Intelligent Information Processing Techniques Research Articles

Advanced approaches for information processing in multimedia, decision‐making, and security systems

Advanced approaches for information processing in multimedia, decision‐making, and security systems

A comprehensive literature review of the demand forecasting methods of emergency resources from the perspective of artificial intelligence

In recent decades, several forecasting methods have been proposed so as to aid in selecting from all optimal alternatives in the demand of emergency resources. Academic research in the field of emergency management has increasingly focused on artificial intelligence. However, more attention has been paid to attempts at simulating the human brain, with little focus on addressing intelligent information processing techniques based on machine learning, big data and smart devices. In this paper, a comprehensive literature review is presented in order to classify and interpret current research on demand forecasting methodologies and applications. A total of 1235 academic papers from 1980 to 2018 in the SpringerLink and Elsevier ScienceDirect databases are categorized as follows: time series analysis, case-based reasoning (CBR), mathematical models, information technology, literature reviews, and discussion and analysis. Application areas from business source premier include papers on the topics of emergency management, decision-making, decision relief, logistics, fuzzy sets and other topics. Academic publications are classified by (1) year of publication, (2) journal of publication, (3) database source, (4) methodology and (5) research discipline. The results of this literature review show that, despite forecasting methods such as ARIMA, CBR and mathematical models appearing to play a pivotal role in promoting prediction performance, there is a need to explore more real-time forecasting approaches based on intelligent information processing techniques so as to achieve appropriate dynamic demand prediction that is adaptable to emergency and rescue situations. The intention for this paper is to be a useful reference point for those with research needs in forecasting methodologies and the applications of emergency resources.

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78%.

A Novel Architecture for Data Management and Control in Autonomous Intelligent Microgrid

Intelligent microgrid with distributed energy sources is considered as the next generation grid to mitigate the present day power system issues. Intelligent microgrid should facilitate monitoring and distributed control of the system using smart components. For effective, reliable and intelligent operation of such a system, it needs to use the advanced communication and intelligent information processing techniques. This paper explores the possibility of managing an autonomous intelligent microgrid with prioritized loads, utilizing the existing communication networks to acquire data and manage from a central location. The central control center runs an energy management algorithm, utilizing the load and source data acquired from the clients, to maximize the power delivery to the higher priority loads. The proposed scheme enables the consumers to dynamically set their load priority and fix the rate for selling the power generated by them within the autonomous grid, thereby ensuring consumer participation in the development of power infrastructure. Further, a load management algorithm for the reliable operation of autonomous intelligent microgrid with prioritized loads is proposed and its effectiveness is illustrated with a case study.

Special Issue on "Intelligent Information Processing: Techniques and Applications"

In today’s digital era, information can be easily acquired from various sources, and is available in many different forms, which include data, text, image, voice, and signal. However, disparate information needs to be processed meticulously and intelligently for it to be useful. In this aspect, intelligent information processing systems play an important role to help extract meaningful and significant information from spurious one, and to help decision makers to make informed decisions. Over the years, researchers have designed and developed a variety of intelligent information processing techniques, and have demonstrated their applicability to undertaking various complex problems. In this special issue, a total of seven articles in the general area of intelligent information processing are presented. It should be noted that these articles represent only a small part of the huge research activities in this exciting and fast growing area. The main aim is to highlight some recent advances in different intelligent information processing techniques and their applications, ranging from natural language processing, affective computing, data mining, to fuzzy information processing. The first and second articles are concerned with natural language processing and affective computing. In the first article, an intelligent natural language processing technique that allows a machine to detect illogical word combinations is described. A knowledge model of human discourse and words, focusing on nouns and adjective phrases, is created. Using a tree-like structure, the knowledge model is used to form general properties of objects. An association model is then used to form word-to-word relationships based on a

Advances in design and application of neural networks

In this special issue, a total of seven articles, based on extended papers from the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2008) as well as from other submissions, are presented. These papers form a small sample of research in demonstrating the usefulness of intelligent information processing techniques in the design and application of neural networks.