scholarly journals Graph Learning-Based Ontology Sparse Vector Computing

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1562
Author(s):  
Jianzhang Wu ◽  
Arun Kumar Sangaiah ◽  
Wei Gao
Keyword(s):  
Learning Algorithm ◽  
Early Stage ◽  
Optimal Solution ◽  
Spectral Graph Theory ◽  
Optimization Techniques ◽  
Ontology Learning ◽  
Structure Information ◽  
Sparse Vector ◽  
Vector Computing ◽  
The Given

The ontology sparse vector learning algorithm is essentially a dimensionality reduction trick, i.e., the key components in the p-dimensional vector are taken out, and the remaining components are set to zero, so as to obtain the key information in a certain ontology application background. In the early stage of ontology data processing, the goal of the algorithm is to find the location of key components through the learning of some ontology sample points, if the relevant concepts and structure information of each ontology vertex with p-dimensional vectors are expressed. The ontology sparse vector itself contains a certain structure, such as the symmetry between components and the binding relationship between certain components, and the algorithm can also be used to dig out the correlation and decisive components between the components. In this paper, the graph structure is used to express these components and their interrelationships, and the optimal solution is obtained by using spectral graph theory and graph optimization techniques. The essence of the proposed ontology learning algorithm is to find the decisive vertices in the graph Gβ. Finally, two experiments show that the given ontology learning algorithm is effective in similarity calculation and ontology mapping in some specific engineering fields.

Traditional and Non-Traditional Optimization Techniques to Enhance Reliability in Process Industries

2020 ◽  
pp. 67-80
Author(s):  
Ravinder Kumar ◽  
Hanumant P. Jagtap ◽  
Dipen Kumar Rajak ◽  
Anand K. Bewoor
Keyword(s):  
Objective Function ◽  
Traditional Method ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Initial Approximation ◽  
Optimization Techniques ◽  
Process Industries ◽  
Advantages And Disadvantages ◽  
Effective Use ◽  
The Given

At present, optimization techniques are popular to solve typical engineering problems. It is the action of making the best or most effective use of a situation or resources. In order to survive in the competitive market, each organization has to follow some optimization technique depending on their requirement. In each optimization problem, there is an objective function to minimize or maximize under the given restrictions or constraints. All techniques have their own advantages and disadvantages. Traditional method starts with the initial solution and with each successive iteration converges to the optimal solution. This convergence depends on the selection of initial approximation. These methods are not suited for discontinuous objective function. So, the need of non-traditional method was felt. Some non-traditional methods are called nature-inspired methods. In this chapter, the authors give the description of the optimization techniques along with the comparison of the traditional and non-traditional techniques.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals OPTIMAL SOLUTION FOR THE DISTRIBUTION OF BY-PRODUCTS IN DISPOSAL UNIT

2017 ◽  
Vol 5 (4RAST) ◽  
pp. 59-63 ◽  
Author(s):  
Jyothi P ◽  
Vatsala G A ◽  
Radha Gupta
Keyword(s):  
Waste Disposal ◽  
Goal Programming ◽  
Programming Model ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Programming Technique ◽  
Emerging Industries ◽  
Goal Programming Model ◽  
Net Profit ◽  
By Products

In present scenario, Waste disposal unit is one of the emerging industries. The process of collection of wastes, segregation of wastes, recycling the wastes and manufacturing by-products and selling the by-products are the major works are undertaken into consideration.  Any business expectation is to get the profit.  Our study is to formulate goal programming model which helps in maximizing the profit by identifying the deviation of goals in the disposal unit. Goal Programming technique is one of the optimization techniques. Manager of the disposal unit can takes the better decision using the deviation of goals. Pre emptive Goals of the study are (i) minimizing the expenditure of the unit and recycling cost of the wastes ii) boosting the net profit of the unit    iii) Maintaining the supply of by-products to each location within the maximum demand iv) Fulfilling demand of by- products in different locations v) Maintaining the minimum supply of recycled by-products to 5 different locations must be at least one.

scholarly journals Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yoonseok Shin
Keyword(s):  
Cost Estimation ◽  
Construction Projects ◽  
High Performance ◽  
Learning Algorithm ◽  
Early Stage ◽  
Construction Project ◽  
Regression Tree ◽  
Building Construction ◽  
Regression Problem ◽  
Additional Information

Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT) is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN) model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project.

Partial multi-dividing ontology learning algorithm

2018 ◽  
Vol 467 ◽  
pp. 35-58 ◽  
Author(s):  
Wei Gao ◽  
Juan L.G. Guirao ◽  
B. Basavanagoud ◽  
Jianzhang Wu
Keyword(s):  
Learning Algorithm ◽  
Ontology Learning

Gradient Optimization of Inverse Dynamics for Robotic Manipulator Motion Planning Using Combined Optimal Control

2017 ◽  
Author(s):  
Shangdong Gong ◽  
Redwan Alqasemi ◽  
Rajiv Dubey
Keyword(s):  
Optimal Control ◽  
Motion Planning ◽  
Inverse Dynamics ◽  
Null Space ◽  
Optimal Solution ◽  
Human Motion ◽  
Optimization Techniques ◽  
Redundant Manipulators ◽  
Robot Arm ◽  
Gradient Optimization

