Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data

Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimisation problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty. Existing CP approaches that accommodate uncertainty are less suited to uncertainty arising due to incomplete and erroneous data, because they do not build reliable models and solutions guaranteed to address the user's genuine problem as she perceives it. Other fields such as reliable computation offer combinations of models and associated methods to handle these types of uncertain data, but lack an expressive framework characterising the resolution methodology independently of the model. We present a unifying framework that extends the CP formalism in both model and solutions, to tackle ill-defined combinatorial problems with incomplete or erroneous data. The certainty closure framework brings together modelling and solving methodologies from different fields into the CP paradigm to provide reliable and efficient approches for uncertain constraint problems. We demonstrate the applicability of the framework on a case study in network diagnosis. We define resolution forms that give generic templates, and their associated operational semantics, to derive practical solution methods for reliable solutions.Comment: Revised versio

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion

Ultrafast and Ultralight Network-Based Intelligent System for Real-time Diagnosis of Ear diseases in Any Devices

Traditional ear disease diagnosis heavily depends on experienced specialists and specialized equipment, frequently resulting in misdiagnoses, treatment delays, and financial burdens for some patients. Utilizing deep learning models for efficient ear disease diagnosis has proven effective and affordable. However, existing research overlooked model inference speed and parameter size required for deployment. To tackle these challenges, we constructed a large-scale dataset comprising eight ear disease categories and normal ear canal samples from two hospitals. Inspired by ShuffleNetV2, we developed Best-EarNet, an ultrafast and ultralight network enabling real-time ear disease diagnosis. Best-EarNet incorporates the novel Local-Global Spatial Feature Fusion Module which can capture global and local spatial information simultaneously and guide the network to focus on crucial regions within feature maps at various levels, mitigating low accuracy issues. Moreover, our network uses multiple auxiliary classification heads for efficient parameter optimization. With 0.77M parameters, Best-EarNet achieves an average frames per second of 80 on CPU. Employing transfer learning and five-fold cross-validation with 22,581 images from Hospital-1, the model achieves an impressive 95.23% accuracy. External testing on 1,652 images from Hospital-2 validates its performance, yielding 92.14% accuracy. Compared to state-of-the-art networks, Best-EarNet establishes a new state-of-the-art (SOTA) in practical applications. Most importantly, we developed an intelligent diagnosis system called Ear Keeper, which can be deployed on common electronic devices. By manipulating a compact electronic otoscope, users can perform comprehensive scanning and diagnosis of the ear canal using real-time video. This study provides a novel paradigm for ear endoscopy and other medical endoscopic image recognition applications.Comment: This manuscript has been submitted to Neural Network

On the use of periodic photothermal methods for materials diagnosis

This work aims the analysis of valuation methods devoted to materials diagnosis in order to provide an efficient estimation in practical operational conditions and environment (by the observation of a thermal tracer representative of a damage). The followed methodology consists in implementing observation techniques based on a periodic photo-thermal excitation so that the observation of the heated structure response allows to identify characteristic parameters of the studied materials. In most cases, simplistic hypotheses required for analytical model validation are not satisfied. Thus, analysis in the frequency domain requires the computing of a specific finite elements method

Fault diagnosis based on identified discrete-event models

International audienceFault diagnosis of Discrete-Event Systems consists of detecting and isolating the occurrence of faults within a bounded number of event occurrences. Recently, a new model for discrete-event system identification with the aim of fault detection, called Deterministic Automaton with Outputs and Conditional Transitions (DAOCT), has been proposed in the literature. The model is computed from observed fault-free paths, and represents the fault-free system behavior. In order to obtain compact models, loops are introduced in the model, which implies that sequences that are not observed can be generated leading to an exceeding language. This exceeding language is associated with possible non-detectable faults, and must be reduced in order to use the model for fault detection. After detecting the fault occurrence, its isolation is carried out by analyzing residuals. In this paper, we present a fault diagnosis scheme based on the DAOCT model. We show that the proposed fault diagnosis scheme is more efficient than other approaches proposed in the literature, in the sense that the exceeding language can be drastically reduced, reducing the number of non-detectable fault occurrences, and, in some cases, reducing also the delay for fault diagnosis. A practical example, consisting of a plant simulated by using a 3D simulation software controlled by a Programmable Logic Controller, is used to illustrate the results of the paper

Network tomography based on 1-D projections

Network tomography has been regarded as one of the most promising methodologies for performance evaluation and diagnosis of the massive and decentralized Internet. This paper proposes a new estimation approach for solving a class of inverse problems in network tomography, based on marginal distributions of a sequence of one-dimensional linear projections of the observed data. We give a general identifiability result for the proposed method and study the design issue of these one dimensional projections in terms of statistical efficiency. We show that for a simple Gaussian tomography model, there is an optimal set of one-dimensional projections such that the estimator obtained from these projections is asymptotically as efficient as the maximum likelihood estimator based on the joint distribution of the observed data. For practical applications, we carry out simulation studies of the proposed method for two instances of network tomography. The first is for traffic demand tomography using a Gaussian Origin-Destination traffic model with a power relation between its mean and variance, and the second is for network delay tomography where the link delays are to be estimated from the end-to-end path delays. We compare estimators obtained from our method and that obtained from using the joint distribution and other lower dimensional projections, and show that in both cases, the proposed method yields satisfactory results.Comment: Published at http://dx.doi.org/10.1214/074921707000000238 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Conceptual model of integrated apiarian consultancy

The socio-economic field researches have indicated the necessity of realizing an integrated consultancy service for beekeepers that will supply technical-economic solutions with a practical character for ensuring the lucrativeness and viability of the apiaries. Consequently, an integrated apiarian consultancy model has been built holding the following features: it realizes the diagnosis of the meliferous resources and supplies solutions for its optimal administration; it realizes the technical-economic of the apiarian exploitation adapted according to its objectives and identifies its optimal administration measures; it manages the local pollination services market; it realizes viable investment projects and ensures the management of their implementation; it elaborates aggregated indicators as efficient instruments of analysis and utilizes and informatics application of apiarian management used for realizing the specific objectives of the apiaries; it integrates the technical, economic and juridical consultancy service.Peer reviewe