Miracle-dz/UniFAR-FTUs huggingface dataset:
Condition Parameter Modeling for Anomaly Detection in Wind Turbines

Data collected from the supervisory control and data acquisition (SCADA) system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs), is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN) for automatic selection of the condition parameters. The SCADA data sets are determined through analysis of the cumulative probability distribution of wind speed and the relationship between output power and wind speed. The automatic BPNN-based parameter selection is for reduction of redundant parameters for anomaly detection in wind turbines. Through investigation of cases of WT faults, the validity of the automatic parameter selection-based model for WT anomaly detection is verified.
Study of Text Mining Using Hybrid Agglomerative Clustering With ACO Algorithms

: Textual document clustering technique was introduced in the area of text mining. The two important main goals in document clustering are achieving high performance or efficiency and obtaining highly accurate data clusters that are closed to their natural classes or textual document cluster quality To enhance this work, we are going to propose a new hybrid clustering algorithm using Agglomerative Clustering with ACO (Ant Colony Optimization) algorithm. ACO algorithms are a class of algorithms inspired by the observation of real ants. In this paper single linkage and K-nearest Neighbor are used to achieve the high efficiency and high quality. And also used four parameters recall, precision, time, document are calculated for high efficiency and high quality.
Fuzzy and neuro-fuzzy modeling of a fermentation process

Neuro-fuzzy modeling may be qualified as a grey-box technique, since it combines the transparency of rule-based fuzzy systems with the learning capability of neural networks. The main problem in the identification of non-linear processes is the lack of complete information. Certain variables are, either immeasurable or difficult to measure, the soft sensors are the necessary tools to solve the problem. Those latter can be used via online estimation, and then they will be implemented in fed- batch fermentation processes for optimal production and online monitoring. The process parameters are estimated through a fuzzy logic system. The fuzzy models of takagi-sugeno type suffer of the problem of poor initialization, which can be solved by the trial-and error method Trial-and-error method is used to solve the poor initialization problem of TS models, this deals with identifying the structure of the model, such structure consists on finding the optimum number of rules, which enters in the model cost reduction. The fuzzy model might not capture the process non-linearity, especially if the number of rules is over- optimized. Bioreactors exhibit a wide range of dynamic behaviours and offer many challenges to modeling, as a result of the presence of living micro-organisms whose growth rate is described by complex equations. We will illustrate the fuzzy and the neuro-fuzzy modeling on the identification of such a system. In order to compare the NF model outputs, we use another fuzzy model that does not incorporate the neural network learning capability, to identify the parameters of the same process. Even though, the two models were trained using levenberg-marquardt algorithm, the corresponding simulation results show that a better modeling is achieved using NF technique, especially that we did not employ any involved optimization procedure to identify the NF structure.
Towards Automatic Construction of Knowledge Bases from Chinese Online Resources

Automatically constructing knowledge bases from online resources has become a crucial task in many research areas. Most existing knowledge bases are built from English resources, while few efforts have been made for other languages. Building knowledge bases for Chinese is of great importance on its own right. However, simply adapting existing tools from English to Chinese yields inferior results. In this paper, we propose to create Chinese knowledge bases from online resources with less human involvement. This project will be formulated in a self-supervised framework which requires little manual work to extract knowledge facts from online encyclopedia resources in a probabilistic view. In addition, this framework will be able to update the constructed knowledge base with knowledge facts extracted from up-to-date newswire. Currently, we have obtained encouraging results in our pilot experiments that extracting knowledge facts from infoboxes can achieve a high accuracy of around 95%, which will be then used as training data for the extraction of plain webpages.
Architecture for Body Sensor Networks

Recent advances in wireless sensor networks have facilitate the realisation of pervasive health monitoring for both homecare and hospital environments. The concept of Body Sensor Networks (BSN) has been introduced recently where miniaturised wearable or implantable wireless sensors are used for continuous monitoring of patients. To facilitate research and development in BSN, a BSN hardware development platform, called BSN node, is proposed by Imperial College London. With its compact, low power and flexible design, the BSN nodes provide a versatile developing environment for pervasive healthcare applications.
Native ATM Videoconferencing based on H . 323

