Academic literature on the topic 'Multiattribute object'

Pairwise comparison based multiattribute decision-making (MADM) methods are widely used and studied in recent years. However, the perception and cognition towards the semantic representation for the linguistic rating scale and the way in which the pairwise comparisons are executed are still open to discuss. The commonly used ratio scale is likely to produce misapplications and the matrix based comparison style needs too many comparisons and is not able to guarantee the consistency of the matrix when the number of objects involved is large. This research proposes a new MADM method CBWM (Cognitive Best Worst Method) which adopts interval scale to represent the pairwise difference and only compares each object to the best object and the worst object rather than all the other objects. CBWM is a vector based method which only needs 2n-3 pairwise comparisons and is more likely to generate consistent comparisons and reliable results. The theoretical analysis and a real world application demonstrate the effectiveness of CBWM.

Decision making is associated with ranking problems aimed to obtain a set of preference order of solutions. People can make mistakes choosing the best object for investments. Due to high cost of such mistakes, such a choice should be well founded. A major goal of paper is to develop a theoretical basis for creating a decision support system aimed to increase building construction and reconstruction investment efficiency by applying multiattribute decision making approaches and mathematical modelling. To achieve the goal, the following problems have to be solved: to analyse new models currently used in developing investment strategies in building construction and reconstruction, to make a classification of construction investment projects and to describe the stages of determining the efficiency of construction investments, to create a family of multiattribute decision methods to be used in the analysis of investment projects in building construction and reconstruction, to create multiple attribute decision support system based on the multiattribute methods developed for determining the efficiency of construction and reconstruction investment projects.

One of the key components of traditional seismic interpretation is to associate or “label” a specific seismic amplitude package of reflectors with an appropriate seismic or geologic facies. The object of seismic clustering algorithms is to use a computer to accelerate this process, allowing one to generate interpreted facies for large 3D volumes. Determining which attributes best quantify a specific amplitude or morphology component seen by the human interpreter is critical to successful clustering. Unfortunately, many patterns, such as coherence images of salt domes, result in a salt-and-pepper classification. Application of 3D Kuwahara median filters smooths the interior attribute response and sharpens the contrast between neighboring facies, thereby preconditioning the attribute volumes for subsequent clustering. In our workflow, the interpreter manually painted [Formula: see text] target facies using traditional interpretation techniques, resulting in attribute training data for each facies. Candidate attributes were evaluated by crosscorrelating their histogram for each facies with low correlation implying good facies discrimination, and Kuwahara filtering significantly increased this discrimination. Multiattribute voxels for the [Formula: see text] interpreter-painted facies were projected against a generative topographical mapping manifold, resulting in [Formula: see text] probability density functions (PDFs). The Bhattacharyya distance between the PDF of each unlabeled voxel to each of [Formula: see text] facies PDFs resulted in a probability volume of each user-defined facies. We have determined the effectiveness of this workflow to a large 3D seismic volume acquired offshore Louisiana, USA.

In today’s increasingly competitive market, consumers usually have to face a huge number of products with different designs but having the same use. Therefore, an important problem for manufacturers is to attract consumers by special designs of the products. This paper aims at the improvement of a consumer-oriented approach in recommending products, and proposing a recommendation system for Japanese traditional crafts based on target-oriented fuzzy method and ontological engineering. Specifically, a target-oriented fuzzy method is used for measuring the fitness of a selected attribute to a certain object. Two aggregation models for dealing with a multiattribute evaluation and ranking are introduced; four ranking methods are also examined for getting a recommendation list. To test the aggregation models and the ranking methods, a recommendation system was developed and a comparison test was conducted.

Abstract The coal mining industry is developing automated and intelligent coal mining processes. Accurate determination of the geological conditions of working faces is an important prerequisite for automated mining. The use of machine learning to extract comprehensive attributes from seismic data and the application of that data to determine the coal strata thickness has become an important area of research in recent years. Conventional coal strata thickness interpretation methods do not meet the application requirements of mines. Determining the coal strata thickness with machine learning solves this problem to a large extent, especially for issues of exploration accuracy. In this study, we use seismic exploration data from the Xingdong coal mine, with the 1225 working face as the research object, and we apply seismic multiattribute machine learning to determine the coal strata thickness. First, through optimal selection, we perform seismic multiattribute extraction and optimal multiparameter selection by selecting the seismic attributes with good responses to the coal strata thickness and extracting training samples. Second, we optimise the model through a trial-and-error method and use machine learning for training. Finally, we illustrate the advantages of this method using actual data. We compare the results of the proposed model with results based on a single attribute, The results show that application of seismic multiattribute machine learning to determine coal strata thickness meets the requirements of geological inspection and has a good application performance and practical significance in complex areas.

