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1
Wang, Y., R. A. Prade, J. Griffith, W. E. Timberlake, and J. Arnold. "A fast random cost algorithm for physical mapping." Proceedings of the National Academy of Sciences 91, no. 23 (November 8, 1994): 11094–98. http://dx.doi.org/10.1073/pnas.91.23.11094.
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Fang, Zhu, and Zhengquan Xu. "Dynamic Random Graph Protection Scheme Based on Chaos and Cryptographic Random Mapping." Information 13, no. 11 (November 14, 2022): 537. http://dx.doi.org/10.3390/info13110537.
Повний текст джерелаАнотація:
Advances in network technology have enhanced the concern for network security issues. In order to address the problem that hopping graph are vulnerable to external attacks (e.g., the changing rules of fixed graphs are more easily grasped by attackers) and the challenge of achieving both interactivity and randomness in a network environment, this paper proposed a scheme for a dynamic graph based on chaos and cryptographic random mapping. The scheme allows hopping nodes to compute and obtain dynamically random and uncorrelated graph of other nodes independently of each other without additional interaction after the computational process of synchronous mirroring. We first iterate through the chaos algorithm to generate random seed parameters, which are used as input parameters for the encryption algorithm; secondly, we execute the encryption algorithm to generate a ciphertext of a specified length, which is converted into a fixed point number; and finally, the fixed point number is mapped to the network parameters corresponding to each node. The hopping nodes are independently updated with the same hopping map at each hopping period, and the configuration of their own network parameters is updated, so that the updated graph can effectively prevent external attacks. Finally, we have carried out simulation experiments and related tests on the proposed scheme and demonstrated that the performance requirements of the random graphs can be satisfied in both general and extreme cases.
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Xia, Xun, and Ling Chen. "Elastic Optical Network Service-Oriented Architecture (SOA) Used for Cloud Computing and Its Resource Mapping Optimization Scheme." Journal of Nanoelectronics and Optoelectronics 15, no. 4 (April 1, 2020): 442–49. http://dx.doi.org/10.1166/jno.2020.2783.
Повний текст джерелаАнотація:
In this study, starting from the elastic optical network, the layered and function isolated service-oriented architecture (SOA) is introduced, so as to propose an elastic optical network SOA for cloud computing, and further study the resource mapping of optical network. Linear mapping model, random routing mapping algorithm, load balancing mapping algorithm and link separation mapping algorithm are introduced respectively, and the resource utilization effect of different mapping algorithms for the proposed optical network is compared. During the experiment, firstly, the elastic optical network is tested. It is found that the node utilization and spectrum utilization of the underlying optical fiber level network are significantly improved. Within the average service time of 0.312 s∼0.416 s, the corresponding node utilization and spectrum utilization are 90% and 80% respectively. In the resource mapping experiment, load balancing algorithm and link separation algorithm can effectively improve the mapping success rate of services. Among them, the link separation mapping algorithm can improve the spectrum resource utilization of optical network by 15.6%. The elastic optical network SOA proposed in this study is helpful to improve the use of network resources.
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Zhu, Sheng Jie, and Xin Huan Feng. "Mountain Mapping Relaying Communication Positioning Algorithm." Applied Mechanics and Materials 336-338 (July 2013): 1804–8. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1804.
Повний текст джерелаАнотація:
Naturally, for the mountain mapping, the communication system's power is limited, As a matter of fact, the stations should be built as high as possible in order to eliminate the terrain shielding and radiate to the farther areas, however the number of users or other aspects also counts, In this paper, we introduced new methods to locate the modified location for the base stations in the mountain areas, and with this method, we use less stations to cover the most areas in the given situation. For a random mountain terrain. the coverage rate of this model is as high as 95.1%.
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Lin, Ke, and Rui Man. "A Similarity Algorithm Based on Hash Feature and Random Mapping." Journal of Physics: Conference Series 1865, no. 4 (April 1, 2021): 042020. http://dx.doi.org/10.1088/1742-6596/1865/4/042020.
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Meng, Lingqi, and Naoki Masuda. "Analysis of node2vec random walks on networks." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2243 (November 2020): 20200447. http://dx.doi.org/10.1098/rspa.2020.0447.
Повний текст джерелаАнотація:
Random walks have been proven to be useful for constructing various algorithms to gain information on networks. Algorithm node2vec employs biased random walks to realize embeddings of nodes into low-dimensional spaces, which can then be used for tasks such as multi-label classification and link prediction. The performance of the node2vec algorithm in these applications is considered to depend on properties of random walks that the algorithm uses. In the present study, we theoretically and numerically analyse random walks used by the node2vec. Those random walks are second-order Markov chains. We exploit the mapping of its transition rule to a transition probability matrix among directed edges to analyse the stationary probability, relaxation times in terms of the spectral gap of the transition probability matrix, and coalescence time. In particular, we show that node2vec random walk accelerates diffusion when walkers are designed to avoid both backtracking and visiting a neighbour of the previously visited node but do not avoid them completely.
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Liu, Yian-Kui, and Baoding Liu. "Expected Value Operator of Random Fuzzy Variable and Random Fuzzy Expected Value Models." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 02 (April 2003): 195–215. http://dx.doi.org/10.1142/s0218488503002016.
Повний текст джерелаАнотація:
Random fuzzy variable is mapping from a possibility space to a collection of random variables. This paper first presents a new definition of the expected value operator of a random fuzzy variable, and proves the linearity of the operator. Then, a random fuzzy simulation approach, which combines fuzzy simulation and random simulation, is designed to estimate the expected value of a random fuzzy variable. Based on the new expected value operator, three types of random fuzzy expected value models are presented to model decision systems where fuzziness and randomness appear simultaneously. In addition, random fuzzy simulation, neural networks and genetic algorithm are integrated to produce a hybrid intelligent algorithm for solving those random fuzzy expected valued models. Finally, three numerical examples are provided to illustrate the feasibility and the effectiveness of the proposed algorithm.
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Peng, Weiping, Shuang Cui, and Cheng Song. "One-time-pad cipher algorithm based on confusion mapping and DNA storage technology." PLOS ONE 16, no. 1 (January 20, 2021): e0245506. http://dx.doi.org/10.1371/journal.pone.0245506.