Due to the potential of videoconference as a communication service, ITU-T has issued several standards that try to regulate its transmission and control protocols. ATM is considered a good transmission media for this kind of traffic, due to its ability to assure quality of service (QoS) parameters. In this paper we identify the disadvantages of the current videoconferencing standards, and propose a new protocol stack for videoconference over ATM. In the new protocol stack, the different streams are transported directly over ATM, without any additional network layer protocol. The feasibility of the proposal is demonstrated by a prototype application.
Automated Extraction of Structural Information from SystemC-based IP for Validation

The increasing complexity and size of system level design models introduces a difficult challenge for validating them. Hence, in most industries, design validation takes a large percentage of the overall design time. In efforts to alleviate this problem, the authors propose a methodology of using structural reflection to extract structural information from design sources allowing the use of tools such as test bench generators and model viewers to seamlessly employ this extracted information. In this paper the authors present a methodology to automatically extract structural information from already existing SystemC projects and we show how this information can be exploited for system management and validation tasks. The authors illustrate example uses such as visualization, design management tasks, and automated test generation
INFLUENCE OF WORK-FAMILY CONFLICT AND FAMILY-WORK CONFLICT ON EMPLOYEES’ TURNOVER INTENTIONS WITH GENDER, SOCIAL SUPPORT AND INDIVIDUAL VALUE AS MODERATING EFFECTS

work interfering with family-WIF and family interfering with work-FIW) and its influences on turnover intention. This research also examined the moderating effect of gender, social support and individual values on the relationship between the work-family conflict and turnover intentions. The participants of this study were 210 low and middle managers of four and five star hotels in Bali. This sample consisted of 126 males and 84 females. Multiple regression and hierarchical methods were used to test the proposed hypotheses. The result showed that WIF positively and significantly influences the turnover intention but FIW did not. It was also found that social support significantly moderates the relationships between variables studied, but gender and individual value had no impact on it.
The shopping assistant Robot design based on ROS and deep learning

As the traditional service robots' artificial intelligence bottlenecks, there is a huge gap between service robots and human intelligence in the cognitive and learning discipline. So the service robots could not be widely applied. As deep learning theory is proposed in 2012, it may lead to a generation leap forward in machine learning discipline, so as to improving the traditional robot's cognitive algorithms. This paper studies the principle of three-dimensional Kinect sensor and the deep learning framework of CNN. We proposed a shopping assistant robot designing method which combined Robot Operation System and deep learning method. Firstly, the ROS packages for the service robot are designed. Secondly, Kinect sensors are used for acquiring the information in the robot. Finally, we use simulation to evaluated the design.
Electromyogram signal processing by using M-transform

The authors propose a new method for reducing both impulsive noise and white noise by use of M-transform and wavelet shrinkage. M-transform is a new signal transformation proposed by the authors, and any periodic time signal can be considered as the output of a filter whose input is an M-sequence. By using the properties of M-transform, it is shown that both impulsive noise and white noise can be eliminated by use of first M-transform and then wavelet shrinkage. In this paper, the proposed noise reduction method is applied to the electromyogram signal which sometimes contains many impulsive noise and white noise. From the results of the experiment, it is shown that the proposed method is very efficient for impulsive noise reduction.
Option Pricing in a Jump-Diffusion Model with Regime Switching

Nowadays, the regime switching model has become a popular model in mathematical finance and actuarial science. The market is not complete when the model has regime switching. Thus, pricing the regime switching risk is an important issue. In Naik (1993), a jump diffusion model with two regimes is studied. In this paper, we extend the model of Naik (1993) to a multi-regime case. We present a trinomial tree method to price options in the extended model. Our results show that the trinomial tree method in this paper is an effective method; it is very fast and easy to implement. Compared with the existing methodologies, the proposed method has an obvious advantage when one needs to price exotic options and the number of regime states is large. Various numerical examples are presented to illustrate the ideas and methodologies.
Achieving Bounded Blocking in Circuit-Switched Networks

