Journal articles on the topic 'Performance optimization of rating system'
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1
Al-Hadi, Ismail Ahmed Al-Qasem, Nurfadhlina Mohd Sharef, Md Nasir Sulaiman, Norwati Mustapha, and Mehrbakhsh Nilashi. "Latent based temporal optimization approach for improving the performance of collaborative filtering." PeerJ Computer Science 6 (December 21, 2020): e331. http://dx.doi.org/10.7717/peerj-cs.331.
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Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers’ drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.
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Choi, Gyeong Seok, Tae Jung Kim, Jae Sik Kang, and Hyun Jung Choi. "Design and Energy Performance Evaluation of Energy Efficiency Windows with Nanotechnology Convergence Type." Applied Mechanics and Materials 521 (February 2014): 752–56. http://dx.doi.org/10.4028/www.scientific.net/amm.521.752.
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The purpose of this study, including an aluminum frame high efficiency window system is to develop and validate. In this study, simulation program based on the Window and Therm simulation program, aluminum frame profile, thermal bridges through optimization design of barrier efficiency of the 6type aluminum window system was constructed. In the results of high-efficiency windows system performance assessment, U value 0.8 ~ 1.1W/m2·K, air-tightness performance ratings of 1 showed the windows energy efficiency rating that meets the Class 1 or 2 ratings showed. This study is based on an aluminum frame with a high-efficiency windows were secure system design technology, future public housing, commercial buildings is expected to be applied to a variety of buildings.
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Wang, J. H., and F. M. Shih. "Threshold Performance Optimization of a Rotor-Bearing System Subjected to Leakage Excitation." Journal of Engineering for Gas Turbines and Power 112, no. 4 (October 1, 1990): 439–44. http://dx.doi.org/10.1115/1.2906187.
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Fluid leakage in blade tips in turbomachinery may induce instability and limit and output rating. In this work, the optimization technique has been used to find diameters of shaft elements and bearing supports so that the optimized rotor-bearing system can sustain a larger fluid leakage force. The results show that the threshold performance of rotor-bearing systems can be significantly improved by slight modifications of the shaft diameters. The results also indicate that the threshold performance can be improved more significantly by the combination of optimum bearing supports and optimum shaft diameters.
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Davidson, J. H., and H. A. Walker. "Design Optimization of a Two-Phase Solar Water Heater Using R-123." Journal of Solar Energy Engineering 114, no. 1 (February 1, 1992): 53–61. http://dx.doi.org/10.1115/1.2929982.
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Design of a vapor transport solar water heater using R-123 is optimized to maximize thermal performance and minimize life cycle cost. Optimal systems for one and two-story homes are identified in a parametric study of collector area, condenser heat transfer area, water storage tank volume and circulating refrigerant volume for the Solar Rating and Certification Corporation rating procedure. Selection of collector and heat exchanger areas is critical to performance. With warm and cloudless ambient conditions, the most economic system is a large collector area, low efficiency system capable of meeting nearly 100 percent of the load. Under more realistic operating conditions, the best design will place more emphasis on efficiency. The condenser should have sufficient surface area to avoid significant increases in collector operating temperatures. Thermal performance is relatively insensitive to either refrigerant or water storage volume.
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Evans, Lee A., and Ki-Hwan G. Bae. "US Army performance appraisal policy analysis: a simulation optimization approach." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 16, no. 2 (August 13, 2018): 191–205. http://dx.doi.org/10.1177/1548512918787969.
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An effective performance appraisal system is critical in identifying officers with the knowledge, skills, and abilities to lead the future military force. The US Army uses a forced distribution performance appraisal system that limits the number of above average evaluations raters can award to their subordinates. Aside from job performance, multiple factors contribute to the rating an individual receives in such systems. These factors include a rater’s span of control (the number of subordinates being rated), the frequency at which individuals change raters, regulatory constraints pertaining to the number of top evaluations a rater can award, and the rater behavior. Using performance appraisal data provided by the US Army Human Resources Command, we develop a discrete-event simulation model that represents Army officers in the current forced distribution performance appraisal system. We then apply ranking and selection simulation optimization techniques to evaluate and optimize controllable input parameters in the simulated system. Our results show the potential of reducing the number of officers not receiving the number of above average evaluations commensurate with their performance level by as much as 24%. The results also further indicate the general applicability of simulation optimization in the fields of manpower modeling and policy analysis.
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Sarbu, Ioan, Emilian Valea, and Gabriel Ostafe. "Optimization of Insulation Design for Refrigerating Systems." Applied Mechanics and Materials 510 (February 2014): 202–7. http://dx.doi.org/10.4028/www.scientific.net/amm.510.202.
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Economic operation with minimal energy consumption and low costs of a cooled room or a refrigerant piping system depends largely on the quality and thickness of their insulation. The classical method of insulation rating for refrigeration systems is based on respect of the condition to prevent condensation of water vapours in the air on the surface of insulation or on limiting heat gain, but rarely leads to optimum in terms of a technical and economic criterion. In this paper are described some types of insulation for refrigeration applications and is developed a rating optimization model of these insulations with a high level of generality. It uses multiple dynamic optimization criteria simple or compound, which better reflects the economic and energy complex aspects, present and future. Based on this model were elaborated two computer programs implemented on PC microsystems. Numerical examples will be presented to demonstrate the accuracy and efficiency of the proposed optimization model. These show the good performance of the new model.
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Effendi Amran, Mohd, and Mohd Nabil Muhtazaruddin. "Assessment of Renewable Distributed Generation in Green Building Rating System for Public Hospital." International Journal of Engineering & Technology 7, no. 3.15 (August 13, 2018): 40. http://dx.doi.org/10.14419/ijet.v7i3.15.17404.
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This paper presents an optimization solution for renewable Distributed Generation (DG), as imposed in the Green Building Rating System (GBRS) for a public hospital. Solar photovoltaic DG unit (PV-DG) is identified as a type of DG used in this paper. The proposed optimization via PV-DG coordination will improve the sustainable energy performance of the green building by power loss reduction within accepted lower losses region using Artificial Bee Colony (ABC) algorithm. The setup input data from one of Malaysian public hospitals’ power distribution system is been adopted and simulation results via MATLAB programming show that the optimization of DG forming into bigger-scale imposed system provides a better outcome in minimization of total power losses within appropriate voltage profile as compared to current PV-DG imposed in GBRS. The objective function representing total power losses which also supported by related literature give a measure that forming sufficient and optimal PV-DG assessment criteria is highly important, thus, current PV-DG assessment in GBRS is proposed to be reviewed into new parameter setting for public hospital due to its’ high energy demand and distinctive electrical load profile.
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Rohde, Gustav T., Michael I. Pinard, and Elzbieta Sadzik. "Long-Term Network Performance: Function of Pavement Management System Maintenance Selection Policy." Transportation Research Record: Journal of the Transportation Research Board 1592, no. 1 (January 1997): 1–7. http://dx.doi.org/10.3141/1592-01.
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Most road agencies operate under a scenario in which the maintenance need exceeds the available maintenance funds. In this environment pavement managers are forced to select maintenance and rehabilitation actions on the basis of a defined methodology. This is typically done through a ranking process or through modern optimization routines. In the ranking process the maintenance priority of a road is determined on the basis of the importance and condition of the road. Projects with a high priority rating are carried out first, and projects are then carried out until the available funds are depleted. The more sophisticated pavement management systems use optimization routines that allow for interproject trade-offs when generating a maintenance plan. The importance of the choice of maintenance selection methodology for the long-term network condition is described. Data from an actual road network were used and were subjected to various ranking and optimization functions. This included policies such as fix worst first, use priority index, maximize asset value, minimize transportation costs, and the traditional maximization of area under a condition curve approach. The outcome was evaluated in terms of network condition, network asset value, overall transportation costs, and road user cost. It is shown that the maintenance policy should be compatible with agencies’ long-term maintenance objectives.
