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This paper aims to propose an algorithm, location-aware opportunistic content forwarding (LOC), to improve message directivity using direction vectors in opportunistic networks. The LOC is based on the assumption that if approximate location of the destination node is known, then overall message delivery and cost can be improved. Efficient message delivery with low communication cost is a major challenge in current opportunistic networks. In these networks, nodes do not have prior knowledge of their recipients, and message forwarding can be achieved by selecting suitable forwarder based on some forwarding criteria, as compared to its ancestor mobile ad hoc networks.

In this paper, the authors tested LOC in two sets of mobility models, synthetic movement model and real mobility data sets. In the first set, working day movement is used as synthetic movement model, where proposed algorithm is compared against Lobby Influence (LI) and Epidemic algorithms. In the second set of experiments, the new algorithm is tested in three mobility data sets, namely, Cambridge, Reality and Sassy, and results compared against LI algorithm. The reason of using various movement models is to establish strengths and weaknesses of the proposed algorithm in different scenarios.

The experimental results show that the new algorithm performed extremely well in different scenarios, not only in terms of overall message delivery but also successfully managed to reduce the communication cost.

The new contribution increases the overall energy and storage efficiency of nodes by targeting relevant forwarding nodes in the network.

The purpose of this paper is to proposes a forward search algorithm for detecting and identifying natural structures arising in human-computer interaction (HCI) and human physiological response (HPR) data.

The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models used in classification algorithms. Data were also simulated based on multivariate normal distribution method and used to compare and validate the original data.

Results shows that the forward search method provides robust features that are capable of repelling over-fitting in physiological and eye movement data.

One of the limitations of the robust forward search algorithm is that when the number of digits for residuals value is more than the expected size for stack flow, it normally yields an error caution; to counter this, the data sets are normally standardized by taking the logarithmic function of the model before running the algorithm.

The authors conducted some of the experiments at individual residence which may affect environmental constraints.

The novel approach to this method is the detection of outliers for data sets based on the Mahalanobis distances on HCI and HPR. And can also involve a large size of data with p possible parameters. The improvement made to the algorithm is application of more graphical display and rendering of the residual plot.

The study aims to propose reverse processing solution to improve the performance of strapdown inertial navigation system (SINS) initial alignment and SINS-/global positioning system- (GPS) integrated navigation. The proposed scheme can be well applied in the fields of aircraft and aerospace navigation.

For the SINS alignment phase, a fast initial alignment scheme is proposed: the initial value of reverse filter is determined by the final result of forward filter, and then, the reverse filter is carried out using the stored data. Multiple iterations are performed until the accuracy is satisfied. For the SINS-/GPS-integrated phase, a forward–reverse navigation algorithm is proposed: first, the standard forward filter is used, and then, the reverse filter is carried out using the initial value determined by the forward filter, and the final fusion results are achieved by the weighted smoothing of the forward and reverse filtering results.

The simulation and the actual test results show that in the initial alignment stage, the proposed reverse processing method can obviously shorten the SINS alignment time and improve the alignment accuracy. In the SINS-/GPS-integrated navigation data fusion stage, the proposed forward–reverse data fusion processing can, obviously, improve the performance of the navigation solution.

The proposed reverse processing technology has an important application in improving the accuracy of navigation and evaluating the performance of real-time navigation. The proposed scheme can be not only used for SINS-/GPS-integrated system but also applied to other integrated systems for general aviation aircraft.

Compared with the common forward filtering algorithm, the proposed reverse scheme can not only shorten alignment time and improve alignment accuracy but also improve the performance of the integrated navigation.

Proposes a heuristic iterative scheduling algorithm for the resource constrained project scheduling problem. Considers a general model where activities are represented by multiple operating modes and each operating mode is constituted of different activity durations and resource requirements. The performance measures considered are the minimization of project duration and the maximization of net present value (NPV). In the cash flow model assumed, activity expenditures take place at their start times and the project payment is made on its completion. The iterative scheduling algorithm consists of forward/ backward scheduling passes, where consecutive scheduling passes are linked by updated activity time windows. The iterative algorithm is supported by a conflict‐based activity selection technique called local constraint based analysis (LCBA). A considerable amount of improvement in both performance criteria is observed when the results of the iterative algorithm are compared with the results given by the initial forward schedule.

