Electrical Design: A Problem for Artificial Intelligence Research

This report outlines the problem of intelligent failure recovery in a problem-solver for electrical design. We want our problem solver to learn as much as it can from its mistakes. Thus we cast the engineering design process on terms of Problem Solving by Debugging Almost-Right Plans, a paradigm for automatic problem solving based on the belief that creation and removal of "bugs" is an unavoidable part of the process of solving a complex problem. The process of localization and removal of bugs called for by the PSBDARP theory requires an approach to engineering analysis in which every result has a justification which describes the exact set of assumptions it depends upon. We have developed a program based on Analysis by Propagation of Constraints which can explain the basis of its deductions. In addition to being useful to a PSBDARP designer, these justifications are used in Dependency-Directed Backtracking to limit the combinatorial search in the analysis routines. Although the research we will describe is explicitly about electrical circuits, we believe that similar principles and methods are employed by other kinds of engineers, including computer programmers.

ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

The seeds of artificial intelligence : SUMEX-AIM /

"March 1980."

State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems - a review

Given the considerable recent attention to distributed power generation and interest in sustainable energy, the integration of photovoltaic (PV) systems to grid-connected or isolated microgrids has become widespread. In order to maximize power output of PV system extensive research into control strategies for maximum power point tracking (MPPT) methods has been conducted. According to the robust, reliable, and fast performance of artificial intelligence-based MPPT methods, these approaches have been applied recently to various systems under different conditions. Given the diversity of recent advances to MPPT approaches a review focusing on the performance and reliability of these methods under diverse conditions is required. This paper reviews AI-based techniques proven to be effective and feasible to implement and very common in literature for MPPT, including their limitations and advantages. In order to support researchers in application of the reviewed techniques this study is not limited to reviewing the performance of recently adopted methods, rather discusses the background theory, application to MPPT systems, and important references relating to each method. It is envisioned that this review can be a valuable resource for researchers and engineers working with PV-based power systems to be able to access the basic theory behind each method, select the appropriate method according to project requirements, and implement MPPT systems to fulfill project objectives.

Outdoor worker sun protection project : a comprehensive applied research project to demonstrate the effectiveness of sun protection measures which influence high risk outdoor workers in Queensland to adopt sun safe behaviour practices 2010 – 2013

The QUT Outdoor Worker Sun Protection (OWSP) project undertook a comprehensive applied health promotion project to demonstrate the effectiveness of sun protection measures which influence high risk outdoor workers in Queensland to adopt sun safe behaviours. The three year project (2010-2013) was driven by two key concepts: 1) The hierarchy of control, which is used to address risks in the workplace, advocates for six control measures that need to be considered in order of priority (refer to Section 3.4.2); and 2) the Ottawa Charter which recommends five action means to achieve health promotion (refer to Section 2.1). The project framework was underpinned by a participatory action research approach that valued peoples’ input, took advantage of existing skills and resources, and stimulated innovation (refer to Section 4.2). Fourteen workplaces (small and large) with a majority outdoor workforce were recruited across regional Queensland (Darling Downs, Northwest, Mackay and Cairns) from four industries types: 1) building and construction, 2) rural and farming, 3) local government, and 4) public sector. A workplace champion was identified at each workplace and was supported (through resource provision, regular contact and site visits) over a 14 to 18 month intervention period to make sun safety a priority in their workplace. Employees and employers were independently assessed for pre- and postintervention sun protection behaviours. As part of the intervention, an individualised sun safety action plan was developed in conjunction with each workplace to guide changes across six key strategy areas including: 1) Policy (e.g., adopt sun safety practices during all company events); 2) Structural and environmental (e.g., shade on worksites; eliminate or minimise reflective surfaces); 3) Personal protective equipment (PPE) (e.g., trial different types of sunscreens, or wide-brimmed hats); 4) Education and awareness (e.g., include sun safety in inductions and toolbox talks; send reminder emails or text messages to workers);5) Role modelling (e.g., by managers, supervisors, workplace champions and mentors); and 6) Skin examinations (e.g., allow time off work for skin checks). The participatory action process revealed that there was no “one size fits all” approach to sun safety in the workplace; a comprehensive, tailored approach was fundamental. This included providing workplaces with information, resources, skills, know how, incentives and practical help. For example, workplaces engaged in farming complete differing seasonal tasks across the year and needed to prepare for optimal sun safety of their workers during less labour intensive times. In some construction workplaces, long pants were considered a trip hazard and could not be used as part of a PPE strategy. Culture change was difficult to achieve and workplace champions needed guidance on the steps to facilitate this (e.g., influencing leaders through peer support, mentoring and role modelling). With the assistance of the project team the majority of workplaces were able to successfully implement the sun safety strategies contained within their action plans, up skilling them in the evidence for sun safety, how to overcome barriers, how to negotiate with all relevant parties and assess success. The most important enablers to the implementation of a successful action plan were a pro-active workplace champion, strong employee engagement, supportive management, the use of highly visual educational resources, and external support (provided by the project team through regular contact either directly through phone calls or indirectly through emails and e-newsletters). Identified barriers included a lack of time, the multiple roles of workplace champions, (especially among smaller workplaces), competing issues leading to a lack of priority for sun safety, the culture of outdoor workers, and costs or budgeting constraints. The level of sun safety awareness, knowledge, and sun protective behaviours reported by the workers increased between pre-and post-intervention. Of the nine sun protective behaviours that were assessed, the largest changes reported included a 26% increase in workers who “usually or always” wore a broad-brimmed hat, a 20% increase in the use of natural shade, a 19% increase in workers wearing long-sleeved collared shirts, and a 16% increase in workers wearing long trousers.