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Li, Lianhuan, Zheng Zhang, and Shaoda Zhang. "Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation." Scientific Programming 2021 (May 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/7427409.
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This paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative filtering. This method not only makes use of the advantages of content filtering but also can carry out similarity matching filtering for all items, especially when the items are not evaluated by any user, which can be filtered out and recommended to users, thus avoiding the problem of early level. At the same time, this method also takes advantage of the advantages of collaborative filtering. When the number of users and evaluation levels are large, the user rating data matrix of collaborative filtering prediction will become relatively dense, which can reduce the sparsity of the matrix and make collaborative filtering more accurate. In this way, the system performance will be greatly improved through the integration of the two. On the basis of the improved collaborative filtering algorithm, a hybrid algorithm based on content and improved collaborative filtering was proposed. By combining user rating with item features, a user feature rating matrix was established to replace the traditional user-item rating matrix. K-means clustering was performed on the user set and recommendations were made. The improved algorithm can solve the problem of data sparsity of traditional collaborative filtering algorithm. At the same time, for new projects, it can also predict users who may be interested in new projects according to the matching of project characteristics and user characteristics scoring matrix and generate push list, which effectively solve the problem of new projects in “cold start.” The experimental results show that the improved algorithm in this paper plays a significant role in solving the speed bottleneck problems of data sparsity, cold start, and online recommendation and can ensure a better recommendation quality.
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Wang, J. H., and F. M. Shih. "Improve the Stability of Rotor Subjected to Fluid Leakage by Optimum Diameters Design." Journal of Vibration and Acoustics 112, no. 1 (January 1, 1990): 59–64. http://dx.doi.org/10.1115/1.2930099.
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For high performance turbomachinery, the fluid leakage in the shroud of the blades and the labyrinth seal may cause instability and limit the output rating. In this work, an optimization technique has been used to find the optimum diameters of shaft elements so that the optimized rotor can sustain maximum fluid leakage excitation. The results show that, even without the increase of total mass of the rotor system, the threshold performance of rotor-bearing systems can be significantly improved by slight modification of the shaft diameters.
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Rácz, Levente, Bálint Németh, Gábor Göcsei, Dimitar Zarchev, and Valeri Mladenov. "Performance Analysis of a Dynamic Line Rating System Based on Project Experiences." Energies 15, no. 3 (January 29, 2022): 1003. http://dx.doi.org/10.3390/en15031003.
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This paper aims to demonstrate the performance and reliability analysis of a dynamic line rating (DLR) system at the Bulgarian demonstration site of the FLEXITRANSTORE project. As part of the project, various manufacturers’ different line monitoring DLR sensors and weather stations were installed on a 110 kV double-circuit overhead line (OHL). These devices provided input parameters to the DLR system based on objective measurements. This paper used statistical tools to examine the reliability and accuracy of installed devices, thus making products from different manufacturers comparable. In addition, two independent line monitoring and DLR models have been developed: the black-box and extended white-box models. The performances of the two models were analyzed for the same input parameters and compared to the field measurements. Based on the presented results, the reliability and accuracy of the applied weather stations of different companies were almost the same. This conclusion cannot be said for DLR line monitoring sensors, where the devices could be differentiated based on reliability and measurement accuracy results. In terms of models, the usability of the extended white-box model seemed to be limited in certain weather conditions, implicating a more significant role for soft-computing-based DLR models in the future. In addition to the results, root causes for the errors and future directions that may provide a framework for further research are also presented.
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Aull, Mark, Andy Stough, and Kelly Cohen. "Design Optimization and Sizing for Fly-Gen Airborne Wind Energy Systems." Automation 1, no. 1 (June 17, 2020): 1–16. http://dx.doi.org/10.3390/automation1010001.
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Traditional on-shore horizontal-axis wind turbines need to be large for both performance reasons (e.g., clearing ground turbulence and reaching higher wind speeds) and for economic reasons (e.g., more efficient land use, lower maintenance costs, and fewer controllers and grid attachments) while their efficiency is scale and mass independent. Airborne wind energy (AWE) system efficiency is a function of system size and AWE system operating altitude is less directly coupled to system power rating. This paper derives fly-gen AWE system parameters from small number of design parameters, which are used to optimize a design for energy cost. This paper then scales AWE systems and optimizes them at each scale to determine the relationships between size, efficiency, power output, and cost. The results indicate that physics and economics favor a larger number of small units, at least offshore or where land cost is small.
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Bochare, Rewa, and Poonam Bagora. "Comparative Analysis of Green Building Rating Systems for Residential House: A Case Study." ECS Transactions 107, no. 1 (April 24, 2022): 7091–101. http://dx.doi.org/10.1149/10701.7091ecst.
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Rating a building for its ecological sustainability and energy efficient is the new black in the construction industry. In this study, we assessed a small house for green building optimization and energy efficient rating with the help of two softwares, namely EDGE (Excellence in Design for Greater Efficiencies) for green building rating and IGBC Green New Building Rating SystemVersion 3.0. The former is cost effective software, furnishing detailed results about optimal design aspects for the green buildings like energy efficiency, water efficiency, and material efficiency. The latter, however, is a certified rating system assessing the drawings on similar criteria as EDGE but delivering overall points for the building (out of 100), thereby providing the performance level in terms of certified levels namely Certified, Silver, Gold, Platinum, and Super Platinum. After performing analysis for the case plan, using EDGE software, the energy efficiency measured 57.93%, water efficiency was 66.24%, and material efficiency was 81%. Upon using the IGBC green house rating system software, 65 points were matched out of 100 for our case plan. Thus, the certification level was Silver and recognition achieved was Best Practices.
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M.A. Alakkad, Moataz, Zulhani Rasin, Mohammed Rasheed, Wahidah Abd Halim, and Rosli Omar. "Real-time switching thirteen-level modified CHB-Multilevel inverter using artificial neural network technique based on selective harmonic elimination." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (December 1, 2020): 1642. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1642-1652.
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<p>Recently, global research is giving more attention to the renewable energy sources due to its sustainability and environmentally friendly nature. The necessity of DC to AC conversion to integrate these sources to the well established AC power system create a significant development of multi-level inverter with its advantages of operating at higher rating system with lower component rating as well as better harmonics performance at it output voltage and current. In this research paper, a modified topology of CHB-MLI to provide 13-level output AC waveform is proposed based on the SHE-PWM strategy using the ANN optimization technique. The system modeling is done with Matlab Simulink software and verification are carried out by both simulation and experiment. Results show that the ANN technique able to reduce the THD significantly as the level of waveform is increased as low as 5.16% THD for the 13-level output voltage. Results from the experiment shows a good agreement with the simulation, thus verifying the effectiveness of the proposed ANN technique as an optimization method. </p>
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Sebelev, Aleksandr, Aleksandr Kirillov, Gennadii Porshnev, Kirill Lapshin, and Aleksandr Laskin. "Thermodynamic analysis of design and part-load operation of a novel waste heat recovery unit." MATEC Web of Conferences 245 (2018): 04010. http://dx.doi.org/10.1051/matecconf/201824504010.
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Organic Rankine Cycle (ORC) thermodynamic optimization is of critical importance while developing new plants. Optimization procedures may be imed at the highest efficiency as well as cost or sizing minimization. Optimization process is generally carried out for plant nominal rating. At the same time, part-load operation has to be carefully considered in case of waste heat recovery from flue gases coming from internal combustion engines or gas turbines. Gas mass flow and temperature variations are specific to this application, significantly influencing ORC plant performance. Secure prediction of part-load operation is of particular importance for assessment of plant power output, providing stability and safety and utilizing proper control strategy. In this paper design and off-design cycle simulation model is proposed. Off-design performance of the ORC cycle recovering waste heat from gas turbine unit installed at gas compressor station is considered. Major factors affecting system performance are outlined.
