What Is Social Loafing Definition and Examples

Social loafing is a phenomenon in which people put in less effort on a task when they are working in a group, compared to when they are working alone. Researchers focusing on the efficiency of groups study why this phenomenon occurs and what can be done to prevent it. Key Takeaways: Social Loafing Psychologists define social loafing as the tendency to put in less effort when working as part of a group, compared to when working individually.Social loafing is one of the reasons why groups sometimes work ineffectively.Although social loafing is a common occurrence, it doesn’t always happen—and steps can be taken to encourage people to put in more effort on group projects. Overview Imagine you’re assigned to complete a group project with your classmates or coworkers. Will you work more effectively as part of a group, or on your own? Some research suggests that people can actually be less effective when they’re working as members of a group. For example, you and your classmates might have difficulty coordinating the tasks. You might divide up the work in an ineffective way, or duplicate each other’s efforts if you don’t coordinate who does what. You might also face difficulties if not everyone in the group puts in the same amount of work—for example, some of your classmates might be less inclined to put in effort on the project, thinking that others’ work will make up for their inaction. If you’re not a fan of group work, you might not be surprised to know that psychologists have found that this really does happen: people tend to put in less effort when they’re part of a group, compared to when they’re completing tasks individually. Key Studies The relative inefficiency of groups was first studied by Max Ringelmann in the early 1900s. He asked people to try to pull as hard as possible on a rope and measured how much pressure they were able to exert while on their own, compared to in groups. He found that a group of two worked less efficiently than two people working independently. Moreover, as the groups got larger, the amount of weight that each individual pulled decreased. In other words, a group as a whole was able to accomplish more than a single person—but, in groups, the amount of weight that each individual group member had pulled was less. Several decades later, in 1979, researchers Bibb Latanà ©, Kipling Williams, and Stephen Harkins published a landmark study on social loafing. They asked male college students to try to clap or shout as loudly as possible. When participants were in groups, the noise made by each person was less than the amount of noise that they had made when they were working individually. In a second study, the researchers sought to test out whether merely thinking that they were part of a group was enough to cause social loafing. To test this, the researchers had participants wear blindfolds and headphones, and told them that other participants would be shouting with them (in actuality, the other participants had not been given the instruction to shout). When participants thought they were acting as part of a group (but were actually in the â€Å"fake† group and were really shouting by themselves), they weren’t as loud as when they thought they were shouting individually. Importantly, the second study by Latanà © and colleagues gets at the reasons why group work can be so ineffective. Psychologists hypothesize that part of the ineffectiveness of group work is due to something called coordination loss (i.e. the group members don’t coordinate their actions effectively) and that part is due to people putting in less effort when part of a group (i.e. social loafing). Latanà © and colleagues found that people were most efficient when working alone, somewhat less efficient when they only thought they were part of a group, and even less efficient when they were actually part of a group. Based on this, Latanà © and colleagues suggested that some of the inefficiency of group work comes from coordination losses (which could only happen in the real groups), but social loafing plays a role too (since coordination loss couldn’t account for why the â€Å"fake† groups were still less efficient). Can Social Loafing Be Reduced? In a 1993 meta-analysis, Steven Karau and Kipling Williams combined the results of 78 other studies to assess when social loafing happens. Overall, they found support for the idea that social loafing occurs. However, they found that some circumstances were able to reduce social loafing or even stop it from happening. Based on this research, Karau and Williams suggest that several strategies can potentially reduce social loafing: There should be a way to monitor each individual group member’s work.The work should be meaningful.People