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The article presents invitation for posters and papers for the 2013 IEEE International Reliability Physics Symposium (IRPS) to be held in Monterey, California from April 14-18, 2013. Among the areas where papers should be focused include electronic systems, automotive reliability, and circuit aging simulation. It notes the awards to be given by IRPS including Outstanding Paper, Best Student Talk, Best Poster, and Best Paper.

THE ADMINISTRATIVE COMMITTEE of the IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society takes great pleasure in announcing the selection of the Outstanding Paper published in its 2009 Transactions, Vol. 56. The award was presented during ceremonies at the 2010 IEEE International Ultrasonics Symposium in San Diego, California, October 12th, 2010. The award winners are Jorge Camacho, Montserrat Parrilla, and Carlos Fritsch for their paper entitled "Phase Coherence Imaging". [ABSTRACT FROM PUBLISHER]

The article discusses on the 32nd International Conference on Plasma Science (ICOPS) held in Monterey, California. This Special Issue focuses on Plenary and Invited Papers from ICOPS. There are invited talks included in the issue which subjects ranged from the traditional interests in basic plasma processes, microwave generation and charged particle beams, and other plasma-related topics.

This paper proposes a modeling approach for characterizing the probability of wildfire ignition caused by faults on power distribution systems. The proposed model serves as a starting point in research literature to illustrate, from an analytical perspective, the many factors that influence wildfire ignitions in power distribution systems. This paper presents the series of events that leads to power-system-related wildfire ignitions, and characterizes the wildfire ignition probability as a combination of the probability that a fault occurs along a power distribution line segment, and the probability that the fault results in the sustained ignition of a vegetation fuel bed surrounding the line. The proposed model integrates a variety of data including environmental conditions, power system protection settings, and power system line flows. A case study is performed on a test 33-bus distribution system using observed historical weather data from a high-threat fire district in California. The California case study is utilized to investigate the effects of three primary factors (wind speed, line congestion, and protection settings) on wildfire ignition probability. [ABSTRACT FROM AUTHOR]

Some of the deadliest wildfires in the U.S., such as California’s 2018 wildfires, have been ignited by power systems. In an effort to prevent and minimize the ignition of wildfires, or control them if ignited, energy companies have developed wildfire mitigation plans. This paper provides energy companies and power system operators, engineers, researchers, and suppliers an overview of the state-of-the-art studies that address key topics in these wildfire mitigation plans and compares the wildfire mitigation plans of several energy companies. The key topics include grid design and system hardening, asset management and inspection, situational awareness and forecasting, operational response, vegetation management, public safety power shutoff, and risk-spend efficiency. This paper also presents a comparison of several energy companies’ decision-making criteria for initiating a public safety power shutoff. Finally, we discuss opportunities for future research studies that could help energy companies prevent wildfire ignitions. [ABSTRACT FROM AUTHOR]

Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is appealing to devise a blended strategy to perform effective resource sharing. In this paper, we exploit deep learning techniques predict vehicles’ mobility patterns. Then we propose an architecture consisting of centralized decision making and distributed channel allocation to maximize the spectrum efficiency of all vehicles involved. To achieve this, we leverage two deep reinforcement learning techniques, namely deep Q-network (DQN) and advantage actor-critic (A2C) techniques. In addition, given the time varying nature of the user mobility, we further incorporate the long short-term memory (LSTM) into DQN and A2C techniques. The combined system tracks user mobility, varying demands and channel conditions and adapt resource allocation dynamically. We verify the performance of the proposed methods through extensive simulations and prove the effectiveness of the proposed LSTM-DQN and LSTM-A2C algorithms using real data obtained from California state transportation department. [ABSTRACT FROM AUTHOR]

