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The sufficient utilization of the mega data acquired from operative power units is a promising way leading to high efficient, clean and safe operation of power plant. Hereinto, data filtering is a critical link to the data-driven dynamic model identification aiming at optimizing the process control. The machine learning is an advantageous approach for filtering usable data from mega databases for identification due to its effective statistical learning strategy to the field data with noises, disturbances and coupling quantities. Therefore, a data filtering method of combining Gaussian Naive Bayesian classifier and prediction error method (Gaussian NB-PEM) is proposed in this paper. Firstly, variables associated with identification model are selected by analyzing the characteristics of the process. Secondly, the GaussianNB classifier is used for coarse data filtering by calculating the priori probability of each attribute from training sample set and the probability with all possible values of the known categories for testing sample set. Thirdly, the prediction error method is used for further data filtering based on model fitting. By using the filtered closed-loop data, the dynamic characteristics of the superheated steam temperature is modeled and verified by closed-loop control simulation, showing the validity of the Gaussian NB-PEM data filtering method.

The paper is devoted to functional and structural testing of programs. Experimental results of a set of programs are presented. The experiments cover selection of functional tests, analysis of function and line coverage and optimization of test suites. The comparison of code coverage results and selection of the most effective tests are discussed in the relation to the test-first approach for program development. The types of the code not covered by the tests are classified for different categories.

The ability to walk is typically related to several biomechanical components that are involved in the gait cycle (or stride), including free mobility of joints, particularly in the legs; coordination of muscle activity in terms of timing and intensity; and normal sensory input, such as vision and vestibular system. As people age, they tend to slow their gait speed, and their balance is also affected. Also, the retirement from the working life and the consequent reduction of physical and social activity contribute to the increased incidence of falls in older adults. Moreover, older adults suffer different kinds of cognitive decline, such as dementia or attention problems, which also accentuate gait disorders and its consequences. In this paper we present a methodology for gait identification using the on-board sensors of a smart rollator: the i-Walker. This technique provides the number of steps performed in walking exercises, as well as the time and distance travelled for each stride. It also allows to extract spatio-temporal metrics used in medical gait analysis from the interpretation of the interaction between the individual and the i-Walker. In addition, two metrics to assess users’ driving skills, laterality and directivity, are proposed.

The classification of color image that contain skin pixel is a challenging process. It is challenging due to certain factor such as brightness, background similar to human skin represent the obstacles thereof. The basic approach of skin detection is color based classification. The human skin image is the composition of RGB color. The data values of skin is calculated by randomly sampling RGB color values of different face images which have different age, race, genders sets. However, its performance has not really been high because of the high overlapped degree between “skin” and “non-skin” pixels. This paper describes the linear norm fuzzy based twin support vector machine (LN-FTSVM)) approach for discriminate skin and non-skin data values of skin dataset and to enhance the skin recognition performance. The concept of fuzzy is resolved the unclassified and overlapped data region problems. If no decision function is positive for a data set, this data set is classified into a class with the large membership value. By computational experiments shows that Experiments result shows that the accuracy is improved by linear norm fuzzy based TSVM (LN-FTSVM)) over the conventional methods.

Through progressive technological advancements, the importance and relevance of platform-based business models are proliferating. On the one hand, in the context of the internet of things, platforms enable the interactions between smart products and service providers and offer basic functionality to develop data-based smart service applications. On the other hand, multi-sided platforms efficiently network different groups of actors—for example, providers and consumers of smart service applications. In both cases, companies face the challenge of developing a complex socio-technical smart service system (SSS) comprising networked actors, smart products, and IT infrastructures, such as digital platforms. Conceptual models are established artifacts to facilitate efficient and unambiguous communication and support the development and integration of technical systems. The purpose of this paper is to design, demonstrate, and evaluate a domain-specific modeling language for the conceptual design of platform-based SSS. The presented modeling language is the first one to integrate hitherto separate research streams on SSS and digital platforms. It is demonstrated with the real case of a mechanical engineering company and evaluated conceptually.

The history of the computer science department at the Institute of Mathematics and Informatics of Bulgarian Academy of Sciences since 1963 is briefly described. Starting with small groups of enthusiastic people initiating the new for Bulgaria research in the field of Informatics, nowadays the department can report some significant achievements. The paper presents some of them in three areas—software quality evaluation and management, data mining and semantic analysis, information systems and communities—which mark some key areas of research during the years.

