Inspired by the quotient space theory and fuzzy concept, it is pointed out that the foundation of success of FCS (fuzzy control system) does not refer to the value-getting of MFs (membership functions) and the success...
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Inspired by the quotient space theory and fuzzy concept, it is pointed out that the foundation of success of FCS (fuzzy control system) does not refer to the value-getting of MFs (membership functions) and the success can be achieved by obtaining the mutual relationships between elements, namely the relationships of their order. The hierarchical structure, which is made up of order relation, is the essential characteristic of fuzzy control. It's the necessary condition of the success of FCS. At the end of this paper, the logical control system based on order relation is given. Its controlling rules are more accustomed to the mental habits of the mankind and it may achieve the ideal performance of steady state. MATLAB's simulation demonstrates the above-mentioned conclusions.
The quotient space theory obtains a fusion model about semi-order structure under the condition of consistency information, harnessing topology relation among elements of the universe space and hierarchical structure....
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The quotient space theory obtains a fusion model about semi-order structure under the condition of consistency information, harnessing topology relation among elements of the universe space and hierarchical structure. It has great significance to forming a unified theory structure about information fusion technology. This paper gets a new semi-order structure fusion model under the condition of inconsistency information and proves incompleteness of its semi-order lattice . An example is presented in the paper at last as well as a new method is indicated for Bayesian network structure learning.
Stereo matching is one of the most active research areas in computer vision. In this paper, a fast stereo matching algorithm by means of epipolar constraint and multiresolution approach was presented. The searching sc...
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Stereo matching is one of the most active research areas in computer vision. In this paper, a fast stereo matching algorithm by means of epipolar constraint and multiresolution approach was presented. The searching scope of corresponding pixels in the original image is obtained and has diminished a lot based on multiresolution approach. Then intensity correlation principle and epipolar constraint can be applied to get the stereo matching results in this scope. In this way, we reduce the search time for correspondence and ensure the validity of matching. The experimental results show this algorithm is effective and efficient.
Analysis of microelectrode recordings (MER) of extracellular neuronal activity has gained increasing interest due to potential improvements to surgical techniques involving ablation or placement of deep brain stimulat...
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Analysis of microelectrode recordings (MER) of extracellular neuronal activity has gained increasing interest due to potential improvements to surgical techniques involving ablation or placement of deep brain stimulators, as is common in the treatment of Parkinson's disease. Critical to these procedures is the identification of different brain structures such as the globus pallidus internus (GPI). Evidence suggests that the spike trains from individual neurons contain enough information to identify the brain structure in which they are located For the work reported here, spike train data gathered during surgical procedure from multiple patients is used. Using a moving window sampling approach, a novel feature extraction method for spike trains was developed. This method is then used in combination with a support vector classification algorithm. Results strongly indicate that the sampling methods reported here are able to extract the necessary information for highly accurate spike source identification.
We describe an algorithm that objectively and automatically identifies target regions in the brain for ablation or stimulation during neurosurgery for Parkinson's disease and other movement disorders. The algorith...
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We describe an algorithm that objectively and automatically identifies target regions in the brain for ablation or stimulation during neurosurgery for Parkinson's disease and other movement disorders. The algorithm uses microelectrode recordings to distinguish between the target and adjacent anatomic structures during stereotactic neurosurgery. This algorithm uses a novel method of signal feature extraction that enables standard classification algorithms such as support vector machines to perform well. The algorithm was validated on microelectrode recordings acquired near the globus pallidus internus and labeled by the neurosurgeon.
Low-resource languages are challenging to process intelligent decision systems due to limited data and resources. As an effective way of processing low-resource languages in intelligent decision systems, fuzzy linguis...
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Low-resource languages are challenging to process intelligent decision systems due to limited data and resources. As an effective way of processing low-resource languages in intelligent decision systems, fuzzy linguistic approaches excel in transforming original uncertain linguistic information into highly structured data and learning valid decision rules between complex data structures. However, existing fuzzy linguistic methods may not fully capture realistic features of multi-attribute group decision-making (MAGDM), such as incomplete and hesitant linguistic expressions, stable information fusion, and bounded rationality of decision-makers (DMs). Therefore, it is necessary to develop a collaborative fuzzy language learning system based on bounded rationality, low-resource and robust decision-making. Specifically, we present a new multi-granularity (MG) group decision-making (GDM) scheme by using MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) and PT (Prospect Theory) for incomplete hesitant fuzzy linguistic information systems (I-HFL-ISs), where MG GDM aims to discover knowledge from complex MAGDM problems with MG features. To achieve the above goal, we first introduce the concept of MG-I-HFL-ISs to represent incomplete, hesitant and imprecise linguistic evaluation information offered by multiple decision-makers (DMs). Then, we apply a valid transformation scheme to convert MG-I-HFL-ISs into MG-HFL-ISs, and use the MG probability rough set (PRS) to develop a series of MG-HFL-PRSs with the support of MULTIMOORA. Afterwards, an HFL MG GDM method is designed by integrating MULTIMOORA and PT for solving MAGDM problems with MG-I-HFL-ISs. The proposed method can effectively synthesize low-resource languages and mine useful decision-making knowledge. At last, a drug selection case and a simulated case are performed for showing the rationality of the designed HFL MG GDM scheme.
The rise of the digital economy and e-commerce has fostered a movement towards efficient low-resource medical information processing, a trend that holds great importance in the healthcare sector. Diabetes, being a wid...
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The rise of the digital economy and e-commerce has fostered a movement towards efficient low-resource medical information processing, a trend that holds great importance in the healthcare sector. Diabetes, being a widespread chronic condition, has witnessed the introduction of glucometers, which offer patients a convenient method of monitoring their blood sugar levels. However, it is worth noting that a considerable proportion of online comments may be subject to emotional bias or contain inaccurate information. Furthermore, the performance of glucometers can be influenced by several attributes, including price, accuracy and portability, thereby potentially complicating the decision-making process for consumers. Semantic analysis can be employed to acquire valuable information, aiding consumers in reasonably choosing the suitable glucometer. This paper utilizes the benefits of granular computing, an emerging computing paradigm, to effectively handle incomplete and uncertain medical information. It employs generalized fuzzy sets, rough sets and three-way decisions (TWD) techniques to boost the accuracy and reliability of medical information fusion. Subsequently, the MABAC (Multi-Attribute Border Approximation Area Comparison) method is utilized to evaluate the reviews of every glucometer, calculate their aggregated scores, and rank and compare them. Ultimately, in light of consumers’ needs and trade-offs, the glucometer with the highest score can be selected. The proposed approach comprehensively considers the weight and priority of multiple attributes, reduces information overload and mitigates selection difficulties, thereby enhancing the accuracy and reliability of low-resource medical information processing.
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