This paper first presents an adaptive switching frequency modulation that optimizes the efficiency of the cascaded buck-boost (CBB) converter. Such bidirectional dc–dc CBB topology is typically deployed in PV-battery-device systems. A precise loss-model that accounts for component nonlinearities is developed to determine power-loss distribution. According to the loss model, optimal switching frequencies are selected that correspond to the lowest total loss in both the buck and the boost modes of the operation. Then, a small volume low-profile planar-nanocrystalline inductor is designed to further increase efficiency and power density. Finite element analysis method is used to evaluate the inductor design. A digital control system is designed to periodically adjust the optimal switching frequencies to modulate the pulsewidth modulation duty cycle and to take into account any load and line regulations. The objective of this paper is to achieve ultra-high efficiency (over 99%) for a well-known CBB converter. A 100 W CBB converter prototype was built to verify the proposed algorithm and designed nanocrystalline inductor. A California Energy Commission weighted efficiency of 98.87% with a peak efficiency of 99.31% was achieved for the buck mode and 98.58% with a peak efficiency of 99.25% was achieved for the boost mode, both at nominal voltage. Also, a power density of 3.67 kW/L was reported. [ABSTRACT FROM AUTHOR]

Penetration of distributed energy resources (DERs) such as wind, solar and battery units are rapidly increasing in distribution power system (DPS). In view of power grid reliability and efficiency, cyber and physical secure of these units are highly important since it can directly impact power system operations. This paper investigates the dynamic behavior of power distribution during cyber, physical and natural strikes on reactive power control in DER units. Various fault scenarios including modifications in voltage-reactive power (Volt-Var) curve pattern such as slope of the curve, dead band; and changes in capacitive or/and inductive reactive power delivery to the power grid are presented. Further, fault scenarios are investigated with the presence of dynamic VAR (volt-ampere reactive) compensation (DVC) units in the system. A southern California 140-bus commercial distribution power system is selected as a test system and faults are analyzed in both Matlab/Simulink and ETAP simulation platforms. The results evidence that the modifications in reactive power control values at DER units significantly affects the voltage quality/security of the system and affect the number of switching actions of voltage regulators (VRs). In contrast, DPS connected with DVCs improves voltage quality and reduces the number of switching actions of VRs, also voltage quality/security is maintained during the faults. [ABSTRACT FROM AUTHOR]

Traditionally voltage control in distribution power system (DPS) is performed through voltage regulating devices (VRDs) including on load tap changers (OLTCs), step voltage regulators (SVRs), and switched capacitor banks (SCBs). The recent IEEE 1547-2018 from March 2018 requires inverter fed distributed energy resources (DERs) to contribute reactive power to support the grid voltage. To accommodate VAR from DERs, well-organized control algorithm is required to use in this mode to avoid grid oscillations and unintended switching operations of VRDs. This paper presents two voltage control strategies (i) static voltage control considering voltage-reactive power mode (IEEE 1547-2018), (ii) dynamic and extensive voltage control with maximum utilization of DER capacity and system stability. Further, effective time-graded control is implemented between VRDs and DER units to reduce the simultaneous and negative operation. The proposed voltage control strategies are tested in a realistic 140-bus southern California distribution power system through extensive time-domain simulation studies. The results show that voltage quality in a distribution system is effectively achieved through the proposed voltage control strategies with a significantly reduction in the number of switching operations of VRDs. In addition, proposed voltage control strategies increase reliability and security of the DPS during unexpected failures. [ABSTRACT FROM AUTHOR]

Focuses on the agenda for the 1999 International Conference of Software Engineering in Los Angeles, California. Enhancement of the industrial presentation programs; List of participating research papers; Topics and writers for the research papers.

This paper presents a methodological framework to evaluate the resilience, with the primary focus on performance degradation, of levee-protected electric power networks to flooding in a changing climate. To this end, a multi-disciplinary framework is established by integrating climate science, hydrology, and electric power network analysis. The framework quantifies the effect of climate change on flood hazard levels in a levee-protected area and the subsequent changes in the resilience of the electric power network. In the first step, the changes in the exposure of levee-protected regions to flooding hazard in a warming climate are calculated. Then, the probabilities of failure of power network components due to flooding under current and projected future climate are determined. Finally, the power system resilience index is used to assess the system resiliency for pre-flooding (baseline), historic flooding, and two projected future flooding scenarios. For demonstration, the proposed framework is applied to a levee-protected area in Northern California. The IEEE 118-bus standard test system is employed to represent the power network in the study area. Results reveal that climate change can considerably decrease the system resilience of the levee-protected electric power network. The findings of this study can contribute towards more resilient power network systems under a changing climate. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic model is proposed for a joint statistical description of solar photovoltaic (PV) power and outdoor temperature. The underlying correlation emerges from solar irradiance that is responsible in part for both the variability in solar PV power and temperature. The proposed model can be used to capture the uncertainty in solar PV power and its correlation with the electric power consumption of thermostatically controlled loads. First, a model for solar PV power that explicitly incorporates the stochasticity due to clouds via a regime-switching process between the three classes of sunny, overcast and partly cloudy is proposed. Then, the relationship between temperature and solar power is postulated using a second-order Volterra model. This joint modeling is leveraged to develop a joint probabilistic forecasting method for solar PV power and temperature. Real-world datasets that include solar PV power and temperature measurements in California are analyzed and the effectiveness of the joint model in providing probabilistic forecasts is verified. The proposed forecasting methodology outperforms several reference methods thus portraying that the underlying correlation between temperature and solar PV power is well defined and only requires a simple lower-complexity sampling space. [ABSTRACT FROM AUTHOR]