The rapid growth of wireless devices and services exacerbates the problem of spectrum scarcity in wireless networks. Recently, spectrum auction has emerged as one of the most promising techniques to enhance spectrum utilization and mitigate this problem. Although there exist some works studying spectrum auction, most of them are designed for single-hop communications, and it is usually not clear whom a winning user communicates with. Moreover, most previous auction schemes only focus on satisfying the incentive compatibility property, also called truthfulness, but ignore another two critical properties: individual rationality, and budget balance. Thus, they may not be economic-robust. In this paper, we propose a transmission opportunity auction scheme, called TOA, which can support multi-hop data traffic, ensure economic-robustness, and generate high revenue for the auctioneer. Specifically, in TOA, instead of spectrum bands as in traditional spectrum auction schemes, users bid for transmission opportunities (TOs). A TO is defined as the permit of data transmission on a specific link using a certain band, i.e., a link-band pair. The TOA scheme is composed of three procedures: TO allocation, TO scheduling, and pricing, which are performed sequentially and iteratively until the aforementioned goals are reached. We prove that TOA is economic-robust, and conduct extensive simulations to show its effectiveness and efficiency.

This paper presents an impedance-based modelling method for length-scalable long transmission cable (LTC), which is able to assess influence of LTC on stability of grid-connected inverter (GCI). Electrical parameters of power cable in per unit-length (p.u.l.) are first extracted from the measured terminal short-circuited and open-circuited admittances. Then, the terminal admittance of power cable in different length can be derived on the basis of the obtained p.u.l. parameters. Finally, a decoupled two-port circuit model is established for the LTC using the derived terminal admittance. Simulation results are given to validate effectiveness of the proposed impedance based modelling method for LTC. The proposed impedance based modelling method is able to avoid repetitive terminal impedance measurement. Also, the proposed model is able to support impedance-based stability criterion for GCI with length scalable LTC.

In modern manufacturing, hundreds of process variables are collected, and it is usually difficult to identify the most informative ones. Partial Least Square Regression provides an efficient way to evaluate each variable, but it cannot evaluate any variable subset as a whole. In the paper, a new framework of key process variable identification is proposed. It combines PLSR model and wrapper feature selection to firstly assess every variable individually and then the top variables in groups. Five datasets are tested, and the average classification accuracy is higher and the key process variables identified are less than the available approaches.

Recent developments show that it is possible to implement drivers using 3D Graphics Hardware for the PC, which obtain performance previously found only in very expensive, specialized graphics systems. Typically, these drivers provide single application support only. This unfortunately results in the lack of having the comfort of a Windowing System and the inability to output to more than one viewing window. In this paper, an implementation to include the missing properties is discussed.

This paper describes a system for the analysis of the diagnostic electrocardiogram (ECG). The main emphasis is placed on the problems of ECG description, medical knowledge representation, clinical decision. In all these fields the use of fuzzy sets is a powerful tool to treat imprecision and vagueness. Electrocardiographic features are represented by fuzzy linguistic expressions; pathological classes are defined in terms of fuzzy descriptors; diagnostic classification is obtained by means of the composition of fuzzy relations. The result is (for all possible pathologies) a group of three fuzzy sets defined in the interval [0,1], each one representing the relative (fuzzy) degree of membership of the ECG under examination in one of the three classes: Normal, Abnormal, Borderline.

In this paper, we consider the age of information (AoI) of a discrete time status updating system, focusing on finding the stationary AoI distribution assuming that the Ber/G/1/1 queue is used. Following the standard queueing theory, we show that by invoking a two-dimensional state vector which tracks the AoI and packet age in system simultaneously, the stationary AoI distribution can be derived by analyzing the steady state of the constituted two-dimensional stochastic process. We give the general formula of the AoI distribution and calculate the explicit expression when the service time is also geometrically distributed. The discrete and continuous AoI are compared, we depict the mean of discrete AoI and that of continuous time AoI for system with M/M/1/1 queue. Although the stationary AoI distribution of some continuous time single-server system has been determined before, in this paper, we shall prove that the standard queueing theory is still appliable to analyze the discrete AoI, which is even stronger than the proposed methods handling the continuous AoI., 9 pages, 3 figures, Accepted for presentation in ITW 2021

This paper describes the functioning of a manufacturing cell, in which several complex systems have been joined, such as: a robot, a computer vision system, and a lathe. To allow communication between these systems in an organised manner, diverse technologies have been used, varying from a field bus to the intranet of the company. The robot takes the role of master, coordinating the functioning of the rest of subsystems. The computer vision takes charge calculating the position and orientation of the pieces of injected plastic and communicating them to the robot. The robot gathers the pieces and takes them up to the lathe. Once the process of the lathe has finished, the robot gathers again the piece and deposits it in the packaging system. The cell is endowed with the possibility of remote adjustment and monitoring across the company's intranet. The cell's functioning conditions can be modified from the laboratory, to assure that the pieces made are within the quality limits specified by the clients.

