Power cycling results for different control strategies

Power cycling is an important method to characterize the lifetime of power semiconductor modules. Application engineers use lifetime curves published by manufacturers to verify that their system design meets the required reliability. An important condition for the lifetime of a module under repeated temperature swings is the control strategy applied during the test. Power cycling tests with identical start condition but different control strategies have been performed, which have been conducted on specially assembled test equipment with ultimate control of all test parameters. The results show, that different control strategies deliver lifetime results that vary by a factor of 3.     

Processing online analytics with classification and association rule mining

Business performance measurements, decision support systems (DSS) and online analytical processing (OLAP) have a common goal i.e., to assist decision-makers during the decision-making process. Integrating DSS and OLAP into existing business performance measurements hopes to improve the accuracy of analysis and provide in-depth, multi-angle view of data. This paper describes a decision support system containing our methodology, Weighted and Layered workflow evaluation (WaLwFA), extended to incorporate business intelligence using C4.5 and association rule algorithms. C4.5 produces more comprehensible decision trees by showing only important attributes. Furthermore, C4.5 can be transformed into IF-THEN rules. However, association rules are preferred as data can be described in rules of multiple granularities. Sorting rules based on rules’ complexities permits OLAP to navigate through layers of complexities to extract rules of relevant sizes and to view data from multidimensional perspectives in each layer. Experimental results on an airline domain are presented.     

Simulated Annealing algorithm for photovoltaic parameters identification

A Simulated Annealing based approach is proposed in this paper for optimal estimation of solar cell model parameters. Different solar cell models, namely single diode, double diode, and photovoltaic module, are used in this study to verify the proposed approach outcomes. The developed technique is used to solve a transcendental function that governs the current–voltage relationship of a solar cell, as no direct general analytical solution exists. Several cases were investigated to test and validate the consistency of accurately estimating various parameters of different solar cell models. Comparative study among different parameter estimation techniques is presented to show the effectiveness of the developed approach. Furthermore, statistical analyses are carried out to measure the accuracy of the estimated parameters and model suitability.     

C波段高稳定度磁控管微波源实验研究

由于物理机制和工艺结构的固有限制,各种微波源均有其功率极限,而以锁相技术为基础的功率合成技术是获得大功率微波的一条重要途径。主要研究了以磁控管为基本单元的注入式锁相方案,并且采用注入锁相方法对现有的c波段磁控管进行了锁相实验验证,研制出频率、相位、幅度可调的高稳定度磁控管微波源,并结合实验分析了系统的性能指标。     

Detecting node replication attacks in wireless sensor networks: A survey

A wireless sensor network (WSN) consists of a number of tiny, low-cost, and resource-constrained sensor nodes, but is often deployed in unattended and harsh environments to perform various monitoring tasks. As a result, WSNs are susceptible to many application-dependent and application-independent attacks. In this paper we consider a typical threat in the latter category known as the node replication attack, where an adversary prepares her own low-cost sensor nodes and deceives the network into accepting them as legitimate ones. To do so, the adversary only needs to physically capture one node, extract its secret credentials, reproduce the node in large quantity, and then deploy the replicas under her control into the network, possibly at strategic positions, to cripple various WSN applications with little effort. Defending against such node replication attacks has recently become an imperative research topic in sensor network security, and the design issues may involve different and more threatening challenges than detecting typical application-dependent attacks. In this survey, we classify existent detections in the literature, and explore the various proposals in each category. We look into necessary technical details and make certain comparisons, so as to demonstrate their respective contributions as well as limitations. We also present the technical challenges and indicate some possible directions for future research.     

Using head-mounted displays to examine adaptation and calibration under varying perturbations

Simulator sickness has been a pervasive problem as head-mounted displays (HMDs) grow in popularity. Recent work showed that people can adapt to constant latency in an HMD, but latency that varies has not been examined. In this study, participants performed a shooting task while wearing an HMD during three sessions separated by 48 h under conditions of constant or varying latency. Performance was assessed for both accuracy (targets hit) and speed (time-to-hit targets). It was hypothesized that participants would adapt to constant, but not varying latency as indicated by decreasing simulator sickness over time. Further, it was hypothesized that participants would improve performance over time for both conditions due to practice, but the constant latency group would improve at a faster rate. Results showed reduced sickness with session regardless of latency condition. A similar trend was shown where performance improved with each session, with no effect of the latency condition. Change in sickness and performance were not correlated, suggesting that the changes were independently driven. These findings showed that people reduced sickness and improved performance with repeated exposure, even when experiencing different perturbations. This finding has implications for both HMDs and general understanding of the relationship between latency and adaptation.     

The random electrode selection ensemble for EEG signal classification

Pattern classification methods are a crucial direction in the current study of brain–computer interface (BCI) technology. A simple yet effective ensemble approach for electroencephalogram (EEG) signal classification named the random electrode selection ensemble (RESE) is developed, which aims to surmount the instability demerit of the Fisher discriminant feature extraction for BCI applications. Through the random selection of recording electrodes answering for the physiological background of user-intended mental activities, multiple individual classifiers are constructed. In a feature subspace determined by a couple of randomly selected electrodes, principal component analysis (PCA) is first used to carry out dimensionality reduction. Successively Fisher discriminant is adopted for feature extraction, and a Bayesian classifier with a Gaussian mixture model (GMM) approximating the feature distribution is trained. For a test sample the outputs from all the Bayesian classifiers are combined to give the final prediction for its label. Theoretical analysis and classification experiments with real EEG signals indicate that the RESE approach is both effective and efficient.     

Computationally‐Light Non‐Lifted Data‐Driven Norm‐Optimal Iterative Learning Control

Computational complexity and model dependence are two significant limitations on lifted norm optimal iterative learning control (NOILC). To overcome these two issues and retain monotonic convergence in iteration, this paper proposes a computationally‐efficient non‐lifted NOILC strategy for nonlinear discrete‐time systems via a data‐driven approach. First, an iteration‐dependent linear representation of the controlled nonlinear process is introduced by using a dynamical linearization method in the iteration direction. The non‐lifted NOILC is then proposed by utilizing the input and output measurements only, instead of relying on an explicit model of the plant. The computational complexity is reduced by avoiding matrix operation in the learning law. This greatly facilitates its practical application potential. The proposed control law executes in real‐time and utilizes more control information at previous time instants within the same iteration, which can help improve the control performance. The effectiveness of the non‐lifted data‐driven NOILC is demonstrated by rigorous analysis along with a simulation on a batch chemical reaction process.     

Simulation based considerations in placement of capacitors near a dynamic voltage restorer

This paper addresses issues of capacitor placement in power distribution systems in the vicinity of a dynamic voltage restorer (DVR). A feedback controlled simulation model of an actual DVR in operation in the US is developed to perform case studies. Validity of this model is established by comparing the waveforms from the model with field measurements. Issues relating to maximization of a DVR in the vicinity of power factor correction capacitors are analyzed. Some considerations regarding the placement of capacitors in the vicinity of a DVR are drawn based on the simulation models.