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Angle of Attack (AOA) is a crucial parameter which directly affects the aerodynamic forces of an aircraft. The measurement of AOA is required to ensure a safe flight within its designed flight envelop. This paper intends to summarise a comprehensive survey on the measurement techniques and estimation methods for AOA, specifically in Unmanned Aerial Vehicle (UAV) applications. In the case of UAVs, weight constraint plays a major role as far as sensor suites are concerned. This results in selecting a suitable estimation method to extract AOA using the available data from the autopilot. The most feasible and widely employed AOA measurement technique is by using the Multi-Hole Probes (MHPs). The MHP measures the AOA regarding the pressure variations between the ports. Due to the importance of MHP in AOA measurement, the calibration methods for the MHP are also included in this paper. This paper discusses the AOA measurement using virtual AOA sensors, their importance and the operation. Keywords: Angle of attack estimation, Angle of attack measurement, Angle of attack vane, Five-hole probe, Multi-hole probe, Multi-hole probe calibration, Nine-hole probe, Seven-hole probe, Virtual angle of attack sensor

This work proposes a new algorithm for training neural networks to solve the problems of feature selection and function approximation. The algorithm applies different weight constraint functions for the hidden and the output layers of a multilayer perceptron neural network. The LASSO operator is applied to the hidden layer; therefore, the training provides automatic selection of relevant features and the standard norm regularization function is applied to the output layer. Therefore, we propose a multi-objective training algorithm that is able to select the important features while solving the approximation problem.

Short-term traffic flow forecast plays an important role in intelligent transportation systems. Existing traffic flow prediction model used deep layer neural network, which can’t prevent over fitting, resulting in performance loss and lack of generalization ability. We propose a regularized LSTM model that fused recurrent dropout and max-norm weight constraint. We apply recurrent dropout to the recurrent connections of LSTM network, and use max-norm weight constraint to arrest the input weights not to grow very large. Simultaneously, we merge ADAM optimizer into our model. We use three datasets from different countries. In order to compare with the other researchers in the field of traffic flow prediction, we introduce same features and perform the same time interval prediction task. The experiment results show that our model has the lowest root mean square error and mean absolute error than the basic LSTM and other machine learning model including BP neural network, RNN, stacked autoencoder.

In the past, most researchers have paid attention to the storage space allocation problem in maritime container terminals, while few have studied this problem in rail&ndash, water intermodal container terminals. Therefore, this paper proposes a storage space allocation problem to look for a symmetry point between the efficiency and effectivity of rail&ndash, water intermodal container terminals and the unbalanced allocations and reallocation operations of inbound containers in the railway operation area, which are two interactive aspects. In this paper, a two-stage model on the storage space allocation problem is formulated, whose objective is to balance inbound container distribution and minimize overlapping amounts, considering both stacking principles, such as container departure time, weight and stacking height, and containers left in railway container yards from earlier planning periods. In Stage 1, a novel simulated annealing algorithm based on heuristics is introduced and a new heuristic algorithm based on a rolling horizon approach is developed in Stage 2. Computational experiments are implemented to verify that the model and algorithm we introduce can enhance the storage effect feasibly and effectively. Additionally, two comparison experiments are carried out: the results show that the approach in the paper performs better than the regular allocation approach and weight constraint is the most important influence on container storage.

Weight-constrained route planning (WRP) over static graphs has been extensively studied due to its wide application to transportation networks. However, real transportation networks often evolve over time and are thus modeled as time-dependent graphs. In this paper, we study the WRP problem over a large time-dependent graph by incorporating continuous time and weight functions into it. Most existing works regarding route planning over time-dependent graphs are based on the first-in-first-out (FIFO) property. Unfortunately, the FIFO property does not hold for our problem. To solve the problem, we propose two novel route planning algorithms, namely, a baseline algorithm and an advanced algorithm. Specifically, the advanced algorithm is even more efficient than the baseline algorithm, as the advanced algorithm incorporates a fast traversal scheme and tight bounds of time functions to terminate the traversal as early as possible. We confirm the effectiveness and efficiency of our algorithms by extensive experiments on real datasets.