Traditional dynamic routing schemes in circuitswitched networks rely on accurate knowledge of the traffic matrix in determining algorithm parameters, etc. But the network traffic matrix is not constant, and so the schemes in general cannot make any guarantees on performance. On the other hand, Valiant load-balancing (VLB), which we study in this paper, uses the same parameters for all traffic matrices, and can provide a theoretical upper bound on the worst case blocking probability (as long as the node aggregate traffic rates do not exceed some bounds). This unusual property of VLB enables the design of circuit-switched networks with performance guarantees. Our main contributions in this paper are: 1) Proposed Valiant load-balancing as a dynamic routing scheme in circuit-switched networks to achieve bounded blocking probabilities under all traffic matrices; 2) Formulated and solved the blocking probability minimization problem for VLB, providing a theoretical bound for the worst case blocking probability; 3) Demonstrated the performance of VLB in simulation.
Electric Power Grid Indications & Warning Tool

The Electric Power grid makes an attractive target because it is the foundational critical infrastructure that underlies all others. A successful attack on the po wer grid causing a wide-area long-term outage would have sig nificant national security, economic, and public health and safety consequences. Power grid outages could even cause a dditional cascading failures in other critical infrastructures due to the high dependency on electrical power. Hence, it is impera tive that public and private authorities receive Indications and Warnings (I&W) when such attacks are in the early operational phases in order to mitigate their consequences. Towards that end, we have developed a prototype Electric Power I&W tool to provide near real-time I&W to alert private and public sector authorities when the likely causes of outage events are malicious ac tivity. We have developed new business intelligence-style metrics t o quantify the consequences of power system outages and we have de veloped techniques to identify multiple, cotemporaneous att acks. We demonstrate the ability to minimize false alarms due to severe weather. Similar techniques can be employed to min imize false alarms caused by high temperatures and wind or geom agnetic storms. For this capability to be used in the private and public sectors, a commercialized product based on this res arch would need be developed that would be: (1) part of a larg er grid management capability or (2) part of state and metr opolitan area fusion centers’ capabilities where potential attack s on multiple critical infrastructure components are monitored continually.
Cloud enabled and cluster based efficient data broadcasting in VANETs

Predominantly a Vehicular Ad-hoc Network (VANET) is contemplated with vehicles being the resource end user. The alternate way of judging it is perhaps the vehicles as resource contributor. Stemmed from this notion, we set down the conception of vehicular cloud where the en route safety services and the like can be aided uses in vehicular resources. In view of the fact that the distinguishable feature of vehicular network i.e. High mobility, congregated movement and routed behavior, latest methods stand in need to adequately manage the compelling resources. We propose an adaptable hierarchical vehicular cloud network model based on Road Side Unit regions for determining in vehicular resources which satisfy for its non-ideal nature by considering the mobility impression of vehicular nodes by controlled flooding of packets using RPM approach. Conclusively we introduce architecture for cloud enabled VANET to back remote data storage, communication and processing.
Cough signal recognition with Gammatone Cepstral Coefficients

Cough Recognition is a valuable classification problem in healthcare. Generally, feature representation contributes a lot to the overall classifying performance. In this paper, a novel feature extraction method, Gammatone Cepstral Coefficients (GTCC), is investigated for cough recognition. The accuracy of GTCC comparing with MFCC is evaluated on a designed cough dataset following a 10 fold cross-validation schemes. Considering the imbalance of that dataset, weighted SVM is applied as the base classifier. The results indicate that GTCC surpass MFCC in modeling cough signals. With combination of GTCC and MFCC, a better performance is achieved. This paper provides a better feature representation prototype in cough recognition.
Resonant DC-DC converters for photovoltaic energy generation systems

Renewable energy generation systems, based on photovoltaic sources, currently represent the most suitable solution, for low power levels, to reduce the energy consumption produced by oil and gas. Moreover, they are becoming more and more attractive as the price per watt of the photovoltaic modules is decreasing. However, the conversion efficiency of commercial photovoltaic modules is still rather low so it is particularly important to be able to achieve, in every operating situation, the maximum possible energy. The paper focuses on double-stage, grid-connected, Photovoltaic conversion systems. In this frame, different structures of resonant DC-DC converters, which allows reduced switching losses, are analyzed accounting the specific behavior of Photovoltaic sources.
Artificial Neural Networks Based Emotion Classification System through Relative Wavelet Energy of EEG Signal

