Information System for Storage, Management, and Usage for Embodied Intelligent Systems

2019 ◽  
pp. 135-159
Author(s):  
Daniel Beßler ◽  
Asil Kaan Bozcuoğlu ◽  
Michael Beetz
Keyword(s):  
Information System ◽  
Intelligent Systems ◽  
Storage Management

Research on Logistics Warehouse Management Information System Based on RFID

2014 ◽  
Vol 687-691 ◽  
pp. 4906-4909
Author(s):  
Yan Li Wang ◽  
Ji Meng Du ◽  
Sai Sai Xu
Keyword(s):  
Information System ◽  
Management Information System ◽  
Storage Management ◽  
Rfid Technology ◽  
Warehouse Management ◽  
Management Information ◽  
Storage Service ◽  
Function Module ◽  
Process Structure ◽  
And Storage

Because of a vast amount of business process, difficult tracking, low turnover efficiency, processing of logistics management information behind time and with antiquated means, the application of advanced RFID technology in the field of logistics and storage management is proposed to solve the above problems on the basis of analyzing the shortages. In this paper, the logistics warehouse management information system based on RFID is conducted. The working flow and process structure of the logistics and storage arc given on the basis of analysis of the traditional logistics and storage service flow. The integral framework of the logistics and storage service system based on RFID technology is established. This thesis mainly does research about the pattern of RFID application in warehouse, the system framework and the information flow. On the base, it designs the information system and realizes its function module.

Research and Design of Railway Emergency Rescue Command System Based on Interoperation

2012 ◽  
Vol 622-623 ◽  
pp. 1443-1446
Author(s):  
Zi Wang ◽  
Yan Hui Wang ◽  
Chen Chen Zhang
Keyword(s):  
Information System ◽  
Hierarchical Structure ◽  
Intelligent Systems ◽  
Logical Structure ◽  
Requirements Analysis ◽  
Emergency Rescue ◽  
Existing Problems ◽  
The Hierarchical Structure ◽  
Research And Design

Development of the railway information and intelligent systems has been progressing greatly but in isolation, research on interoperation with information system would solve existing problems in informatization of railway. Through research of construction status of railway information system in china and requirements analysis of interoperability system, we design and build the railway emergency rescue command system and give the hierarchical structure and logical structure of the system.

AHKME eLearning Information System

2013 ◽  
pp. 283-291
Author(s):  
H. Rego ◽  
T. Moreira ◽  
F. J. García-Peñalvo
Keyword(s):  
Information System ◽  
Intelligent Systems ◽  
Learning Strategy ◽  
Learning Resources ◽  
Web Based ◽  
Web 3.0 ◽  
Collaborative Tools ◽  
Semantic Component ◽  
Teachers And Students ◽  
E Learning

AHKME (Adaptive Hypermedia Knowledge Management E-Learning Platform) is an eLearning Information System that has evolved to fulfill the web-based learning requirements when they are compliant with a Web 3.0 philosophy. The ideas behind Web 3.0 are related to more semantic and intelligent systems. AHKME allows teachers to access standardized resources and evaluate integration and reuse possibilities in eLearning systems, not only content but also learning strategy. The educational resources adaptation in AHKME is supported by a set of collaborative tools, which also allow the users’ feedback provision that is stored in system database. The semantic component in AHKME is based on a set of tools for the instructional designer to create and customize specifications and ontologies to give structure and meaning to resources, manual and automatic search with recommendation of resources and instructional design based on the context, and recommendation of adaptations in learning resources. Finally, AHKME takes into account the mobile learning (mLearning) capabilities, allowing access by teachers and students to learning resources, regardless of time and space.

scholarly journals COUNTERFACTUAL TEMPORAL MODEL OF CAUSAL RELATIONSHIPS FOR CONSTRUCTING EXPLANATIONS IN INTELLIGENT SYSTEMS

