scholarly journals Study of Multimedia Delivery over Software Defined Networks

2016 ◽  
Vol 7 (4) ◽  
pp. 37 ◽  
Author(s):  
Jose Miguel Jimenez ◽  
Oscar Romero ◽  
Albert Rego ◽  
Avinash Dilendra ◽  
Jaime Lloret
Keyword(s):  
Artificial Intelligence ◽  
Personal Computer ◽  
Network Protocols ◽  
Performance Comparison ◽  
Distributed Artificial Intelligence ◽  
Software Defined Networks ◽  
Multimedia Delivery ◽  
Real Network ◽  
Multimedia Streams ◽  
The Difference

Software Defined Networks (SDN) have become a new way to make dynamic topologies. They have great potential in both the creation and development of new network protocols and the inclusion of distributed artificial intelligence in the network. There are few emulators, like Mininet, that allow emulating a SDN in a single personal computer, but there is lack of works showing its performance and how it performs compared with real cases. This paper shows a performance comparison between Mininet and a real network when multimedia streams are being delivered. We are going to compare them in terms of consumed bandwidth (throughput), delay and jitter. Our study shows that there are some important differences when these parameters are compared. We hope that this research will be the basis to show the difference with real deployments when Mininet is used.

scholarly journals On the Juridical Relevance of the Phenomenological Notion of Person in Max Scheler and Edith Stein

2021 ◽  
Author(s):  
Francesco Galofaro
Keyword(s):  
Artificial Intelligence ◽  
Edmund Husserl ◽  
Phenomenological Analysis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Edith Stein ◽  
Max Scheler ◽  
Human Decision ◽  
The Difference ◽  
Social Units

AbstractThe paper presents a semiotic interpretation of the phenomenological debate on the notion of person, focusing in particular on Edmund Husserl, Max Scheler, and Edith Stein. The semiotic interpretation lets us identify the categories that orient the debate: collective/individual and subject/object. As we will see, the phenomenological analysis of the relation between person and social units such as the community, the association, and the mass shows similarities to contemporary socio-semiotic models. The difference between community, association, and mass provides an explanation for the establishment of legal systems. The notion of person we inherit from phenomenology can also be useful in facing juridical problems raised by the use of non-human decision-makers such as machine learning algorithms and artificial intelligence applications.

An IBM PC Based Computer Program for the Analysis of Infrared Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1739-1745
Author(s):  
Min J. Yang ◽  
Paul W. Yang
Keyword(s):  
Artificial Intelligence ◽  
Operating System ◽  
Computer Program ◽  
Infrared Spectra ◽  
Personal Computer ◽  
Research Laboratory ◽  
General Purpose ◽  
Disk Operating System ◽  
Interpretation System ◽  
Ibm Pc

A computerized infrared interpreter has been developed on an IBM personal computer (PC) running under the Microsoft disk operating system (DOS). Based on the original Merck Sharp & Dhome Research Laboratory Program for the Analysis of InfRared Spectra (PAIRS), this infrared interpreter, PC PAIRS+, is capable of analyzing infrared spectra measured from a wide variety of spectrophotometers. Modifications to PAIRS now allow the application of both artificial intelligence and library searching techniques in the program. A new algorithm has been devised to combine the results from the library searching and the PAIRS program to enhance the dependability of interpretational data. The increased capability of this infrared interpreter along with its applicability on a personal computer results in a powerful, general-purpose, and easy-to-use infrared interpretation system. Applications of PC PAIRS+ on petrochemical samples are described.

scholarly journals MODELING OF THE SPREAD OF MOTOR TRANSPORT NOISE IN ŠIAULIAI CITY / AUTOTRANSPORTO TRIUKŠMO SKLAIDOS ŠIAULIUOSE MODELIAVIMAS / МОДЕЛИРОВАНИЕ РАССЕЯНИЯ ТРАНСПОРТНОГО ШУМА В ГОРОДЕ ШЯУЛЯЙ

2011 ◽  
Vol 19 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Leonas Paulauskas ◽  
Robertas Klimas
Keyword(s):  
Personal Computer ◽  
Noise Level ◽  
Negative Impact ◽  
Noise Measurement ◽  
Motor Transport ◽  
City Environment ◽  
Measurement Results ◽  
The Difference ◽  
Environment Noise ◽  
Level Detector