This paper addresses the Joint Angle and Delay estimation problem of a received multi-carrier frame through multiple antennas. In particular, each source is parameterized through its complex gain, Time-of-arrival (ToA) and Angle-of-arrival (AoA). We develop three novel JADE methods in a partially relaxed model, where one relaxes the interference zone, while focusing on a certain direction at a given time. Computer simulations are given with comparison to the Cramer-Rao Bound.

To improve the intelligent image recognition abilities of edge devices, a parallel-optimization-based framework called POWER is introduced in this paper. With FPGA (Field-Programmable Gate Array) as its hardware module, POWER provides well extensibility and flexible customization capability for developing intelligent firmware suitable for different types of edge devices in various scenarios. Through an actual case study, we design and implement a firmware prototype following the specification of POWER and explore its performance improvement using parallel optimization. Our experimental results show that the firmware prototype we implement exhibits good performance and is applicable to substation inspection robots, which also validate the effectiveness of our POWER framework in designing edge intelligent firmware modules indirectly.

Network coding has been applied successfully in peer-to-peer systems to shorten the distribution time, particularly in extreme conditions where peers in the network are clustered and separated by physical links with limited bandwidth. In this paper, we focus more closely on the use of source coding, i.e. encoding is done only at the source, as an alternative to network coding to facilitate content distribution under such limited bandwidth configuration. We observe analytically and experimentally that, in this specific case, with appropriately chosen expansion factors, source coding can have comparable performance to network coding in terms of distribution time, yet consuming much less computational resources than the latter approach does.

The dynamic programming is an important procedure for speech understanding and image recognition. In this paper, the application of dynamic programming to recognize the image basing on its contour is recalled, then the parallelism of this process is analyzed and exploited. Some parallel dynamic programming algorithms for evaluating the smallest value of the minimum intersegment distance path in a data table are designed in detail on several kinds or the specialized parallel computer, such as 2D Systolic Array, ID Systolic Array and an associative computer of the SIMD type. Their computation complexities are respectively estimated as O(k), O(k) and O(kw), while the cost O(mn) is obtained on serial computer, where k=max(2n,m), m is the number of segments of a contour to be recognized, n gives the number of segments of the sample contour and w is the bit-width of the used associative fields.

Data Protection (DP) and Universal Human Rights are extremely relevant to biometrics, where inherently private data is used for authentication purposes. In this context this paper stresses that there are significant challenges beyond biometric authentication. For example, it has been shown in the existing literature that medical information of a skin disease from a fingerprint, symptoms of diabetes on the retina, or diseases affecting one's walk can be extracted from biometric recordings. We address the derived privacy challenges in biometrics by a careful review of relevant aspects of the universal human rights from UN documents and the EU General Data Protection Regulation (GDPR) with a first identification and enumeration of relevant attributes. From the derived privacy sensitive attributes and respective requirements, de-identification approaches to protection of soft biometrics in face and fingerprints are explored. In consideration of these techniques, there is the question of what constitutes legal and moral biometric signal processing presently in the state-of-the-art, as well as motivation for further work towards fulfilling the criteria.

Interconnection networks play a crucial role in the performance of massively parallel computers. Hierarchical interconnection networks provide high performance at low cost by exploring the locality that exists in the communication patterns of massively parallel computer systems. The Tori-connected mESH (TESH) Network is a 2D-torus network of multiple basic modules, in which the basic modules are 2D-mesh networks that are hierarchically interconnected for higher level networks. In this paper, we present a deadlock-free routing algorithm for the TESH network using 2 virtual channels - 2 being the minimum number for dimension-order routing - and evaluate the network's dynamic communication performance under various nonuniform traffic patterns, using the proposed routing algorithm. We evaluate the dynamic communication performance of TESH, mesh, and torus networks by computer simulation. It is shown that the dynamic communication performance of the TESH network is better than that of the mesh and torus networks.

Current wake vortex separation minima are a major impediment to increasing traffic capacity since they require greater separations than radar separation minima. The concept of dynamic wake vortex separation minima, which would allow reduced separations in favorable meteorological conditions when wake durations on flight paths become shorter, would allow an increase in capacity. This paper proposes a methodology that uses a probabilistic wake vortex prediction model to probabilistically assure that wake vortex encounter risks at reduced separations are equal or lower than the risks at current separations.

