scholarly journals Statistics based Optimization Technique for Query Processing

2019 ◽  
Vol 8 (10) ◽  
pp. 714-718
Keyword(s):  
Semantic Web ◽  
Optimization Technique ◽  
Optimization Method ◽  
Structured Data ◽  
Query Graph ◽  
User Input ◽  
Search Result ◽  
Query Recommendation ◽  
User Query ◽  
The Cost

Semantic web consists of the data in the structure manner and query searching methods can access these structured data to provide effective search result. The query recommendation in the semantic web relevance is needed to be improved based on the user input query. Many existing methods are used to improve the query recommendation efficiency using the optimization technique such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO). These methods involve in the use of many features which are selected from the user query. This in-turn increases the cost of a query in the semantic web. In this research, the query optimization was carried out by using the statistics method. The statistics based optimization method requires fewer features such as triple pattern and node priority etc., for finding the relevant results. The LUBM dataset contains the semantic queries and this dataset is used to measure the efficiency of the proposed Statistical based optimization method. The SPARQL queries are used to plot the query graph and triple scores are extracted from the graph. The cost value of the triple scores is measured and given as input to the proposed statistics method. The execution time of the statistics based optimization method for the query is 35 ms while the existing method has 48 ms.

scholarly journals Maximum Loadability Enhancement with a Hybrid Optimization Method

2018 ◽  
Vol 7 (3) ◽  
pp. 323-330
Author(s):  
E. E. Hassan ◽  
T. K. A. Rahman ◽  
Z. Zakaria ◽  
N. Bahaman ◽  
M. H. Jifri
Keyword(s):  
Transmission Lines ◽  
Power Plants ◽  
Voltage Stability ◽  
Reactive Power ◽  
Optimization Technique ◽  
Optimization Method ◽  
Stressed Condition ◽  
Bacterial Foraging ◽  
Reactive Power Planning ◽  
The Cost

Nowadays, a power system is operating in a stressed condition due to the increase in demand in addition to constraint in building new power plants. The economics and environmental constraints to build new power plants and transmission lines have led the system to operate very close to its stability limits. Hence, more researches are required to study the important requirements to maintain stable voltage condition and hence develop new techniques in order to address the voltage stability problem. As an action, most Reactive Power Planning (RPP) objective is to minimize the cost of new reactive resources while satisfying the voltage stability constraints and labeled as Secured Reactive Power Planning (SCRPP). The new alternative optimization technique called Adaptive Tumbling Bacterial Foraging (ATBFO) was introduced to solve the RPP problems in the IEEE 57 bus system. The comparison common optimization Meta-Heuristic Evolutionary Programming and original Bacterial Foraging techniques were chosen to verify the performance using the proposed ATBFO method. As a result, the ATBFO method is confirmed as the best suitable solution in solving the identified RPP objective functions.

scholarly journals Two-Stage Optimization Method for the Bearing Layout of Isolated Structure

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Dang ◽  
GenXiong Zhao ◽  
HongTu Tian ◽  
Guobao Li
Keyword(s):  
Optimization Technique ◽  
Optimal Solution ◽  
Nonlinear Behavior ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Design Parameters ◽  
Two Stage ◽  
Factors Affecting ◽  
Lead Rubber Bearing ◽  
The Cost

Design of seismic isolated building is often a highly iterative and tedious process due to the nonlinear behavior of the system, a large range of design parameters, and uncertainty of ground motions. It is needed to consider a comprehensive optimization procedure in the design of isolated buildings with optimized performances. This can be accomplished by applying a rigorous optimization technique. However, due to many factors affecting the performance of isolated buildings, possible solutions are abundant, and the optimal solution is difficult to obtain. In order to simplify the optimization process, an isolated building is always modeled as a shear-type structure supported on the isolated layer, and the optimal results are the parameters of the isolated layer which could not be used as a practical design of the isolated structure. A two-stage optimization method for designing isolated buildings as a practical and efficient guide is developed. In the first stage, a 3D isolated building model is adopted that takes into account of nonlinear behavior in building and isolation devices. The isolation devices are simplified as a kind of lead-rubber bearing. The genetic algorithm is used to find the optimal parameters of the isolated layer. In the second stage, the location parameters of isolation bearing layout are optimized. Moreover, the cost of the isolation bearing layout should be as low as possible. An integer programming method is adopted to optimize the number of each type of isolator. Considering vertical bearing capacity of isolators and the minimum eccentricity ratio of the isolated layer, the optimal bearing layout of the isolated building can be obtained. The proposed method is demonstrated in a typical isolated building in China. The optimum bearing layout of the isolated building effectively suppresses the structural seismic responses, but the cost of the isolated layer might slightly increase.

