Journal articles: 'Multi-cover Problem'

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Mestre, Julián. "On the multi-radius cover problem." Information Processing Letters 99, no. 5 (September 2006): 195–98. http://dx.doi.org/10.1016/j.ipl.2006.04.012.

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Hochbaum, Dorit S., and Asaf Levin. "The multi-integer set cover and the facility terminal cover problem." Networks 53, no. 1 (January 2009): 63–66. http://dx.doi.org/10.1002/net.20265.

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Shi, Yishuo, Yingli Ran, Zhao Zhang, James Willson, Guangmo Tong, and Ding-Zhu Du. "Approximation algorithm for the partial set multi-cover problem." Journal of Global Optimization 75, no. 4 (July 3, 2019): 1133–46. http://dx.doi.org/10.1007/s10898-019-00804-y.

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Hu, Shuli, Xiaoli Wu, Huan Liu, Yiyuan Wang, Ruizhi Li, and Minghao Yin. "Multi-Objective Neighborhood Search Algorithm Based on Decomposition for Multi-Objective Minimum Weighted Vertex Cover Problem." Sustainability 11, no. 13 (July 2, 2019): 3634. http://dx.doi.org/10.3390/su11133634.

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The multi-objective minimum weighted vertex cover problem aims to minimize the sum of different single type weights simultaneously. In this paper, we focus on the bi-objective minimum weighted vertex cover and propose a multi-objective algorithm integrating iterated neighborhood search with decomposition technique to solve this problem. Initially, we adopt the decomposition method to divide the multi-objective problem into several scalar optimization sub-problems. Meanwhile, to find more possible optimal solutions, we design a mixed score function according to the problem feature, which is applied in initializing procedure and neighborhood search. During the neighborhood search, three operators ( A d d , D e l e t e , S w a p ) explore the search space effectively. We performed numerical experiments on many instances, and the results show the effectiveness of our new algorithm (combining decomposition and neighborhood search with mixed score) on several experimental metrics. We compared our experimental results with the classical multi-objective algorithm non-dominated sorting genetic algorithm II. It was obviously shown that our algorithm can provide much better results than the comparative algorithm considering the different metrics.

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Kim, Suhwan. "Direction finder deployment based on the partial set multi-cover problem." International Journal of Management Science and Engineering Management 14, no. 2 (August 2, 2018): 94–101. http://dx.doi.org/10.1080/17509653.2018.1500952.

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Soleiman Mohammadi, Limaei, Lohmander Peter, and Olsson Leif. "Dynamic growth models for continuous cover multi-species forestry in Iranian Caspian forests." Journal of Forest Science 63, No. 11 (November 30, 2017): 519–29. http://dx.doi.org/10.17221/32/2017-jfs.

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This study concerns some of the relevant topics of the Iranian Caspian forestry planning problem, in particular the first central components in this modelling process, such as forest modelling, forest statistics and growth function estimations. The required data was collected from Iranian Caspian forests. To do so, 201 sample plots were determined and the parameters such as number of trees, tree diameter at breast height and tree height were measured at each sample plot. Three sample plots at different 3 elevations were chosen to measure the tree increment. Data has been used to estimate a modified logistic growth model and a model that describes the growth of the basal area of individual trees as a function of basal area. General function analysis has been applied in combination with regression analysis. The results are interpreted from ecological perspectives. Furthermore, a dynamic multi-species growth model theory is developed and analysed with respect to dynamic behaviour, equilibria, convergence and stability. Logistic growth models have been found applicable for continuous cover forest management optimization. Optimization of management decisions in a changing and not perfectly predictable world should always be based on adaptive optimization.

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Zakarai, Mohammad, Hegazy Zaher, and Naglaa Ragaa. "Stochastic Local Search for Solving Chance-Constrained Multi-Manned U-shaped Assembly Line Balancing Problem with Time and Space Constraints." Journal of University of Shanghai for Science and Technology 23, no. 04 (April 28, 2021): 278–95. http://dx.doi.org/10.51201/jusst/21/04242.

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The assembly line balancing problems have great importance in research and industry fields. They allow minimizing the learning aspects and guaranteeing a fixed number of products per day. This paper introduces a new problem that combines the multi-manned concept with the U-shaped lines with time and space constraints under uncertainty. The processing time of the tasks is considered as random variables with known means and variances. Therefore, chance-constraints appear in the cycle time constraints. In addition, each task has an associated area, where the assigned tasks per station are restricted by a total area. The proposed algorithm for solving the problem is a stochastic local search algorithm. The parameter levels of the proposed algorithm are optimized by the Taguchi method to cover the small, medium, and large-sized problems. Well-known benchmark problems have been adapted to cover the new model. The computational results showed the importance of the new problem and the efficiency of the proposed algorithm.

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Nguyen, Lan N., and My T. Thai. "Minimum Robust Multi-Submodular Cover for Fairness." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 28, 2021): 9109–16. http://dx.doi.org/10.1609/aaai.v35i10.17100.

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In this paper, we study a novel problem, Minimum Robust Multi-Submodular Cover for Fairness (MinRF), as follows: given a ground set V; m monotone submodular functions f_1,...,f_m; m thresholds T_1,...,T_m and a non-negative integer r; MinRF asks for the smallest set S such that f_i(S \ X) ≥ T_i for all i ∈ [m] and |X| ≤ r. We prove that MinRF is inapproximable within (1- ε) ln m; and no algorithm, taking fewer than exponential number of queries in term of r, is able to output a feasible set to MinRF with high certainty. Three bicriteria approximation algorithms with performance guarantees are proposed: one for r = 0, one for r = 1, and one for general r. We further investigate our algorithms' performance in two applications of MinRF, Information Propagation for Multiple Groups and Movie Recommendation for Multiple Users. Our algorithms have shown to outperform baseline heuristics in both solution quality and the number of queries in most cases.

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Stojkovic, Mirela, and François Soumis. "The operational flight and multi-crew scheduling problem." Yugoslav Journal of Operations Research 15, no. 1 (2005): 25–48. http://dx.doi.org/10.2298/yjor0501025s.