Adaptive consensus seeking in group decision making.Mutual influence modeling.Taking into account human attitude.Multi-criteria problem using bipolarity concept.Consensus processes with or not individual changes. This paper addresses the collaborative group decision making problems considering a consensus processes to achieve a common legitimate solution. The proposed resolution model is based on individual bipolar assessment. Each decision maker evaluates alternatives through selectability and rejectability measures which respectively represent the positive and negative aspects of alternatives considering objectives achievement. The impact of human behavior (influence, individualism, fear, caution, etc.) on decisional capacity has been taken into account. The influence degrees exerted mutually by decision makers are modeled through concordance and discordance measures. The individualistic nature of decision makers has been taken into account from the individualism degree. In order to achieve a common solution(s), models of consensus building are proposed based on the satisficing game theory formalism for collective decision problems. An application example is given to illustrate the proposed concepts.

This paper presents the theoretical analysis, design and FPGA implementation of a pass-band chaos-based CDMA system. The system uses a digital modulation technique called Chaos-Phase-Shift-Keying (CPSK) in a complex form, where chaotic sequences are used for spreading digital messages. Conventional multi-rate signal processing techniques are employed for carrier modulation and demodulation, which changed the structure of the theoretical model of the analysed system. The model of a pass-band additive white Gaussian noise channel is also constructed. The system is implemented in Altera Stratix II EP2S180 DSP Development Board. The FPGA design flow is carried out using Alter DSP Builder integrated into MathWorks Simulink algorithm development environment. The BER characteristics obtained on the prototype and simulation confirmed the theoretical findings.

In order to improve the classification accuracy in the field of handwriting character recognition (HCR), the number of derivative algorithms has improved and the interest in feature extraction has increased. In this paper, we propose a metaheuristic method for feature extraction algorithm with Whale Optimisation Algorithm (WOA) based HCR. WOA is a swarm-based techniques that mimic the social behavior of groups of animals, which mimics the social behavior of humpback whales. Freeman chaincode (FCC) is utilised as a data representations of handwritten text images. Nevertheless, the representations of FCC depends on the length of the path and the branching of the character’s nodes. To solve this problem, we propose a metaheuristic approach through WOA to find the shortest path length and minimum computational time for handwriting recognition. Finally, the results were compared with the existing proposed Flower Pollination Algorithm (FPA) at the time of FCC extraction. The results show that WOA is a bit better at getting shorter path lengths than FPA in terms of path lengths. In terms of calculation time, WOA calculates faster calculation time by feature extraction than FPA.

Simulated annealing's high computational intensity has stimulated researchers to experiment with various parallel and distributed simulated annealing algorithms for shared memory, message-passing, and hybrid-parallel platforms. MapReduce is an emerging distributed computing framework for large-scale data processing on clusters of commodity servers; to our knowledge, MapReduce has not been used for simulated annealing yet. In this paper, we investigate the applicability of MapReduce to distributed simulated annealing in general, and to the TSP in particular. We (i) design six algorithmic patterns of distributed simulated annealing with MapReduce, (ii) instantiate the patterns into MR implementations to solve a sample TSP problem, and (iii) evaluate the solution quality and the speedup of the implementations on a cloud computing platform, Amazon's Elastic MapReduce. Some of our patterns integrate simulated annealing with genetic algorithms. The paper can be beneficial for those interested in the potential of MapReduce in computationally intensive nature-inspired methods in general and simulated annealing in particular.

A feature descriptor aims at the characterization of images whose discriminative data are used by different applications, as image recognition and classification. An efficient feature descriptor can accurately describe the content, and maintain low computational resources. This paper proposes a new feature descriptor, called Modified Center-Symmetric Local Mapped Pattern (MCS-LMP), which is based on the Center-Symmetric Local Mapped Pattern (CS-LMP) descriptor. By using the central pixel in the calculations, we can better characterize an image content, while using a reduced descriptor size. The descriptor was evaluated in an object classification task through the comparison of the results with those of CS-LMP, SIFT and LIOP. It could improve the sensibility of classification at shorter computational time.