Повний текст джерелаАнотація:
In order to solve the problems of low computational security in the encoding mapping and difficulty in practical operation of biological experiments in DNA-based one-time-pad cryptography, we proposed a one-time-pad cipher algorithm based on confusion mapping and DNA storage technology. In our constructed algorithm, the confusion mapping methods such as chaos map, encoding mapping, confusion encoding table and simulating biological operation process are used to increase the key space. Among them, the encoding mapping and the confusion encoding table provide the realization conditions for the transition of data and biological information. By selecting security parameters and confounding parameters, the algorithm realizes a more random dynamic encryption and decryption process than similar algorithms. In addition, the use of DNA storage technologies including DNA synthesis and high-throughput sequencing ensures a viable biological encryption process. Theoretical analysis and simulation experiments show that the algorithm provides both mathematical and biological security, which not only has the difficult advantage of cracking DNA biological experiments, but also provides relatively high computational security.
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Hussain, Muhammad Afaq, Zhanlong Chen, Run Wang, Safeer Ullah Shah, Muhammad Shoaib, Nafees Ali, Daozhu Xu, and Chao Ma. "Landslide Susceptibility Mapping using Machine Learning Algorithm." Civil Engineering Journal 8, no. 2 (February 1, 2022): 209–24. http://dx.doi.org/10.28991/cej-2022-08-02-02.
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Landslides are natural disasters that have resulted in the loss of economies and lives over the years. The landslides caused by the 2005 Muzaffarabad earthquake heavily impacted the area, and slopes in the region have become unstable. This research was carried out to find out which areas, as in Muzaffarabad district, are sensitive to landslides and to define the relationship between landslides and geo-environmental factors using three tree-based classifiers, namely, Extreme Gradient Boosting (XGBoost), Random Forest (RF), and k-Nearest Neighbors (KNN). These machine learning models are innovative and can assess environmental problems and hazards for any given area on a regional scale. The research consists of three steps: Firstly, for training and validation, 94 historical landslides were randomly split into a proportion of 7/3. Secondly, topographical and geological data as well as satellite imagery were gathered, analyzed, and built into a spatial database using GIS Environment. Nine layers of landslide-conditioning factors were developed, including Aspect, Elevation, Slope, NDVI, Curvature, SPI, TWI, Lithology, and Landcover. Finally, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) value were used to estimate the model's efficiency. The area under the curve values for the RF, XGBoost, and KNN models are 0.895 (89.5%), 0.893 (89.3%), and 0.790 (79.0%), respectively. Based on the three machine learning techniques, the innovative outputs show that the performance of the Random Forest model has a maximum AUC value of 0.895, and it is more efficient than the other tree-based classifiers. Elevation and Slope were determined as the most important factors affecting landslides in this research area. The landslide susceptibility maps were classified into four classes: low, moderate, high, and very high susceptibility. The result maps are useful for future generalized construction operations, such as selecting and conserving new urban and infrastructural areas. Doi: 10.28991/CEJ-2022-08-02-02 Full Text: PDF
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Purwanto, Anang Dwi, Ketut Wikantika, Albertus Deliar, and Soni Darmawan. "Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia." Remote Sensing 15, no. 1 (December 21, 2022): 16. http://dx.doi.org/10.3390/rs15010016.
Повний текст джерелаАнотація:
Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI+NDSI+DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI+NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root (), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI+NDSI+DEM parameters with the number of trees = 100, mtry = all variables (, and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.
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He, Yuanhuizi, Changlin Wang, Fang Chen, Huicong Jia, Dong Liang, and Aqiang Yang. "Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm." Remote Sensing 11, no. 5 (March 5, 2019): 535. http://dx.doi.org/10.3390/rs11050535.
Повний текст джерелаАнотація:
Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.
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Zhao, Yue, and Lingfeng Liu. "A Bit Shift Image Encryption Algorithm Based on Double Chaotic Systems." Entropy 23, no. 9 (August 30, 2021): 1127. http://dx.doi.org/10.3390/e23091127.
Повний текст джерелаАнотація:
A chaotic system refers to a deterministic system with seemingly random irregular motion, and its behavior is uncertain, unrepeatable, and unpredictable. In recent years, researchers have proposed various image encryption schemes based on a single low-dimensional or high-dimensional chaotic system, but many algorithms have problems such as low security. Therefore, designing a good chaotic system and encryption scheme is very important for encryption algorithms. This paper constructs a new double chaotic system based on tent mapping and logistic mapping. In order to verify the practicability and feasibility of the new chaotic system, a displacement image encryption algorithm based on the new chaotic system was subsequently proposed. This paper proposes a displacement image encryption algorithm based on the new chaotic system. The algorithm uses an improved new nonlinear feedback function to generate two random sequences, one of which is used to generate the index sequence, the other is used to generate the encryption matrix, and the index sequence is used to control the generation of the encryption matrix required for encryption. Then, the encryption matrix and the scrambling matrix are XORed to obtain the first encryption image. Finally, a bit-shift encryption method is adopted to prevent the harm caused by key leakage and to improve the security of the algorithm. Numerical experiments show that the key space of the algorithm is not only large, but also the key sensitivity is relatively high, and it has good resistance to various attacks. The analysis shows that this algorithm has certain competitive advantages compared with other encryption algorithms.
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Noroozi, H., and M. Hasanlou. "FULL POLARIMETRIC TIME SERIES IMAGE ANALYSIS FOR CROP TYPE MAPPING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 14, 2023): 603–8. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-603-2023.
Повний текст джерелаАнотація:
Abstract. Crop information and quality are not only fundamental for experts using spatial decision support systems but also have many applications in irrigation management, economic analysis for import or export, food safety, and achieving sustainable agriculture. Remote sensing is a cheap and fast way of reaching this goal. Full polarimetric SAR unlike optical sensors is an all-weather system providing geometrical and physical properties of the earth’s surface events. Due to the dynamic changes in crop properties through their phenological stages, crop type mapping has been challenging. As a result, accurate, reliable, and cost-effective crop type mapping using minimum data and processing has been the goal of the remote sensing and precision agriculture community. In this study, a new method based on time series analysis of full polarimetric SAR data combined with radar indices, polarimetric decompositions followed by the three αs extracted from H/A/α decomposition, and unsupervised H/α/Wishart classification bands as features generated from only 5 dates of RADARSAT CONSTELLATION MISSION 2 data were used for classification of crops. Applying random forest and cat boost algorithm as classifiers an accuracy of 87.4% and 75% was respectively achieved. indicating that both algorithms have promising results. Although the random forest algorithm had better results, the cat boost algorithm had less noise in each field and more homogenous farms were detected.
