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The smart meters installed at the customer premises are one of the main apparatuses promoting the modernization of the distribution systems. These devices collect a huge amount of data, demanding the development of analytic techniques to transform these data into useful information. In addition, by proposing new applications to data supplied by smart meters, more value is added to this equipment, allowing higher return on the associated investments. Utilities can have a business case by increasing their operational efficiency if smart meters are used, for instance, to identify non-technical losses, which represent an important cause of revenue losses. This paper presents a new data analytic technique for detection and location of non-technical losses caused by illegal connections of loads to distribution systems in the presence of smart meters. The data analytic technique relies on bad data analysis, similar to the ones used in state estimation methods, developed specifically for this application. A real 34-bus low voltage system is used to illustrate the main concepts of the proposed algorithm. Systematic tests are also conducted on a real 1682-bus distribution system to evaluate the method performance considering electricity theft caused by medium and low voltage customers. [ABSTRACT FROM AUTHOR]

In order to effectively improve the accuracy of related analysis models in the application of government risk investment, a government risk investment prediction model based on fuzzy clustering discrete algorithm is put forward in this paper. First of all, government risk investment problem is analyzed. Based on Markowitz theory, the general government risk investment model is considered, and the market value constraint and the upper bound constraint are combined to improve the government risk investment model and obtain the mixed constraint government risk investment model. Secondly, the fuzzy clustering discrete algorithm is introduced in the analysis process of government venture investment model, and it is used to solve the mixed constraint analysis model of government venture investment. In addition, to further improve the performance of discrete algorithm based on fuzzy clustering in the model solving process, automatic contraction and expansion of factors is used to carry out adaptive learning of related parameters based fuzzy clustering discrete algorithm, and improve the convergence of the algorithm. Finally, the simulation experiments on some stock samples of investment sector show that the algorithm in this paper can obtain more ideal government venture investment schemes, so as to reduce investment risk and obtain greater investment returns. [ABSTRACT FROM AUTHOR]

This research has two objectives: (1) to model and analyze the momentum effect and (2) to propose a portfolio-reconstruction algorithm that uses the momentum effect to obtain excess return. The momentum effect tends to be present in the stock market and describes the phenomenon whereby rising (declining) stocks tend to continue to rise (decline). However, because existing research does not separate momentum effects from stock price fluctuations, it is not always possible to obtain an excess return when working with an unknown dataset that contains a momentum effect. In this research, we define a new externalforce momentum-effect (EFME) model based on bias in stock price rises (declines). We prepared an artificial stock dataset that contained this momentum effect and constructed a portfolio with the proposed algorithm. Then, we analyzed the relationship between the EFME model and excess return and verify that excess return is obtained. Additionally, we confirmed that the proposed method yields higher excess return than the existing method when applied to artificial and real stock datasets. [ABSTRACT FROM AUTHOR]

Abstract: Aims: To inform the transferability of tobacco control‐related economic evidence to resource‐poor countries. Methods: We ran a univariate sensitivity analysis on a return on investment (ROI) model, the European study on Quantifying Utility of Investment in Protection from Tobacco model (EQUIPTMOD), to identify key input values to which the ROI estimates were sensitive. The EQUIPTMOD used a Markov‐based state transition model to estimate the ROI of several tobacco control interventions in five European countries (England, Germany, Spain, Hungary and the Netherlands). Base case ROI estimates were obtained through average values of model inputs (throughout the five countries), which were then replaced one at a time with country‐specific values. Tornado diagrams were used to evaluate the significance of sensitivity, defined as a ≥ 10% difference in ROI estimates from the base case estimates. Results: The ROI estimates were sensitive to 18 (of 46) input values. Examples of model inputs to which ROI estimates were sensitive included: smoking rate, costs of smoking‐related diseases (e.g. lung cancer) and general population attributes. Conclusion: Countries that have limited research time and other resources can adapt EQUIPTMOD to their own settings by choosing to collect data on a small number of model inputs. EQUIPTMOD can therefore facilitate transfer of tobacco control related economic evidence to new jurisdictions. [ABSTRACT FROM AUTHOR]

Optimal asset replacement is analysed under non-exponential technological improvement, whereby the asset capital cost and the operational cost reduce for newer assets, and the relative rate of this decrease varies with time. We establish that the optimal lifetime of sequentially replaced assets increases if the relative rate of technological change decreases, and converse. Next, the two-cycle replacement algorithm is employed to take the unequal asset lifetime into account. The algorithm is adjusted to the case of several discrete estimates of changing asset costs and demonstrates good performance in various scenarios of improving technology. [ABSTRACT FROM AUTHOR]