Rapidly growing urbanization causes the increase of noise level of various sources, that have a negative impact upon people's health. The contribution of noise caused by motor transport in city environment composes up to 80% of general impact of all the sources. The article presents the results of modeling of the spread of motor transport noise of Šiauliai city, maps of motor transport noise, recommendations for management of environment noise. MapNoise programme module, adapted to work in the ArcGIS Desktop 9.1 environment, was used for modeling motor transport noise. Noise measurement researches have been carried out using digital noise isolator Nor121, completed with digital level detector. NorXfar software was used to send the data to personal computer. Having evaluated the validity of modeling results it has been determined that the difference between the night noise modeling and measurement results does not exceed 2.2%, and varies from 0.5dB(A) to 1.1 dB(A). The obtained results indicate that 7.2% of the apartments of all city residents are influenced by the LDEN noise that exceeds the permitted noise level (LDEN >65 dB(A)) and 31.2% of the apartments of the residents are influenced by night noise that exceeds the permitted noise level (LN > 55 dB(A)). Santrauka Sparčiai vykstant urbanizacijos procesui, kinta įvairių šaltinių keliamo triukšmo lygis, didėja neigiama įtaka žmonių sveikatai. Miestų aplinkoje iki 80 % visuminio visų triukšmo šaltinių poveikio tenka autotransporto keliamam triukšmui.Straipsnyje pateikta autotransporto triukšmo sklaidos Šiauliuose modeliavimo rezultatai, autotransporto triukšmo žemėlapiai, aplinkos triukšmo valdymo rekomendacijos. Autotransporto triukšmui modeliuoti naudotas MapNoise programinismodulis, pritaikytas darbui ArcGIS Desktop 9.1 aplinkoje. Iš rezultatų matyti, kad 7,2% visų miesto gyventojų būstų yra veikiami paros triukšmo, viršijančio leidžiamąjį triukšmo lygį (LDVN > 65 dB(A)), ir 31,2% gyventojų būstų veikiami nakties triukšmo, viršijančio leidžiamąjį triukšmo lygį (LN > 55 dB(A)). Įvertinus modeliavimo rezultatų patikimumą nustatyta, kad paros ir nakties triukšmo modeliavimo ir matavimo rezultatų neatitiktis neviršija 2,2 % ir svyruoja nuo 0,5dB(A) iki 1,1dB(A). Резюме При быстром росте урбанизации увеличивается уровень шума, создаваемого разными источниками и отрицательно влияющего на здоровье населения. Шум от автотранспорта в городах составляет около 80% от всех источников шума. В статье представлены результаты моделирования рассеяния шума от автотранспорта в городеШяуляй, карты автотранспортного шума, рекомендации по управлению шумом в окружающей среде. Приизмерении шума был использован числовой анализатор шума № 121, укомплектованный с числовым детекторомуровня RMS. Для передачи данных в персональный компьютер использована программа NorXfer. Для моделирования автотранспортного шума использован программный модуль MapNoise, приспособленный дляработы в среде ArcGIS desktop 9.1. При анализе достоверности результатов моделирования было установлено, чтоих отличие от результатов измерения шума в течение суток и ночное время не превышает 2,2% и колеблется от0,5дБ(A) до 1,1дБ(A). Результаты исследования свидетельствуют о том, что 7,2% жилых помещений городаподвергаются суточному шуму, уровень которого превышает допустимый (LDVN > 65 дБ(A)) и 31,2% жилыхпомещенийгородаподвергаютсяшумувночноевремя,уровень которогопревышаетдопустимый (LN > 55дБ(A)).

scholarly journals Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA

2013 ◽  
Vol 7 (2) ◽  
pp. 189
Author(s):  
Silviani E Rumagit ◽  
Azhari SN
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Statistical Method ◽  
Moving Average ◽  
Electric Energy ◽  
Backpropagation Neural Network ◽  
The Difference ◽  
The Government ◽  
Increasing Demand

AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan.            ARIMA merupakan salah satu metode statistik yang banyak digunakan untuk prediksi dimana ARIMA mengikuti model autoregressive (AR) moving average (MA). Syarat dari ARIMA adalah data harus stasioner, data yang tidak stasioner harus distasionerkan dengan differencing. Selain metode statistik, prediksi juga dapat dilakukan dengan teknik kecerdasan buatan, dimana dalam penelitian ini jaringan syaraf tiruan backpropagation dipilih untuk melakukan prediksi. Dari hasil pengujian yang dilakukan selisih MSE ARIMA, JST dan penggabungan ARIMA, jaringan syaraf tiruan tidak berbeda secara signifikan. Kata Kunci— ARIMA, jaringan syaraf tiruan, kelompok tarif.  AbstractBackground this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.            ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly different. Keyword—ARIMA, neural network, tarif groups

scholarly journals The next step for Artificial Intelligence in a globally connected economy

2021 ◽  
Vol 129 ◽  
pp. 04001
Author(s):  
Dumitru Alexandru Bodislav ◽  
Florina Bran ◽  
Carol Cristina Gombos ◽  
Amza Mair
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Factor ◽  
Value Added ◽  
Point Of View ◽  
Business Analytics ◽  
Economic Point ◽  
Technological Breakthrough ◽  
Big Picture ◽  
The Difference

Research background: This research paper represents an overview of what artificial intelligence is, what are its roots, and what is the next big thing regarding the domain. In this paper we try to highlight how the domain is growing and what is the difference between the ideology, the business factor and the human factor. We try to create a big picture on the entire phenomenon by creating a parallel between machine learning, artificial intelligence and the influence of technological breakthrough from a hardware perspective. Purpose of the article: The paper is built as a tool in understanding technology, globalization and the pathway to success and scientific glory for what can be seen as the industry of artificial intelligence. The tools presented in the research have the purpose to create an easier path to how we can develop this domain by accelerating theoretical processing and business analytics that come together to form the next level of machine learning/artificial intelligence; research and development, everything being filtered from an economic point of view. Methods: The used research method is based on fundamental analysis of the artificial intelligence domain and its purpose in the complexity of globalization and economic development. Findings & Value added: The paper tries to offer a tool for building a better understanding of the next decade in the domain of artificial intelligence.

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