scholarly journals NEURAL NETWORKS SURROGATE MODELS FOR SIMULATING PAYMENT RISK IN PAVEMENT CONSTRUCTION/NEURONINIŲ TINKLŲ PAKAITALO MODELIS MOKĖJIMO RIZIKAI KELIŲ STATYBOJE NUSTATYTI

2008 ◽  
Vol 14 (4) ◽  
pp. 235-240 ◽  
Author(s):  
Anshu Manik ◽  
Kasthurirangan Gopalakrishnan ◽  
Abhishek Singh ◽  
Shengquan Yan
Keyword(s):  
Neural Networks ◽  
Simulation Models ◽  
Detailed Examination ◽  
Surrogate Models ◽  
Average Error ◽  
Speed Up ◽  
Pavement Construction ◽  
To Receive ◽  
Highway Pavement

A common provision in quality control/quality assurance (QC/QA) highway pavement construction contracts is the adjustment of the pay that a contractor receives on the basis of the quality of the construction. It is important to both the contractor and the contracting agency to examine the amount of pay that the contractor can expect to receive for a given level of construction quality. Previous studies have shown that computer simulations can provide a better, more detailed examination of the pay schedule than is possible by simply determining the expected pay. In particular, the simulation process can provide an indication of the variability of pay at various quality levels and can identify the factors most responsible for pay adjustments. Stochastic simulation models are very useful in estimating and analyzing payment risk in highway pavement construction. However, such models are constrained by their computational requirements, and it is often necessary to couple them with simpler models to speed up the process of decision‐making. This paper investigates the use of Neural Networks (NN) to build surrogate models for a pavement construction payment‐risk prediction model. The results show that although the average error associated with the NN predictions are acceptable; in some particular cases the errors may be unacceptably high. Santrauka Bendroji sąlyga kokybės kontrolės užtikrinimo (QC/QA) kelių tiesimo sutartyse yra užmokesčio nustatymas. Jį rangovas gauna atsižvelgdamas į statybos kokybę. Svarbu rangovui ir agentūrai išnagrinėti užmokesčio kiekį, kurio rangovas gali tikėtis gauti už tam tikrą statybų kokybę. Ankstesni tyrimai parodė, kad pasitelkiant kompiuterinį modeliavimą galima gauti geresnį, daug išsamesnį apmokėjimo vaizdą. Tai galima padaryti paprasčiausiai nustatant tikėtiną užmokestį. Modeliavimo procesas rodo užmokesčio kitimą paisant kokybės ir gali pateikti veiksnius, nuo kurių priklauso kainos nustatymas. Tikimybinis modelis yra labai naudingas apskaičiuojant ir analizuojant užmokesčio riziką tiesiant kelių dangas. Tačiau tokie modeliai yra suvaržyti kompiuterinių reikalavimų, ir dažnai juos reikia susieti su paprastesniais modeliais norint greitinti sprendimų priėmimo procesą. Šiame straipsnyje tyrinėjamas neuroninių tinklų naudojimas pakeičiantiems modeliams sukurti norint teisingai nustatyti kelių dangos tiesimo užmokestį. Rezultatai parodė, kad vidutinė paklaida, susijusi su neuroninių tinklų spėjimu, yra priimtina, tačiau kai kuriais atvejais paklaidos gali būti neleistinai didelės.

scholarly journals The application of the artificial neural network in analysing the spread quality

2018 ◽  
Vol 226 ◽  
pp. 04042
Author(s):  
Marko Petkovic ◽  
Marija Blagojevic ◽  
Vladimir Mladenovic
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Food Processing ◽  
New Approach ◽  
Confectionery Products ◽  
Speed Up ◽  
Artificial Neural

In this paper, we introduce a new approach in food processing using an artificial intelligence. The main focus is simulation of production of spreads and chocolate as representative confectionery products. This approach aids to speed up, model, optimize, and predict the parameters of food processing trying to increase quality of final products. An artificial intelligence is used in field of neural networks and methods of decisions.

Application of Artificial Neural Networks for Processing and Interpretation of Data from a Scanning Magnetic Introscope

2021 ◽  
Author(s):  
Victor Evgenevich Kosarev ◽  
Ekaterina Anatolevna Yachmeneva ◽  
Aleksandr Vladimirovich Starovoyto ◽  
Dmitrii Ivanovich Kirgizov ◽  
Rustem Ramilevich Mukhamadiev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Machine Learning Algorithms ◽  
Test Machine ◽  
Subjective Factor ◽  
Mathematical Algorithms ◽  
Speed Up ◽  
Artificial Neural

Summary This paper presents the efficiency of using artificial neural networks for solving problems of processing and interpreting geophysical data obtained by scanning magnetic introscopy. Neural networks of various architectures have been implemented to solve the problems of processing primary material, searching for well structure objects,identifying casing defects. The analysis of the capabilities of neural networks in comparison with mathematical algorithms is carried out. To test machine learning algorithms and mathematical algorithms for processing, visualizing and storing the results, a software shell was created in which all tasks are solved using a set of tools. It was found that the use of artificial neural networks can significantly speed up the process of data processing and interpretation, as well as improve the quality of the results in comparison with individual mathematical algorithms. Nevertheless, the use of mathematical algorithms in solving some problems gives consistently better results. In particular, the problematic aspects were identified at the stage of interpretation when identifying defects. This is due to the presence of conventions in the isolation of defects by the operator at the stage of preparing data for training neural networks, which is a subjective factor and requires a deeper study.

