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1.
The important field of research on ship operation is related to the high efficiency of transportation, the convenience of maneuvering ships, and the safety of navigation. This paper proposes an ontology-based fuzzy support agent for ship steering control and desires to testify the validity of the proposal by applying the fuzzy control model to the steering control system based on linguistic instruction. The fuzzy support agent is presented to build the maneuvering models of steersman and the miniature model for steering control system. The proposed fuzzy agent contains three main mechanisms, including the interpretation mechanism of linguistic instruction, the self-regulation mechanism, and the task performance mechanism. Furthermore, the task performance mechanism includes the kinematics module and the performance ontology. The simulation results show that the proposed approach can work effectively for ship steering control.  相似文献   
2.
A genetic fuzzy agent using ontology model for meeting scheduling system   总被引:1,自引:0,他引:1  
A Genetic Fuzzy Agent (GFA) using the ontology model for Meeting Scheduling System (MSS) is presented in this paper. The ontology model includes the Fuzzy Meeting Scheduling Ontology (FMSO) and the Fuzzy Personal Ontology (FPO) that can support to construct the knowledge base of the GFA. The FMSO is utilized to record and describe the meeting scheduling domain knowledge for the GFA. In addition, we implement a FMSO editor for generating the Web Ontology Language, OWL, that will be utilized by the GFA. Furthermore, the GFA will infer the suitable meeting time slots based on the ontology model. Moreover, it also adjusts the FMSO and FPO based on the results of the genetic learning mechanism for the next meeting. The experimental results show that our approach can effectively work for MSS.  相似文献   
3.
In this paper, we propose a Genetic-based Fuzzy Image Filter (GFIF) to remove additive identical independent distribution (i.i.d.) impulse noise from highly corrupted images. The proposed filter consists of a fuzzy number construction process, a fuzz filtering process, a genetic learning process, and an image knowledge base. First, the fuzzy number construction process receives sample images or the noise-free image and then constructs an image knowledge base for the fuzzy filtering process. Second, the fuzzy filtering process contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy decision process to perform the task of noise removal. Finally, based on the genetic algorithm, the genetic learning process adjusts the parameters of the image knowledge base. By the experimental results, GFIF achieves a better performance than the state-of-the-art filters based on the criteria of Peak-Signal-to-Noise-Ratio (PSNR), Mean-Square-Error (MSE), and Mean-Absolute-Error (MAE). On the subjective evaluation of those filtered images, GFIF also results in a higher quality of global restoration.  相似文献   
4.
Antimalware application is one of the most important research issues in the area of cyber security threat. Nowadays, because hackers continuously develop novel techniques to intrude into computer systems for various reasons, many security researchers should analyze and track new malicious program to protect sensitive and valuable information in the organization. In this paper, we propose a novel soft-computing mechanism based on the ontology model for malware behavioral analysis: Malware Analysis Network in Taiwan (MAN in Taiwan, MiT). The core techniques of MiT contain two parts listed as follows: (1) collect the logs of network connection, registry, and memory from the operation system on the physical-virtual hybrid analysis environment to get and extract more unknown malicious behavior information. The important information is then extracted to construct the ontology model by using the Web Ontology Language and Fuzzy Markup Language. Additionally, MiT is also able to automatically provide and share samples and reports via the cloud storage mechanism; (2) apply the techniques of Interval Type-2 Fuzzy Set to construct the malware analysis domain knowledge, namely the Interval Type-2 Fuzzy Malware Ontology (IT2FMO), for malware behavior analysis. Simulation results show that the proposed approach can effectively execute the malware behavior analysis, and the constructed system has also released under GNU General Public License version 3. In the future, the system is expected to largely collect and analyze malware samples for providing industries or universities to do related applications via the established IT2FMO.  相似文献   
5.