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelity for postanalysis. Because supercomputers and HPC applications are becoming heterogeneous using accelerator-based architectures, in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existing state-of-the-art GPU-based lossy compressors suffer from either low compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version, cuSZ, for one of the best error-bounded lossy compressors-SZ. To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data. Our contributions are fourfold. (1) We propose a dual-quantization scheme to entirely remove the data dependency in the prediction step of SZ such that this step can be performed very efficiently on GPUs. (2) We develop an efficient customized Huffman coding for the SZ compressor on GPUs. (3) We implement cuSZ using CUDA and optimize its performance by improving the utilization of GPU memory bandwidth. (4) We evaluate our cuSZ on five real-world HPC application datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and GPUs. Experiments show that our cuSZ improves SZ's compression throughput by up to 370.1x and 13.1x, respectively, over the production version running on single and multiple CPU cores, respectively, while getting the same quality of reconstructed data. It also improves the compression ratio by up to 3.48x on the tested data compared with another state-of-the-art GPU supported lossy compressor., Comment: 13 pages, 8 figures, 9 tables, published in PACT '20

Wireless sensor network (WSN) is composed by a set of sensor nodes with low energy consumption and low cost which could sense quantities like humidity, temperature, pressure and send them to the central node. The routing optimization in IoT networks is required taking into account the traffic, congestion, and failure of some or all network services. In fact, low traffic and network congestion means high quality routing in that network. Increasing the network lifetime and improving the routing quality is one of the main concerns of IoT-based sensor networks. In this paper, a new clustering algorithm is proposed to increase the network efficiency in the IoT-based sensor networks using the fuzzy logic and particle swarm optimization (PSO) algorithm. In this approach, the cluster was created in WSN using energy modelling for effective routing of data packages via using fuzzy logic and (PSO). The stimulation results of the proposed method showed higher capabilities by comparison with reported algorithms (e.g. LEACH, FLCFP, HEED, FBCFP) s that were used for energy consumption optimization in WSN.

In this paper, a new configuration for a multi-terminal offshore wind farms based VSC-HVDC and large scale photovoltaic system using modified unified adaptive linear neuron (Adaline) algorithm is presented. In the offshore station, the permanent magnet synchronous generator (PMSG)-based wind turbines are tied to the bus via back-to-back converters and a step-up transformer. The large scale photovoltaic power is connected to the dc bus between the offshore voltage source converters (VSC) and onshore VSC-HVDC stations via a modular multilevel converter (MMC). The Power management and the synchronization between two buses are achieved by controlling the two VSC converters. The performance of the topology proposed is tested using Matlab/Simulink during permanent operation mode and in the presence of DC fault.

The number of channels of a multichannel rejection filter (MRF) has been optimized by the energy and probabilistic criteria. The matrix of MRF coefficients is defined. The paper includes analysis of the gain in signal detection effectiveness, in the presence of interference, as compared to the single-channel rejection filter.

This paper presents a novel method to define the calibration standards for the line-reflect-reflect-match method in a reciprocal 16-term error network. The procedure is based on four two-port calibration measurements: a thru line, a pair of matches, and two pure reactance pairs. The line standard and the resistance of the match standard need to be exactly known. The reactances of the match and lossless open and short standards are found using their raw S-parameter measurement data. The algorithm is limited to second-tier calibration of a reciprocal error network with a pre-calibrated network analyzer. Detailed theory of the method is presented and its functionality is demonstrated by practical on-wafer measurements and simulation.

In this paper, the problem of stability analysis of nonlinear stochastic Hopfield neural networks(FHNNs) with time-varying delays is investigated by using the Takagi-Sugeno(T-S) approach. Combined with both the fuzzy relaxed technique and an improved free-weighting matrix approach with weighting-dependent Lagrange multipliers, less conservative stability criteria is proposed via the Lyapunov-Krasovskii functional approach. Furthermore, related algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis and the obtained stability criteria is in terms of Linear matrix inequalities. Finally, an illustrative example shows less conservatism of the proposed approaches.

Sudoku is a very popular mathematical and logical curiosity with abundant variations. Many artificial intelligence (AI) programs that imitate human expertise have been developed to solve sudoku puzzles. However, most popular methods, such as Recurrent Relational Networks and Double Q-learning, are primarily reinforcement learning (RL) algorithms. These algorithms are rather complex since they consist of two or more fundamental models for achieving high accuracy. To circumvent this drawback, this paper proposes a simple Multi-Layer Perceptron (MLP) model that not only produces correct results but is easy enough for implementation as well. Since the MLP model is a supervised learning method and is widely used in classification problems, this paper first discusses how the training data is generated. It then demonstrates a coding method that can be used to convert 4 × 4 sudoku puzzles to classification problems. Afterward, it shows the MLP settings and how the predictions from the model can be transformed into legal solutions. This model has been thoroughly tested on sudoku puzzles with various levels of difficulty, and the resulting data illustrate that its accuracy is no worse than those reinforcement learning methods and other non-AI algorithms. Moreover, with slight modification, the MLP model can be utilized on larger scales, such as 9 × 9 sudoku puzzles, or even harder ones.

