In this paper, an enhanced artificial potential field (EAPF) planner is introduced. This planner is proposed to rapidly find online solutions for the mobile robot path planning problems, when the underlying environment contains obstacles with unknown locations and sizes. The classical artificial potential field represents both the repulsive force due to the detected obstacle and the attractive force due to the target. These forces can be considered as the primary directional indicator for the mobile robot. However, the classical artificial potential field has many drawbacks. So, we suggest two secondary forces which are called the midpoint repulsive force and the off-sensors attractive force. These secondary forces and modified primary forces are merged to overcomethe drawbacks like dead ends and U shape traps. The proposed algorithm acquirs information of unknown environment by collecting the readings of five infrared sensors with detecting range of 0.8 m. The proposed algorithm is applied on two different environments also it is compared with another algorithm. The simulation and experimental results confirm that the proposed algorithm always converges to the desired target. In addition, the performance of algorithm is well and meets the requirements in terms of saved time and computational resources.
This research deals with the study of the obstacles to peaceful coexistence in Iraq. Therefore, our hypothesis is that these obstacles are not attributed to one reason or to one source. Therefore, peaceful coexistence among the components of Iraqi society remains fragile and vulnerable to collapse and threat at any moment. This society, which is already in need of social relations, needs more than ever to coexist and consolidate it. Through the exercise of democracy in Iraq in a manner that guarantees rights and duties for all, to restore mutual trust between the components of this community in a way that feels they are citizens of the state.
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Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a
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This paper provides an attempt for modeling rate of penetration (ROP) for an Iraqi oil field with aid of mud logging data. Data of Umm Radhuma formation was selected for this modeling. These data include weight on bit, rotary speed, flow rate and mud density. A statistical approach was applied on these data for improving rate of penetration modeling. As result, an empirical linear ROP model has been developed with good fitness when compared with actual data. Also, a nonlinear regression analysis of different forms was attempted, and the results showed that the power model has good predicting capability with respect to other forms.
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The aim of the research was to prepare a scale of obstacles to achieving quality performance, field application from the point of view of fourth-stage female students, to identifyOn the difficulties and obstacles to achieving quality performance through field application from the students’ point of view for the topic (school administrative procedures, learners, cognitive achievement, school tools).The researchers used the descriptive approach in the research procedures as it is an appropriate approach to achieve the research objectives.,The following questions were asked:There are obstacles and difficulties faced by female students during the process of choosing schools, dealing with school administrations, and the difficulties fa
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Reservoir characterization is an important component of hydrocarbon exploration and production, which requires the integration of different disciplines for accurate subsurface modeling. This comprehensive research paper delves into the complex interplay of rock materials, rock formation techniques, and geological modeling techniques for improving reservoir quality. The research plays an important role dominated by petrophysical factors such as porosity, shale volume, water content, and permeability—as important indicators of reservoir properties, fluid behavior, and hydrocarbon potential. It examines various rock cataloging techniques, focusing on rock aggregation techniques and self-organizing maps (SOMs) to identify specific and
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In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha
... Show MoreIn this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func