A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies

Redundancy allocation problem (RAP) is one of the best-developed problems in reliability engineering studies. This problem follows to optimize the reliability of a system containing s sub-systems under different constraints, including cost, weight, and volume restrictions using redundant components for each sub-system. Various solving methodologies have been used to optimize this problem, including exact, heuristic, and meta-heuristic algorithms. In this paper, an efficient multi-objective meta-heuristic algorithm based on simulated annealing (SA) is developed to solve multi-objective RAP (MORAP). This algorithm is knowledge-based archive multi-objective simulated annealing (KBAMOSA). KBAMOSA applies a memory matrix to reinforce the neighborhood structure to achieve better quality solutions. The results analysis and comparisons demonstrate the performance of the proposed algorithm for solving MORAP.     

Reasoning about non-immediate triggers in biological networks

Modeling molecular interactions in signalling networks is important from various perspectives such as predicting side effects of drugs, explaining unusual cellular behavior and drug and therapy design. Various formal languages have been proposed for representing and reasoning about molecular interactions. The interactions are modeled as triggered events in most of the approaches. The triggering of events is assumed to be immediate: once an interaction is triggered, it should occur immediately. Although working well for engineering systems, this assumption poses a serious problem in modeling biological systems. Our knowledge about biological systems is inherently incomplete, thus molecular interactions are constantly elaborated and refined at different granularity of abstraction. The model of immediate triggers can not consistently deal with this refinement. In this paper we propose an action language to address this problem. We show that the language allows for refinements of biological knowledge, although at a higher cost in terms of complexity.      

Ontology-Centered Knowledge Management Using Information Retrieval Techniques

The paper argues that an effective solution to information and knowledge management (KM) needs of practitioners in the construction industry can be found in the provision of an adapted knowledge environment that makes use of user profiling and document summarization techniques based on information retrieval sciences. The conceptualization of the domain through ontology takes a pivotal role in the proposed knowledge environment and provides a semantic referential to ensure relevance, accuracy, and completeness of information. A set of KM services articulated around the selected ontology have been developed, using the Web services model, tested, and validated in real organizational settings. This provided the basis for formulating recommendations and key success factors for any KM project development.     

基于多变性设计的回转体零件挤压模具结构智能设计方法和研究

为了实现回转体零件挤压模具的快速设计，减少不必要的重复性工作，通过变量化建模和装配技术，并结合人工智能技术和多变性设计技术，在UG／NX平台上开发了回转体零件的挤压模具智能设计系统，实例研究表明所建立的系统是有效的。     

Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method

In this paper, a new approach for detecting previously unencountered malware targeting mobile device is proposed. In the proposed approach, time-stamped security data is continuously monitored within the target mobile device (i.e., smartphones, PDAs) and then processed by the knowledge-based temporal abstraction (KBTA) methodology. Using KBTA, continuously measured data (e.g., the number of sent SMSs) and events (e.g., software installation) are integrated with a mobile device security domain knowledge-base (i.e., an ontology for abstracting meaningful patterns from raw, time-oriented security data), to create higher level, time-oriented concepts and patterns, also known as temporal abstractions. Automatically-generated temporal abstractions are then monitored to detect suspicious temporal patterns and to issue an alert. These patterns are compatible with a set of predefined classes of malware as defined by a security expert (or the owner) employing a set of time and value constraints. The goal is to identify malicious behavior that other defensive technologies (e.g., antivirus or firewall) failed to detect. Since the abstraction derivation process is complex, the KBTA method was adapted for mobile devices that are limited in resources (i.e., CPU, memory, battery). To evaluate the proposed modified KBTA method a lightweight host-based intrusion detection system (HIDS), combined with central management capabilities for Android-based mobile phones, was developed. Evaluation results demonstrated the effectiveness of the new approach in detecting malicious applications on mobile devices (detection rate above 94% in most scenarios) and the feasibility of running such a system on mobile devices (CPU consumption was 3% on average).     

Experience-consistent modeling: Regression and classification problems

In this study, we are concerned with system modeling which involves limited data and reconciles the developed model with some previously acquired domain knowledge being captured in the format of already constructed models. Each of these previously available models was formed on a basis of extensive data sets which are not available for the current identification pursuits. To emphasize the nature of modeling being guided by the reconciliation mechanisms, we refer to this mode of identification as experience-consistent modeling. The paper presents the conceptual and algorithmic framework by focusing on regression models. By forming a certain extended form of the performance index, it is shown that the domain knowledge captured by regression models can play a similar role as a regularization component used quite commonly in system identification. Experimental results involve both synthetic low-dimensional data and selected data coming from Machine Learning repository. The data used in the experiments tackle regression models as well as classification problems (two-class classifiers).     

RMAIN: Association rules maintenance without reruns through data

Association rules are well recognised as a data mining tool for analysis of transactional data, currently going far beyond the early basket-based applications. A wide spectrum of methods for mining associations have been proposed up to date, including batch and incremental approaches. Most of the accurate incremental methods minimise, but do not completely eliminate reruns through processed data. In this paper we propose a new approximate algorithm RMAIN for incremental maintenance of association rules, which works repeatedly on subsequent portions of new transactions. After a portion has been analysed, the new rules are combined with the old ones, so that no reruns through the processed transactions are performed in the future. The resulting set of rules is kept similar to the one that would be achieved in a batch manner. Unlike other incremental methods, RMAIN is fully separated from a rule mining algorithm and this independence makes it highly general and flexible. Moreover, it operates on rules in their final form, ready for decision support, and not on intermediate representation (frequent itemsets), which requires further processing. These features make the RMAIN algorithm well suited for rule maintenance within knowledge bases of autonomous systems with strongly bounded resources and time for decision making. We evaluated the algorithm on synthetic and real datasets, achieving promising results with respect to either performance or quality of output rules.     

An Intelligent Decision Support System for Investment Analysis

We present an intelligent decision support system that combines decision analysis and traditional investment evaluation and analysis. The system brings to the ordinary user expertise in both decision analysis and investment evaluation techniques, as well as domain knowledge about the market. The system supports the entire decision analysis cycle and provides facilities and tools for decision modeling, probability assessment, model evaluation, and sensitivity analysis. An example based on the Shanghai Stock Market is presented. We demonstrate how an investment decision model for the stock market is constructed by the system and how the optimal decision is obtained. Sensitivity and value of information analysis were also carried out by the system. Received 12 April 1999 / Revised 8 February 2000 / Accepted 2 March 2000