Multi-agent collaborative 3D design with geometric model at different levels of detail

This paper presents a scheme for collaborative 3D design using product model at various levels of detail (LODs). Design features are selectively hidden at each level from certain participants, depending on their actual needs and individual accessibility in the collaboration. A tree data structure represents the feature hierarchy of CAD construction, the link between feature and LOD, and 2D mesh data for display control of each feature. An XML/XSLT-based approach is proposed to enable real-time visualization of different LOD models in distributed environment. A collaborative design system is implemented using multi-agent technologies, which focuses on function design of each agent, interactions among agents, the client–server structure, and generation of the LOD data using the XML/XSLT approach. A scenario of synchronous 3D mold assembly demonstrates that geometric categorization of product model provides an operational mechanism for assuring security of information sharing in engineering collaborations over the Internet. It also validates the effectiveness of the agent technologies for automating complex engineering activities.     

Work fatigue and physiological symptoms in different occupations of high-elevation construction workers

The objective of this study is to investigate whether work fatigue and physiological symptoms that high-elevation construction workers experience would be affected by the occupations. Questionnaires of demographic data and subjective fatigue symptoms as well as some physiological measurements were carried out, pre- and post-shift, on scaffolders, steel fixers, formworkers, electrician-plumbers, concreters and miscellaneous workers at a high-rise building construction site. This study found that some subjective fatigue symptoms coincide with the life style of some workers and that the extent of fatigue symptoms and physiological strains varies among different occupations of construction workers. Scaffolders, steel fixers and formworkers are categorized as physically demanding fatigue type of workers, while concreters, electrician-plumbers and miscellaneous workers as general type. The prevalence and occurrence of subjective fatigue symptoms indicate high-elevation workers have more complaints of “projection of physical impairment” than “drowsiness and dullness” and “difficulty in concentration”. Some unexpected changes (i.e., post-shift measurements are greater than pre-shift ones) in some strength tests in scaffolders and concreters were consistent with the observations of how they exercised their bodies during work shift. Considerable variation of average heart rate among occupations was found, with scaffolders the highest and concreters the lowest. This study concludes that questionnaires of subjective fatigue symptoms and some physiological measurements can be used as indicators to predict the extent of strains or hazards which construction workers encounter. In terms of management program of safety and health, more attention should be paid to those physically demanding workers, such as scaffolders, workers with lower sense of safety and health, such as miscellaneous workers, and workers with older age, such as concreters.     

Long paths in hypercubes with conditional node-faults

Let F be a set of f?2n-5 faulty nodes in an n-cube Q_n such that every node of Q_n still has at least two fault-free neighbors. Then we show that Q_n-F contains a path of length at least 2ⁿ-2f-1 (respectively, 2ⁿ-2f-2) between any two nodes of odd (respectively, even) distance. Since the n-cube is bipartite, the path of length 2ⁿ-2f-1 (or 2ⁿ-2f-2) turns out to be the longest if all faulty nodes belong to the same partite set. As a contribution, our study improves upon the previous result presented by [J.-S. Fu, Longest fault-free paths in hypercubes with vertex faults, Information Sciences 176 (2006) 759-771] where only n-2 faulty nodes are considered.     

A novel user identification scheme with key distribution preserving user anonymity for distributed computer networks

Recently, Yang et al. proposed an efficient user identification scheme with key distribution, in which it is possible for the user to anonymously log into a system and establish a secret key shared with the system. Mangipudi and Katti later demonstrated a Deniable-of-Service (DoS) attack on the Yang et al. scheme and then proposed an improvement to withstand such an attack. However, this paper demonstrates an identity disclosure attack to show that neither schemes’ claimed user anonymity requirement can be achieved. We further propose a novel user identification scheme with key distribution preserving user anonymity for distributed computer networks. The proposed scheme not only withstands the attacks mentioned above, but also achieves the following: (i) user anonymity, (ii) key distribution, (iii) mutual authentication, and (iv) key confirmation. The performance of our scheme is of greater efficiency than that of previously proposed schemes in terms of communication costs and computational complexities.     