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G, Dhanya, and J. Jayakumari. "Speech Scrambling Based on Chaotic Mapping and Random Permutation for Modern Mobile Communication Systems." APTIKOM Journal on Computer Science and Information Technologies 2, no. 1 (March 1, 2017): 20–25. http://dx.doi.org/10.11591/aptikom.j.csit.95.
Повний текст джерелаАнотація:
The expanding significance of securing data over the network has promoted growth of strong encryption algorithms. To enhance the information protection in network communications, this paper presents a Random permutation, chaotic mapping and pseudo random binary scrambling. It involves transforming the intelligible speech signal into an unintelligible form to protect it from interrupters. In this report, suggest a simple and secure procedure to secure the speech signal. The speech scrambling process makes use of two Permutations. In the first step, Random permutation algorithm is used to swap the rows of the original speech followed by swapping of rows using chaotic Bernoulli mapping. This produces an intermediary scrambled speech. In the second measure, pseudo random binary generator is used to make the final scrambled signal. Various analysis tests are then executed to determine the quality of the encrypted image. The test results determine the efficiency of the proposed speech scrambling process.
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Yang, Jianbo, Jianchu Xu, and De-Li Zhai. "Integrating Phenological and Geographical Information with Artificial Intelligence Algorithm to Map Rubber Plantations in Xishuangbanna." Remote Sensing 13, no. 14 (July 16, 2021): 2793. http://dx.doi.org/10.3390/rs13142793.
Повний текст джерелаАнотація:
Most natural rubber trees (Hevea brasiliensis) are grown on plantations, making rubber an important industrial crop. Rubber plantations are also an important source of household income for over 20 million people. The accurate mapping of rubber plantations is important for both local governments and the global market. Remote sensing has been a widely used approach for mapping rubber plantations, typically using optical remote sensing data obtained at the regional scale. Improving the efficiency and accuracy of rubber plantation maps has become a research hotspot in rubber-related literature. To improve the classification efficiency, researchers have combined the phenology, geography, and texture of rubber trees with spectral information. Among these, there are three main classifiers: maximum likelihood, QUEST decision tree, and random forest methods. However, until now, no comparative studies have been conducted for the above three classifiers. Therefore, in this study, we evaluated the mapping accuracy based on these three classifiers, using four kinds of data input: Landsat spectral information, phenology–Landsat spectral information, topography–Landsat spectral information, and phenology–topography–Landsat spectral information. We found that the random forest method had the highest mapping accuracy when compared with the maximum likelihood and QUEST decision tree methods. We also found that adding either phenology or topography could improve the mapping accuracy for rubber plantations. When either phenology or topography were added as parameters within the random forest method, the kappa coefficient increased by 5.5% and 6.2%, respectively, compared to the kappa coefficient for the baseline Landsat spectral band data input. The highest accuracy was obtained from the addition of both phenology–topography–Landsat spectral bands to the random forest method, achieving a kappa coefficient of 97%. We therefore mapped rubber plantations in Xishuangbanna using the random forest method, with the addition of phenology and topography information from 1990–2020. Our results demonstrated the usefulness of integrating phenology and topography for mapping rubber plantations. The machine learning approach showed great potential for accurate regional mapping, particularly by incorporating plant habitat and ecological information. We found that during 1990–2020, the total area of rubber plantations had expanded to over three times their former area, while natural forests had lost 17.2% of their former area.
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Amani, M., A. Ghorbanian, S. Mahdavi, and A. Mohammadzadeh. "IRANIAN LAND COVER MAPPING USING LANDSAT-8 IMAGERY AND RANDOM FOREST ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 77–81. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-77-2019.
Повний текст джерелаАнотація:
Abstract. Land cover classification is important for various environmental assessments. The opportunity of imaging the Earth’s surface makes remote sensing techniques efficient approaches for land cover classification. The only country-wide land cover map of Iran was produced by the Iranian Space Agency (ISA) using low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and a basic classification method. Thus, it is necessary to produce a more accurate map using advanced remote sensing and machine learning techniques. In this study, multi-temporal Landsat-8 data (1,321 images) were inserted into a Random Forest (RF) algorithm to classify the land cover of the entire country into 13 categories. To this end, all steps, including pre-processing, classification, and accuracy assessment were implemented in the Google Earth Engine (GEE) platform. The overall classification accuracy and Kappa Coefficient obtained from the Iran-wide map were 74% and 0.71, respectively, indicating the high potential of the proposed method for large-scale land cover mapping.
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Zhang, Yang, Weicheng Wu, Yaozu Qin, Ziyu Lin, Guiliang Zhang, Renxiang Chen, Yong Song, et al. "Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China." ISPRS International Journal of Geo-Information 9, no. 11 (November 23, 2020): 695. http://dx.doi.org/10.3390/ijgi9110695.
Повний текст джерелаАнотація:
Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.
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Grigoriev, Andrei, Richard Mott, and Hans Lehrach. "An Algorithm to Detect Chimeric Clones and Random Noise in Genomic Mapping." Genomics 22, no. 2 (July 1994): 482–86. http://dx.doi.org/10.1006/geno.1994.1416.
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Wang, Xiaolong, Haowen Yan, and Liming Zhang. "Vector Map Encryption Algorithm Based on Double Random Position Permutation Strategy." ISPRS International Journal of Geo-Information 10, no. 5 (May 7, 2021): 311. http://dx.doi.org/10.3390/ijgi10050311.
Повний текст джерелаАнотація:
Encryption of vector maps, used for copyright protection, is of importance in the community of geographic information sciences. However, some studies adopt one-to-one mapping to scramble vertices and permutate the coordinates one by one according to the coordinate position in a plain map. An attacker can easily obtain the key values by analyzing the relationship between the cipher vector map and the plain vector map, which will lead to the ineffectiveness of the scrambling operation. To solve the problem, a vector map encryption algorithm based on a double random position permutation strategy is proposed in this paper. First, the secret key sequence is generated using a four-dimensional quadratic autonomous hyperchaotic system. Then, all coordinates of the vector map are encrypted using the strategy of double random position permutation. Lastly, the encrypted coordinates are reorganized according to the vector map structure to obtain the cipher map. Experimental results show that: (1) one-to-one mapping between the plain vector map and cipher vector map is prevented from happening; (2) scrambling encryption between different map objects is achieved; (3) hackers cannot obtain the permutation key value by analyzing the pairs of the plain map and cipher map.
