Temat: artificial intelligence system - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems
Autorzy:: Hayder, Gasim
Solihin, Mahmud Iwan
Kushiar, Khairul Faizal Bin
Powiązania:: https://bibliotekanauki.pl/articles/1955428.pdf
Data publikacji:: 2021
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: sediment estimation
artificial intelligence
machine learning prediction
river system
visual programming
hydro-informatics system
Opis:: Sediment is a universal issue that is generated in the river catchment and affects the river flow, reservoir capacity, hydropower generation and dam structure. This paper aims to present the result of experimentation in sediment load estimation using various machine learning algorithms as a powerful AI approach. The data was collected from eight locations in upstream area of Ringlet reservoir catchment. The input variables are discharge and suspended solid. It was found that there is strong correlation between sediment and suspended solid with correlation coefficient of R = 0.9 . The developed ML model successfully estimated the sediment load with competitive results from ANN, Decision Tree, AdaBoost and SVM. The best result was produced by SVM (v-SVM version) where very low RMSE was generated for both training and testing dataset despite its more complicated hyperparameters setup. The results also show a promising application of machine learning for future prediction in hydro-informatic systems.
Źródło:: Journal of Ecological Engineering; 2021, 22, 7; 20-27
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A skeleton rule-based expert system of new generation
Autorzy:: Brzozowski, W.
Powiązania:: https://bibliotekanauki.pl/articles/384945.pdf
Data publikacji:: 2013
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: expert system
artificial intelligence
computer program
algorithm
inference process
fact
rule
technical diagnostics
Opis:: The paper presents skeleton rule-based expert system of a new generation, named EXPERT 3.0, worked out and programmed by the Author. Notion of a new generation refers here to implementation of a knowledge base of the system in a form of a computer database; previous skeleton expert systems implemented knowledge bases as text files. At first, a theory of expert systems, as one of the branches of Artificial Intelligence, is briefly presented. Then the Author’s original algorithms of the system are described in the paper. Using the EXPERT 3.0 system, execution of the inference processes: forward, backwards or mixed, as well as of falsification of the main hypothesis, is possible. The EXPERT 3.0 system may be loaded with any number of parallel knowledge bases from such domains as technical, medical or financial diagnostics, as well as providing equipment, forecast and many other systems; in the paper, the inference process is illustrated by an example of the diagnostics of the damage to a MKM33 coal mill, working in a 200 MW power unit. Finally, conclusions and recommendations are formulated in the paper.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2013, 7, 3; 10-21
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Agent approach in machine diagnosis
Podejście agentowe w diagnostyce maszyn
Autorzy:: Klemm, M.
Tabaszewski, M.
Powiązania:: https://bibliotekanauki.pl/articles/328055.pdf
Data publikacji:: 2011
Wydawca:: Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:: diagnostyka
system agentowy
diagnostyka wielosymptomowa
sztuczna inteligencja
eksploracja danych
condition monitoring
agent system
multisymptom diagnostic
artificial intelligence
data mining
Opis:: The paper presents a new approach to software development of diagnostic machines. The proposed system is a collection of many independent applications called agents which gain the diagnostic information, process it and inform the user of the system of the occurrence of significant events concerning the operation of the object. This allows a comprehensively support of the operation process by detecting the current condition and forecast a failure. An important feature of the proposed system is the speed, ability to learn through the use of artificial intelligence and openness that allows for any development of the system by adding more new items pursuing new activities or the same action on a different basis (increasing the reliability of inference).
W pracy przedstawiono nowe podejście do tworzenia oprogramowania diagnostycznego maszyn. Zaproponowany system jest zbiorem wielu niezależnych aplikacji nazwanych agentami, które pozyskują informację diagnostyczną, przetwarzają ją i informują użytkownika systemu o wystąpieniu istotnych zdarzeń dotyczących eksploatacji obiektu. Pozwala to kompleksowo wspomagać proces eksploatacji poprzez wykrywanie aktualnego stanu i prognozę do awarii. Istotną cechą zaproponowanego systemu jest szybkość działania, zdolność uczenia się poprzez zastosowanie metod sztucznej inteligencji oraz otwartość pozwalająca na dowolny rozwój systemu poprzez dodawanie kolejnych nowych elementów realizujących nowe działania lub te same działania w oparciu o inne zasady (zwiększanie niezawodności wnioskowania).
