Abstract: Pedestrian re-recognition is an important research because it affects applications such as intelligent monitoring, content-based video retrieval, and human-computer interaction. It can help relay tracking and criminal suspect detection in large-scale video surveillance systems. Although the existing traditional pedestrian re-recognition methods have been widely applied to address practical problems, they have deficiencies such as low recognition accuracy, inefficient computation, and difficulty to adapt to specific applications. In recent years, the pedestrian re-recognition algorithms based on deep learning have been widely used in the pedestrian re-recognition field because of their strong adaptive ability and high recognition accuracy. The deep learning models provide a technical approach for pedestrian re-recognition tasks with their powerful learning ability. However, the pedestrian re-recognition method based on deep learning also has the following problems: First, the existing deep learning pedestrian re-recognition methods lack memory and prediction mechanisms, and the deep learning methods offer only limited improvement to pedestrian re-recognition accuracy. Second, they exhibit overfitting problems. Finally, initializing the existing LSTM parameters is problematic. In view of this, this paper introduces a revertive connection into the pedestrian re-recognition detector, making it more similar to the human cognitive process by converting a single image into an image sequence; then, the memory image sequence pattern reidentifies the pedestrian image. This approach endows deep learning-based pedestrian re-recognition algorithms with the ability to memorize image sequence patterns and allows them to reidentify pedestrians in images. At the same time, this paper proposes a selective dropout method for shallow learning. Selective dropout uses the classifier obtained through shallow learning to modify the probability that a node weight in the hidden layer is set to 0, thereby eliminating the overfitting phenomenon of the deep learning model. Therefore, this paper also proposes a greedy layer-by-layer pretraining algorithm for initializing LSTM and obtains better generalization performance. Based on the above explanation, this paper proposes a pedestrian re-recognition algorithm based on an optimized LSTM deep learning-sequence memory learning model. Experiments show that the pedestrian re-recognition method proposed in this paper not only has strong self-adaptive ability but also identifies the average accuracy. The proposed method also demonstrates a significant improvement compared with other mainstream methods because it can better memorize and learn the continuous motion of pedestrians and effectively avoid overfitting and parameter initialization in the deep learning model. This proposal provides a technical method and approach for adaptive pedestrian re-recognition algorithms. PubDate: Mon, 14 Oct 2019 13:30:00 +000

Abstract: A technoeconomic optimization problem for a domestic grid-connected PV-battery hybrid energy system is investigated. It incorporates the appliance time scheduling with appliance-specific power dispatch. The optimization is aimed at minimizing energy cost, maximizing renewable energy penetration, and increasing user satisfaction over a finite horizon. Nonlinear objective functions and constraints, as well as discrete and continuous decision variables, are involved. To solve the proposed mixed-integer nonlinear programming problem at a large scale, a competitive swarm optimizer-based numerical solver is designed and employed. The effectiveness of the proposed approach is verified by simulation results. PubDate: Sun, 13 Oct 2019 00:05:58 +000

Abstract: In this paper, we use fixed-point index to study the existence of positive solutions for a system of Hadamard fractional integral boundary value problems involving nonnegative nonlinearities. By virtue of integral-type Jensen inequalities, some appropriate concave and convex functions are used to depict the coupling behaviors for our nonlinearities . PubDate: Sun, 13 Oct 2019 00:05:56 +000

Abstract: Either in microlevel organizations or macrolevel societies, the individuals acquire benefits or payoffs by forming interdependency groups linked by common interests. Conducting research on the effects of interdependency groups on the evolution of cooperation could have a better understanding of the social dilemma problem. In this paper, we studied a spatial public goods game with nonlocal interdependency groups where each of participants is located in a two-dimensional square lattice or Watts–Strogatz small-world network with payoffs obtaining from the interactions with nearest neighbors. In terms of the enhancement factor, the effects of group density on the evolutionary cooperation can be quite different. For a low enhancement factor, the cooperation level is a nonmonotonic function with the varying density of interdependency groups in the system, which means a proper density of interdependency groups can best promote the cooperative level. For a moderate enhancement factor, a higher density of interdependency groups can always correspond to a higher cooperative level. However, if the enhancement factor is too high, a high density of interdependency groups can impede the evolutionary cooperation. We give the explanations for the different roles of group density of interdependency by using the transition probabilities of C players into D players as well as the reverse. Our findings are very helpful for the understanding of emergence cooperation as well as the cooperation regulation in the selfish individuals. PubDate: Sun, 13 Oct 2019 00:05:55 +000

