GRID STATE EVALUATION OF A LVDC TRACTION NETWORK: METHODS FOR THE ANALYSIS OF FORECAST DATA

2021 ◽  
Author(s):  
M. Wazifehdust ◽  
D. Baumeister ◽  
M. Salih ◽  
P. Steinbusch ◽  
M. Zdrallek ◽  
...  
Keyword(s):  
State Evaluation ◽  
Traction Network ◽  
Network Methods ◽  
Forecast Data

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

Calculation of Short-Circuit Fault Currents in an Electric Traction Network with Double Ended Power Supply

2019 ◽  
Vol 5 (5) ◽  
pp. 19-23
Author(s):  
Leonid A. GERMAN ◽  
◽  
Alexandr S. SEREBRYAKOV ◽  
Aleksey B. LOSKUTOV ◽  
Vladimir L. OSOKIN ◽  
...  
Keyword(s):  
Power Supply ◽  
Short Circuit ◽  
Fault Currents ◽  
Traction Network ◽  
Electric Traction ◽  
Short Circuit Fault

Using the Quality Ability of Neural Networks to Determine Emergency Modes in a Traction Network

2020 ◽  
Vol 91 (9) ◽  
pp. 546-551
Author(s):  
V. A. Grechishnikov ◽  
N. D. Kurov ◽  
D. A. Kurov
Keyword(s):  
Neural Networks ◽  
Traction Network

Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rizqa Raaiqa Bintana ◽  
Chastine Fatichah ◽  
Diana Purwitasari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Question Answering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Community Based ◽  
Network Methods ◽  
The Mean ◽  
Index Terms ◽  
Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

A Review of Drug Side Effect Identification Methods

2020 ◽  
Vol 26 (26) ◽  
pp. 3096-3104 ◽  
Author(s):  
Shuai Deng ◽  
Yige Sun ◽  
Tianyi Zhao ◽  
Yang Hu ◽  
Tianyi Zang
Keyword(s):  
Side Effects ◽  
Future Research ◽  
Drug Side Effects ◽  
Important Indicator ◽  
Advantages And Disadvantages ◽  
Key Points ◽  
Network Methods ◽  
Future Research Directions ◽  
The One ◽  
Approved Drugs

Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.

An Introduction to Statistical Models for Networks

2020 ◽  
pp. 218-233
Author(s):  
Valentina Kuskova ◽  
Stanley Wasserman
Keyword(s):  
Social System ◽  
Exponential Family ◽  
Behavioral Science ◽  
Social Science Research ◽  
Science Research ◽  
Uniform Distributions ◽  
The Social ◽  
Special Cases ◽  
Social And Behavioral Science ◽  
Network Methods

Network theoretical and analytic approaches have reached a new level of sophistication in this decade, accompanied by a rapid growth of interest in adopting these approaches in social science research generally. Of course, much social and behavioral science focuses on individuals, but there are often situations where the social environment—the social system—affects individual responses. In these circumstances, to treat individuals as isolated social atoms, a necessary assumption for the application of standard statistical analysis is simply incorrect. Network methods should be part of the theoretical and analytic arsenal available to sociologists. Our focus here will be on the exponential family of random graph distributions, p*, because of its inclusiveness. It includes conditional uniform distributions as special cases.

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