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This paper introduces a new kind of operational multi-crew scheduling problem which consists in simultaneously modifying, as necessary, the existing flight departure times and planned individual work days (duties) for the set of crew members, while respecting predefined aircraft itineraries. The splitting of a planned crew is allowed during a day of operations, where it is more important to cover a flight than to keep planned crew members together. The objective is to cover a maximum number of flights from a day of operations while minimizing changes in both the flight schedule and the next-day planned duties for the considered crew members. A new type of the same flight departure time constraints is introduced. They ensure that a flight which belongs to several personalized duties, where the number of duties is equal to the number of crew members assigned to the flight, will have the same departure time in each of these duties. Two variants of the problem are considered. The first variant allows covering of flights by less than the planned number of crew members, while the second one requires covering of flights by a complete crew. The problem is mathematically formulated as an integer nonlinear multi-commodity network flow model with time windows and supplementary constraints. The optimal solution approach is based on Dantzig-Wolfe decomposition/column generation embedded into a branch-and-bound scheme. The resulting computational times on commercial-size problems are very good. Our new simultaneous approach produces solutions whose quality is far better than that of the traditional sequential approach where the flight schedule has been changed first and then input as a fixed data to the crew scheduling problem.

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ABU-AFFASH, A. KARIM, PAZ CARMI, MATTHEW J. KATZ, and GILA MORGENSTERN. "MULTI COVER OF A POLYGON MINIMIZING THE SUM OF AREAS." International Journal of Computational Geometry & Applications 21, no. 06 (December 2011): 685–98. http://dx.doi.org/10.1142/s021819591100386x.

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We consider a geometric optimization problem that arises in sensor network design. Given a polygon P (possibly with holes) with n vertices, a set Y of m points representing sensors, and an integer k, 1 ≤ k ≤ m. The goal is to assign a sensing range, ri, to each of the sensors yi ∈ Y, such that each point p ∈ P is covered by at least k sensors, and the cost, [Formula: see text], of the assignment is minimized, where α is a constant. In this paper, we assume that α = 2, that is, find a set of disks centered at points of Y, such that (i) each point in P is covered by at least k disks, and (ii) the sum of the areas of the disks is minimized. We present, for any constant k ≥ 1, a polynomial-time c1-approximation algorithm for this problem, where c1 = c1(k) is a constant. The discrete version, where one has to cover a given set of n points, X, by disks centered at points of Y, arises as a subproblem. We present a polynomial-time c2-approximation algorithm for this problem, where c2 = c2(k) is a constant.

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Zhou, Taoqing, Zhipeng Lü, Yang Wang, Junwen Ding, and Bo Peng. "Multi-start iterated tabu search for the minimum weight vertex cover problem." Journal of Combinatorial Optimization 32, no. 2 (May 29, 2015): 368–84. http://dx.doi.org/10.1007/s10878-015-9909-3.

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Ran, Yingli, Yishuo Shi, Changbing Tang, and Zhao Zhang. "A primal-dual algorithm for the minimum partial set multi-cover problem." Journal of Combinatorial Optimization 39, no. 3 (December 13, 2019): 725–46. http://dx.doi.org/10.1007/s10878-019-00513-y.

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Lei, Tao, Linze Li, Zhiyong Lv, Mingzhe Zhu, Xiaogang Du, and Asoke K. Nandi. "Multi-Modality and Multi-Scale Attention Fusion Network for Land Cover Classification from VHR Remote Sensing Images." Remote Sensing 13, no. 18 (September 20, 2021): 3771. http://dx.doi.org/10.3390/rs13183771.

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Land cover classification from very high-resolution (VHR) remote sensing images is a challenging task due to the complexity of geography scenes and the varying shape and size of ground targets. It is difficult to utilize the spectral data directly, or to use traditional multi-scale feature extraction methods, to improve VHR remote sensing image classification results. To address the problem, we proposed a multi-modality and multi-scale attention fusion network for land cover classification from VHR remote sensing images. First, based on the encoding-decoding network, we designed a multi-modality fusion module that can simultaneously fuse more useful features and avoid redundant features. This addresses the problem of low classification accuracy for some objects caused by the weak ability of feature representation from single modality data. Second, a novel multi-scale spatial context enhancement module was introduced to improve feature fusion, which solves the problem of a large-scale variation of objects in remote sensing images, and captures long-range spatial relationships between objects. The proposed network and comparative networks were evaluated on two public datasets—the Vaihingen and the Potsdam datasets. It was observed that the proposed network achieves better classification results, with a mean F1-score of 88.6% for the Vaihingen dataset and 92.3% for the Potsdam dataset. Experimental results show that our model is superior to the state-of-the-art network models.

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Shi, Yishuo, Yingli Ran, Zhao Zhang, and Ding-Zhu Du. "A bicriteria algorithm for the minimum submodular cost partial set multi-cover problem." Theoretical Computer Science 803 (January 2020): 1–9. http://dx.doi.org/10.1016/j.tcs.2019.03.004.

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Vasegaard, Alex Elkjær, Mathieu Picard, Florent Hennart, Peter Nielsen, and Subrata Saha. "Multi Criteria Decision Making for the Multi-Satellite Image Acquisition Scheduling Problem." Sensors 20, no. 5 (February 25, 2020): 1242. http://dx.doi.org/10.3390/s20051242.

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The multi-satellite image acquisition scheduling problem is traditionally seen as a complex optimization problem containing a generic objective function that represents the priority structure of the satellite operator. However, the majority of literature neglect the collective and contemporary effect of factors associated with the operational goal in the objective function, i.e., uncertainty in cloud cover, customer priority, image quality criteria, etc. Consequently, the focus of the article is to integrate a real-time scoring approach of imaging attempts that considers these aspects. This is accomplished in a multi-satellite planning environment, through the utilization of the multi-criteria decision making (MCDM) models, Elimination and Choice Expressing Reality (ELECTRE-III) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the formulation of a binary linear programming model. The two scoring approaches belong to different model classes of MCDM, respectively an outranking approach and a distance to ideal point approach, and they are compared with a naive approach. Numerical experiments are conducted to validate the models and illustrate the importance of criteria neglected in previous studies. The results demonstrate the customized behaviour allowed by MCDM methods, especially the ELECTRE-III approach.

