Combines Case-based Reasoning Research Articles

Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning

Construction projects are highly risk-prone due to both internal factors (e.g., organizational, contractual, project, etc.) and external factors (e.g., environmental, economic, political, etc.). Construction risks can thus have a direct or indirect impact on project objectives, such as cost, time, safety, and quality. Identification of these risks is crucial in order to fulfill project objectives. Many tools and techniques have been proposed for risk identification, including literature review, questionnaire surveys, and expert interviews. However, the majority of these approaches are highly reliant on expert knowledge or prior knowledge of the project. Therefore, the application of such tools and techniques in risk identification for renewable energy projects (e.g., wind farm and solar power plant projects) is challenging due to their novelty and the limited availability of historical data or literature. This paper addresses these challenges by introducing a new risk identification framework for renewable energy projects, which combines case-based reasoning (CBR) with fuzzy logic. CBR helps to solve problems related to novel projects (e.g., renewable energy projects) based on their similarities to existing, well-studied projects (e.g., conventional energy projects). CBR addresses the issue of data scarcity by comparing novel types of construction projects to other well-studied project types and using the similarities between these two sets of projects to solve the different problems associated with novel types of construction projects, such as risk identification of renewable energy projects. Moreover, the integration of fuzzy logic with CBR, to develop fuzzy case-based reasoning (FCBR), increases the applicability of CBR in construction by capturing the subjective uncertainty that exists in construction-related problems. The applicability of the proposed framework was tested on a case study of an onshore wind farm project. The objectives of this paper are to introduce a novel framework for risk identification of renewable energy projects and to identify the risks associated with the construction of onshore wind farm projects at the work package level. The results of this paper will help to improve the risk management of renewable energy projects during the construction phase.

Application of the CBR method for aiding the process of manipulators designing

The article presents a method based on knowledge and engineering experience for adding the process of manipulators designing. The method, proposed by the authors, is based on a hybrid structure that combines Case Based Reasoning (CBR) and the advisory system (AS). CBR is a problem-solving method that involves searching for similarities between the currently solved task and earlier cases that, appropriately described, are stored in the computer’s memory. The advisory system is based on the knowledge gained from the experts. In the presented article, the authors have presented the structure of the developed system, the algorithm and the formal description of functioning of the application for computer aided designing of manipulators. Also, the method of functioning of the program is illustrated on the example of the manipulators designing using Festo and Item modules.

Case-based Reasoning for Design Patterns Searching System

patterns are graceful solutions to specific software design problems. However, choosing proper design patterns for given software design problems might not be an easy task especially for novice developers. The design patterns searching tools are therefore needed to solve the problem. One major problem of the existing researches in this field is the indexing problem. This paper aims to solve the problem by presenting an elegant design pattern searching model that combines Case Based Reasoning (CBR) and Formal Concept Analysis (FCA) techniques. This model proposes a newly refinement technique. The technique allows experts to organize indexes to gain more complete software problem description in order to retrieve more appropriate design patterns. The indexes and cases similarity is calculated using FCA. The learning model to store new knowledge for retention process is also provided. Mean Average Precision (MAP) is used to assess the performance of the model. The preliminary experimental results show that the presented model has more retrieval ability in term of MAP comparing to the traditional model of CBR.

Combination of case-based reasoning and analytical hierarchy process for providing intelligent decision support for product recycling strategies

Waste electric and electronic equipment (WEEE) may cause environmental problems during the waste management phase if it is not designed to be environment friendly. Many governments have made considerable efforts to frame policies to reduce the use of hazardous materials and improve a product’s recyclability. In this research, recyclability parameters are introduced to represent a product component’s recyclability and to aid a designer in evaluating the component’s recyclability. The recyclability index is defined as a measure of the ability of a material to regain its valued properties when subjected to a special recycling process. A method is proposed to determine the recyclability of a material during the design phase. This research method combines case-based reasoning (CBR) and the analytical hierarchy process to simplify the calculation of the recyclability index for a product. Designers can extract experiences from past cases to (1) calculate the recyclability rate of a designed product, (2) determine the possibility of recycling end-of-life (EOL) products, and (3) finalise a recycling plan for EOL products when including a new product in a recycling and treatment system.

