scholarly journals Partial Observer Decision Process Model for Crane-Robot Action

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Asif Khan ◽  
Jian Ping Li ◽  
Amin ul Haq ◽  
Shah Nazir ◽  
Naeem Ahmad ◽  
...  
Keyword(s):  
Process Model ◽  
Learning Algorithm ◽  
Interaction Model ◽  
Cooperative Work ◽  
Human Robot Interaction ◽  
Related Information ◽  
Desirable Output ◽  
Long Time ◽  
Error Amplification ◽  
New Vision

The most common use of robots is to effectively decrease the human’s effort with desirable output. In the human-robot interaction, it is essential for both parties to predict subsequent actions based on their present actions so as to well complete the cooperative work. A lot of effort has been devoted in order to attain cooperative work between human and robot precisely. In case of decision making , it is observed from the previous studies that short-term or midterm forecasting have long time horizon to adjust and react. To address this problem, we suggested a new vision-based interaction model. The suggested model reduces the error amplification problem by applying the prior inputs through their features, which are repossessed by a deep belief network (DBN) though Boltzmann machine (BM) mechanism. Additionally, we present a mechanism to decide the possible outcome (accept or reject). The said mechanism evaluates the model on several datasets. Hence, the systems would be able to capture the related information using the motion of the objects. And it updates this information for verification, tracking, acquisition, and extractions of images in order to adapt the situation. Furthermore, we have suggested an intelligent purifier filter (IPF) and learning algorithm based on vision theories in order to make the proposed approach stronger. Experiments show the higher performance of the proposed model compared to the state-of-the-art methods.

scholarly journals Predicting Fraud Victimization Using Classical Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 300
Author(s):  
Mark Lokanan ◽  
Susan Liu
Keyword(s):  
Machine Learning ◽  
Financial Literacy ◽  
Learning Algorithm ◽  
Demographic Characteristics ◽  
Financial Knowledge ◽  
The Past ◽  
Machine Learning Model ◽  
Long Time ◽  
Regulatory Organization ◽  
Investment Fraud

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals HOANG XUAN HAN AND GIROLAMO MAIORICA’S NOM WORKS IN NATIONAL LIBRARY OF FRANCE IN PARIS

2021 ◽  
Vol 1 (42) ◽  
pp. 72-77
Author(s):  
Phuong Thi Phuong Tran
Keyword(s):  
Seventeenth Century ◽  
Society Of Jesus ◽  
Viet Nam ◽  
National Library ◽  
Related Information ◽  
Long Time

Girolamo Maiorica was an Italian priest of Society of Jesus (Latin name: Societas  Iesu). He came to Viet Nam at the beginning of the seventeenth century, he lived and preached in Tonkin at the same time as Alexandre deRhodes did, from 1631 until his death in Thang Long. His legacy was a large number of works in various genres of Catholic literature written in Nom script, which for a long time had been considered lost, until Vietnamese scholar Hoang Xuan Han found some in the National Library of France in 1951. Hoang Xuan Han wroteabout his discovery in an article published in the Archivum Historicum Soietatis Iesu journal in 1953. Although this article was short, it was of great significance for the study of Catholic literature in Nom script of the seventeenth century. This paper aims to introduce Hoang Xuan Han’s article and some related information

scholarly journals Development of Agriculture Chatbot using Machine Learning Techniques

2019 ◽  
Vol 9 (2S) ◽  
pp. 24-28
Keyword(s):  
Question Answering ◽  
Learning Algorithm ◽  
Related Question ◽  
Machine Learning Techniques ◽  
Meaningful Information ◽  
Related Information ◽  
Exact Answer ◽  
Deep Learning Algorithm ◽  
The People ◽  
Learning Techniques

Agriculture data is a main source of country’s economic growth. It is important to provide agriculture related information to all the people who are involved in agriculture activities as and when required. This meaningful information is used by people who supply services to agriculture domain and to take some correct decision related to agriculture to apply for their field. The solutions to this problem are given by the efficient interaction of computer with human. Chatbot system provides ability to extract the exact answer to the queries posed by farmers. The proposed system is called as Agriculture Chatbot system or even it is called as Question-Answering system for agriculture domain, where farmer is asking the agriculture related question which fetches the precise answers for the asked questions by farmers in natural language and processes the query using RNN (Recurrent Neural Network) deep learning algorithm to extract correct answer.

Multi-Agent Architecture for Developing Cooperative E-Business Applications

2010 ◽  
pp. 377-396
Author(s):  
Mahmoud Brahimi ◽  
Lionel Seinturier ◽  
Mahmoud Boufaida
Keyword(s):  
Web Services ◽  
Web Service ◽  
External World ◽  
Cooperative Work ◽  
Service Ontology ◽  
Business Applications ◽  
Multi Agent ◽  
New Vision ◽  
A Company ◽  
Different Parts

This article describes a multi-agent approach that provides solutions to the problems raised during the development of cooperative e-business applications. This approach is organized in the form of cooperative application groups representing the different parts of a company. Agent coordinators orchestrate the cooperative work of these groups. The most requested functionalities inside the company and those offered to the external world can be exported as Web Services. These Web Services are described with DAML-based Web Service ontology (OWL-S) and managed with an intermediate agent called Web Service Finder Agent. The proposed solution provides a new vision of the cooperation context where the companies and their partners share knowledge and offer functionalities as agents and Web Services.

scholarly journals Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2699 ◽  
Author(s):  
Redhwan Algabri ◽  
Mun-Taek Choi
Keyword(s):  
Deep Learning ◽  
Obstacle Avoidance ◽  
Learning Algorithm ◽  
Human Robot Interaction ◽  
Target Person ◽  
Single Shot ◽  
Color Feature ◽  
Mapping Algorithm ◽  
Deep Learning Algorithm ◽  
Illumination Changes

Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.