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Guo, Shaocui, Xiangrong Tong, and Xu Yang. "Shell Analysis and Optimisation of a Pure Electric Vehicle Power Train Based on Multiple Software." World Electric Vehicle Journal 9, no. 4 (December 5, 2018): 49. http://dx.doi.org/10.3390/wevj9040049.

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Motor end cover mounting fracture is a problem recently encountered by novel pure electric vehicles. Regarding the study of the traditional vehicle engine mount bracket and on the basis of the methods of design and optimisation available, we have analysed and optimised the pure electric vehicle end cover mount system. Multi-body dynamic software and finite element software have been combined. First, we highlight the motor end cover mount bracket fracture engineering problems, analyse the factors that may produce fracture, and propose solutions. By using CATIA software to establish a 3D model of the power train mount system, we imported it into ADAMS multi-body dynamic software, conducted 26 condition analysis, obtained five ultimate load conditions, and laid the foundations for subsequent analysis. Next, a mount and shell system was established by the ANSYS finite element method, and modal, strength, and fatigue analyses were performed on the end cover mount. We found that the reason for fracture lies in the intensity of the end cover mount joint, which leads to the safety factor too small and the fatigue life not being up to standard. The main goal was to increase the strength of the cover mount junction, stiffness, safety coefficient, and fatigue life. With this aim, a topology optimisation was conducted to improve the motor end cover. A 3D prototype was designed accordingly. Finally, stiffness, strength, modal, and fatigue were simulated. Our simulation results were as follows. The motor end cover suspension stiffness increases by 20%, the modal frequency increases by 2.3%, the quality increases by 3%, the biggest deformation decreases by 52%, the maximum stress decreases by 28%, the minimum safety factor increases by 40%, and life expectancy increases 50-fold. The results from sample and vehicle tests highlight that the component fracture problem has been successfully solved and the fatigue life dramatically improved.

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Hartmanns, Arnd, Sebastian Junges, Joost-Pieter Katoen, and Tim Quatmann. "Multi-cost Bounded Tradeoff Analysis in MDP." Journal of Automated Reasoning 64, no. 7 (July 28, 2020): 1483–522. http://dx.doi.org/10.1007/s10817-020-09574-9.

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Abstract We provide a memory-efficient algorithm for multi-objective model checking problems on Markov decision processes (MDPs) with multiple cost structures. The key problem at hand is to check whether there exists a scheduler for a given MDP such that all objectives over cost vectors are fulfilled. We cover multi-objective reachability and expected cost objectives, and combinations thereof. We further transfer approaches for computing quantiles over single cost bounds to the multi-cost case and highlight the ensuing challenges. An empirical evaluation shows the scalability of our new approach both in terms of memory consumption and runtime. We discuss the need for more detailed visual presentations of results beyond Pareto curves and present a first visualisation approach that exploits all the available information from the algorithm to support decision makers.

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Shavarani, Seyed Mahdi. "Multi-level facility location-allocation problem for post-disaster humanitarian relief distribution." Journal of Humanitarian Logistics and Supply Chain Management 9, no. 1 (April 30, 2019): 70–81. http://dx.doi.org/10.1108/jhlscm-05-2018-0036.

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Purpose Previously use of drones as a relief distribution vehicle was studied in several studies where required number of drones and the best locations for the relief centers were investigated. The maximum travel distance of drones without a need to recharge is limited by their endurance. Recharge stations can be used to extend the coverage area of the drones. The purpose of this paper is to find the best topology for both relief centers and recharge stations to cover a large-scale area with minimum and feasible incurred costs and waiting times. Design/methodology/approach A multi-level facility location problem (FLP) is utilized to find the optimum number of relief centers and refuel stations and their locations. It is supposed that the demand occurs according to Poisson distribution. The allocation of the demand is based on nearest neighborhood method. A hybrid genetic algorithm is proposed to solve the model. The performance of the algorithm is examined through a case study. Findings The proposed method delivers increased efficiency and responsiveness of the humanitarian relief system. The coverage area of the drones is extended by refuel stations, total costs of the system are reduced and the time to respond an emergency, which is an important factor in survival rate, is significantly decreased. Originality/value This study proposes a multi-level FLP to simultaneously account for recharge stations, relief centers and the number of required drones to cover all the demand for relief in a post-disaster period.

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Arteconi, Alessia. "An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems." Environments 5, no. 12 (December 4, 2018): 130. http://dx.doi.org/10.3390/environments5120130.

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The urgent need for the reduction of greenhouse gas (GHG) emissions requires efficient and integrated energy communities in order to decrease final energy demand and sustain energy transitions towards renewable energy sources (RES). This introduces a big research challenge for future energy systems design and optimization, given the multi-level and inter-sectorial dimensions of the problem. Buildings cover a central role in this context, because they represent the nexus between different energy carrier networks. The study of the management of multi-sector and multi-energy systems presents several challenges, such as the need to take into account uncertainties, to represent the heterogeneity of the energy demand and the scalability of the problem. This paper aims at outlining such a framework, and at showing the state-of-the-art nature of the above-mentioned challenges.

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Burhan, Hussein M., Bara’a A. Attea, Amenah D. Abbood, Mustafa N. Abbas, and Mayyadah Al-Ani. "Evolutionary multi-objective set cover problem for task allocation in the Internet of Things." Applied Soft Computing 102 (April 2021): 107097. http://dx.doi.org/10.1016/j.asoc.2021.107097.

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Zhang, Wenjie, Jianhua Tu, and Lidong Wu. "A multi-start iterated greedy algorithm for the minimum weight vertex cover P3 problem." Applied Mathematics and Computation 349 (May 2019): 359–66. http://dx.doi.org/10.1016/j.amc.2018.12.067.

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Levi, Retsef, Andrea Lodi, and Maxim Sviridenko. "Approximation Algorithms for the Capacitated Multi-Item Lot-Sizing Problem via Flow-Cover Inequalities." Mathematics of Operations Research 33, no. 2 (May 2008): 461–74. http://dx.doi.org/10.1287/moor.1070.0305.

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Bahrami, Amir, and Saeed Ostadzadeh. "Comprehensively Efficient Analysis of Nonlinear Wire Scatterers Considering Lossy Ground and Multi-tone Excitations." Applied Computational Electromagnetics Society 35, no. 8 (October 7, 2020): 878–86. http://dx.doi.org/10.47037/2020.aces.j.350805.

