Two Routes to Trust Calibration

Trust calibration takes place prior to and during system interaction along the available information. In an online study N = 519 participants were introduced to a conditionally automated driving (CAD) system and received different a priori information about the automation's reliability (low vs high) and brand of the CAD system (below average vs average vs above average reputation). Trust was measured three times during the study. Additionally, need for cognition (NFC) and other personality traits were assessed. Both heuristic brand information and reliability information influenced trust in automation. In line with the Elaboration Likelihood Model (ELM), participants with high NFC relied on the reliability information more than those with lower NFC. In terms of personality traits, materialism, the regulatory focus and the perfect automation scheme predicted trust in automation. These findings show that a priori information can influence a driver's trust in CAD and that such information is interpreted individually.

PROCEDURE FOR FORMING AN EFFECTIVE SELECTION PROCESS RATIONAL REHABILITATION MEASURES FOR TREATMENT GYNECOLOGICAL DISEASES

В статье рассматривается ряд методов интеллектуальной поддержки принятия решений в диагностике и лечении гинекологических заболеваний. При применении высоких медицинских технологий возрастает роль лечащего врача (ЛВ), который по-прежнему остается лицом, принимающим решение (ЛПР). Однако применение этих методов принятия решений должно увязываться с логикой деятельности ЛВ, быть доступно для практического использования, освобождать ЛВ от "рутинной" работы и способствовать целенаправленному и эффективному лечебно-диагностическому процессу. Процессы диагностики и лечения гинекологических заболеваний характеризуются большим числом переменных и определяются индивидуальными характеристиками пациенток, рядом неопределенности при выборе тактики лечения. Процедура математического описания процессов лечения состоит из выбора метода моделирования в условиях неоднородностей. Выбор тактики лечения сводится к поиску эффективных алгоритмов, индивидуализирующих особенности каждой больной в процессе лечения, и позволяет врачу на основе опыта и интуиции принимать адекватные решения в любой момент времени. Вот почему, прежде всего, при выборе рациональных реабилитационных мероприятий в условиях неполной априорной информации требуется интеллектуальная поддержка принимаемых решений ЛВ. Для интеллектуальной поддержки принимаемых решений применяются методы имитационного эксперимента, основанные на априорной информации лечащего врача и эксперта для организации и алгоритмизации диалогового режима в ускоренном и реальном масштабе времени. Рассматривается алгоритмическая процедура процессов лечения The article discusses a number of methods of intellectual support for decision-making in the diagnosis and treatment of gynecological diseases. With the use of high medical technologies, the role of the attending physician (PD) increases, who still remains a decision-maker (DM). However, the application of these decision-making methods should be linked to the logic of the drug's activity, be available for practical use, free the drug from "routine" work and contribute to a purposeful and effective treatment and diagnostic process. The processes of diagnosis and treatment of gynecological diseases are characterized by a large number of variables and are determined by the individual characteristics of patients, a number of uncertainties in the choice of treatment tactics. The procedure for the mathematical description of treatment processes consists of the choice of a modeling method under conditions of inhomogeneities. The choice of treatment tactics is reduced to the search for effective algorithms that individualize the characteristics of each patient in the treatment process, and allows the doctor, based on experience and intuition, to make adequate decisions at any time. That is why, first of all, when choosing rational rehabilitation measures in conditions of incomplete a priori information, intellectual support for the decisions made by the dispensary is required. For the intellectual support of the decisions made, the methods of the simulation experiment are used, based on the a priori information of the attending physician and the expert for the organization and algorithmization of the dialogue mode in accelerated and real time. An algorithmic procedure for treatment processes is considered

Interpretation of Magnetic Anomalies over 2D Fault and Sheet-Type Mineralized Structures Using Very Fast Simulated Annealing Global Optimization: An Understanding of Uncertainty and Geological Implications

Identification of intraterrane dislocation zones and associated mineralized bodies is of immense importance in exploration geophysics. Understanding such structures from geophysical anomalies is challenging and cumbersome. In the present study, we present a fast and competent algorithm for interpreting magnetic anomalies from such dislocation and mineralized zones. Such dislocation and mineralized zones are well explained from 2D fault and sheet-type structures. The different parameters from 2D fault and sheet-type structures such as the intensity of magnetization (k), depth to the top (z1), depth to the bottom (z2), origin location (x0), and dip angle (θ) of the fault and sheet from magnetic anomalies are interpreted. The interpretation suggests that there is uncertainty in defining the model parameters z1 and z2 for the 2D inclined fault; k, z1, and z2 for the 2D vertical fault and finite sheet-type structure; and k and z for the infinite sheet-type structure. Here, it shows a wide range of solutions depicting an equivalent model with smaller misfits. However, the final interpreted mean model is close to the actual model with the least uncertainty. Histograms and crossplots for 2D fault and sheet-type structures also reveal the same. The present algorithm is demonstrated with four theoretical models, including the effect of noises. Furthermore, the investigation of magnetic data was also applied from three field examples from intraterrane dislocation zones (Australia), deep-seated dislocation zones (India) as a 2D fault plane, and mineralized zones (Canada) as sheet-type structures. The final estimated model parameters are in good agreement with the earlier methods applied for these field examples with a priori information wherever available in the literature. However, the present method can simultaneously interpret all model parameters without a priori information.