should feel that the group is cohesive.The tasks should be set up so that each person in the group is able to make a unique contribution and each person feels that their part of the work matters. Comparison to Related Theories Social loafing is related to another theory in psychology, the idea of diffusion of responsibility. According to this theory, individuals feel less responsible for acting in a given situation if there are other people present who could also act. For both social loafing and diffusion of responsibility, a similar strategy can be used to combat our tendency for inaction when we’re part of a group: assigning people unique, individual tasks to be responsible for. Sources and Additional Reading: Forsyth, Donelson R. Group Dynamics. 4th ed., Thomson/Wadsworth, 2006. https://books.google.com/books?idjXTa7Tbkpf4CKarau, Steven J., and Kipling D. Williams. Social Loafing: A Meta-Analytic Review and Theoretical Integration.  Journal of Personality and Social Psychology,  vol. 65, no. 4, 1993, pp. 681-706. https://psycnet.apa.org/record/1994-33384-001Latanà ©, Bibb, Kipling Williams, and Stephen Harkins. Many Hands Make Light the Work: The Causes and Consequences of Social Loafing.  Journal of Personality and Social Psychology, vol. 37, no. 6, 1979: pp. 822-832. https://psycnet.apa.org/record/1980-30335-001Simms, Ashley, and Tommy Nichols. Social Loafing: A Review of the Literature.  Journal of Management Policy and Practice, vol. 15, no.1, 2014: pp. 58-67. https://www.researchgate.net/publication/285636458_Social_loafing_A_review_of_the_literature

Deposits in thermal power plant condensers - Free Essay Example

Sample details Pages: 16 Words: 4778 Downloads: 5 Date added: 2017/06/26 Category Statistics Essay Did you like this example? 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 Don’t waste time! Our writers will create an original "Deposits in thermal power plant condensers" essay for you Create order 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 introduced in [17] according to the probability analysis method. The improved asymptotic model of the 40th cleaning period is the following [17]. ( ) ? 41.3?[1? ?(t ?4.31) /14.57 ] f R t e (18) Fig. 5 predicted fouling thermal resistance evolution Fig. 6 predicted fouling thermal resistance evolution in the first cleaning period in the 18th cleaning period At the 27th hour of the cleaning period, the velocity of cooling water increased from 2.0m/s to 2.5m/s to adapt the steam load change, and the experiment result shows in Table 3. From the Table 3, we can see that from 0 to the 27th hour in this cleaning period, the improved asymptotic model is able to well forecast the fouling evolution. At the time of 27th hour, the velocity of cooling water increased suddenly, and the fouling thermal resistance will slowly increase after decreasing a period of time. However, the improved asymptotic model is not able to forecast the tendency. The models of SVMs and T-S fuzzy logic are able to predict the tendency, but the SVMs model has more accurate results than T-S model. Fig. 7 demonstrates the predicted fouling thermal resistance evolution based on the optimized SVMs model and the improved asymptotic model. Table 3 fouling thermal resistance prediction results of the 40th 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 1289 2.0 10.5 28.4 0.1065 0.1070 0.1066 0.1058 0.47 0.01 0.66 1294 2.0 10.1 28.6 0.2031 0.2011 0.2026 0.1950 0.98 0.25 3.99 1299 2.0 10.8 30.1 0.2660 0.2676 0.2574 0.2583 0.60 3.23 2.89 1304 2.0 12.0 32.0 0.3130 0.3161 0.3053 0.3032 0.99 2.46 3.13 1309 2.0 12.5 32.9 0.3422 0.3408 0.3398 0.3351 0.41 0.70 2.07 1314 2.5 11.1 32.3 0.3672 0.3687 0.3625 0.3578 0.41 1.28 2.56 1316 2.5 10.1 32.0 0.3734 0.3728 0.3712 0.3648 0.16 0.59 2.30 1319 2.5 10.0 32.2 0.3173 0.3152 0.3327 0.3738 0.66 4.85 17.81 1324 2.5 11.7 33.5 0.3048 0.3054 0.3135 0.3852 0.20 2.85 26.38 1329 2.5 13.2 34.8 0.3079 0.3097 0.3202 0.3933 0.58 3.99 27.73 Fig. 7 predicted fouling thermal resistance evolution Fig. 8 predicted fouling thermal resistance evolution in the 40th cleaning period in the 85th cleaning period The 85th cleaning period in winter is also considered in the experiment to further evaluate the effectiveness of the proposed approach. Table 4 shows the fouling thermal resistance prediction results of this period. From the Table 4 we can observe that the prediction model based on the optimized SVMs has more satisfactory results than the improved asymptotic model and T-S model. Fig. 8 demonstrates the predicted fouling thermal resistance evolution based on the optimized SVMs model and the improved asymptotic model. Table 4 fouling thermal resistance prediction results of the 85th 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 2830 