Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 6 Articles
Considering that the probability distribution of random variables in stochastic\nprogramming usually has incomplete information due to a perfect sample\ndata in many real applications, this paper discusses a class of two-stage stochastic\nprogramming problems modeling with maximum minimum expectation\ncompensation criterion (MaxEMin) under the probability distribution\nhaving linear partial information (LPI). In view of the nondifferentiability of\nthis kind of stochastic programming modeling, an improved complex algorithm\nis designed and analyzed. This algorithm can effectively solve the nondifferentiable\nstochastic programming problem under LPI through the variable\npolyhedron iteration. The calculation and discussion of numerical examples\nshow the effectiveness of the proposed algorithm....
Spatial crowdsourcing assigns location-related tasks to a group of workers (people equipped with smart devices and willing to\ncomplete the tasks), who complete the tasks according to their scope of work. Since space crowdsourcing usually requires workersâ??\nlocation information to be uploaded to the crowdsourcing server, it inevitably causes the privacy disclosure of workers. At the\nsame time, it is difficult to allocate tasks effectively in space crowdsourcing. Therefore, in order to improve the task allocation\nefficiency of spatial crowdsourcing in the case of large task quantity and improve the degree of privacy protection for workers, a\nnew algorithm is proposed in this paper, which can improve the efficiency of task allocation by disturbing the location of workers\nand task requesters through k-anonymity. Experiments show that the algorithm can improve the efficiency of task allocation\neffectively, reduce the task waiting time, improve the privacy of workers and task location, and improve the efficiency of space\ncrowdsourcing service when facing a large quantity of tasks....
With the increasing interconnection of computer networks and sophistication\nof cyber-attacks, Cryptography is one way to make sure that confidentiality,\nauthentication, integrity, availability, and identification of data user\ncan be maintained as well as security and privacy of data provided to the\nuser. Symmetric key cryptography is a part of the cryptographic technique\nwhich ensures high security and confidentiality of data transmitted through\nthe communication channel using a common key for both encryption and\ndecryption. In this paper I have analyzed comparative encryption algorithms\nin performance, three most useful algorithms: Data Encryption\nStandard (DES), Triple DES (3DES) also known as Triple Data Encryption\nAlgorithm (TDEA), and Advanced Encryption Standard (AES). They have\nbeen analyzed on their ability to secure data, time taken to encrypt data and\nthroughput the algorithm requires. The performance of different algorithms\ndiffers according to the inputs....
The inverse kinematics of redundant manipulators is one of the most important and complicated problems in robotics. Simultaneously,\nit is also the basis for motion control, trajectory planning, and dynamics analysis of redundant manipulators. Taking the\nminimum pose error of the end-effector as the optimization objective, a fitness function was constructed. Thus, the inverse kinematics\nproblem of the redundant manipulator can be transformed into an equivalent optimization problem, and it can be solved\nusing a swarm intelligence optimization algorithm. Therefore, an improved fruit fly optimization algorithm, namely, the hybrid\nmutation fruit fly optimization algorithm (HMFOA), was presented in this work for solving the inverse kinematics of a redundant\nrobot manipulator. An olfactory search based on multiple mutation strategies and a visual search based on the dynamic real-time\nupdates were adopted in HMFOA. The former has a good balance between exploration and exploitation, which can effectively solve\nthe premature convergence problem of the fruit fly optimization algorithm (FOA). The latter makes full use of the successful search\nexperience of each fruit fly and can improve the convergence speed of the algorithm........................
Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging\nalgorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level\nwavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and\nlearns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching\napproach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to\nget the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the\nimage. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the\ncomparison algorithm....
In this article, we introduce a novel iterative algorithm to approximate fixed point of mappings with.....................
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