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Sun, Chang-Jian, and Fang Gao. "A Tent Marine Predators Algorithm with Estimation Distribution Algorithm and Gaussian Random Walk for Continuous Optimization Problems." Computational Intelligence and Neuroscience 2021 (December 28, 2021): 1–17. http://dx.doi.org/10.1155/2021/7695596.
Повний текст джерелаАнотація:
The marine predators algorithm (MPA) is a novel population-based optimization method that has been widely used in real-world optimization applications. However, MPA can easily fall into a local optimum because of the lack of population diversity in the late stage of optimization. To overcome this shortcoming, this paper proposes an MPA variant with a hybrid estimation distribution algorithm (EDA) and a Gaussian random walk strategy, namely, HEGMPA. The initial population is constructed using cubic mapping to enhance the diversity of individuals in the population. Then, EDA is adapted into MPA to modify the evolutionary direction using the population distribution information, thus improving the convergence performance of the algorithm. In addition, a Gaussian random walk strategy with medium solution is used to help the algorithm get rid of stagnation. The proposed algorithm is verified by simulation using the CEC2014 test suite. Simulation results show that the performance of HEGMPA is more competitive than other comparative algorithms, with significant improvements in terms of convergence accuracy and convergence speed.
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Li, Hongjun, Miguel Barão, Luís Rato, and Shengjun Wen. "HMM-Based Dynamic Mapping with Gaussian Random Fields." Electronics 11, no. 5 (February 26, 2022): 722. http://dx.doi.org/10.3390/electronics11050722.
Повний текст джерелаАнотація:
This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments.
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Yang, Zeng Xiang, and Sai Jin. "UAV Active SLAM Trajectory Programming Based on Optimal Control." Advanced Materials Research 765-767 (September 2013): 1932–35. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1932.
Повний текст джерелаАнотація:
To decrease the uncertainty of simultaneous localization and mapping of UAV, and at the same time, to increase the speed of searching the unknown environment at which UAV locates, an active SLAM trajectory programming algorithm is proposed based on optimal control. Therefore, UAV SLAM is tackled as a combined optimization problem, considering the precision of UAV location and mapping integrity. Based on the simplified UAV plane motion model, this algorithm is simulated and tested by comparing with the random SLAM algorithm. Simulation results show that this algorithm is effective.
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Wu, Fan, Yufen Ren, and Xiaoke Wang. "Application of Multi-Source Data for Mapping Plantation Based on Random Forest Algorithm in North China." Remote Sensing 14, no. 19 (October 3, 2022): 4946. http://dx.doi.org/10.3390/rs14194946.
Повний текст джерелаАнотація:
The expansion of plantation poses new challenges for mapping forest, especially in mountainous regions. Using multi-source data, this study explored the capability of the random forest (RF) algorithm for the extraction and mapping of five forest types located in Yanqing, north China. The Google Earth imagery, forest inventory data, GaoFen-1 wide-field-of-view (GF-1 WFV) images and DEM were applied for obtaining 125 features in total. The recursive feature elimination (RFE) method selected 32 features for mapping five forest types. The results attained overall accuracy of 87.06%, with a Kappa coefficient of 0.833. The mean decrease accuracy (MDA) reveals that the DEM, LAI and EVI in winter and three texture features (entropy, variance and mean) make great contributions to forest classification. The texture features from the NIR band are important, while the other texture features have little contribution. This study has demonstrated the potential of applying multi-source data based on RF algorithm for extracting and mapping plantation forest in north China.
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Prins, Adriaan Jacobus, and Adriaan van Niekerk. "Regional Mapping of Vineyards Using Machine Learning and LiDAR Data." International Journal of Applied Geospatial Research 11, no. 4 (October 2020): 1–22. http://dx.doi.org/10.4018/ijagr.2020100101.
Повний текст джерелаАнотація:
This study evaluates the use of LiDAR data and machine learning algorithms for mapping vineyards. Vineyards are planted in rows spaced at various distances, which can cause spectral mixing within individual pixels and complicate image classification. Four resolution where used for generating normalized digital surface model and intensity derivatives from the LiDAR data. In addition, texture measures with window sizes of 3x3 and 5x5 were generated from the LiDAR derivatives. The different combinations of the resolutions and window sizes resulted in eight data sets that were used as input to 11 machine learning algorithms. A larger window size was found to improve the overall accuracy for all the classifier–resolution combinations. The results showed that random forest with texture measures generated at a 5x5 window size outperformed the other experiments, regardless of the resolution used. The authors conclude that the random forest algorithm used on LiDAR derivatives with a resolution of 1.5m and a window size of 5x5 is the recommend configuration for vineyard mapping using LiDAR data.
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Kamejima, Kohji. "Stochastic-Computational Approach to Self-Similarity Detection in Random Image Fields." Journal of Robotics and Mechatronics 11, no. 2 (April 20, 1999): 88–97. http://dx.doi.org/10.20965/jrm.1999.p0088.
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We present an integrated stochastic-computational scheme for detecting self-similarity in random image fields. By modeling imaging as a Brownian motion in a successively reduced domain, capture probability is induced on the image plane. Attractor distribution is simultaneously identified with fixed points corresponding to mapping sequences generated by imaging. The computational structure of local maxima of capture probability is extracted through invariance and observability analysis to match observed attractors with a preassigned mapping dictionary. Proposed scheme was implemented as digital algorithm and verified through simulation.
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Drzewiecki, Wojciech. "Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping." Geodesy and Cartography 66, no. 2 (December 20, 2017): 171–210. http://dx.doi.org/10.1515/geocart-2017-0012.
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AbstractWe evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.
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Yang, Annan, Chunmei Wang, Guowei Pang, Yongqing Long, Lei Wang, Richard M. Cruse, and Qinke Yang. "Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models." ISPRS International Journal of Geo-Information 10, no. 10 (October 9, 2021): 680. http://dx.doi.org/10.3390/ijgi10100680.
Повний текст джерелаАнотація:
Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.
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Li, Guodong, and Xuejuan Han. "A Color Image Encryption Algorithm with Cat Map and Chaos Map Embedded." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, Supp01 (March 26, 2021): 73–87. http://dx.doi.org/10.1142/s0218488521400043.