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Kim, Joseph J., Marco A. Sanchez, Michelle Del Aguila, and Sunkuk Kim. "GREEN BUILDING STRATEGIES FOR LEED CERTIFIED RECREATIONAL FACILITIES." Journal of Green Building 12, no. 2 (March 2017): 149–66. http://dx.doi.org/10.3992/1943-4618.12.2.149.
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Student recreational facilities at higher education institutions are increasingly utilizing green building strategies to decrease their impact on the campus environment and to increase economic and social impacts on the community. Through nearly two decades of LEED building development, comparative studies of LEED strategies among buildings are not rare, but still in demand due to the lessons learned from other LEED certified buildings. This paper presents case studies on the green building strategies utilized for six student recreational facilities certified by the U.S. Green Building Council's Leadership in Energy and Environmental Design rating system and examines favorable strategies that lead to the success of obtaining higher rating levels. The comparative analysis results showed that the energy and atmosphere and indoor environmental quality categories that utilized energy performance optimization and innovative architectural designs for better daylights and views were major components for higher rating levels apart from conventional design criteria such as location, climate, and density. The recreational facilities having higher ratings were more favorable in obtaining credits using central plants, stormwater design, innovative wastewater technologies, and light control. The findings not only provide specific green buildings strategies for student recreational facilities, but they also enhance the green initiatives for future education facilities on university campuses.
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Barrella, Elise, Kelsey Lineburg, and Peter Hurley. "Applying a transportation rating system to advance sustainability evaluation, planning and partnerships." International Journal of Sustainability in Higher Education 18, no. 4 (May 2, 2017): 608–26. http://dx.doi.org/10.1108/ijshe-05-2015-0087.
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Purpose The purpose of this paper is to describe a pilot application of the Sustainable Transportation Analysis & Rating System (STARS), and highlight how a sustainability rating system can be used to promote sustainable urban development through a university–city partnership. STARS is an example of a second-generation “green” rating system focused on transportation planning, design, operations and maintenance. Design/methodology/approach In Fall 2013, James Madison University (JMU) initiated a STARS pilot demonstration using a local corridor that connects the university and the city of Harrisonburg. The pilot’s purposes were to develop attainable transportation-development targets, evaluate infrastructure and programmatic options in the context of a credit-based system and demonstrate a decision-making framework centered on sustainability optimization. The paper provides an overview of the STARS framework and the pilot’s collaborations, analysis, findings and recommendations for credits across sustainability dimensions. Findings Upon applying the rating system, the research team found that STARS may initially be easier to integrate into a comprehensive transportation planning process than a corridor-level evaluation due to data needs, in-house expertise and planning timelines for campus and city developments. A campus-wide master plan based on STARS would enable a university and a city to apply sustainability principles to their physical and/or policy interfaces to systemically create change and achieve quantifiable targets. Originality/value The STARS framework provides a novel approach for integrating multiple stakeholders (faculty, the university and city staff, students and community members) in a process of capacity building, evaluating options, policy-making, implementation and performance monitoring. The JMU pilot is the first application of STARS at a university and the only US East Coast application to date.
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Molan, Amirarsalan Mehrara, and Khaled Ksaibati. "Developing the New Barrier Condition Index (BCI) to Unify the Barrier Assessments - A Case Study in Wind River Indian Reservation, Wyoming." Open Transportation Journal 12, no. 1 (May 31, 2018): 182–91. http://dx.doi.org/10.2174/1874447801812010182.
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Based on the statistics, roadsides include a considerable portion of the fatal crashes annually. Barrier systems are one of the important components of roadside that plays a key role in mitigating the severity of these crashes and save more lives. Despite the substantial role of barrier systems, unfortunately, there is no unified rating system in the barrier condition assessments to utilize for the optimization of the improvements. As the primary contribution in this study, a new Barrier Condition Index (BCI) was proposed to unify the barrier assessment studies to use the same reference for rating barrier systems. This fact makes the decision-makers able to establish a prioritized ranking for optimizing the barrier improvements. For this purpose, the research investigated a comprehensive list of damages in barrier systems. Then, appropriate coefficients were given to the damage types based on their effects on the performance of the system reviewing the previous works. To show the practicability of the new BCI as an example for the whole procedure of the assessment, six different barrier systems located in Wind River Indian Reservation (WRIR), Wyoming, were also evaluated in the study. Based on the evaluation done, the BCI was used to rank the performance of the barriers. Three of the barrier segments at WRIR was introduced as the high-severity level which should be considered in the first priority of the improvement phase.
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Marzouk, Mohamed. "FUZZY MONTE CARLO SIMULATION OPTIMIZATION FOR SELECTING MATERIALS IN GREEN BUILDINGS." Journal of Environmental Engineering and Landscape Management 28, no. 2 (April 27, 2020): 95–104. http://dx.doi.org/10.3846/jeelm.2020.12087.
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Global interest in sustainable and green building design has been increasing in the last few decades. This interest is strengthened by the fact that sustainable measures help in reducing negative social and environmental impacts of buildings. For that, this paper aims to develop a mixed integer optimization model that aids architects/designers and owner representatives during design stage in selecting building materials taking into consideration costs and risks that are involved in the selection process. The model is developed as a simulation optimization tool based on the Leadership in Energy and Environmental Design (LEED) rating system for new construction. The developed model allows deterministic and probabilistic cost analysis of various design alternatives. In addition, it identifies the least possible cost to gain the LEED credits and the risks associated with materials’ quantities and materials’ unit prices. To illustrate the use of the proposed tool, a case study of an office building project constructed in Egypt is presented. An integrated Fuzzy Monte Carlo Simulation (FMCS) analysis is performed to account for the associated risks of using new materials in the considered case study. The proposed model is capable to capture the cost uncertainty of building materials and to identify the cost and sustainability performance of various building materials by relating the LEED rating system for new construction.
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Ponomareva, L. A., O. N. Romashkova, A. N. Belyakova, and V. S. Zabolotnikova. "Automation of multicriteria ranking of students using ePortfolio." Vestnik of Don State Technical University 19, no. 4 (January 3, 2020): 382–88. http://dx.doi.org/10.23947/1992-5980-2019-19-4-382-388.
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Introduction. The paper considers improving the management quality of an educational organization through automating the personalized data collection, storage and ranking. The authors have developed a module of the university corporate system, an ePortfolio of students, which implements a multicriteria optimization method for calculating the students’ rating on the basis of the collected and stored data. The study object is multicriteria ranking methods. The subject of the study is the automated calculation of student’s rating within the electronic portfolio. The study objective is to develop tools for collecting, storing and processing data on individual achievements of students and the implementation of the mathematical method of multicriteria optimization for ranking students on the basis of the portfolio data. Practical implications include development of a tool for an effective management of the educational process.Materials and Methods. A prototype module of the university corporate system “ePortfolio” on the platform of 1C: Enterprise 8.3 is presented. To rank students, a special case of the alternative ranking method is implemented in the block of the data analysis module – pairwise comparison in order of their relative importance. At that, a unified scale of relations was used.Research Results. The authors described the scheme of the information system (IS) operation “ePortfolio”, presented a process interaction pattern for the portfolio formation, as well as a diagram of business processes under calculating an individual rating. A fragment of the sample is shown on which the performance of the multicriteria optimization block has been tested. The criteria of the calculations are described, as well as the rule of screening alternatives for solving for the optimum.Discussion and Conclusion. The paper presents the results of the IS block operation on ranking a list of students. The calculation results coincide with the practical data.
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Alqaili, Abdulraaof, Mohammed Qais, and Abdullah Al-Mansour. "Integer Search Algorithm: A New Discrete Multi-Objective Algorithm for Pavement Maintenance Management Optimization." Applied Sciences 11, no. 15 (August 3, 2021): 7170. http://dx.doi.org/10.3390/app11157170.