Źródło:: Diagnostyka; 2011, 4(60); 59-64
1641-6414
2449-5220
Pojawia się w:: Diagnostyka
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: AI-based Yolo v4 intelligent traffic light control system
Autorzy:: Prathap, Boppuru Rudra
Kumar, Kukatlapalli Pradeep
Chowdary, Cherukuri Ravindranath
Hussain, Javid
Powiązania:: https://bibliotekanauki.pl/articles/27314354.pdf
Data publikacji:: 2022
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: traffic jam
traffic light system
traffic management
intelligent monitoring
signal switching algorithm
artificial intelligence
Opis:: With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, wait times for commuters at traffic signal points are not reduced. The proposed methodology employs artificial intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in shorter vehicle waiting times.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 4; 53--61
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: AI-supported reasoning in physiotherapy
Wnioskowanie w fizjoterapii wspierane sztuczną inteligencją
Autorzy:: Mikołajewski, Dariusz
Mikołajewska, Emilia
Powiązania:: https://bibliotekanauki.pl/articles/41203435.pdf
Data publikacji:: 2024
Wydawca:: Uniwersytet Kazimierza Wielkiego w Bydgoszczy
Tematy:: artificial intelligence
machine learning
clinical reasoning
clinical decision support system
interview
musculoskeletal pain disorders
physiotherapy
usability
recommender system
self-management
mHealth
sztuczna inteligencja
uczenie maszynowe
wnioskowanie kliniczne
system wspomagania decyzji klinicznych
wywiad
zaburzenia bólowe układu mięśniowo-szkieletowego
fizjoterapia
użyteczność
system rekomendacji
samokontrola
mZdrowie
Opis:: Artificial intelligence (AI)-based clinical reasoning support systems in physiotherapy, and in particular data-driven (machine learning) systems, can be useful in making and reviewing decisions regarding functional diagnosis and formulating/maintaining/modifying a rehabilitation programme. The aim of this article is to explore the extent to which the opportunities offered by AI-based systems for clinical reasoning in physiotherapy have been exploited and where the potential for their further stimulated development lies.
Systemy wspomagania wnioskowania klinicznego w fizjoterapii oparte na sztucznej inteligencji, a w szczególności na danych (uczenie maszynowe), mogą być przydatne w podejmowaniu i weryfikacji decyzji dotyczących diagnostyki funkcjonalnej ora formułowania/utrzymywania/modyfikowania programu rehabilitacji. Celem niniejszego artykułu jest zbadanie, w jakim stopniu możliwości oferowane przez systemy oparte na sztucznej inteligencji w zakresie rozumowania klinicznego w fizjoterapii zostały wykorzystane i gdzie leży potencjał ich dalszego stymulowanego rozwoju.
Źródło:: Studia i Materiały Informatyki Stosowanej; 2024, 16, 2; 21-27
1689-6300
Pojawia się w:: Studia i Materiały Informatyki Stosowanej
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: An evaluation of the use of expert systems in economics
Autorzy:: Sonnet, Daniel
Powiązania:: https://bibliotekanauki.pl/articles/518250.pdf
Data publikacji:: 2017
Wydawca:: Uniwersytet Gdański. Wydział Ekonomiczny
Tematy:: expert system
artificial intelligence
economic modelling
SWOT analysis
Opis:: This article presented the hypothesis that expert systems can extend traditional economic modelling. Before this hypothesis was examined, expert systems were specified and example swere given. Expert systems use a knowledge base, often of the form of “if A, then B” rules. It has been highlighted that expert systems have not as often been applied in economics as in other disciplines, like medicine or business science. A SWOT analysis for the stated hypothesis was conducted. Based on this qualitative analysis, the hypothesis was not rejected. An expert system for supporting inflation forecasts will be built in the nearer future.
