An Evolutionary Sequential Transfer Optimization Algorithm for Well Placement Optimization Based on Task Characteristics

The paper presents an overview of design methodology and results of experiments with a Prototype of highly efficient optimal adaptive feedback communication systems (AFCS), transmitting low frequency analog signals without coding. The paper emphasizes the role of the forward transmitter saturation as the factor that blocked implementation of theoretical results of pioneer (1960-1970s) and later research on FCS. Deepened analysis of the role of statistical fitting condition in adequate formulation and solution of AFCS optimization task is given. Solution of the task – optimal transmission/reception algorithms is presented in the form useful for elaboration of the hardware/software Prototype. A notable particularity of the Prototype is absence of the encoding/decoding units, whose functions are realized by the adaptive pulse amplitude modulator (PAM) of the forward transmitter (FT) and estimating/controlling algorithm in the receiver of base station (BS). Experiments confirm that the Prototype transmits signals from FT to BS “perfectly”: with the bit rate equal to the capacity of the system, and with limit energy [J/bit] and spectral [bps/Hz] efficiency. Another, not less important and confirmed experimentally, particularity of AFCS is its capability to adjust parameters of FT and BS to the characteristics of scenario of application and maintain the ideal regime of transmission including spectralenergy efficiency. AFCS adjustment can be made using BS estimates of mean square error (MSE). The concluding part of the paper contains discussion of the presented results, stressing capability of AFCS to solve problems appearing in development of dense wireless networks.

The purpose of this article is to investigate the relationship between task characteristics, business intelligence (BI) quality, and task compatibility. It is essential to investigate this relationship, as BI often builds up data from the organization's existing information systems, and thus, is a supplement. In addition, there is a gap within existing research about task characteristics and BI. We conducted a survey of three companies, where 104 BI end users answered the questionnaire. Our findings reveal that BI users who experience high information quality solve difficult tasks, have a specified job, and their task solution is independent of others experiencing higher task compatibility. The main contribution of the survey is a better understanding of how tasks and BI fit together. In conclusion, the model could be tested in different industries and with different information systems.

School-related reading requirements can be challenging and are entwined with the general ability to perform academically in subjects such as mathematics. However, it is empirically still unclear in how far linguistic requirements affect regular secondary school students ability to interact with subject content. The project reported here investigated the effects of academic German language characteristics on text comprehension difficulty of regular students in a German comprehensive school (“Gesamtschule”, 10 % heritage language users) in 25 text-heavy modelling tasks on mathematical functions. For each text, three versions with different amounts of academic lexical and syntactic features were constructed, while the content was not altered. N = 407 regular secondary school students (grades 7–10) solved text comprehension items specifically relevant to the mathematical task solution. General measures of language and mathematical proficiency and cultural capital were elicited. The main findings were that a), considerable numbers of students did in fact experience text comprehension difficulties of mathematically relevant information in mathematical modeling tasks; b), a systematic increase of academic language features in the task texts increased comprehension difficulty even when students characteristics were controlled for; c) higher grade level, mathematical and general language competence, and male gender, predicted strongly the ability to solve the comprehension items correctly; and d) number of books at home and language use at home did not moderate text comprehension after all other variables were controlled.

ZAPROS, a method to support rank ordering tasks using ordinal input from decision makers, is discussed and compared with a preference cone technique and the analytic hierarchy process (AHP). It provides a means to identify inconsistencies in ordinal decision tasks, yielding verification and explanation of results for partial ordering of a large set of alternatives. The results indicate that ZAPROS provides no less accuracy in task solution, while having some advantages from a behavioural point of view. Comparative analysis of the effectiveness of the methods under consideration in accordance with differences in task characteristics is carried out.

