Automatic Decision-making Research Articles

Statistical analysis of coronary blood flow monitoring data for hemodynamic assessment of the degree of coronary artery stenosis

Statistical relationships between the pressure curves Pa(t), Pd(t) and blood flow velocity Va(t), recorded in vivo in the coronary arteries of patients before and after stenosis, as part of the standard clinical procedure for calculating dynamic indices FFR, HSR, CFR, and a number of other ones generally accepted in surgical practice are studied. It is shown that in the case of insignificant stenosis that does not require surgical intervention, there is a correlation between the curves, and their spectrum is represented by three main harmonics. In the case of significant stenosis requiring immediate stenting, the positive correlation between Pa(t) and Pd(t) is less pronounced, and there is a negative correlation with the Va(t) curve. The spectrum of the curves is much more complex and contains high-frequency harmonics. For patients from the so-called “gray zone”, an expert decision on the need for stenting can be made based on the appearance of additional harmonics in the spectrum and a negative correlation between the Pa(t), Pd(t) and Va(t) curves. The proposed approach can be used for automatic decision-making based on machine learning and the development of appropriate mathematical models.

Continuous support for rehabilitation using machine learning

Abstract Providing a suitable rehabilitation at home after an acute episode or a chronic disease is a major issue as it helps people to live independently and enhance their quality of life. However, as the rehabilitation period usually lasts some months, the continuity of care is often interrupted in the transition from the hospital to the home. Relieving the healthcare system and personalizing the care or even bringing care to the patients’ home to a greater extent is, in consequence, the superior need. This is why we propose to make use of information technology to come to participatory design driven by users needs and the personalisation of the care pathways enabled by technology. To allow this, patient rehabilitation at home needs to be supported by automatic decision-making, as physicians cannot constantly supervise the rehabilitation process. Thus, we need computer-assisted patient rehabilitation, which monitors the fitness of the current patient plan to detect sub-optimality, proposes personalised changes for a patient and eventually generalizes over patients and proposes better initial plans. Therefore, we will explain the use case of patient rehabilitation at home, the basic challenges in this field and machine learning applications that could address these challenges by technical means.

The design and simulation of an autonomous system for aircraft maintenance scheduling

Operational support is a key issue for aircraft maintenance, which aims to improve operational efficiency and reduce operating costs under the premise of ensuring flight safety. Although many works have emerged to achieve this aim, they mostly address the concept of maintenance systems, the relationship between stakeholders and the loop of maintenance information separately. Hence, the cooperation between stakeholders could be impeded especially when urgent decisions should be made, relying on historical data and real-time data. In this paper, we propose an innovative design of an autonomous system supporting the automatic decision-making for maintenance scheduling. The design starts from the proposition of the analysis framework, to concept formulation of the system, to information transitional level interface, and ends with an instance of system module interactions. The underlying architecture illustrates the high-level fusion of technical and business drives; optimizes strategies and plans with regard to maintenance costs, service level and reliability. An agent-based simulation system is developed as a proof to illustrate the feasibility of the system principle and algorithms. Furthermore, the simulation experiment analyzing the impact of maintenance sequence strategies on maintenance costs and service level has demonstrated the algorithm functionality and the feasibility of the proposed approach.

Definition of a Framework for Acquiring and Acquisition Subprocesses in a Collective Knowledge Processing in the Integrated Management Information System

Efficient operation of the integrated management information systems (IMISs), especially multi-agent systems, is related to their ability to automatically process collective knowledge. On the basis of this knowledge the decision-making process is realized in the business organizations. This paper presents issues related to framework for acquiring and acquisition subprocesses in a collective knowledge of business organization processing in IMIS. The main novelty of the developed framework is the coverage of all the areas of operation of an organization. Additionally, the inter-area knowledge for automatic strategic-level decision-making has been taken into consideration. The main improvements of this framework are that it allows for processing of the whole collective knowledge of business organization and it can be directly implemented in IMIS.