2.0 2.5 24.9 0.1594 0.1698 0.1632 0.1058 0.99 2.38 33.63 2835 2.0 2.6 25.4 0.2467 0.2541 0.2345 0.1950 0.64 4.95 20.96 2840 2.0 2.8 26.3 0.3055 0.3065 0.2827 0.2583 0.01 7.46 15.45 2845 2.0 2.6 27.1 0.3410 0.3414 0.3177 0.3032 0.12 6.83 11.09 2850 2.0 2.5 27.7 0.3696 0.3702 0.3548 0.3351 0.17 4.01 9.33 2855 2.5 2.4 28.1 0.3891 0.3932 0.3776 0.3578 1.05 2.96 8.04 2860 2.5 2.4 29.2 0.4005 0.4003 0.3883 0.3648 0.05 3.05 8.92 2865 2.5 2.6 29.6 0.4087 0.4075 0.3979 0.3738 0.28 2.64 8.54 2870 2.5 2.7 29.4 0.4138 0.4132 0.4062 0.3852 0.14 1.84 6.91 7 Conclusions The fouling accumulating process in condensers is very complex, which is influenced by a lot of operating conditions. Hence, fouling prediction is a very difficult task, and the traditional techniques, such as asymptotic fouling model and heat transfer coefficient method, are not efficient to describe it. In this paper, the condenser fouling forecasting model based on SVMs was proposed. The parameters of the SVMs model were optimized by an improved differential evolution. Practice application experiment results show that, compared with traditional asymptotic model, the forecasting model based on optimized SVMs is able to effectively eliminate the influence of residual fouling to forecasting accuracy, has good dynamic self-adaptive ability and can obtain satisfactory forecasting accuracy when the condenser operating conditions varied in a large range. The proposed approach is also compared with the T-S fuzzy logic model, experiment results in four different cleaning periods present that the optimized SVMs model has higher forecasting accuracy than T-S model. The relevance of these results shows that the technique of support vector machines is able to estimate well the evolution of the fouling in thermal power plant condensers without being much affected by the operating conditions change and periodic residual fouling, and the general principles of application of SVMs developed here would be useful in other problems of fouling. Acknowledgement: This work was partly supported by the National High Technology Research and Development Program of China (Grant no. 2008AA04Z214, 2007AA04Z244), the State Key Program of National Natural Science Foundation of China (Grant no. 60835004, 60775047), the Scientific Research Fund of Hunan Provincial Education Department (08C337), and the Program for New Century Excellent Talents in University. References:

Nursing Terminology System Vs. The International...

Nursing Terminology System Comparison Nursing terminology could be described as the formal and informal communication that occurs in a nurses daily activity. According to McGonigle Mastrian (2009) nursing terminology allows nurses to communicate nursing data, information and knowledge specific to nursing. In addition, standardized nursing terminology refers to a system. This means they have undergone approval by a specific authority. The American Nurses Association (ANA) is one authority with a standardized nursing practice and information infrastructure committee, which recognizes twelve standardized terminologies, both nursing specific and interdisciplinary (p. 98). The Clinical Care Classification system and the International†¦show more content†¦The CCC system’s major developer is Dr. Virginia K. Saba. Dr. Saba has pioneered the integration of computer technology and nursing. The ICNP was developed by the International Council of Nurses (ICN) to cover diagnoses, nursing actions and nursing outcomes a s it relates to nursing phenomenon (p. 99). The CCC website (2012) describes this system of nursing terminology as a â€Å"unique coding structure for assessing, documenting, and classifying patient care by nurses and other clinical professionals in any health care setting† (www.sabacare.com). The CCC system serves nursing as well as other health care providers such as physical, occupational and speech therapists and medical social workers. The ICN website (2015) reports ICNP was created to improve worldwide healthcare as an integral part of the global information infrastructure to influence health care practice and policy. This database is intended for use by nurses, who can describe and report in detail, nursing assessments and interventions, as opposed to being a system for other clinical healthcare professionals. The ICNP is reported reliable to support care and effective decision making and useful for nursing education, research and health policy (www.icn.ch/what-we-do/about-icnpr/). The CCC website (2012) states there are a total of 176 nursing diagnoses and 804 concepts and core interventions to make up the nursing interventions and actions. There are a total of 21 care components representing functional,

Why Effective Communication Is Important Developing...

