Knowledge-based Procedure Research Articles

Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction

The design of contemporary antenna systems encounters multifold challenges, one of which is a limited size. Compact antennas are indispensable for new fields of application such as the Internet of Things or 5G/6G mobile communication. Still, miniaturization generally undermines electrical and field performance. When attempted using numerical optimization, it turns into a constrained problem with costly constraints requiring electromagnetic (EM) simulations. At the same time, due to the parameter redundancy of compact antennas, size reduction poses a multimodal task. In particular, the achievable miniaturization rate heavily depends on the starting point, while identifying a suitable starting point is a challenge on its own. These issues indicate that miniaturization should be addressed using global optimization methods. Unfortunately, the most popular nature-inspired algorithms cannot be applied for solving size reduction tasks because of their inferior computational efficacy and difficulties in handling constraints. This work proposes a novel methodology for the globalized size reduction of antenna structures. Our methodology is a multi-stage knowledge-based procedure, initialized with the detection of the approximate location of the feasible region boundary, followed by the construction of a dimensionality-reduced metamodel and global optimization thereof; the last stage is the miniaturization-oriented local refinement of geometry parameters. For cost reduction, the first stages of the procedure are realized with the use of a low-fidelity EM antenna model. Our approach is verified using four broadband microstrip antennas and benchmarked against multi-start local search as well as nature-inspired methods. Superior size reduction rates are demonstrated for all considered cases while maintaining reasonably low computational costs.

Knowledge-Based Investigation of Seismic Vulnerability Assessment and Compatible Strengthening Design of an Existing Masonry Building

Most existing masonry buildings were built without following code regulations for seismic criteria. Hence, their performance compared to the demands of the current codes, for both seismic and static actions, could be very low. In engineering practice, strengthening interventions can be pursued at different levels, based on qualitative assessments of masonry structures. Firstly, the structures are evaluated through in situ inspections. Then, based on the structural system of the building a strengthening design is proposed. The design can be targeted at guaranteeing a box behavior of structures acting through macro-elements, or at strengthening the in-plane capacity of piers and spandrels. However, lower cognitive levels may drive inaccurate impressions about the capacity of structures, leading to unreliable strengthening solutions. While in some cases the confidence factor could underestimate the effects of the reinforcements, in other cases lower knowledge levels could lead to incorrect reinforcements that do not provide helpful solutions. In this paper, the issues concerning the strengthening design of masonry structures are presented and discussed with regard to an existing building. The construction is a 20th century masonry building used as a nursing home for elderly persons. The study follows a knowledge-based procedure where, after achievement of the highest knowledge level and the proposal of compatible and effective strengthening interventions, the analysis of the potential effects of achieving lower knowledge levels is discussed both in terms of vulnerability computations and in terms of strengthening effectiveness. Regarding vulnerability assessment, a lower knowledge level leads to non-conservative results, with an increase in the estimated capacity of around 80%. Critical evaluation of the different configurations for the case study makes it clear that for the considered masonry building the mortar investigation represented the most important parameter to be investigated. The proposal of strengthening interventions before assessment of this parameter may lead to ineffective improvements resulting in index values lower than 0.60. The study represents the opportunity to assess comparisons in the setting of different investigation strategies and their effect on the definition of the strengthening solutions.

Knowledge-based expedited parameter tuning of microwave passives by means of design requirement management and variable-resolution EM simulations