Portfolio selection using artificial intelligence

The application of artificial intelligence in finance is relatively new area of research. This project employed artificial neural networks (ANNs) that use both fundamental and technical inputs to predict future prices of widely held Australian stocks and use these predicted prices for stock portfolio selection over a long investment horizon. The research involved the creation and testing of a large number of possible network configurations and draws conclusions about ANN architectures and their overall suitability for the purpose of stock portfolio selection.

The role of basic nutritional research in pediatric liver disease : an historical perspective

The advent of liver transplantation for end-stage liver disease (ESLD) in children has necessitated a major rethink in the preoperative preparation and management from simple palliative care to active directed intervention. This is particularly evident in the approach to the nutritional care of these patients with the historical understanding of the nutritional pertubations in ESLD being described from a single pediatric liver transplant center. ESLD in children is a hypermetabolic process adversely affecting nutritional status, metabolic, and non-metabolic body compartments. There is a complex dynamic process affecting metabolic activity within the metabolically active body cell mass, as well as lipid oxidation during fasting and at rest, with other factors operating in conjunction with daily activities. We have proposed that immediately ingested nutrients are a more important source of energy in patients with ESLD than in healthy children, among whom energy may be stored in various body compartments.

Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

The status of shark and ray fishery resources in the Gulf of California: applied research to improve management and conservation

Seasonal surveys were conducted during 1998–1999 in Baja California, Baja California Sur, Sonora, and Sinaloa to determine the extent and activities of artisanal elasmobranch fisheries in the Gulf of California. One hundred and forty–seven fishing sites, or camps, were documented, the majority of which (n = 83) were located in Baja California Sur. Among camps with adequate fisheries information, the great majority (85.7%) targeted elasmobranchs during some part of the year. Most small, demersal sharks and rays were landed in mixed species fisheries that also targeted demersal teleosts, but large sharks were usually targeted in directed drift gillnet or, to a lesser extent, surface longline fisheries. Artisanal fishermen were highly opportunistic, and temporally switched targets depending on the local productivity of teleost, invertebrate, and elasmobranch fishery resources. Major fisheries for small sharks (< 1.5 m, “cazón”) were documented in Baja California during spring, in Sonora during autumn–spring, and in Sinaloa during winter and spring. Triakid sharks (Mustelus spp.) dominated cazón landings in the northern states, whereas juvenile scalloped hammerheads (Sphyrna lewini) primarily supported the fishery in Sinaloa. Large sharks (> 1.5 m, “tiburón”) were minor components of artisanal elasmobranch fisheries in Sonora and Sinaloa, but were commonly targeted during summer and early autumn in Baja California and Baja California Sur. The pelagic thresher shark (Alopias pelagicus) and silky shark (Carcharhinus falciformis) were most commonly landed in Baja California, whereas a diverse assemblage of pelagic and large coastal sharks was noted among Baja California Sur landings. Rays dominated summer landings in Baja California and Sinaloa, when elevated catch rates of the shovelnose guitarfish (Rhinobatos productus, 13.2 individuals/vessel/trip) and golden cownose ray (Rhinoptera steindachneri, 11.1 individuals/vesse/trip) primarily supported the respective fisheries. The Sonoran artisanal elasmobranch fishery was the most expansive recorded during this study, and rays (especially R. productus) dominated spring and summer landings in this state. Seasonal catch rates of small demersal sharks and rays were considerably greater in Sonora than in other surveyed states. Many tiburón populations (e.g., C. leucas, C. limbatus, C. obscurus, Galeocerdo cuvier) have likely been overfished, possibly shifting effort towards coastal populations of cazón and rays. Management recommendations, including conducting demographic analyses using available life history data, determining and protecting nursery areas, and enacting seasonal closures in areas of elasmobranch aggregation (e.g., reproduction, feeding), are proposed. Without effective, enforceable management to sustain or rebuild targeted elasmobranch populations in the Gulf of California, collapse of many fisheries is a likely outcome. (PDF contains 243 pages)

Impact pathway analysis for research planning: the case of aquatic resources research in the WorldFish Center

In line with its mandate of poverty reduction and sustainable development, the WorldFish Center is orienting its research towards high impact scientific activity. Identifying such activities is the task of prospective impact assessment, in turn based on impact pathway analysis. The paper describes a framework for analyzing benefits from aquatic resources research, the relevant research categories, pathways to impact by category, and indicators along each pathway that can be estimated in order to quantify probable research impact.

A History of Benthic Research in the NMFS Northeast Fisheries Science Center

The Northeast Fisheries Science Center of NOAA's National Marine Fisheries Service has a long history of research on benthic invertebrates and habitats in support of the management of living marine resources. These studies began in the 1870's under Spencer F. Baird's guidance as part of an effort to characterize the Nation's fisheries and living marine resources and their ecological interactions. This century and a quarter of research has included many benthic invertebrate studies, including community characterizations, shellfish biology and culture, pathology, ecosystem energy budget modeling, habitat evaluations, assessments of human impacts, toxic chemical bioaccumulation in demersal food webs, habitat or endangered species management, benthic autecology, systematics (to define new species and species population boundaries), and other benthic studies. Here we review the scope of past and current studies as a background for strategic research planning and suggest areas for further research to support NOAA's goals of sustainable fisheries management, healthy coastal ecosystems, and protected species populations.