Emotion classification from EEG brain signal has been a widely research topic recently, because of the complexity of processing the multi channels of the brain signal and also the problem on mapping the human emotion itself. This paper discusses the technique to determine the emotion classification from EEG brain signal using a relative wavelet energy (RWE) as a feature vector and an artificial neural networks (ANN) as a classifier. In this research, two types of ANN classifier was utilized and analyzed, namely, Back-propagation Neural Networks (BPNN) and Probabilistic Neural Networks (PNN). Also reducing the number of the EEG channel to be processed is investigated, in order to decrease the computational cost of the classification system. Results showed that the recognition rate of the reduced utilized channels up to 4 channel are incomparable with that of the full utilization of 32 channels. However, 8 and 14 channels still produced sufficient recognition rate. It is also confirmed from experiments that the BPNN shown as a more reliable classifier compare with the PNN method.
Consensus-based cooperative formation control with collision avoidance for a multi-UAV system

In this paper, we study cooperative control problems with a multi-UAV system. Specifically, we propose a c consensus-based cooperative formation control strategy with collision-avoidance capability for a group of multiple unmanned aerial vehicles (UAVs). To cooperatively fly in formation, a consensus-based algorithm and a leader-follower structure are applied to the UAVs. Collisions among the UAVs can occur while they are flying with the cooperative control algorithm. Therefore, we describe how the cooperatively controlled UAVs avoid collisions while they are flying to desired position for formation. The collision-avoidance strategy is based on an artificial potential approach. The convergence is guaranteed even when the cooperative formation control algorithm and the collision-avoidance control algorithm are simultaneously applied to the quadrotors. Finally, the proposed approach is validated by some simulations.
Disparity Channels in Early Vision

The past decade has seen a dramatic increase in our knowledge of the neural basis of stereopsis. New cortical areas have been found to represent binocular disparities, new representations of disparity information (e.g., relative disparity signals) have been uncovered, the first topographic maps of disparity have been measured, and the first causal links between neural activity and depth perception have been established. Equally exciting is the finding that training and experience affects how signals are channeled through different brain areas, a flexibility that may be crucial for learning, plasticity, and recovery of function. The collective efforts of several laboratories have established stereo vision as one of the most productive model systems for elucidating the neural basis of perception. Much remains to be learned about how the disparity signals that are initially encoded in primary visual cortex are routed to and processed by extrastriate areas to mediate the diverse capacities of three-dimensional vision that enhance our daily experience of the world.
Text mining with information extraction

The popularity of the Web and the large number of documents available in electronic form has motivated the search for hidden knowledge in text collections. Consequently, there is growing research interest in the general topic of text mining. In this dissertation, we develop a text-mining system by integrating methods from Information Extraction (IE) and Data Mining (Knowledge Discovery from Databases or KDD). By utilizing existing IE and KDD techniques, text-mining systems can be developed relatively rapidly and evaluated on existing text corpora for testing IE systems. We present a general text-mining framework called DISCOTEX which employs an IE module for transforming natural-language documents into structured data and a KDD module for discovering prediction rules from the extracted data. When discovering patterns in extracted text, strict matching of strings is inadequate because textual database entries generally exhibit variations due to typographical errors, misspellings, abbreviations, and other sources. We introduce the notion of discovering “soft-matching” rules from text and present two new learning algorithms. TEXTRISE is an inductive method for learning soft-matching prediction rules that integrates rule-based and instance-based learning methods. Simple, interpretable rules are discovered using rule induction, while a nearest-neighbor algorithm provides soft matching. SOFTAPRIORI is a text-mining algorithm for discovering association rules from texts that uses a similarity measure to allow flexible matching to variable database items. We present experimental results on inducing prediction and association rules from natural-language texts demonstrating that TEXTRISE and SOFTA PRIORI learn more accurate rules than previous methods for these tasks. We also present an approach to using rules mined from extracted data to improve the accuracy of information extraction. Experimental results demonstrate that such discovered patterns can be used to effectively improve the underlying IE method.
A linear-time nearest point algorithm for the lattice An*

The lattice An* is an important lattice because of its covering properties in low dimensions. Two algorithms exist in the literature that compute the nearest point in the lattice AAn* in O(n log n) arithmetic operations. In this paper we describe a new algorithm that requires only O(n) operations. The new algorithm makes use of an approximate sorting procedure called a bucket sort. This is the fastest known nearest point algorithm for this lattice.