scholarly journals Rotated Black Hole: A New Heuristic Optimization for Reducing Localization Error of WSN in 3D Terrain

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qing-Wei Chai ◽  
Jerry Wangtao Zheng

Wireless sensor network (WSN) attracts the attention of more and more researchers, and it is applied in more and more environment. The localization information is one of the most important information in WSN. This paper proposed a novel algorithm called the rotated black hole (RBH) algorithm, which introduces a rotated optimal path and greatly improves the global search ability of the original black hole (BH) algorithm. Then, the novel algorithm is applied in reducing the localization error of WSN in 3D terrain. CEC 2013 test suit is used to verify the performance of the novel algorithm, and the simulation results show that the novel algorithm has better search performance than other famous intelligence computing algorithms. The localization simulation experiment results reveal that the novel algorithm also has an excellent performance in solving practical problems. WSN localization 3D terrain intelligence computing rotated the black hole algorithm.

Author(s):  
Loránd Lehel Tóth ◽  
Raymond Pardede ◽  
Gábor Hosszú

The article presents a method to decipher Rovash inscriptions made by the Szekelys in the 15th-18th centuries. The difficulty of the deciphering work is that a large portion of the Rovash inscriptions contains incomplete words, calligraphic glyphs or grapheme errors. Based on the topological parameters of the undeciphered symbols registered in the database, the presented novel algorithm estimates the meaning of the inscriptions by the matching accuracies of the recognized graphemes and gives a statistical probability for deciphering. The developed algorithm was implemented in software, which also contains a built-in dictionary. Based on the dictionary, the novel method takes into account the context in identifying the meaning of the inscription. The proposed algorithm offers one or more words in a different random values as a result, from which users can select the relevant one. The article also presents experimental results, which demonstrate the efficiency of method.


2018 ◽  
Vol 8 (10) ◽  
pp. 1832
Author(s):  
Zhixian Chen ◽  
Baoliang Zhao ◽  
Shijia Zhao ◽  
Ying Hu ◽  
Jianwei Zhang

For a service robot, learning appropriate behaviours to acquire task knowledge and deliberation in various situations is essential, but the existing methods do not support merging the plan-based activity experiences from multiple situations in the same task. In this paper, an abstract method is introduced to integrate the empirical activity schemas of multiple situations, and a novel algorithm is presented to learn activity schema with abstract methods. Furthermore, a novel planner called the schema-based optimized planner (SBOP) is developed based on the learned activity schema, in which actions merging optimization and partially backtracking techniques are adopted. A simulation with a PR2 robot and a physical experiment is conducted to validate the proposed method. The results show that the robot can generate a plan to recover from failure automatically using the novel learning and planning method, given that the experienced exception has been merged in the activity schema. Owing to the improved autonomy, the proposed SBOP exhibits increased efficiency in dealing with tasks containing loops and multiple activity schema instances. This research presents a novel solution of how to merge activity experiences from multiple situations and generate an intelligent and efficient plan that could adapt to a dynamically changing environment.


Author(s):  
Jing Zhao ◽  
Xiaoli Wang ◽  
Ming Li

Image segmentation is a classical problem in the field of computer vision. Fuzzy [Formula: see text]-means algorithm (FCM) is often used in image segmentation. However, when there is noise in the image, it easily falls into the local optimum, which results in poor image boundary segmentation effect. A novel method is proposed to solve this problem. In the proposed method, first, the image is transformed into a neutrosophic image. In order to improve the ability of global search, a combined FCM based on particle swarm optimization (PSO) is proposed. Finally, the proposed algorithm is applied to the neutrosophic image segmentation. The results of experiments show that the novel algorithm can eliminate image noise more effectively than the FCM algorithm, and make the boundary of the segmentation area clearer.


SLEEP ◽  
2020 ◽  
Vol 43 (3) ◽  
Author(s):  
Takashi Abe ◽  
Kazuo Mishima ◽  
Shingo Kitamura ◽  
Akiko Hida ◽  
Yuichi Inoue ◽  
...  