SUMMARY A comprehensive context management approach is necessary in the era of ubiquitous technologies, and efficient context data collection is one of the most fundamental and important processes for realizing comprehensive context management. Traditional context data collection approaches are based on Transmission Control Protocol (TCP) or User Datagram Protocol (UDP) over Internet Protocol (IP), which has several disadvantages, such as lack of efficient mobility support, security and data transfer efficiency. Content-Centric Networking (CCN), on the other hand, provides advantages in terms of mobility, security and bandwidth efficiency in comparison with IP. In this paper, we introduce our user-centric comprehensive context management framework, and propose a secure and efficient context data collection and provision approach based on the framework using CCN as a network and transport layer. This context collection approach provides a flexible security mechanism by introducing three levels of security type. It also provides bandwidth efficiency by taking advantage of CCN's content caching; performance evaluation results show that our approach can reduce bandwidth consumption up to 99% for pull and up to 46% for push in comparison to a UDP/IP-based system. Our approach also provides advantages in supporting mobility and leveraging multiple interfaces. Copyright © 2013 John Wiley & Sons, Ltd.

We report on the design and evaluation of the initial results of operation of a prototype of an advanced system for maritime security. The system is autonomous and is designed to remain in the ocean for extended periods up to two months. It is based on the Bottom Stationing Ocean Profiler (BSOP), an un-tethered, autonomous platform that stations itself on the sea floor and ascends to the surface at specific time intervals or, potentially, when triggered by certain events such as recognizable acoustic signals, collected and analyzed on board. The surface operations of the system include optical data acquisition, image data analysis, communication with the ground station, and retrieval based functionality. The system is designed to take video and imagery of the surrounding ocean surface and analyze it for the presence of ships, thus, potentially enabling automatic detection and tracking of marine vehicles as they transit in the vicinity of the platform. The system transmits the data to the ground control via bi-directional RF satellite link and can have its mission parameters reprogrammed during the deployment. The described unit is low cost, easy to deploy and recover, and does not reveal itself to the potential targets. The paper describes the system hardware, architecture, algorithms for visual ship detection and tracking.

The quality and size of training set have a great impact on the results of deep learning-based face-related tasks. However, collecting and labeling adequate samples with high-quality and balanced distributions still remains a laborious and expensive work, and various data augmentation techniques have thus been widely used to enrich the training dataset. In this paper, we review the existing works of face data augmentation from the perspectives of the transformation types and methods, with the state-of-the-art approaches involved. Among all these approaches, we put the emphasis on the deep learning-based works, especially the generative adversarial networks which have been recognized as more powerful and effective tools in recent years. We present their principles, discuss the results and show their applications as well as limitations. Different evaluation metrics for evaluating these approaches are also introduced. We point out the challenges and opportunities in the field of face data augmentation and provide brief yet insightful discussions.

Data interoperability between computer-aided design (CAD) systems remains a major obstacle in the information integration and exchange in a collaborative engineering environment. The standards for CAD data exchange have remained largely restricted to geometric representations, causing the design intent portrayed through construction history, features, parameters, and constraints to be discarded in the exchange process. In this research paper, an ontology-based framework is proposed to allow for the full exchange of semantic feature data. The Ontology Web Language (OWL) is used to represent feature types as well as the concepts and properties that define features, which allows the use of existing ontology reasoning tools to infer new relationships and information between heterogeneous data. Boundary representation (B-Rep) data corresponding to the output of the feature operation is also stored for purposes of feature identification and translation verification. The base ontology and a small feature library are built in OWL, and a combination of OWL and SWRL (Semantic Web Rule Language) rules are developed to allow a feature from an arbitrary source system to be automatically classified and translated into the target system through the use of a reasoner. These rules relate input parameter and reference types to expected B-Rep objects, allowing classification even when feature definitions vary or when little is known about the source system. In cases when the source system is well known, this approach also permits direct translation rules to be implemented. With such a flexible framework, a neutral feature exchange format could be developed.Copyright © 2011 by ASME

Wireless Power Transfer (WPT) is a new technology for charging the electric vehicles that can make free humans from annoying wires. In recent years, WPT systems have been developed and have significant advances. Design of an inductive power transfer (IPT) has several sections; the two important of which are design of the power electronic devices and design of the pad topology and electromagnetic issues. In this paper, we have discussed the second section and examined parameters that affect the magnetic coupling coefficient between the two pads especially for DD Pad structure.