Communication Sciences and Disorders PhD Graduates' Perceptions of Their PhD Program

2020 ◽  
Vol 5 (2) ◽  
pp. 463-478
Author(s):  
Elizabeth Crais ◽  
Melody Harrison Savage
Keyword(s):  
Detailed Examination ◽  
Career Expectations ◽  
Doctoral Preparation ◽  
Creative Solutions ◽  
Phd Education ◽  
Doctor Of Philosophy ◽  
First Time ◽  
Students Success ◽  
Phd Program

Purpose The shortage of doctor of philosophy (PhD)–level applicants to fill academic and research positions in communication sciences and disorders (CSD) programs calls for a detailed examination of current CSD PhD educational practices and the generation of creative solutions. The intended purposes of the article are to encourage CSD faculty to examine their own PhD program practices and consider the perspectives of recent CSD PhD graduates in determining the need for possible modifications. Method The article describes the results of a survey of 240 CSD PhD graduates and their perceptions of the challenges and facilitators to completing a PhD degree; the quality of their preparation in research, teaching, and job readiness; and ways to improve PhD education. Results Two primary themes emerged from the data highlighting the need for “matchmaking.” The first time point of needed matchmaking is prior to entry among students, mentors, and expectations as well as between aspects of the program that can lead to students' success and graduation. The second important matchmaking need is between the actual PhD preparation and the realities of the graduates' career expectations, and those placed on graduates by their employers. Conclusions Within both themes, graduate's perspectives and suggestions to help guide future doctoral preparation are highlighted. The graduates' recommendations could be used by CSD PhD program faculty to enhance the quality of their program and the likelihood of student success and completion. Supplemental Material https://doi.org/10.23641/asha.11991480

Pengaruh Kualitas Sumber Daya Manusia, Pemanfaatan Teknologi Informasi, Dan Sistem Pengendalian Intern Terhadap Ketepatwaktuan Pelaporan Keuangan Pemerintah Desa

2018 ◽  
Vol 26 (2) ◽  
pp. 131-143
Author(s):  
Marlinawati Marlinawati ◽  
Dewi Kusuma Wardani
Keyword(s):  
Information Technology ◽  
Control System ◽  
Human Resources ◽  
Financial Reporting ◽  
Internal Control ◽  
Positive Influence ◽  
Internal Control System ◽  
Speed Up ◽  
Village Government

The purpose of this research is to know the influence between the Quality of Human Resources, Utilization of Information Technology and Internal Control System Against Timeliness of Village Government Financial Reporting at Gunungkidul Regency. This research is causative research. The population is the village government in Gunungkidul Regency, especially in Gedangsari subdistrict. Criteria of respondents in the study were to village and village apparatus. We use questionnaire to collect data. We use multiple regression with SPSS program version 16.0 to analyze data. We find that quality of human resources and internal control system have a positive influence on the timeliness of village government financial reporting. On the other hand, utilization of information technology does not influence the timeliness of village government financial reporting. These imply that the quality of human resources and internal control system can speed up the preparation of village government financial reporting.

Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems

2016 ◽  
pp. 141-149
Author(s):  
S.V. Yershov ◽  
◽  
R.М. Ponomarenko ◽  
Keyword(s):  
Dynamic Models ◽  
Fuzzy Inference ◽  
Knowledge Bases ◽  
Comparative Characteristic ◽  
Software Systems ◽  
Scientific Papers ◽  
Speed Up ◽  
Diagnostic Software ◽  
Takagi Sugeno

Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

Lesson Study dan Implikasinya Terhadap Pembelajaran Berbasis Kurikulum Tingkat Satuan Pendidikan

2016 ◽  
Vol 1 ◽  
pp. 189-196
Author(s):  
Vian Harsution
Keyword(s):  
Learning Environment ◽  
Learning Process ◽  
Lesson Study ◽  
Learning Difficulties ◽  
Learning Needs ◽  
Democratic Leadership ◽  
Entrepreneurial Spirit ◽  
To Receive

Lesson study is a systematic, collaborative, and sustainable method of improving the quality of learning. Lesson study emphasizes the exploration of students’ learning needs; teacher openness towards learning difficulties encountered; the willingness of teachers to receive and provide advice and solutions to the difficulties encountered; and the consistency of the various parties to follow up the suggestions and solutions. Implementation of lesson study involving teachers, principals, and experts in the field of education. Kurikulum tingkat satuan pendidikan or abbreviated KTSP is operational curriculum formulated and implemented by each educational unit. KTSP has the characteristics, namely: giving broad autonomy to the educational unit, involving the community and parent participation, involving the democratic leadership of the principal, and require the support of a working team that is synergistic and transparent. KTSP based on the learning process, needs to be supported by a conducive learning environment and fun to be created by teachers.Teachers and principals in a professional, systematic and collaborative create an atmosphere that fosters independence, tenacity, entrepreneurial spirit, adaptive and proactive nature of the learning process. Thus, the learning needs of students who fulfilled optimally and professional ability of teacher who have increased on an ongoing basis, may usher in success – based learning KTSP. It means that the lesson study provides positive implications for the KTSP – based learning.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Reduction of number of postoperative complications depends on quality of dressing material and wound treatment

2020 ◽  
pp. 61-63
Author(s):  
Larisa Katkasova ◽  
Svetlana Kropotova
Keyword(s):  
At Risk ◽  
Wound Healing ◽  
Postoperative Complications ◽  
Wound Treatment ◽  
Postoperative Wound ◽  
Speed Up ◽  
Modern Technologies

Operated patients suffering from diabetes are at risk of developing postoperative complications. Modern technologies of postoperative wound treatment and modern dressings allow to avoid complications and speed up the process of postoperative wound healing.

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