Ning Xiong

Multi-sensor management for information fusion: issues and approaches

Abstract Multi-sensor management concerns the control of environment perception activities by managing or coordinating the usage of multiple sensor resources. It is an emerging research area, which has become increasingly important in research and development of modern multi-sensor systems. This paper presents a comprehensive review of multi-sensor management in relation to multi-sensor information fusion, describing its place and role in the larger context, generalizing main problems from existing application needs, and highlighting problem solving methodologies.

Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments

The health sciences are, nowadays, one of the major application areas for case-based reasoning (CBR). The paper presents a survey of recent medical CBR systems based on a literature review and an e-mail questionnaire sent to the corresponding authors of the papers where these systems are presented. Some clear trends have been identified, such as multipurpose systems: more than half of the current medical CBR systems address more than one task. Research on CBR in the area is growing, but most of the systems are still prototypes and not available in the market as commercial products. However, many of the projects/systems are intended to be commercialized.

A Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching

Stress diagnosis based on finger temperature (FT) signals is receiving increasing interest in the psycho‐physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret, and analyze complex, lengthy sequential measurements to make a diagnosis and treatment plan. The paper presents a case‐based decision support system to assist clinicians in performing such tasks. Case‐based reasoning (CBR) is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the CBR system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty‐nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness‐of‐fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR is a valuable approach in domains, where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable, where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho‐physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.

Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system

OBJECTIVE An important procedure in diagnosing stress-related disorders caused by dysfunction in the interaction of the heart with breathing, i.e., respiratory sinus arrhythmia (RSA), is to analyse the breathing first and then the heart rate. Analysing these measurements is a time-consuming task for the diagnosing clinician. A decision-support system in this area would reduce the analysis task of the clinician and enable him/her to give more attention to the patient. We have created a decision-support system which contains a signal classifier and a pattern identifier. The system performs an analysis of the physiological time series concerned which would otherwise be performed manually by the clinician. METHODS The signal-classifier, HR3Modul, classifies heart-rate patterns by analysing both cardio- and pulmonary signals, i.e., physiological time series. HR3Modul uses case-based reasoning (CBR), using a wavelet-based method for retrieving features from the signals. The system searches for familiar shapes in the signals by comparing them with shapes already stored. We have applied a best fit scheme for handling signals of different lengths, as the length of a breath is highly dynamic. We also apply automatic weighting to the features to obtain a more autonomous system. The classified heart signals indicate if a patient may be suffering from a stress-related disorder and the nature of the disorder. These classified signals are thereafter sent to the second subsystem, the pattern-identifier. The pattern-identifier analyses the classified signals and searches for familiar patterns by identifying sequences in the classified signals. The identified sequences give clinicians a more complete analysis of the measurements, providing them with a better basis for diagnosis. RESULTS AND CONCLUSION We have shown that a case-based classifier with a wavelet feature extractor and automatic weighting is a viable option for building a decision-support system for the psychophysiological domain, as it is at par, or even outperforms other retrieval techniques and is less complex.

Reduction of fuzzy control rules by means of premise learning - method and case study

Rule number reduction is important for fuzzy control of complex processes with high dimensionality. It is stated in the paper that this issue can be treated effectively by means of learning premises with general structure. Since conditions of rules are generalised by a genetic algorithm (GA) rather than enumerated according to every AND-connection of input fuzzy sets, a parsimonious knowledge base with a reduced number of rules can be expected. On the other hand, to give a numerical evaluation of possible conflicts among rules, a consistency index of the rule set is established. This index is integrated into the fitness function of the GA to search for a set of optimal rule premises yielding not only good control performance but also little or no inconsistency in the fuzzy knowledge base. The advantage of the proposed method is demonstrated by the case study of development of a compact fuzzy controller to balance an inverted pendulum in the laboratory.