In this paper, a highly efficient isolated resonant converter with a novel modulation method is proposed for delivering power from photovoltaic (PV) modules to the dc microgrid. The proposed modulation method allows the converter to boost low input voltages and regulate a wide input voltage range. The converter design is based on a series resonant converter (SRC) that operates at the resonant frequency to achieve highest efficiency under nominal input voltage condition. Under shadowed or low irradiance conditions of PV panels, the converter will operate with the proposed “double-pulse duty cycle” modulation method to step up the voltage for the dc microgrid connection. With the proposed modulation method, the output switches serve for both synchronous rectification and voltage boost function. This method enables a higher voltage boost ratio than the SRC without adding additional switches while operating at the resonant frequency. A 300-W hardware prototype with gallium-nitride devices is built to verify the performance of the proposed converter and modulation method. The converter achieved a peak efficiency of 98.9% and a California Energy Commission weighted efficiency of 98.7% under nominal input voltage condition. [ABSTRACT FROM AUTHOR]

The Third International Conference on Data Engineering was held in Los Angeles, California on February 2 to 6, 1987. The conference was designed as an international forum for bringing together researchers, developers, managers, strategic planners and other users with an interest in the research, design and development of data engineering methodologies, strategies and systems. Its scope includes computer science, artificial intelligence, electrical engineering, and computer engineering. It featured papers from all major areas of data engineering, including database design and modeling, performance evaluation, algorithms, integrity, security, fault tolerance, query language, artificial intelligence approaches, knowledge bases, database machines, distributed databases and data engineering applications.

The Symposium on Foundations in Software Engineering is an annual conference sponsored by Association for Computing Machinery, a computer portal and dedicated to the presentation of innovative research results contributing to an engineering discipline for software systems. This special section of the July 1998 issue of the journal IEEE Transactions on Software Engineering features three of the best papers from the fourth symposium, held in October 1996 in San Francisco, California. During its first half-decade, research results presented at this conference have covered engineering research topics. However, a number of distinguishing properties tend to characterize the best of the papers and have helped set the standards for high-quality software engineering research. The three papers in this special section illustrate these properties. One of the critical challenges for software engineering research is to find practical tools that help system builder restructure existing systems. Mark Moriconi was general chair for the symposium and deserves considerable credit for its success.

A number of models for the critical surface of Nb3 Sn, and in general A15 superconductors, have been developed in the past years. This paper compares the most common parameterizations using consistent notation. Although the parameterizations appear dissimilar at first sight, they are in reality all based on a fit of the normalized pinning force vs. the reduced field, and have similar scalings for the critical field and critical temperature based on a Unified Scaling Law. In this paper we take the various parameterizations as a basis for a generic scaling proposed for the characterization and production follow-up of the ITER Nb3 Sn strands. The accuracy of the scaling is estimated using the fitting residuals on various sets of Ic (B, T, ϵ) data available in literature. We discuss the results, and give our view of the work towards a unified, practical parameterization. [ABSTRACT FROM AUTHOR]