2021 ◽  
pp. 41-46
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi ◽  
Irina Leshchynska
Keyword(s):  
Information System ◽  
Intelligent Systems ◽  
Intelligent System ◽  
System Development ◽  
Causal Relationships ◽  
Temporal Properties ◽  
Intellectual System ◽  
Transitivity Property ◽  
Temporal Model ◽  
Intelligent Information

The subject of the research is the processes of constructing explanations based on causal relationships between states or actions of an intellectualsystem. An explanation is knowledge about the sequence of causes and effects that determine the process and result of an intelligent informationsystem. The aim of the work is to develop a counterfactual temporal model of cause-and-effect relationships as part of an explanation of the process offunctioning of an intelligent system in order to ensure the identification of causal dependencies based on the analysis of the logs of the behavior ofsuch a system. To achieve the stated goals, the following tasks are solved: determination of the temporal properties of the counterfactual description ofcause-and-effect relationships between actions or states of an intelligent information system; development of a temporal model of causal connections,taking into account both the facts of occurrence of events in the intellectual system, and the possibility of occurrence of events that do not affect theformation of the current decision. Conclusions. The structuring of the temporal properties of causal links for pairs of events that occur sequentially intime or have intermediate events is performed. Such relationships are represented by alternative causal relationships using the temporal operators"Next" and "Future", which allows realizing a counterfactual approach to the representation of causality. A counterfactual temporal model of causalrelationships is proposed, which determines deterministic causal relationships for pairs of consecutive events and pairs of events between which thereare other events, which determines the transitivity property of such dependencies and, accordingly, creates conditions for describing the sequence ofcauses and effects as part of the explanation in intelligent system with a given degree of detail The model provides the ability to determine cause-andeffect relationships, between which there are intermediate events that do not affect the final result of the intelligent information system.

Emergency Ethics, Law, Policy and IT Innovation in Crises

2019 ◽  
pp. 1584-1610
Author(s):  
Xaroula Kerasidou ◽  
Monika Buscher ◽  
Michael Liegl ◽  
Rachel Oliphant
Keyword(s):  
Information System ◽  
Science And Technology Studies ◽  
Intelligent Systems ◽  
Organizational Innovation ◽  
Crisis Response ◽  
Information System Design ◽  
Law And Policy ◽  
It Innovation ◽  
It Ethics ◽  
Ethics Law

Ethics, law, and policy are cornerstones for effective IT innovation in crisis response and management. While many researchers and practitioners recognise this, it can be hard to find good resources for circumspect innovation approaches. This paper reviews The Library of Essays on Emergency Ethics, Law and Policy (2013), a four Volume series edited by Tom D. Campbell, that presents a collection of 113 seminal articles and chapters on emergency ethics, law and policy, and emergency research ethics. Building on a selective summary overview of each volume, the authors draw out core themes and discuss their relevance to research concerned with the design and use of intelligent systems for crisis response and management. The series brings together important insights for information system design and organizational innovation, but there is a lack of attention to socio-technical dimensions of emergency response and management. The authors conclude by discussing research within ISCRAM and the related fields of science and technology studies and IT Ethics, showing that entering into a conversation would be highly productive.

Historical Research in Information System Field

2009 ◽  
pp. 140-160
Author(s):  
Erja Mustonen-Ollila ◽  
Jukka Heikkonen
Keyword(s):  
Information System ◽  
Intelligent Systems ◽  
Historical Research ◽  
Historical Study ◽  
Self Organizing Maps ◽  
Multiple Data ◽  
Semistructured Interviews ◽  
Bayesian Network Modeling ◽  
Practical Guidelines ◽  
Collection Methods

This chapter gives important methodological, theoretical, and practical guidelines to the information system (IS) researchers to carry out a historical study. This study shows how a new theory can be discovered inductively from historical studies using a methodological guideline from Mason, McKenney, and Copeland (1997b), using multiple data collection methods, such as semistructured interviews, archival files, and published news, and using novel data analysis methods from learning and intelligent systems, such as the Self-Organizing Maps (SOMs), SOMs combined with U-matrices, and the Bayesian network modeling. It also outlines the benefits, the main problems, the characteristics, and the implications of historical research in the information system field. Finally this chapter gives future some research directions of historical research.

