149,081 research outputs found
Visual analysis of sensor logs in smart spaces: Activities vs. situations
Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS
Friction ridge skin - Automated Fingerprint Identification System (AFIS)
This contribution describes the development and the forensic use of automated fingerprint identification systems (AFISs). AFISs were initially developed in order to overcome the limitations of the paper-based fingerprint collections, by digitizing the ten-print cards in computerized databases and to translate the manual pattern classification into computer-friendly codes. Then, technologies to automate the fingerprint feature extraction and comparison were developed, and AFISs were implemented on a large scale in order to improve the process of identification of repetitive offenders based on the ten-print cards. Further development of the fingerprint biometric technology allowed for the inclusion of palmprint reference databases and for the processing of fingermarks and palmmarks with, as a result, the partial automation of the forensic investigation and intelligence process. In the field of AFIS, the challenges for the future call for further automation of the feature extraction from low-quality fingerprint and fingermark images, for more transparency in the processes, for the improvement of the interoperability of the systems on a global level and the combination of biometric modalities as well as for the use of fingerprint biometric technology and scientific methodology, to further develop the forensic friction ridge evaluation process
Artificial intelligence in the sorting of municipal waste as an enabler of the circular economy
The recently finalized research project "ZRR for municipal waste" aimed at testing and evaluating the automation of municipal waste sorting plants by supplementing or replacing manual sorting, with sorting by a robot with artificial intelligence (ZRR). The objectives were to increase the current recycling rates and the purity of the recovered materials; to collect additional materials from the current rejected flows; and to improve the working conditions of the workers, who could then concentrate on, among other things, the maintenance of the robots. Based on the empirical results of the project, this paper presents the main results of the training and operation of the robotic sorting system based on artificial intelligence, which, to our knowledge, is the first attempt at an application for the separation of bulky municipal solid waste (MSW) and an installation in a full-scale waste treatment plant. The key questions for the research project included (a) the design of test protocols to assess the quality of the sorting process and (b) the evaluation of the performance quality in the first six months of the training of the underlying artificial intelligence and its database
Tendencias actuales en los recursos humanos
New trends in human resource management include human talent management, focus on job performance, recruitment of qualified candidates,andemployeeperformance evaluation. Today, there is a growing adoption of cutting-edge technologies in the field of humancapitalmanagement.These technologies include artificial intelligence, data analytics, process automation and cloud platforms. One of the most prominent trends is the use of artificial intelligence and machine learning to improve efficiency and decision making in humancapitalmanagement.These technologies make it possible to analyze large amounts of data to identify patterns and trends, which helps HR professionals make more informed and strategic decisions. Anotherimportanttrendisprocess automation. More and more organizations are adopting systems and tools that automate repetitive and administrative tasks, such as payrollmanagement,recruitingand performance tracking. This frees up time for HR professionals to focus on more strategic activities, such as talent development and succession planning
Ways of Applying Artificial Intelligence in Software Engineering
As Artificial Intelligence (AI) techniques have become more powerful and
easier to use they are increasingly deployed as key components of modern
software systems. While this enables new functionality and often allows better
adaptation to user needs it also creates additional problems for software
engineers and exposes companies to new risks. Some work has been done to better
understand the interaction between Software Engineering and AI but we lack
methods to classify ways of applying AI in software systems and to analyse and
understand the risks this poses. Only by doing so can we devise tools and
solutions to help mitigate them. This paper presents the AI in SE Application
Levels (AI-SEAL) taxonomy that categorises applications according to their
point of AI application, the type of AI technology used and the automation
level allowed. We show the usefulness of this taxonomy by classifying 15 papers
from previous editions of the RAISE workshop. Results show that the taxonomy
allows classification of distinct AI applications and provides insights
concerning the risks associated with them. We argue that this will be important
for companies in deciding how to apply AI in their software applications and to
create strategies for its use
Computational Intelligence Techniques for Control and Optimization of Wastewater Treatment Plants
The development of novel, practice-oriented and reliable instrumentation and control strategies for
wastewater treatment plants in order to improve energy efficiency, while guaranteeing process stability and
maintenance of high cleaning capacity, has become a priority for WWTP operators due to increasing
treatment costs. To achieve these ambitious and even contradictory objectives, this thesis investigates a
combination of online measurement systems, computational intelligence and machine learning methods as
well as dynamic simulation models. Introducing the state-of-the-art in the fields of WWTP operation,
process monitoring and control, three novel computational intelligence enabled instrumentation, control
and automation (ICA) methods are developed and presented. Furthermore, their potential for practical
implementation is assessed. The methods are, on the one hand, the automated calibration of a simulation
model for the Rospe WWTP that provides a basis for the development and evaluation of the subsequent
methods, and on the other hand, the development of soft sensors for the WWTP inflow which estimate the
crucial process variables COD and NH4-N, and the estimation of WWTP operating states using Self-
Organising Maps (SOM) that are used to determine the optimal control parameters for each state. These
collectively, provide the basis for achieving comprehensive WWTP optimization. Results show that energy
consumption and cleaning capacity can be improved by more than 50%
Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm
Industry 4.0 aims at achieving mass customization at a
mass production cost. A key component to realizing this is accurate
prediction of customer needs and wants, which is however a
challenging issue due to the lack of smart analytics tools. This
paper investigates this issue in depth and then develops a predictive
analytic framework for integrating cloud computing, big data
analysis, business informatics, communication technologies, and
digital industrial production systems. Computational intelligence
in the form of a cluster k-means approach is used to manage
relevant big data for feeding potential customer needs and wants
to smart designs for targeted productivity and customized mass
production. The identification of patterns from big data is achieved
with cluster k-means and with the selection of optimal attributes
using genetic algorithms. A car customization case study shows
how it may be applied and where to assign new clusters with
growing knowledge of customer needs and wants. This approach
offer a number of features suitable to smart design in realizing
Industry 4.0
Evaluation of Cognitive Architectures for Cyber-Physical Production Systems
Cyber-physical production systems (CPPS) integrate physical and computational
resources due to increasingly available sensors and processing power. This
enables the usage of data, to create additional benefit, such as condition
monitoring or optimization. These capabilities can lead to cognition, such that
the system is able to adapt independently to changing circumstances by learning
from additional sensors information. Developing a reference architecture for
the design of CPPS and standardization of machines and software interfaces is
crucial to enable compatibility of data usage between different machine models
and vendors. This paper analysis existing reference architecture regarding
their cognitive abilities, based on requirements that are derived from three
different use cases. The results from the evaluation of the reference
architectures, which include two instances that stem from the field of
cognitive science, reveal a gap in the applicability of the architectures
regarding the generalizability and the level of abstraction. While reference
architectures from the field of automation are suitable to address use case
specific requirements, and do not address the general requirements, especially
w.r.t. adaptability, the examples from the field of cognitive science are well
usable to reach a high level of adaption and cognition. It is desirable to
merge advantages of both classes of architectures to address challenges in the
field of CPPS in Industrie 4.0
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