Short texts have become a kind of prevalent source of information, and the classification of these short texts in various forms is valuable to many applications. However, most existing short text classification approaches circumvent the sparsity problem by extending short texts (or their feature representations) or exploiting additional information to adapt short texts to traditional text classification approaches. In this paper, we try to solve the sparsity problem of short text classification in a different direction: adapting the classifier to short texts. We propose a sparse representation short text classification method based on entropy weighted constraint. The main idea behind this study is to consider that the short texts are similar in potentially specific subspace. Specifically, we first introduce word embedding to represent the initial sparse representation dictionary, and then a fast feature subset selection algorithm is used to filter the dictionary. Again, we design an objective function based on sparse representation of entropy weight constraint. The optimal value of the objective function is obtained by Lagrange multiplier method. Finally, the distance between the short text to be classified and the short text in each class is calculated under the subspace, and the short text is classified according to three classification rules. Experiments over five datasets show that the proposed approach can effectively alleviate the problem of sparse feature of short text and is more efficient and effective than the existing short text classification method.

Stiffened conical shells in launch vehicles are very sensitive to various forms of imperfections. As a type of equivalent imperfections, several perturbation load approaches are used to investigate the influence of dimple-shape imperfections on the load-carrying capacity of stiffened conical shells. Firstly, the effect of axial location of dimple is examined by single perturbation load approach (SPLA), since the stiffness of stiffened conical shells varies along axial direction. Then, worst multiple perturbation load approach (WMPLA) is employed to find the lower bound of the collapse load of stiffened conical shells, and also provides the knowledge to determine the number of dimples in the multiple perturbation load approach (MPLA). After that, the optimization of stiffened conical shells for imperfection-insensitive design is carried out, where the equivalent MPLA is adopted during the optimization process to reduce the computational cost. Illustrative example indicates that stiffened conical shells exhibit more complicated imperfection sensitivity compared to cylindrical shells, and the proposed optimization framework can find an imperfection-insensitive design under structural weight constraint in an efficient manner.

In this paper, an efficient concurrent optimization method of macrostructures, and material microstructures and orientations is proposed for maximizing natural frequency. It is assumed that the macrostructure is composed of uniform material with the same microstructure but with various orientation. The bi-directional evolutionary structural optimization (BESO) method is applied to optimize the macrostructure and its material microstructure under a given weight constraint. Meanwhile, the optimality condition with respect to local material orientation is derived and embedded in the two-scale design of macrostructures and material microstructures. Numerical examples are presented to demonstrate the capability and effectiveness of the proposed optimization algorithm. The results show that the current design of macrostructures, material microstructures, and local material orientation greatly improves structural dynamic performance.

A common strategy to discovering cancer associated copy number aberrations (CNAs) from a cohort of cancer samples is to detect recurrent CNAs (RCNAs). Although the previous methods can successfully identify communal RCNAs shared by nearly all tumor samples, detecting subgroup-specific RCNAs and their related subgroup samples from cancer samples with heterogeneity is still invalid for these existing approaches. In this paper, we introduce a novel integrated method called HetRCNA, which can identify statistically significant subgroup-specific RCNAs and their related subgroup samples. Based on matrix decomposition framework with weight constraint, HetRCNA can successfully measure the subgroup samples by coefficients of left vectors with weight constraint and subgroup-specific RCNAs by coefficients of the right vectors and significance test. When we evaluate HetRCNA on simulated dataset, the results show that HetRCNA gives the best performances among the competing methods and is robust to the noise factors of the simulated data. When HetRCNA is applied on a real breast cancer dataset, our approach successfully identifies a bunch of RCNA regions and the result is highly correlated with the results of the other two investigated approaches. Notably, the genomic regions identified by HetRCNA harbor many breast cancer related genes reported by previous researches.

The goal of the Nurse Scheduling Problem (NSP) is to find an assignment of nurses to shifts according to specific requirements. Given its practical relevance, many researchers have developed different strategies for solving several variants of the problem. One of such variants was recently addressed by an approach based on Answer Set Programming (ASP), obtaining promising results. Nonetheless, the original ASP encoding presents some intrinsic weaknesses, which are identified and eventually circumvented in this paper. The new encoding is designed by taking into account both intrinsic properties of NSP and internal details of ASP solvers, such as cardinality and weight constraint propagators. The performance gain of clingo and wasp is empirically verified on instances from ASP literature. As an additional contribution, the performance of clingo and wasp is compared to other declarative frameworks, namely SAT and ILP; the best performance is obtained by clingo running the new ASP encoding.

