Case-based Reasoning Technology Research Articles

On-line combustion optimization framework for coal-fired boiler combining improved cultural algorithm, deep learning, multi-objective evolutionary algorithm with improved case-based reasoning technology

It is important to gain the on-line combustion adjustment strategy under ensuring the running working condition of boiler unchanged. Because it can not only significantly improve the whole combustion environment of boiler, but can ensure the stability and safety of boiler in optimization. This article proposes a kind of new-style on-line combustion optimization framework for boiler combustion system, and this study is to quickly acquire the combustion adjustment strategies to improve the whole combustion environment of boiler under ensuring the operating working condition of boiler unchanged. First, improved culture algorithm based on improved particle swarm optimization and double mutation evolution strategy (CIPSODM) and long-short term memory neural network (LSTM) are combined to establish the boiler dynamic combustion model with self-adaptive capability. The improvement of CIPSODM involves two aspects: introducing clustering analysis to the population space of culture algorithm and adding the double mutation thought to the belief space of culture algorithm. And then domain maximum substitution strategy (DMS) is introduced into the decomposition-based multiple-objective evolutionary algorithm (DMS- D/MOEA) to obtain a great deal of combustion optimization cases offline, and an optimization cases base is established by gathering all optimization cases. Whereafter, mutation strategy is added to the case-based reasoning (MSCBR) to online obtain the combustion optimization adjustment strategy with the same running working condition as the current running working condition of the boiler for stable and safety combustion. Meanwhile, to illustrate the availability of the combination of CIPSODM-LSTM, DMS-D/ MOEA and MSCBR, several different on-line optimization approaches are adopted to acquire the combustion adjustment strategies online for comparison. The experiment results illustrated that the thermal efficiency could be increased by 0.588 % and NOx emission could be reduced 14.183 mg/m3 using proposed online combustion optimization approaches. Furthermore, the difference in operating load between adjustment strategy and current boiler reached the minimum value 0.696 MW in all optimization results. Consequently, proposed the on-line combustion optimization framework can gain the on-line combustion adjustment strategies to maximize thermal efficiency and decrease NOx emission so as to improve the whole running environment of boiler.

Emergency Response Plan for Spontaneous Combustion Based on Case-Based Reasoning

In order to avoid missing the best opportunity for emergency rescue in the event of spontaneous combustion and to prevent accidents from developing further, this paper proposed a method to generate an emergency response plan for spontaneous combustion based on case-based reasoning (CBR). Firstly, we adopted Hadoop big data retrieval technology to establish a case library for spontaneous combustion; then, our team applied CBR technology and introduced the differential determination symbol to calculate case similarity; furthermore, we quickly generated an emergency response plan for spontaneous combustion with the help of the Python program; and finally, we used a case to verify the effectiveness of the method. Overall, the results showed that the emergency response plan generated using this proposed method was consistent with the actual situation of the accident case and, compared with other relevant representative algorithms, the results in this paper were more accurate. In practice, this method may be helpful in providing support for generating emergency response plans for spontaneous combustion.

Quick prediction of seismic casualties using fuzzy Case-Based Reasoning technology

Rapid prediction of earthquake casualties is vital to improve the efficiency of emergency rescue and reduce social losses. Using the Delphi process, nine feature attributes affecting post-earthquake casualties are identified. Corresponding membership functions for the feature attributes are established based on fuzzy theory. The objective weights of feature attributes obtained from the entropy technology are applied to modify the subjective weights from Analytical Hierarchy Process (AHP). To expand the size of the case base, a new idea of collecting cases based on seismic intensity scenarios is proposed. A numerical experiment is carried out for all cases in the case base along the proposed fuzzy Case-Based Reasoning technical route. The average prediction error is only 14.93%.

Emergency Decision-Making for Middle Route of South-to-North Water Diversion Project Using Case-Based Reasoning and Prospect Theory

The middle route of the South-to-North Water Diversion Project is one of the crucial frameworks of China’s water network and an essential channel for water resource allocation in North China. The safe operation of the project has a huge impact on regional economic development, social stability and other aspects. The objectives of this research are to improve the disposal efficiency of all kinds of accidents during the operation of the Middle Route of the South-to-North Water Diversion Project, reduce people’s property losses and ensure the safety of water supply along the line. This paper will put forward a new emergency decision-making method based on case-based reasoning technology and prospect theory. The method is divided into two parts: (1) Collecting the historical case information and building the case library. The frame representation in the case-based reasoning technology is used to describe the characteristics of historical cases and adopt the two-level method of historical cases fast retrieval and similarity fuzzy matching retrieval to complete the preliminary selection of emergency plans; (2) The decision-making and optimization model of disposal plans based on prospect theory, namely, using the value function and probability weight classification to measure the prospect value of similar schemes and selecting the optimal disposal scheme, in order to improve the science and rationality of the decision-making results. Finally, examples are taken to verify the feasibility and effectiveness of the method.