Unit 3. Assessment Criteria 3.1.1. Explain why effective communication is important in developing positive relationships with children, young people and adults. Effective communication is important when developing positive relationships with anyone, as it builds trust and establishes rapport between the people who are communicating with one another. Respect is an important element in effective communication, and the development of a relationship. Actively listening to another person’s point of view or opinion, remembering personal details, showing an interest and responding in an appropriate manner, all show that you have listened and placed importance upon what has been said, which leads to positive relationships and respectful, effective communication. Effective communication, and respectful relationships are vital when talking with children, young people, and adults as they both allow accurate gathering of information and feelings and the subsequent passing along of any issues that may need to be followed up on. Both effective communication and positive relationships require effort, consideration and respect from all parties involved in order to continue to be effective and productive. Assessment Criteria 3.1.2. Explain the principles of relationship building with children, young people and adults. Respect of and understanding that other people have differing opinions, views and religious/cultural beliefs, and how these beliefs may impact on building an effective,Show MoreRelatedWhy Effective Communication Is Important? 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Perry Smith A Passion to Kill - 1354 Words

Serial killers have long eluded law enforcement while simultaneously grabbing the attention of the public, and now more than ever, criminal psychologists are beginning to understand what makes a serial killer. In his true-crime documentary, In Cold Blood, Truman Capote depicts the horrifying murders of four members of the Clutter family and the search to find the criminals responsible for the deaths. Eventually, two killers are caught, one being Perry Smith, a detached and emotionless man. And although his carnage of the Clutters labeled him a mass murderer, many of Perry’s personality traits are characteristic of a serial killer. In fact, if detectives had not caught him, Perry could have easily become a serial killer. 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Benefits of Digital Technology-Free-Samples-Myassignmenthelp.com

Question: Discuss about the how law firms could successfully utilise Digital Technology in their Workplace to provide Benefits to their Employees and Clients. Answer: With the advent of technology, while digital tools have become an integral aspect of the life of every individual, digital innovation has also invaded the large law firms as well as different in-house legal departments. With increased online presence of lawyers as well as clients, it has become easier for the lawyers to create technology-based solutions that not only helps in driving down the client costs, but also helps in enhancing the effectiveness of legal services. Some of the law firms have been executing all its functions with the help of technology and employs the effective use of technology in a wide variety of practice areas such as litigation, intellectual property and transactional works (Bharadwaj et al. 2013). Nowadays, a person seeking legal help does not literally require to go out in search of an efficient lawyer, as he can gain access to effective legal help from reputed law firms, via his tablets and smart phones. While it has become easy for clients to get profess ional legal help as and when required, it has become even easier for the lawyers to offer comprehensive solution to legal problems via online platforms. However, despite the multiple benefits, it is nevertheless a bad news that as per research reports, half of the total employment opportunities available all over the world, is predicted to get destroyed because of the invasion of technology. With the rise of digital technology in professional domains, the chance of unemployment has also increased at a steady rate. Yet nevertheless, the benefits of the digital technology can never be undermined. In this connection, it is important to state how present research reports suggest that 88% of the companies have already provided digital tools to its employees while 90% of the organizations have exhibited their willingness to invest in technology that can ensure smooth and cost-effective operation of the business activities (Rabinovich and Katsh 2014). In order to review digital information sources, for reliability and credibility, the law firms should have strict Human Resource