Automated Estimation of Seismic Shaking Intensity from Instrumental Data in Quasi-Real-time Mode and Its Use in the Operation of the of Seismic Early Warning Service in the Kamchatka Region

Early warning systems are becoming increasingly important in the modern world. These systems combine several components: predictive systems (For example, tsunami warning systems), earthquake early warning systems, emergency message services, and systems of seismic damage monitoring. Information about shaking intensity becomes especially important in the case of a strong earthquake occurrence. These data are necessary for planning emergency rescue operations, but they are difficult to collect in a natural disasters situation because of possible communication problems. Application of data on instrumental seismic intensity may make it possible to solve this problem. Early warning systems predicting seismic intensity distributions just after the occurrence of an earthquake have already been developed in many seismically active regions of the world. Such a system also needs to be implemented in Kamchatka, where the strongest earthquakes can produce extremely high values of strong motion acceleration. As a result of the development of a system for seismological observation in Kamchatka, a unified specialized system for collection, transmission, archiving, and processing of seismic information was created. Seismological observations in Kamchatka were significantly improved with the update of the tsunami warning service in 2006–2011. As a result, a network of strong motion stations is currently operating in Kamchatka and can serve as a basis for creating a quasi-real-time seismic early warning system under the auspices the Kamchatka Branch of the Geophysical Survey, Russian Academy of Sciences (KB GS RAS). It uses data from strong motion stations to estimate the instrumental seismic intensity in quasi-real-time mode and visualizes the results. During the operational period while the service is being intensively used in the framework of the Seismic Early Warning Reports Tsunami Warning Service in the Kamchatka and Sakhalin branches of the GS RAS for real-time warning of interested parties about the shaking intensities at observation points, the technology implemented in this service has proved highly informative. In total, 75 messages on instrumental intensity in various places of Kamchatka krai and the northern Kuril Islands (Paramushir Islands) have been sent since the service was commissioned at the end of 2014. The currently operating version of the service has proved its informativeness and applicability for special departments of the Emergency Situations Ministry. In addition, real-time warning has improved coordination between the departments of KB GS RAS, and the results of this system are being used in a number of basic research projects. Further development of the service is related to the creation of denser instrumental networks to record strong ground motions and the transition to automatic decision-making and message sending.

About the problems and methods for finding of small size objects in the image

This work is devoted to the development of a new system of search and identification unmanned vehicle using a machine vision system, namely of guidance system. The problem of automatic aerial target recognition based on of the digital filtering streaming video and questions of informative signature formation. Machine vision systems are used for the treatment and recognition of different images obtained from a camera. A vision system is a means of tracking, monitoring and automatic decision-making in certain situations. This article discusses of design features of guidance system for unmanned aerial vehicle. The guidance system operates in real time. Is proposed detection algorithm of small-sized unmanned aerial vehicle. From the results obtained, generated control command for the guidance the unmanned aircraft to target. The main objective of the search algorithm and identification of air targets, there is a search for the reference point of the desired geometric object. To find the sought point of the contour, the used to group of digital filters for three channels of streaming video. Represented and implemented algorithm the block guidance when using frame difference. The proposed algorithm for finding of small objects can detect moving objects according to the optical sensor to the changing position of the optical sensor. The work concludes by demonstrating the proposed algorithm.

The searching algorithms and identification of reference points for aircraft

This work is devoted to the development of a new system of search and identification unmanned vehicle using a machine vision system, namely its takeoff and flight in offline mode.This article discusses the problem of automatic aerial target recognition based on of the digital filtering streaming video and questions of informative signature formation. Machine vision systems are used for the treatment and recognition of different images obtained from a camera. A vision system is a means of tracking, monitoring and automatic decision-making in certain situations. The survey conducted a series of tests monitoring and identification of the flight, with the aim of finding his contour lines and the development of targeted algorithms to create software module tracking aircraft. The main objective of the search algorithm and identification of air targets, there is a search for the reference point of the desired geometric object. To find the sought point of the contour, the used to group of digital filters for three channels of streaming video. To solve this problem, we used digital filters: Gauss filter, the local filter, the median filter, Sobel filter, the binary filter. The model includes the algorithm for finding reference points and allows us to identify specific geometric features (attributes) in real time. On the basis of mathematical modeling in Matlab algorithm for search and identification software module was created in the language C ++ DetectorScanAir, which tracks moving objects in the frame via WebCam Sony.