W niniejszym artykule postawiono hipotezę, że system ekspercki może rozszerzyć tradycyjne podejście modelowe w ekonomii, a także, że systemy te z powodzeniem możemy zastosować do problemów ekonomicznych. Zanim przetestowano hipotezę przedstawiono przykłady zastosowań systemu eksperckiego oraz wykazano, w jaki sposób korzysta ów system z bazy wiedzy. System ten w wielu przypadkach opiera się na warunku, „jeżeli A to B”. Zaznaczono także, że stosowanie systemu eksperckiego jest nieporównywalnie mniej popularne w ekonomii niż w medycynie czy w praktyce biznesowej. Przeprowadzono analizę SWOT w celu weryfikacji hipotezy. Wyniki analizy jakościowej nie pozwalają odrzucić hipotezy, system ekspercki pozwala zmniejszyć niepewność zmiennych ekonomicznych, gdy tylko dostarczymy jemu rzetelną bazę wiedzy. System ekspercki także pomógłby w prognozowaniu inflacji, dlatego ciekawym rozwinięciem powyższych rozważań byłaby budowa systemu eksperckiego wspomagająca prognozowanie inflacji.
Źródło:: Zeszyty Studenckie Wydziału Ekonomicznego „Nasze Studia”; 2017, 8; 190-199
1731-6707
Pojawia się w:: Zeszyty Studenckie Wydziału Ekonomicznego „Nasze Studia”
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: An Expert System for Supporting the Design and Selection of Mechanical Equipment for Recreational Crafts
Autorzy:: Gonciarz, T.
Powiązania:: https://bibliotekanauki.pl/articles/116387.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:: Naval Architecture
Ship Construction and Design
Expert System
Mechanical Equipment
Recreational Craft, Yachting
Computer Program
artificial intelligence
Opis:: Expert Systems can be defined as computer programs, whose main task is to simulate a human expert, usually in a narrow field of expertise. Expert Systems have experienced tremendous growth and popularity since their commercial introduction in the early 1970’s. Today, Expert Systems are used in business, science, engineering, manufacturing and other engineering applications such as planning, scheduling, diagnosing equipment failures and are used in almost every stage of the manufacturing process and also in medicine and many other fields. Expert Systems belong to the field of artificial intelligence. An intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for the solution. The purpose of this paper is to present an Expert System which assists with the design of yachts and supports the selection of mechanical equipment for yachts and includes knowledge in the field of yachting engineering. Using the presented Expert System reduces the time during the design and production preparation process.
Źródło:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2014, 8, 2; 275-280
2083-6473
2083-6481
Pojawia się w:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Analiza sygnałów z procesu zarządzania incydentami w systemach IT i ich wykorzystanie w podejmowaniu decyzji z użyciem metod sztucznej inteligencji
Analysis of signals from incident management process in IT systems to use them in decision making using artificial intelligence methods
Autorzy:: Gościniak, T.
Powiązania:: https://bibliotekanauki.pl/articles/321024.pdf
Data publikacji:: 2015
Wydawca:: Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:: analiza
incydent
system IT
sztuczna inteligencja
analysis
incident
IT system
artificial intelligence
Opis:: Niezawodność systemów produkcyjnych coraz częściej zależy od niezawodności systemów IT, które nadzorują procesy produkcyjne oraz nimi sterują. Skuteczne wsparcie systemów IT pod względem naprawczym oraz utrzymania ich wysokiej niezawodności musi być optymalne kosztowo. Najczęstszym czynnikiem powodującym powstawanie błędów w procesie wsparcia są ludzie, stąd należy rozważyć możliwość wsparcia procesu decyzyjnego przy wykorzystaniu metod sztucznej inteligencji. W artykule przeprowadzono analizę sygnałów dostępnych w procesie naprawczym IT Zarządzania Incydentami oraz ich użyteczności w zastosowaniu w procesie komputerowo wspomaganych decyzji przy wykorzystaniu metod sztucznej inteligencji.