Повний текст джерелаАнотація:
In order to deal with the problem of encryption algorithms being overly simplistic, and the relatively low security of color images that creates potential to be attacked in the transmission process, this paper will introduce a new encryption algorithm that is designed to divide color images into R, G and B layers. In the scrambling operation: the first scrambling is aimed to block the clear text image scrambling; The second scrambling is the dynamic Arnold scrambling of the ciphertext after the first scrambling. In the diffusion operation, the scrambled ciphertext image was taken as the input, and the pseudo-random sequence generated by Tent mapping and Sine mapping was embedded. The sequence generated by Logistic mapping was used to select sub-blocks for block diffusion of the image. Tent-Sine mapping was applied to the second diffusion to obtain the final ciphertext image. The algorithm designed in this paper combines image block scrambling and dynamic Arnold scrambling, the scrambling degree of each layer of image pixels would be greatly improved, thus improving the security of color images. In the process of diffusion, chaos sequence is selected for diffusion operation, which increases the difficulty of decoding ciphertext. The simulation results show that the new algorithm has desirable encryption effect, strong key sensitivity and large key space, and complex encryption algorithm can effectively resist attacks, which certainly has value in image information security.
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Bachri, Imane, Mustapha Hakdaoui, Mohammed Raji, Abdelmajid Benbouziane, and Hicham Si Mhamdi. "Identification of Lithology Using Sentinel-2A Through an Ensemble of Machine Learning Algorithms." International Journal of Applied Geospatial Research 13, no. 1 (January 2022): 1–17. http://dx.doi.org/10.4018/ijagr.297524.
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Remotely sensed data has become an effective, operative and applicable tool that provide critical support for geological surveys and studies by reducing the costs and increasing the precision. Advances in remote-sensing data analysis methods, like machine learning algorithms, allow for easy and impartial geological mapping. This study aims to carry out a rigorous comparison of the performance of three supervised classification methods: Random Forest, k-Nearest Neighbor and maximum likelihood using remote sensing data and additional information in Souk El Had N’Befourna region. The enhancement of remote sensing geological classification by using geomorphometric features, principal component analysis, gray level co-occurrence matrix (GLCM) and multispectral data of the Sentinel-2A imagery was highlighted. The Random Forest algorithm showed reliable results and discriminated limestone, dolomite, conglomerate, sandstone and rhyolite, silt and Alluvium, ignimbrite, granodiorite, Lutite, granite, and quartzite. The best overall accuracy (~91%) was achieved by Random Forest algorithm.
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Huang, Penghe, Dongyan Li, Yu Wang, Huimin Zhao, and Wu Deng. "A Novel Color Image Encryption Algorithm Using Coupled Map Lattice with Polymorphic Mapping." Electronics 11, no. 21 (October 24, 2022): 3436. http://dx.doi.org/10.3390/electronics11213436.
Повний текст джерелаАнотація:
Some typical security algorithms such as SHA, MD4, MD5, etc. have been cracked in recent years. However, these algorithms have some shortcomings. Therefore, the traditional one-dimensional-mapping coupled lattice is improved by using the idea of polymorphism in this paper, and a polymorphic mapping–coupled map lattice with information entropy is developed for encrypting color images. Firstly, we extend a diffusion matrix with the original 4 × 4 matrix into an n × n matrix. Then, the Huffman idea is employed to propose a new pixel-level substitution method, which is applied to replace the grey degree value. We employ the idea of polymorphism and select f(x) in the spatiotemporal chaotic system. The pseudo-random sequence is more diversified and the sequence is homogenized. Finally, three plaintext color images of 256×256×3, “Lena”, “Peppers” and “Mandrill”, are selected in order to prove the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm has a large key space, better sensitivity to keys and plaintext images, and a better encryption effect.
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Huang, Xuewei, Shiyuan Wang, and Kui Xiong. "The Cauchy Conjugate Gradient Algorithm with Random Fourier Features." Symmetry 11, no. 10 (October 22, 2019): 1323. http://dx.doi.org/10.3390/sym11101323.
Повний текст джерелаАнотація:
Random Fourier mapping (RFM) in kernel adaptive filters (KAFs) provides an efficient method to curb the linear growth of the dictionary by projecting the original input data into a finite-dimensional space. The commonly used measure in RFM-based KAFs is the minimum mean square error (MMSE), which causes performance deterioration in the presence of non-Gaussian noises. To address this issue, the minimum Cauchy loss (MCL) criterion has been successfully applied for combating non-Gaussian noises in KAFs. However, these KAFs using the well-known stochastic gradient descent (SGD) optimization method may suffer from slow convergence rate and low filtering accuracy. To this end, we propose a novel robust random Fourier features Cauchy conjugate gradient (RFFCCG) algorithm using the conjugate gradient (CG) optimization method in this paper. The proposed RFFCCG algorithm with low complexity can achieve better filtering performance than the KAFs with sparsification, such as the kernel recursive maximum correntropy algorithm with novelty criterion (KRMC-NC), in stationary and non-stationary environments. Monte Carlo simulations conducted in the time-series prediction and nonlinear system identification confirm the superiorities of the proposed algorithm.
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Chen, Jinlin, Yiquan Wu, Yeguo Sun, and Chunzhi Yang. "Image Encryption Algorithm Using 2-Order Bit Compass Coding and Chaotic Mapping." Symmetry 14, no. 7 (July 20, 2022): 1482. http://dx.doi.org/10.3390/sym14071482.
Повний текст джерелаАнотація:
This paper proposes a novel image encryption algorithm based on an integer form of chaotic mapping and 2-order bit compass diffusion technique. Chaotic mapping has been widely used in image encryption. If the floating-point number generated by chaotic mapping is applied to image encryption algorithm, it will slow encryption and increase the difficulty of hardware implementation. An innovative pseudo-random integer sequence generator is proposed. In chaotic system, the result of one-iteration is used as the shift value of two binary sequences, the original symmetry relationship is changed, and then XOR operation is performed to generate a new binary sequence. Multiple iterations can generate pseudo-random integer sequences. Here integer sequences have been used in scrambling of pixel positions. Meanwhile, this paper demonstrates that there is an inverse operation in the XOR operation of two binary sequences. A new pixel diffusion technique based on bit compass coding is proposed. The key vector of the algorithm comes from the original image and is hidden by image encryption. The efficiency of our proposed method in encrypting a large number of images is evaluated using security analysis and time complexity. The performance evaluation of algorithm includes key space, histogram differential attacks, gray value distribution(GDV),correlation coefficient, PSNR, entropy, and sensitivity. The comparison between the results of coefficient, entropy, PSNR, GDV, and time complexity further proves the effectiveness of the algorithm.
