8,967 research outputs found
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
Review of Machine Learning Approaches In Fault Diagnosis applied to IoT System
International audienceWith increasing complex systems, low production costs, and changing technologies, for this reason, the automatic fault diagnosis using artificial intelligence (AI) techniques is more in more applied. In addition, with the emergence of the use of reconfigurable systems, AI can assist in self-maintenance of complex systems. The purpose of this article is to summarize the diagnosis research of systems using AI approaches and examine their application particularly in the field of diagnosis of complex systems. It covers articles published from 2002 to 2018 using Machine Learning tools for fault diagnosis in industrial systems
Optimizing construction of scheduled data flow graph for on-line testability
The objective of this work is to develop a new methodology for behavioural synthesis using a flow of synthesis, better suited to the scheduling of independent calculations and non-concurrent online testing. The traditional behavioural synthesis process can be defined as the compilation of an algorithmic specification into an architecture composed of a data path and a controller. This stream of synthesis generally involves scheduling, resource allocation, generation of the data path and controller synthesis. Experiments showed that optimization started at the high level synthesis improves the performance of the result, yet the current tools do not offer synthesis optimizations that from the RTL level. This justifies the development of an optimization methodology which takes effect from the behavioural specification and accompanying the synthesis process in its various stages. In this paper we propose the use of algebraic properties (commutativity, associativity and distributivity) to transform readable mathematical formulas of algorithmic specifications into mathematical formulas evaluated efficiently. This will effectively reduce the execution time of scheduling calculations and increase the possibilities of testability
NASA SBIR abstracts of 1991 phase 1 projects
The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
Wearable electroencephalography for long-term monitoring and diagnostic purposes
Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG
units, which are the current standard for long-term EEG monitoring. Replacing
these short lifetime, bulky units with long-lasting, miniature and wearable devices
that can be easily worn by patients will result in more EEG data being collected for
extended monitoring periods. This thesis presents three new fabricated systems, in
the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of
epilepsy and sleep disorders by detecting specific clinically important EEG events
on the sensor node, while discarding background activity. The power consumption
of the WEEG monitoring device incorporating these systems can be reduced since
the transmitter, which is the dominating element in terms of power consumption,
will only become active based on the output of these systems.
Candidate interictal activity is identified by the developed analog-based interictal
spike selection system-on-chip (SoC), using an approximation of the Continuous
Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike
selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW.
Experimental results reveal that the SoC is able to identify 87% of interictal spikes
correctly while only transmitting 45% of the data.
Sections of EEG data containing likely ictal activity are detected by an analog
seizure selection SoC using the low complexity line length feature. This SoC is
fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental
results, the fabricated SoC is able to correctly detect 83% of seizure
episodes while transmitting 52% of the overall EEG data.
A single-channel analog-based sleep spindle detection SoC is developed to aid
the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic
events of sleep. The system identifies spindle events by monitoring abrupt changes
in the input EEG. An approximation of the median frequency calculation, incorporated
as part of the system, allows for non-spindle activity incorrectly identified
by the system as sleep spindles to be discarded. The sleep spindle detection SoC
is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC
achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces
Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals
Neuromodulation techniques have emerged as promising approaches for treating
a wide range of neurological disorders, precisely delivering electrical
stimulation to modulate abnormal neuronal activity. While leveraging the unique
capabilities of artificial intelligence (AI) holds immense potential for
responsive neurostimulation, it appears as an extremely challenging proposition
where real-time (low-latency) processing, low power consumption, and heat
constraints are limiting factors. The use of sophisticated AI-driven models for
personalized neurostimulation depends on back-telemetry of data to external
systems (e.g. cloud-based medical mesosystems and ecosystems). While this can
be a solution, integrating continuous learning within implantable
neuromodulation devices for several applications, such as seizure prediction in
epilepsy, is an open question. We believe neuromorphic architectures hold an
outstanding potential to open new avenues for sophisticated on-chip analysis of
neural signals and AI-driven personalized treatments. With more than three
orders of magnitude reduction in the total data required for data processing
and feature extraction, the high power- and memory-efficiency of neuromorphic
computing to hardware-firmware co-design can be considered as the
solution-in-the-making to resource-constraint implantable neuromodulation
systems. This could lead to a new breed of closed-loop responsive and
personalised feedback, which we describe as Neuromorphic Neuromodulation. This
can empower precise and adaptive modulation strategies by integrating
neuromorphic AI as tightly as possible to the site of the sensors and
stimulators. This paper presents a perspective on the potential of Neuromorphic
Neuromodulation, emphasizing its capacity to revolutionize implantable
brain-machine microsystems and significantly improve patient-specificity.Comment: 17 page
- …