This paper assesses the feasibility of three-phase pulse-width modulated (PWM) three-level voltage-source converters (VSC), namely neutral-point-clamped (3L-NPC) and T-type (3L-T) as alternatives to two-level converter (2L-VSC) for low-voltage multimegawatt renewable energy grid-connected converter applications. For this purpose, a novel design algorithm that takes switching frequency, capacity factor, modulation index, PWM scheme(s), and converter topology(ies) as input is unveiled to provide the design steps and performance benchmark for the specified topologies and modulation methods. Design of the LCL filter is also addressed, and equivalent output ripple performance criteria is used to benchmark the converter performances for a set of selected filter parameters. Main contribution of this algorithm is that it covers cost and operating factors of the design during the hardware design phase by introducing/modifying the terms of total cost of ownership (TCO) and return on investment (ROI) time considering the major energy markets. The design is detailed for 1-MVA system, and the results are summarized for 0.5 and 2-MVA systems. The study shows that 3L topologies perform more efficiently than 2L-VSC (T-type being the most prominent—with the shortest ROI), whereas the difference becomes much more prominent at the multimegawatt range, provided that all three yield equivalent outputs fulfilling the stringent grid codes. [ABSTRACT FROM PUBLISHER]

Energy storage technologies all have different characteristics that make them suitable to particular purposes. A hybrid energy storage (HES) arrangement can take advantage of these differences. An example of this is a compressed-air energy storage plant with a flywheel mounted on the same shaft as the motor/generator and turbo machinery, where the flywheel would be used for high-frequency transactions with the grid while the compressed air store, with lower efficiency but also lower cost per unit of energy storage, could be used for longer term storage. This paper reports an algorithm which can be used to find the maximum arbitrage value achievable with a given HES plant for a given set of electricity prices. This is used in an optimisation routine to find the configuration of hybrid flywheel/compressed air plant that maximises potential arbitrage value. With realistic costs and efficiencies, it is found that the maximum annual return on investment is 0.29%, corresponding to a plant with 10% of its total energy storage capacity in the form of flywheel and the rest as compressed air. [ABSTRACT FROM AUTHOR]

This paper provides a new market implied calibration based on a moment matching methodology where the moments of the risk-neutral density function are inferred from at-the-money and out-the-money European vanilla option quotes. In particular, we derive a model-independent risk-neutral formula for the moments of the asset log-return distribution function by expanding power returns as a weighted sum of vanilla option payoffs (based on results of Breeden and Litzenberger and Carr and Madan). For the numerical study, we develop different popular exponential Lévy models, namely the VG, NIG and Meixner models. The new calibration methodology rests on closed-form formulae only: it is shown that the moment matching system can be transformed into a system of algebraic equations that computes directly the optimal value of themodel parameters in terms of the second to theth market standardized moments under the different Lévy models under investigation. Hence, the proposed calibration can be performed almost instantaneously. Furthermore, for the models considered in this paper, the method does not require any search algorithm and hence any starting value for the model parameters and avoids the problem of becoming stuck in local minima. [ABSTRACT FROM AUTHOR]

Abstract: Since the security market is complex, sometimes the future security returns are available mainly based on experts judgements. This paper discusses a portfolio selection problem in which security returns are given subject to experts’ estimations. The use of uncertain measure is justified, and two new mean–variance and mean–semivariance models are proposed. In addition, a hybrid intelligent algorithm for solving the optimization models is given. To illustrate the application of the new models, the method to obtain the uncertainty distributions of the security returns based on experts’ evaluations is given, and two selection examples are provided. [Copyright &y& Elsevier]

Abstract: Risk and return are interdependent in a stock portfolio. To achieve the anticipated return, comparative risk should be considered simultaneously. However, complex investment environments and dynamic change in decision making criteria complicate forecasts of risk and return for various investment objects. Additionally, investors often fail to maximize their profits because of improper capital allocation. Although stock investment involves multi-criteria decision making (MCDM), traditional MCDM theory has two shortfalls: first, it is inappropriate for decisions that evolve with a changing environment; second, weight assignments for various criteria are often oversimplified and inconsistent with actual human thinking processes. In 1965, Rechenberg proposed evolution strategies for solving optimization problems involving real number parameters and addressed several flaws in traditional algorithms, such as their use of point search only and their high probability of falling into optimal solution area. In 1992, Hillis introduced the co-evolutionary concept that the evolution of living creatures is interactive with their environments (multi-criteria) and constantly improves the survivability of their genes, which then expedites evolutionary computation. Therefore, this research aimed to solve multi-criteria decision making problems of stock trading investment by integrating evolutionary strategies into the co-evolutionary criteria evaluation model. Since co-evolution strategies are self-calibrating, criteria evaluation can be based on changes in time and environment. Such changes not only correspond with human decision making patterns (i.e., evaluation of dynamic changes in criteria), but also address the weaknesses of multi-criteria decision making (i.e., simplified assignment of weights for various criteria). Co-evolutionary evolution strategies can identify the optimal capital portfolio and can help investors maximize their returns by optimizing the preoperational allocation of limited capital. This experimental study compared general evolution strategies with artificial neural forecast model, and found that co-evolutionary evolution strategies outperform general evolution strategies and substantially outperform artificial neural forecast models. The co-evolutionary criteria evaluation model avoids the problem of oversimplified adaptive functions adopted by general algorithms and the problem of favoring weights but failing to adaptively adjust to environmental change, which is a major limitation of traditional multi-criteria decision making. Doing so allows adaptation of various criteria in response to changes in various capital allocation chromosomes. Capital allocation chromosomes in the proposed model also adapt to various criteria and evolve in ways that resemble thinking patterns. [Copyright &y& Elsevier]