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In this paper based on intelligent water drops algorithm (IWD), comprehensively nonlinear analysis of nonlinearly loaded wire scatterers are carried out. The analyses involve two stages. First, the problem is modeled as a nonlinear multi-port equivalent circuit and it is then reformulated into an optimization problem which is solved by the IWD. The simulation results are compared with harmonic balance (HB), arithmetic operator method (AOM), approximate methods and experiment. Analysis of the problem under strongly nonlinear loads, presence of lossy ground, multi-port structures, and multi-ton excitations are included to cover all the complex aspects. In one hand, the proposed modeling approach is in excellent agreement with other conventional techniques. On the other hand, the run time is considerably reduced.

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Li, Lin, Dianxi Shi, Songchang Jin, Ying Kang, Chao Xue, Xing Zhou, Hengzhu Liu, and XiaoXiao Yu. "Complete coverage problem of multiple robots with different velocities." International Journal of Advanced Robotic Systems 19, no. 2 (March 1, 2022): 172988062210916. http://dx.doi.org/10.1177/17298806221091685.

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Complete coverage, which is integral to many robotic applications, aims to cover an area as quickly as possible. In such tasks, employing multiple robots can reduce the overall coverage time by appropriate task allocation. Several multi-robot coverage approaches divide the environment into balanced subareas and minimize the maximum subarea of all robots. However, balanced coverage in many situations, such as in the cases of robots with different velocities and heterogeneous multi-robot systems, may have inefficient results. This study addresses the unbalanced complete coverage problem of multiple robots with different velocities for a known environment. First, we propose a novel credit model to transform the unbalanced coverage problem into a set of single-objective optimization problems, which can find a combinational optimal solution by optimizing each separate objective function of the single-objective optimization problem to alleviate the computational complexity. Then, we propose a credit-based algorithm composed of a cyclic region growth algorithm and a region fine-tuning algorithm. The cyclic region growth algorithm finds an initial solution to the single-objective optimization problems set by a regional growth strategy with multiple restricts, whereas the region fine-tuning algorithm reallocates the tasks of the partitions with too many tasks to the partitions with too few tasks by constructing a search tree, thereby converging the initial solution to the optimal solution. Simulation results indicate that compared with conventional multi-robot complete coverage problem algorithms, the credit-based algorithm can obtain the optimal solution with the increased number of robots and enlarged size of the mission environment.

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Jia, Jintong, Jiarui Song, Qingqiang Kong, Huan Yang, Yunhe Teng, and Xuan Song. "Multi-Attention-Based Semantic Segmentation Network for Land Cover Remote Sensing Images." Electronics 12, no. 6 (March 12, 2023): 1347. http://dx.doi.org/10.3390/electronics12061347.

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Semantic segmentation is a key technology for remote sensing image analysis widely used in land cover classification, natural disaster monitoring, and other fields. Unlike traditional image segmentation, there are various targets in remote sensing images, with a large feature difference between the targets. As a result, segmentation is more difficult, and the existing models retain low accuracy and inaccurate edge segmentation when used in remote sensing images. This paper proposes a multi-attention-based semantic segmentation network for remote sensing images in order to address these problems. Specifically, we choose UNet as the baseline model, using a coordinate attention-based residual network in the encoder to improve the extraction capability of the backbone network for fine-grained features. We use a content-aware reorganization module in the decoder to replace the traditional upsampling operator to improve the network information extraction capability, and, in addition, we propose a fused attention module for feature map fusion after upsampling, aiming to solve the multi-scale problem. We evaluate our proposed model on the WHDLD dataset and our self-labeled Lu County dataset. The model achieved an mIOU of 63.27% and 72.83%, and an mPA of 74.86% and 84.72%, respectively. Through comparison and confusion matrix analysis, our model outperformed commonly used benchmark models on both datasets.

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Flathmann, Christopher, Nathan McNeese, and Lorenzo Barberis Canonico. "Using Human-Agent Teams to Purposefully Design Multi-Agent Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1425–29. http://dx.doi.org/10.1177/1071181319631238.

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With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams.

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Zhang, Bin, Pei De Xu, Jian Jiang Wang, and Chao Bo Wu. "Optimization Deployment of Multi-Platform Earth Observation Resources for Emergencies." Advanced Materials Research 765-767 (September 2013): 552–56. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.552.

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in emergency, to improve the collaborative observation capacity of multiple platform resources, including satellite, airship and UAV (Unmanned Aerial Vehicle), an approach for deploying resources based on genetic optimization is proposed in this paper. We model the observation area into grids, and formulate the multi-platform resource deployment as a combination optimization problem, which has the goal of maximizing the cover probability of the observation area in the constraint of response time. Integer encoding is adopted in the approach to represent the positions of resources. Besides, elitism strategy is applied to improve the convergence rate. Experiment results demonstrate that the approach based on genetic can get satisfactory multi-platform resources deployment solutions in a short time, solving multi-platform resource optimization deployment problem effectively.

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Abdul, Arif Mohammad, Arshad Ahmad Khan Mohammad, Matti Kiran Sastry, and Jyothi Bankapalli. "Brute Force Attack on Distributed data Hiding in the Multi-Cloud Storage Environment More Diminutive than the Exponential Computations." Ingénierie des systèmes d information 27, no. 6 (December 31, 2022): 915–21. http://dx.doi.org/10.18280/isi.270607.

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Classical steganography is designed to hide data by cover media. Recent approaches fragmented the data and hide them in a distributed manner by embedding each fragmented data into a distinct cover media. This approach makes a secret message extremely difficult for an attacker to detect. However, cover media modification leaves fingerprints that could expose a secret channel to an attacker. To overcome the problem, a novel steganography concept designed by two technical contributions. I). cover media does not undergo any modification, i.e., the cover media act as a pointer to fragmented data. II). A secret message is stored in the multi-cloud storage environment. The approach claimed that it is computationally infeasible for an attacker to detect and extract the hidden message despite of having fully access to the accounts of the different clouds. In this paper, we analysed the security strength of the novel steganography concept and concluded that, attacker can get the secret value stored in multi-cloud storage environment using the brute force attacks more diminutive than exponential computations.

