting, good exercise and less strenuous workouts. Not an environment where winning is the prime concern. There are man... ...)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   As you can see there are several problems that lye within the gymnastics society, but we the outside force must come to learn, understand and teach the athletes and coaches some of the correct ways in which they can handle situations. I have come across some major problems throughout this paper, along with some good solution which I hope everyone can take into account. It is important for not only the athletes of this country to be aware of the problems they have, but also to inform the rest of society about the situations hence forth. I know things can change when we put our minds together and create action upon our solutions. I hope this information has helped anyone who was having a difficult time understanding some of the issues that arise with gymnastics, or anyone who had a question. â€Å" Don’t let a problem or situation get in the way of a dream.†

art of the hula :: essays research papers

gArt of the Hulaf What is one thing that stands out in most peoples’ minds when they think of Hawaii? Most people would probably say the hula dance. The hula dance descended from, or can be traced to Polynesia and India. The Hula was a form of poetry for the Hawaiians in all of its sacred and ceremonial forms. In hula dancing, the hands are very important: they tell a story. However, more important are the chants. Chanting is an extension of speaking that started as a means of communicating to the gods. The hula can be performed with or without music, but not without the chant. Bamboo sticks, drums, and gourds, are some of the instruments that are played to support the chanting. The chants are very poetic and have many levels of meaning. They believe chanting is a very personal way of expressing feelings and thoughts on a higher level of communication. The topics of the chants may include warfare, death, sex, birth, chiefs, gods, the beauty of the island and water, or even surfing. This exotic culture was hidden from the world until 1778, when Captain James Cook and his men became the first westerners to discover the islands of Hawaii. When they arrived at Kaua’i, the islanders performed the hula dance as a way of greeting the strangers. Later in 1820, Christian missionaries from New England came to the islands, armed with the Bible and narrow-minded thoughts. They were shocked by the â€Å"heathenish† hula, and tried to abolish the dance. The missionaries eventually convinced the royalty, which had been converted to Christianity, to make the hula dance illegal. It was hard for the Hawaiians to retain their culture because the missionaries banned the Hawaiian language from the schools. However, the Hawaiians treasured their culture and dance, and did not let them die. In 1874, King David Kalakaua came to the throne. He is credited with returning the ancient hula dance to the people. European clad, he was known as the Merrie Monarch. He dined with prominent figures including the ever-corrupt President Grant. He had mastered ancient chants taught to him by his grandmother. During his reign, he brought a lot of European style to the hula dance. He integrated hymn singing and band music into traditional hula dance forms. The ukulele and steel guitar were also introduced. It was also during this period when the ti leaf skirt appeared as a hula dance costume.

Hamlet: Finding Courage to Die :: Shakespeare Hamlet

Hamlet: Finding Courage to Die In William Shakespeare's "Hamlet" we see a young man paralyzed with grief over his father. So much so that he is believed to have gone mad. Hamlet is such a complex character that one must look deeply to find what drives him. Did he really have the courage to kill the king or was it madness? Hamlet's character will be illuminated by explaining both soliloquies and finally Hamlet himself. "To be, or not to be, that is the question," (Beaty, 1348) is one of the most famous and well known excerpts from the play "Hamlet." What most people do not realize is the significance it has in the portrayal of the character Hamlet. During this soliloquy Hamlet is debating his fate. Hamlet is asking himself whether it is more noble, in the mind, to passively accept and suffer through all the pains of life fate throws at him, or to actively destroy, in death, these numerous troubles, and ultimately end his pain. Hamlet is questioning whether it is better to live in a world where he cannot see any goodness or take his own life. Hamlet has a very intense, philosophical personality. For this reason, he cannot take his life because he does not know what happens after one dies. He is not positive of an afterlife, therefore he doesn't have the courage to end his life. "Now might I do it prat," (Beaty, 1363) is a soliloquy in which we see a shift in Hamlet's rationalization. Hamlet, as his fathers only son, is seeking revenge for his fathers death, but is afraid that a quick death for Claudius would not be enough. Hamlet feels that waiting until Claudius is in an immoral situation would make him suffer in death because he would not be allowed to repent for his sins. During this soliloquy Hamlet is caught up in his plot for revenge and has foregone, for the moment, his plan of suicide. The contradictions in these two soliloquies sheds much needed light on Hamlet's personality. Hamlet is very outraged by the immoral actions of some of the other characters. He is deeply offended by his mothers hasty marriage to her brother-in-law and king. Hamlet begs his mother to stop being intimate with Claudius and to think more upon her late husband. This shows that Hamlet has a very clear perception of right and wrong. He also shows this characteristic by being suspicious and even hurt by his childhood friends loyalty to Claudius. Hamlet: Finding Courage to Die :: Shakespeare Hamlet Hamlet: Finding Courage to Die In William Shakespeare's "Hamlet" we see a young man paralyzed with grief over his father. So much so that he is believed to have gone mad. Hamlet is such a complex character that one must look deeply to find what drives him. Did he really have the courage to kill the king or was it madness? Hamlet's character will be illuminated by explaining both soliloquies and finally Hamlet himself. "To be, or not to be, that is the question," (Beaty, 1348) is one of the most famous and well known excerpts from the play "Hamlet." What most people do not realize is the significance it has in the portrayal of the character Hamlet. During this soliloquy Hamlet is debating his fate. Hamlet is asking himself whether it is more noble, in the mind, to passively accept and suffer through all the pains of life fate throws at him, or to actively destroy, in death, these numerous troubles, and ultimately end his pain. Hamlet is questioning whether it is better to live in a world where he cannot see any goodness or take his own life. Hamlet has a very intense, philosophical personality. For this reason, he cannot take his life because he does not know what happens after one dies. He is not positive of an afterlife, therefore he doesn't have the courage to end his life. "Now might I do it prat," (Beaty, 1363) is a soliloquy in which we see a shift in Hamlet's rationalization. Hamlet, as his fathers only son, is seeking revenge for his fathers death, but is afraid that a quick death for Claudius would not be enough. Hamlet feels that waiting until Claudius is in an immoral situation would make him suffer in death because he would not be allowed to repent for his sins. During this soliloquy Hamlet is caught up in his plot for revenge and has foregone, for the moment, his plan of suicide. The contradictions in these two soliloquies sheds much needed light on Hamlet's personality. Hamlet is very outraged by the immoral actions of some of the other characters. He is deeply offended by his mothers hasty marriage to her brother-in-law and king. Hamlet begs his mother to stop being intimate with Claudius and to think more upon her late husband. This shows that Hamlet has a very clear perception of right and wrong. He also shows this characteristic by being suspicious and even hurt by his childhood friends loyalty to Claudius.