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Alzahrani, Ali, and Awos Kanan. "Machine Learning Approaches for Developing Land Cover Mapping." Applied Bionics and Biomechanics 2022 (June 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/5190193.
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In remote sensing data processing, cover classification on decimeter-level data is a well-studied but tough subject that has been well-documented. The majority of currently existent works make use of orthographic photographs or orthophotos and digital surface models that go with them (DSMs). Urban land cover classification plays a significant role in the field of remote sensing to enhance the quality of different applications including environment protection, sustainable development, and resource management and planning. Novelty of the research done in this area is focused on extracting features from high-resolution satellite images to be used in the classification process. However, it is well known in machine learning literature that some of the extracted features are irrelevant to the classification process with a negative or no effect on its accuracy. In this work, a genetic algorithm-based feature selection approach is used to enhance the performance of urban land cover classification. Neural networks (NNs) and random forest (RF) classifiers were used to evaluate the proposed approach on a recent urban land cover dataset of nine different classes. Experimental results show that the proposed approach achieved better performance with RF classifier using only 27% of the features. The random forest tree has achieved highest accuracy 84.27%; it is concluded that the RF algorithm is an appropriate algorithm for classifying cover land.
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Naghibi, Seyed Amir, Kourosh Ahmadi, and Alireza Daneshi. "Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping." Water Resources Management 31, no. 9 (April 19, 2017): 2761–75. http://dx.doi.org/10.1007/s11269-017-1660-3.
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Lou, Peiqing, Bolin Fu, Hongchang He, Ying Li, Tingyuan Tang, Xingchen Lin, Donglin Fan, and Ertao Gao. "An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data." Remote Sensing 12, no. 8 (April 17, 2020): 1270. http://dx.doi.org/10.3390/rs12081270.
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Discriminating marsh vegetation is critical for the rapid assessment and management of wetlands. The study area, Honghe National Nature Reserve (HNNR), a typical freshwater wetland, is located in Northeast China. This study optimized the parameters (mtry and ntrees) of an object-based random forest (RF) algorithm to improve the applicability of marsh vegetation classification. Multidimensional datasets were used as the input variables for model training, then variable selection was performed on the variables to eliminate redundancy, which improved classification efficiency and overall accuracy. Finally, the performance of a new generation of Chinese high-spatial-resolution Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite images for marsh vegetation classification was evaluated using the improved object-based RF algorithm with accuracy assessment. The specific conclusions of this study are as follows: (1) Optimized object-based RF classifications consistently produced more than 70.26% overall accuracy for all scenarios of GF-1 and ZY-3 at the 95% confidence interval. The performance of ZY-3 imagery applied to marsh vegetation mapping is lower than that of GF-1 imagery due to the coarse spatial resolution. (2) Parameter optimization of the object-based RF algorithm effectively improved the stability and classification accuracy of the algorithm. After parameter adjustment, scenario 3 for GF-1 data had the highest classification accuracy of 84% (ZY-3 is 74.72%) at the 95% confidence interval. (3) The introduction of multidimensional datasets improved the overall accuracy of marsh vegetation mapping, but with many redundant variables. Using three variable selection algorithms to remove redundant variables from the multidimensional datasets effectively improved the classification efficiency and overall accuracy. The recursive feature elimination (RFE)-based variable selection algorithm had the best performance. (4) Optical spectral bands, spectral indices, mean value of green and NIR bands in textural information, DEM, TWI, compactness, max difference, and shape index are valuable variables for marsh vegetation mapping. (5) GF-1 and ZY-3 images had higher classification accuracy for forest, cropland, shrubs, and open water.
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Guan , Zhaoxiong, Junxian Li, Linqing Huang, Xiaoming Xiong, Yuan Liu, and Shuting Cai. "A Novel and Fast Encryption System Based on Improved Josephus Scrambling and Chaotic Mapping." Entropy 24, no. 3 (March 9, 2022): 384. http://dx.doi.org/10.3390/e24030384.
Повний текст джерелаАнотація:
To address the shortcomings of weak confusion and high time complexity of the existing permutation algorithms, including the traditional Josephus ring permutation (TJRP), an improved Josephus ring-based permutation (IJRBP) algorithm is developed. The proposed IJRBP replaces the remove operation used in TJRP with the position exchange operation and employs random permutation steps instead of fixed steps, which can offer a better scrambling effect and a higher permutation efficiency, compared with various scrambling methods. Then, a new encryption algorithm based on the IJRBP and chaotic system is developed. In our scheme, the plaintext feature parameter, which is related to the plaintext and a random sequence generated by a chaotic system, is used as the shift step of the circular shift operation to generate the diffusion matrix, which means that a minor change in the source image will generate a totally different encrypted image. Such a strategy strikes a balance between plaintext sensitivity and ciphertext sensitivity to obtain the ability to resist chosen-plaintext attacks (CPAs) and the high robustness of resisting noise attacks and data loss. Simulation results demonstrate that the proposed image cryptosystem has the advantages of great encryption efficiency and the ability to resist various common attacks.
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Yang, Zi Heng, Na Li, Li Yuan Liu, Ren Ji Qi, and Ling Ling Yu. "Research on Improved AES Encryption Algorithm." Advanced Materials Research 989-994 (July 2014): 1861–64. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1861.
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AES (Advanced Encryption Standard) in May 26, 2002 became effective standard. AES algorithm research has become a hot topic at home and abroad, and the algorithm has been widely applied in the field of information security. Since the algorithm of AES key expansion part is open, so the key is between the wheel can be derived from each other, the AES algorithm designed for this security risk by generating pseudo-random number. Logistic mapping a certain length, after quantization is used as a key to improve the security of the AES algorithm.
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Yi, Nengjun, and Shizhong Xu. "A Random Model Approach to Mapping Quantitative Trait Loci for Complex Binary Traits in Outbred Populations." Genetics 153, no. 2 (October 1, 1999): 1029–40. http://dx.doi.org/10.1093/genetics/153.2.1029.