policies whereby a selected personnel should be entrusted with the duty of preserving digital records. It is also important to institutionalize these digital contents so as to ensure complete periodic updates of each digital content review results. Since digital information is a continually evolving field, it is important to review the latest trends by researching about the latest innovation in cloud computing, conducting survey on the rival companies to know which latest technology they are using for data management (Barrett et al. 2015). Reference List: Bharadwaj, A., El Sawy, O.A., Pavlou, P.A. and Venkatraman, N.V., 2013. Digital business strategy: toward a next generation of insights. Rabinovich-Einy, O. and Katsh, E., 2014. Digital Justice: Reshaping Boundaries in an Online Dispute Resolution Environment.IJODR,1, p.5. Hansen, H.K. and Flyverbom, M., 2015. The politics of transparency and the calibration of knowledge in the digital age.Organization,22(6), pp.872-889. Barrett, M., Davidson, E., Prabhu, J. and Vargo, S.L., 2015. Service innovation in the digital age: key contributions and future directions.MIS quarterly,39(1), pp.135-154.

A Report Critical evaluation of luxury brand stores

Question: Explain Critical evaluation of luxury brand stores? Compare and contrast the stores (DOS or Flagship) of Louis Vuitton Gucci. You should analyse and compare the consistency of marketing message, which the stores provide in relation to their other brand touch points. Include an analysis of the respective brands heritage and DNA and how these are communicated via the retail store. Answer: 1. Introduction: Gucci is one of the worlds prominent luxury Brands in the Fashion world. The Company was founded in the year 1921, which became renowned for its high quality Italian craftsmanship, high quality of fashion goods and innovative designs. The company manufactures and distributes, high end fashion goods including, handbags, luggage bags, fine jewellery, time pieces, shoes, fragrances, ready to wear silks and eye wear products. The Gucci products are mostly available exclusively at directly operated boutiques and with a handful of authorized department and specialty stores (Lloyd et al., 2006). Louis Vuitton is a French fashion house and the worlds largest and valuable brand of luxury goods. The company is in to manufacturing of leather goods, watches, jewellery, shoes, trunks, handbags and other accessories that are adorned with the LV monogram. This international fashion house sells its products via standalone outlets, high end departmental stores, and through online retailing of its products through its company website. Many of the products manufactured by this company have the signature brown Damier and monogram Canvas Material used that adds iconic value to its products. The Company has its presence in over fifty countries, with more than 460 exclusive boutiques operated worldwide (Edberg, 2010). 2. Brand Overview: A Brand is a graphical identity of image of a company that represents the company and helps build emotional connectivity with the Target Audiences and customers of the company. It is a symbol of collection of experiences, associations and trust of the stakeholders with a company. The Brand image of a company can be further strengthened through visual representation and efficient brand communication by the company (Atwal Williams, 2009). (a) Brand Positioning and Perceptual Mapping:The Brand Positioning for both the brands Gucci as well as Louis Vuitton has always been close to differentiation by positioning the brands as sophisticated, innovative high quality and elite brands. The strategic orientation of this positioning for both these brands is based on utmost adherence to the desired quality and performance of the products offered by the brands (Flueckiger, 2009). The Symbolic expressive value of these brands further ads up to the high class positioning of these brands in the international markets. The concept of perpetual mapping revolves around the display of the location of these brands or products using more than two dimensions. The act of perpetual mapping through development of ad campaigns and web promotions thus pulls customers towards these brands and increases the overall brand appeal for the product and offerings by these brands (Vi Nguyen, 2004). (b) Brand Heritage and DNA:Brand Heritage has an indispensable connection with the history and DNA of organizations, that posse these brands. The Brand heritage is one of the prominent strengths of several responsible and ethical organizations, which work as a link to strengthen the emotional connection between the company and its target Audience (Fabrikant, 2002). The Brand Louis Vuitton is