This paper presents a parallelized, solution adaptive, multi-grid hybrid unstructured finite volume solver (UNIC-UNS) for allspeed flows. The methods and techniques applicable for all speeds were implemented in the general-purpose CFD computer code. The hanging node solution adaptation, parallel computing algorithm with multi options of serial, PVM or MPI to accommodate any type of machines, algebraic multi-grid speeding-up convergence method were all facilitated in the solver. With the enhancement of higher order scheme data reconstruction, higher order upwind-biased differencing, multipressure-correction, and Bi-CGSTAB and GMRES matrix solvers, the solver can efficiently and accurately handle flow problems in several cases including benchmark and practical. Very good convergence characteristics have been demonstrated even for highly local enriched grids and high Reynolds number turbulent flow conditions.

Visual lifelogging is the process of automatically recording images and other sensor data for the purpose of aiding memory recall. Such lifelogs are usually created using wearable cameras. Given the vast amount of images that are maintained in a visual lifelog, it is a significant challenge for users to deconstruct a sizeable collection of images into meaningful events. In this paper, random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using SenseCam lifelog data streams to identify such events. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to "distinct significant events" in the visual lifelogs. Finally, the cross-correlation matrix C is cleaned by separating the noisy part from the non-noisy part. Overall, the RMT technique is shown to be useful to detect major events in SenseCam images.

Outlying Aspect Mining (OAM) aims to find the subspaces (a.k.a. aspects) in which a given query is an outlier with respect to a given data set. Existing OAM algorithms use traditional distance/density-based outlier scores to rank subspaces. Because these distance/density-based scores depend on the dimensionality of subspaces, they cannot be compared directly between subspaces of different dimensionality. Z-score normalisation has been used to make them comparable. It requires to compute outlier scores of all instances in each subspace. This adds significant computational overhead on top of already expensive density estimation—making OAM algorithms infeasible to run in large and/or high-dimensional datasets. We also discover that Z-score normalisation is inappropriate for OAM in some cases. In this paper, we introduce a new score called Simple Isolation score using Nearest Neighbor Ensemble (SiNNE), which is independent of the dimensionality of subspaces. This enables the scores in subspaces with different dimensionalities to be compared directly without any additional normalisation. Our experimental results revealed that SiNNE produces better or at least the same results as existing scores; and it significantly improves the runtime of an existing OAM algorithm based on beam search.

The track 1 problem in KDD Cup 2013 is to discriminate between papers confirmed by the given authors from the other deleted papers. This paper describes the winning solution of team National Taiwan University for track 1 of KDD Cup 2013. First, we conduct the feature engineering to transform the various provided text information into 97 features. Second, we train classification and ranking models using these features. Last, we combine our individual models to boost the performance by using results on the internal validation set and the official Valid set. Some effective post-processing techniques have also been proposed. Our solution achieves 0.98259 MAP score and ranks the first place on the private leaderboard of Test set.

High speed transmission of information suffers inevitably by intersymbol interference (ISI) induced by multipath propagation over frequency selective channels. ISI has been a major obstacle preventing the successful implementation of physical layer network coding (PLNC) systems in these environments. Another major obstacle is the high computational complexity of existing algorithms dealing with ISI, which is a determining factor in the design of relay nodes. In this paper, we propose a novel and computationally efficient decision feedback equalizer structure that is utilized at the end node and in conjunction with a simple amplify and forward (AF) technique employed at the relay. We derive the optimal equations for the decision feedback equalizer (DFE) coefficients and evaluate the end-to-end bit error rate performance in frequency selective channels, and provide both analytical and numerical simulation results that demonstrate the feasibility of the proposed approach under realistic channel conditions. The obtained results show that E2E performance is dominated by the channel with most severe frequency selectivity.

DEM has been well established as a powerful numerical technique to study the fundamentals of granular materials. It has limited application in dealing with practical particulate systems which often involve billions of particles. Graphical Processing Units (GPU) offers a solution to this problem. This paper explores GPU-based DEM development, and its performance is assessed under various conditions. For a packing case using 300,000 spheres, it is demonstrated that the speed up ratio (single GPU to single CPU) varies from 40 to 75, depending on the parallel algorithms used. The GPU-based DEM is then developed for other systems involving arbitrary wall geometries, moving wall boundaries, and non-spherical particles. Particle flow patterns in the top charging system of blast furnace, screw conveyor and rotating drums are studied. For ellipsoids in the rotating drums, when aspect ratio increases or decreases from 1.0, the dynamic angle of repose increases. Multiple GPUs with message passing interface (MPI) are also used. It is demonstrated that the speed using 32 GPUs can be 18 times faster than a single GPU. It can handle large granular systems with more than 10 million particles. Thus, the GPU-based DEM developed in this work makes the simulation of real industrial processes possible.