Fast iterative algorithms for designing birefringent filters with any specified spectral response are proposed. From the Jones formalism, we derive two polynomials representing the transmitted and rejected response of the filter, respectively. Once the coefficients of the filters are obtained, the orientation angle of each birefringent section and the phase shift introduced by each compensator can be determined by an iterative algorithm that gives an efficient solution to the birefringent filter design problem. Afterward, some design examples are presented to demonstrate the effectiveness of the proposed approach. In comparison with results reported in the literature, this approach provides the best performance in terms of accuracy and time complexity.

The purpose of this paper is to present a new effective integration method for cyclic plasticity models.

By defining an integrating factor and an augmented stress vector, the system of differential equations of the constitutive model is converted into a nonlinear dynamical system, which could be solved by an exponential map algorithm.

The numerical tests show the robustness and high efficiency of the proposed integration scheme.

The von‐Mises yield criterion in the regime of small deformation is assumed. In addition, the model obeys a general nonlinear kinematic hardening and an exponential isotropic hardening.

Integrating the constitutive equations in order to update the material state is one of the most important steps in a nonlinear finite element analysis. The accuracy of the integration method could directly influence the result of the elastoplastic analyses.

The paper deals with integrating the constitutive equations in a nonlinear finite element analysis. This subject could be interesting for the academy as well as industry. The proposed exponential‐based integration method is more efficient than the classical strategies.

The spatial conflicts and congestion of construction resources are challenges that lead to the reduction in efficiency. The purpose of this paper is to enable users to detect and resolve workspace conflicts by implementing four resolution strategies in a five-dimensional (5D) CAD model. In addition to resolving conflicts, the model should be able to optimize time and cost of the projects. In other words, three variables of spatial conflicts, time and cost of project are considered simultaneously in the proposed model to find the optimum solution.

In the first step, a 5D simulation model is developed that includes time, cost and geometrical information of a project. Then, time-cost trade-off analysis was carried out to distinguish optimum schedule. The schedule was imported to the 5D CAD model to detect spatial conflicts. Finally, a novel algorithm was implemented to solve identified conflicts while imposing minimum project’s time and cost. Several iterations are performed to resolve all clashes using conflict resolution algorithm and visual simulation model.

The proposed methodology in this research was applied to a real case. Results showed that in comparison to the normal and initial schedule with 19 conflicts, the finalized schedule has no conflict, while time and cost of the project are both reduced.

Implementing the proposed methodology in construction projects requires proper technical basis in this field. In this regard, the executive user should have a proper understanding of the principles, concepts and tools of building information modeling and have project management knowledge. Also, the implementation conditions of the basic model requires the determination of the construction methods, estimated volumes of working items, scheduling and technical specification. The designed methodology also has two limitations regarding to its implementation. The first is the fact that strategies should be applied manually to the schedule. The other one pertains to the number of strategies used in the research. Four strategies have been used in the conflict resolution algorithm directly and the two others (spatial divisibility and activities breakdown strategies) have been used as default strategies in the visual simulation model. Since the unused strategies including the changing of construction method and the activity resources are subjective and depend upon the planner and project manager’s personal opinion, the authors have avoided using them in this research.

The method proposed in this research contributes the coordination of the working teams at the planning and execution phases of the project. In fact, the best location and work direction for each working team is presented as a schedule, so that the space conflict may not come about and the cost can be minimized. This visual simulation not only deepens the planners’ views about the executive barriers and the spatial conditions of the worksite, it also makes the construction engineers familiar on a daily basis with their executive scope. Therefore, it considerably improves the interactions and communication of the planning and construction teams. Another advantage and application of this methodology is the use of initial and available projects’ documents including the schedule and two-dimensional drawings. The integration of these basic documents in this methodology helps identify the spatial conflicts efficiently. To achieve this, the use of the existing and widely-used construction tools has facilitated the implementation of the methodology. Using this system, planners have applied the strategies in an order of priority and can observe the results of each strategy visually and numerically in terms of time, cost and conflicts. This methodology by providing the effective resolution strategies guides the practitioner to remove conflicts while optimum time and cost are imposed to project.

