A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding

According to the requirements of point cloud simplification for T-profile steel plate welding in shipbuilding, the disadvantages of the existing simplification algorithms are analyzed. In this paper, a point cloud simplification method is proposed based on octree coding and the threshold of the surface curvature feature. In this method, the original point cloud data are divided into multiple sub-cubes with specified side lengths by octree coding, and the points that are closest to the gravity center of the sub-cube are kept. The k-neighborhood method and the curvature calculation are performed in order to obtain the curvature features of the point cloud. Additionally, the point cloud data are divided into several regions based on the given adjustable curvature threshold. Finally, combining the random sampling method with the simplification method based on the regional gravity center, the T-profile point cloud data can be simplified. In this study, after obtaining the point cloud data of a T-profile plate, the proposed simplification method is compared with some other simplification methods. It is found that the proposed simplification method for the point cloud of the T-profile steel plate for shipbuilding is faster than the three existing simplification methods, while retaining more feature points and having approximately the same reduction rates.

GIS Spatial Optimization for Corridor Alignment Using Simulated Annealing

Planning corridors for new facilities such as pipeline or transmission lines through geographical spaces is a topographical constraint optimization problem. The corridor planning problem requires finding an optimal route or a set of alternative paths between two locations. This article presents a simulated-annealing-based (SA) approach applying a variable neighborhood strategy in a continuous space to generate competitive and different alternative paths to solve the corridor planning problem. The variable neighborhood method randomly selects two points from a variable interval of the current solution generated by SA creating pseudo-random paths inside a corridor and finding spatially different alternatives. The proposed approach is evaluated with three practical problems using real topographic data from the Veracruz Basin in Mexico. The experimental results show that this approach obtains efficient and competitive solutions with improvements above 18% over those gotten by the compared method.

Determination of positioning accuracies by using fingerprint localisation and artificial neural networks

Fingerprint localisation technique is an effective positioning technique to determine the object locations by using radio signal strength, values in indoors. The technique is subject to big positioning errors due to challenging environmental conditions. In this paper, initially, a fingerprint localisation technique is deployed by using classical k-nearest neighborhood method to determine the unknown object locations. Additionally, several artificial neural networks, are employed, using fingerprint data, such as single-layer feed forward neural network, multi-layer feed forward neural network, multi-layer back propagation neural network, general regression neural network, and deep neural network to determine the same unknown object locations. Fingerprint database is built by received signal strength indicator measurement signatures across the grid locations. The construction and the adapted approach of different neural networks using the fingerprint data are described. The results of them are compared with the classical k-nearest neighborhood method and it was found that deep neural network was the best neural network technique providing the maximum positioning accuracies.

A Study on the Effect of Local Neighbourhood Parameter towards the Performance of SAFIRO

Single-agent Finite Impulse Response Optimizer (SAFIRO) is a recently proposed metaheuristic optimization algorithm which adopted the procedure of the ultimate unbiased finite impulse response filter (UFIR) in state estimation. In SAFIRO, a random mutation with shrinking local neighborhood method is employed during measurement phase to balance the exploration and the exploitation process. Beta, β, is one of the parameters used in the local neighborhood to control the step size. In this study, the effect of β towards the performance of SAFIRO is observed by assigning the value of 1, 5, 10, 15, and 20. The best setting of β for SAFIRO is also determined. The CEC2014 Benchmark Test Suite is used to evaluate the SAFIRO performance with different β values. Results show that the performance of β is depending on the problems to be optimized. 17 out of 30 functions show the best performance of SAFIRO by setting β = 10. Statistical analysis using Friedman test and Holm post hoc test were performed to rank the performance. β = 10 has the highest rank where its performance is significantly better than other values, but equivalent to β = 5 and β = 15. Hence, it is recommended to tune the β for best performance, however, β = 10 is a good value to be used in SAFIRO for solving optimization problems.

PREDIKSI DISTRIBUSI PENCEMARAN AIR SUNGAI DAS BRANTAS HULU KOTA BATU PADA MUSIM HUJAN DAN KEMARAU MENGGUNAKAN METODE SPASIAL INVERSE DISTANCE WEIGHTED

The diversity of activities along the Brantas watershed causes waste disposal, which contributes to an increase in pollution load in the Brantas watershed and a decrease in water quality. It is necessary to periodically monitor water quality changes, but the constraints faced are the high cost of sample testing. The solution that can be done is to predict changes in water quality through the neighborhood method to reduce the number of samples. The purpose of this study is to predict the water quality conditions spatially by using the Inverse Distance Weighted (IDW) method, especially in the watershed area of ??Batu Upper Brantas and find out the differences in the spread of pollution water in the conditions of the rainy and dry season, as the last output is to determine the water purification zone. The IDW method is able to visualize the spread of water pollution with distance-based interpolation calculations, where the advantages of IDW compared to other methods are that the calculations are at the minimum limit and the maximum limit of input values. The results obtained from this study were from 13 sampling points obtained 1 point experienced an increase in pollution status in the rainy season to dry season, namely from weight to moderate, 1 sample point that experienced changes in pollution status from mild to moderate, 7 points experienced a change in status from moderate to severe and 4 sample points experiencing changes in status from mild to severe. Branats River degradation zone upstream of Batu City at a distance of 2 km has experienced a decrease in DO during the rainy and dry season, the decomposition zone of Sunga Upper Brantas in Batu City is at a distance of 3 km to 6 km in the rainy season, and at a distance of 3-4 km dry season conditions , rehabilitation zone in the Brantas River in the Upper Batu City at a distance of 7 km to 8 km in the rainy season and at a distance of 5-11 km during the dry season and a re-purification zone, there was no purification zone at a distance of 11 km along the Brantas River in the Upper Batu City. Keywords : distribution, prediction, purification zone, water pollution