Abstract: A model of the attitude system for a quadrotor unmanned aerial vehicle (QUAV), assumed to be a rigid body, is developed. For specific parameter configurations, a chaotic region with a saddle and two stable node-focus equilibrium points is identified. The chaotic model provides an important reference for dynamic analysis and a challengeable task of controller design once the flight enters the chaotic region of parameters. The pitchfork bifurcation of the equilibrium points is provided. Rich dynamics of the system are revealed by two bifurcation regions, which demonstrates the diversity of the flight behaviors as the parameters vary. One bifurcation analysis is with respect to the speed of the front propeller and the speed difference of the front and left propellers, and another one is with respect to the speed of the front propeller and moment of inertia. The dynamic characteristics of the QUAV are further verified by the Casimir power bifurcations. The trajectories of three settings with different structural parameters are analyzed in detail. The stability of the QUAV is found to be enhanced for certain optimized values of the structural parameters. Finally, using the Casimir power and Lagrange multiplier method, a supremum bound of the chaotic attractor is presented. PubDate: Sun, 13 Oct 2019 00:05:53 +000

Abstract: This paper focuses on the problem of event-triggered control for a class of uncertain nonlinear strict-feedback systems with zero dynamics via backstepping technique. In the design procedure, the adaptive controller and the triggering event are designed at the same time to remove the assumption of the input-to-state stability with respect to the measurement errors. Besides, we propose an assumption to deal with the problem of zero dynamics. Three different event-triggered control strategies are designed, which guarantees that all the closed-loop signals are globally bounded. The effectiveness of the proposed methods is illustrated and compared using simulation examples. PubDate: Thu, 10 Oct 2019 00:05:05 +000

Abstract: Based on the structures of unmanned aerial vehicle (UAV) wings, nonlinear dynamic analysis of macrofiber composite (MFC) laminated shells is presented in this paper. The effects of piezoelectric properties and aerodynamic forces on the dynamic stability of the MFC laminated shell are studied. Firstly, under the flow condition of ideal incompressible fluid, the thin airfoil theory is employed to calculate the effects of the mean camber line to obtain the circulation distribution of the wings in subsonic air flow. The steady aerodynamic lift on UAV wings is derived by using the Kutta–Joukowski lift theory. Then, considering the geometric nonlinearity and piezoelectric properties of the MFC material, the nonlinear dynamic model of the MFC laminated shell is established with Hamilton’s principles and the Galerkin method. Next, the effects of electric field, external excitation force, and nonlinear parameters on the stability of the system are studied under 1 : 1 internal resonance and the effects of material parameters on the natural frequency of the structure are also analyzed. Furthermore, the influence of the aerodynamic forces and electric field on the nonlinear dynamic responses of MFC laminated shells is discussed by numerical simulation. The results indicate that the electric field and external excitation have great influence on the structural dynamic responses. PubDate: Wed, 09 Oct 2019 09:05:02 +000

Abstract: SFA (Surface Fitting Algorithm) for continuous displacement is an important method for digital image correlation with antinoise ability and computational efficiency advantages in practical applications. In order to improve the algorithm accuracy and expand its application range, this paper tries to improve the SFA and studies the modified cubic surface fitting algorithm CTSFA (Corrected Three Surface Fitting Algorithm), which is suitable for solving the initial value of continuous displacement. Bilinear interpolation and adjacent interpolation are used to analyze the gray level at any integer-pixel position in the displacement matrix and the weight coefficient is given. The distance-weighted method is used to approximate the true initial displacement value of the continuum, and the algorithm suitable for digital image processing is extended to the continuum displacement solution. The cubic surface expression of the CTSFA programmatic application is solved by the least squares method, and the correlation coefficient of the power basis function is calculated. In the computer simulation of speckle test, the comparison between CTSFA and SFA on the calculation results of linear and nonlinear displacement fields shows that the calculated amount of CTSFA is basically the same as that of SFA, but the calculation accuracy is doubled. The study of analysing the Brazilian splitting test using CTSFA and SFA reveals that CTSFA is better than SFA in observing the development of cracks. PubDate: Mon, 30 Sep 2019 09:05:01 +000