Constitution in USA Essay

A constitution is either a written (codified) or unwritten (uncodified) body of fundamental principles or established precedents according to which a state is acknowledged to be governed. Generally, a constitution is only written after a major event. In the case of America their constitution was written in 1787, after the American Revolutionary War came to a close. There are a number of issues with America’s constitution which make it, arguably, unfit for the 21st century. The most apparent issue with the constitution is the overall ambiguity that it’s based upon. Take for example the eighth amendment which forbids the federal government from imposing cruel or unusual punishments on American citizens. However, what’s classed as a cruel or unusual punishment? Many have argued that capital punishment can be seen as a cruel punishment, which has led to some states, such as Illinois, abolishing the death penalty altogether. Though, despite eighteen states abolishing the death penalty so far, there has been no move to amend the constitution to include the death penalty as an example of ‘cruel punishment’. Another example of an ambiguous amendment is the first amendment which protects the right to free speech as well as freedom of religion. However, how far this amendment applies has been questioned on numerous occasions. For example, Texas passed a law which prevented flag desecration (burning of the American flag), however the Supreme Court overturned the Texas law due to it violating citizen’s first amendment right as flag desecration is seen as an expression of belief, which the first amendment protects. This has led to numerous calls for flag desecration being outlawed via a constitutional amendment, however just like the eight amendment this would be incredibly hard to achieve. This is purely due to how difficult it is to amend the American constitution. The American constitution’s amendment process is long and difficult, which is mainly due to it requiring a supermajority. A supermajority is where 2/3rds of both houses of Congress have to agree to the amendment put forward. Even if either house falls short by one vote, the amendment is dropped. This process was made to be hard intentionally by the Founding Fathers. This was  because they believed that the constitution shouldn’t be constantly changing, and so they created the need for a supermajority to stop the federal government from making rash, in the moment, decisions which they could grow to regret later on. However, it is this founding belief that has made the constitution, arguably, untenable for the 21st century, which can be contributed to Congress’ explosive growth over the last 200 years. For example, in 1789 there were only 65 Representatives in the House of Representatives, which grew to 435 by 1963 and plateaued due to the House of Representative s being capped in 1911. This is an increase of 370 over a period of 174 years (meaning that there were two new Representatives every year). This continually increased the amount of people who had to work in unison to pass constitutional amendments, and as evidenced by the 1911 Act which capped the size of the House of Representatives, America grew far more than the Founding Fathers had originally intended. The constitution can also be seen as unfit for the 21st century due to an ever increasing political pace, as well as rapidly changing circumstances which have led to very different outcomes when compared to the British political system. This can mainly be seen with gun control which is protected in America by the second amendment (â€Å"Right to bear arms†) despite the amount of shootings which have occurred in recent times. An example of this would be the Sandy Hook shooting, which occurred on December 14th 2012 at an elementary school in Connecticut. This caused nationwide outrage which in turn caused support for disarmament groups to increase. However, after several months the support fell away and no constitutional amendments were put through, despite pledges and campaigns from Barrack Obama and Joe Biden. Now, when compared to England, there was a shooting spree in a Dunblane Primary School in 1996. Following national outrage, much alike that caused by Sandy Hook, guns were criminalised by an Act of Parliament, which was significantly easier to do as the UK does not have a written constitution, rather an unwritten one which is drawn from several sources. However, despite the faults with the American constitution, it must be fit for purpose if it still exists. This is because if it wasn’t fit for  purpose, and didn’t work at all, it would have been scraped by one of the American administrations after its conception. This is mainly aided by the argument that the constitution’s ambiguity is what allows it to adapt to changing circumstances as well as its ability to change without formal review. What is meant by this is the fact that the Supreme Court can uphold or repeal earlier decisions made in relation to the constitution, meaning that if the correct decisions were repealed the constitution could be drastically changed.