Reliability of production systems increasingly depends on performance of IT systems, which monitor and control production systems. Effective support of IT systems in the area of repair and maintaining of their high reliability must be performed at optimal costs. The most common factor of the support process errors are people and should consider the possibility of making decision in a different way. The article analyzes available signals from Incident Management process and their usability in the computer aided decision using artificial intelligence methods.
Źródło:: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2015, 78; 143-154
1641-3466
Pojawia się w:: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Aquarium: augmented knowledge and wisdom in the age of the Fourth Industrial Revolution
Autorzy:: Paprocki, Wojciech
Powiązania:: https://bibliotekanauki.pl/articles/18723860.pdf
Data publikacji:: 2019-07-30
Wydawca:: Szkoła Główna Handlowa w Warszawie. Kolegium Zarządzania i Finansów
Tematy:: cognitive process
axiological system
knowledge and wisdom
digital technologies
artificial intelligence
Opis:: The first two decades of the 21st century witnessed the emergence of technical and technological solutions that have enabled digital transformations in the global economy. Registration of unstructured data and bringing them together into large datasets have enriched the cognitive process comprising traditional generation of human knowledge and the digital transformation of data into information and then into industrial knowledge. As a result, we obtain augmented knowledge. Yet, robots and bots (i.e., a device which, unlike a robot, is not connected with mechanisms and other peripheral devices) fuelled by technologies collectively referred to as artificial intelligence, primarily by the machine learning technology, are not able to develop wisdom. That is because robots and bots operate only within two dimensions: perception and context, while a human being has his/her autonomous and subjectively defined axiological system, which shapes his/her capacity to make value judgements and generate wisdom. This system is the third dimension in the aquarium with the glass front wall and the remaining three walls carved in stone. It provides stability ensured by a solid foundation of an axiological system that is thousands of years old and has been approved by the entire global community. Subjectivity of value judgements made by individuals and social groups produces a great variety of assessments of available knowledge. That is evidenced, inter alia, by decisions about the use of nuclear energy or decisions regulating free access to personal data and interfering with private life.
Źródło:: Journal of Management and Financial Sciences; 2019, 36; 9-29
1899-8968
Pojawia się w:: Journal of Management and Financial Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems
Autorzy:: Patton, R. J.
Lopez-Toribio, C. J.
Uppal, F. J.
Powiązania:: https://bibliotekanauki.pl/articles/908290.pdf
Data publikacji:: 1999
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: metoda sztucznej inteligencji
rozpoznanie błędu
modelowanie rozmyte
system rozmyty
artificial intelligence methods
fault diagnosis
residual generation
fuzzy modelling
neuro-fuzzy systems
Opis:: Recent approaches to fault detection and isolation (FDI) for dynamic systems using methods of integrating quantitative and qualitative model information, based upon artificial intelligence (AI) techniques are surveyed. In this study, the use of AI methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained artificial neural network (ANN) can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model behaviour; the model is explicit rather than implicit in form. This main difficulty can be overcome using qualitative modelling or rule-based inference methods. For example, fuzzy logic can be used together with state-space models or neural networks to enhance FDI diagnostic reasoning capabilities. The paper discusses the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of real process systems.
Źródło:: International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 471-518
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 11.

Tytuł:: Artificial intelligence at universities in Poland
Autorzy:: Stachowicz-Stanusch, A.
Amann, W.