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Optimization techniques keep road performance at a good level using a cost-effective maintenance strategy. Thus, the trade-off between cost and road performance is a multi-objective function. This paper offers a new multi-objective stochastic algorithm for discrete variables, which is called the integer search algorithm (ISA). This algorithm is applied to an optimal pavement maintenance management system (PMMS), where the variables are discrete. The PMMS optimization can be achieved by maximizing the condition of pavement with a minimum cost at specified constraints, so the PMMS is a constrained multi-objective problem. The ISA and genetic algorithm (GA) are applied to improve the performance condition rating (PCR) of the pavement in developing countries, where the annual budget is limited, so a minimum cost for three years’ maintenance is scheduled. Study results revealed that the ISA produced an optimal solution for multi-function objectives better than the optimal solution of GA.
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Vygodchikova, I. Yu. "MINIMAX MODEL AND BALL-RATING APPROACH FOR REMUNERATION OF EMPLOYEES THE SPORTS SECTIONS OF UNIVERSITY." Business Strategies 8, no. 4 (April 22, 2020): 91–93. http://dx.doi.org/10.17747/2311-7184-2020-4-91-93.
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In article offers solution of problem the improving of quality the management in field of sports services of university. Point-rating system and minimax optimization model are used to assess the level of staff performance in two main divisions: experienced qualified employees and young developing employees. On average, the amount of remuneration for coaches of the first (higher) level of qualification will be higher than for coaches of the second (lower) level, since employees of the highest level will receive an additional point for experience and qualifications. For coaches of the second level, maximum diligence and absence of errors in work are necessary, which will significantly increase level of remuneration. The introduction of such system of remuneration for employees of sports sections of universities will encourage coaches to increase their professional status and improve their skills.
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Kim, Myung-Hun, and Eul-Bum Lee. "A Forecast Model for the Level of Engineering Maturity Impact on Contractor’s Procurement and Construction Costs for Offshore EPC Megaprojects." Energies 12, no. 12 (June 16, 2019): 2295. http://dx.doi.org/10.3390/en12122295.
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This paper focuses on the influence of detailed engineering maturities on offshore engineering, procurement, and construction (EPC) project procurement and construction cost performance. The authors propose a detailed engineering completion rating index system (DECRIS) to estimate the engineering maturities, from contract award to beginning of construction or steel cutting. The DECRIS is supplemented in this study with an artificial neural network methodology (ANN) to forecast procurement and construction cost performances. The study shows that R2 and mean error values using ANN functions are 20.2% higher and 19.7% lower, respectively, than cost performance estimations using linear regressions. The DECRIS cutoff score at each gate and DECRIS forecasting performance of total cost impact were validated through the results of fifteen historical offshore EPC South Korean mega-projects, which contain over 300 procurement cost performance data points in total. Finally, based on the DECRIS and ANN findings and a trade-off optimization using a Monte-Carlo simulation with a genetic algorithm, the authors propose a cost mitigation plan for potential project risks based on optimizing the engineering resources. This research aids both owners and EPC contractors to mitigate cost overrun risks, which could be continuously monitored at the key engineering gates, and engineering resources could be adjusted per optimization results.
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Makarenko, Serhii, Nataliia Oliinyk, and Tatyana Kazakova. "IMPROVING THE METHOD APPROACH TO THE RATING EVALUATION OF EMPLOYEES AS PROFESSIONAL CAREER DEVELOPMENT." Baltic Journal of Economic Studies 4, no. 5 (February 11, 2019): 179. http://dx.doi.org/10.30525/2256-0742/2018-4-5-179-187.
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Theoretical and methodical aspects of rating performance appraisal of academic workers of higher educational institutions are the object of research. The purpose of the article is to study peculiarities of the functioning of institutions of higher education and to improve processes of rating assessment of employees as a part of the development of professional careers. Methodological basis of research consisted of scientific works of domestic and foreign scientists and leading specialists, statistical and analytical materials of state authorities. Results are obtained through the use of methods: statistical and economic – to determine the peculiarities and trends of the development of institutions of higher education in Ukraine; economics and mathematics – to study the impact of the main indicators of activity and performance of higher education institutions and purchasing power of the population on the total volume of gross domestic product; abstract-logical – for theoretical generalization and formulation of conclusions. The results of the conducted research testify to the necessity of introducing a set of measures for optimization of labour costs for scientific and pedagogical workers of higher education institutions without worsening the quality of provided educational services. It has been revealed that the lack of a sound methodological approach to employee rating and appropriate means of motivation for professional development can lead to the loss of existing intellectual potential and competitive positions of higher education institutions in the market for educational services in general. To maintain the existing intellectual potential justified the introduction of an organizational and economic system for optimizing labour costs, which should consist of the following five main stages: collecting information, processing information received using expert and econometric and mathematical models, calculating the predictive values of factors and productive indicator, development of measures and directions of optimization of labour costs, development of a system of planning career development of personnel. With the introduction of a rating assessment of the activities of academic workers and its attachment to the development of professional careers, it is necessary to avoid possible professional burnout due to overload. A further study of the mechanism of determining the optimal predictive model, taking into account the influence of the shadow sector on the official statistical indicators of the development of the economy and the educational sector, in particular, deserves further study.
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Zhao, Bingqian, Xiaojing Lyu, and Ning Qi. "Construction and Optimization of Transboundary Business Financial Credit Network in the Era of 5G Communication." Wireless Communications and Mobile Computing 2022 (March 24, 2022): 1–14. http://dx.doi.org/10.1155/2022/6481340.
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The e-commerce industry has broken through the original geographical restrictions, successfully integrated with world trade, and evolved new e-commerce model. The e-commerce model, that is, e-commerce transactions across international trade, has been rapidly expanded. However, e-commerce transactions across international trade have innate virtuality and information imbalance. These problems have caused many credibility problems and, at the same time, restrict and hinder the healthy and sustainable evolution of transactions across international trade. Credit evaluation of transaction entities through credit evaluation models is an effective way to restrict the behavior of transaction entities. However, the existing credit evaluation models lack pertinence and effectiveness when applied in the context of cross-border e-commerce. In the era of 5G communication, building a complete credit evaluation system through 5G-related technologies will certainly become a new way for the stable evolution of transactions across international trade. This not only can effectively control the risks of cross-border trade and improve efficiency but also properly resolve the uncertainty caused by information imbalance. In order to better promote the development of e-commerce, this article establishes an e-business evaluation module based on integrated fusion performance rating. The weight and the membership of each factor are determined by AHP. Finally, the model was verified by an example. The results show that the evaluation system and the method proposed in this paper are feasible and effective for solving practical problems and provide a solid foundation for the construction of the network of my country’s e-commerce credit rating system. Establishing a scientific and reasonable e-commerce integrity evaluation system has very good practical significance.
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Shao, Chen, and Yue zhong yi Sun. "Shilling attack detection for collaborative recommender systems: a gradient boosting method." Mathematical Biosciences and Engineering 19, no. 7 (2022): 7248–71. http://dx.doi.org/10.3934/mbe.2022342.
