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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 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.

Various control strategies are being used to achieve high performance operation of linear motor drives. To maintain minimum volume and weight of the power supply unit on board the transportation vehicle, peak thrust per unit current operation is a desirable objective. True peak thrust per unit current through slip control is difficult to achieve because the parameters of linear induction machines vary during normal operation. This paper first develops a peak thrust per unit current control law based on the per-phase equivalent circuit for linear induction machines. The algorithm for identification of the variable parameters in induction machines is then presented. Application to an operational linear induction machine (LIM) demonstrates the utility of this algorithm. The control strategy is then simulated, based on an operational transit LIM, to show the capability of achieving true peak thrust operation for linear induction machines.

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.

The Cygnus Dual Beam Radiographic Facility consists of two identical radiographic sources each with a dose rating of ∼4-rad at 1 m, and a ∼1-mm diameter spot size. The development of the rod pinch diode (RPD) was responsible for the ability to meet these criteria. The RPD in each machine uses a 0.75-mm tungsten diameter tapered anode rod extended through a 9-mm diameter cathode aperture. When properly configured, the electron beam born off the aperture edge can self-insulate and pinch onto the tip of the rod creating an intense, small x-ray source. These sources are utilized in concert with an imaging system on complex experiments, which are single-shot, high-value events. In such an application there is an emphasis on machine performance (reliability and reproducibility). Vacuum quality is a significant determinator for source performance. In this paper we discuss the Cygnus Dual Beam Radiographic Facility, the associated vacuum systems, vacuum monitor calibration and a multi-purpose vacuum chamber. Vacuum system performance, using various enhancements, will be reported. Using the vacuum system enhancements, as well as other machine tweaks, recent source performance has improved. This new level of performance will be presented via dosimetry and electrical measurements.

This paper describes a new framework of laser triangulation method for measuring the distance according to the basic principles of traditional laser triangulation method, which spread the range of the traditional measuring method. This method measure the distance for particle subsection by using three independent CCD that distribute uniformly along the optical axis and detect by using the plain reflected mirror as virtual detector which is equivalent to another CCD in the system when the beam image on the CCD. Collimator filter is composed of collimation system, polarizer and diaphragm, which reduce the impact of accurate measurement that measured surface is not ideal optical spot by reducing diameter of the imaging point on photosensitive surface of virtual detector, at the same time add light intensity control system to ensure stability of beam that control light intensity only fluctuating in a small range. The results of the system are effective agreement with the experimental observation.

This paper presents a method for characterizing chemical solutions using nanowire field effect transistors. A pulsed gate potential method is used to prevent instabilities related to the dynamics of ions and other charged species present in the solution. Applying this method realizes a significant increase of the stability of the drain current versus gate potential characteristics of the devices, enabling reproducible characterization of chemical solutions with nanowire field effect transistors in aqueous environments.

The paper employs fuzzy automata for emotion control by audio-visual stimulus. In the proposed design, emotions are represented by states, and transition of emotions is controlled by selective audio-visual stimulus. Experiments are undertaken to determine the fuzzy membership for state transitions in the automata. A fuzzy relational algebra has been used to determine the current membership distribution of the emotions from their previous value and fuzzy state transition memberships. Experiments with fifty audio-visual stimulus and one hundred subjects confirm that automatic control of emotion to a desired state from a given state can be attained with a success rate of approximately 86%.

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

Remote sensing multispectral images are extensively used by applications in various fields. The degradation generated by haze or smoke negatively influences the visual analysis of the represented scene. In this paper, a deep neural network based method is proposed to address the visualization improvement of hazy and smoky images. The method is able to entirely exploit the information contained by all spectral bands, especially by the SWIR bands, which are usually not contaminated by haze or smoke. A dimensionality reduction of the spectral signatures or angular signatures is rapidly obtained by using a stacked autoencoders (SAE) trained based on contaminated images only. The latent characteristics obtained by the encoder are mapped to the R - G - B channels for visualization. The haze and smoke removal results of several Sentinel 2 scenes present an increased contrast and show the haze hidden areas from the initial natural color images.

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.

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.

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.

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.