REVIEW AND COMPARISON OF THE DEMAND ANALYSIS METHODS OF MARITIME EMERGENCY RESOURCES

The demand analysis method of maritime emergency resources is the key technology to promote a reasonable emergency resource allocation during maritime emergency management. It is widely used to improve the efficiency of maritime emergency rescue and reduce the loss of maritime accidents. However, it requires a scientific and effective method of the demand analysis of maritime emergency resources. This paper aims to analyze the underlying modeling paradigms and to assess the extent to which the demand analysis methods of maritime emergency resources can meet the requirements. Focusing on the demand analysis methods, this paper provides a broad overview of the current literature on maritime emergency resources of the last decades, by considering the models’ purposes, theoretical frameworks, factors, and outputs. The results indicate that the existing methods can be classified into three concepts: the linear regression theory, Back Propagation (BP) Neural Network, and Case-based Reasoning (CBR) technology. Combined with the characteristics of China's maritime emergency management field, the interaction between theoretical framework and applications is not sufficiently understood and thus needs to be further studied. Being familiar with knowledge gaps acts as a catalyst for further research on scientific and efficient demand analysis methods of maritime emergency resources in various navigation conditions.

Research on Key Technologies of Personalized Intervention for Chronic Diseases Based on Case-Based Reasoning.

In recent years, with the acceleration of industrialization, urbanization, and aging process, the number of patients with chronic diseases in the world is increasing year by year. In China, the number of chronic diseases has increased tenfold in 10 years. The percentage of the disease burden in the whole society accounts for 79.4%. Chronic diseases have become the top killer for Chinese people's health. However, for chronic diseases, prevention is more important than treatment. It is the best way to keep healthy. Therefore, health intervention is the key to prevent chronic diseases. Especially now, with the spread of COVID-19 pandemic, reducing the times of hospital check-ups and treatments for chronic patients is practically significant for releasing the stress on medical staffs and decreasing the rate of transmission and infection of COVID-19. In this paper, case-based reasoning (CBR) technology is used to assist personalized intervention for chronic diseases, and the key technologies of personalized intervention for chronic diseases based on case-based reasoning are proposed. The case organization, case retrieval, and case retention techniques of CBR technology in chronic disease personalized intervention are designed, and the calculation of interclass dispersion is added to the distribution of feature words, which is used to describe the distribution of feature attributes in different categories of cases. It provides an effective method for the establishment of personalized intervention model for chronic disease.

Fault Diagnosis Method for Certain Equipment Based on Case-based Reasoning

Absrtact: With the increasing complexity, information and intelligence of combat systems and weapons equipment, the traditional fault diagnosis technology can not meet the requirements of rapid and accurate fault diagnosis of equipment. In this paper, according to the fault characteristics and maintenance status of a certain type of equipment, combined with case-based reasoning technology, an equipment maintenance system which can realize intelligent query, case accumulation and fault reasoning is proposed. Finally, the feasibility of the method is proved by an example.

CBI + R: A Fusion Approach to Assist Dermatological Diagnoses

With the emerge of advanced technologies such as high-resolution cameras and computational power, it seems to ease to built a better dermatological diagnostic system. However, the identification of skin disease is still a challenging problem with the origination of various skin diseases. In this paper, we proposed a new fusion architecture — CBI [Formula: see text] R to support the diagnosis in multiple skin diseases. The architecture combines Content-Based Image Retrieval (CBIR) and Case-Based Reasoning (CBR) technology together to facilitate medical diagnosis. CBIR is to retrieve digital dermoscopy images from a data repository using the shape, texture and color features. Along with these features, CBR is incorporated which contains symptoms, case history and treatment plan of the disease. Experiments on a set of 1210 images yielded an accuracy of 98.2%. This was a superior retrieval and diagnosis performance in comparison with the state-of-the-art works.