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<abstract> <p>Organized malicious shilling attackers influence the output of the collaborative filtering recommendation systems by inserting fake users into the rating matrix within the database. The existence of shilling attack poses a serious risk to the stability of the system. To counter this specific security threat, many attack detection methods are proposed. Some of the past methods suffer from two disadvantages, the first being that they only analyze the rating matrix from a single perspective of user rating values and ignore other perspectives. Another is that some methods only use a single classifier to handle the classification of malicious attackers. Considering the above disadvantages, this paper proposes a gradient boosting method (named XGB-SAD) to achieve attack detection by combining double-view and gradient boosting. We first analyze the rating matrix with a double-view of time and item, which in turn defines the TPUS collection. Then our method uses eXtreme Gradient Boosting to perform heuristic iterative optimization of the model's objective function and uses the idea of ensemble learning to integrate multiple sets of base classifiers into strong classifier. The integrated strong classifiers are used to complete the detection of malicious attackers. Finally, we perform several experiments and the results demonstrate that XGB-SAD outperforms the comparison methods in terms of small-scale attack detection and overall detection, which proves the performance of our method.</p> </abstract>
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Shah, Khant, and Devanshu Pandit. "Factors Affecting Productivity of Refuse Compactor Vehicles in India." Journal of Solid Waste Technology and Management 47, no. 3 (August 1, 2021): 529–35. http://dx.doi.org/10.5276/jswtm/2021.529.
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Shift towards mechanization in the municipal solid waste management (MSWM) is aimed at improving health wellbeing of the workers who are manually handling waste. With huge investment and operating costs, proper evaluation and monitoring of the performances of these equipment is essential. Identifying and rating the factors which impact the performance of equipment is the first step towards improving efficiency of a system. This research identifies the critical factors and issues in the operation optimization of refuse compactor vehicles. Interviews with experts and field studies helped in identification of 30 factors which were divided in five categories. The research uses analytical hierarchy process (AHP) for finding out the priority vectors of thus identified factors and categories. The study also highlights equipment selection parameters for the best suited model and make.
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Lu, Jiazheng, Qingjun Huang, Xinguo Mao, Yanjun Tan, Siguo Zhu, and Yuan Zhu. "Optimized Design of Modular Multilevel DC De-Icer for High Voltage Transmission Lines." Electronics 7, no. 9 (September 17, 2018): 204. http://dx.doi.org/10.3390/electronics7090204.
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Ice covering on overhead transmission lines would cause damage to transmission system and long-term power outage. Among various de-icing devices, a modular multilevel converter based direct-current (DC)de-icer (MMC-DDI) is recognized as a promising solution due to its excellent technical performance. Its principle feasibility has been well studied, but only a small amount of literature discusses its economy or hardware optimization. To fill this gap, this paper presents a quantitative analysis and calculation on the converter characteristics of MMC-DDI. It reveals that, for a given DC de-icing requirement, the converter rating varies greatly with its alternating-current (AC) -side voltage, and it sometimes far exceeds the melting power. To reduce converter rating and improve its economy, an optimized configuration is proposed in which a proper transformer should be configured on the input AC-side of converter under certain conditions. This configuration is verified in an MMC-DDI for a 500 kV transmission line as a case study. The result shows, in the case of outputting the same de-icing characteristics, the optimized converter is reduced from 151 MVA to 68 MVA, and the total cost of the MMC-DDI system is reduced by 48%. This conclusion is conducive to the design optimization of multilevel DC de-icer and then to its engineering application.
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Inkollu, Sai Ram, and Venkata Reddy Kota. "A Multi-objective Hybrid Heuristic Approach for Optimal Setting of FACTS Devices in Power System Operation." International Journal of Applied Power Engineering (IJAPE) 5, no. 3 (December 1, 2016): 120. http://dx.doi.org/10.11591/ijape.v5.i3.pp120-129.
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<p>Improvement of power system performance in terms of increased voltage profile and decreased transmission loss is becoming one of the challenging tasks to the system operators under open access environment. Apart from traditional power flow controlling devices, use of Flexible AC Transmission System (FACTS) devices can give an attractive solution for the operation and control of deregulated power system. The type, size, location and number of FACTS devices are to be optimized appropriately in order to get the targeted benefits. In this paper, two FACTS devices, Thyristor Controller Phase Shift Transformer (TCPST) and Interline Power Flow Controller (IPFC) are selected to obtain the required performance such as improvement of voltage profile and loss minimization. To search the optimal location and optimal rating of the selected FACTS devices, a hybrid algorithm which formulated with Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) is proposed. At the first step, the optimization problem is solved for finding the optimal location of FACTS devices using PSO with an objective of voltage profile maximization and later GSA is implemented to optimize their parameters with an objective of transmission loss minimization. The proposed method is implemented on IEEE 30-bus test system and from the simulation results it can be proved that this technique is well suited for real-time application. </p><p align="center"><strong><br /></strong></p>
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K A, Shirien, Neethu George, and Surekha Mariam Varghese. "Descriptive Answer Script Grading System using CNN-BiLSTM Network." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 139–44. http://dx.doi.org/10.35940/ijrte.e5212.019521.
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Descriptive answer script assessment and rating program is an automated framework to evaluate the answer scripts correctly. There are several classification schemes in which a piece of text is evaluated on the basis of spelling, semantics and meaning. But, lots of these aren’t successful. Some of the models available to rate the response scripts include Simple Long Short Term Memory (LSTM), Deep LSTM. In addition to that Convolution Neural Network and Bi-directional LSTM is considered here to refine the result. The model uses convolutional neural networks and bidirectional LSTM networks to learn local information of words and capture long-term dependency information of contexts on the Tensorflow and Keras deep learning framework. The embedding semantic representation of texts can be used for computing semantic similarities between pieces of texts and to grade them based on the similarity score. The experiment used methods for data optimization, such as data normalization and dropout, and tested the model on an Automated Student Evaluation Short Response Scoring, a commonly used public dataset. By comparing with the existing systems, the proposed model has achieved the state-of-the-art performance and achieves better results in the accuracy of the test dataset.
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Tarife, Rovick, Yosuke Nakanishi, Yining Chen, Yicheng Zhou, Noel Estoperez, and Anacita Tahud. "Optimization of Hybrid Renewable Energy Microgrid for Rural Agricultural Area in Southern Philippines." Energies 15, no. 6 (March 19, 2022): 2251. http://dx.doi.org/10.3390/en15062251.
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Microgrids, or distributed systems of local energy generation, transmission, and demand, are now technologically and operationally capable of providing power to communities, especially in rural and peri-urban regions of developing nations. The reliability of the system, the cost of power generation, and the operating environmental impact are the major issues when designing and evaluating the performance of an off-grid hybrid renewable energy microgrid (HREM). This paper presents an integrated method for optimal sizing and operation of an HREM for rural agricultural communities in the Southern Philippines composed of run-of-the-river hydropower, photovoltaics (PV), diesel generator, and a battery energy storage system (BESS) using multi-objective particle swarm optimization (MOPSO) and a proposed multi-case power management strategy. The three conflicting objective functions that were simultaneously minimized were: loss of power supply probability (LPSP), levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions, subject to several constraints. The optimization generated 200 non-dominated or Pareto optimal alternative solutions, 4 of which were selected as solutions of interest. Based on the results, the optimal sizes of the main components for the reliable operation of the system are 100 panels with a rating of 0.25 kW for PV, 100 kWh for BESS, and 13 kW for the diesel generator, with corresponding LCOE, LPSP, and GHG emission values of 0.1795 USD/kWh, 0.05%, and 7874 kg, respectively, for 1 year. The effectiveness of the proposed HREM design was also analyzed, and the study yielded plenty of useful findings that could aid the electrification of the area.
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Khan, Atif Naveed, Kashif Imran, Muhammad Nadeem, Anamitra Pal, Abraiz Khattak, Kafait Ullah, Muhammad Waseem Younas, and Muhammad Shahzad Younis. "Ensuring Reliable Operation of Electricity Grid by Placement of FACTS Devices for Developing Countries." Energies 14, no. 8 (April 19, 2021): 2283. http://dx.doi.org/10.3390/en14082283.