AUTOMATIC CONTROL OF CAMERA OPTICAL AXIS BASED ON VISION SYSTEM USING THE METHOD OF OBJECT RECOGNITION BY COLOR

Machine vision systems are used for the treatment and recognition of different images obtained from a camera. A vision system is a means of tracking, monitoring and automatic decision-making in certain situations.This work is dedicated to the development of a new system of automatic control pilotless vehicle using a machine vision system, namely its landing offline on a special sign / symbol, posted on the ground.The practical application of such systems: pilotless vehicle specified by coordinates reaches the final destination, using the camera (machine vision system) finds an appropriate symbol in place of the final position, recognizes it and goes to land; everything goes offline.Object detection and segmentation is the most important and challenging fundamental task of a computer (machine) vision. It is a critical part in many applications such as image search, scene understanding, etc. However, it is still an open problem due to the variety and complexity of object classes and backgrounds.The optimal way to detect and segment an object from an image is the color-based method. The object and the background should have a significant color difference in order to successfully segment objects using color based methods. This work for object detection based on color with using software Python 3, OpenCV 3, Web Camera, and microcontroller Raspberry Pi 2.On the microcontroller Raspberry Pi 2, we run a web server written in Python that provides a web interface, and which listens for commands over the WebSockets protocol. When it gets commands, it sends them onto the Mini Driver via serial USB. This is a custom Raspberry Pi camera program we’ve written that streams JPEG images over the network. It can also stream reduced size images (160×120) for computer vision.The efficiency of the system is confirmed by tests on the laboratory bench.

Evolutionary game dynamics of controlled and automatic decision-making.

We integrate dual-process theories of human cognition with evolutionary game theory to study the evolution of automatic and controlled decision-making processes. We introduce a model in which agents who make decisions using either automatic or controlled processing compete with each other for survival. Agents using automatic processing act quickly and so are more likely to acquire resources, but agents using controlled processing are better planners and so make more effective use of the resources they have. Using the replicator equation, we characterize the conditions under which automatic or controlled agents dominate, when coexistence is possible and when bistability occurs. We then extend the replicator equation to consider feedback between the state of the population and the environment. Under conditions in which having a greater proportion of controlled agents either enriches the environment or enhances the competitive advantage of automatic agents, we find that limit cycles can occur, leading to persistent oscillations in the population dynamics. Critically, however, these limit cycles only emerge when feedback occurs on a sufficiently long time scale. Our results shed light on the connection between evolution and human cognition and suggest necessary conditions for the rise and fall of rationality.

Understanding and shifting drug-related decisions: contributions of automatic decision-making processes.

While substance use is common, only a minority of individuals who use drugs or alcohol develop problematic use. An understanding of the factors underlying the transition from substance use to misuse may improve prevention and intervention efforts. A key feature of substance misuse is ongoing decisions to use drugs or alcohol despite escalating negative consequences. Research findings highlight the importance of both relatively automatic, associative cognitive processes and relatively controlled, deliberative, and rational-analytic cognitive processes, for understanding situational decisions to use drugs. In this review, we discuss several cognitive component processes that may contribute to decision-making that promotes substance use and misuse, with a focus on more automatic processes. A growing body of evidence indicates that relative differences in the strength of these component processes can account for individual differences in the transition from substance use to misuse and may offer important avenues for developing novel intervention strategies.

Trust-based decision-making for smart and adaptive environments

Abstract Smart environments are able to support users during their daily life. For example, smart energy systems can be used to support energy saving by controlling devices, such as lights or displays, depending on context information, such as the brightness in a room or the presence of users. However, proactive decisions should also match the users’ preferences to maintain the users’ trust in the system. Wrong decisions could negatively influence the users’ acceptance of a system and at worst could make them abandon the system. In this paper, a trust-based model, called User Trust Model (UTM), for automatic decision-making is proposed, which is based on Bayesian networks. The UTM’s construction, the initialization with empirical data gathered in an online survey, and its integration in an office setting are described. Furthermore, the results of a live study and a live survey analyzing the users’ experience and acceptance are presented.