scholarly journals AHKME eLearning Information System

2011 ◽  
Vol 2 (2) ◽  
pp. 71-79
Author(s):  
H. Rego ◽  
T. Moreira ◽  
F. J. García-Peñalvo
Keyword(s):  
Information System ◽  
Intelligent Systems ◽  
Learning Strategy ◽  
Learning Resources ◽  
Web 3.0 ◽  
Collaborative Tools ◽  
Learning Platform ◽  
Semantic Component ◽  
Teachers And Students ◽  
Automatic Search

AHKME (Adaptive Hypermedia Knowledge Management E-Learning Platform) is an eLearning Information System that has evolved to fulfill the web-based learning requirements when they are compliant with a Web 3.0 philosophy. The ideas behind Web 3.0 are related to more semantic and intelligent systems. AHKME allows teachers to access standardized resources and evaluate integration and reuse possibilities in eLearning systems, not only content but also learning strategy. The educational resources adaptation in AHKME is supported by a set of collaborative tools, which also allow the users’ feedback provision that is stored in system database. The semantic component in AHKME is based on a set of tools for the instructional designer to create and customize specifications and ontologies to give structure and meaning to resources, manual and automatic search with recommendation of resources and instructional design based on the context, and recommendation of adaptations in learning resources. Finally, AHKME takes into account the mobile learning (mLearning) capabilities, allowing access by teachers and students to learning resources, regardless of time and space.

scholarly journals HIERARCHICAL REPRESENTATION OF CAUSAL RELATIONSHIPS TO DETAIL EXPLANATIONS IN INTELLIGENT SYSTEMS

2021 ◽  
Vol 5 (4) ◽  
pp. 103-108
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi
Keyword(s):  
Decision Making ◽  
Information System ◽  
Intelligent Systems ◽  
Input Data ◽  
Intelligent System ◽  
Decision Making Process ◽  
Causal Relationships ◽  
Hierarchical Representation ◽  
Cause And Effect ◽  
Intelligent Information

The subject of research in the article is the processes of constructing explanations in intelligent systems based on the use of causal dependencies. The aim is to develop a hierarchical representation of causal relationships between the actions of an intelligent system to form an explanation of the process of the system's operation with a given degree of generalization or detailing. Representation of the hierarchy of cause-and-effect relationships allows you to form an explanation at a given level of detail using the input data in the form of a temporally ordered sequence of events reflecting the known actions of an intelligent system. Tasks: structuring the hierarchy of cause-and-effect relationships for known variants of the decision-making process in an intelligent information system, considering the temporal ordering of the corresponding actions; development of a model of a multi-level representation of causal dependencies for description for explanations in an intelligent system. The approaches used are: counterfactual analysis of causality, used to describe alternative dependencies for possible decision-making options; linear temporal logic to reflect the temporal aspect of causation. The following results were obtained. A generalized hierarchy of cause-and-effect relationships is highlighted for the known variants of the process of obtaining recommendations in an intelligent information system based on the temporal ordering of the corresponding decision-making actions. A model of hierarchical representation of causal dependencies has been developed to describe explanations in an intellectual system with a given degree of detail. Conclusions. The scientific novelty of the results obtained is as follows. A model of hierarchical representation of time-ordered causal relationships is proposed to describe the explanations of the operation of an intelligent system with a given degree of detail. At the top level of the hierarchy, the model defines a generalized causal relationship between the event of using the input data and the event of the result of the system's operation. This connection describes the current task that the intelligent information system solves. At the lower level, cause-and-effect relationships are set between events sequential in time, between which there are no other events. At intermediate levels of the hierarchical representation, the causal dependencies of pairs of events are determined, between which there are other events. The developed model creates conditions for constructing explanations with a given degree of detailing of the actions of the decision-making process in an intelligent system. The model also provides the ability to describe early and late anticipation of alternative sequences of the decision-making process by describing causal dependencies for events between which there are other events.

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