THE COMPLETE TREATMENT OF THE TIME EVOLUTION IN THE CASE OF A DISCRETIZED ATOM-FIELD INTERACTION MODEL

2001 ◽  
Vol 15 (21) ◽  
pp. 883-894
Author(s):  
J. SEKE ◽  
A. V. SOLDATOV ◽  
N. N. BOGOLUBOV
Keyword(s):  
State Energy ◽  
Interaction Model ◽  
Time Interval ◽  
Short Time Interval ◽  
Field Interaction ◽  
Markovian Dynamics ◽  
Complete Treatment ◽  
Time Regime ◽  
Long Time ◽  
Short Time

The dynamics of a discretized atom-field interaction model with a physically relevant form factor is analyzed. It is shown that after some short time interval only a small fraction of eigenvalues and eigenstates (belonging to the close vicinity of the excited atomic state energy E = ω0/2) contributes to the nondecay probability amplitudes in the long-time regime, whereas the contribution of all other eigenstates and eigenvalues is negligible. Nevertheless, to describe correctly the non-Markovian dynamics in the short-time regime the contribution of all eigenstates and eigenvalues must be taken into account.

A New Concept for Requirements Driven Project Planning in an Integrated PLM Environment

2008 ◽  
Author(s):  
Michael Maletz ◽  
Dan Brisson ◽  
Yong Zeng
Keyword(s):  
Process Model ◽  
Project Planning ◽  
Domain Specific ◽  
Related Information ◽  
System A ◽  
Requirements Modeling ◽  
Methodical Approach ◽  
Key Factor ◽  
Natural Language Requirements ◽  
Product And Process

Integration in today’s heterogeneous PLM environments is a key factor in all development phases. This paper describes a methodical approach to integrating requirements modeling into a PLM environment. The specific focus of integration aspects is on project planning of complex mechatronic products with recurrent character based on requirements specification documents. Function and process orientation serves as a basis for the integration. It is discussed how development projects teams can benefit by generating project plans including resource estimations and predefined interfaces to bordering disciplines along the development process. With the help of semantic parsing methods of natural language requirements and through a generic classification system a requirement based product and process model is generated. This model is then taken as the basis for deriving product and process related information. Through domain specific ontology’s generic project and resource plans are generated with the help of the proposed methodology.

Wave Breaking Dissipation in the Wave-Driven Ocean Circulation

2007 ◽  
Vol 37 (7) ◽  
pp. 1749-1763 ◽  
Author(s):  
Juan M. Restrepo
Keyword(s):  
Time Scales ◽  
Ocean Circulation ◽  
Wave Breaking ◽  
Interaction Model ◽  
Stokes Drift ◽  
Dissipative Term ◽  
Flow Vorticity ◽  
Proper Mass ◽  
Long Time ◽  
Current Interaction

Abstract If wave breaking modifies the Lagrangian fluid paths by inducing an uncertainty in the orbit itself and this uncertainty on wave motion time scales is observable as additive noise, it is shown that within the context of a wave–current interaction model for basin- and shelf-scale motions it persists on long time scales. The model of McWilliams et al. provides the general framework for the dynamics of wave–current interactions. In addition to the deterministic part, the vortex force, which couples the total flow vorticity to the residual flow due to the waves, will have a part that is associated with the dissipative mechanism. At the same time the wave field will experience dissipation, and tracer advection is affected by the appearance of a dissipative term in the Stokes drift velocity. Consistency leads to other dynamic consequences: the boundary conditions are modified to take into account the diffusive process and proper mass/momentum balances at the surface of the ocean. In addition to formulating how a wave–current interaction model is modified by the presence of short-time events that induce dissipation, this study proposes a stochastic parameterization of dissipation. Its relation to other alternative parameterizations is given. Two focal reasons make stochastic parameterizations attractive: one can draw from extensive practical modeling experience in other fields, and it ties in a very natural way to a wealth of observational data via statistics.

scholarly journals Process-Oriented Development of Failure Reporting, Analysis, and Corrective Action System

2010 ◽  
Vol 2010 ◽  
pp. 1-8
Author(s):  
Jae Hoon Lee ◽  
SungIl Chan ◽  
Joong Soon Jang
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Management ◽  
Corrective Action ◽  
Prototype System ◽  
Time Activity ◽  
Action System ◽  
Related Information ◽  
Process Oriented ◽  
Failure Reporting

Although failure reporting, analysis, and corrective action system (FRACAS) has two management perspectives, its tasks and related information, the previous researches and applications mainly have focused on the data management. This study is to develop a process-oriented FRACAS which supports the operation of the failure-related activities. The development procedures are (1) to define the reporting and analysis tasks, (2) to define the information to be used at each task, and (3) to design a computerized business process model and set the attributes such as durations, rules, and document types. This computerized FRACAS process can be activated in a business process management system (BPMS) which employs the enactment functions, deliver tasks to the proper workers, provide the necessary information, and alarm the abnormal status of the tasks (delay, incorrect delivery, cancellation). Through implementing the prototype system, improvements are found for automation of the tasks, prevention of disoperation, and real-time activity monitoring.

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