accordingly a French brand, who DNA may be traces down to the country France in the European region. The Several Art forms used by this brand in its designs are observed to have a deeper connection with the French culture. The Brand heritage of Gucci may be accordingly traced down to the Italian culture and its contemporary traditional designs (McKnight, 1987). (c) Brand Identity:The Brand identity is a way in which an organization wants its target Audience to perceive its brand name, logo, communication style and other visual elements associated with the brand (Tungate, 2009). The luxury brands Gucci and Louis Vuitton have indeed developed and extended their respective brand identities through iconic yet innovative luxury products that have attracted huge reputation and popularity to these brands. The inscribed G monogram for the Italian brand Gucci, is explicit representation of luxury and glamour that is recognized globally (Appendix 1.2). The Brand Louis Vuitton also inscribes the letters LV on few of its signature products that displays a potent symbol of modern luxury (Appendix 1.1). The exclusive boutiques by Gucci as well as Louis Vuitton, and the established distribution networks limited only to high end stores, further enhances the Brand identity of these luxury brands (Luxury Daily, 2013). 3. Brand Communication and Message/ Brand Touch points: Directly owned Stores or Flagship stores for Gucci are about 280 stores across the globe, mostly in the developed and developing markets in the world. These stores are directly operated by the company and houses exclusive Gucci products and offerings. Gucci was awarded the 38th most valuable brand in the world by Forbes in the year 2013. The company enjoys high brand equity throughout the world and has helped the society in with standing several economic down turns (Kalfopoulos, 2012). The Brand Message of the company Gucci has always been closely associated with sex appeal (Secara, 2012). Attracting the target Audience through its vibrant and controversial visuals as a Brand Messages has always been a part of the Communications and advertising promotions of the Brand Gucci. The company has experimented using floral patterns in its mens spring collection of the year 2014, which was a grand success (Cognizant 20-20 Insights, 2012). The Company had participated in several fashion shows to reach out to its target customers and convey its brand message (Refer to Appendix 2.2). Exclusivity is the prominent message that this brand has conveyed from time to time in all of its advertising campaigns. Many of the advertising campaigns, especially the print ads reflect a sexy and vibrant image of female characters. The same is also displayed undoubtedly thorough participation in the numerous fashion shows across the European region. Gucci has also attracted a lot of attention from the elite target audience through several controversial advertisings and promotion. The Promotion of the perfume Opium by using the naked image of celebrity Sophie Dahl, was one of the most controversial promotions of this brand. Gucci even had an extreme print advertisement in Vogue that created controversy due to the inscribed G in the public hair of a womans image (Luxury Daily, 2013). These actions and brand touch points helped gain better media coverage for the company that boosted brand aw areness of Gucci.The Brand Louis Vuitton has more than 500 stores around the world and is one of the largest luxury brands in terms of total Revenues and sales attracted by its parent company. The company however, is working on a strategy to limit its new store openings, and in turn focus on preserving its exclusive image (Palmitessa, 2012). The company had successfully managed to present its print campaigns to the Audience through a new mobile application (Refer to Appendix 3.1). The Brand Louis Vuitton maintains its effluent brand touch points by neither having any discount nor any duty free stores across the globe (Jones, 2014). The company in addition to its flag ship stores sells its exclusive products through its online shopping platform, Louisvuitton.com that can be accessed from any part of the world (Cognizant 20-20 Insights, 2012). Its newest mobile application Amble, allows all the users of this application to follow their favorite celebrities favorite city experiences an d has several features of creating ones own videos and notes with those celebrities (Clow et al., 2010). The Brand Louis Vuitton has always preferred celebrity advertisement and endorsement of its newly arrived products thorough celebrities (Refer to Appendix 2.1). Thus, the logo of this brand, the visual identity of the style displayed by its products and the exclusive monogram canvas of LV on its products all protects and strengthens Louis Vuittons Brand Identity and sails it effectively conveying a Heritage Brand Message. 