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Chraibi, Abdelahad, Said Kharraja, Ibrahim H. Osman, and Omar Elbeqqali. "A Multi-Agents System for Solving Facility Layout Problem: Application to Operating Theater." Journal of Intelligent Systems 28, no. 4 (September 25, 2019): 601–19. http://dx.doi.org/10.1515/jisys-2017-0081.

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Abstract Facility layout problem (FLP) has a great impact on the efficiency of any organization. It is concerned with defining the optimal location for each facility in order to optimize the supply chain productivity. In this kind of problems, the choice of resolution approach depends on the complexity and the size of the problem. Operating theaters are generally big structures containing a lot of facilities, which makes the conception of their layout a complex problem. In the literature, exact methods are powerless when faced with problem sizes up to 18 facilities. This leads us to explore other approaches, looking for efficient solutions. This paper presents a novel approach using a multi-agents system where agents’ skills are exploited to cover a wide research space, to accelerate the decision-making process and to deal with real-life problem sizes. This decision-making tool is based on several mixed integer linear programming models for solving the FLP, and considers two types of environments with deterministic and variant patient demand. Several experiments have been performed to demonstrate the effectiveness of our approach, and several problem instances with >80 facilities have been solved in reasonable time.

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Garai, Arindam, and Tapan Kumar Roy. "Intuitionistic Fuzzy Modeling to Travelling Salesman Problem." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 9 (December 5, 2013): 3015–24. http://dx.doi.org/10.24297/ijct.v11i9.3414.

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This paper presents solution technique for travelling salesman problem (TSP) under intuitionistic fuzzy environment. Travelling salesman problemÂ is a non-deterministic polynomial-time (NP) hardÂ problem inÂ combinatorial optimization,Â studied inÂ graph theory, operations researchÂ andÂ theoretical computer science. It must be noted that a traveling sales man even face a situation in which he is not able to achieve his objectives completely. There must be a set of alternatives from which he can select one that best meets his aspiration level. For Multi-Objective Symmetric TSP, in fuzzy environment, it is converted into a Linear Program using Fuzzy Multi-Objective Linear Programming technique. A route cannot be simply chosen just as it will most minimize time or it will cover the least possible distance. Examples with requirements to consider the degree of rejection or hesitation (or both) are overflowing in our materialistic world. Here comes the need to consider TSP under intuitionistic fuzzy environment. The degree of rejection as well as the degree of hesitancy must be studied to find the solution in a truly optimum sense! Proposed technique is an extension as well as collaboration of ideas of fuzzy traveling salesperson problem and intuitionistic fuzzy (IF) optimization technique.

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Pamukcu, Duygu, and Burcu Balcik. "A multi-cover routing problem for planning rapid needs assessment under different information-sharing settings." OR Spectrum 42, no. 1 (October 10, 2019): 1–42. http://dx.doi.org/10.1007/s00291-019-00563-y.

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Fluck, Julia. "Investigating the Comparability of Two Multi-Item-Scales for Cyber Bullying Measurement." International Journal of Environmental Research and Public Health 15, no. 11 (October 25, 2018): 2356. http://dx.doi.org/10.3390/ijerph15112356.

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In current cyberbullying literature, researchers assess the phenomenon using a large variety of measurement approaches. This poses a problem in light of comparability of study results. The most common approaches are singular global questions or multi-item scales that list several forms of cyberbullying. Such lists contain either different types of actions or different types of media. This study explores different measurement approaches. Two multi-item scales, one listing different actions and one listing different media, are compared to investigate whether they measure the same latent construct. Confirmatory factor analysis is used to model one factor for each of the multi-item scales. In the first study, the items cover victimization experiences while in the second study they cover estimation of severity. Results show that the two different multi-item scales measure the same latent construct. These results have a substantial impact on the future of cyberbullying research with regard to study comparability.

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MONGIOVÌ, MISAEL, RAFFAELE DI NATALE, ROSALBA GIUGNO, ALFREDO PULVIRENTI, ALFREDO FERRO, and RODED SHARAN. "SIGMA: A SET-COVER-BASED INEXACT GRAPH MATCHING ALGORITHM." Journal of Bioinformatics and Computational Biology 08, no. 02 (April 2010): 199–218. http://dx.doi.org/10.1142/s021972001000477x.

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Network querying is a growing domain with vast applications ranging from screening compounds against a database of known molecules to matching sub-networks across species. Graph indexing is a powerful method for searching a large database of graphs. Most graph indexing methods to date tackle the exact matching (isomorphism) problem, limiting their applicability to specific instances in which such matches exist. Here we provide a novel graph indexing method to cope with the more general, inexact matching problem. Our method, SIGMA, builds on approximating a variant of the set-cover problem that concerns overlapping multi-sets. We extensively test our method and compare it to a baseline method and to the state-of-the-art Grafil. We show that SIGMA outperforms both, providing higher pruning power in all the tested scenarios.

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Chang, Chen Yang, Jing Mei Zhai, Qin Xiang Xia, and Bin Cai. "Application of Particle Swarm Optimization Based on Support Vector Machine in Multi-Objective Structure Optimization." Applied Mechanics and Materials 201-202 (October 2012): 283–86. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.283.

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Aiming at addressing optimization problems of complex mathematical model with large amount of calculation, a method based on support vector machine and particle swarm optimization for structure optimization design was proposed. Support Vector Machine (SVM) is a powerful computational tool for problems with nonlinearity and could establish approximate structures model. Grey relational analysis was utilized to calculate the coefficient between target parameters in order to change the multi-objective optimization problem into a single objective one. The reconstructed models were solved by Particle Swam Optimization (PSO) algorithm. A slip cover at medical treatment was adopted as an example to illustrate this methodology. Appropriate design parameters were selected through the orthogonal experiment combined with ANSYS. The results show this methodology is accurate and feasible, which provides an effective strategy to solve complex optimization problems.

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Pérez-Peló, Sergio, Jesús Sánchez-Oro, Ana Dolores López-Sánchez, and Abraham Duarte. "A Multi-Objective Parallel Iterated Greedy for Solving the p-Center and p-Dispersion Problem." Electronics 8, no. 12 (December 1, 2019): 1440. http://dx.doi.org/10.3390/electronics8121440.

