A Dirichlet Process based type-1 and type-2 fuzzy modeling for systematic confidence bands prediction

This paper presents a new methodology for fuzzy logic systems modeling based on the Dirichlet process Gaussian mixture models (DPGMM). The proposed method simultaneously allows for the systematic elicitation of confidence bands as well as the automatic determination of model complexity. This work is new since existing fuzzy model elicitation techniques use ad hoc methods for confidence band estimations, which do not meet the stringent requirements of today's challenging environments where data are sparse, incomplete, and characterized by noise as well as uncertainties. The proposed approach involves an integration of fuzzy and Bayesian topologies and allows for the generation of confidence bands based on both the random and linguistic uncertainties embedded in the data. Additionally, the proposed method provides a “right-first time approach” to fuzzy modeling as it does not require an iterative model complexity determination. In order to see how the proposed framework performs across a variety of challenging data modeling problems, the proposed approach was tested on a nonlinear synthetic dataset as well as two real multidimensional datasets generated by the authors from materials science and bladder cancer studies. Results show that the proposed approach consistently provides better generalization performances than other well-known soft computing modeling frameworks-in some cases, improvements of up to 20% in modeling accuracy were achieved. The proposed method also provides the capability to handle uncertainties via the generation of systematic confidence intervals for informing on model reliability. These results are significant since the generic methodologies developed in this paper should help material scientists as well as clinicians, for example, assess the risks involved in making informed decisions based on model predictions

A New Fuzzy Modeling Framework for Integrated Risk Prognosis and Therapy of Bladder Cancer Patients

This paper presents a new fuzzy modelling approach for analysing censored survival data and finding risk groups of patients diagnosed with bladder cancer. The proposed framework involves a new procedure for integrating the frameworks of interval type-2 fuzzy logic and Cox modelling intrinsically. The output of this synergistic framework is a risk score/prognostics index which is indicative of the patient's level of mortality risk. A threshold value is selected whereby patients with risk scores that are greater than this threshold are classed as high risk patients and vice versa. Unlike in the case of black-box type modelling approaches, the paper shows that interpretability and transparency are maintained using the proposed fuzzy modelling framework

Assessment of Radiological Hazards Indices in Vegetables Grown Around Ririwai Tin Mines, Kano State, North Western Nigeria

Mining industry in Nigeria provides economic benefits of wealth creation and employment opportunities. Presently there are numbers of artisanal and large scale mining activities going on across Nigeria and most of these artisanal miners currently under take only surface mining. The process produced large volumes of tailings and waste that may contain naturally occurring radioactive materials (NORMs). Some of the NORMs are soluble in water and have the tendency to leach into water bodies and farm lands.    This work assessed the radiological hazard indices in vegetable grown around Ririwai Tin Mine Kano State North Western Nigeria using Direct Gamma Spectroscopy (NaI (Tl)), The results shows that the mean activity concentration in vegetable samples were 259.25±4.77, 28.05±4.97 and 54.56±2.58Bq/kg respectively for 40K, 226Ra and 232Th, the mean absorbed dose rate was 45.043±1.98nGyh-1 the mean committed effective dose for 40K is 0.091±0.002mSv/year, 226Ra has a mean committed effective dose of 0.471±0.083mSv/year while 232Th has a mean committed effective dose of 0.753±0.036mSv/year. The total committed effective dose in vegetable has a mean value of 1.320±0.125mSv/year. The risk estimated for fatality cancer, lifetime  fatality cancer risk, severe hereditary effect and life time hereditary effect in vegetable were 7.26 x 10-5, 5.29 x 10-3, 2.60 x 10-6 and 1.84 x 10-4 respectively. The values obtained in this study are relatively high such that consumption of vegetable grown in the area could pose  radiological health hazards. Keywords: Activity Concentrations, Absorbed dose, Committed effective dose, Risk

Vulvovaginal candidasis among female patients attending Yusuf Dantsoho Memorial Hospital and Barau Dikko Specialist Hospital Kaduna

Vulvovaginal candidiasis (VVC) is a major cause of lower genital infections in women, especially in developing countries and cause significant morbidity and financial burden on the already weak economy. The objective was to isolate and identify Candida spp responsible for Vulvovaginitis. Two hundred (200) vaginal swabs were collected from female patients 15years and above, attending Yusuf Dantsoho Memorial Hospital (YDMH) and Barau Dikko Specialist Hospital (BDSH) all within Kaduna metropolis. The samples were analysed for the presence of Candida spp using standard procedures of microscopy, culture and biochemical identification. The overall incidence of VVC was 79.5% (159/200), with higher incidence among patients attending Yusuf Dantsoho Memorial Hospital 84.8% (117/138), than Barau Dikko Memorial Hospital 67.7% (42/62).  Four (4) yeast species were isolated and identified which include Candida krusie, Candida parapsilosis and Candida glabrata. C. krusie had the highest percentage occurrence of 42.5% (39/159), while C. parapsilosis had the least percentage occurrence of 11.3% (18/159). Higher incidence was recorded among age group 40 and above 92.8% (13/14) and lowest among age group 31-40 76.5% (39/51). Highest incidence was also recorded among widow 100% (18/18) and lowest among married 76.8% (109/142). The high percentage of positive samples is an indication that there is a high incidence of candidiasis among the study population. There is need for regular screening of women for VVC and other sexually transmitted infections. Key words: Incidence, Vulvovaginal Candidiasis, Isolation, Kaduna