The current lack of a robust standardized technique for geophysical mapping of karst systems can be attributed to the complexity of the environment and prior technological limitations. Abrupt lateral variations in physical properties that are inherent to karst systems generate significant geophysical noise, challenging conventional seismic signal processing and interpretation. The application of neural networks (NNs) to multiattribute seismic interpretation can provide a semiautomated method for identifying and leveraging the nonlinear relationships exhibited among seismic attributes. The ambiguity generally associated with designing NNs for seismic object detection can be reduced via statistical analysis of the extracted attribute data. A data-driven approach to selecting the appropriate set of input seismic attributes, as well as the locations and suggested number of training examples, provides a more objective and computationally efficient method for identifying karst systems using reflection seismology. This statistically optimized NN technique is demonstrated using 3D seismic reflection data collected from the southeastern portion of the Florida carbonate platform. Several dimensionality reduction methods are applied, and the resulting karst probability models are evaluated relative to one another based on quantitative and qualitative criteria. Comparing the preferred model, using quadratic discriminant analysis, with previously available seismic object detection workflows demonstrates the karst-specific nature of the tool. Results suggest that the karst multiattribute workflow presented is capable of approximating the structural boundaries of karst systems with more accuracy and efficiency than a human counterpart or previously presented seismic interpretation schemes. This objective technique, using solely 3D seismic reflection data, is proposed as a practical approach to mapping karst systems for subsequent hydrogeologic modeling.

This paper is concerned with the uncertain multiattribute decision-making (UMADM) of which the attribute value is triangular fuzzy number. Firstly, the max-relative similarity degree and min-relative similarity degree of alternative decision-making objects are given based on the relative similarity degree of triangular fuzzy number, the advantage relation theories to comparative relative similarity degree of triangular fuzzy number are proposed, and some good properties, relations, and conclusions are derived. Secondly, in order to determine the attribute weight vector, a triangular fuzzy number-based decision-making object relative similarity programming model is established with the help of maximizing possibility degree algorithm rules in the cooperative game theory. Subsequently, by aggregating the comparison overall relative similarity degree values of all decision-making objects, we could pick over and sort the set of alternative objects and gather a new model algorithm for the relative similarity programming of triangular fuzzy number-based multiple attribute decision-making alternatives. Finally, an example is given to illustrate the feasibility and practicability of the model algorithm presented in this paper.

Current trends towards the Future Internet are envisaging the conception of novel services endowed with context-aware and autonomic capabilities to improve end users’ quality of life. The Internet of Things paradigm is expected to contribute towards this ambitious vision by proposing models and mechanisms enabling the creation of networks of “smart things” on a large scale. It is widely recognized that efficient mechanisms for discovering available resources and capabilities are required to realize such vision. The contribution of this work consists in a novel discovery service for the Internet of Things. The proposed solution adopts a peer-to-peer approach for guaranteeing scalability, robustness, and easy maintenance of the overall system. While most existing peer-to-peer discovery services proposed for the IoT support solely exact match queries on a single attribute (i.e., the object identifier), our solution can handle multiattribute and range queries. We defined a layered approach by distinguishing three main aspects: multiattribute indexing, range query support, peer-to-peer routing. We chose to adopt an over-DHT indexing scheme to guarantee ease of design and implementation principles. We report on the implementation of a Proof of Concept in a dangerous goods monitoring scenario, and, finally, we discuss test results for structural properties and query performance evaluation.

When dealing with cases, judges must consult a large number of relevant materials and carefully consider before they can write the final judgment. So, we want to use intelligent systems to assist the judicial system in handling cases. The essence of the system is automatic text classification. The system can predict the judgment result according to the previous prediction and can also provide support for judicial judgment and individual litigation. Because the evaluation of intelligent judicial decision-making system has the characteristics of complexity and fuzziness, we establish a comprehensive evaluation model of intelligent judicial decision-making system with subjective and objective combination by introducing the TOPSIS model. In the experiment, firstly, we use nine multiattribute comprehensive evaluation index systems such as acquisition cost and use cost to grade the indexes. Secondly, AHP and entropy weight methods are used to calculate the subjective weight and objective weight of the index, respectively; the combined weight of the index is determined according to the expert forced scoring method, the attribute measurement function of a single index is constructed according to the classification of the index, the comprehensive attribute measurement is calculated, and the comprehensive evaluation grade is judged according to the attribute identification standard. Finally, taking the intelligent judicial decision-making system as the research object, combined with the system report and expert score, this paper makes a multiattribute comprehensive evaluation and analysis of the intelligent judicial decision-making system and analyzes the results. The final experimental results show that the evaluation results of the model are reasonable and consistent with the actual situation, which verifies the adaptability of the combined weighted attribute recognition model in the multiattribute comprehensive evaluation of intelligent judicial judgment system. This result provides ideas and theoretical follow-up work for the intelligent judgment of judicial cases and has certain significance for the development of the field of judicial judgment.