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This paper generalizes the iterated greedy algorithm to solve a multi-objective facility location problem known as the Bi-objective p-Center and p-Dispersion problem ( B p C D ). The new algorithm is coined as Multi-objective Parallel Iterated Greedy (MoPIG) and optimizes more than one objective at the same time. The B p C D seeks to locate p facilities to service or cover a set of n demand points, and the goal is to minimize the maximum distance between facilities and demand points and, at the same time, maximize the minimum distance between all pairs of selected facilities. Computational results demonstrate the effectiveness of the proposed algorithm over the evolutionary algorithms NSGA-II, MOEA/D, and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), comparing them with the optimal solution found by the ϵ -constraint method.

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Liu, Yongbei, Naiming Qi, Weiran Yao, Jun Zhao, and Song Xu. "Cooperative Path Planning for Aerial Recovery of a UAV Swarm Using Genetic Algorithm and Homotopic Approach." Applied Sciences 10, no. 12 (June 17, 2020): 4154. http://dx.doi.org/10.3390/app10124154.

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To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of “launch-recovery-relaunch” will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.

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Sidorov, Vladimir N., and Alim M. Primkulov. "Semi-analytical solution to steady-state and transient heat transfer problem with variable conductivity properties of the domain." Vestnik MGSU, no. 5 (May 2023): 685–96. http://dx.doi.org/10.22227/1997-0935.2023.5.685-696.

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Introduction. Present paper studies the case of heat transfer problem where the conductivity property of the domain is not a constant value but varies depending on its temperature. In particular, a conductivity that linearly dependent onthe temperature is reviewed. A semi-analytical formulation is developed and proposed to solve steady-state and transient types of these kinds of heat transfer problems. Materials and methods. The domain is mathematically modeled by using finite element method where non-linearity property of the problem has been incorporated. For time-dependent (transient) case, the temporal solution has been achieved by analytical methods wherefore all necessary formulation has been derived. Non-linear equations have been solved by both Newton and Picard methods. Results. In order to proof check, the proposed calculations a wall with three layers has been calculated by using the proposed transient problem formulation. The derivative boundary conditions at the faces of the wall are given as the functions of time. Non-linearity has been solved by two different methods that gave the identical results. Having solved the non-linearity by these two methods, allowed to compare the efficiency of Newton method versus Picard method. Both methods reached the solution with the same number of iterations. The paper proposes the algorithms for solving the problems. The authors have used MatLab environment to implement those algorithms. Conclusions. Proposed formulation solves coupled heat transfer problems in multi-layered exterior walls. Multi-layered calculation ability allows to cover all non-homogeneous cases of the computation domain. The formulation solves the heat loss problems through the multi-layered walls with due and reliable accuracy.

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Zhang, Yu, and Meiqi Dai. "Analysis of the Cooling and Humidification Effect of Multi-Layered Vegetation Communities in Urban Parks and Its Impact." Atmosphere 13, no. 12 (December 7, 2022): 2045. http://dx.doi.org/10.3390/atmos13122045.

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As urbanization continues to accelerate, the urban heat island effects have become one of the most important issues affecting the urban environment and people’s living experience. Numerous studies have shown that urban parks and green spaces can effectively alleviate the problem of the urban heat island effect and provide cooling and humidifying effects. Vegetation communities are a fundamental part of urban parklands, and multi-layered vegetation communities are considered to have better cooling and humidifying effects. Previous studies have focused on comparative analyses between different cover types of vegetation communities but have not explored the differences in the cooling and humidifying effects of multi-layered vegetation communities of the same cover type. Therefore, the Olympic Forest Park in Beijing was selected as the subject of this study, and multi-layered vegetation-covered (tree-shrub-grass) with different degrees of densities and uncovered squares were selected for the control and comparison. The cooling and humidifying effects of multi-layered vegetation communities with different canopy densities at different times of the day through field measurements were studied, and the influencing factors for this were analyzed. The results show that the tree cover is the core factor affecting temperature; the degree of the canopy density of multi-layered vegetation communities is significantly and positively correlated with the intensity of cooling and humidification, and the cooling and humidifying effect of multi-layered vegetation communities increases as the degree of canopy density increases. The results of this study can provide some references for the planning and design of urban parks and green spaces.

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Kwan, Chiman, David Gribben, Bulent Ayhan, Sergio Bernabe, Antonio Plaza, and Massimo Selva. "Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data." Remote Sensing 12, no. 9 (April 28, 2020): 1392. http://dx.doi.org/10.3390/rs12091392.

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Hyperspectral (HS) data have found a wide range of applications in recent years. Researchers observed that more spectral information helps land cover classification performance in many cases. However, in some practical applications, HS data may not be available, due to cost, data storage, or bandwidth issues. Instead, users may only have RGB and near infrared (NIR) bands available for land cover classification. Sometimes, light detection and ranging (LiDAR) data may also be available to assist land cover classification. A natural research problem is to investigate how well land cover classification can be achieved under the aforementioned data constraints. In this paper, we investigate the performance of land cover classification while only using four bands (RGB+NIR) or five bands (RGB+NIR+LiDAR). A number of algorithms have been applied to a well-known dataset (2013 IEEE Geoscience and Remote Sensing Society Data Fusion Contest). One key observation is that some algorithms can achieve better land cover classification performance by using only four bands as compared to that of using all 144 bands in the original hyperspectral data with the help of synthetic bands generated by Extended Multi-attribute Profiles (EMAP). Moreover, LiDAR data do improve the land cover classification performance even further.

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Pan, Ping, Zeming Wu, Chen Yang, and Bing Zhao. "Double-Matrix Decomposition Image Steganography Scheme Based on Wavelet Transform with Multi-Region Coverage." Entropy 24, no. 2 (February 7, 2022): 246. http://dx.doi.org/10.3390/e24020246.

