In power grids and towers of tens of kilometers, it is impractical to use only one drone for inspection and cleaning operations. The joint operation of multiple UAVs complies with the effectiveness and efficiency arrangements. However, in the case of joint flight and operation of multiple UAVs, GOS becomes important, which is also the important core of this research. This study mainly applies the combined optimization of hyperbolic partial differential equations through the mathematical wave vibration model to achieve the GOS of multi-unit UAV collaborative power grids and towers detection and cleaning system. This study analyzed the flight position convergence, and found that the OCS of multi-unit UAV collaborative power grids and towers detection and cleaning system in this study is more capable of controlling the flight position than the previous method, and the data is similar to 90 m. Also, the time delay error is also compared. The result can also be seen that the OCS of this study is better than the traditional method by about 0.2 under the same iteration steps. To sum up the foregoing, This study believes that the OCS of multi-unit UAV collaborative power grids and towers detection and cleaning system in this study has better tracking and matching capabilities of position and flight speed of UAV, with less delay error and better convergence, so it can provide The robustness and reliability of OCS that meet our needs.
Risk Assessment contributes to optimizing the allocation of resources at the enterprise level, which achieves its goals, so the matter needs centralized management of risks on the enterprise level, not for each project. The risk assessment is carried out through several stages and by using various methods. This research provides an analytical view of risk assessment in concurrent multiple software projects environment. Research experiment has proven high levels of accuracy, reaching almost from 93% by using Simple Logistic into 98% using REP Tree technique. in determining risk levels in a concurrent multi-project environment.
For years, the study on human-computer interaction in the mixed reality has been exploring in more natural and efficient interacting ways. As a common interacting component, the traditional menu on tangible user interface is not well suited to virtual interactive scenes in mixed reality and virtual menu represents a new category of efficient interacting methods in mixed reality. Recently, the combination of gesture recognition with the virtual menu has attracted little attention from researchers. To implement a natural, intuitive, and efficient interaction, in this work, a virtual menu using gesture recognition is proposed for the 3D object manipulation in mixed reality. In particular, several gesture states with respect to the different stages of menu interaction are first defined; then, the gestures on the screen are mapped to 3D virtual objects through coordinate transformation; and finally, the style and interaction logic of the menu is introduced to. In the experiment, the user’s interaction results using different menus are evaluated by comparing the time needed and the result’s accuracy with the same tasks. The experimental results show that our proposal can effectively reduce the number of interacting operations, and improve the efficiency of interacting process.
There are incredibly renewable outlets such as solar and wind intermittent, and machine reliability is hard to sustain with the intolerable balance of green energy resources. Because of population growth and technological development, global energy demand is increasing exponentially. However, civilization must address a stable energy source for two benefits, a cost-effective and sustainable basis for future renewable energy production. Solar Energy is a platform for green energy and is a potential means of solving problems that face a possible energy system. The power output is primarily determined by the properties of the incoming radiation and solar panels. Precise and accurate knowledge of these variables provides a consistent model for predicting the solar future. Solar power forecasts would have a significant effect on the viability of massive solar energy projects. This study presents a new approach to predict the expected amount of energy to be produced from Photovoltaics using a hybrid deep neural network based on a combination of extended short-term memory networks (LSTM) and convolutional neural networks (CNN), taking into account the energy of electrons and photons as well as elements and climatic conditions as input data.
With an improved algorithm based on the Otsu method, this paper proposes solving the shadow region problem caused by non-uniform illumination. The recent image processing algorithms for handling the shaded area's issue for the massive difference in the image grey value will not be practical. It is not easy to directly set the shaded area's content to "black" based on this problem. This paper proposes improved threshold image segmentation for Non-uniform illumination based on the Otsu optimization Approach. It uses the local area threshold and window image pixel by sliding to enhance the image recognition rate of a shaded area. Experimental results verify that the proposed segmentation method has more robust adaptability to images with shadows. Compared with the classical global OTSU algorithm and genetic algorithm optimization, the image recognition rate is 96%.