Abstract Vigilance deficits account for a substantial number of accidents and errors. Current techniques to detect vigilance impairment measure only the most severe level evident in eyelid closure and falling asleep, which is often too late to avoid an accident or error. The present study sought to identify ocular biometrics of intermediate impairment of vigilance and develop a new technique that could detect a range of deficits in vigilant attention (VA). Sixteen healthy adults performed well-validated Psychomotor Vigilance Test (PVT) for tracking vigilance attention while undergoing simultaneous recording of eye metrics every 2 hours during 38 hours of continuous wakefulness. A novel marker was found that measured VA when the eyes were open—the prevalence of microsaccades. Notably, the prevalence of microsaccades decreased in response to sleep deprivation and time-on-task. In addition, a novel algorithm for detecting multilevel VA was developed, which estimated performance on the PVT by integrating the novel marker with other eye-related indices. The novel algorithm also tracked changes in intermediate level of VA (specific reaction times in the PVT, i.e. 300–500 ms) during prolonged time-on-task and sleep deprivation, which had not been tracked previously by conventional techniques. The implication of the findings is that this novel algorithm, named “eye-metrical estimation version of the PVT: PVT-E,” can be used to reduce human-error-related accidents caused by vigilance impairment even when its level is intermediate.


2018 ◽  
Vol 7 (1.9) ◽  
pp. 6 ◽  
Author(s):  
Pandi Selvam Raman ◽  
Shankar K ◽  
Ilayaraja M

Mobile ad hoc networks (MANETs) are wireless infrastructure-less network consisting collection of autonomous nodes that communicate with each other in decentralized manner. Security remains major challenge due to its some unique characteristics like open medium, mobility and hence topology changes. Therefore, routing protocol for MANETs is much vulnerable to attacks. Black Hole is a type of attack, where malicious node falsely advertises itself having the shortest or optimal path to the destination node. This attack is more dangerous while a group of nodes are cooperating with each other.The objective of this paper is to design cluster based routing protocol and prevent it from the black hole attack. The simulation results show improvement in packet delivery ratio and control overhead.


1995 ◽  
Vol 11 (2) ◽  
pp. 187-194
Author(s):  
Michael L. Metzker ◽  
Kyle M. Allain ◽  
Richard A. Gibbs

2011 ◽  
Vol 187 ◽  
pp. 371-376
Author(s):  
Ping Zhang ◽  
Xiao Hong Hao ◽  
Heng Jie Li

In order to avoid the over fitting and training and solve the knowledge extraction problem in fuzzy neural networks system. Ying Learning Dynamic Fuzzy Neural Network (YL-DFNN) algorithm is proposed. The Learning Set based on K-VNN is constituted from message. Then the framework of is designed and its stability is proved. Finally, Simulation indicates that the novel algorithm is fast, compact, and capable in generalization.


2013 ◽  
Vol 21 (1) ◽  
pp. 7-11 ◽  
Author(s):  
Matija Strbac ◽  
Nebojsa Malesevic ◽  
Radoje Cobeljic ◽  
Laszlo Schwirtlich

We present a novel system for control of elbow movements by electrical stimulation of the biceps and triceps in tetraplegic patients. The operation of the system uses the novel algorithm and applies closed loop control. Movement of the arm is generated via multi-pad electrodes developed by Tecnalia Serbia, Ltd. by the stimulator that allows asynchronous activation of individual pads. The electrodes are positioned over the innervation of the biceps and triceps muscles on the upper arm. This layout allows distributed activation; thereby, selective and low fatiguing activation of paralyzed muscles. The sensory feedback comes from the image acquired by the Microsoft Kinect system and the depth stream analysis is performed in real time by the computer running in the MatLab environment. The image based feedback allows control of the hand position at the target by cocontraction of the antagonists. The control adjusts the stimulation intensity and results with the tracking of the desired movement. The algorithm was proven to operate efficiently in a tetraplegic patient.


Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 154 ◽  
Author(s):  
Ignacio Huitzil ◽  
Jorge Bernad ◽  
Fernando Bobillo

Fuzzy description logics, the formalism behind fuzzy ontologies, are an important mathematical method with applications in many artificial intelligence scenarios. This paper proposes the first specific algorithms to solve two reasoning tasks with respect to a fuzzy ontology: the instance retrieval and the realization problem. Our algorithms are based on a reduction of the number of optimization problems to solve by merging some of them. Our experimental evaluation shows that the novel algorithm to solve the instance retrieval outperforms the previous algorithm, and that in practice it is common to be able to solve a single optimization problem.


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