The advent of high-speed, lightweight, and durable sensor technologies opens new possibilities for field monitoring applications. In particular, under natural or man-made loading conditions, applying these new technologies to the monitoring of building interiors may substantially help rescue and reconnaissance crews during postevent evaluations. To test such a methodology, in this paper, we develop a specialized network of conventional analog and digital (camera) sensors and use them in monitoring nonstructural components subjected to vibration loading within a demonstration building structure. A full-scale vibration experiment is conducted with a research team from the University of California, Los Angeles, on a vacant structure damaged during the 1994 Northridge Earthquake. The building of interest is a four-story office building located in Sherman Oaks, CA. The investigation has two primary objectives: 1) to characterize the seismic response of an important class of equipment and building contents and 2) to study the applicability of tracking the response of these equipment and contents using arrays of image-based monitoring systems. In this paper, we describe the experimental field setup, including the analog and camera sensor systems and the networking hardware used to collect data, present the testing matrix, and sample the processed analog data results. We summarize the difficulties encountered in the field implementation of these types of monitoring systems while highlighting their potential benefits. In a companion paper, we present the analysis methodology applied to the image sequences collected and summarize needs for future work if such systems are to be robustly employed in the field. [ABSTRACT FROM AUTHOR]

This paper presents a behavior-based adaptive mission planner (AMP) to trace a chemical plume to its source and reliably declare the source location. The proposed AMP is implemented on a REMUS autonomous underwater vehicle (AUV) equipped with multiple types of sensors that measure chemical concentration, the flow velocity vector, and AUV position, depth, altitude, attitude, and speed. This paper describes the methods and results from experiments conducted in November 2002 on San Clemente Island, CA, using a plume of Rhodamine dye developed in a turbulent fluid flow (i.e., near-shore ocean conditions). These experiments demonstrated chemical plume tracing over 100 m and source declaration accuracy relative to the nominal source location on the order of tens of meters. The designed maneuvers are divided into four behavior types: finding a plume, tracing the plume, reacquiring the plume, and declaring the source location. The tracing and reacquiring behaviors are inspired by male moths flying upwind along a pheromone plume to locate a sexually receptive female. All behaviors are formulated by perception and action modules and translated into chemical plume-tracing algorithms suitable for implementation on a REMUS AUV. To coordinate the different behaviors, the subsumption architecture is adopted to define and arbitrate the behavior priorities. AUVs capable of such feats would have applicability in searching for environmentally interesting phenomena, unexploded ordnance, undersea wreckage, and sources of hazardous chemicals or pollutants. [ABSTRACT FROM AUTHOR]

Highlights the activities and topics discussed at the International Electrical and Electronics Engineering (IEEE) Microwave Theory and Techniques Society (MTTS) International Microwave Symposium held from June 13 to 19, 1999 in Anaheim, California. Guest speakers; Presentation of the IEEE Electromagnetics Award and the IEEE MTT-S 1999 IEEE Fellows; Scientific paper competition.

Microwave radiometers measure weak thermal emission from the Earth, which is broadband in nature. Radio frequency interference (RFI) originates from active transmitters and is typically narrow band, directional, and continuous or intermittent. The Global Precipitation Measurement (GPM) Microwave Imager (GMI) has seen RFI caused by ocean reflections from direct broadcast and communication satellites in the shared 18.7-GHz allocated band. This paper focuses on the use of a complex signal kurtosis algorithm to detect direct broadcast satellite (DBS) signals at 18.7 GHz. An experiment was conducted in August 2017 at the Harvest oil platform, located about 10 km off the coast of central California. Data were collected for direct and ocean reflected DBS transmissions in the K- and Ku-bands from a commercial geostationary satellite. Results are presented for the complex kurtosis performance for a five-channel quadrature phase-shift keying (QPSK) signal versus the seven-channel case. As the spectrum becomes more occupied, detector performance decreases. Filtering of RFI in the fully occupied spectrum is very difficult, and detection using the complex kurtosis detector is only possible for very large interference-to-noise ratio (INR) values at −5 dB and higher. This corresponds to over 100 K in a real system such as GMI; therefore, other detection approaches might be more appropriate. [ABSTRACT FROM AUTHOR]

The article discusses information related to the periodical "IEEE Transactions on Reliability." Ralph Evans is retiring from service as the managing editor, after serving the periodical for 35 years. Evans is the founding editor of the periodical "ASQ Reliability Review," and has been trained as a physicist at the University of California, Berkeley, California. Jason Rupe will be replacing Evans for the periodical. Rupe has served the periodical as an associate editor, and holds a degree in industrial engineering from Iowa State University.