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Abstract Mapping quantitative trait loci (QTL) for complex binary traits is more challenging than for normally distributed traits due to the nonlinear relationship between the observed phenotype and unobservable genetic effects, especially when the mapping population contains multiple outbred families. Because the number of alleles of a QTL depends on the number of founders in an outbred population, it is more appropriate to treat the effect of each allele as a random variable so that a single variance rather than individual allelic effects is estimated and tested. Such a method is called the random model approach. In this study, we develop the random model approach of QTL mapping for binary traits in outbred populations. An EM-algorithm with a Fisher-scoring algorithm embedded in each E-step is adopted here to estimate the genetic variances. A simple Monte Carlo integration technique is used here to calculate the likelihood-ratio test statistic. For the first time we show that QTL of complex binary traits in an outbred population can be scanned along a chromosome for their positions, estimated for their explained variances, and tested for their statistical significance. Application of the method is illustrated using a set of simulated data.
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Lu, Liang, Adrian Carrio, Carlos Sampedro, and Pascual Campoy. "A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor." Remote Sensing 13, no. 9 (May 5, 2021): 1796. http://dx.doi.org/10.3390/rs13091796.
Повний текст джерелаАнотація:
Collision-avoidance is a crucial research topic in robotics. Designing a collision-avoidance algorithm is still a challenging and open task, because of the requirements for navigating in unstructured and dynamic environments using limited payload and computing resources on board micro aerial vehicles. This article presents a novel depth-based collision-avoidance method for aerial robots, enabling high-speed flights in dynamic environments. First of all, a depth-based Euclidean distance field mapping algorithm is generated. Then, the proposed Euclidean distance field mapping strategy is integrated with a rapid-exploration random tree to construct a collision-avoidance system. The experimental results show that the proposed collision-avoidance algorithm has a robust performance at high flight speeds in challenging dynamic environments. The experimental results show that the proposed collision-avoidance algorithm can perform faster collision-avoidance maneuvers when compared to the state-of-art algorithms (the average computing time of the collision maneuver is 25.4 ms, while the minimum computing time is 10.4 ms). The average computing time is six times faster than one baseline algorithm. Additionally, fully autonomous flight experiments are also conducted for validating the presented collision-avoidance approach.
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LEI, KIN FONG, SHIH-CHUNG CHENG, MING-YIH LEE, and WEN-YEN LIN. "MEASUREMENT AND ESTIMATION OF MUSCLE CONTRACTION STRENGTH USING MECHANOMYOGRAPHY BASED ON ARTIFICIAL NEURAL NETWORK ALGORITHM." Biomedical Engineering: Applications, Basis and Communications 25, no. 02 (April 2013): 1350020. http://dx.doi.org/10.4015/s1016237213500208.
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Muscle contraction strength estimation using mechanomyographic (MMG) signal is typically calculated by the root mean square (RMS) amplitude. Raw MMG signal is processed by rectification, low-pass filtering, and mapping. In this work, beside RMS amplitude, another significant parameter of MMG signal, i.e. frequency variance (VAR), is introduced and used for constructing an algorithm for estimating the muscle contraction strength. Seven participants produced isometric contractions about the elbow while MMG signal and generated torque (resultant of muscle contraction strength) of biceps brachii were recorded. We found that MMG RMS increased monotonously and VAR decreased under incremental voluntary contractions. Based on these results, a two-layer neural network was utilized for the model of estimating the muscle contraction strength from MMG RMS and VAR. Experimental evaluation was performed under constant posture and sinusoidal contractions at 0.5 Hz, 0.25 Hz, 0.125 Hz, and random frequency. The results of the proposed algorithm and MMG RMS linear mapping were also compared. The proposed algorithm has better accuracy than linear mapping for all contraction frequencies. The mean absolute error decreased 6% for the 0.5Hz contraction, 43% for 0.25 Hz contraction, 52% for 0.125 Hz contraction, and 30% for random frequency contraction.
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Woo, Seungjun, Francisco Yumbla, Chanyong Park, Hyouk Ryeol Choi, and Hyungpil Moon. "Plane-based stairway mapping for legged robot locomotion." Industrial Robot: the international journal of robotics research and application 47, no. 4 (April 23, 2020): 569–80. http://dx.doi.org/10.1108/ir-09-2019-0189.
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Purpose The purpose of this study is to propose a novel plane-based mapping method for legged-robot navigation in a stairway environment. Design/methodology/approach The approach implemented in this study estimates a plane for each step of a stairway using a weighted average of sensor measurements and predictions. It segments planes from point cloud data via random sample consensus (RANSAC). The prediction uses the regular structure of a stairway. When estimating a plane, the algorithm considers the errors introduced by the distance sensor and RANSAC, in addition to stairstep irregularities, by using covariance matrices. The plane coefficients are managed separately with the data structure suggested in this study. In addition, this data structure allows the algorithm to store the information of each stairstep as a single entity. Findings In the case of a stairway environment, the accuracy delivered by the proposed algorithm was higher than those delivered by traditional mapping methods. The hardware experiment verified the accuracy and applicability of the algorithm. Originality/value The proposed algorithm provides accurate stairway-environment mapping and detailed specifications of each stairstep. Using this information, a legged robot can navigate and plan its motion in a stairway environment more efficiently.
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Saini, R., and S. K. Ghosh. "EXPLORING CAPABILITIES OF SENTINEL-2 FOR VEGETATION MAPPING USING RANDOM FOREST." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1499–502. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1499-2018.
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Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice. This study aims to explore the capabilities of Sentinel-2 data over Landsat-8 Operational Land Imager (OLI) data for vegetation mapping. Two combination of Sentinel-2 dataset have been considered, first combination is 4-band dataset at 10m resolution which consists of NIR, R, G and B bands, while second combination is generated by stacking 4 bands having 10 m resolution along with other six sharpened bands using Gram-Schmidt algorithm. For Landsat-8 OLI dataset, six multispectral bands have been pan-sharpened to have a spatial resolution of 15 m using Gram-Schmidt algorithm. Random Forest (RF) and Maximum Likelihood classifier (MLC) have been selected for classification of images. It is found that, overall accuracy achieved by RF for 4-band, 10-band dataset of Sentinel-2 and Landsat-8 OLI are 88.38 %, 90.05 % and 86.68 % respectively. While, MLC give an overall accuracy of 85.12 %, 87.14 % and 83.56 % for 4-band, 10-band Sentinel and Landsat-8 OLI respectively. Results shown that 10-band Sentinel-2 dataset gives highest accuracy and shows a rise of 3.37 % for RF and 3.58 % for MLC compared to Landsat-8 OLI. However, all the classes show significant improvement in accuracy but a major rise in accuracy is observed for Sugarcane, Wheat and Fodder for Sentinel 10-band imagery. This study substantiates the fact that Sentinel-2 data can be utilized for mapping of vegetation with a good degree of accuracy when compared to Landsat-8 OLI specifically when objective is to map a sub class of vegetation.