Evaluating the allocation efficiency of agricultural flood and drought mitigation project is to investigate that whether the benefit brought by the project is effective from the economics angle of input-output. For the non-standardized weight distribution of the input-output indicators in the data envelopment analysis model, an improved data envelopment analysis model is proposed. Through constructing the decision-making unit and strengthening the weight constraint, the range of efficiency values is expanded and the weight of input-output indicators is standardized. In the end, this model is used to evaluate the allocation efficiency of agricultural flood and drought mitigation project of China's 31 provinces, municipalities and autonomous regions.

This paper deals with a variant of minimum spanning tree problem with multiple constraints, which includes degree, weight and budgeting constraints simultaneously together. To model the problem, a zero-one programming is incorporated. An exact solution procedure called pattern recognition technique-based lexi-search algorithm is developed. A suitable numerical illustration is given to check the applicability of the developed algorithm. Furthermore, the algorithm is programmed in C and tested with randomly generated hard instances, computational results are also reported. The overall results reveal that the proposed algorithm is fairly proficient in the sense of both acquiring the optimal solutions and computational time.

Frequent graph pattern mining is one of the most interesting areas in data mining, and many researchers have developed a variety of approaches by suggesting efficient, useful mining techniques by integration of fundamental graph mining with other advanced mining works. However, previous graph mining approaches have faced fatal problems that cannot consider important characteristics in the real world because they cannot process both (1) different element importance and (2) multiple minimum support thresholds suitable for each graph element. In other words, graph elements in the real world have not only frequency factors but also their own importance, in addition, various elements composing graphs may require different thresholds according to their characteristics. However, traditional ones do not consider such features. To overcome these issues, we propose a new frequent graph pattern mining method, which can deal with both different element importance and multiple minimum support thresholds. Through the devised algorithm, we can obtain more meaningful graph pattern results with higher importance. We also demonstrate that the proposed algorithm has more outstanding performance compared to previous state-of-the-art approaches in terms of graph pattern generation, runtime, and memory usage.

Since frequent graph pattern mining was proposed, various approaches have been suggested by devising efficient techniques or integrating graph mining with other mining areas. However, previous methods have limitations that cannot reflect the following important characteristics in the real world to their mining processes. First, elements in the real world have their own importance as well as frequency, but traditional graph mining methods do not consider such features. Second, various elements composing graph databases may need thresholds different from one another according to their characteristics. However, since traditional approaches mine graph patterns on the basis of only a single threshold, losses of important pattern information can be caused. Motivated by these problems, we propose a new graph mining algorithm that can consider both different importance and multiple thresholds for each element of graphs. We also demonstrate outstanding performance of the proposed algorithm by comparing ours with previous state-of-the-art approaches.

Weighted Frequent Itemset Mining (WFIM) has been proposed as an alternative to frequent itemset mining that considers not only the frequency of items but also their relative importance. However, an important limitation of WFIM is that it does not consider how recent the patterns are. To address this issue, we extend WFIM to consider the recency of patterns, and thus present the Recent Weighted Frequent Itemset Mining (RWFIM). A projection-based algorithm named RWFIM-P is designed to mine Recent Weighted Frequent Itemsets (RWFIs) based on a novel upper-bound downward closure property. Moreover, an improved algorithm named RWFIM-PE is also proposed, which introduces a new pruning strategy named Estimated Weight of 2-itemset Pruning (EW2P) to prune unpromising candidate of RWFIs early. An experimental evaluation against a state-of-the-art WFIM algorithm on the real-world and synthetic datasets show that the proposed algorithms are highly efficient.

This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach. The optimal and feasible packing of goods for transportation and distribution to various locations by satisfying the practical constraints are the key points in this project work. As the number of boxes for packing can not be predicted in advance and the boxes may not be of same category always. It also involves many practical constraints that are why the optimal packing makes much importance to the industries. This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering most of the practical constraints facing in logistic industries. This goal was achieved in this research by optimizing the empty volume inside the container using genetic approach. Feasible packing pattern was achieved by satisfying various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint. 3D bin packing problem consists of 'n' number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and in-turn profit. Furthermore, Boxes to be packed may be of arbitrary sizes. The user input data are the number of bins, its size, shape, weight, and constraints if any along with standard container dimension. This user input were stored in the database and encoded to string (chromosomes) format which were normally acceptable by GA. GA operators were allowed to act over these encoded strings for finding the best solution.