We have conducted a clinical trial to investigate the receptiveness and signal accuracy of our in-house light-weight single lead wearable wireless ECG device with 20 outpatients, who were suspected of cardiac rhythm issues. The receptiveness was measured via a survey score sheet while the signal accuracy was evaluated by comparing the Holter’s hourly heart rate report (the gold-standard) against the ones from our device. In terms of receptiveness, a score of 8.6 indicates good patient compliance. In terms of accuracy, the mean absolute error is 1.4 bpm (beats per minute) with precision of ±1.6 bpm. In addition, measurements from both devices were found to be linearly related with coefficient of determination, r2, of 0.97. Furthermore, the limits of agreement were calculated to be +3.54 and –4.71 based on the Altman and Bland technique, which indicated good agreement for most of our measurements against the Holter device. In addition, this paper also discusses the unexpected challenges of conducting a trial on actual outpatients which can be used as a reference for similar subsequent studies.

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.

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 collision experiment apparatuses that have currently developed can not able to assist students in understanding relation of physical parameters on the experiment that involves a collision restitution coefficient. This paper describes the development of the collision experiment apparatus for restitution coefficient determination based on free fall motion experiment. The system uses three couples of infra-red (IR) LEDs CQY99 as transmitter and photodiode sensors SFH 203FA as receiver while the data recording is driven by an SMD based Arduino Uno with an ATmega328 microcontroller. The sensors were mounted on a vertical transparent acrylic pipe in diameter of 100 mm, thickness of 5 mm and height of 1200 mm. The measuring used objects are spherical masses with varied diameter and type. The apparatus is installed on a 3-points configuration where the sensors are placed on three positions; sensor 1 is placed on the top, sensor 2 is in the middle, and sensor 3 at the bottom of the acrylic pipe. Sensor 1 and 2 can be adjusted according to the desired measuring distance. The work system of this apparatus is divided into three parts, that is: determining the acceleration of falling objects, application of the restitution coefficient, and helping students explore physical parameters related to the coefficient of restitution. The electronic circuit is designed with in two functions so that apparatus can calculate the period when the objects fall (t 1 ) and bounce (t 2 ). The surface of the apparatus is optimized to minimize air friction, so that the falling objects acceleration is equivalent to the Earth's gravity ( $\mathbf{g}=9.8\ \mathbf{m}/\mathbf{s}^{2}$ ). To obtain the collision restitution coefficients (e) over the calculation, the bouncing surfaces were varied. The results of the collision experiment have shown the persistence of the collision restitution coefficient parameter, with the results of $\mathbf{g}=9.79\pm 0.0032\ \mathbf{m}/\mathbf{s}^{2}, \mathbf{u} , and as shown in the results, the value of e of a freely falling object is 0 < e < 1. Based on this result, the apparatus can be used as collision experiment tool at schools to help students in understanding the concept of free fall and collision restitution coefficients.

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.

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.

There are considered interaction of accelerator-subcritical reactor in energy producing and nuclear waste transmutation problem. Review of some papers connected with this topic is given. Separate tasks which are related to this problem are control of nuclear reactor with accelerator, optimal choice of accelerator and reactor types, beam dynamics optimization and Structure of active reactor zone optimization. Possible construction of compact energy-producing nuclear station is proposed.

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.

A compact microwave bandpass filter with λ/4 stepped impedance resonators (SIRs) is presented in this paper. λ/4 SIRs are employed here not only to miniaturize the circuit size, but also to eliminate spurious resonance at 2f 0 . To achieve the maximal number of finite transmission zeros, this filter is synthesized based on the 4th-order fully canonical coupling topology. As a result, four finite transmission zeroes are analytically achieved, and used to sharpen filter rejection skirts. Three capacitive J couplings are systematically designed to meet the required coupling strengths for the filter frequency response. Besides, they also adjust their coupling zeros for 3f 0 spurious suppression. Lastly, a 4th-order fully canonical bandpass filter is designed and fabricated with central frequency of 1.8 GHz and fractional bandwidth of 11.0% The proposed filter possesses preferred merits: compact size, 4 transmission zeros and up to 4.8f 0 20 dB attenuated stopband.

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.

A broadband annual dielectric loaded cylindrical dielectric resonator Antenna (ADLCDRA) is designed and analyzed in this paper. The proposed antenna has a simple structure of two parts providing the monopole-type radiation pattern. It has a wide impedance bandwidth of 55% covering the frequency range from 0.71 to 1.25GHz and has a low profile of 0.173 λ 0 (λ 0 is the wavelength of the center frequency).