Дисертація на здобуття наукового ступеня кандидата технічних наук (доктора філософії) за спеціальністю 05.13.06 "Інформаційні технології" (122 – Комп'ютерні науки). – Національний технічний університет "Харківський політехнічний інститут", Харків, 2017. Об'єкт дослідження – процес комплексного оцінювання багатоознакових об'єктів в задачах підтримки прийняття рішень. Предмет дослідження – моделі, методи та інформаційні технології комплексного оцінювання багатоознакових об'єктів в задачах підтримки прийняття рішень. Дисертацію присвячено вирішенню науково-практичної задачі – розробці моделей та інформаційної технології комплексного оцінювання складних об'єктів багатоознакової природи для підвищення обґрунтованості процесу прийняття рішень. У дисертаційній роботі вирішена актуальна науково-практична задача розробки моделей та інформаційної технології комплексного оцінювання складних об'єктів багатоознакової природи для підвищення обґрунтованості процесу прийняття рішень. Проаналізовано існуючі інформаційні технології, моделі та методи оцінювання складних об'єктів та процесів і сформульовано основні вимоги до розробки інформаційної технології комплексного оцінювання багатоознакових об'єктів. Розроблено моделі побудови множини первинних показників, агрегування показників та інтерпретації комплексної оцінки. Розроблено метод комплексного оцінювання багатоознакових об'єктів з використанням кваліметричної методики та методу послідовного агрегування показників. Удосконалено інформаційну технологію комплексного оцінювання багатоознакових об'єктів. Результати дослідження впроваджено в практику побудови підсистем оцінювання в системах підтримки прийняття рішень підприємств Харкова, Києва, а також у навчальний процес кафедри програмної інженерії та інформаційних технологій управління НТУ "ХПІ".
The dissertation for a candidate degree in technical sciences (PhD), specialty 05.13.06 "Information Technologies" (122 – Computer science). – National Technical University "Kharkiv Polytechnic Institute", Kharkiv, 2017. The object of the study is the process of multiattribute object comprehensive assessment in the tasks of decision-making support. The subject of research – models, methods and information technologies of of multiattribute object comprehensive assessment in the tasks of decision-making support. The dissertation is devoted to the solution of the scientific and practical problem - the development of models and information technology for complex objects of multisign nature comprehensive assessment to increase the validity of the decision-making process. Relevant scientific and practical task is solved in the thesis. The models and information technology of multiattribute object comprehensive assessment was developed in order to enhance the validity of the decision-making process. The existing information technologies, models and methods of multiattribute object comprehensive assessment were analyzed. Main requirements for the information technology of multiattribute object comprehensive assessment were designed. Models for the set of primary indicators, aggregation and interpretation of comprehensive assessment were developed. The method of multiattribute object comprehensive assessment was designed using qualimetric methods and method of indicators step-by-step aggregation. Information technology of multiattribute object comprehensive assessment was improved. Research results were implemented into the practice of assessment subsystems constructing in decision-making systems of Kharkiv and Kiev enterprises, as well as in the educational process of the Software engineering and information technology management department of NTU "KhPI".

Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.06 – інформаційні технології. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2018. У дисертаційній роботі вирішена актуальна науково-практична задача розробки моделей та інформаційної технології комплексного оцінювання складних об'єктів багатоознакової природи для підвищення обґрунтованості процесу прийняття рішень. Проаналізовано існуючі інформаційні технології, моделі та методи оцінювання складних об'єктів та процесів і сформульовано основні вимоги до розробки інформаційної технології комплексного оцінювання багатоознакових об'єктів. Розроблено моделі побудови множини первинних показників, агрегування показників та інтерпретації комплексної оцінки. Розроблено метод комплексного оцінювання багатоознакових об'єктів з використанням кваліметричної методики та методу послідовного агрегування показників. Удосконалено інформаційну технологію комплексного оцінювання багатоознакових об'єктів. Результати дослідження впроваджено в практику побудови підсистем оцінювання в системах підтримки прийняття рішень підприємств Харкова, Києва, а також у навчальний процес кафедри програмної інженерії та інформаційних технологій управління НТУ "ХПІ".
The dissertation for a candidate degree in technical sciences, specialty 05.13.06 – Information Technologies. – National Technical University "Kharkiv Polytechnic Institute", Kharkiv, 2018. Relevant scientific and practical task is solved in the thesis. The models and information technology of multiattribute object comprehensive assessment was developed in order to enhance the validity of the decision-making process. The existing information technologies, models and methods of multiattribute object comprehensive assessment were analyzed. Main requirements for the information technology of multiattribute object comprehensive assessment were designed. Models for the set of primary indicators, aggregation and interpretation of comprehensive assessment were developed. The method of multiattribute object comprehensive assessment was designed using qualimetric methods and method of indicators step-by-step aggregation. Information technology of multiattribute object comprehensive assessment was improved. Research results were implemented into the practice of assessment subsystems constructing in decision-making systems of Kharkiv and Kiev enterprises, as well as in the educational process of the Software engineering and information technology management department of NTU "KhPI".