Abstract: In this paper, the concept of consensus is generalized to weighted consensus, by which the conventional consensus, the bipartite consensus, and the cluster consensus problems can be unified in the proposed weighted consensus frame. The dynamics of agents are modeled by the general linear time-invariant systems. The interaction topology is modeled by edge- and node-weighted directed graphs. Under both state feedback and output feedback control strategies, the weighted consensus problems are transformed into the equivalent conventional consensus problems. Then, some distributed state feedback and output feedback protocols are proposed to solve the weighted consensus problems. For output feedback case, a unified frame to construct the state-observer-based weighted consensus protocols is proposed, and different design approaches are discussed. As special cases, some related results for bipartite consensus and cluster consensus can be obtained directly. Finally, a simple example is given to illustrate the effectiveness of our proposed approaches. PubDate: Mon, 30 Sep 2019 07:05:04 +000

Abstract: Pavement surveying and distress mapping is completed by roadway authorities to quantify the topical and structural damage levels for strategic preventative or rehabilitative action. The failure to time the preventative or rehabilitative action and control distress propagation can lead to severe structural and financial loss of the asset requiring complete reconstruction. Continuous and computer-aided surveying measures not only can eliminate human error when analyzing, identifying, defining, and mapping pavement surface distresses, but also can provide a database of road damage patterns and their locations. The database can be used for timely road repairs to gain the maximum durability of the asphalt and the minimum cost of maintenance. This paper introduces an autonomous surveying scheme to collect, analyze, and map the image-based distress data in real time. A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. Typically, CNN-based schemes require a relatively large processing power to detect desired objects in images in real time. However, the portability objective of this work requires to utilize low-weight processing units. To that end, the CNN training was optimized by the Bayesian optimization algorithm (BOA) to achieve the maximum accuracy and minimum processing time with minimum neural network layers. First, a database consisting of a diverse population of crack distress types such as longitudinal, transverse, and alligator cracks, photographed at multiple angles, was prepared. Then, the database was used to train a CNN whose hyperparameters were optimized using BOA. Finally, a heuristic algorithm is introduced to process the CNN’s output and produce the crack map. The performance of the classifier and mapping algorithm is examined against still images and videos captured by a drone from cracked pavement. In both instances, the proposed CNN was able to classify the cracks with 97% accuracy. The mapping algorithm is able to map a diverse population of surface cracks patterns in real time at the speed of 11.1 km per hour. PubDate: Sun, 29 Sep 2019 00:05:34 +000

Abstract: We study the planar symmetric central configurations of the 1 + 4-body problem where the symmetry axis does not contain any infinitesimal masses. Under certain assumptions, we find analytically some central configurations for suitable positive masses and also get some numerical results of symmetric central configurations where infinitesimal masses are not necessarily equal. PubDate: Sun, 29 Sep 2019 00:05:32 +000

Abstract: In this paper, based on the earlier research, a new fractional-order chaotic Genesio-Tesi model is established. The chaotic phenomenon of the fractional-order chaotic Genesio-Tesi model is controlled by designing two suitable time-delayed feedback controllers. With the aid of Laplace transform, we obtain the characteristic equation of the controlled chaotic Genesio-Tesi model. Then by regarding the time delay as the bifurcation parameter and analyzing the characteristic equation, some new sufficient criteria to guarantee the stability and the existence of Hopf bifurcation for the controlled fractional-order chaotic Genesio-Tesi model are derived. The research shows that when time delay remains in some interval, the equilibrium point of the controlled chaotic Genesio-Tesi model is stable and a Hopf bifurcation will happen when the time delay crosses a critical value. The effect of the time delay on the stability and the existence of Hopf bifurcation for the controlled fractional-order chaotic Genesio-Tesi model is shown. At last, computer simulations check the rationalization of the obtained theoretical prediction. The derived key results in this paper play an important role in controlling the chaotic behavior of many other differential chaotic systems. PubDate: Sun, 29 Sep 2019 00:05:29 +000