Frequent itemset mining aims at discovering patterns the supports of which are beyond a given threshold. In many applications, including network event management systems, which motivated this work, patterns are composed of items each described by a subset of attributes of a relational table. As it involves an exponential mining space, the efficient implementation of user preferences and mining constraints becomes the first priority for a mining algorithm. User preferences and mining constraints are often expressed using patterns attribute structures. Unlike traditional methods that mine all frequent patterns indiscriminately, we regard frequent itemset mining as a two-step process: the mining of the pattern structures and the mining of patterns within each pattern structure. In this paper, we present a novel architecture that uses pattern structures to organize the mining space. In comparison with the previous techniques, the advantage of our approach is two-fold: (i) by exploiting the interrelationships among pattern structures, execution times for mining can be reduced significantly; and (ii) more importantly, it enables us to incorporate high-level simple user preferences and mining constraints into the mining process efficiently. These advantages are demonstrated by our experiments using both synthetic and real-life datasets.  相似文献   
6.
Ontological fuzzy agent for electrocardiogram application   总被引:1,自引:0,他引:1  
The electrocardiogram (ECG) signal is adopted extensively as a low-cost diagnostic procedure to provide information concerning the healthy status of the heart. However, the QRS complex must be calculated accurately before proceeding with the heart rate variability (HRV). In particular, the R peak needs to be detected reliably. This study presents an adaptive fuzzy detector to detect the R peak correctly. Additionally, an ontological fuzzy agent is presented to process the collection of ECG signals. The required knowledge is stored in the ontology, which comprises some personal ontologies and predefined by domain experts. The ontological fuzzy agent retrieves the ECG signals with R peaks marked for HRV analysis and ECG further applications. It contains a personal fuzzy filter, an HRV analysis mechanism, and a fuzzy normed inference engine. Moreover, the ECG fuzzy signal space and some important properties are presented to define the working environment of the agent. An experimental platform has been constructed to test the performance of the agent. The results indicate that the proposed method can work effectively.  相似文献   
7.
Evaluating cardiac health through semantic soft computing techniques   总被引:5,自引:5,他引:0  
Heart Rate Variability (HRV) represents a physiological phenomenon which consists in the oscillation in the interval between consecutive heartbeats. Based on the HRV analysis, cardiology experts can make a assessment for both the cardiac health and the condition of the autonomic nervous system that is responsible for controlling heart activity and, consequently, they try to prevent cardiovascular mortality. In this scenario, one of the most widely accepted and low-cost diagnostic procedures useful for deriving and evaluating the HRV is surely the electrocardiogram (ECG), i.e., a transthoracic interpretation of the electrical activity of the heart over a period of time. With the advent of modern signal processing techniques, the diagnostic power of the ECG is increased exponentially due to the huge number of features that are typically extracted from the ECG signal. Even though this expanded set of features could allow medical staffs to diagnose various pathologies in an accurate way, it is too complex to manage in a manual way and, for this reason, methods for feature representation and evaluation are necessary for supporting medical diagnosis. Starting from this consideration, this paper proposes an enhanced ECG-based decision making system exploiting a collection of ontological models representing the ECG and HRV feature sets and a fuzzy inference engine based on Type-2 Fuzzy Markup Language capable of evaluating the ECG and HRV properties related to a given person and infer detailed information about his health quality level. As will be shown in the experimental section, where the proposed approach has been tested on a set of under exams students, our diagnostic framework yields good performances both in terms of precision and recall.  相似文献   
8.
9.
A fuzzy ontology and its application to news summarization.   总被引:7,自引:0,他引:7  
In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization.  相似文献   
10.
Time series are often generated by continuous sampling or measurement of natural or social phenomena. In many cases, events cannot be represented by individual records, but instead must be represented by time series segments (temporal intervals). A consequence of this segment-based approach is that the analysis of events is reduced to analysis of occurrences of time series patterns that match segments representing the events.A major obstacle on the path toward event analysis is the lack of query languages for expressing interesting time series patterns. We have introduced SQL/LPP (Perng and Parker, 1999). Which provides fairly strong expressive power for time series pattern queries, and are now able to attack the problem of specifying queries that analyze temporal coupling, i.e., temporal relationships obeyed by occurrences of two or more patterns.In this paper, we propose SQL/LPP+, a temporal coupling verification language for time series databases. Based on the pattern definition language of SQL/LPP (Perng and Parker, 1999), SQL/LPP+ enables users to specify a query that looks for occurrences of a cascade of multiple patterns using one or more of Allen's temporal relationships (Allen, 1983) and obtain desired aggregates or meta-aggregates of the composition. Issues of pattern composition control are also discussed.  相似文献   
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