Research on Temporal Fire Distribution of Shipboard-Gun Neutralization Firing Anti-Shore

In order to satisfy the need of automatic decision-making while shipboard-gun oppressive fire to the shore, This paper research the problem of shipboard-gun temporal fire distribution .This paper put forward the elements of oppressive fire distribution and solve out the calculation of every oppressive fire distribution elements(such as quick-firing’s quantity, surveillance firing’s quantity, every quick-firing’s ammunition wastage, every surveillance firing’s ammunition wastage and firing occasion). It could provide technical support for the realization of automatic decision-making while shipboard-gun suppression firing anti-shore.

Trust-based Decision-making for the Adaptation of Public Displays in Changing Social Contexts

Public displays may adapt intelligently to the social context, tailoring information on the screen, for example, to the profiles of spectators, their gender or based on their mutual proximity. However, such adaptation decisions should on the one hand match user preferences and on the other maintain the user’s trust in the system. A wrong decision can negatively influence the user’s acceptance of a system, cause frustration and, as a result, make users abandon the system. In this paper, we propose a trust-based mechanism for automatic decision-making, which is based on Bayesian Networks. We present the process of network construction, initialization with empirical data, and validation. The validation demonstrates that the mechanism generates accurate decisions on adaptation which match user preferences and support user trust.

Automatic decision-making and reliability in robotic systems: some implications in the case of robot weapons

In this article, I shall examine some of the issues and questions involved in the technology of autonomous robots, a technology that has developed greatly and is advancing rapidly. I shall do so with reference to a particularly critical field: autonomous military robotic systems. In recent times, various issues concerning the ethical implications of these systems have been the object of increasing attention from roboticists, philosophers and legal experts. The purpose of this paper is not to deal with these issues, but to show how the autonomy of those robotic systems, by which I mean the full automation of their decision processes, raises difficulties and also paradoxes that are not easy to solve. This is especially so when considering the autonomy of those robotic systems in their decision processes alongside their reliability. Finally, I would like to show how difficult it is to respond to these difficulties and paradoxes by calling into play a strong formulation of the precautionary principle.

Exposing Digital Image Forgeries by Illumination Color Classification

For decades, photographs have been used to document space-time events and they have often served as evidence in courts. Although photographers are able to create composites of analog pictures, this process is very time consuming and requires expert knowledge. Today, however, powerful digital image editing software makes image modifications straightforward. This undermines our trust in photographs and, in particular, questions pictures as evidence for real-world events. In this paper, we analyze one of the most common forms of photographic manipulation, known as image composition or splicing. We propose a forgery detection method that exploits subtle inconsistencies in the color of the illumination of images. Our approach is machine-learning-based and requires minimal user interaction. The technique is applicable to images containing two or more people and requires no expert interaction for the tampering decision. To achieve this, we incorporate information from physics- and statistical-based illuminant estimators on image regions of similar material. From these illuminant estimates, we extract texture- and edge-based features which are then provided to a machine-learning approach for automatic decision-making. The classification performance using an SVM meta-fusion classifier is promising. It yields detection rates of 86% on a new benchmark dataset consisting of 200 images, and 83% on 50 images that were collected from the Internet.

Ubiquitous computing in sports: A review and analysis

Ubiquitous (pervasive) computing is a term for a synergetic use of sensing, communication and computing. Pervasive use of computing has seen a rapid increase in the current decade. This development has propagated in applied sport science and everyday life. The work presents a survey of recent developments in sport and leisure with emphasis on technology and computational techniques. A detailed analysis on new technological developments is performed. Sensors for position and motion detection, and such for equipment and physiological monitoring are discussed. Aspects of novel trends in communication technologies and data processing are outlined. Computational advancements have started a new trend – development of smart and intelligent systems for a wide range of applications – from model-based posture recognition to context awareness algorithms for nutrition monitoring. Examples particular to coaching and training are discussed. Selected tools for monitoring rules' compliance and automatic decision-making are outlined. Finally, applications in leisure and entertainment are presented, from systems supporting physical activity to systems providing motivation. It is concluded that the emphasis in future will shift from technologies to intelligent systems that allow for enhanced social interaction as efforts need to be made to improve user-friendliness and standardisation of measurement and transmission protocols.

