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.     

BASE: an assistant tool to precisely simulate energy consumption and reliability of energy‐efficient storage systems

The concept of green storage in cluster computing has recently attracted enormous interest among researchers. Consequently, several energy‐efficient solutions, such as multi‐speed disks and disk spin down methods, have been proposed to conserve power in storage systems and improve disk access. Some researchers have assessed their proposed solutions via simulations, while others have used real‐world experiments. Both methods have advantages and disadvantages. Simulations can more swiftly assess the benefits of energy‐efficient solutions, but various measurement errors can arise from procedural shortcomings. For instance, many power simulation tools fail to consider how heat increases the power overhead of disk operations. Some researchers claim that their modeling methods reduce the measurement error to 5% in the single disk model. However, the demand for large‐scale storage systems is growing rapidly. Traditional power measurement using a single disk model is unsuited to such systems because of their complex storage architecture and the unpredictability of numerous disks. Consequently, a number of studies have conducted real machine experiments to assess the performance of their solutions in terms of power conservation, but such experiments are time consuming. To address this problem, this study proposes an efficient simulation tool called Benchmark Analysis Software for Energy‐efficient Solution (BASE), which can accurately estimate disks' power consumption in large‐scale storage systems. We evaluate the performance of BASE on real‐world traces of Academia Sinica (Taiwan) and Florida International University. BASE incorporates an analytical method for assessing the reliability of energy‐efficient solutions. The analytical results demonstrate that the measurement error of BASE is 2.5% lower than that achieved in real‐world experiments involving energy‐estimation experiments. Moreover, the results of simulations to assess solution reliability are identical to those obtained through real‐world experiments. Copyright © 2015 Copyright © 2015 John Wiley & Sons, Ltd.     

A cross‐cultural study of the effect of a graph‐oriented computer‐assisted project‐based learning environment on middle school students' science knowledge and argumentation skills

The purpose of this mixed‐methods study was to explore how seventh graders in a suburban school in the United States and sixth graders in an urban school in Taiwan developed argumentation skills and science knowledge in a project‐based learning environment that incorporated a graph‐oriented, computer‐assisted application (GOCAA). A total of 42 students comprised the treatment condition and were engaged in a project‐based learning environment that incorporated a GOCAA. Of these 42 students, 21 were located in the United States and 21 were located in Taiwan. A total of 26 students comprised the control condition and were engaged in a project‐based learning environment without the GOCAA. Of these 26 students, 15 were in the United States and 11 were in Taiwan. In each country, verbal collaborative argumentation was recorded and the students' post‐essays were collected. A one‐way analysis of variance (ANOVA) was conducted for each measure of science knowledge about alternative energies. The results showed a significant treatment effect for the outcome of scientific explanation among U.S. students, while among Taiwanese students, a significant treatment effect on scientific facts was observed. A one‐way ANOVA was additionally conducted for each measure of argumentation skills and a significant treatment effect on counterarguments and rebuttals was observed among the U.S. students, while in Taiwan, a significant treatment effect on reasoning and rebuttals was observed. A qualitative analysis was conducted to examine how the GOCAA supported students' development of argumentation skills in different countries. This study found distinct argumentation patterns between the U.S. and Taiwanese intervention teams. Additionally, a distinct gender difference in the use of evidence and division of labour was noted when the Taiwanese teams were compared with the U.S. teams, which may be explained by cultural differences. This study concluded that, in both the United States and Taiwan, a project‐based learning environment incorporating a GOCAA was effective in improving students' science knowledge and developing their scientific argumentation skills.