Powiązania:: https://bibliotekanauki.pl/articles/392992.pdf
Data publikacji:: 2018
Wydawca:: Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:: artificial intelligence
chatbot
intelligent tutoring system
sztuczna inteligencja
wirtualny asystent
inteligentny system nauczania
Opis:: Artificial intelligence (AI) technologies are one of top investment priorities in these days. They are aimed at finding applications in fields of special value for humans, including education. Chatbots are one of those AI-driven solutions that support learning and teaching processes also in higher education institutions. In this paper there are presented two cases of chatbot technology implementation at Polish universities. Chatbots develop students’ technical and programming skills, but also provide the possibility of gaining linguistic expertise. However, a chatbot’s teaching mastery depends also on its users. That is why it is important to get students to truly understand AI systems and feel responsible for the conversation. But above all, we should ensure that chatbots respect human and civil rights.
Źródło:: Organizacja i Zarządzanie : kwartalnik naukowy; 2018, 2; 65-82
1899-6116
Pojawia się w:: Organizacja i Zarządzanie : kwartalnik naukowy
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 12.

Tytuł:: Artificial intelligence methods in diagnostics of analog systems
Autorzy:: Bilski, P.
Wojciechowski, J.
Powiązania:: https://bibliotekanauki.pl/articles/908112.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: fault detection
artificial intelligence
analog system
detekcja uszkodzeń
sztuczna inteligencja
system analogowy
Opis:: The paper presents the state of the art and advancement of artificial intelligence methods in analog systems diagnostics. Firstly, the diagnostic domain is introduced and its problems explained. Then, computational intelligence approaches usable for fault detection and identification are reviewed. Particular groups of methods are presented in detail, explaining their usefulness and drawbacks. Examples, such as the induction motor or the electronic filter, are provided to show the applicability of the presented approaches for monitoring the state of analog objects from engineering domains. The discussion section reviews the presented approaches, their future prospects and problems to be solved.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2014, 24, 2; 271-282
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 13.

Tytuł:: Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing
Autorzy:: Lazaroiu, George
Androniceanu, Armenia
Grecu, Iulia
Grecu, Gheorghe
Neguriță, Octav
Powiązania:: https://bibliotekanauki.pl/articles/19322650.pdf
Data publikacji:: 2022
Wydawca:: Instytut Badań Gospodarczych
Tematy:: cognitive manufacturing
Artificial Intelligence of Things
cyber-physical system
big data-driven deep learning
real-time scheduling algorithm
smart device
sustainable product lifecycle management
Opis:: Research background: With increasing evidence of cognitive technologies progressively integrating themselves at all levels of the manufacturing enterprises, there is an instrumental need for comprehending how cognitive manufacturing systems can provide increased value and precision in complex operational processes. Purpose of the article: In this research, prior findings were cumulated proving that cognitive manufacturing integrates artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production. Methods: Throughout April and June 2022, by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms including "cognitive Industrial Internet of Things", "cognitive automation", "cognitive manufacturing systems", "cognitively-enhanced machine", "cognitive technology-driven automation", "cognitive computing technologies", and "cognitive technologies". The Systematic Review Data Repository (SRDR) was leveraged, a software program for the collecting, processing, and analysis of data for our research. The quality of the selected scholarly sources was evaluated by harnessing the Mixed Method Appraisal Tool (MMAT). AMSTAR (Assessing the Methodological Quality of Systematic Reviews) deployed artificial intelligence and intelligent workflows, and Dedoose was used for mixed methods research. VOSviewer layout algorithms and Dimensions bibliometric mapping served as data visualization tools. Findings & value added: Cognitive manufacturing systems is developed on sustainable product lifecycle management, Internet of Things-based real-time production logistics, and deep learning-assisted smart process planning, optimizing value creation capabilities and artificial intelligence-based decision-making algorithms. Subsequent interest should be oriented to how predictive maintenance can assist in cognitive manufacturing by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.
Źródło:: Oeconomia Copernicana; 2022, 13, 4; 1047-1080
2083-1277
Pojawia się w:: Oeconomia Copernicana
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 14.