Automatic decision-making: Neural firing and response

In contrast with conscious decision-making, there are numerous subconscious decisions that we make without thinking about them. Some are biological and are made by different parts of our body to keep it alive and functioning normally. Others are a result of repetition and training that we can then do without thinking about them. The mathematics of conscious decisions with the Analytic Hierarchy and Network Processes is discrete, and has been discussed in the first three parts published on the subject. The mathematics of subconscious decisions is continuous. Here, we generalize the discrete mathematics of conscious decisions to the continuous case and develop it in some depth to apply to the synthesis of firings in the brain.

Feedback Control Applied to Survivability: A Host-Based Autonomic Defense System

We address the problem of information system survivability, or dynamically preserving intended functionality & computational performance, in the face of malicious intrusive activity. A feedback control approach is proposed which enables tradeoffs between the failure cost of a compromised information system and the maintenance cost of ongoing defensive countermeasures. Online implementation features an inexpensive computation architecture consisting of a sensor-driven recursive estimator followed by an estimate-driven response selector. Offline design features a systematic empirical procedure utilizing a suite of mathematical modeling and numerical optimization tools. The engineering challenge is to generate domain models and decision strategies offline via tractable methods, while achieving online effectiveness. We illustrate the approach with experimentation results for a prototype autonomic defense system which protects its host, a Linux-based web-server, against an automated Internet worm attack. The overall approach applies to other types of computer attacks, network-level security and other domains which could benefit from automatic decision-making based on a sequence of sensor measurements.

Moral Schemas and Tacit Judgement or How the Defining Issues Test is Supported by Cognitive Science

Ideas from cognitive science are increasingly influential and provide insight into the nature of moral judgement. Three core ideas are discussed: modern schema theory, the frequency of automatic decision-making and implicit processes as the default mode of human information processing. The Defining Issues Test (DIT) measures the beginnings of moral understanding, which are largely non-verbal and intuitive, in contrast to the Moral Judgement Interview (MJI), which measures the highest level of verbal understanding. The positive attributes of the DIT and its conceptualisation of moral judgement schemas are more apparent in a time of increasing respect for implicit knowledge and processing. The DIT offers a means of measuring moral judgement that fits with current views in cognitive science. Although the MJI and interview techniques generally are worthwhile for measuring production competence, the DIT is better able to measure understanding at the level that drives most decisions for most people.

Image Analyzer를 이용한 수삼등급의 자동판정 II. 수삼의 적변판정

본 연구는 현재 수삼수납 시 품질검사요원의 관능검사로 판정되고 있는 6년근수삼의 적변판정에 대하여 영상분석기를 이용한 수삼의 적변 자동판정 시스템을 개발하고자 수행하였다. 영상분석기에 의한 수삼 총면적에 대한 grey valuequf 면적비율을 조사하여 이에 대한 회귀 및 판별분석을 실시한 결과, 정상삼과 적변삼을 구별할 수 있는 임계값은 총 면적에 대한 grey value 148에서의 면적비율(G148)로 분석되었으며, 임계값인 G(148)을 이용하여 다음과 같은 판별식을 구하였다. 적변삼의 판별식 : 6.67951<TEX>$\times$</TEX>G(148)+3.74122, 정상삼의 판별식 : 2.85942<TEX>$\times$</TEX>G(148)+9.95612, 본 판별식의 적중율은 89.8%로 이는 앞으로 계속적인 연구로 적변삼의 판정 적중율을 증가시킬 수 있을 것인 바, 컴퓨터 등 기계에 의한 적변 자동판정의 가능성은 매우 클 것으로 사료된다. This study was undertaken to evaluate the automatic decision-making on the rusty root of fresh ginseng (Panax ginseng C.A. Meyer) by an image analyzer. Critical value of rusty root of ginseng by image analyzing was the percentage of grey value 0∼148 area (G 148) to the total area of grey value 0∼255. And the discriminant formula of rusty root of ginseng as follows; rusty root of ginseng : 6.68<TEX>$\times$</TEX>G(148) +3.74, normal ginseng : 2.86<TEX>$\times$</TEX>G(148) +9.96, and fitness rates of this formula were 89.8%. Also, we developed the automatic rusty root of decision-making program. As the result of this study, the automatic decision-making on the rusty root of fresh ginseng by an image analyzer seems to have high possibility.