Abstract: The fact that photovoltaic panels are very sensitive to non-uniform insolation conditions, which can occur several times a day, causes a decrease of efficiency and so increases time for return on investment. This work presents a maximum power point tracking algorithm (MPPT) operating on the load characteristic in order to take the converters losses into account. In addition, the proposed MPPT deals with the problems of shadowing for which the power-load characteristic can present two or more local maximums close to each other. Considering the converters losses it can be shown that the maximum output power of the photovoltaic panels does not necessarily coincide with the maximum output power of the converters. The proposed MPPT algorithm tracks the maximum power with the intention of reducing the total losses including those of converters. Its performance is verified by simulation and confirmed by experimental results. [Copyright &y& Elsevier]

Abstract: This paper deals with a portfolio selection problem with fuzzy return rates. A possibilistic mean variance (FMVC) portfolio selection model was proposed. The possibilistic programming problem can be transformed into a linear optimal problem with an additional quadratic constraint by possibilistic theory. For such problems there are no special standard algorithms. We propose a cutting plane algorithm to solve (FMVC). The nonlinear programming problem can be solved by sequence linear programming problem. A numerical example is given to illustrate the behavior of the proposed model and algorithm. [Copyright &y& Elsevier]

Prognostics and Health Management (PHM) provides opportunities for lowering sustainment costs, improving maintenance decision-making, and providing product usage feedback into the product design and validation process. However, support for PHM is predicated on the articulation of clear business cases that quantify the expected cost and benefits of its implementation. The realization of PHM requires implementation at different levels of scale, and complexity. The maturity, robustness, and applicability of the underlying predictive algorithms impact the overall efficacy of PHM within an enterprise. The utility of PHM to inform decision-makers within tight scheduling constraints, and under different operational profiles likewise affects the cost avoidance that can be realized. This paper discusses the calculation of Return on Investment (ROl) for PHM activities, and presents a study conducted using a stochastic discrete event simulation model to deter- mine the potential ROl offered by electronics PHM. The case study of a multifunctional display in a Boeing 737 compares the life cycle costs of a system employing unscheduled maintenance to the same system using a precursor to failure PHM approach. [ABSTRACT FROM AUTHOR]

This paper researches portfolio selection problem in combined uncertain environment of randomness and fuzziness. Due to the complexity of the security market, expected values of the security returns may not be predicted accurately. In the paper, expected returns of securities are assumed to be given by fuzzy variables. Security returns are regarded as random fuzzy variables, i.e. random returns with fuzzy expected values. Following Markowitz's idea of quantifying investment return by the expected value of the portfolio and risk by the variance, a new type of mean-variance model is proposed. In addition, a hybrid intelligent algorithm is provided to solve the new model problem. A numeral example is also presented to illustrate the optimization idea and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new and improved solution for random yield improvement at the post-routing stage. The proposed solution is better suited for current processes, where a clustering effect has been observed resulting in differing particle densities in the metal and empty regions of the chip. To account for this clustering effect, we introduce the concept of weighted critical area to serve as a proxy for random yield loss. A new algorithm for weighted critical area minimization is also introduced. The proposed optimization solution derives a weighted critical area based on the user-specified particle densities. It then uses this weighted critical area information to dynamically select the appropriate critical area reduction technique in each local region to guarantee a reduction of the weighted critical area in both the local region and the whole layer. This makes the algorithm flexible and readily applicable to different process lines. It consistently improves the random yield irrespective of the particle densities in the metal and empty regions of the chip. [ABSTRACT FROM AUTHOR]