Multi-Frequency GPR Microwave Imaging of Sparse Targets through a Multi-Task Bayesian Compressive Sensing Approach

An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data by jointly processing the multi-frequency (MF) spectral components of the collected radargrams. On the other hand, it enforces sparsity priors on the problem unknowns to yield regularized solutions of the fully non-linear scattering equations. Towards this end, a multi-task Bayesian compressive sensing (MT-BCS) methodology is adopted and suitably customized to take full advantage of the available frequency diversity and of the a-priori information on the class of imaged targets. Representative results are reported to assess the proposed MF-MT-BCS strategy also in comparison with competitive state-of-the-art alternatives.

A Backward Monte Carlo method for fast-ion-loss simulations

This paper presents a novel scheme to improve the statistics of simulated fast-ion loss signals and power loads to plasma-facing components in fusion devices. With the so-called Backward Monte Carlo method, the probabilities of marker particles reaching a chosen target surface can be approximately traced from the target back into the plasma. Utilizing the probabilities as {\it a priori} information for the well-established Forward Monte Carlo method, statistics in fast-ion simulations are significantly improved. For testing purposes, the scheme has been implemented to the ASCOT suite of codes and applied to a realistic ASDEX Upgrade configuration of beam-ion distributions.

Magnetic Target Position Estimation Method Based on Prior Information

Aiming at the problem of detection and location of magnetic targets in water beach, the acoustic magnetic composite detection method is studied. After the sonar obtains the image of the suspicious object in the target area, the magnetic target recognition and location are realized by using the abnormal magnetic field distribution data near the target area measured by the shipborne magnetic sensor and the multi-sensor information fusion method. A target recognition and location method based on a priori information is proposed to solve the problem that the measurement results of magnetic sensor can not fully reflect the influence of ferromagnetic target on the surrounding magnetic field due to terrain constraints. In order to make up for this lack of information, taking the sonar measurement results as a priori information, the hypothesis test method is adopted to make full use of all the measurement results of different types of sensors to realize the recognition and positioning of magnetic targets.

Inverse Problems and Artificial Intelligence

В данной работе приведен анализ взаимосвязей теории обратных и некорректных задач и математических аспектов искусственного интеллекта. Показано, что при анализе вычислительных алгоритмов, которые условно можно отнести к вычислительному искусственному интеллекту (машинное обучение, природоподобные алгоритмы, методы анализа и обработки данных), возможно, а подчас и необходимо, использовать результаты и подходы, развитые в теории и численных методах решения обратных и некорректных задач, такие как регуляризация, условная устойчивость и сходимость, использование априорной информации, идентифицируемость, чувствительность, усвоение данных. This paper analyzes the relationship between the theory of inverse and incorrect problems and the mathematical aspects of artificial intelligence. It is shown that computational algorithms that can be categorized as computational artificial intelligence (machine learning, nature-like algorithms, data analysis and processing) can or should be analyzed with the approaches developed for the theory and numerical methods for solving inverse and incorrect problems. They are regularization, conditional stability and convergence, the use of a priori information, identifiability, sensitivity, data assimilation.

Modified simplified algorithm of the method of group consideration of arguments in simulation processes

There are many types and methods of simulation, but among them special attention should be paid to methods based on the theory of heuristic self-organization. All algorithms of the method of group argumentation (MGVA) are characterized by structural commonality on the principle of self-organization, which require insignificant requirements for a priori information to search for an infinite number of options. The advantage of the algorithm of the method of group consideration of arguments in comparison with other algorithms of this class is the presence of possibilities of expansion of the vector of initial data and the device for elimination of collinearity - reception of orthogonalization. MGVA consists of two blocks: pre-processing of observations taking into account the system of selected reference functions and calculation of selection applicants. As a result of the algorithm, models capable of controlling the process taking into account the phenomena accompanying a certain process are obtained. Given the commonality of the main provisions of the theory of self-organization of artificial neural networks and MGVA, the network variables are added to the model as a variable Z. As a result, we obtain a neural network that describes the physical phenomena accompanying the process. This will significantly increase the efficiency and accuracy of process management.

Planning of technical objects and systems

There are many types and methods of simulation, but among them special attention should be paid to methods based on the theory of heuristic self - organization. All algorithms of the method of group argumentation (MGVA) are characterized by structural commonality on the principle of self - organization, which require insignificant requirements for a priori information to search for an infinite number of options. The advantage of the algorithm of the method of group consideration of arguments in comparison with other algorithms of this class is the presence of possibilities of expansion of the vector of initial data and the device for elimination of collinearity - reception of orthogonalization. MGVA consists of two blocks: pre - processing of observations taking into account the system of selected reference functions and calculation of selection applicants. As a result of the algorithm, models capable of controlling the process taking into account the phenomena accompanying a certain process are obtained. Given the commonality of the main provisions of the theory of self - organization of artificial neural networks and MGVA, the network variables are added to the model as a variable Z. As a result, we obtain a neural network that describes the physical phenomena accompanying the process. This will significantly increase the efficiency and accuracy of process management.