The paper presents an improved terrain profiling system with high-precision range measurement method for underwater surveyor robot. Extensive research has been carried out in the area of terrain profiling in different scenarios; however, limited work has been performed for underwater environment. In the present work, a surveyor robot has been designed using ultrasonic range sensor for terrain profiling in underwater environment. The dynamic nature of underwater scenario adds significant noise to the acoustic signals leading to inaccurate range measurements. The noise embedded in the data in underwater environment degrades the working of the surveyor robot leading to uncertainty in terrain profiling, surveying, and navigation. Different digital signal filtering techniques are used to remove noise from data, leading to a better estimate of the range data and improved signal-to-noise ratio. Two sets of range measurement data in an underwater setup have been estimated using signal processing techniques. The results show that low pass FIR filter improved the results as compared to Moving Average method; however, the high value of standard deviation of the results depicts that FIR filtering is not adequate for accurate range measurement and thereby faithful working of underwater robot. Kalman filter being a recursive estimator provides an optimal solution for estimation and data prediction tasks and is efficient in filtering the noisy data from an input sensor. However, its filtering performance is dependent on the selection of measurement noise covariance R used in the predictor-corrector model. In an actual underwater data streaming environment, it is very difficult to obtain the optimal value of R from complex device configuration. In order to avoid a poor selection of R and to determine its best estimate directly from the sensor data, an analytical method using Denoising Autoencoder is used in the present work. The results show that the Kalman filter method using Denoising Autoencoder estimated the range data accurately. The terrain profile for an underwater test setup was generated by simultaneously recording the position of the robot and the elevation data filtered using the above method. The results are in good agreement with the actual terrain profile of the test setup.

The article regards chunking for English in the dependency model using n-grams. The main problem arising during the solution of task is to choose the best tree among a set of dependency trees. We suggested a solution within the framework of multi-objective optimization using lexicographic method, which can considerably improve the quality of the decision. The results are analyzed in detail.

Summary For highly nonlinear problems, the objective function f(x) may have multiple local optima and it is desired to locate all of them. Analytical or adjoint-based derivatives may not be available for most real optimization problems, especially, when responses of a system are predicted by numerical simulations. The distributed-Gauss-Newton (DGN) optimization method performs quite efficiently and robustly for history-matching problems with multiple best matches. However, this method is not applicable for generic optimization problems, e.g., life-cycle production optimization or well location optimization. In this paper, we generalized the distribution techniques of the DGN optimization method and developed a new distributed quasi-Newton (DQN) optimization method that is applicable to generic optimization problems. It can handle generalized objective functions F(x,y(x))=f(x) with both explicit variables x and implicit variables, i.e., simulated responses, y(x). The partial derivatives of F(x,y) with respect to both x and y can be computed analytically, whereas the partial derivatives of y(x) with respect to x (sensitivity matrix) is estimated by applying the same efficient information sharing mechanism implemented in the DGN optimization method. An ensemble of quasi-Newton optimization tasks is distributed among multiple high-performance-computing (HPC) cluster nodes. The simulation results generated from one optimization task are shared with others by updating a common set of training data points, which records simulated responses of all simulation jobs. The sensitivity matrix at the current best solution of each optimization task is approximated by either the linear-interpolation (LI) method or the support-vector-regression (SVR) method, using some or all training data points. The gradient of the objective function is then analytically computed using its partial derivatives with respect to x and y and the estimated sensitivities of y with respect to x. The Hessian is updated using the quasi-Newton formulation. A new search point for each distributed optimization task is generated by solving a quasi-Newton trust-region subproblem for the next iteration. The proposed DQN method is first validated on a synthetic history matching problem and its performance is found to be comparable with the DGN optimizer. Then, the DQN method is tested on different optimization problems. For all test problems, the DQN method can find multiple optima of the objective function with reasonably small numbers of iterations (30 to 50). Compared to sequential model-based derivative-free optimization methods, the DQN method can reduce the computational cost, in terms of the number of simulations required for convergence, by a factor of 3 to 10.