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On the basis of ensuring the quality and concealment of steganographic images, this paper proposes a double-matrix decomposition image steganography scheme with multi-region coverage, to solve the problem of poor extraction ability of steganographic images under attack or interference. First of all, the cover image is transformed by multi-wavelet transform, and the hidden region covering multiple wavelet sub-bands is selected in the wavelet domain of the cover image to embed the secret information. After determining the hidden region, the hidden region is processed by Arnold transform, Hessenberg decomposition, and singular-value decomposition. Finally, the secret information is embedded into the cover image by embedding intensity factor. In order to ensure robustness, the hidden region selected in the wavelet domain is used as the input of Hessenberg matrix decomposition, and the robustness of the algorithm is further enhanced by Hessenberg matrix decomposition and singular-value decomposition. Experimental results show that the proposed method has excellent performance in concealment and quality of extracted secret images, and secret information is extracted from steganographic images attacked by various image processing attacks, which proves that the proposed method has good anti-attack ability under different attacks.

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Chabert, Maxime, and Christine Solnon. "A Global Constraint for the Exact Cover Problem: Application to Conceptual Clustering." Journal of Artificial Intelligence Research 67 (March 12, 2020): 509–47. http://dx.doi.org/10.1613/jair.1.11870.

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We introduce the exactCover global constraint dedicated to the exact cover problem, the goal of which is to select subsets such that each element of a given set belongs to exactly one selected subset. This NP-complete problem occurs in many applications, and we more particularly focus on a conceptual clustering application. We introduce three propagation algorithms for exactCover, called Basic, DL, and DL+: Basic ensures the same level of consistency as arc consistency on a classical decomposition of exactCover into binary constraints, without using any specific data structure; DL ensures the same level of consistency as Basic but uses Dancing Links to efficiently maintain the relation between elements and subsets; and DL+ is a stronger propagator which exploits an extra property to filter more values than DL. We also consider the case where the number of selected subsets is constrained to be equal to a given integer variable k, and we show that this may be achieved either by combining exactCover with existing constraints, or by designing a specific propagator that integrates algorithms designed for the NValues constraint. These different propagators are experimentally evaluated on conceptual clustering problems, and they are compared with state-of-the-art declarative approaches. In particular, we show that our global constraint is competitive with recent ILP and CP models for mono-criterion problems, and it has better scale-up properties for multi-criteria problems.

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Liu, B., J. Chen, H. Xing, H. Wu, and J. Zhang. "A SPIRAL-BASED DOWNSCALING METHOD FOR GENERATING 30 M TIME SERIES IMAGE DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 817–20. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-817-2017.

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The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. <br><br> A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. <br><br> Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

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Li, Shi. "Constant Approximation Algorithm for Nonuniform Capacitated Multi-Item Lot Sizing via Strong Covering Inequalities." Mathematics of Operations Research 45, no. 3 (August 2020): 947–65. http://dx.doi.org/10.1287/moor.2019.1018.

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We study the nonuniform capacitated multi-item lot-sizing problem. In this problem, there is a set of demands over a planning horizon of T discrete time periods, and all demands must be satisfied on time. We can place an order at the beginning of each period s, incurring an ordering cost Ks. In this order, we can order up to Cs units of products. On the other hand, carrying inventory from time to time incurs an inventory holding cost. The goal of the problem is to find a feasible solution that minimizes the sum of ordering and holding costs. Levi et al. [Levi R, Lodi A, Sviridenko M (2008) Approximation algorithms for the capacitated multi-item lot-sizing problem via flow-cover inequalities. Math. Oper. Res. 33(2):461–474.] gave a two-approximation for the problem when the capacities Cs are the same. Extending the result to the case of nonuniform capacities requires new techniques as pointed out in the discussion section of their paper. In this paper, we solve the problem by giving a 10-approximation algorithm for the capacitated multi-item lot-sizing problem with general capacities. The constant approximation is achieved by adding an exponential number of new covering inequalities to the natural facility location–type linear programming (LP) relaxation for the problem. Along the way of our algorithm, we reduce the lot-sizing problem to two generalizations of the classic knapsack-covering problem. We give LP-based constant approximation algorithms for both generalizations via the iterative rounding technique.

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Sharifi, Farid, Mostafa Mirzaei, Youmin Zhang, and Brandon W. Gordon. "Cooperative Multi-Vehicle Search and Coverage Problem in an Uncertain Environment." Unmanned Systems 03, no. 01 (January 2015): 35–47. http://dx.doi.org/10.1142/s230138501550003x.

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A distributed approach is proposed in this paper to address a cooperative multi-vehicle search and coverage problem in an uncertain environment such as forest fires monitoring and detection. Two different types of vehicles are used for search and coverage tasks: search and service vehicles. The search vehicles have a priori probability maps of targets in the environment. These vehicles update the probability maps based on their sensors measurements during the search mission. The search vehicles use a limited look-ahead dynamic programming algorithm to find their own path individually while their objective is to maximize the amount of information gathered by the whole team. The task of the service vehicles is to optimally spread out over the environment to cover the interested area for a mission. A Voronoi-based coverage control strategy is proposed to modify the configuration of service vehicles in such a way that a prescribed coverage cost function is minimized using the updated probability maps which are provided by the search vehicles. The improved performance of the proposed approach compared to conventional coverage methods is demonstrated by numerical simulation and experimental results.

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Noor Azam, Muhammad Harith, Farida Hazwani Mohd Ridzuan, and M. Norazizi Sham Mohd Sayuti. "Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm." Journal of Information and Communication Technology 22, no. 2 (April 3, 2023): 255–82. http://dx.doi.org/10.32890/jict2023.22.2.5.

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Existing embedding techniques depend on cover audio selected by users. Unknowingly, users may make a poor cover audio selectionthat is not optimised in its capacity or imperceptibility features, which could reduce the effectiveness of any embedding technique. As a trade-off exists between capacity and imperceptibility, producing a method focused on optimising both features is crucial. One ofthe search methods commonly used to find solutions for the trade-off problem in various fields is the Multi-Objective Evolutionary Algorithm (MOEA). Therefore, this research proposed a new method for optimising cover audio selection for audio steganography using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which falls under the MOEA Pareto dominance paradigm. The proposed method provided suggestions for cover audio to users based on imperceptibility and capacity features. The sample difference calculation was initially formulated to determine the maximum capacity for each cover audio defined in the cover audio database. Next, NSGA-II was implemented to determine the optimised solutions based on the parameters provided by each chromosome. The experimental results demonstrated the effectiveness of the proposed method as it managed to dominate thesolutions from the previous method selected based on one criterion only. In addition, the proposed method considered that the trade-off managed to select the solution as the highest priority compared to the previous method, which put the same solution as low as 71 in the priority ranking. In conclusion, the method optimised the cover audio selected, thus, improving the effectiveness of the audio steganography used. It can be a response to help people whose computers and mobile devices continue to be unfamiliar with audio steganography in an age where information security is crucial.