Abstract: The stability of iron tailings dam is affected by the permeability of tailings. Considering the influence of it, it is necessary to analyze the permeability of tailings so as to prevent the recurrence of Brazilian iron tailings dam accidents. Nevertheless, the results of iron tailings permeability from some prediction equations (Terzaghi equation, Hazen equation, and Kozeny equation) cannot be accurate. Iron tailings are various as they can be divided into three categories: (1) silt content is less than 40%; (2) silt content is more than 40%, while clay content is less than 15%; and (3) clay content is more than 15% and less than 30%. Correspondingly, three equations are proposed to calculate the disturbed and iron undisturbed tailings permeability for the three types. And more accurate results come from it. The water-flow paths of the iron tailings are blocked after compaction, and the critical pressure of iron tailings blockage is 200 kPa. Although the porosity is large, some of the pores are isolated from each other when the pressure is larger than 200 kPa. However, porosity becomes too large for permeability calculation after compaction and the calculated permeability gets larger as well (equations (24)–(26)). Correcting the permeability calculation equations is an absolute must. The calculated permeability by the revised equations becomes more accurate (equations (27)–(29)). In fact, the granulometric characteristics necessarily play a vital role in the evolution of the pore interconnections by blocking the water-flow paths and modifying the morphological parameters. More research studies are required to be done in the future. PubDate: Wed, 25 Sep 2019 06:05:02 +000

Abstract: The prediction information has effects on the emergency prevention and advanced control in various complex systems. There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time-series impact on each other to make the prediction more difficult. Then, a solution of time-series prediction for the multivariate was explored in this paper. Firstly, a compound neural network framework was designed with the primary and auxiliary networks. The framework attempted to extract the change features of the time series as well as the interactive relation of multiple related variables. Secondly, the structures of the primary and auxiliary networks were studied based on the nonlinear autoregressive model. The learning method was also introduced to obtain the available models. Thirdly, the prediction algorithm was concluded for the time series with multiple variables. Finally, the experiments on environment-monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term. PubDate: Sun, 22 Sep 2019 00:05:25 +000

Abstract: In this paper, we focus on a class of singular fractional differential equation, which arises from many complex processes such as the phenomenon and diffusion interaction of the ecological-economic-social complex system. By means of the iterative technique, the uniqueness and nonexistence results of positive solutions are established under the condition concerning the spectral radius of the relevant linear operator. In addition, the iterative scheme that converges to the unique solution is constructed without request of any monotonicity, and the convergence analysis and error estimate of unique solution are obtained. The numerical example and simulation are also given to demonstrate the application of the main results and the effectiveness of iterative process. PubDate: Mon, 02 Sep 2019 07:05:05 +000

Abstract: Software stability means the resistance to the amplification of changes in software. It has become one of the most important attributes that affect maintenance cost. To control the maintenance cost, many approaches have been proposed to measure software stability. However, it is still a very difficult task to evaluate the software stability especially when software becomes very large and complex. In this paper, we propose to characterize software stability via change propagation simulation. First, we propose a class coupling network (CCN) to model software structure at the class level. Then, we analyze the change propagation process in the CCN by using a simulation way, and by doing so, we develop a novel metric, (software stability), to measure software stability. Our metric is validated theoretically using the widely accepted Weyuker’s properties and empirically using a set of open source Java software systems. The theoretical results show that our metric satisfies most of Weyuker’s properties with only two exceptions, and the empirical results show that our metric is an effective indicator for software quality improvement and class importance. Empirical results also show that our approach has the ability to be applied to large software systems. PubDate: Thu, 29 Aug 2019 07:05:00 +000

Abstract: New sufficient conditions for the oscillation of all solutions to a class of third-order Emden–Fowler differential equations with unbounded neutral coefficients are established. The criteria obtained essentially improve related results in the literature. In particular, as opposed to known results, new criteria can distinguish solutions of third-order differential equations with different behaviors. Examples are also provided to illustrate the results. PubDate: Wed, 28 Aug 2019 10:05:01 +000

Abstract: A fuzzy predictive fault-tolerant control (FPFTC) scheme is proposed for a wide class of discrete-time nonlinear systems with uncertainties, interval time-varying delays, and partial actuator failures as well as unknown disturbances, in which the main opinions focus on the relevant theory of FPFTC based on Takagi-Sugeno (T-S) fuzzy model description of these systems. The T-S fuzzy model represents the discrete-time nonlinear system in the form of the discrete uncertain time-varying delay state space, which is firstly constructed by a set of local linear models and the nonlinear membership functions. The novel improved state space model can be further obtained by extending the output tracking error to the constructed model. Then the fuzzy predictive fault-tolerant control law based on this extended model is designed, which can increase more control degrees of freedom. Utilizing Lyapunov-Krasovskill theory, less conservative delay-range-dependent stable conditions in terms of linear matrix inequality (LMI) constraints are given to ensure the asymptotically robust stability of closed-loop system. Meanwhile, the optimized cost function and H-infinity performance index are introduced to the stable conditions to guarantee the robust performance and antidisturbance capability. The simulation results on the temperature control of a strong nonlinear continuous stirred tank reactor (CSTR) show that the proposed control scheme is feasible and effective. PubDate: Sun, 25 Aug 2019 13:30:00 +000