Motion planning of redundant manipulators is an active and widely studied area of research. The inverse kinematics problem can be solved using various optimization methods within the null space to avoid joint limits, obstacle constraints, as well as minimize the velocity or maximize the manipulability measure. However, the relation between the torques of the joints and their respective positions can complicate inverse dynamics of redundant systems. It also makes it challenging to optimize cost functions, such as total torque or kinematic energy. In addition, the functional gradient optimization techniques do not achieve an optimal solution for the goal configuration. We present a study on motion planning using optimal control as a pre-process to find optimal pose at the goal position based on the external forces and gravity compensation, and generate a trajectory with optimized torques using the gradient information of the torque function. As a result, we reach an optimal trajectory that can minimize the torque and takes dynamics into consideration. We demonstrate the motion planning for a planar 3-DOF redundant robotic arm and show the results of the optimized trajectory motion. In the simulation, the torque generated by an external force on the end-effector as well as by the motion of every link is made into an integral over the squared torque norm. This technique is expected to take the torque of every joint into consideration and generate better motion that maintains the torques or kinematic energy of the arm in the safe zone. In future work, the trajectories of the redundant manipulators will be optimized to generate more natural motion as in humanoid arm motion. Similar to the human motion strategy, the robot arm is expected to be able to lift weights held by hands, the configuration of the arm is changed along from the initial configuration to a goal configuration. Furthermore, along with weighted least norm (WLN) solutions, the optimization framework will be more adaptive to the dynamic environment. In this paper, we present the development of our methodology, a simulated test and discussion of the results.

A Heuristic Application-Specific Path Planner for Robot Motion Planning

1992 ◽  
Author(s):  
David G. Alciatore
Keyword(s):  
Degrees Of Freedom ◽  
Optimal Solution ◽  
Plant Design ◽  
Planning Techniques ◽  
Construction Automation ◽  
Path Planner ◽  
Joint Limits ◽  
The Given ◽  
Application Specific ◽  
Equipment Operator

Abstract This paper presents the development and simulation results of a Heuristic Application-Specific Path Planner (HASPP) that can be used to automatically plan trajectories for a manipulator operating around obstacles. Since the implementation of HASPP is inherently application-specific due to dependence on heuristics, the application of HASPP to an eight degree of freedom Pipe Manipulator is presented as an illustrative example. This development and simulation was implemented on a Silicon Graphics Personal IRIS with the aid of WALKTHRU, a 3-D simulation and animation tool, and software developed in C. HASPP uses extensive knowledge of the manipulator’s workspace and makes certain assumptions about the environment in finding trajectories. The algorithm also makes use of the manipulator’s redundant degrees of freedom to avoid obstacles and joint limits during the trajectory while obtaining a heuristic near-optimal solution. The algorithm is rule-based, governed by heuristics and well-defined geometric tests, providing extremely fast results. It finds “good” trajectories that are optimal within the defined heuristics. When a trajectory is not feasible for the given geometry, the algorithm offers a diagnosis of the limiting constraints. The Pipe Manipulator HASPP implementation has been tested thoroughly with the computer graphics model and it has demonstrated the ability to reliably determine near-optimal collision-free erection trajectories completely automatically. No other planning techniques available in the literature have demonstrated the ability to solve problems as complex as the example presented here. The use of HASPP with simulation offers many application opportunities including plant design constructability studies, assembly and maintenance planning, pre-planning and pre-programming of equipment tasks, and equipment operator assistance. This work was the result of construction automation research sponsored by the National Science Foundation.

scholarly journals A Framework for the Selection of Optimum Offshore Wind Farm Locations for Deployment

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1855 ◽  
Author(s):  
Varvara Mytilinou ◽  
Estivaliz Lozano-Minguez ◽  
Athanasios Kolios
Keyword(s):  
Decision Making ◽  
Wind Energy ◽  
Wind Farm ◽  
Cost Effective ◽  
Optimization Techniques ◽  
Offshore Wind ◽  
Moray Firth ◽  
The Uk ◽  
The Given ◽  
Nondominated Sorting

This research develops a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework includes optimization techniques, decision-making methods and experts’ input in order to support investment decisions. Further, techno-economic evaluation, life cycle costing (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision-making process. A process on the criteria selection is also presented and seven conflicting criteria are being considered for implementation in the technique for the order of preference by similarity to the ideal solution (TOPSIS) method in order to suggest the optimum location that was produced by the nondominated sorting genetic algorithm (NSGAII). For the given inputs, Seagreen Alpha, near the Isle of May, was found to be the most probable solution, followed by Moray Firth Eastern Development Area 1, near Wick, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make better informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK.

scholarly journals Analysis of Firefly–Fuzzy Hybrid Algorithm for Navigation of Quad-Rotor Unmanned Aerial Vehicle

Inventions ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 48
Author(s):  
Brijesh Patel ◽  
Bhumeshwar Patle
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Fuzzy Controller ◽  
Optimal Solution ◽  
Obstacle Detection ◽  
Optimization Techniques ◽  
Multiple Features ◽  
Hybrid Controller ◽  
Aerial Vehicle ◽  
Single Controller ◽  
The Individual

In the present scenario for the development of the unmanned aerial vehicle (UAV), artificial intelligence plays an important role in path planning and obstacle detection. Due to different environments, it is always a task to achieve the proper moment for achieving the target goal while avoiding obstacles with minimum human interference. To achieve the goal with the avoidance of obstacles, individual optimization techniques with metaheuristic algorithms such as fuzzy, particle swarm optimization (PSO), etc. were implemented in various configurations. However, the optimal solution was not attained. Thus, in order to achieve an optimal solution, a hybrid model was developed by using the firefly algorithm and the fuzzy algorithm, establishing multiple features of the individual controller. The path and time optimization were achieved by a standalone controller and a hybrid firefly–fuzzy controller in different conditions, whereby the results of the controller were validated by simulation and experimental results, highlighting the advantages of the hybrid controller over the single controller.

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