As compared with conventional methods for measuring ocean surface currents, spaceborne synthetic aperture radar (SAR) offers cloud-penetrating ocean-current observation capability at high spatial resolution. While some studies have shown the potential of SAR for studying ocean surface currents through feature tracking, they have only analyzed a few images to demonstrate the basic measurement technique, and no concise general technique has been developed. This paper shows the application of the maximum cross correlation (MCC) method to generate ocean surface currents from nearly two years of available sequential spaceborne C-band SAR imagery from the Envisat ASAR and ERS-2 Advanced Microwave Instrument SAR sensors over the coastal California Current System. The data processing strategies are discussed in detail, and results are compared with HF radar measured currents. One-dimensional wavenumber spectra of the SAR-derived surface currents agree with the k-2 power law, as predicted by sub mesoscale resolution models. Comparisons with HF radar currents show encouraging results with MCC SAR vectors oriented slightly counterclockwise relative to HF radar vectors. MCC SAR surface currents are found to have larger magnitudes than HF radar currents (≈11 cm/s), which may be due to the fact that SAR penetrates only a few centimetres into the ocean surface whereas HF radar currents are averaged over the top 1 m of the ocean surface. The larger part of this magnitude difference is contained in the along-shore component, which can be attributed to higher HF radar accuracy in the direct radial cross-shore measurements as compared with along-shore components derived from multiple cross-shore radial measurements. [ABSTRACT FROM PUBLISHER]

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key environmental remote-sensing instruments onboard the Suomi National Polar-Orbiting Partnership spacecraft, which was successfully launched on October 28, 2011 from the Vandenberg Air Force Base, California. Following a series of spacecraft and sensor activation operations, the VIIRS nadir door was opened on November 21, 2011. The first VIIRS image acquired signifies a new generation of operational moderate resolution-imaging capabilities following the legacy of the advanced very high-resolution radiometer series on NOAA satellites and Terra and Aqua Moderate-Resolution Imaging Spectroradiometer for NASA's Earth Observing system. VIIRS provides significant enhancements to the operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for 23 environmental data records including aerosol, cloud properties, fire, albedo, snow and ice, vegetation, sea surface temperature, ocean color, and nigh-time visible-light-related applications. Preliminary results from the on-orbit verification in the postlaunch check-out and intensive calibration and validation have shown that VIIRS is performing well and producing high-quality images. This paper provides an overview of the on-orbit performance of VIIRS, the calibration/validation (cal/val) activities and methodologies used. It presents an assessment of the sensor initial on-orbit calibration and performance based on the efforts from the VIIRS-SDR team. Known anomalies, issues, and future calibration efforts, including the long-term monitoring, and intercalibration are also discussed. [ABSTRACT FROM AUTHOR]

The UN Economic Commission's Statistics of Road Traffic Accidents report of 2011 shows that every year, about 150 000 human beings lose their lives on the roads of the western world. Although it is a common belief that this figure could shrink with the use of new sensor and communication technologies, unfortunately, none such systems have hit the road to date. Ideally, if such technologies were put into place, vehicles could be part of a vehicular ad hoc network (VANET) capable of spreading relevant information about dangerous events (e.g., car accidents) to all approaching drivers. However, all this is mainly supported by simulation studies, as no practical results have been published to date, revealing the effective performances of such systems at work. In this paper, we fill this gap, presenting a detailed description of the greatest experiments (a few thousand throughout the streets of Los Angeles), to date, ever performed with an accident warning system specifically devised for highway scenarios. In particular, among all the possible candidate schemes, we ran a few thousand experiments with the accident warning system algorithm that was proven to be optimal in terms of bandwidth usage and covered distance in realistic scenarios. Our experiments confirm what has been observed before in theory and simulation, i.e., the use of such a system can reduce, by as much as 40%, the amount of vehicles involved in highway pileups. [ABSTRACT FROM PUBLISHER]

A pseudo-tie resource model is a means by which a resource (i.e., generation unit or load) is operationally transferred from one balancing authority (BA) area in which the resource is physically located (i.e., native BA) to another (i.e., attaining BA area). The attaining BA has responsibility for operational control of the pseudo-tie resource for both system reliability and electricity market purposes. By implementing a pseudo-tie, the California Independent System Operator Corporation (ISO) BA is able to attain control of resources physically located external to its jurisdiction for its BA area services. [ABSTRACT FROM PUBLISHER]