Commercial metal-oxide semiconductor (MOS) gas sensors have been widely used by recent studies as detection units of electronic noses (e-nose) in various applications including disease diagnosis. However, the enoses employing the MOS sensors can only discriminate a limited number of odor groups due to their poor selectivity. Preliminary studies have shown that the selectivity of the MOS sensors can be enhanced by jointly integrating with other sensory units such as QCM or potentiometric sensors, which, however, involves complex interface circuitry and measurement procedures. In contrast, this paper presents a hybrid electronic nose that combines olfactory information from an MOS sensor array together with a compact paperbased colorimetric sensor array which is simpler and easier to utilize. The proposed system employs total 8 MOS sensors, and the compact paper-based colorimetric sensors are fabricated with indicator dyes such as phenol red, methyl red, and methylene blue. Color profiles of the paper-based sensors are captured using a USB-microscope and the alterations of the dyes during the gas exposure are monitored. The improvement of the system performance in classifying six volatile organic compounds (VOC) are investigated by comparing the classification results of the system with and without the colorimetric sensors. The measurement data from both sensor arrays are mapped to the feature space using principal component analysis (PCA) for pattern extraction. It was confirmed that pattern separation among the target VOCs could be improved based on data fusion of these two sensor arrays. This hybrid e-nose system may be useful for improvement of VOC classification performance.

Fabrication of metal matrix composites (MMC) requires the use of sophisticated manufacturing processes. One such process is hot isostatic pressing (HIP) which is becoming a key technology for manufacturing MMC airframe structural panels for advanced aircrafts, superalloys and titanium alloys. We present in this paper the results of applying modern control techniques such as model predictive control (MPC) to HIP furnaces to achieve optimal temperature and pressure profiles-this would minimize the ultimate cost per unit of production. A HIP furnace is primarily a nonlinear distributed parameter process. The problem was solved in three steps. First, a nonlinear physical model was developed and validated from experimental data from a real 3-zone HIP furnace located at Industrial Materials Technology (IMT), Andover, Mass. The model was linearized in the second step to approximate the steady state behavior of a HIP furnace. Finally, in the third step, the linear model was used to design an MPC controller. Another control technique similar to H-infinity known as risk sensitive optimal (RSO) control was used in combination with MPC to increase robustness and improve steady state regulation. The controllers were tested on the nonlinear simulator. The response time of the combined MPC-RSO controlled system was less than one-third of the closed loop time response of the system under current PID control. The combined controller was also found to attenuate low frequency disturbances to a satisfactory level.

Survey of research papers is not an easy task for novice researchers, because they are not always good at finding all appropriate keywords for the survey. Moreover, it is not easy for them to understand positions of papers in their research fields instantly, even when they use famous search engines like Google Scholar, it may often take a long time for them to find scholarly literature. On the other hand, many researchers have presented citation visualization techniques for surveying research papers. However, it is still often difficult to observe the complicated relations across multiple research fields or traverse the entire relations in their interest. In this paper, we proposed a visualization technique for citation networks applying topic-based paper clustering. Our technique categorizes papers applying LDA (Latent Dirichlet Allocation), and constructs clustered networks consisting of the papers.

This paper examines the current CT2 system and proposes a simple method of capacity expansion using embedded ADPCM coding. Existing objective measures of speech quality are analyzed based on a simple error model.

In this paper, we present a dynamic model for simulating a car-like robot with trailer taking into account suspension system and tires deformations. The model describing the wheel/ground contact is a linear model and the joint tractor/trailer is modeled as a spherical-joint. The obtained results are compared with those given by the Pc-Crash software. We noted a good correlation for various scenarios. Furthermore, the proposed model has been successfully applied to study the unbalanced braking in a straight line and the jack-knife problem.

Machine vision based automatic apparatus is a necessary component of an automated production line. In this paper, taking can-end surface defect detection as an example, we presented a visual inspector and proposed a new surface defect detection framework. Firstly, the Region of Interest (ROI) of the input image is got by circle detection and prior radius knowledge of can-end. Then, the saliency map and texture filtering image are computed to overcome the problems of irregular texture and non-uniform illumination. Finally, all suspected defects are obtained by saliency map segmentation, connected component analysis. Following, the size, the average saliency value and the mean texture filtering value of each suspected defective region are calculated, and the 3-dimensional features are used to distinguish true defects in the central panel region of can-end. We employ the designed equipment to grab can-end images and do tests divided into four groups on the grabbed images. The accuracy is 98.16% and the execution time is about 65.7 milliseconds. Experimental results demonstrate that the proposed method can inspect faint and low contrast defect accurately. Meanwhile, the proposed method is simple and computationally efficient and meets the high speed of industrial automation.

