A novel application to evaluate the bridge health after retrofitting using vibration and static measurements

This paper proposes a novel method based on vibration and static measurement data to evaluate bridge health. This method is verified in Kenh 7 bridge. Kenh 7 Bridge is located in Long An district, Vietnam. The structural condition of the bridge was surveyed in July 2016. At that time, the girder was in good condition, whereas the deck’s concrete spalled in many areas. Then the deck was decided to punch out and be replaced with the new ones. The dynamic and static experiments of both before and after retrofitted bridges were carried out in the campaign. This research analyses the vibration data and the main girder deflection under the static load to evaluate the stiffness condition of the bridge girder, deck, and cross beam. A finite element (FE) model of the bridge is created in FE software. The Grey Wolf Optimizer algorithm will be used to update the unknown parameters. By model updating, the natural frequency and the main girder deflection differences between the experimental and the numerical results are minimized, and the concrete properties of each component are estimated. Comparing the stiffness between the before and retrofit bridge, the conclusion about the health of the Kenh 7 bridge after repair can be made. It is recommended that the cross beam should be strengthened

Solving Weapon-Target Assignment Problem with Salp Swarm Algorithm

The weapon target problem is a combinatorial optimization problem. It aims to have the weapons on target properly assigned for the intended purposes. When focused on its target, it does things with its effective attack research in mind. It is an ongoing problem program to minimize survivors. This study, using the weapon target assignment model calculates the expected probabilities on the target with the salp model. The nature of this SHA model is designed to be appropriately predicted for this particular use. The Salp Swarm Algorithm (SSA) is a metaheuristic method of methods approaching the solution set as an approximation. Optimum solution or optimum example is in a working example. This study was done with 12 problem examples (200 training and 200 targets with pleasure to observe, to test the efficiency of SSA). In the problem, the iteration resulted in optimum results at the end of the definite usage time. Best value included 48.355 for WTA1, 92.654 for WTA2, 174.432 for WTA3, 155.658 for WTA4, 250.784 for WTA5, 284.967 for WTA6, 247.458 for WTA7, 362.636 for WTA8, 524.732 for WTA9, 548.580 for WTA10, 601.654 for WTA11, and WTA16812. It was obtained by finding in 200,000 iterations and the result value was 50. After 200000 improvements, it was observed to relax to increase iteration. The use of barter when generating new solutions to the problem. To find out the fitness values, mean, best, and worst values were found

A novel sketch based face recognition in unconstrained video for criminal investigation

Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is taken as an input. Firstly, input video is converted into frames and summarized using the proposed quality indexed three step cross search algorithm. Next, faces are detected by proposed modified Viola-Jones algorithm. Then, necessary features are selected using the proposed salp-cat optimization algorithm. Finally, these features are fused with scale-invariant feature transform (SIFT) features and Euclidean distance is computed between feature vectors of sketch and each face in a video. Face from the video having lowest Euclidean distance with query sketch is considered as suspect’s face. The proposed method’s performance is analyzed on Chokepoint dataset and the system works efficiently with 89.02% of precision, 91.25% of recall and 90.13% of F-measure

Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition

© 2019 Elsevier Ltd This paper proposes a novel bio-inspired optimization method named memetic salp swarm algorithm (MSSA). It is developed by extending the original salp swarm algorithm (SSA) with multiple independent salp chains, thus it can implement a wider exploration and a deeper exploitation under the memetic computing framework. In order to enhance the convergence stability, a virtual population based regroup operation is used for the global coordination between different salp chains. Due to partial shading condition (PSC) and fast time-varying weather conditions, photovoltaic (PV) systems may not be able to generate the global maximum power. Hence, MSSA is applied for an effective and efficient maximum power point tracking (MPPT) of PV systems under PSC. To evaluate the MPPT performance of the proposed algorithm, four case studies are undertaken using Matlab/Simulink, e.g., start-up test, step change of solar irradiation, ramp change of solar irradiation and temperature, and field atmospheric data of Hong Kong. The obtained PV system responses are compared to that of eight existing MPPT algorithms, such as incremental conductance (INC), genetic algorithm (GA), particle swarm optimization (PSO), artificial bees colony (ABC), cuckoo search algorithm (CSA), grey wolf optimizer (GWO), SSA, and teaching-learning-based optimization (TLBO), respectively. Simulation results demonstrate that the output energy generated by MSSA in Spring in HongKong is 118.57%, 100.73%, 100.96%, 100.87%, 101.35%, 100.36%, 100.81%, and 100.22% to that of INC, GA, PSO, ABC, CSA, GWO, SSA, and TLBO, respectively. Lastly, a hardware-in-the-loop (HIL) experiment using dSpace platform is undertaken to further validate the implementation feasibility of MSSA

Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery

Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening

A New Quadratic Binary Harris Hawk Optimization For Feature Selection

Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms

Binary Competitive Swarm Optimizer Approaches For Feature Selection

Feature selection is known as an NP-hard combinatorial problem in which the possible feature subsets increase exponentially with the number of features. Due to the increment of the feature size, the exhaustive search has become impractical. In addition, a feature set normally includes irrelevant, redundant, and relevant information. Therefore, in this paper, binary variants of a competitive swarm optimizer are proposed for wrapper feature selection. The proposed approaches are used to select a subset of significant features for classification purposes. The binary version introduced here is performed by employing the S-shaped and V-shaped transfer functions, which allows the search agents to move on the binary search space. The proposed approaches are tested by using 15 benchmark datasets collected from the UCI machine learning repository, and the results are compared with other conventional feature selection methods. Our results prove the capability of the proposed binary version of the competitive swarm optimizer not only in terms of high classification performance, but also low computational cost