The importance of numerical optimization techniques has been continually growing in the design of microwave components over the recent years. Although reasonable initial designs can be obtained using circuit theory tools, precise parameter tuning is still necessary to account for effects such as electromagnetic (EM) cross coupling or radiation losses. EM-driven design closure is most often realized using gradient-based procedures, which are generally reliable as long as the initial design is sufficiently close to the optimum one. Otherwise, the search process may end up in a local optimum that is of insufficient quality. Furthermore, simulation-based optimization incurs considerable computational expenses, which are often impractically high. This paper proposes a novel parameter tuning procedure, combining a recently reported design specification management scheme, and variable-resolution EM models. The former allows for iteration-based automated modification of the design goals to make them accessible in every step of the search process, thereby improving its immunity to poor starting points. The knowledge-based procedure for the adjustment of the simulation model fidelity is based on the convergence status of the algorithm and discrepancy between the current and the original performance specifications. Due to using lower-resolution EM simulations in early phase of the optimization run, considerable CPU savings can be achieved, which are up to 60 percent over the gradient-based search employing design specifications management and numerical derivatives. Meanwhile, as demonstrated using three microstrip circuits, the computational speedup is obtained without design quality degradation.

A Stochastic Flight Problem Simulation to Minimize Cost of Refuelling

Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses for airline activities. A nonlinear mixed binary mathematical programming model for the airline fuel task is presented to minimize the total cost of refueling in an entire flight route problem. The model is enhanced to include possible discounts in fuel prices, which are performed by adding dummy variables and some restrictive constraints, or by fitting a suitable distribution function that relates prices to purchased quantities. The obtained fuel plan explains exactly the amounts of fuel in gallons to be purchased from each airport considering tankering strategy while minimizing the pertinent cost of the whole flight route. The relation between the amount of extra burnt fuel taken through tinkering strategy and the total flight time is also considered. A case study is introduced for a certain flight rotation in domestic US air transport route. The mathematical model including stepped discounted fuel prices is formulated. The problem has a stochastic nature as the total flight time is a random variable, the stochastic nature of the problem is realistic and more appropriate than the deterministic case. The stochastic style of the problem is simulated by introducing a suitable probability distribution for the flight time duration and generating enough number of runs to mimic the probabilistic real situation. Many similar real application problems are modelled as nonlinear mixed binary ones that are difficult to handle by exact methods. Therefore, metaheuristic approaches are widely used in treating such different optimization tasks. In this paper, a gaining sharing knowledge-based procedure is used to handle the mathematical model. The algorithm basically based on the process of gaining and sharing knowledge throughout the human lifetime. The generated simulation runs of the example are solved using the proposed algorithm, and the resulting distribution outputs for the optimum purchased fuel amounts from each airport and for the total cost and are obtained.

Knowledge-Based Approach for the Structural Assessment of Monumental Buildings: Application to Case Studies

Historical buildings are characterised by high level of complexity due to a long realization process often resulting in an overall lack of information and in a structural behaviour that is more like to ‘structural aggregates’ rather than ‘single building’. The assessment of the static safety and seismic vulnerability then requires a multidisciplinary and multi-level approach including a deep and accurate preliminary knowledge phase before performing structural analyses. In the present paper, a consolidated knowledge-based procedure is presented and applied to four case studies in Italy. Interest is focused on the knowledge phase, joining critical-historical analysis to in-situ architectural, geometrical, structural, material and geotechnical aspects. The knowledge phase proves to be fundamental to understand the structural behaviour of cultural heritage, with special attention to the determination and analysis of local mechanisms and vulnerability elements and allowing to validate and give reason to numerical results.

Fault Diagnosis of Planetary Gear Carrier Packs: A Class Imbalance and Multiclass Classification Problem

Fault diagnosis plays a key role in monitoring manufactured products for the purpose of quality control. Among the several fault diagnosis approaches, knowledge-based fault diagnosis, which uses signals from sensors and machine learning algorithms instead of a priori information, is widely employed to diagnose the status of products. In this paper, we propose a knowledge-based procedure to establish a fault diagnosis model. The model is aimed to diagnose planetary gear carrier packs, which have many fault types and an unbalanced number of samples in the sample classes, using transmission error. In the procedure, the best feature subset that contains the most important features is selected using two different feature selection processes. Several ensemble algorithms are used during the model training process. The imbalance ratio between classes of samples is addressed. The number of weak learners is automatically determined by a genetic algorithm. Finally, the performance of the proposed procedure is validated by comparison with other models trained without applying the proposed procedure. We observed that it is important to incorporate the class imbalance technique in the training process as it reduces the misclassification of faulty products as normal ones. This reduction is important in production quality control.