Inspection by exception: a new machine learning-based approach for multistage manufacturing

Manufacturing processes usually consist of multiple different stages, each of which is influenced by a multitude of factors. Therefore, variations in product quality at a certain stage are contributed to by the errors generated at the current, as well as preceding, stages. The high cost of each production stage in the manufacture of high-quality products has stimulated a drive towards decreasing the volume of non-added value processes such as inspection. This paper presents a new method for what the authors have referred to as ‘inspection by exception’ – the principle of actively detecting and then inspecting only the parts that cannot be categorized as healthy or unhealthy with a high degree of certainty. The key idea is that by inspecting only those parts that are in the corridor of uncertainty, the volume of inspections are considerably reduced. This possibility is explored using multistage manufacturing data and both unsupervised and supervised learning algorithms. A case study is presented whereby material conditions and time domain features for force, vibration and tempering temperature are used as input data. Fuzzy C-Means (FCM) clustering is implemented to achieve inspection by exception in an unsupervised manner based on the normalized Euclidean distances between the principal components and cluster centres. Also, deviation vectors for product health are obtained using a comparator system to train neural networks for supervised learning-based inspection by exception. It is shown that the volume of inspections can be reduced by as much as 82% and 93% using the unsupervised and supervised learning approaches, respectively

Development of a new machine learning-based informatics system for product health monitoring

Manufacturing informatics aims to optimize productivity by extracting information from numerous data sources and making decisions based on that information about the process and the parts being produced. Manufacturing processes usually include a series of costly operations such as heat treatment, machining, and inspection to produce high-quality parts. However, performing costly operations when the product conformance to specifications cannot be achievable is not desirable. This paper develops a new machine learning-based informatics system capable of predicting the end product quality so that non-value-adding operations such as inspection can be minimized and the process can be stopped before completion when the part being manufactured fails to meet the design specifications

A type-2 fuzzy modelling framework for aircraft taxi-time prediction

Knowing aircraft taxi-time precisely a-priori is increasingly important for any airport management system. This work presents a new approach for estimating and characterising the taxi-time of an aircraft based on historical information. The approach makes use of the interval type-2 fuzzy logic system, which provides more robustness and accuracy than the conventional type-1 fuzzy system. To compensate for erroneous modelling assumptions, the error distribution of the model is further analysed and an error compensation strategy is developed. Results, when tested on a real data set for Manchester Airport (U.K.), show improved taxi-time accuracy and generalisation capability over a wide range of modelling assumptions when compared with existing fuzzy systems and linear regression-based methods

Multi-objective fuzzy rule-based prediction and uncertainty quantification of aircraft taxi time

The ever growing air traffic demand and highly connected air transportation networks put considerable pressure for the sector to optimise air traffic management (ATM) related performances and develop robust ATM systems. Recent efforts made in accurate aircraft taxi time prediction have shown significant advancement in generating more efficient taxi routes and schedules, benefiting other key airside operations, such as runway sequencing and gate assignment. However, little study has been devoted to quantification of uncertainty associated with taxiing aircraft. Routes and schedules generated based on deterministic and accurate taxi time prediction for an aircraft may not be resilient under uncertainties due to factors such as varying weather conditions, operational scenarios and pilot behaviours, impairing system-wide performance as taxi delays can propagate throughout the network. Therefore, the primary aim of this paper is to utilise multi-objective fuzzy rule-based systems to better quantify such uncertainties based on historic aircraft taxiing data. Preliminary results reveals that the proposed approach can capture uncertainty in a more informative way, and hence represents a promising tool to further develop robust taxi planning to reduce delays due to uncertain taxi times

Real-time four-dimensional trajectory generation based on gain-scheduling control and a high-fidelity aircraft model

Aircraft ground movement plays a key role in improving airport efficiency, as it acts as a link to all other ground operations. Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations. Moreover, autonomous taxiing is envisioned as a key component in future digitalized airports. However, state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases. The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions. This paper proposes a new approach for generating efficient four-dimensional trajectories (4DTs) on the basis of a high-fidelity aircraft model and gain-scheduling control strategy. Working in conjunction with a routing and scheduling algorithm that determines the taxi route, waypoints, and time deadlines, the proposed approach generates fuel-efficient 4DTs in real time, while respecting operational constraints. The proposed approach can be used in two contexts: ① as a reactive decision support tool to generate new trajectories that can resolve unprecedented events; and ② as an autopilot system for both partial and fully autonomous taxiing. The proposed methodology is realistic and simple to implement. Moreover, simulation studies show that the proposed approach is capable of providing an up to 11% reduction in the fuel consumed during the taxiing of a large Boeing 747 jumbo jet