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Zhou, Huailai, Yuanjun Wang, Tengfei Lin, Fangyu Li, and Kurt J. Marfurt. "Value of nonstationary wavelet spectral balancing in mapping a faulted fluvial system, Bohai Gulf, China." Interpretation 3, no. 3 (August 1, 2015): SS1—SS13. http://dx.doi.org/10.1190/int-2014-0128.1.
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Seismic data with enhanced resolution allow interpreters to effectively delineate and interpret architectural components of stratigraphically thin geologic features. We used a recently developed time-frequency domain deconvolution method to spectrally balance nonstationary seismic data. The method was based on polynomial fitting of seismic wavelet magnitude spectra. The deconvolution increased the spectral bandwidth but did not amplify random noise. We compared our new spectral modeling algorithm with existing time-variant spectral-whitening and inverse [Formula: see text]-filtering algorithms using a 3D offshore survey acquired over Bohai Gulf, China. We mapped these improvements spatially using a suite of 3D volumetric coherence, energy, curvature, and frequency attributes. The resulting images displayed improved lateral resolution of channel edges and fault edges with few, if any artifacts associated with amplification of random noise.
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Adugna, Tesfaye, Wenbo Xu, and Jinlong Fan. "Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images." Remote Sensing 14, no. 3 (January 25, 2022): 574. http://dx.doi.org/10.3390/rs14030574.
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The type of algorithm employed to classify remote sensing imageries plays a great role in affecting the accuracy. In recent decades, machine learning (ML) has received great attention due to its robustness in remote sensing image classification. In this regard, random forest (RF) and support vector machine (SVM) are two of the most widely used ML algorithms to generate land cover (LC) maps from satellite imageries. Although several comparisons have been conducted between these two algorithms, the findings are contradicting. Moreover, the comparisons were made on local-scale LC map generation either from high or medium resolution images using various software, but not Python. In this paper, we compared the performance of these two algorithms for large area LC mapping of parts of Africa using coarse resolution imageries in the Python platform by the employing Scikit-Learn (sklearn) library. We employed a big dataset, 297 metrics, comprised of systematically selected 9-month composite FegnYun-3C (FY-3C) satellite images with 1 km resolution. Several experiments were performed using a range of values to determine the best values for the two most important parameters of each classifier, the number of trees and the number of variables, for RF, and penalty value and gamma for SVM, and to obtain the best model of each algorithm. Our results showed that RF outperformed SVM yielding 0.86 (OA) and 0.83 (k), which are 1–2% and 3% higher than the best SVM model, respectively. In addition, RF performed better in mixed class classification; however, it performed almost the same when classifying relatively pure classes with distinct spectral variation, i.e., consisting of less mixed pixels. Furthermore, RF is more efficient in handling large input datasets where the SVM fails. Hence, RF is a more robust ML algorithm especially for heterogeneous large area mapping using coarse resolution images. Finally, default parameter values in the sklearn library work well for satellite image classification with minor/or no adjustment for these algorithms.
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Erichsen, Lars, and Per Bruun Brockhoff. "An application of latent class random coefficient regression." Journal of Applied Mathematics and Decision Sciences 8, no. 4 (January 1, 2004): 247–60. http://dx.doi.org/10.1155/s1173912604000161.
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In this paper we apply a statistical model combining a random coefficient regression model and a latent class regression model. The EM-algorithm is used for maximum likelihood estimation of the unknown parameters in the model and it is pointed out how this leads to a straightforward handling of a number of different variance/covariance restrictions. Finally, the model is used to analyze how consumers' preferences for eight coffee samples relate to sensory characteristics of the coffees. Within this application the analysis corresponds to a model-based version of the so-called external preference mapping.
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Mariano, Córdoba, and Balzarini Mónica. "A random forest-based algorithm for data-intensive spatial interpolation in crop yield mapping." Computers and Electronics in Agriculture 184 (May 2021): 106094. http://dx.doi.org/10.1016/j.compag.2021.106094.
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Lu, Yijie, Zhen Zhang, and Danni Huang. "Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir." Water 12, no. 11 (November 18, 2020): 3231. http://dx.doi.org/10.3390/w12113231.
Повний текст джерелаАнотація:
Debris-covered glaciers are common features on the eastern Pamir and serve as important indicators of climate change promptly. However, mapping of debris-covered glaciers in alpine regions is still challenging due to many factors including the spectral similarity between debris and the adjacent bedrock, shadows cast from mountains and clouds, and seasonal snow cover. Considering that few studies have added movement velocity features when extracting glacier boundaries, we innovatively developed an automatic algorithm consisting of rule-based image segmentation and Random Forest to extract information about debris-covered glaciers with Landsat-8 OLI/TIRS data for spectral, texture and temperature features, multi-digital elevation models (DEMs) for elevation and topographic features, and the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) for movement velocity features, and accuracy evaluation was performed to determine the optimal feature combination extraction of debris-covered glaciers. The study found that the overall accuracy of extracting debris-covered glaciers using combined movement velocity features is 97.60%, and the Kappa coefficient is 0.9624, which is better than the extraction results using other schemes. The high classification accuracy obtained using our method overcomes most of the above-mentioned challenges and can detect debris-covered glaciers, illustrating that this method can be executed efficiently, which will further help water resources management.
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Fu-Lai, Wang. "A universal algorithm to generate pseudo-random numbers based on uniform mapping as homeomorphism." Chinese Physics B 19, no. 9 (September 2010): 090505. http://dx.doi.org/10.1088/1674-1056/19/9/090505.
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Aktas, Hakan, and Bekir Taner San. "Landslide susceptibility mapping using an automatic sampling algorithm based on two level random sampling." Computers & Geosciences 133 (December 2019): 104329. http://dx.doi.org/10.1016/j.cageo.2019.104329.
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Wang, Yong, Shuying Zang, and Yang Tian. "Mapping paddy rice with the random forest algorithm using MODIS and SMAP time series." Chaos, Solitons & Fractals 140 (November 2020): 110116. http://dx.doi.org/10.1016/j.chaos.2020.110116.
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