Real-Time Intelligent Recognition Method for Horizontal Well Marker Bed

The accurate identification of the horizontal well marker bed is to guarantee the soft landing of the well trajectory. With the intelligent development of the petroleum industry, it is feasible to apply computers to identify the marker bed automatically. In case-based reasoning technology, the data of well logging while drilling (LWD) as characteristic parameters are compared with those of adjacent well. By taking the depth sequence of LWD data as time series and using Dynamic Time Warping (DTW) similarity measure algorithm, the similarity index of each drilling depth is calculated corresponding to the marker bed in the adjacent well. The total similarity curve is obtained by giving different weights of different feature parameters. Selecting natural gamma, deep resistivity, and shallow resistivity LWD curves as characteristic parameters, two horizontal wells in JL block of Junggar basin are analysed by this method. The result of similarity curve indicates the location of the marker bed and the total similarity value reaches 78%. The research shows that the method based on case-based reasoning can identify the marker bed of the horizontal well accurately and effectively, assist the geologist to carry out formation correlation of multiple wells at the same time, reduce the cost of human labour force, and improve work efficiency.

Problem Solving Strategy for Product Variant Design Combined with Improved Neural Network Method

Aiming at the fast solution of product variant design problem, a solving strategy combined with improved neural network method was proposed. Past similar cases were extracted as training samples through case-based reasoning (CBR) technology, empirical parameters were predicted based on neural network; computational parameters were solved by the existing calculation templates. The design results visualization was realized through parametric design. To reduce the influence of artificial sample construction on neural network training effect, the sample construction strategy of the multiple input/single output (MI/SO) structure was proposed to improve the parameter prediction quality. Finally, the feasibility and effectiveness of the proposed method was demonstrated by taking the design of a single-cylinder recuperator for example.

A Methodological Approach to Model CBR-Based Systems

Artificial intelligence (AI) has been used in various areas to support system optimization and find solutions where the complexity makes it challenging to use algorithmic and heuristics. Case-based Reasoning (CBR) is an AI technique intensively exploited in domains like management, medicine, design, construction, retail and smart grid. CBR is a technique for problem-solving and captures new knowledge by using past experiences. One of the main CBR deployment challenges is the target system modeling process. This paper presents a straightforward methodological approach to model CBR-based applications using the concepts of abstract and concrete models. Splitting the modeling process with two models facilitates the allocation of expertise between the application domain and the CBR technology. The methodological approach intends to facilitate the CBR modeling process and to foster CBR use in various areas outside computer science.

RESEARCH ON AUXILIARY DIAGNOSIS OF POWER COMMUNICATION FIELD OPERATION AND MAINTENANCE BASED ON CBR

Aiming at managing on-site operation and maintenance of power communication, based on the case-based reasoning (CBR) technology, an optimization method using GRNN neural network is proposed to improve CBR retrieval efficiency, which realize the self-learning and self-growth of an on-site problem diagnosis, and the problems of the traditional CBR algorithm such as low matching degree and slow convergence speed are effectively avoided. The simulation results show that the CBR retrieval algorithm based on the GRNN neural network has a high recall rate and precision rate, which can provide more credible auxiliary diagnosis cases for on-site operation and maintenance personnel and has strong practicability.

An IoMT-based geriatric care management system for achieving smart health in nursing homes

PurposeThe purpose of this paper is to develop an Internet of medical things (IoMT)-based geriatric care management system (I-GCMS), integrating IoMT and case-based reasoning (CBR) in order to deal with the global concerns of the increasing demand for elderly care service in nursing homes.Design/methodology/approachThe I-GCMS is developed under the IoMT environment to collect real-time biometric data for total health monitoring. When the health of an elderly deteriorates, the CBR is used to revise and generate the customized care plan, and hence support and improve the geriatric care management (GCM) service in nursing homes.FindingsA case study is conducted in a nursing home in Taiwan to evaluate the performance of the I-GCMS. Under the IoMT environment, the time saving in executing total health monitoring helps improve the daily operation effectiveness and efficiency. In addition, the proposed system helps leverage a proactive approach in modifying the content of a care plan in response to the change of health status of elderly.Originality/valueConsidering the needs for demanding and accurate healthcare services, this is the first time that IoMT and CBR technologies have been integrated in the field of GCM. This paper illustrates how to seamlessly connect various sensors to capture real-time biometric data to the I-GCMS platform for responsively supporting decision making in the care plan modification processes. With the aid of I-GCMS, the efficiency in executing the daily routine processes and the quality of healthcare services can be improved.