Abstract: In this paper, we study the number of limit cycles emerging from the period annulus by perturbing the Hamiltonian system . The period annulus has a heteroclinic cycle connecting two hyperbolic saddles as the outer boundary. It is proved that there exist at most and at least limit cycles emerging from the period annulus, and limit cycles are near the boundaries. PubDate: Sun, 25 Aug 2019 10:05:00 +000

Abstract: In a supply chain system, the prices with which the suppliers supply its local commodity to the retailers should satisfy the requirements of the retailers and the consumers. The supply and demand scheme satisfying these requirements is reduced into fuzzy relation inequalities (FRIs) with min-product composition. Due to the difference between the min-product composition and the classical max-t-norm one, we first study the resolution of such min-product FRI system. For optimization management in the supply chain system, we further investigate a maximin programming problem subject to the min-product FRIs. An algorithm is proposed to obtain the optimal solution based on the quasi-maximal matrix and corresponding index set. To illustrate the efficiency of our proposed algorithm, we provide a simple numerical example. The obtained optimal solution reflects an optimal pricing scheme, which maximizes the minimum prices of the commodity from the suppliers. PubDate: Sun, 25 Aug 2019 09:05:06 +000

Abstract: Aiming at the problem of fixed-time trajectory tracking control for high-order dynamic systems with external time-varying disturbance and input dead-zone, an adaptive fixed-time sliding mode control algorithm is proposed by employing a fixed-time sliding mode disturbance observer (FTSMDO) and high-order fixed-time sliding mode algorithm. Firstly, a FTSMDO is presented for the problem that estimating the compound disturbance is composed of input dead-zone and time-varying external disturbance in the higher-order dynamic system, which cannot be measured accurately. Furthermore, for the case that the total disturbance of the system has an unknown upper bound, the corresponding adaptive law is designed to estimate the unknown upper bound, and the fixed-time controller is designed based on FTSMDO algorithm to make all state variables converge in a fixed-time. Based on Lyapunov technique, the fixed-time convergence performance of the proposed algorithm is proved. The effectiveness of the presented fixed-time control algorithm is verified by simulating the depth tracking control of the underactuated underwater vehicle. PubDate: Thu, 22 Aug 2019 12:05:07 +000

Abstract: We consider a distributed constrained optimization problem over graphs, where cost function of each agent is private. Moreover, we assume that the graphs are time-varying and directed. In order to address such problem, a fully decentralized stochastic subgradient projection algorithm is proposed over time-varying directed graphs. However, since the graphs are directed, the weight matrix may not be a doubly stochastic matrix. Therefore, we overcome this difficulty by using weight-balancing technique. By choosing appropriate step-sizes, we show that iterations of all agents asymptotically converge to some optimal solutions. Further, by our analysis, convergence rate of our proposed algorithm is under local strong convexity, where is the number of iterations. In addition, under local convexity, we prove that our proposed algorithm can converge with rate . In addition, we verify the theoretical results through simulations. PubDate: Tue, 20 Aug 2019 13:30:00 +000

Abstract: In the paper, we use the differential game method to test the impact of joint implementation (JI) mechanism on pollution control in two bilateral countries. The Hamilton-Jacobi-Bellman (HJB) equations of the models are obtained by using the dynamic programming principle. We obtain the optimal emissions, optimal local and foreign investments in environment projects, optimal revenues, and optimal trajectories of carbon stock under three situations, namely, situation without JI, with JI (noncooperative), and with JI (cooperative), of the two countries by solving these equations. We also compare their optimal Nash equilibrium solutions. We find that the introduction of JI mechanism can slow down the growth of the carbon stocks by reducing emissions or increasing investment in emission reduction projects, compared to the situation without JI mechanism. However, the JI mechanism does not reduce the revenue of the two countries under certain conditions. Finally, some numerical tests are provided to illustrate the theoretical results. PubDate: Tue, 20 Aug 2019 13:05:12 +000