Reasoning about accessibility for disabled using building graph models based on BIM/IFC

BackgroundBuilding Information Modelling (BIM) is becoming a standard in Architecture, Building and Construction (ABC) industries. It provides a method for a unified representation of information about building throughout its lifecycle – from the design phase to the building maintenance. At the design phase the focus is usually on fulfilling the requirements and applicable norms and standards. One of the issues that is considered to be very important deals with the accessibility of buildings. While the design tools usually support the legal requirements for accessibility for disabled persons, little effort has been observed to address the quality of the access routes in terms of time, length and convenience of the route to be taken.MethodsThis paper introduces a method of searching routes accessible for disabled people in public buildings, which is based on the building-related knowledge. This knowledge is stored in graph-based representations of buildings. A graph model of the considered building is effectively created by extracting information about the building from the IFC file, which is an interoperable BIM standard. The information about the topology of spatial layouts of buildings is stored in the graph representation together with characteristics of elements ensuring access between spaces. This model allows for homogenous encoding of both spatial and non-spatial information of different types, which is necessary for route calculation. Attributes assigned to graph nodes encode types of spaces and their sizes. Attributes of graph edges represent costs of moving between spaces, which depend on width of openings, equipping stairs with ramps, door types and their opening directions. As for people moving around on wheelchairs easiness of passing a route between two places can depend on the route direction, the directed graphs are used to represent building layouts. The optimal routes accessible for disabled are specified using the modified algorithm of graph search for single-source minimum-cost paths, where costs of passing through different spaces and between them are considered. This approach is applied to find best routes to a chosen room starting from all possible building entrances.ResultsThe proposed application offers intelligent knowledge-based navigation, which is based on the graph representation of buildings. The system is aimed to aid people to decide about the routes they should take using mobile devices. It enables them to select from all accessible routes the one which requires the lowest effort in traversing it. Apart from the user interface allowing for visualization the results of design analysis in respect to the accessible routes, a visual tool dedicated for designers, which shows on the floor layout the actual route a disabled person has to take, is proposed. Offering a designer such a visualization can shift the focus of the design from mere existence of accessible routes to existence of quality routes for disabled. The presented examples illustrate the proposed approach by depicting the found accessible routes on the building layout.ConclusionsBuilding-specific knowledge, which is extracted from the IFC file and stored in the graph building representation, is used to find the best routes accessible for disabled people. The search for these routes is performed by a knowledge-based graph-search procedure, which takes into consideration costs of passing through different spaces and between them. There is a possibility of adding in future more elements which can be treated as different obstacles in moving around the building.

Heavy Prenatal Alcohol Exposure is Related to Smaller Corpus Callosum in Newborn MRI Scans.

Magnetic resonance imaging (MRI) studies have consistently demonstrated disproportionately smaller corpus callosa in individuals with a history of prenatal alcohol exposure (PAE) but have not previously examined the feasibility of detecting this effect in infants. Tissue segmentation of the newborn brain is challenging because analysis techniques developed for the adult brain are not directly transferable, and segmentation for cerebral morphometry is difficult in neonates, due to the latter's incomplete myelination. This study is the first to use volumetric structural MRI to investigate PAE effects in newborns using manual tracing and to examine the cross-sectional area of the corpus callosum (CC). Forty-three nonsedated infants born to 32 Cape Coloured heavy drinkers and 11 controls recruited prospectively during pregnancy were scanned using a custom-designed birdcage coil for infants, which increases signal-to-noise ratio almost 2-fold compared to the standard head coil. Alcohol use was ascertained prospectively during pregnancy, and fetal alcohol spectrum disorders diagnosis was conducted by expert dysmorphologists. Data were acquired using a multi-echo FLASH protocol adapted for newborns, and a knowledge-based procedure was used to hand-segment the neonatal brains. CC was disproportionately smaller in alcohol-exposed neonates than controls after controlling for intracranial volume. By contrast, CC area was unrelated to infant sex, gestational age, age at scan, or maternal smoking, marijuana, or methamphetamine use during pregnancy. Given that midline craniofacial anomalies have been recognized as a hallmark of fetal alcohol syndrome in humans and animal models since this syndrome was first identified, the CC deficit identified here in newborns may support early identification of a range of midline structural impairments. Smaller CC during the newborn period may provide an early indicator of fetal alcohol-related cognitive deficits that have been linked to this critically important brain structure in childhood and adolescence.