Autonomic self healing and recovery informed by environment knowledge

An important goal of autonomic computing is the development of computing systems that are capable of self healing with a minimum of human intervention. Typically, recovery from even a simple fault will require knowledge of the environment in which a computing system operates. To meet this need, we present an approach to self healing and recovery informed by environment knowledge that combines case based reasoning (CBR) and rule based reasoning. Specifically, CBR is used for fault diagnosis and rule based reasoning for fault remediation, recovery, and referral. We also show how automated information gathering from available sources in a computing system's environment can increase problem solving efficiency and help to reduce the occurrence of service failures. Finally, we demonstrate the approach in an intelligent system for fault management in a local printer network.

PREDICTING INFLUENZA WAVES WITH HEALTH INSURANCE DATA

In recent years, many of the most developed countries have started to create influenza surveillance systems, because influenza still is very costly, not just concerning the health systems, but also economically. In most of these systems statistical methods are applied, unfortunately with rather moderate results. In contrast to statistical methods case‐based reasoning explicitly uses former episodes. Because we already successfully applied our prognostic method, which combines case‐based reasoning (CBR) with temporal abstraction, to kidney functions, we use it again to forecast influenza. Because health centers collect extensive laboratory data but their availability is usually delayed for at least two weeks, we use quickly available data from the main German health insurance scheme. In this article, we propose the use of CBR for influenza forecast and we show promising results.

A new hybrid case-based architecture for medical diagnosis

This paper proposes a new hybrid case-based architecture, which supports multiple-disease diagnosis and the learning of new adaptation knowledge. The architecture combines case-based reasoning (CBR), neural networks, fuzzy theory, induction, utility theory, and knowledge-based planning technology together to facilitate medical diagnosis. The basic mechanism is that of CBR. A distributed fuzzy neural network is employed to perform approximate matching and thus to tolerate potential noise in case retrieval. The induction technology along with utility theory is used to select valuable features of the target case and prune unnecessary search space. Knowledge-based planning is a general-purpose mechanism for case adaptation. It creates a case adaptation plan from an adaptation tree, which contains all relevant problem features, satisfies all relevant constraints, and contains all cases whose expected utilities are greater than a threshold. Execution of the case adaptation plan leads to the diagnosis of multiple diseases. The adaptation tree facilitates the reuse of cases and the learning of various types of knowledge-including relationships between disease types and features, case-specific verification knowledge, and differential diagnosis rules. Integrating these techniques in the CBR paradigm can effectively produce a high-quality diagnosis for a given medical consultation.

Knowledge architecture and system design for intelligent operation support systems

Intelligent operation support systems emerged from the complexity of modern industrial plants and the availability of inexpensive computer hardware. Modern industrial plants often collect vast amount of process data in distributed control systems and management information systems. Time stress due to data overload and decision uncertainty increases the risk of operator errors. Comprehensive operation support to the operator in abnormal situations to reduce operator errors is strongly suggested. The solution to this problem is to design intelligent operation support systems that reduce the cognitive load placed on operators by providing guidance for knowledge-based decision making. This paper presents an intelligent operation support system (IOSS) structure using rich knowledge representation and hybrid reasoning strategy. The functional requirements and desired features for IOSS are defined. The human operator's recognition behavior is analyzed. It is shown that a hybrid reasoning environment that combines case-based reasoning (CBR), model-based reasoning (MBR) and rule-based reasoning (RBR) is consistent with operator's problem solving. A multidimensional problem solving model is proposed to incorporate these requirements and human recognition behavior. The IOSS is designed by using the problem solving model as the guide.

Intelligent control system for process monitoring

On-line monitoring and fault diagnosis based on artificial intelligence (AI) technology is an important Al application. Hazardous accidents and equipment failures always result in environmental pollution and poor product quality, and jeopardize the safety of equipment and human resources. As a result, accidents cost money, decrease productivity and profits. In this article, a generic integrated distributed intelligent multimedia system architecture for process monitoring, fault diagnosis and maintenance is discussed. The architecture consists of four individual modules: data calibration, condition monitoring, fault diagnosis, and maintenance assistance. It integrates different AI techniques such as heuristic reasoning, case-based reasoning and hypermedia. An industrial application using the architecture is presented which is used to implement an intelligent operation support system (IPMS) using rich knowledge representation and hybrid reasoning strategy. The functional requirements and desired features for IPMS are defined. The human operator's recognition behavior is analyzed. It is shown that a hybrid reasoning environment that combines case-based reasoning (CBR), model-based reasoning (MBR) and rule-based reasoning (RBR) is consistent with operator's problem solving. A multidimensional problem solving model is proposed to incorporate these requirements and human recognition behavior. IPMS is designed by using the problem solving model as the guide. The implementation of IPMS in a pulp production process is presented.