4. Critical Comparison of Gucci and Louis Vuitton Companies: Gucci has built a very strong image in the Fashion market, like its competitor Brand Louis Vuitton. The Brand Gucci has always tried to market its brand in innovative ways. The Advertisements of this company, like a girl performing martial arts, gives a clear message of the comfort level of the fabric that this brand offers. The Brand Louis Vuitton on the other hand, prefers to highlight eminent personalities and celebrities while advertising and endorsements. The use of Artistic symbols in the product designs of the Brand Louis Vuitton are widely spotted as compared to the traditional designs of the Brand Gucci. The Brand Gucci has shown a higher degree of corporate social responsibility over the Brand Louis Vuitton for its partnership with the UNICEF since the year 2005. The Brand Gucci makes a generous donation every year to UNICEF, by offering a fixed percentage of revenues generated by the directly owned flagship stores of the company (Bengtsen, 2007). The products and offerings by the brand Louis Vuitton are more influence with artistic patterns and graffiti. The company has always strived to collaborate with several renowned artists to reflect various art forms on their products designs and offerings. Takashi Murakami, the artist who created the much copied cherry blossom print for the Brand, is an example of how the Brand is highly reflects the innovative art forms by renowned artist across the world, from time to time basis. 5. Recommendations: 1. Remaining Consistent and Authentic in terms of the range of products and offerings to the elite target segment is the primary recommendation for the Brands Louis Vuitton and Gucci. 2. These Brands may look at constantly updating and modifying the designs of their luxury offerings to suit the tastes and preferences of the modern consumer at a global level. The Luxury brands have to more often server as the cultural references points towards the innovations and changes in the global lifestyle habits of the respective target consumers (Okonkwo, 2009). 3. The Brands can also think of innovative communication channels for connecting well with their Target consumer and keeping these high-end target audiences, loyal to their respective brands. Maintaining an updated global customer database and sending them cards and goodies on special occasions like Anniversaries and Birthdays would strengthening the customer relationship with these brands. 4. These Luxury brands Gucci may follow Louis Vuittons innovative offering idea of providing customized offerings to its customers. Many of the products offered by Louis Vuitton are made to order, beaming in exotic skins that create invitation spaces in their exclusive brand stores (Palmitessa, 2012). 5. Maintaining enhanced personal relationships with the customers through Social media networks is a technology enabled strategy that has become the need of the hour for all the premium brands (Kim Ko, 2010). Also a lot of demographic information of the prominent customers using these luxury brands may be extracted through social media. This information may be further used to track the tastes and preferences of the luxury goods users, to provide vital inputs for research and developmental activities in terms of manufacturing innovative product offerings. 6. Conclusion The both the luxury brands Gucci and Louis Vuitton have adopted a brand strategy of addressing the niche elite segment in different manners. The various aspects of Brand communication and messages delivered to the target audience though differing to a high extent, both these brands have been successful in managing the cut throat competition in the luxury products segment across the international markets. It is now imperative for both these brands to extend its retail horizon beyond the brick and mortar stores to virtual stores maintaining the desired brand heritage and brand exclusivity and creativity of these luxury brands. References Atwal, G., Williams, A., 2009, Luxury Brand Marketing -The Experience Is Everything, Brand Management, Vol. 16, 338-346. Bengtsen Peter, 2007, Branding of Brand Hacking, MA dissertation, Department of Arts and cultural sciences, Lund University. Camps Samuel and Jones Mayuko, 2014, The Art of Luxury Branding, ADMAP. Clow Kenneth and Baack Donald, 2010, Integrated Advertising, promotion, and marketing communications, fourth Edition, London: Prentice Hall. Fabrikant Geraldine, 2002, The media business: Advertising, Guccis current Campaign: recovering its elite image, the New York Times.