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Flexible AC Transmission Systems (FACTS) are essential devices used for the efficient performance of modern power systems and many developing countries lack these devices. Due to the non-existence of these advanced technologies, the national grid remains weak and vulnerable to power stability issues that can jeopardize system stability. This study proposes novel research to solve issues of an evolving national grid through the installation of FACTS devices. FACTS devices play a crucial role in minimizing active power losses while managing reactive power flows to keep the voltages within their respective limits. Due to the high costs of FACTS, optimization must be done to discover optimal locations as well as ratings of these devices. However, due to the nonlinearity, it is a challenging task to find the optimal locations and appropriate sizes of these devices. Shunt VARs Compensators (SVCs) and Thyristor-Controlled Series Compensators (TCSCs) are the two FACTS devices considered for the study. Optimal locations for SVCs and TCSCs are determined by Voltage Collapse Proximity Index (VCPI) and Line Stability Index (Lmn), respectively. Particle Swarm Optimization (PSO) is employed to find the ideal rating for FACTS devices to minimize the system operating cost (cost due to active power loss and capital cost of FACTS devices). This technique is applied to IEEE (14 and 30) bus systems. Moreover, reliable operation of the electricity grid through the placement of FACTS for developing countries has also been analysed; Pakistan being a developing country has been selected as a case study. The planning problem has been solved for the present as well as for the forecasted power system. Consequently, in the current national network, 6.21% and 6.71% reduction in active and reactive power losses have been observed, respectively. Moreover, voltage profiles have been improved significantly. A detailed financial analysis covering the calculation of Operation Cost (OC) of the national grid before and after the placement of FACTS devices is carried out.
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Zheng, Qinghua. "Information System Security Evaluation Algorithm Based on PSO-BP Neural Network." Computational Intelligence and Neuroscience 2021 (August 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/6046757.
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With the deepening of big data and the development of information technology, the country, enterprises, organizations, and even individuals are more and more dependent on the information system. In recent years, all kinds of network attacks emerge in an endless stream, and the losses are immeasurable. Therefore, the protection of information system security is a problem that needs to be paid attention to in the new situation. The existing BP neural network algorithm is improved as the core algorithm of the security intelligent evaluation of the rating information system. The input nodes are optimized. In the risk factor identification stage, most redundant information is filtered out and the core factors are extracted. In the risk establishment stage, the particle swarm optimization algorithm is used to optimize the initial network parameters of BP neural network algorithm to overcome the dependence of the network on the initial threshold, At the same time, the performance of the improved algorithm is verified by simulation experiments. The experimental results show that compared with the traditional BP algorithm, PSO-BP algorithm has faster convergence speed and higher accuracy in risk value prediction. The error value of PSO-BP evaluation method is almost zero, and there is no error fluctuation in 100 sample tests. The maximum error value is only 0.34 and the average error value is 0.21, which proves that PSO-BP algorithm has excellent performance.
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C, Jaymar, and Marifel Grace Capili-Kummer. "Giddy Ion Reloaded: Desktop Manager, Optimizer with Multi Utility Tool." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 154–58. http://dx.doi.org/10.35940/ijrte.e5238.019521.
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The performance of our computer is vital in fulfilling the task of the user. The paper presents a solution for maintaining the performance of the computer specifically computers with Windows operating systems. In this article, the fundamental difference and problem of the Windows operating system are defined which roots in the architectural design of using single configuration storage. The security hole of windows authentication, the exploitation of Microsoft EFS, and the acquisition of password hashes from Microsoft SAM are also discussed. Various existing utility software is evaluated to investigate if they meet the user define criteria. This paper also proposes a user-level implementation of the AES 256 encryption algorithm for securing user files and a Network Blocking algorithm based on ARP Spoofing techniques that provide a user-level network monitoring capability. The proposed application is called “Giddy-ION Reloaded” which consists of four main modules; machine information acquisition and monitoring, machine optimization, machine cleaning, and tools module that is divided into submodule; encryption and decryption, network monitoring, desktop management, network optimization/ control, and task automation. The testing was conducted with the participants coming from a computer college, continuing education trainer/faculty, and various IT experts. The response from these groups was statistically treated and analyzed, where the Giddy ION rank top and shows promising results. The study is limited to windows machines with 64-bit support architecture. The developed application is ready for implementation and deployment as evidenced by its high overall performance rating as evaluated by the participants against the ISO 25010 standards.
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Li, Wei. "Optimization and Application of Random Forest Algorithm for Applied Mathematics Specialty." Security and Communication Networks 2022 (May 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/1131994.
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For unbalanced data classification, RF (Random forest) algorithm will cause problems such as poor classification performance and a large DT scale. With the advent of the era of big data, RF algorithms should have the ability to process large-scale data. Aiming at the problem that RF cannot handle unbalanced data well, this paper improves the feature selection method built in RF and proposes a new feature selection algorithm. On the basis of feature importance ranking, randomness is introduced to ensure the strength of each tree and reduce the correlation between trees. In the extended transform data set, the sensitivity of the RF model has exceeded 0.8, and that of other models has increased to about 0.65. The prediction accuracy of the centralized RF model for the company’s credit rating reached 100%, while the CART model misjudged companies C6 and C7, while the Logit model misjudged companies C3, C5, and C8. Experiments prove the extrapolation of the RF model and its excellent prediction ability. In the practical application of applied mathematics specialty, the RF optimization algorithm proposed in this study can well handle continuous variables and improve the classification accuracy of RF. This paper holds that the advantages of the RF algorithm in data processing and model performance will make it more widely used in the field of enterprise credit risk evaluation.
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Kim, Myung-Hun, Eul-Bum Lee, and Han-Suk Choi. "A Forecast and Mitigation Model of Construction Performance by Assessing Detailed Engineering Maturity at Key Milestones for Offshore EPC Mega-Projects." Sustainability 11, no. 5 (February 27, 2019): 1256. http://dx.doi.org/10.3390/su11051256.
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The main subject of this research is to develop a forecast and mitigation model of schedule and cost performance during a detailed engineering stage of offshore engineering, procurement and construction (EPC) projects. The weight factors of major elements in detailed engineering completion rating index system (DECRIS) were measured using a fuzzy inference system (FIS) and an analytic hierarchy process (AHP). At five key engineering milestones, from an EPC contract being awarded to the start of construction, detailed engineering maturities were assessed in fourteen historical offshore EPC projects using the DECRIS model. DECRIS cutoff scores for successful project execution were defined at the key engineering milestones. A schedule and cost performance was forecasted and validated through comparison of DECRIS and other models using statistical confidence of a fuzzy set qualitative comparative analysis (fsQCA) and a regression analysis. As a mitigation method for engineering risks to EPC contractors, engineering resource enhancement is recommended for trade-off optimization of cost overrun using a Monte Carlo simulation. The main contribution of this research is that EPC contractors could continuously forecast construction costs and schedule performance utilizing the DECRIS model, and could review the adequacy of engineering resources, assessing the trade-off between said resources and cost/schedule risk mitigation.
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Zhang, Shuang. "Optimal Model of Horse Racing Competition Decision Management Based on Association Rules and Neural Network." Scientific Programming 2022 (March 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/4240244.
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With the vigorous development of horse racing, people’s attention to horse racing has increased significantly. Some experts and scholars have conducted research on the decision-making management and predictive analysis methods of horse racing. Today, with the rapid development of information technology, the amount of data and data dimensions of horse racing competitions continue to explode. The increase in data scale and feature dimensions provides new challenges for competition management and competition prediction research. At present, traditional prediction algorithms can no longer meet the needs of horse racing situation prediction, but research has found that association rules and neural network algorithms provide a good solution to the classification and prediction problem. Based on the advantages of association rules and neural networks in analyzing data, according to the requirements of horse racing decision management, this paper adopts the B/S structure to realize the construction of the horse racing decision management optimization model from the three aspects of hierarchical structure, functional structure, and forecasting process. Combined with the horse racing decision management optimization model, based on a large number of experimental training data, the final conclusion is drawn: first, the factors that affect the horse racing performance are from large to small. The order of arrangement is: race schedule > age > gender > weight > rating > horse top three rate > jockey > weight load > harness > ranking > field nature > field > trainer; the second is the prediction and actual results of the neural network algorithm. The closest one, which is slightly higher than 90%, has the highest prediction accuracy; third, the average value of the horse racing performance prediction of this system during the review is only 2.01 s, and the misappraisal rate is 0.12%, indicating that the application value of this system is significant; fourth, in the average time spent in the two seasons, the average time spent in the second season was reduced compared with the average time spent in the first season, with a maximum reduction of 0.984 s, indicating a slight improvement in the performance of the 2020 season. Using this system to predict horse racing, results can improve the optimization of horse racing decision management to a certain extent.