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

scholarly journals The Development of Patient Scheduling Groups for an Effective Appointment System

2016 ◽  
Vol 07 (01) ◽  
pp. 43-58 ◽  
Author(s):  
Yu Li Huang
Keyword(s):  
Access To Care ◽  
Treatment Time ◽  
Optimization Technique ◽  
Urgent Care ◽  
Patient Scheduling ◽  
Total Cost ◽  
Patient Type ◽  
Clinic Efficiency ◽  
The Cost ◽  
The Impact

SummaryPatient access to care and long wait times has been identified as major problems in outpatient delivery systems. These aspects impact medical staff productivity, service quality, clinic efficiency, and health-care cost.This study proposed to redesign existing patient types into scheduling groups so that the total cost of clinic flow and scheduling flexibility was minimized. The optimal scheduling group aimed to improve clinic efficiency and accessibility.The proposed approach used the simulation optimization technique and was demonstrated in a Primary Care physician clinic. Patient type included, emergency/urgent care (ER/UC), follow-up (FU), new patient (NP), office visit (OV), physical exam (PE), and well child care (WCC). One scheduling group was designed for this physician. The approach steps were to collect physician treatment time data for each patient type, form the possible scheduling groups, simulate daily clinic flow and patient appointment requests, calculate costs of clinic flow as well as appointment flexibility, and find the scheduling group that minimized the total cost.The cost of clinic flow was minimized at the scheduling group of four, an 8.3% reduction from the group of one. The four groups were: 1. WCC, 2. OV, 3. FU and ER/UC, and 4. PE and NP. The cost of flexibility was always minimized at the group of one. The total cost was minimized at the group of two. WCC was considered separate and the others were grouped together. The total cost reduction was 1.3% from the group of one.This study provided an alternative method of redesigning patient scheduling groups to address the impact on both clinic flow and appointment accessibility. Balance between them ensured the feasibility to the recognized issues of patient service and access to care. The robustness of the proposed method on the changes of clinic conditions was also discussed.

Optimization Design of an Axial-Flow Fan Used in Local Ventilation in Mining Industry

2011 ◽  
Author(s):  
Dhaval Desai ◽  
Jiang Zhou
Keyword(s):  
Optimization Design ◽  
Optimization Technique ◽  
Mining Industry ◽  
Axial Flow ◽  
Optimization Method ◽  
Low Noise ◽  
Design Constraint ◽  
Engineering Research ◽  
Blade Element Theory ◽  
Local Ventilation

In a world where the increasing demand on developing energy-efficient systems is probably the most stringent design constraint, the trend in engineering research in recent years has been to optimize the existing technologies rather than to implement new ones. The present work addresses a robust axial-type fan design technique developed using an optimization technique. A fan is indispensable equipment for primary and local ventilation in mining industries. We always pursue the fan with high working efficiency and low noise. In this paper, an optimization method is developed to improve the pneumatic properties of the fan based on the blade element theory. A new type of fan used in local ventilation is designed with the help of computer. It is shown that the new design enhanced the efficient up to 88%. Numerical analysis is also conducted to validate the optimization design results.

scholarly journals An enhanced krill herd optimization technique used for classification problem

2021 ◽  
Vol 30 (2) ◽  
pp. 354-364
Author(s):  
Firas Al-Mashhadani ◽  
Ibrahim Al-Jadir ◽  
Qusay Alsaffar
Keyword(s):  
Machine Learning ◽  
Simulated Annealing ◽  
Optimization Technique ◽  
Optimization Method ◽  
Classification Problem ◽  
Test Results ◽  
Internal Evaluation ◽  
Evaluation Measures ◽  
Search Optimization ◽  
Krill Herd

In this paper, this method is intended to improve the optimization of the classification problem in machine learning. The EKH as a global search optimization method, it allocates the best representation of the solution (krill individual) whereas it uses the simulated annealing (SA) to modify the generated krill individuals (each individual represents a set of bits). The test results showed that the KH outperformed other methods using the external and internal evaluation measures.

scholarly journals A multi-level optimization technique for electricity consumption management and pricing in residential neighborhoods

2021 ◽  
Author(s):  
Ramyar Rashed Mohassel
Keyword(s):  
Smart Grids ◽  
New Technologies ◽  
Power Transmission ◽  
Optimization Technique ◽  
Electricity Consumption ◽  
Optimization Method ◽  
Residential Neighborhood ◽  
Upper Level ◽  
Advanced Metering ◽  
Power Management Unit

With the introduction of new technologies, concepts and approaches in power transmission, distribution and utilization such as Smart Grids (SG), Advanced Metering Infrastructures (AMI), Distributed Energy Resources (DER) and Demand Side Management (DSM), new capabilities have emerged that enable efficient use and management of power consumption. These capabilities are applicable at micro level in households and building complexes as well as at macro level for utility providers in form of resource and revenue management initiatives. On the other hand, integration of Information Technology (IT) and instrumentation has brought Building Management Systems (BMS) to our homes and has made it possible for the ordinary users to take advantage of more complex and sophisticated energy and cost management features as an integral part of their BMS. The idea of combining capabilities and advantages offered by SG, AMI, DER, DSM and BMS is the backbone of this thesis and has resulted in developing a unique, two-level optimization method for effective deployment of DSM at households and residential neighborhoods. The work consists of an optimization algorithm for households to maximize utilization of DER as the lower level of the envisioned two-level optimization technique while using a customized Game Theoretic optimization for optimizing revenue of utility providers for residential neighborhood as the upper level. This work will also introduce a power management unit, called Load Moderation Center (LMC), to host the developed optimization algorithms as an integrated part of BMS. LMC, upon successful completion, will be able to automatically plan consumption, effectively utilize available sources including grid, renewable energies and storages, and eliminate the need for residences to manually program their BMS for different market scenarios.

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