Design of intelligent power controller for DC–DC converters using CMAC neural network

DC–DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popularly used because of the high efficiency and small size. This paper proposes an intelligent power controller for the DC–DC converters via cerebella model articulation controller (CMAC) neural network approach. The proposed intelligent power controller is composed of a CMAC neural controller and a robust controller. The CMAC neural controller uses a CMAC neural network to online mimic an ideal controller, and the robust controller is designed to achieve L ₂ tracking performance with desired attenuation level. Finally, a comparison among a PI control, adaptive neural control and the proposed intelligent power control is made. The experimental results are provided to demonstrate the proposed intelligent power controller can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response and simple computation.     

Ontology-based data mining approach implemented for sport marketing

Since sport marketing is a commercial activity, precise customer and marketing segmentation must be investigated frequently and it would help to know the sport market after a specific customer profile, segmentation, or pattern come with marketing activities has found. Such knowledge would not only help sport firms, but would also contribute to the broader field of sport customer behavior and marketing. This paper proposes using the Apriori algorithm of association rules, and clustering analysis based on an ontology-based data mining approach, for mining customer knowledge from the database. Knowledge extracted from data mining results is illustrated as knowledge patterns, rules, and maps in order to propose suggestions and solutions to the case firm, Taiwan Adidas, for possible product promotion and sport marketing.     

Fault-tree analysis for liquefied natural gas terminal emergency shutdown system

Natural gas, one of the cleanest, most efficient and useful of all energy sources, is a vital component of the world’s supply of energy. To make natural gas more convenient for storage and transportation, it is refined and condensed into a liquid called liquefied natural gas (LNG). In a LNG site, safety is a long-team and critical issue. The emergency shutdown (ESD) system in the LNG receiving terminal is used to automatically stop the pumps and isolate the leakage section. Fault-tree analysis (FTA) has been widely used for providing logical functional relationships among subsystems and components of a system and identifying the root causes of the undesired failures in a system. In the conventional FTA for the ESD system, we usually assume that exact failure probabilities of events are collected. However, in most real applications, first, the FTA for the ESD system needs to be made at a early design or manufacturing stage, certain new components normally used without failure data; secondly, sometimes the environmental change in the system during the operation periods. This makes more difficult to gather past exact failures data for the FTA. To complete the FTA of the ESD system under these uncertain situations, we apply the intuitionistic fuzzy sets (IFS) theory to the FTA. We generate the intuitionistic fuzzy fault-tree interval, and the intuitionistic fuzzy reliability interval for the ESD system. We also present an algorithm to find the critical components in the system based on IFS–FTA and determine weak paths in the ESD system, where the key improvement must be made.     

Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference

In this paper, an intelligent diagnosis for fault gear identification and classification based on vibration signal using discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) is presented. The discrete wavelet transform (DWT) technique plays one of the important roles for signal feature extraction in the proposed system. The abnormal transient signals will show in different decomposition levels and can be used to recognize the various faults by the DWT figure. However, many fault conditions are hard to inspect accurately by the naked eye. In the present study, the feature extraction method based on discrete wavelet transform with energy spectrum is proposed. The different order wavelets are considered to identify fault features accurately. The database is established by feature vectors of energy spectrum which are used as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to identify and classify the fault gear positions and the gear fault conditions in the fault diagnosis system. The proposed ANFIS includes both the fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental results verified that the proposed ANFIS has more possibilities in fault gear identification. The ANFIS achieved an accuracy identification rate which was more satisfactory than traditional vision inspection in the proposed system.     

Constructing intelligent living-space controlling system with blue-tooth and speech-recognition microprocessor

This study is a creative and initial work focused on designing an intelligent living-space with automatic control system to control all home-appliances in the living-space. First of all, the system was constructed with two parts; one is remote-control mechanism with blue-tooth (BT) module mounting on both master controller module (remote-controller) and slave receiver module (on every home appliance), the other is to design the remote-controller with speech-recognition and function of self-learning human voice so that people could directly give command to control appliances Therefore, the technique of BT communication and speech-recognition microprocessor became the most important in this study. After implementing the finished projects, the whole system was proved to be great successful and the final result of this research has achieved both National patent authorized by Patent Bureau of Taiwan Economy Ministry, and The First Prize of National Award of Practical Projects Design authorized by Taiwan Education Ministry.