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Seyyedi, Mir Hossein, Amir Mohammad Fakoor Saghih, Alireza Pooya, and Zahra Naji Azim. "Designing a Multi-Objective Mathematical Model for Flexible Job Shop Scheduling Problem With the Earliness/Tardiness Penalty." Journal of Social Sciences Research, no. 510 (October 5, 2019): 1375–82. http://dx.doi.org/10.32861/jssr.510.1375.1382.

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The scheduling of flexible job shop systems is one of the most important issues in the various fields of production and is currently being addressed by many researchers in the field of optimization issues. The present research includes flexible job shop scheduling problem (FJSSP) with multi-objective, minimizing maximum completion time (makespan), maximum machine workload, total machines workload and also, earliness/tardiness penalty with different constraints. In this research, the researcher is looking to design a mathematical model that can cover all the constraints and assumptions related to the problem. Therefore, the mathematical model was designed with multi-objective and different constraints with exact details and different assumptions that are consistent with the actual situation of the problem and implemented at Comex Company. What that distinguishes this research from other similar researches is the approach of multi-objective with different constraints, which, at the same time, it raises the complexity of the problem but the problem gets closer to the actual situation, with less research done. Finally, the results of the study showed that this mathematical model designed, as well as in the real environment which has the flexible job shop system, can be implemented within a reasonable time with the highest efficiency before the implementation of the model.

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Bejiga, Mesay Belete, Farid Melgani, and Pietro Beraldini. "Domain Adversarial Neural Networks for Large-Scale Land Cover Classification." Remote Sensing 11, no. 10 (May 14, 2019): 1153. http://dx.doi.org/10.3390/rs11101153.

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Learning classification models require sufficiently labeled training samples, however, collecting labeled samples for every new problem is time-consuming and costly. An alternative approach is to transfer knowledge from one problem to another, which is called transfer learning. Domain adaptation (DA) is a type of transfer learning that aims to find a new latent space where the domain discrepancy between the source and the target domain is negligible. In this work, we propose an unsupervised DA technique called domain adversarial neural networks (DANNs), composed of a feature extractor, a class predictor, and domain classifier blocks, for large-scale land cover classification. Contrary to the traditional methods that perform representation and classifier learning in separate stages, DANNs combine them into a single stage, thereby learning a new representation of the input data that is both domain-invariant and discriminative. Once trained, the classifier of a DANN can be used to predict both source and target domain labels. Additionally, we also modify the domain classifier of a DANN to evaluate its suitability for multi-target domain adaptation problems. Experimental results obtained for both single and multiple target DA problems show that the proposed method provides a performance gain of up to 40%.

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Yao, Yao, Jun-Hua Cao, Yi Guo, Zhun Fan, Bing Li, Biao Xu, and Ke Li. "Adaptive coverage control for multi-USV system in complex environment with unknown obstacles." International Journal of Distributed Sensor Networks 17, no. 6 (June 2021): 155014772110215. http://dx.doi.org/10.1177/15501477211021525.

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Aiming at the shortcomings of the existing control law based on the global information, this article studies the coverage problem of a given region in the plane using a team of USVs. The coverage goal, which is to cover a given search domain using multiple mobile sensors so that each point is surveyed until a certain preset level is achieved, is formulated in a mathematically precise problem statement. The adaptive control law is presented which enables multi-USV to navigate in a complex environment in the presence of unknown obstacles and guarantees that a fully connected multi-USV system attains the coverage goal. In particular, the dangerous area was divided into two different parts in order to enhance the searching efficiency. Finally, simulation results are presented to validate the feasibility and efficiency of the proposed approach.

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Rukhovich, D. D. "2D-to-3D Projection for Monocular and Multi-View 3D Multi-class Object Detection in Indoor Scenes." Programmnaya Ingeneria 12, no. 9 (December 16, 2021): 459–69. http://dx.doi.org/10.17587/prin.12.459-469.

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In this paper, we propose a novel method of joint 3D object detection and room layout estimation. The proposed method surpasses all existing methods of 3D object detection from monocular images on the indoor SUN RGB-D dataset. Moreover, the proposed method shows competitive results on the ScanNet dataset in multi-view mode. Both these datasets are collected in various residential, administrative, educational and industrial spaces, and altogether they cover almost all possible use cases. Moreover, we are the first to formulate and solve a problem of multi-class 3D object detection from multi-view inputs in indoor scenes. The proposed method can be integrated into the controlling systems of mobile robots. The results of this study can be used to address a navigation task, as well as path planning, capturing and manipulating scene objects, and semantic scene mapping.

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Kweon, Hyeokjoon, Jinsun Park, Sanghyun Woo, and Donghyeon Cho. "Deep Multi-Image Steganography with Private Keys." Electronics 10, no. 16 (August 9, 2021): 1906. http://dx.doi.org/10.3390/electronics10161906.

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In this paper, we propose deep multi-image steganography with private keys. Recently, several deep CNN-based algorithms have been proposed to hide multiple secret images in a single cover image. However, conventional methods are prone to the leakage of secret information because they do not provide access to an individual secret image and often decrypt the entire hidden information all at once. To tackle the problem, we introduce the concept of private keys for secret images. Our method conceals multiple secret images in a single cover image and generates a visually similar container image containing encrypted secret information inside. In addition, private keys corresponding to each secret image are generated simultaneously. Each private key provides access to only a single secret image while keeping the other hidden images and private keys unrevealed. In specific, our model consists of deep hiding and revealing networks. The hiding network takes a cover image and secret images as inputs and extracts high-level features of the cover image and generates private keys. After that, the extracted features and private keys are concatenated and used to generate a container image. On the other hand, the revealing network extracts high-level features of the container image and decrypts a secret image using the extracted feature and a corresponding private key. Experimental results demonstrate that the proposed algorithm effectively hides and reveals multiple secret images while achieving high security.

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Journal articles on the topic 'Multi-cover Problem'

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