Tytuł:: Blast-induced noise level prediction model based on Brain Inspired Emotional Neural Network
Autorzy:: Temeng, Victor Amoako
Ziggah, Yao Yevenyo
Arthur, Clement Kweku
Powiązania:: https://bibliotekanauki.pl/articles/1839067.pdf
Data publikacji:: 2021
Wydawca:: Główny Instytut Górnictwa
Tematy:: artificial intelligence
blast-induced noise level
emotional neural network
limbic system theory
sztuczna inteligencja
poziom hałasu wywołanego wybuchem
sieć neuronowa
teoria układu limbicznego
Opis:: Although a major portion of the emitted energy from mine blast is sub-audible (lower frequency), there exist a component that is audible (high frequencies from 20 Hz to 20 KHz) and as such within the range of human hearing as noise. Unlike blast air overpressure (low frequency occurrence), noise prediction from mine blasting has received little scholarly attention in mining sciences. Noise from mine blast is considered a major detrimental blasting effect and can be a menace to nearby residents and workers in the mine. In this paper, a blast-induced noise level prediction model based on Brain Inspired Emotional Neural Network (BENN) is presented. The objective of this paper was to investigate the implementation possibility of the proposed BENN approach along with six other artificial intelligent methods, such as Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Generalised Regression Neural Network (GRNN), Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM) and Support Vector Machine (SVM). The study also implemented the standard Multiple Linear Regression (MLR) for comparison purposes. The statistical analysis carried out revealed that the BENN performed better than the other investigated methods. Thus, the BENN achieved very promising testing results of 1.619 dB, 3.076%, 0.0925%, 0.911 and 82.956% for root mean squared error (RMSE), mean absolute percentage error (MAPE), normalised root mean squared error (NRMSE), correlation coefficient (R) and variance accounted for (VAF). The implemented BENN can be useful in managing noise from mine blasting using site specific data.
Źródło:: Journal of Sustainable Mining; 2021, 20, 1; 28-38
2300-1364
2300-3960
Pojawia się w:: Journal of Sustainable Mining
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 15.

Tytuł:: Box Selectivity in Different Container Cargo-handling Systems
Autorzy:: Kuznetsov, A. L.
Kirichenko, A. L.
Semenov, A. D.
Powiązania:: https://bibliotekanauki.pl/articles/116336.pdf
Data publikacji:: 2019
Wydawca:: Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:: cargo handling
box selectivity
cargo handling system
container
container cargo-handling system
simulation model
theoretical selectivity
artificial intelligence
Opis:: The box selectivity in operational stack of container terminal is a quite common and long studied question. The pure random choice is governed by the theory of probability offering some combinatorial estimations. The introduction of operational rules like import/export separation, storage by shipping lines, sorting by rail or truck transportation etc., as well as the most notorious ‘sinking’ effect, i.e. covering of boxes arrived earlier by next cargo parties – all these blur the clear algebraiс picture and lead to appearance of many heuristic outlooks of the problem. A new impetus to this problem in last decades was given by the rapid development of IT, AI and simulation techniques. There are quite many examples of the models described in the scientific publication reflecting many real and arbitrary terminals, which embed very advanced and complicated mechanisms reflecting selected features and strategies. Unfortunately, these models usually are created ad hoc, with some pragmatic objectives and under the demand of closest possible proximity to the simulating objects. There are much less models designated to pure scientific study of the deep inner mechanisms responsible for the primal behavior of the operating container stack, enabling to introduce step by step new rules and restrictions, providing regular proving of every next stage’s adequacy and easy to use. This paper describes one attempt of this kind to create a new theoretical tool to put into the regular toolkit of the container terminal designer. The study starts with mathematical (combinatorial) considerations, proceeds with some restrictions caused by physical and technological characteristics, and ends up with the simulation model, which adequacy is confirmed by practical results.
Źródło:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 4; 797-801
2083-6473
2083-6481
Pojawia się w:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:: Biblioteka Nauki

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