A novel alternative to classify tissues from T 1 and T 2 relaxation times for prostate MRI

To segment and classify the different attenuation regions from MRI at the pelvis level using the T 1 and T 2 relaxation times and anatomical knowledge as a first step towards the creation of PET/MR attenuation maps. Relaxation times were calculated by fitting the pixel-wise intensities of acquired T 1- and T 2-weighted images from eight men with inversion-recovery and multi-echo multi-slice spin-echo sequences. A decision binary tree based on relaxation times was implemented to segment and classify fat, muscle, prostate, and air (within the body). Connected component analysis and an anatomical knowledge-based procedure were implemented to localize the background and bone. Relaxation times at 3T are reported for fat (T 1=385ms, T 2=121ms), muscle (T 1=1295ms, T 2=40ms), and prostate (T 1=1700ms, T 2=80ms). The relaxation times allowed the segmentation-classification of fat, prostate, muscle, and air, and combined with anatomical knowledge, they allowed classification of bone. The good segmentation-classification of prostate [mean Dice similarity score (mDSC)=0.70] suggests a viable implementation in oncology and that of fat (mDSC=0.99), muscle (mDSC=0.99), and bone (mDSCs=0.78) advocates for its implementation in PET/MR attenuation correction. Our method allows the segmentation and classification of the attenuation-relevant structures required for the generation of the attenuation map of PET/MR systems in prostate imaging: air, background, bone, fat, muscle, and prostate.

Researching into Bio-Energy Change and Sustainable Land Use

Sustainable land management (SLM) (also sometimes referred to as “sustainable land use” by policy makers, developmental planners, and researchers) is a knowledge-based procedure that aims at integrating the management of land, water, biodiversity, and other environmental resources to meet human needs while sustaining ecosystem services and livelihoods. The term sustainable land management is used, for example, in regional planning and soil or environmental protection, as well as in property and estate management. The World Bank defines SLM as “a process in a charged environment between environmental protection and the guarantee claim of ecosystem services on the one hand”. On the other hand, it is about “productivity of agriculture and forestry with respect to demographic growth and increasing pressure in land use”. It is estimated that the human footprint has affected 83% of the global terrestrial land surface. Land use and land cover (LUCC) change has been the most visible indicator of the human footprint and the most important driver of loss of biodiversity and other forms of land degradation. Recent trends on global demand for food and bio-energy change (which are closely linked to food and energy price spikes and volatility) have raised concerns on the impact of LUCC change on biodiversity and other environmental impacts. This paper aims to assess the LUCC change and explores factors which could be addressed to ensure sustainable development. In terms of methodology employed, secondary data have been analyzed using descriptive research method. The paper concludes that integrated economic, geographic and ecological models are required to capture the multiple drivers of LUCC and objectives of ecosystems.

Stakeholder orientated sustainable land management: The Ruhr Area as a role model for urban areas

The concept of sustainable land management (SLM) can help create a balance between the different demands of economy, society and nature. SLM means to actively develop land based on a knowledge-based procedure. It should aim on long term enhancements. The basis of SLM is the interests and the goals of the stakeholders. It is the result of a bargaining process between different institutions by creating win-win-situations or adequate conflict solutions to reach a consensus. The Ruhr Area is Germany’s biggest metropolitan area. Land management regarding economic, social and environmental issues, which has a long tradition in this area, goes back to 1920. Being the centre of the heavy industry in Germany for more than 150years, the Ruhr Area today is a green metropolis. The ecological, social and economical change of this industrial region can serve as a role model for SLM in urban areas all over the world.