Smart meters are the basic equipment for data collection in a smart grid system and responsible for collecting, measuring, and transmitting original electricity data. The smart meter data is first transmitted to the data aggregation point (DAP) through wireless communication and then transmitted to the control center. Each DAP installation incurs costs, but the number and locations of DAPs have an impact on the quality of communication services due to the distance between DAP and smart meters and the related transmission routes. Therefore, it is important to choose the best positions for installing DAPs in a smart meter network. This study investigates the DAP placement problem and proposes a solution to reduce the number of DAPs and the distance between DAPs and smart meters by using a grid-based model. Without loss of generality, simulations for performance evaluation are conducted based on random data. The simulation results show that the proposed solution can reduce the number of DAPs in a smart meter network.
Ontology matching can solve the heterogeneity problem between two ontologies, and EA represents a state-of-the-art technique for matching ontologies. However, there are two defects concerning the EA-based ontology matching technique: (1) a reference alignment between two ontologies to be matched is required in advance; (2) the confidence of entity similarity measure is low computational complexity of measuring the similarity value is high. To overcome these drawbacks, in this paper, an Evolutionary Algorithm with Context-based Reasoning method (EA-CR) is proposed, where: (1) an approximate metric without the reference alignment is utilized for evaluating the alignment’s quality; (2) a Context-based Reasoning method is presented to distinguish the heterogeneous entities. The experimental results show that the proposed approach is effective.
Parallel processing is an effective measure of inefficient and effective computations of function optimization. Now we propose a new communication strategy for the parallel Bat Algorithm to solve numerical optimization problems. Firstly, the population of bats is divided into several independent groups, and they are independent. With every fixed number of iterations, different groups will exchange information and update. We use benchmark functions to test accuracy convergence behavior. From the analysis and summary of the experimental results, we get the following conclusions. The communication strategy improves the accuracy of BA in finding the best solution. The algorithm has improved significantly.
Due to the relatively fixed route and obvious difference between different routes, how to reduce the energy consumption level of pure electric buses is a research hotspot at home and abroad. Based on the actual operation of pure electric buses in a city as the research object and through the analysis of its starting data, the K-means clustering method is used to study the influencing factors and differences of energy consumption under different starting modes of electric buses. Then, the evaluation system of drivers’ starting behavior is built. The results show that the energy consumption of pure electric buses varies greatly under different starting modes. The energy consumption of radical starting buses is much higher than that of safe starting buses. The evaluation system can objectively and effectively evaluate drivers’ starting behaviors, provide a sufficient basis for the supervision of operating enterprises, and have certain reference value.
Recently Natural Language Processing (NLP) has excessive attention due to increased data available online and needs processing. Nevertheless, the huge development in the NLP but still Arabic Natural Language Processing (ANLP) faces many challenges and grief from researchers' leakage compared with English NLP. Mainly this study has three divisions. It aims to find out the challenges facing ANLP, especially text classification. Furthermore, it aims to examine the effect of using deep learning in ANLP text classification from both sides' advantages and disadvantages. Moreover, it aims to find the most efficient deep learning algorithm that sufficiently classifies and categorizes an Arabic text. To fulfill the objectives of this study, three different methods have been used. The first method, Survey / Systematic Review, to find out the challenges. The second method, Literature review; to examine the effect of using deep learning algorithms to classify Arabic text. The third method, experiment to find an efficient algorithm that reverts better accuracy. In this study, we have determined the data collection methods. Where searching for good quality research papers is the key to accomplish the first two divisions. But the last division needs the full implementation to deploy Arabic text classification models. The Data Analysis explained the sub-steps extensively in each division, how it will be implemented, and each division's expected outcome. Also, this study briefly gives the resources needed and the required timeline. Deploying the models will be the longest process; hence it needs preprocessing and building models covering all types of neural networks to determine the most efficient one.