A Novel Text Retrieval Algorithm for Public Crisis Cases

Public crisis has the characteristics of suddenness and uncertainty, and it is necessary to combine the knowledge with the experience of other similar situations to make decisions effectively and quickly. This work combines artificial intelligent theory with information technology and brings case-based reasoning to build models consisting of the features of public crisis. We explore the case-representation approach and build a case-based retrieval algorithm. Combining the specificness of Case-based reasoning (CBR) technology in the monitoring of public crisis events, a new case retrieval algorithm for public crisis cases, named as Combined multi-similarity with set of simi-larity matching algorithm based on sememe (CMSBS), is proposed to analyze the cases with high similarity to current case. The CMSBS algorithm considers the structural and semantic similarities between two public crisis cases comprehensively. Simulation experiments are performed to validate the representation method of the knowledge, and the simulation results demonstrate that the CMSBS algorithm has superior performance in the average number of matching cases and matching accuracy rate and can work well in providing reference cases for subsequent events.

Product Lifecycle Management as Data Repository for Manufacturing Problem Solving.

Fault diagnosis presents a considerable difficulty to human operators in supervisory control of manufacturing systems. Implementing Internet of Things (IoT) technologies in existing manufacturing facilities implies an investment, since it requires upgrading them with sensors, connectivity capabilities, and IoT software platforms. Aligned with the technological vision of Industry 4.0 and based on currently existing information databases in the industry, this work proposes a lower-investment alternative solution for fault diagnosis and problem solving. This paper presents the details of the information and communication models of an application prototype oriented to production. It aims at assisting shop-floor actors during a Manufacturing Problem Solving (MPS) process. It captures and shares knowledge, taking existing Process Failure Mode and Effect Analysis (PFMEA) documents as an initial source of information related to potential manufacturing problems. It uses a Product Lifecycle Management (PLM) system as source of manufacturing context information related to the problems under investigation and integrates Case-Based Reasoning (CBR) technology to provide information about similar manufacturing problems.

A Decision Support System to Enhance Self-Management of Low Back Pain: Protocol for the selfBACK Project.

BackgroundLow back pain (LBP) is a leading cause of disability worldwide. Most patients with LBP encountered in primary care settings have nonspecific LBP, that is, pain with an unknown pathoanatomical cause. Self-management in the form of physical activity and strength and flexibility exercises along with patient education constitute the core components of the management of nonspecific LBP. However, the adherence to a self-management program is challenging for most patients, especially without feedback and reinforcement. Here we outline a protocol for the design and implementation of a decision support system (DSS), selfBACK, to be used by patients themselves to promote self-management of LBP.ObjectiveThe main objective of the selfBACK project is to improve self-management of nonspecific LBP to prevent chronicity, recurrence and pain-related disability. This is achieved by utilizing computer technology to develop personalized self-management plans based on individual patient data.MethodsThe decision support is conveyed to patients via a mobile phone app in the form of advice for self-management. Case-based reasoning (CBR), a technology that utilizes knowledge about previous cases along with data about the current patient case, is used to tailor the advice to the current patient, enabling a patient-centered intervention based on what has and has not been successful in previous patient cases. The data source for the CBR system comprises initial patient data collected by a Web-based questionnaire, weekly patient reports (eg, symptom progression), and a physical activity-detecting wristband. The effectiveness of the selfBACK DSS will be evaluated in a multinational, randomized controlled trial (RCT), targeting care-seeking patients with nonspecific LBP. A process evaluation will be carried out as an integral part of the RCT to document the implementation and patient experiences with selfBACK.ResultsThe selfBACK project was launched in January 2016 and will run until the end of 2020. The final version of the selfBACK DSS will be completed in 2018. The RCT will commence in February 2019 with pain-related disability at 3 months as the primary outcome. The trial results will be reported according to the CONSORT statement and the extended CONSORT-EHEALTH checklist. Exploitation of the results will be ongoing throughout the project period based on a business plan developed by the selfBACK consortium. Tailored digital support has been proposed as a promising approach to improve self-management of chronic disease. However, tailoring self-management advice according to the needs, motivation, symptoms, and progress of individual patients is a challenging task. Here we outline a protocol for the design and implementation of a stand-alone DSS based on the CBR technology with the potential to improve self-management of nonspecific LBP.ConclusionsThe selfBACK project will provide learning regarding the implementation and effectiveness of an app-based DSS for patients with nonspecific LBP.Registered Report IdentifierRR1-10.2196/9379

Application of case-based reasoning-Tabu search hybrid algorithm for rolling schedule optimization in tandem cold rolling