Abstract: In this paper, we derive analytical solutions of the (2+1)-dimensional Kadomtsev-Petviashvili (KP) equation by two different systematic methods. Using the -expansion method, exact solutions of the mentioned equation including hyperbolic, exponential, trigonometric, and rational function solutions have been obtained. Based on the work of Yuan et al., we proposed the extended complex method to seek exact solutions of the (2+1)-dimensional KP equation. The results demonstrate that the applied methods are efficient and direct methods to solve the complex nonlinear systems. PubDate: Tue, 20 Aug 2019 13:05:11 +000

Abstract: This paper discusses the out lag synchronization of fractional order complex networks (FOCN) including both internal delay and coupling delay and with the employment of pinning control scheme. Using comparison theorem and constructing the auxiliary function, several synchronization criterions by linear feedback pinning control are presented. The model and the obtained results in this work are more general than the previous works. Correctness and effectiveness of the theoretical results are validated through numerical simulations. PubDate: Tue, 20 Aug 2019 08:05:20 +000

Abstract: Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of multigeometry information between data. To solve the above problem, this paper proposed a new method called Hypergraph Regularized Discriminative Nonnegative Matrix Factorization (HDNMF), which captures intrinsic geometry by constructing hypergraphs rather than simple graphs. The introduction of the hypergraph method allows high-order relationships between samples to be considered, and the introduction of label information enables the method to have discriminative effect. Both the hypergraph Laplace and the discriminative label information are utilized together to learn the projection matrix in the standard method. In addition, we offered a corresponding multiplication update solution for the optimization. Experiments indicate that the method proposed is more effective by comparing with the earlier methods. PubDate: Mon, 19 Aug 2019 10:05:00 +000

Abstract: A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method. PubDate: Mon, 19 Aug 2019 07:05:05 +000

Abstract: The classical Bertrand game is assumed that players are perfectly rational. However, many empirical researches indicate that people have bounded rational behavior with fairness concern, which is important in the two-person game and has attracted much attention. In this paper, fairness concern is incorporated into the Bertrand game with two homogeneous products and the effect of fairness concern on this extended Bertrand game is explored. Nash bargaining solution of player is applied to be his own fairness reference point. Then, a Bertrand game model with fairness concern is established, and its equilibrium price is also derived and analyzed. It is shown from some numerical examples that fairness concern and bargaining power of players have a significant influence on their equilibrium price, expected profits, and utilities. As a player gets more fair-minded, if the other player has a less focus on fairness, the price competition between them will be intensified and both of them suffer loss. Thus, fairness concern may be advantageous or disadvantageous for players. In most situations, the fairness concern behavior is not beneficial for players. Additionally, the effect of bargaining power is relative to fairness concern. A player who manufactures a low-cost product should have a weak bargaining power if he terribly focuses on fairness and should have a strong bargaining power if he pays little attention to fairness. However, a player who manufactures a high-cost product should have a weak bargaining power if he is rarely concerned about fairness. Anyway, the same bargaining power is the best for two players. PubDate: Mon, 19 Aug 2019 00:05:07 +000

Abstract: In the last years the reputation of medical, economic, and scientific expertise has been strongly damaged by a series of false predictions and contradictory studies. The lax application of statistical principles has certainly contributed to the uncertainty and loss of confidence in the sciences. Various assumptions, generally held as valid in statistical treatments, have proved their limits. In particular, since some time it has emerged quite clearly that even slightly departures from normality and homoscedasticity can affect significantly classic significance tests. Robust statistical methods have been developed, which can provide much more reliable estimates. On the other hand, they do not address an additional problem typical of the natural sciences, whose data are often the output of delicate measurements. The data can therefore not only be sampled from a nonnormal pdf but also be affected by significant levels of Gaussian additive noise of various amplitude. To tackle this additional source of uncertainty, in this paper it is shown how already developed robust statistical tools can be usefully complemented with the Geodesic Distance on Gaussian Manifolds. This metric is conceptually more appropriate and practically more effective, in handling noise of Gaussian distribution, than the traditional Euclidean distance. The results of a series of systematic numerical tests show the advantages of the proposed approach in all the main aspects of statistical inference, from measures of location and scale to size effects and hypothesis testing. Particularly relevant is the reduction even of 35% in Type II errors, proving the important improvement in power obtained by applying the methods proposed in the paper. It is worth emphasizing that the proposed approach provides a general framework, in which also noise of different statistical distributions can be dealt with. PubDate: Sun, 18 Aug 2019 09:05:00 +000