Educated search—A generic platform for partial modification at conceptual design phase of multidisciplinary problems

This paper presents a goal programming algorithm utilising a weight-free aggregate function for producing enhanced design alternatives and a knowledge-based procedure for the selection of the final solution from a pool of enhanced alternatives. Normally, in a multi-disciplinary design problem several teams of designers with different preference and background knowledge are involved in the decision making processes, such as constructing aggregate functions for multi-objective optimisation and trade-off study towards selecting the final solution. In constructing an aggregate function, designers need to identify how important each objective is with respect to the other objectives. However, in the absence of a final decision maker with expertise in all disciplines, the predicates such as “as important as” or “more important than” cannot be used to compare objectives from different disciplines, and therefore the establishment of a weighted aggregate function is not viable. Introducing the concepts of unsatisfactoriness and tolerated margin, “how important is a design quality with respect to other design qualities” is replaced with “to what extent can the unsatisfactoriness of a design quality be tolerated”. This removes the predicament arising from the usual subjective decision making when forming an aggregate function and also transforms the final solution selection from a negotiation process to a straightforward and knowledge based procedure. A software tool comprising of two modules, a multi-deme genetic algorithm, for producing enhanced alternatives, and an assessment module, which includes visualisation, ranking and filtering facilities, is developed and its performance is shown using an illustrative multi-disciplinary design space.

Vehicle detection algorithm based on light pairing and tracking at nighttime

Detecting a vehicle to obtain traffic information at nighttime is difficult. This study proposes a vehicle detection algorithm, called the headlight extraction, pairing, and tracking (HLEPT) algorithm, which can acquire traffic information in the rain at nighttime by identifying vehicles through the location of their headlights and other indicative lights. A knowledge-based connected-component procedure, in which vehicles are located by grouping their lights and estimating their features, is proposed. The features of a complex nighttime traffic scene were also analyzed. The HLEPT algorithm includes a headlight extraction algorithm, as well as regulations for the pairing and grouping of lights and light tracking using a Kanade-Lucas-Tomasi tracker to measure traffic flow and velocity. Experimental results demonstrate the feasibility and effectiveness of the proposed approach on vehicle detection in the rain at nighttime.

Dipe-D: A Tool for Knowledge-Based Query Formulation in Information Retrieval

The paper reports the development of Dipe-D, a knowledge-based procedure for the formulation of Boolean queries in information retrieval. Dipe-D creates a query in two steps: (1) the user's information need is developed interactively, while identifying the concepts of the information need, and subsequently (2) the collection of concepts identified is automatically transformed into a Boolean query. In the first step, the subject area—as represented in a knowledge base—is explored by the user. He does this by means of specifying the (concepts that meet his) information need in an artificial language and looking through the solution as provided by the computer. The specification language allows one to specify concepts by their features, both in precise terms as well as vaguely. By repeating the process of specifying the information need and exploring the resulting concepts, the user may precisely single out the concepts that describe his information need. In the second step, the program provides the designations (and variants) for the concepts identified, and connects them by appropriate operators. Dipe-D is meant to improve on existing procedures that identify the concepts less systematically, create a query manually, and then sometimes expand that query. Experiments are reported on each of the two stepss they indicate that the first step identifies only but not all the relevant concepts, and the second step performs (at least) as good as human beings do.