Contrary to the previous models that ignore cost, the proposed model is a 5D visual simulation model, which considers the variable of cost as a main factor for conflict identification and resolution. Moreover, a forward-pass approach is introduced to implement resolution strategies that are novel compared to other investigations.

The proposed algorithm successfully optimizes complex error functions, which are difficult to differentiate, ill conditioned or discontinuous. It is a benchmark to identify initial solutions in artificial neural network (ANN) training.

A multi‐directional ANN training algorithm that needs no derivative information is introduced as constrained one‐dimensional problem. A directional search vector examines the ANN error function in weight parameter space. The search vector moves in all possible directions to find minimum function value. The network weights are increased or decreased depending on the shape of the error function hyper surface such that the search vector finds descent directions. The minimum function value is thus determined. To accelerate the convergence of the algorithm a momentum search is designed. It avoids overshooting the local minimum.

The training algorithm is insensitive to the initial starting weights in comparison with the gradient‐based methods. Therefore, it can locate a relative local minimum from anywhere of the error surface. It is an important property of this training method. The algorithm is suitable for error functions that are discontinuous, ill conditioned or the derivative of the error function is not readily available. It improves over the standard back propagation method in convergence and avoids premature termination near pseudo local minimum.

Classifications problems are efficiently classified when using this method but the complex time series in some instances slows convergence due to complexity of the error surface. Different ANN network structure can further be investigated to find the performance of the algorithm.

The search scheme moves along the valleys and ridges of the error function to trace minimum neighborhood. The algorithm only evaluates the error function. As soon as the algorithm detects flat surface of the error function, care is taken to avoid slow convergence.

The algorithm is efficient due to incorporation of three important methodologies. The first mechanism is the momentum search. The second methodology is the implementation of directional search vector in coordinate directions. The third procedure is the one‐dimensional search in constrained region to identify the self‐adaptive learning rates, to improve convergence.

The purpose of this paper is to help enterprises to define and refresh their specific vendor selection criteria according to changing situations.

This paper firstly analyzes the variety of vendor selection criteria according to the diverse business environment. Furthermore, an approach of vendor selection based on MW‐OBS (an artificial neural network pruning algorithm) is put forward. MW‐OBS contributes a lot in distinguishing the crucial items of selection criteria based on certain enterprise's operational data, instead of assuming the criteria set subjectively. Meanwhile MW‐OBS evaluates the importance weights of these crucial items in criteria by data training.

The vendor selection criteria is believed to change for diverse enterprises and even for an enterprise's mutative business conditions because of the attribute of materials, cooperation relationships, and supplier's performance. The approach establishes the vendor selection criteria for different enterprises based on their own conditions, and once business environment changes, with new data being generated, the set can be refreshed dynamically and timely.

This approach extends the research of neural network pruning algorithm, for example the importance of all reserved criteria can be achieved from trained network without extra optimization,

This approach put emphasis on distinguishing dynamic criteria consistent with enterprise's circumstance. Enterprises are capable of constructing their various criteria collections conveniently according to their own specific situations with the application of approach.

This paper seeks to examine the case of the multi‐period optimisation problem where decisions are obtained from a finite horizon model and will be implemented in a situation in which the system will operate indefinitely.

The production planning problem is addressed in which the quantity of the product required (demand) in future periods is being forcast, from which one must decide when and how much to produce.

Finds that a regeneration set is key for finding forecast horizon (FH) and decision horizon (DH) in the dynamic lot size model (DLSM). A regeneration set contains the optimal regeneration points in some conceivable future horizon.

This paper extends the research on horizons which has accumulated in the literature over the last 40 years.