The California energy crisis in 2000-2001 showed what could happen to an electricity market if it did not go through a comprehensive and rigorous testing before its implementation. Due to the complexity of the market structure, strategic interaction between the participants, and the underlying physics, it is difficult to fully evaluate the implications of potential changes to market rules. This paper presents a flexible and integrative method to assess market designs through agent-based modeling. Realistic simulation scenarios are constructed for evaluation of the proposed PJM-like market power mitigation rules of the California electricity market. Simulation results show that in the absence of market power mitigation, generation company (GENCO) agents facilitated by Q-learning are able to exploit the market flaws and make significantly higher profits relative to the competitive benchmark. The incorporation of PJM-like local market power mitigation rules is shown to be effective in suppressing the exercise of market power. [ABSTRACT FROM AUTHOR]

The measurement of the current sharing temperature of a Cable in Conduit Conductor is a complex task. The voltage traces measured on the conduit (jacket) of these conductors during tests, give V∙I characteristics that significantly differ from the typical transition from the superconducting to the resistive state measured on single strands. In these measurements, after an initial ramping of the transport current up to the test value, the temperature is increased by steps until the resistive transition occurs. However, even in the first phase of the current ramping, when the cable is still fully or almost superconductive, early voltages are measured along the jacket at different angular positions around the cable. In particular, this was observed in the ITER TF conductors recently tested in the SULTAN facility at Villigen PSI, Switzerland. These samples showed significant ramps of voltages taken in the proximity of the joints and terminations. The present paper gives a possible qualitative and quantitative explanation for this mechanism. Numerical simulations are also shown which reproduce the scattering of the voltages measured around the jacket at a given location, showing the effect of the voltage taps position along the jacket in a six sub-cable model. [ABSTRACT FROM AUTHOR]

The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) are local learning machines for solving problems and treat unseen samples near the training samples to be more important. In this paper, we propose a localized generalization error model which bounds from above the generalization error within a neighborhood of the training samples using stochastic sensitivity measure. It is then used to develop an architecture selection technique for a classifier with maximal coverage of unseen samples by specifying a generalization error threshold. Experiments using 17 University of California at Irvine (UCI) data sets show that, in comparison with cross validation (CV), sequential learning, and two other ad hoc methods, our technique consistently yields the best testing classification accuracy with fewer hidden neurons and less training time. [ABSTRACT FROM AUTHOR]

Convergence bidding, a.k.a., virtual bidding, has been widely adopted in wholesale electricity markets in recent years. It provides opportunities for market participants to arbitrage on the difference between the day-ahead market locational marginal prices and the real-time market locational marginal prices. Given the fact that convergence bids (CBs) have a significant impact on the operation of electricity markets, it is important to understand how market participants strategically select their CBs in real-world electricity markets. We address this open problem with focus on the electricity market that is operated by the California Independent System Operator (ISO). In this regard, we use the publicly available electricity market data to learn, characterize, and evaluate different types of convergence bidding strategies that are currently used by market participants. Our analysis includes developing a data-driven reverse engineering method that we apply to three years of real-world California ISO market data. Our analysis involves feature selection and density-based data clustering. It results in identifying three main clusters of CB strategies in the California ISO market. Different characteristics and the performance of each cluster of strategies are analyzed. Interestingly, we unmask a common real-world strategy that does not match any of the existing strategic convergence bidding methods in the literature. Next, we build upon the lessons learned from the advantages and disadvantages of the existing real-world strategies in order to propose a new CB strategy that can significantly outperform them. Our analysis includes developing a new strategy for convergence bidding. The new strategy has three steps: net profit maximization by capturing price spikes, dynamic node labeling, and strategy selection algorithm. We show through case studies that the annual net profit for the most lucrative market participants can increase by over 40% if the proposed convergence bidding strategy is used. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of obtaining a reduced-dimension parameterization of a propagation medium for the purpose of estimating the medium from transmission data. The application addressed is microwave remote sensing of tropospheric index-of-refraction profiles over the sea surface, using radar clutter returns. The proposed parameterization balances the desire to represent features prominent in the a priori statistics of the profiles versus the need to capture elements of the profile that significantly affect the observed clutter data. In linear estimation problems, basis vectors for the unknown parameter vector that optimizes this tradeoff have been derived as the reduced-rank Wiener filter or, equivalently, the generalized Karhunen-Loeve transform (GKLT). In this paper, we reinterpret the linear result, producing an extension to the nonlinear refractivity estimation problem. The resulting procedure produces basis vectors for tropospheric refractivity that are less dependent on features that have little effect on the clutter measurements. This results in a more efficient parameterization and reduces mean-square estimation error relative to an approach driven purely by the statistical prior. Application of the generalized KL technique to finding efficient basis vectors for refractivity profiles taken off the southern California coast is presented. [ABSTRACT FROM AUTHOR]