The humanity have entered into one unprecedented “learning society” in ubiquitous knowledge environment, while the academic network knowledge community is precisely intelligent network knowledge platform where education lifelong, education initiative and education for all can be promoted. Academic network community includes knowledge space, user space and social organization rule. This article elaborated four knowledge communication mechanism of academic community, namely, knowledge building rule, knowledge transfer rule, knowledge sharing and knowledge innovation rule. This paper discusses knowledge building system framework, knowledge transition process, knowledge sharing mechanism as well as knowledge innovative general model.

Video synopsis provides a means for fast browsing of activities in video. Principal background selection (PBS) is an important step in video synopsis. Existing methods make PBS in an offline way and at a high memory cost. In this paper we propose a novel background selection method, ``online principal background selection'' (OPBS). The OPBS selects n principal backgrounds from N backgrounds in an online fashion with a low memory cost, making it possible to build an efficient online video synopsis system. Another advantage is that, with OPBS, the selected backgrounds are related to not only background changes over time but also video activities. Experimental results demonstrate the advantages of the proposed OPBS.

Problem of selection of informative frequency band (IFB) for local damage detection using vibration signal is often discussed in the literature. One of the approaches used in this context is based on the analysis of sub-signals obtained in time-frequency representation of the vibration signal. Mentioned sub-signals are analyzed using appropriate statistics (called selectors). Till now, the most popular statistic was kurtosis, one of the measures that can point out these frequency bins on time-frequency map that reveal the most impulsive nature. However for many real signals the spectral kurtosis does not give expected results because it can be sensitive for impulses not related to damage (i.e. artifacts). In this paper we extend the idea of spectral kurtosis. We propose a novel method combining time-frequency representation, namely spectrogram, dependency measure suitable for heavy-tailed distributions, statistical features for novel time-frequency representation and statistical modeling of such features. The new time-frequency representation is based on the measure of dependence appropriate for more general class of distribution, namely heavy tailed. This measure, called autocovariation, is an extension of the classical measures of dependences, namely autocovariance or autocorrelation. The new time-frequency representation was developed in order to enhance informative parts of the signal while reducing inadvisable parts of the signal like artifact or high-energy deterministic parts. Next, the Kuiper statistic is applied to novel time-frequency representation for IFB detection and we design the Alter characteristic. Moreover, we propose also the automatic procedure of filter characteristic thresholding based on the alpha-stable distribution approach.

Multi-level AC-AC converter has the advantages of reducing switch voltage stress, adapting to high voltage input and reducing waveform distortion. In this paper, a symmetrical switched capacitor multi-level (SSCML) AC-AC converter is studied. The switched capacitor (SC) technology is introduced into the AC-AC converter, which has the advantages of small size, high efficiency and high power density. Firstly, the topology of SSCML AC-AC converter and its control strategy are introduced. Then, the working principle of the topology is analyzed, and the relationship between input voltage and output voltage is derived in detail, as well as the expression of switch voltage stress. Finally, a 300W prototype is built to verify the proportional relationship between input voltage and output voltage. The prototype and experimental results prove that the multi-level AC-AC converter using SC technology has the advantages of small size and low voltage stress. The experimental results also prove that the voltage stress is proportional to the modulation ratio, which is the key to reducing the voltage stress.

Dividing sonar images into regions that have similar seabeds is often done by expert interpretation. Automated classification systems are becoming more widely used. This paper describes techniques, based on image amplitudes and texture, that lead to useful and practical automated segmentation of multibeam images. Seabed (or riverbed or lakebed) type affects amplitudes and texture, but so do system operating details, water conditions, and survey geometry. Effects of the last three must be compensated to isolate the effects of seabed type. Images from multibeam surveys are accompanied by bathymetric data from which grazing angles of all sonar footprints can be calculated. By compiling tables of amplitude against range and grazing angle, systematic changes in amplitude with these two variables can be removed consistently. If sound absorption varies, though, this empirical method compensates with some average absorption and local effects get lost. This paper explores using independent absorption data for calculated range compensation, and shows that the accuracy of maps of acoustic classes is thereby improved.