There had been obvious system insecurity in Nigerian power systems’ grid which often lead to frequent power system collapse. Studies have shown that the Privatized power companies that emanated as a result of power sector reform carried out recently in Nigeria are not fully on ground in network argumentation, planning and analysis of the network. For example, it is required that for any proposed capital project execution which would add to the existing Networks; certain analysis via system simulation need to be carried out. Such analysis would display the likely situation after the project is completed and commissioned. These analyses include investigating the bus voltage profile, power flow, losses, overvoltage condition etc. Consequently, this paper presents a development of Q-basic computer software package on Newton- Raphson power flow algorithm and subsequent testing on a 3 - Bus power system network with known solutions. Steady state operating conditions of busbars, generation, branch power flows and circuit system losses were determined. The obtained results are presented and discussed. These results are accurate and reliable, because they agree with the known solutions of the network. Conclusions are drawn, and necessary recommendations are presented.

The Multiple travelling salesman problem (MTSP) is a typical computationally complex combinatorial optimization problem, which is an extension of the famous travelling salesman problem (TSP) classified as NP-Hard. The MTSP can be generalized to a wide variety of routing and scheduling problems. The paper proposes a novel metaheuristic called Elephant Herding Optimization (EHO) to solve the MTSP. The EHO algorithm is bio-inspired from the natural herding behavior of elephant groups, which has proved its efficiency in solving the classic TSP and continued optimization problems. So, to extend the application of this algorithm, The paper makes the attempt to show how the EHO algorithm can be applied to the MTSP by testing some random benchmark instances of TSPLIB. The obtained results shows the performance of the proposed method.

This paper presents a novel algorithm for automatically locating, extracting, and recognizing human poses. The proposed algorithm consists of four stages: (i) training of typical poses in 2D and 3D configurations, (ii) Hausdorff matching for locating the pose, (iii) silhouette representation of the located pose using deformable templates, and (iv) joint extraction from the silhouette. In order to reduce the computational overhead, an a priori obtained training set, Hausdorff matching with principal component analysis (PCA), and a heuristic approach were used. The algorithm was tested on typical human poses such as standing, sitting, walking, crouching, and stretching, and experimental results show the efficiency and accuracy of the proposed algorithm.

Due to the exhaustion of IPv4 addresses, the transition to IPv6 has already begun using various transition mechanisms. In this paper comparison between two transition mechanism 6PE and 6to4 tunneling over MPLS (Multiprotocol Label Switching) based network has been performed. The experimental platform used is based on GNS3/Dynagen using emulated routers on which multiple TCP and UDP flows were generated between separated IPv6 network traversing through IPv4 based core. Traffic were generated using both transition mechanism and are compared to native IPv4 based MPLS network. Performance metric used are end-to-end delay, jitter and throughput, these parameters are also analyzed statistically using ANOVA, F-Test and T-Test to support a conclusion. Further, MPLS Traffic-Engineering (TE) using RSVP-TE signalling is applied, describing the implementation and performance test and analysis of TE tunnels on 6PE for IPv6 traffic. The same test-bed is used to implement and observe MPLS-TE behavior and throughput of 6PE based MPLS-TE to that of native IPv4 based MPLS-TE. Concluding, throughput of 6PE and 6to4 is around 99% and 96%, respectively, as compared to that of native IPv4.

Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms, and phishing. More than 86% of received e-mails are spam. Historical approaches to combating these messages, including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Many current solutions feature machine-learning algorithms trained using statistical representations of the terms that most commonly appear in such e-mails. However, these methods are merely syntactic and are unable to account for the underlying semantics of terms within messages. In this paper, we explore the use of semantics in spam filtering by introducing a pre-processing step of Word Sense Disambiguation (WSD). Based upon this disambiguated representation, we apply several well-known machine-learning models and show that the proposed method can detect the internal semantics of spam messages.

The interest in multivariate statistical process control (MSPC) has increased as the industrial processes have become more complex. This paper presents an industrial process involving a plastic part in which, due to the number of correlated variables, the inversion of the covariance matrix becomes impossible, and the classical MSPC cannot be used to identify physical aspects that explain the causes of variation or to increase the knowledge about the process behaviour. In order to solve this problem, a Multivariate Statistical Process Control based on Principal Component Analysis (MSPC-PCA) approach was used and an R code was developed to implement it according some commercial software used for this purpose, namely the ProMV (c) 2016 from ProSensus, Inc. (www.prosensus.ca). Based on used dataset, it was possible to illustrate the principles of MSPC-PCA. This work intends to illustrate the implementation of MSPC-PCA in R step by step, to help the user community of R to be able to perform it., FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2013)