This paper presents a Fractional Order Proportional-Integral-Derivative (FOPID) controller optimized using Social Spider Optimization (SSO) algorithm to Load Frequency Control (LFC) of a two-area multi-source power system in the presence of Distributed Generations (DGs). Here, the effect of DGs consist of wind and diesel power plants for damping frequency deviation of two-area multi-source power system including thermal, hydro and gas units considering nonlinearities such as generation rate constraint and governor dead band has been investigated. The FOPID controller has three main control parameters as known gain, fractional orders of integrator and derivative part that provide the better performance in comparison with the PID controller. On the other hand, to achieve the good performance in the power system, tuning of these parameters is very important. Therefore, the SSO algorithm has been used to optimal tuning of the FOPID based LFC controller. One of the important specifications of the employed algorithm is that it can balance the local and global solutions in the search space that cause to overcome the local optimal and premature convergence. Also, a High Voltage Direct Current link (HVDC) is used to improve the dynamic performance of the LFC task. Finally, the proposed controller has been applied on a two case studies and analyzed based on investigated performance criteria. Results show the good effect of DGs for the solution of LFC task and enhancing system performance characteristics under different operating conditions and considering system nonlinearities.

Standard models of visual search have focused upon asking participants to search for a single target in displays where the objects do not overlap one another, and where the objects are presented on a single depth plane. This stands in contrast to many everyday visual searches wherein variations in overlap and depth are the norm, rather than the exception. Here, we addressed whether presenting overlapping objects on different depths planes to one another can improve search performance. Across 4 different experiments using different stimulus types (opaque polygons, transparent polygons, opaque real-world objects, and transparent X-ray images), we found that depth was primarily beneficial when the displays were transparent, and this benefit arose in terms of an increase in response accuracy. Although the benefit to search performance only appeared in some cases, across all stimulus types, we found evidence of marked shifts in eye-movement behavior. Our results have important implications for current models and theories of visual search, which have not yet provided detailed accounts of the effects that overlap and depth have on guidance and object identification processes. Moreover, our results show that the presence of depth information could aid real-world searches of complex, overlapping displays. (PsycINFO Database Record

This study investigated into the effects of the "Advanced search" feature of three digital libraries on users' search performance and search efforts. Three operational digital libraries, i.e., the ACM digital library, the IEEE Computer Society digital library, and the IEEE Xplore digital library, were used in this study. Thirty-five students participated in an experiment and completed the assigned search task, using their preferred search feature(s). The results demonstrate that for ACM and IEEE CS, the use of Advanced search did not have a significant effect on improving search performance. Only in Xplore, the use of Advanced search significantly improved search performance. The search efforts increased significantly for the combined-use of Advanced search and Basic search in ACM and IEEE CS. The reasons leading to the results are discussed.

Effects of design features of train electronic guidance interface on visual search, behavior, and usability

Lower-Level Decision Task Solution While Optimising a Construction Project Schedule

An opportunity of finite-length optimal signals application to excess Nyquist limit without energy losses and within defined occupied bandwidth is considered in this article. Finite-length optimal signals with different duration can be found by numerical solution of corresponding multistep optimization task. Optimality criterion of signal form is based on defined reduction rate of out-of-band emissions in condition of controlled intersymbol interference. Controlled intersymbol interference determines possibility of application of simple algorithm of coherent bit-by-bit detection. Solutions of optimization tasks for different conditions are compared with known signal forms in term of Euclidean distance between signal sequences according to Mazo method. It is shown that optimal signals provide higher symbol rate without significant losses in BER performance and may be applied in 5G standards instead of signals formed with the use of digital filtration.

HTSA: A novel hybrid task scheduling algorithm for heterogeneous cloud computing environment

This paper presents a new approach to the improvement of the sensor node - base station (SNBS) links of physical layer of wireless sensor networks performance. The key idea is the consideration of SNBS as the remote measurement and estimation systems and application of the corresponding analytical tools. It is shown that the main difficulty in the improvement of digital SNBS performance is impossibility to formulate and solve the optimization task using well developed methods of the optimal estimation theory. In turn, the SNBS transmitting the signals formed by the sensors using analog pulse-amplitude transmitter adjusted over the feedback channel permit the formulation of analytical form of the mean square error (MSE) and solution of optimization task. The obtained optimal transmission-reception algorithm enables designing of a low energy-size-cost analog SNBS, which transmit the signals with minimal MSE and a bit rate equal to the capacity of the link. Moreover, the MSE of transmission determines the information characteristics of the links, as well as enables a development of the unified methods of the SNBS real performance evaluation and measurement.