The development of Advanced Driver Assistance Systems (ADAS) is rapidly growing. However, most of the ADAS require field test, which is expensive, unpredictable and time consuming. In this paper we propose a multiagent-based driving simulator which integrates a human factor analysis suite and enables rapid and low-cost experimentation of mobile-device ADAS. Our architecture uses a microscopic simulator and a serious-game-based driving simulator. The latter allows the user to control a vehicle and change the correspondent simulation state in the microscopic simulator. The driving simulator also connects to an Android device and sends several kinds of data, such as current GPS coordinates or transportation network data. One important feature of this architecture is its suitability to serve as an appropriate means to conduct behaviour elicitation through peer-designed agents, so as to improve modelling of various driving styles accounting for different aspects of preferences and perception abilities, as well as other performance measures related to drivers' interaction with ADAS solutions. The potentials of our approach to aid experiments in human factor analysis are still to be tested, but are undoubtedly huge and encouraging.

Hyperspectral target detection has been widely used in practice, but its performance is seriously affected by spatial redundancy and spectral variation. In this paper, we design a novel network block including the region of interest (RoI) feature transformation (RFT) and convolution layer, which is called RS block, that can automatically attach different importance to pixels and provide guidance to discriminative feature extraction. Furthermore, a deep neural network (termed as RFTD) is proposed by stacking several RS blocks for hyperspectral target detection. In this way, the RS block enforces RFTD to concentrate on RoI feature extraction, and further increase distinction between target and hard-detected background (false alarm) pixels. Additionally, a constraint loss is introduced to exploit the sparsity and low rank property of hyperspectral images (HSI). Finally, we apply nearest neighbors (NN) for target detection in the feature space. Experimental results on two HSIs demonstrate that the proposed RFTD algorithm outperforms other detection methods.

The power optimization of integrated circuits must be observed in all levels of abstraction of the design flow. The traditional standard cell flow don't really takes care of power minimization at physical level, because there is a limited number of logical functions in a cell library, as well a limited number of sizing versions. To really obtain an optimization at physical level, it is needed to allow the use of any possible logical function, by also using complex cells (Static CMOS complex gates - SCCG) that are not available in a cell library. To have a “freedom” in the logic design step, it is needed the use of an EDA set of tools to let the automatic design of any transistor network (even with a different number of P and N transistors). This approach can reduce the amount of transistors needed to implement a circuit, reducing the power consumption, mainly the leakage power that is proportional to the number of components (transistors). This paper presents some examples and comparisons between the standard cell approach and the network of transistors approach. The flexibility of the approach can also let the designers to define layout parameters to cope with problems like tolerance to transient effects, yield improvement, printability and DFM. The designer can also manage the sizing of transistors to reduce power consumption, without compromising the clock frequency.

In this paper, we focus on the identification of discrete-time piecewise affine (PWA) systems from noisy data. This problem consists of the estimation of both the local affine subsystems and the partition of the regression space. A two-stage robust identification approach is proposed to estimate the local affine subsystems in the presence of noise. This approach includes an optimization-based initial estimation process and a least-squares-based refinement procedure. In addition, to estimate the partition of the regression space for continuous dynamic PWA systems, an intersection approach is proposed as an alternative to the widely used pattern recognition approaches. Simulation studies demonstrate the effectiveness of the two-stage identification approach and the intersection approach in noisy case.

During the past decades, inversion of ocean environment parameters by ocean ambient noise has gradually made great progress. It has been suggested that the time-domain Green's function between two receivers can possibly be extracted by using ocean ambient noise. The correct location of each element of an hydrophone array can be calibrated according to information about Green's function. With the beam-forming technique, a vertical array can be used as a passive fathometer to measure layered sea bottom information. For conventional ambient noise data processing, the down-going and up-going noise signals are applied in the process of cross-correlating, and then the effect of the horizontal-propagating noise can be reduced by a beamforming method. But in some cases, especially in shallow water, interference signals such as the noise from shipping traffic is too great to reduce. So, it is necessary to perform some additional modified data processing in order to achieve better results. In this paper, ambient noise measurements near Qingdao are analyzed, and the minimum variance distortionless response (MVDR) adaptive beamforming technique is applied to extract more clearly layered sea bottom information, and the layered sea bottom information at depths of 29m, 31m and 34m become more distinct. Empirical orthogonal function (EOF) analysis is applied to separate different characteristic noise signals, then by combining some higher order EOF components, the vertically propagating noise signal can be enhanced and those horizontally propagating components are removed well. Results show that combined MVDR and EOF processing can effectively optimize the sea bottom inversion of coastal ambient noise measurement. The signal-to-noise ratio (SNR) can be effectively improved, by about 2dB on average, and the maximum SNR improvement is about 4–5dB.

