Confidence voting method ensemble applied to off-line signature verification

In this paper, a new approximation to off-line signature verification is proposed based on two-class classifiers using an expert decisions ensemble. Different methods to extract sets of local and a global features from the target sample are detailed. Also a normalization by confidence voting method is used in order to decrease the final equal error rate (EER). Each set of features is processed by a single expert, and on the other approach proposed, the decisions of the individual classifiers are combined using weighted votes. Experimental results are given using a subcorpus of the large MCYT signature database for random and skilled forgeries. The results show that the weighted combination outperforms the individual classifiers significantly. The best EER obtained were 6.3 % in the case of skilled forgeries and 2.31 % in the case of random forgeries.     

IR and Raman studies and effect of γ radiation on crystallization of some lead borate glasses containing Al2O3

IR and Raman spectra of glass ceramics based on PbO-Cr₂O₃-B₂O₃glass composition system have been studied. The bands characteristic of BO₃. and BO₄ functional groups are present in all the samples. An incorporation of Al₂O₃(up to 5 mol%) in the initial glass composition considerably changes the glass network structure and relative concentrations of BO₃ and BO₄ groups. The composition 50 PbO-20 Cr₂O₃-25 B₂O₃-5 Al₂O₃(in mol%) reveals a maximum fraction of boron in the BO₄ group. A sample of this composition heat treated at 850° C for 25 h shows a maximum crystallization fraction with Pb₂O · CrO₄ as a prominent crystalline phase. The glasses irradiated with rays inhibit the crystallization into the Pb₂O · CrO₄ phase. They also show relatively smaller thermal conductivity.     

Operational optimization of a building-level integrated energy system considering additional potential benefits of energy storage

As a key component of an integrated energy system (IES), energy storage can effectively alleviate the problem of the times between energy production and consumption. Exploiting the benefits of energy storage can improve the competitiveness of multi-energy systems. This paper proposes a method for day-ahead operation optimization of a building-level integrated energy system (BIES) considering additional potential benefits of energy storage. Based on the characteristics of peak-shaving and valley-filling of energy storage, and further consideration of the changes in the system’s load and real-time electricity price, a model of additional potential benefits of energy storage is developed. Aiming at the lowest total operating cost, a bi-level optimal operational model for day-ahead operation of BIES is developed. A case analysis of different dispatch strategies verifies that the addition of the proposed battery scheduling strategy improves economic operation. The results demonstrate that the model can exploit energy storage’s potential, further optimize the power output of BIES and reduce the economic cost.     

Impact of communication time delays on combined LFC and AVR of a multi-area hybrid system with IPFC-RFBs coordinated control strategy

In this paper, the impact of communication time delays (CTDs) on combined load frequency control (LFC) and automatic voltage regulation (AVR) of a multi-area system with hybrid generation units is addressed. Investigation reveals that CTDs have significant effect on system performance. A classical PID controller is employed as a secondary regulator and its parametric gains are optimized with a differential evolution - artificial electric field algorithm (DE-AEFA). The superior performance of the presented algorithm is established by comparing with various optimization algorithms reported in the literature. The investigation is further extended to integration of redox flow batteries (RFBs) and interline power flow controller (IPFC) with tie-lines. Analysis reveals that IPFC and RFBs coordinated control enhances system dynamic performance. Finally, the robustness of the proposed control methodology is validated by sensitivity analysis during wide variations of system parameters and load.     

Artificial Intelligence System Approach for Optimization of Drilling Parameters of Glass-Carbon Fiber/Polymer Composites

Silicon - In recent times, the study on machining characteristics of combined (hybrid) fiber polymer composites has drawn a remarkable research attention because of its emerging industrial...     

金属粉末注射成形技术的最新研究动态与发展趋势

简要阐述了粉末注射成形技术的特点与应用 ,并从粉末的制备、粘结剂体系的设计、注射成形过程及其计算机模拟等几个方面阐明了粉末注射成形技术的最新研究动态 ,最后提出了粉末注射成形技术的重要发展趋势     

2-DOF vibratory gyroscope fabricated by SU-8 based UV-LIGA process

This paper reports fabrication of 2-DOF vibratory gyroscope using SU-8 based UV-LIGA process. The device structure is designed to be symmetrical in order to match the resonance frequencies of drive and sense mode oscillators and also to minimize their relative temperature dependent drift. The overall arrangement is such that the two vibration modes do not affect each other and therefore, mechanical decoupling is achieved which helps in minimizing bias drift. The design is optimized to be compatible with the UV-LIGA process having 10 μm thick electroformed nickel as structural layer. Photolithography to create 11 μm thick SU-8 molds for electroforming sacrificial copper and structural nickel layer is optimized using multiple exposure technique that ensures near vertical side walls. Since the highly cross-linked SU-8 remaining after development is difficult to remove reliably from high aspect ratio structures without damage or alteration to the electroformed metals, a 2.45 GHz MW plasma etching process is developed with CF₄/O₂ mixes. The fabricated device is checked for off-plane misalignment between the stationary and movable comb fingers using white light interferometry and it is found to be almost negligible. Also, the prototype device is characterized for amplitude and phase spectral responses using Polytec MSA-500 Micro System Analyzer. The drive and sense mode resonance frequencies are observed at 7.3 and 7.1 kHz respectively against the mode matched designed frequency of 7.5 kHz.     

Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. This paper presents a new method for classification of ictal and seizure-free EEG signals. The proposed method is based on the empirical mode decomposition (EMD) and the second-order difference plot (SODP). The EMD method decomposes an EEG signal into a set of symmetric and band-limited signals termed as intrinsic mode functions (IMFs). The SODP of IMFs provides elliptical structure. The 95% confidence ellipse area measured from the SODP of IMFs has been used as a feature in order to discriminate seizure-free EEG signals from the epileptic seizure EEG signals. The feature space obtained from the ellipse area parameters of two IMFs has been used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier. It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance. Experimental results on EEG database available by the University of Bonn, Germany, are included to illustrate the effectiveness of the proposed method.     

Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system

In this paper, we describe how to detect abnormal human activities taking place in an outdoor surveillance environment. Human tracks are provided in real time by the baseline video surveillance system. Given trajectory information, the event analysis module will attempt to determine whether or not a suspicious activity is currently being observed. However, due to real-time processing constrains, there might be false alarms generated by video image noise or non-human objects. It requires further intensive examination to filter out false event detections which can be processed in an off-line fashion. We propose a hierarchical abnormal event detection system that takes care of real time and semi-real time as multi-tasking. In low level task, a trajectory-based method processes trajectory data and detects abnormal events in real time. In high level task, an intensive video analysis algorithm checks whether the detected abnormal event is triggered by actual humans or not.