PurposeThe purpose of this study is to improve the global optimization ability of the Tabu search (TS) algorithm, and then improve the calculation efficiency and accuracy of rolling schedule in tandem cold rolling.Design/methodology/approachA case-based reasoning–Tabu search hybrid algorithm (CBRTS) has been presented. First, the case-based reasoning technology was adopted to obtain high-quality initial solution and then the TS algorithm was used for global optimization.FindingsThe optimization effect of CBRTS is compared with that of the traditional TS algorithm, and the analysis result indicates that the CBRTS has a faster convergence rate than TS, and the optimization results are closer to the global optimal. Meanwhile, the rolling schedule calculated by CBRTS is more reasonable, which can increase the production efficiency while giving full play to the capacity of equipment.Originality/valueA CBRTS hybrid algorithm is presented. The strong dependence of the TS algorithm on the initial solution has been solved. The rolling schedule multi-objective optimization functions are established. The proposed algorithm is applied in a 1,450-mm tandem cold rolling production line. The improved method can reduce about half the iterations compared with the traditional one.

Modeling of the functioning of territorial systems with the purpose of identification of problem situations

The purposeful territorial system (country, region), containing production, consumption, management, and environmental spheres is considered. In the process of system functioning, problem situations arise. The method for modeling the functioning of a territorial system by a complex of interacting linear programming problems, reflecting the inextricable link between the mechanism of centralized planning of production and consumption of goods, resources and the mechanism of market pricing is developed. The dependence of system characteristics of the cyclical dynamics on spectral properties of the generalized technological matrices of circulation of goods and resources is proved. The obtained results are relevant for modern Ukraine, other transitional national economies, characterized by an increased occurrence of problem situations. In this case, the territorial system can come very close to the bifurcation point, in which an alternative to further development arises. The choice of any alternative immerses the territorial system in the transition period. To identify problem situations, it is proposed to form the generalized technological matrixes of circulation of goods and resources on the basis of reporting data, to determine their majorizing roots that characterize the structural stability of the system. It is shown that the right and left eigenvectors found, corresponding to the majorizing matrix roots, determine the effective structures of supply of goods, resources and market prices for them. The system of informative features that determine the deviations of the actual characteristics of the territorial system from efficient ones is constructed. The proposed system of features makes it possible to detect national economic disparities that give rise to problem situations. Classes of problem situations are distinguished. The class contains problem situations for which the overall technology for their resolution is effective. This allowed assigning a standard case to each class that sets the reference problem situation and the way to resolve it (using an adequate pricing technology, adjusting the structure of the minimum needs of the population, the structure of available resources, forming the main properties of the system). The obtained results can serve as a platform for creating the information technology of detection and identification of problem situations, which implements the case-based reasoning technology.

Three dimensional movement control system applying CBR-technology

In this paper the basic aspects of elaborating the three dimensional movement control device are presented. This device is used if the movement control of the suspended object is required. A multipoint light emitter is firmly attached to the controlled object. A set of light receivers registering the light emitters’ signals determines the object location. The aspects of elaborating the three dimensional movement control system structure are presented. The system is elaborated upon the movement control device. Case-Based Reasoning technology is used to determine the location of the controlled subject.

Simultaneously simulate vertical and horizontal expansions of a future urban landscape: a case study in Wuhan, Central China

ABSTRACTWhile there are extensive studies of urban 2D forms, research on the varying geometric features and spatial distribution patterns of urban 3D spaces is comparatively rare. In this paper, we propose a coupled model, known as BPANN-CBRSortCA, which is based on a back propagation artificial neural network (BPANN) and case-based reasoning technology with sort cellular automaton (CBRSortCA) to simulate future urban building heights and their spatial distribution. BPANN–CBRSortCA uses BPANN to predict the vertical extrusion of building heights and uses CBRSortCA to simulate horizontal urban expansion. The BPANN–CBRSortCA model is innovative because of its capabilities to simultaneously project urban growth in the vertical and horizontal dimensions. The proposed model also overcomes the limitations of the traditional cellular automata models that cannot simulate ‘diffused’ urban expansion. This research used Wuhan City as a case study to simulate vertical and horizontal urban expansion from 2015 to 2025. The results showed the following: (1) in the next 10 years, new build-up will mainly appear along the edge of Hongshan and Hanyang Districts or will occupy bare land in the form of ‘filling’ and (2) the tallest buildings will be mainly located to the south of East Lake in Hongshan District and on undeveloped land within the city. These simulation results can provide a reference for future urban planning.