System losses, power generation and generation cost are some of the most important factors that significantly influence the efficiency of power systems. Integrating distributed generation systems into distribution networks is one of the effective solutions. Many studies focused on finding the optimal planning of distributed generation systems to get the maximum benefits from them. In this paper, a brief recent review on a variety of heuristic optimization techniques applied in literature to optimally allocate and size distributed generations will be presented.

Elasticity makes cloud computing an attractive platform for executing complex large-scale expensive dataflows, as it enables different trade-offs between execution time and monetary cost, by varying the number of resources to be provisioned. With cloud providers offering heterogeneous types of resources with different performance and price characteristics, the problem of identifying the various trade-offs available is a great challenge, as the number of possible alternative configurations increases significantly compared to a homogeneous environment, which is itself already computationally difficult. This paper proposes a novel algorithm for dataflow scheduling on heterogeneous clouds that identifies solutions (schedules) close to the optimal pareto front, by exploring the search space in an efficient way. The results of an experimental comparison with the state of the art show that, in several cases, the proposed algorithm provides a richer, more diverse set of solutions, several of which are characterized by significantly better time-money trade-offs.

Based on the principle of piezoelectric driving and ultrasonic near-field levitation technology, this paper designs a piezoelectrically driven ultrasonic levitation bearing, which is mainly composed of a rectangular piezoelectric sheet and a metal cylinder. The piezoelectric sheet drives cylinder wall to squeeze air in bearing gap under the action of high-frequency alternating voltage to form a gas squeezed film. According to the theory of viscous fluid mechanics, squeeze film Reynolds equation is derived. From the equation, it can be seen that cylinder radial deformation and squeeze number are the key parameters that determine the bearing capacity of gas squeeze film. With finite element software, radial deformation displacement function of cylinder is obtained, and the relationship between squeeze number and bearing capacity is analyzed by solving Reynolds equation. Based on above simulation and theoretical analysis, bearing is studied experimentally. The experimental results prove that theoretical calculation method is accurate, which has guiding significance for theoretical research and structural design of ultrasonic levitation bearings.

In this paper a new radome structure made from styrene foam coated with Efrethan® is described together with experiments on its performance. Sufficient strength to endure environmental stress is obtained from the Efrethan layer. Optimal losses are obtained from 1mm or 2mm coating at three frequencies in W-band. The developed radome performed with − .18 dB loss at 76 GHz without any disturbance of antenna pattern.

The high-speed railway load is a single-phase load with high power, which can generate large negative sequence current (NSC). Many compensation methods have been applied to compensate NSC which is a big power quality problem of railway power supply system, and each method has its own characteristics. A hybrid compensation system is proposed in this paper. The system consists of two different compensation methods. One is based on railway power conditioner(RPC) with modular multilevel converter(MMC) structure(MRPC), and the other is based on Steinmetz compensation principle with magnetic static var compensation (MSVC). The MRPC has a fast response speed but expensive price, while the MSVC has a low response speed but much cheaper price. Compared with MRPC-ONLY compensation method, the hybrid compensation system has a lower cost by reducing the compensation capacity of MRPC with MSVC. Firstly, the compensation principles of the hybrid compensation system is analyzed in detail. Then, the collaboration control strategy of compensation system is proposed. Moreover, the cost of the hybrid compensation system and the MRPC-ONLY compensation method are compared. Finally, simulation and experiment results are both presented to verify the effectiveness of the hybrid compensation system and its control method.

Since the COVID-19, the Flipped Classroom has been gradually promoted and applied from offline to online. With the control of COVID-19 and the reopening of schools, the focus of Flipped Classroom application has changed to an online and offline combining mode. Many different professional academic disciplines are trying to apply online teaching platform or online course platform to the teaching practice of Flipped Classroom, so as to improve teaching effect and learning quality. Compared with online teaching platform or online course platform, we found a technology that can better serve the Flipped Classroom in the collaboration office automation system. Whether it is the completely online teaching mode of Flipped Classroom or the online and offline combining Flipped Classroom, this technology can better improve the efficiency and effect of the Flipped Classroom. This paper aims to discuss the application mode of collaboration office automation technology in Flipped Classroom, carries out feasibility analysis, and discusses problems and challenges that may appear in the application process.

Conventional traffic light detection methods often suffers from false positives in urban environment because of the complex backgrounds. To overcome such limitation, this paper proposes a method that combines a conventional approach, which is fast but weak to false positives, and a DNN, which is not suitable for detecting small objects but a very powerful classifier. Experiments on real data showed promising results.