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Vitoi, Lais Abrantes, Danilo Brandao, and Elisabetta Tedeschi. "Active Power Filter Pre-Selection Tool to Enhance the Power Quality in Oil and Gas Platforms." Energies 14, no. 4 (February 16, 2021): 1024. http://dx.doi.org/10.3390/en14041024.
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This paper proposes a preliminary design tool for active power filters’ (APFs) solutions to be applied in offshore oil and gas platforms, where power quality indices are typically low, and reactive power compensation and current harmonic mitigation are often desired. The proposed approach considers that APF selection and rating is a trade-off between performance and size, and that both component and system aspects need to be optimized to achieve a well-tailored solution. As size and weight are critical constraints in offshore applications, possible benefits of using Silicon Carbide (SiC) switches for the APF implementation are investigated. Moreover, different compensation strategies are compared, varying the connection point of the APF between two different voltage levels and assigning the APFs different compensation goals. Improvements in power quality indices, as well as APFs rating, number of components, power losses, and filter size, have been considered for both SiC and Silicon-based solutions to identify the best trade-offs suitable for the considered, energy intensive industrial application.
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Khan, Naseruddin, Yousif Al-Sagheer, and Robert Steinberger-Wilckens. "PEFC System Reactant Gas Supply Management and Anode Purging Strategy: An Experimental Approach." Energies 15, no. 1 (January 1, 2022): 288. http://dx.doi.org/10.3390/en15010288.
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In this report, a 5 kW PEFC system running on dry hydrogen with an appropriately sized Balance of Plant (BoP) was used to conduct experimental studies and analyses of gas supply subsystems. The improper rating and use of BoP components has been found to increase parasitic loads, which consequently has a direct effect on the polymer electrolyte fuel cell (PEFC) system efficiency. Therefore, the minimisation of parasitic loads while maintaining desired performance is crucial. Nevertheless, little has been found in the literature regarding experimental work on large stacks and BoP, with the majority of papers concentrating on modelling. A particular interest of our study was the anode side of the fuel cell. Additionally the rationale behind the use of hydrogen anode recirculation was scrutinised, and a novel anode purging strategy was developed and implemented. Through experimental modelling, the use of cathode air blower was minimised since it was found to be the biggest contributor to the parasitic loads.
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Flintsch, Gerardo W., and John P. Zaniewski. "Expert Project Recommendation Procedure for Arizona Department of Transportation’s Pavement Management System." Transportation Research Record: Journal of the Transportation Research Board 1592, no. 1 (January 1997): 26–34. http://dx.doi.org/10.3141/1592-04.
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The Arizona Department of Transportation (ADOT) uses a network-level pavement management system to determine budget requirements for its annual pavement preservation program. Although this is a valuable tool for preservation programming, it does not assist the engineers with the selection of projects and rehabilitation treatments. The documented research was designed to enhance the capability of ADOT’s pavement management system to include project selection. An automatic project recommendation procedure was developed and implemented in a user-friendly, modular computer program. This automatic system is expected to reduce considerably the effort required to develop the preservation programs. It should improve the consistency of the decision process. The analysis starts with a section delineation procedure that delineates uniform roadway sections. It then computes the remaining service life of each uniform section by using linear performance equations and trigger points defined for each condition indicator. An artificial neural network simulator is used to screen and recommend roadway sections for the preservation program. The trained artificial neural network prepares a list of candidate sections, using the criteria learned from past selections and the current condition of all pavement sections. This preliminary list of candidate sections is further analyzed by a project recommendation procedure. This procedure recommends a preservation treatment, assigns a priority rating to each section in the list, and sorts the projects by priority. Funding is assigned to the highest-priority sections within each roadway group until the budget recommendation provided by the network optimization process is reached.
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Messaoud, Mayouf. "Comparative Functional Analysis of Three MPPT Techniques Applied on a Stand-Alone Photovoltaic System with a Charging Battery." Journal Européen des Systèmes Automatisés 55, no. 2 (April 30, 2022): 259–66. http://dx.doi.org/10.18280/jesa.550214.
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This paper presents a comparative functional analysis of a typical stand-alone photovoltaic system (SPV) with a charging battery. Considering the nonlinear behavior of the PV system, a specific methodology based on Tayler’s expansion series is opted for the modelling, control, and optimization. During the proposed steps, a practical PID controller is designed using a dynamic state space averaging approach. This controller is used to track the maximum power point (MPP) and control the battery charging process. To check the validity of this controller, it is applied with three disparate MPPT algorithms to enhance system performances by preventing drift phenomenon against fast changing in cell temperature and solar irradiance, and control the battery pack to conform with the load rating voltage. A typical 2525 W SPV is simulated in short term to capture fast dynamics transitional details. By comparison with practical manufacturer's specifications of the PV panel, the load, and the battery pack; simulation results show particularly, good performances with fuzzy logic controller in terms of speed tracking, MPPT tracking accuracy, voltage quality, and reducing transient fluctuations. The findings of this research substantiate its efficacy, which may serve as a prototype study for the design and realization of stand-alone photovoltaic systems with energy storage.
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Adelman, Daniel. "An Efficient Frontier Approach to Scoring and Ranking Hospital Performance." Operations Research 68, no. 3 (May 2020): 762–92. http://dx.doi.org/10.1287/opre.2019.1972.
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For many years, stakeholders have been complaining about how hospital scores are computed in the Centers for Medicare and Medicaid Services (CMS) hospital star ratings. In “An Efficient Frontier Approach to Scoring and Ranking Hospital Performance,” author Dan Adelman shows how the current system can lower the scores even of hospitals that improve along every quality measure. He proposes a new approach, based on an optimization framework, that he proves does not exhibit this behaviour, and thus creates better incentives for hospitals to improve. The approach scores hospitals as closely as possible to the best scoring hospital on the efficient frontier of hospital performance, under the same measure weights. It is flexible enough to incorporate constraints that represent stakeholder interests, such as giving higher weight to measures that impact more people. Using this new approach, he computes new scores for nearly every hospital in the United States and shows that there are significant differences with the current CMS hospital star ratings.
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Alowaigl, Amjad, Khalil H. A. Al-Shqeerat, and Mohammed Hadwan. "A multi-criteria assessment of decision support systems in educational environments." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (May 1, 2021): 985. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp985-996.
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<span>Decision support systems (DSS) are useful business intelligence (BI) tools as they help managers in large organizations make the best out of many decisions. Decisions are based on various types of raw data, models, documents, knowledge, and past experiences. This paper examines numerous criteria of decision support systems in the educational environment. Two effective methods were discovered and applied in this research, the analytic hierarchy process (AHP) and simple multi-attribute rating technique (SMART). These methods were selected due to their abilities to deal with complex decisional environments in general and widely used in practice for the educational environment in specific. The performance of methods is compared using two datasets called xApi-Education and IPEDS datasets. The obtained results based on the measurement of space complexity showed the level of convergence and similarity between these two methods. However, the experiments show that the Simple Multi-Attribute Rating Technique outperformed the analytic hierarchy process in terms of accuracy, deviation, and time complexity measurement.</span>
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Liu, Fang, Xiaoli Liu, Changyou Yin, and Hongrong Wang. "Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm." Gastroenterology Research and Practice 2022 (January 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/7874751.