CASE‐BASED REASONING AND KNOWLEDGE DISCOVERY IN MEDICAL APPLICATIONS WITH TIME SERIES

This paper discusses the role and integration of knowledge discovery (KD) in case‐based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case‐specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by a real application scenario in medicine in which time series of patterns are to be analyzed and classified. As single pattern cannot convey sufficient information in the application, sequences of patterns are more adequate. Hence it is advantageous if sequences of patterns and their co‐occurrence with categories can be discovered. Evaluation with cases containing series classified into a number of categories and injected with indicator sequences shows that the approach is able to identify these key sequences. In a clinical applica‐
tion and a case library that is representative of the real world, these key sequences would improve the classification ability and may spawn clinical research to explain the co‐occurrence between certain sequences and classes.

A multi-module case-based biofeedback system for stress treatment

OBJECTIVE Biofeedback is today a recognized treatment method for a number of physical and psychological problems. Experienced clinicians often achieve good results in these areas and their success largely builds on many years of experience and often thousands of treated patients. Unfortunately many of the areas where biofeedback is used are very complex, e.g. diagnosis and treatment of stress. Less experienced clinicians may even have difficulties to initially classify the patient correctly. Often there are only a few experts available to assist less experienced clinicians. To reduce this problem we propose a computer-assisted biofeedback system helping in classification, parameter setting and biofeedback training. METHODS The decision support system (DSS) analysis finger temperature in time series signal where the derivative of temperature in time is calculated to extract the features. The case-based reasoning (CBR) is used in three modules to classify a patient, estimate parameters and biofeedback. In each and every module the CBR approach retrieves most similar cases by comparing a new finger temperature measurement with previously solved measurements. Three different methods are used to calculate similarity between features, they are: modified distance function, similarity matrix and fuzzy similarity. RESULTS AND CONCLUSION We explore how such a DSS can be designed and validated the approach in the area of stress where the system assists in the classification, parameter setting and finally in the training. In this case study we show that the case based biofeedback system outperforms trainee clinicians based on a case library of cases authorized by an expert.

Construction of fuzzy knowledge bases incorporating feature selection

Constructing concise fuzzy rule bases from databases containing many features present an important yet challenging goal in the current researches of fuzzy rule-based systems. Utilization of all available attributes is not realistic due to the “curse of dimensionality” with respect to the rule number as well as the overwhelming computational costs. This paper proposes a general framework to treat this issue, which is composed of feature selection as the first stage and fuzzy modeling as the second stage. Feature selection serves to identify significant attributes to be employed as inputs of the fuzzy system. The choice of key features for inclusion is equivalent to the problem of searching for hypotheses that can be numerically assessed by means of case-based reasoning. In fuzzy modeling, the genetic algorithm is applied to explore general premise structure and optimize fuzzy set membership functions at the same time. Finally, the merits of this work have been demonstrated by the experiment results on a real data set

A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends

Metaheuristics has attained increasing interest for solving complex real-world problems. This paper studies the principles and the state-of-the-art of metaheuristic methods for engineering optimization. Both the classic and emerging approaches to optimization using metaheuristics are reviewed and analyzed. All the methods are discussed in three basic types: trajectory-based, in which in each step a new solution is created from the previous one; multi-trajectory-based, in which a multi-start mechanism is used; and population-based, where multiple new solutions are created considering a population of approximate solutions. We further discuss algorithms and strategies to handle multi-modal and multi-objective optimization tasks as well as methods for parallel implementation of metaheuristic algorithms. Then, different software frameworks for metaheuristics are introduced. Finally, several interesting directions are pointed out as future research trends.

Perception management: an emerging concept for information fusion ☆

Abstract The state-of-art of sensor management has been advanced to the extent where high-level information plays an increasingly important role. Since situation state and associated request for information are device independent, a question arises as whether the term (sensor management) is sufficient enough to encompass the functions on the information level. Recognizing the essential need of intelligent agents to perceive the environment to take appropriate actions, this letter proposes the concept of perception management. It refers to controlling the process of data acquisition from the external world to enhance percepts obtained. The content of perception management is outlined and its relationship with sensor management is also discussed.