A simulation-based decision support system for business process planning

Over the last few years, there has been wide discussion in the field of economics research with regard to the process-oriented approach. Extensive restructuring took place in practice. To improve business processes, a generally applicable, object-oriented, simulation-based decision support system abbreviated as GEPSIS has been developed. It is used to model business processes and to evaluate different process alternatives quantitatively. The aim is to determine the optimal process. Within a business process context, tasks, work flows and decisions are influenced by persons whose behavior is not deterministic. Such a behavior cannot be described adequately taking crisp approaches. For this reason, consideration of uncertainty and vagueness is of special importance. Both stochastic and linguistic types of uncertainty are considered. The vagueness of verbal formulations is modeled using linguistic variables. Several specific procedures are developed for process control. They result from specific priority rules for the object classes. Furthermore, a knowledge-based procedure comprising approximate inference is developed. In this way, rules for the sequencing of workflow objects can be modeled approximating reality. The suitability of GEPSIS and the effects of the application of different procedures to process control are demonstrated. This is achieved by considering a mail order book and record store and analyzing its actual business – e.g. the order process. The example covers some typical flow structures which can be found in many order processing situations.

Antibody Modeling: Implications for Engineering and Design

Our understanding of the rules relating sequence to structure in antibodies has led to the development of accurate knowledge-based procedures for antibody modeling. Information gained from the analysis of antibody structures has been successfully exploited to engineer antibody-like molecules endowed with prescribed properties, such as increased stability or different specificity, many of which have a broad spectrum of applications both in therapy and in research. Here we describe a knowledge-based procedure for the prediction of the antibody-variable domains, based on the canonical structures method for the antigen-binding site, and discuss its expected accuracy and limitations. The rational design of antibody-based molecules is illustrated using as an example one of the most widely employed modifications of antibody structures: the humanization of animal-derived antibodies to reduce their immunogenicity for serotherapy in humans.

Knowledge-Based Structures for Fault Diagnosis and Its Applications

The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This paper describes structures for advanced methods of fault detection and diagnosis. It begins with a consideration of a knowledge-based procedure which is based on analytical and heuristic information. Then different methods of fault detection are considered, which extract features from measured signals and use process and signal models. These methods are based on parameter estimation, state estimation and parity equations. By comparison with the normal behaviour, analytic symptoms are generated. Human operators may be a further source of information, and support the generation of heuristic symptoms. For fault diagnosis, all symptoms have to be processed in order to determine possible faults. This can be performed by classification methods or approximate reasoning, using probabilistic or possibilistic (fuzzy) approaches based on if-then-rules. The application of these methods is shown for the fault detection and diagnosis of Dieselengines.

Supervision, fault-detection and fault-diagnosis methods — An introduction

The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This paper gives an introduction to the field of fault detection and diagnosis. It begins with a consideration of a knowledge-based procedure that is based on analytical and heuristic information. Then different methods of fault detection are considered, which extract features from measured signals and use process and signal models. These methods are based on parameter estimation, state estimation and parity equations. By comparison with the normal behaviour, analytic symptoms are generated. Human operators are another source of information, and support the generation of heuristic symptoms. For fault diagnosis, all symptoms have to be processed in order to determine possible faults. This can be performed by classification methods or approximate reasoning, using probabilistic or possibilistic (fuzzy) approaches based on if-then-rules.

Knowledge-based organ identification from CT images

This paper describes a new knowledge-based procedure for identifying and extracting organs from normal CT imagery. Our procedure differs from previous attempts in its use of a wide variety of knowledge about both the anatomy and the image processing operations. The system features the use of constraint-based dynamic thresholding, negative-shape constraints to rapidly rule out infeasible segmentations, and progressive landmarking that takes advantage of the different degrees of certainty of successful identification of each organ. The results of a series of tests on training data of 100 images from five patients plus additional test data of 75 images from three more patients indicate that the knowledge-based approach is promising.

Wissensbasierte Sensorfehlerdetektion an Gasturbinen unter Verwendung unscharfer Verfahren

Gas path analysis is a useful aid in modelling for jet engines and gas turbines. The models are used to diagnose the condition of these machines. However, errors from the measuring sensors distort the true states of the equipment. The detection and compensation of sensor errors is thus an important task in the fild of diagnosis. In jet engines with several working points the problem is solved by a model-based procedure; powerful software packages already exist for this purpose. A knowledge-based procedure is requiredfor gas turbines with only one working point. To this end an expert system has been developed using a rule network which considers the uncertanties of the system