Determining whether a parameterized problem is kernelizable and has a small kernel size has recently become one of the most interesting topics of research in the area of parameterized complexity and algorithms. Theoretically, it has been proved that a parameterized problem is kernelizable if and only if it is fixed-parameter tractable. Practically, applying a data reduction algorithm to reduce an instance of a parameterized problem to an equivalent smaller instance (i.e., a kernel) has led to very efficient algorithms and now goes hand-in-hand with the design of practical algorithms for solving NP-hard problems. Well-known examples of such parameterized problems include the vertex cover problem, which is kernelizable to a kernel of size bounded by 2k, and the planar dominating set problem, which is kernelizable to a kernel of size bounded by 335k. In this paper we develop new techniques to derive upper and lower bounds on the kernel size for certain parameterized problems. In terms of our lower bound results, we show, for example, that unless P = NP, planar vertex cover does not have a problem kernel of size smaller than 4k⁄3, and planar independent set and planar dominating set do not have kernels of size smaller than 2k. In terms of our upper bound results, we further reduce the upper bound on the kernel size for the planar dominating set problem to 67k, improving significantly the 335k previous upper bound given by Alber, Fellows, and Niedermeier [J. ACM, 51 (2004), pp. 363-384]. This latter result is obtained by introducing a new set of reduction and coloring rules, which allows the derivation of nice combinatorial properties in the kernelized graph leading to a tighter bound on the size of the kernel. The paper also shows how this improved upper bound yields a simple and competitive algorithm for the planar dominating set problem. [ABSTRACT FROM AUTHOR]

This paper discusses portfolio selection problem in fuzzy environment. In the paper, security returns are regarded as fuzzy variables. Based on credibility measure and the expected value operator and variance operator based on credibility, two new types of fuzzy mean-variance models are proposed. A hybrid intelligent algorithm is provided to give a general solution of the optimization problems. Numerical examples are also presented to illustrate the optimization idea and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This paper selects the portfolio with fuzzy returns by criteria of chance represented by credibility measure. In the paper, two types of credibility-based portfolio selection model are provided according to two types of chance criteria. By one chance criterion, the objective is to maximize the investor’s return at a given threshold confidence level; by another chance criterion, the objective is to maximize the credibility of achieving a specified return level subject to the constraints. To provide a general method to solve the new models, a hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed in the paper. Two numerical examples with security returns taking different types of membership function are also given to illustrate the modelling idea of the paper and to demonstrate the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: The admissible efficient portfolio selection problem for risky assets has been discussed by Zhang and Nie. In this paper, the admissible efficient portfolio model is proposed under general investment constrains on risky assets and a risk-less asset. The closed form solution of the admissible efficient frontiers are derived from three cases: the risk-less asset can either be only lent, or borrowed, or both lent and borrowed. The upper and lower admissible efficient frontiers are developed by the expected return and risk to be estimated optimistically and pessimistically, respectively. [Copyright &y& Elsevier]

A model of intraday financial time series is developed. The model is a dynamic factor model consisting of two equations. First, a rate of return of a 'stock' in a single day is assumed to be generated by several common factors plus some additive error ('intraday equation'). Secondly, the joint distribution of those common factors is assumed to depend on the hidden state of the day, which fluctuates according to a Markov chain ('day-by-day equation'). Together the equations compose a hidden Markov model. We investigate properties of the model. Among them is a central limit theorem for cumulative returns, which agrees with the well-known empirical phenomenon in the stock markets that the distributions of longer-horizon returns are closer to the normal. We propose a two-step procedure consisting of the method of principal components and the EM algorithm to estimate the model parameters as well as the unobservable states. In addition, we propose a procedure for predicting intraday returns. Finally, the model is fitted to empirical data, the Standard&Poors 500 Index 5 min return data, to see if the model is capable of describing intraday movements of the index. [ABSTRACT FROM AUTHOR]

Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called "continuous trend labeling" is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation was given, the definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data. [ABSTRACT FROM AUTHOR]

The study of the quality of hospital emergency services is based on analyzing a set of indicators such as the average time of first medical attention, the average time spent in the emergency department, degree of completion of the medical report and others. In this paper, an analysis is presented of one of the quality indicators: the rate of return of patients to the emergency service less than 72 h from their discharge. The objective of the analysis was to know the variables that influence the rate of return and which prediction model is the best. In order to do this, the data of the activity of the emergency service of a hospital of a reference population of 290,000 inhabitants were analyzed, and prediction models were created for the binary objective variable (rate of return to emergencies) using the logistic regression techniques, neural networks, random forest, gradient boosting and assembly models. Each of the models was analyzed and the result shows that the best model is achieved through a neural network with activation function tanh, algorithm levmar and three nodes in the hidden layer. This model obtains the lowest mean squared error (MSE) and the best area under the curve (AUC) with respect to the rest of the models used. [ABSTRACT FROM AUTHOR]