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Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. Initially, the dataset is collected, and preprocessing is done. Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. The performance metrics like classification accuracy, area under the receiver-operating characteristic curve (AUROC), area under the curve (AUC), sensitivity, specificity, and precision are analyzed and compared with the traditional approaches. In individuals with GIB, the suggested technique had a good–excellent prognostic efficacy, and it outperformed other traditional models.
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Demirkiran, Emin T., Muhammet Y. Pak, and Rasim Cekik. "Multi-criteria collaborative filtering using rough sets theory." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 907–17. http://dx.doi.org/10.3233/jifs-201073.
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Recommender systems have recently become a significant part of e-commerce applications. Through the different types of recommender systems, collaborative filtering is the most popular and successful recommender system for providing recommendations. Recent studies have shown that using multi-criteria ratings helps the system to know the customers better. However, bringing multi aspects to collaborative filtering causes new challenges such as scalability and sparsity. Additionally, revealing the relation between criteria is yet another optimization problem. Hence, increasing the accuracy in prediction is a challenge. In this paper, an aggregation-function based multi-criteria collaborative filtering system using Rough Sets Theory is proposed as a novel approach. Rough Sets Theory is used to uncover the relationship between the overall criterion and the individual criteria. Experimental results show that the proposed model (RoughMCCF) successfully improves the predictive accuracy without compromising on online performance.
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Hossain, Md Sanwar, Khondoker Ziaul Islam, Abdullah G. Alharbi, Md Shafiullah, Md Rabiul Islam, and Afef Fekih. "Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network." Sustainability 14, no. 4 (February 15, 2022): 2201. http://dx.doi.org/10.3390/su14042201.
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The increased penetration of renewable energy sources (RESs) along with the rise in demand for wireless communication had led to the need to deploy cellular base stations powered by locally accessible RESs. Moreover, networks powered by renewable energy sources have the ability to reduce the costs of generating electricity, as well as greenhouse gas emissions, thus maintaining the quality of service (QoS). This paper examines the techno-economic feasibility of developing grid-tied solar photovoltaic (PV)/biomass generator (BG)-powered heterogeneous networks in Bangladesh, taking into account the dynamic characteristics of RESs and traffic. To guarantee QoS, each macro and micro-base station is supplied through a hybrid solar PV/BG coupled with enough energy storage devices. In contrast, pico and femto BSs are powered through standalone solar PV units due to their smaller power rating. A hybrid optimization model for electric renewables (HOMER)-based optimization algorithm is considered to determine the optimum system architecture, economic and environmental analysis. MATLAB-based Monte-Carlo simulations are used to assess the system’s throughput and energy efficiency. A new weighted proportional-fair resource method is presented by trading power consumption and communication latency in non-real-time applications. Performance analysis of the proposed architecture confirmed its energy efficiency, economic soundness, reliability, and environmental friendliness. Additionally, the suggested method was shown to increase the battery life of the end devices.
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Atabaki, Mohammad Saeid, Mohammad Mohammadi, and Bahman Naderi. "Hybrid Genetic Algorithm and Invasive Weed Optimization via Priority Based Encoding for Location-Allocation Decisions in a Three-Stage Supply Chain." Asia-Pacific Journal of Operational Research 34, no. 02 (April 2017): 1750008. http://dx.doi.org/10.1142/s0217595917500087.
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In this paper, location–allocation problem of a three-stage supply chain network, including suppliers, plants, distribution centers (DCs) and customers is investigated. With respect to the total cost, the aim is determining opened plants and DCs and designing transportation trees between the facilities. Considering the capacity of suppliers, plants and DCs are limited and there is a limitation on the maximum number of opened plants and DCs, a mixed-integer linear programming (MILP) model of the problem is presented. Since multi-stage supply chain networks have been recognized as NP-hard problems, applying priority-based encoding and a four-step backward decoding procedure, a meta-heuristic algorithm, namely GAIWO, based on the best features of genetic algorithm (GA) and invasive weed optimization (IWO) is designed to solve the problem. In small size problems, the efficiency of the GAIWO is checked by solutions of GAMS software. For larger size problems, the performance of the proposed approach is compared with four evolutionary algorithms in both aspects of the structure of the GAIWO and the efficiency of the proposed encoding–decoding procedure. Besides usual evaluation criteria, Wilcoxon test and a chess rating system are used for evaluating and ranking the algorithms. The results show higher efficiency of the proposed approach.
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Lo Franco, Francesco, Mattia Ricco, Riccardo Mandrioli, and Gabriele Grandi. "Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization." Energies 13, no. 19 (September 23, 2020): 5003. http://dx.doi.org/10.3390/en13195003.
Full textAbstract:
In the context of electric vehicle (EV) development and positive energy districts with the growing penetration of non-programmable sources, this paper provides a method to predict and manage the aggregate power flows of charging stations to optimize the self-consumption and load profiles. The prediction method analyzes each charging event belonging to the EV population, and it considers the main factors that influence a charging process, namely the EV’s characteristics, charging ratings, and driver behavior. EV’s characteristics and charging ratings are obtained from the EV model’s and charging stations’ specifications, respectively. The statistical analysis of driver behavior is performed to calculate the daily consumptions and the charging energy request. Then, a model to estimate the parking time of each vehicle is extrapolated from the real collected data of the arrival and departure times in parking lots. A case study was carried out to evaluate the proposed method. This consisted of an industrial area with renewable sources and electrical loads. The obtained results show how EV charging can negatively impact system power flows, causing load peaks and high energy demand. Therefore, a charging management system (CMS) able to operate in the smart charging mode was introduced. Finally, it was demonstrated that the proposed method provides better EV integration and improved performance.
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Li, Tao, Liyuan Zhang, and Shan Gao. "Performance of an M/M/1 Retrial Queue with Working Vacation Interruption and Classical Retrial Policy." Advances in Operations Research 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4538031.
Full textAbstract:
An M/M/1 retrial queue with working vacation interruption is considered. Upon the arrival of a customer, if the server is busy, it would join the orbit of infinite size. The customers in the orbit will try for service one by one when the server is idle under the classical retrial policy with retrial ratenα, wherenis the size of the orbit. During a working vacation period, if there are customers in the system at a service completion instant, the vacation will be interrupted. Under the stable condition, the probability generating functions of the number of customers in the orbit are obtained. Various system performance measures are also developed. Finally, some numerical examples and cost optimization analysis are presented.
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Mehrabian, M. A., and A. E. Shahbeik. "Thermodynamic modelling of a single-effect LiBr-H2O absorption refrigeration cycle." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 219, no. 3 (August 1, 2005): 261–73. http://dx.doi.org/10.1243/095440805x8656.
Full textAbstract:
The objective of this paper is to develop a computer program for design and thermodynamic analysis of a single effect absorption chiller using LiBr-H2O solution as working fluid. The conditions of hot water entering and leaving the desorber, cooling water entering the absorber and leaving the condenser, chilled water entering and leaving the evaporator, as well as the approach temperatures in condenser, evaporator, desorber, and absorber, the effectiveness of solution heat exchanger, the chiller refrigeration power, and the ambient temperature are used as input data. The program then gives the thermodynamic properties of all state points, the design information of all heat exchangers in the cycle and the overall cycle performance. The results deduced from the computer program are used to study the effect of design parameters on cycle performance. For example, increasing the evaporator and generator temperatures or decreasing the condenser and desorber temperatures can improve the second-law efficiency of the cycle. It is also noticed that the temperatures of hot water, cooling water, and chilled water, respectively, at the inlet of the desorber, condenser, and evaporator have a great effect on cycle coefficient of performance. The results of this program can be used either for sizing a new refrigeration cycle or rating an existing system. It can also be used for optimization purposes. The predictions of the present program are compared with other simulating programs and qualitative agreement is achieved.