Because of the complexity and uncertainty of sentences, ambiguous information often appears in sentence segmentation. To the sentence ambiguity division problem, we proposed a method that can solve the semantic information in the sentence and can select the breakpoint of word dynamically. This study proposed and designed a single-objective discrete optimization model based on the ontology technology of lexeme semantic information. This model analyzed the semantic information of the sentences through the ontology data analysis, combined the case data to calculate the relevant semantic word frequency information; then proposed an optimized artificial bee colony algorithm to solve the model, by introduced an adaptive competition strategy in the selection and elimination of the population to accelerate the convergence speed of the algorithm. Finally, this study experimented on the word segmentation module of the library automatic question-answer consultation system. The experimental results showed that this method can meet the user’s requirements, improve the question segmentation accuracy, accelerate the convergence speed of the bee colony, which verified the effectiveness of the proposed method.
The smart and automatic bag-changing trash can is controlled by a circuit chip and consists of an infrared detection device and a mechanical, electronic drive system. As long as an object approaches the sensing area, the lid will automatically open, and the lid will automatically close after a few seconds after the object or hand leaves the sensing area. It does not require an external power source. It is powered by a battery and has low power consumption. The exquisite streamlined appearance sensor flip design, which combines infrared sensors and microcomputers, is flexible and convenient, and it can easily throw away garbage without manual or foot stepping. It is also convenient and hygienic and has reliable performance, which helps you effectively prevent contact infections. It comprises functional modules such as ultrasonic distance measurement, photoelectric sensor modules, motor drive modules, and voice alarm modules. It takes an ultrasonic sensor, RPR220 photoelectric sensor, and single-chip computer as the core. Distance information is collected through the sensor, and then the single-chip computer sends instructions through AD conversion. When the ultrasonic sensor detects that the distance between the person and the trash can is less than the set distance, the trash can automatically flip the lid. If the RPR220 photoelectric detects the full trash can, the system will give a voice prompt. This system has a simple structure, stable performance, convenient operation, and low cost. This paper designs multifunctional trash can control system based on STC89C52RC. This system has found a feasible solution to further isolate people from garbage and prevent people from infecting bacteria.
In actual decision-making, recognizing, analyzing, and reasoning is vital to solving problems in which information plays an important part. The evidential reasoning (ER) approach provides a great way to address multi-attribute decision-making (MADM) problems, including qualitative and quantitative attributes, based on a distributed assessment framework. However, in the ER context, choosing the optimal schemes is based primarily on the aggregation of attributes’ distributed assessments. The consistency of assessments for each attribute is ignored, all that will affect the evaluation's reliability. This study puts forward a new model based on the ER to handle MADM problems, considering the consistency of attributes assessment. A reliable measure for assessments, calculated with the entropy, is figured out to represent the discrete degree of evaluating so that effectiveness and reliability and are both considered. A numerical example illustrates the decision-making process, and the properties of the new model are investigated. It is shown that the new approach is more reasonable and effective.
Accurate teeth segmentation in panoramic radiographic X-Ray images is importance for orthodontic treatment and research. This paper evaluates the method of LeNet-5 convolutional neural network with input data of windowed image patches for automated tooth root segmentation. In total, 103,984 image patches created from 798 images are used for training and validation sets. The proposed method produced an accuracy of 87.94%, which is higher than comparative Sobel- and Canny-processed cases. A visual evaluation of the segmentation method shows a close resemblance to the ground truth. The method achieved high performance for automated tooth root segmentation on dental panoramic images. With some slight further modification and improvement, the proposed method might be applicable to be used in the first step of dental diagnosis or analysis systems, which involves similar segmentation tasks.
The purpose of the study is toThis is to inform you that corresponding author has been identified as per the information available in the Copyright form.explore the role of business excellence in improving global competitive advantage in the UAE’s healthcare sector. The study explores various business excellence models and recognizes widely used models and the best model applicable to the UAE context, and evaluates factors that influence business excellence such as leadership, HRM, Quality management, customer satisfaction, information, and analysis on the quality results competitive advantage. The paper used a quantitative approach to gather primary data on the selected sector besides statistical analysis to evaluate the influence of business excellence factors on improving competitive advantage. The findings show a high maturity of organizational functioning in the healthcare industry to implement business excellence to improve their competitive advantage.
As the current computer design level continues to improve, the process equipment and production line management levels are relatively stable. The intelligent and automatic 3D garage-type has shown a diversified development trend. Several garage types are currently in everyday use, including vertical lifting type, vertical lifting type, simple lifting type, horizontal shifting circulation type, channel stacking type, etc. The 3D lifting garage has a simple structure, small floor space, low operating cost, and the most extensive application requirements. However, this study summarizes the application status and development prospects of smart lifting 3D garages first. Then the system analyzes the working principle and mechanical structure characteristics of the smart lifting 3D garage, analyzes the control requirements, and designs the semi-automatic control system of the 3D garage S7–1200—it including the PLC control program and the man-machine interface. Finally, the overall system control effect is verified through simulation. This study meter has superficial operation characteristics, a high degree of automation, fast operation speed, high stability, and has deep development value and application prospects in specific markets.
The difference between the subjective emotional preference and the objective cognitive formation of product attributes is an important factor affecting the online reputation of goods, but there is little research on it. This paper introduces vertical product attributes (with a unified preference standard) and horizontal attribute segmentation from the perspective of consumer cognition and emotion. It discusses the internal impact mechanism of online reputation judgment based on the Elaboration Likelihood Model. By capturing the relevant data of online reviews and product popularity on the website of JD.COM, we constructed a sentiment analysis model and conducted an empirical study on it. The results show that consumers generally pay more attention to mobile phones' vertical attributes, such as batteries and cameras. The change in reputation attitude caused by this part of attribute evaluation is more stable. Horizontal attributes such as feel and color focus are ranked lower, and even inconsistent sentiment orientation generally does not hurt reputation. But price ranks high as a horizontal attribute due to consumer preference concern. This paper puts forward specific suggestions for enterprises to improve product attributes, carry out product research and development, implement differentiated marketing, and promote brand image communication.
At present, cloud data centers' high energy consumption is an urgent problem in the development of cloud computing. One reason for the high energy consumption of cloud data centers is the mismatching scheduling between tasks and resources. The existing cloud computing task scheduling algorithms assigned tasks to resource nodes by certain rules according to the task information, and they ignored that the resource nodes are also the main body of the task scheduling algorithms. This paper proposes a new task scheduling algorithm based on bilateral selection. It fully considers the task and the resource and combines the Dynamic Voltage/Frequency Scheduling (DVFS) technology to help tasks and resources to match more appropriately. It can assign tasks to resources with higher credibility. The experimental results show that the cloud computing task scheduling algorithm based on the bilateral selection can obtain better execution performance and lower execution energy consumption and solve the high energy consumption problem caused by the mismatch between tasks and resources.
The ontology matching technique aims at establishing the correspondence between two ontologies’ entities to solve their heterogeneity problem. In the matching process, the quality of the single matcher’s alignment can not be ensured. Usually, multiple matches are required to work together to increase the evidence of potential matches or mismatches. Selecting, combining, and adjusting these ontology matches to obtain high-quality ontology comparisons is one of the main challenges in ontology matching. This paper proposes an Argumentation Framework (A.F.) based on the ontology extracting technique to address this challenge. First, the proposed approach obtains the alignments obtained by different ontology matches. It extracts the final alignment by debating the disputed entity correspondences with A.F. The experiment is conducted on the Bibliographic track provided by Ontology Alignment Evaluation Initiative (OAEI), and the statistical comparison with the state-of-the-art ontology matching techniques shows the effectiveness of the proposed approach.
This paper investigates the influence of credibility on trust as it received scant attention in academia, especially in cloud computing, and to test compatibility against the intention towards usage or adoption of SaaS services. A quantitative survey instrument used, and the authors selected respondents from Malaysian public universities from different schools, races, ages, and genders. Valid responses were 233. Partial least squares structural equation modeling (PLS-SEM) used to analyze the model. The main results show a statistically positive and significant effect of credibility and trust on attitude, compatibility on behavior intention, and behavior intention on the outcome. Notably, credibility exerted a potent significant positive effect on trust. The framework has proven its appropriateness, predictive accuracy, and relevance.