Under the appropriately titled banner of "The Magic of Global Connectivity," IEEE GLOBECOM 2012 recently held its 55th annual event at the fabled Disneyland® Hotel in Anaheim, California with more than 2,500 attendees participating in 1,500 presentations focused on the "new world" of IPv6, smart grid, cloud computing and network infrastructure advancements. [ABSTRACT FROM PUBLISHER]

The 1 m long Nb3Sn dipole magnet HD2, fabricated and tested at Lawrence Berkeley National Laboratory, represents a step towards the development of block-type accelerator quality magnets operating in the range of 13-15 T. The magnet design features two coil modules composed of two layers wound around a titanium-alloy pole. The layer 1 pole includes a round cutout to provide room for a bore tube with a clear aperture of 36 mm. After a first series of tests where HD2 reached a maximum bore field of 13.8 T, corresponding to an estimated peak field on the conductor of 14.5 T, the magnet was disassembled and reloaded without the bore tube and with a clear aperture increased to 43 mm. We describe in this paper the magnet training observed in two consecutive tests after the removal of the bore tube, with a comparison of the quench performance with respect to the previous tests. An analysis of the voltage signals recorded before and after training quenches is then presented and discussed, and the results of coil visual inspections reported. [ABSTRACT FROM AUTHOR]

This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class. [ABSTRACT FROM AUTHOR]

Field measurements of dielectric properties of hypersaline deposits were realized over an arid site located in Death Valley, CA. The dielectric constant of salt and water mixtures is usually high but can show large variations, depending on the considered salt. We confirmed values observed on the field with laboratory measurements and used these results to model both the amplitude and phase behaviors of the synthetic aperture radar (SAR) signal at C- and L-bands. Our analytical simulations allow reproducing specific copolar signatures observed in both Airborne SAR (AIRSAR) and Spaceborne Imaging Radar (SIR-C) data, corresponding to the saltpan of the Cottonball Basin. More precisely, the main objective of the present paper is to understand the influence of soil salinity as a function of soil moisture on the dielectric constant of soils and then on the backscattering coefficients recorded by airborne and spaceborne SAR systems. We also propose the copolarized backscattering ratio and phase difference as indicators of moistened and salt-affected soils. More precisely, we show that these copolar indicators should allow monitoring of the seasonal variations of the dielectric properties of saline deposits at both C- and L-bands. Because of the frequency dependence of the ionic conductivity, we also show that L-band SAR systems should be efficient tools for detecting both soil moisture and salinity, while C-band SAR systems are more suitable for the monitoring of soil moisture only. Through the study of terrestrial evaporitic environments by means of spaceborne SAR systems, our results could also be of great interest for defining future planetary missions, particularly for the exploration of Mars. [ABSTRACT FROM AUTHOR]

In this paper we report the main test results obtained on the Poloidal Field Conductor Insert coil (PFI) for the International Thermonuclear Experimental Reactor (ITER), built jointly by the EU and RF ITER parties, recently installed and tested in the CS Model Coil facility, at JAEA-Naka. During the test we (a) verified the DC and AC operating margin of the NbTi Cable-in-Conduit Conductor in conditions representative of the operation of the ITER PF coils, (b) measured the intermediate conductor joint resistance, margin and loss, and (c) measured the AC loss of the conductor and its changes once subjected to a significant number of Lorentz force cycles. We compare the results obtained to expectations from strand and cable characterization, which were studied extensively earlier. We finally discuss the implications for the ITER PF system. [ABSTRACT FROM AUTHOR]

General Electric, under contract with the Air Force Research Labs (AFRL), has successfully developed and tested a high speed, multimegawatt superconducting generator. The generator was built to demonstrate high temperature superconducting (HTS) generator technology for application in a high power density Multimegawatt Electric Power System (MEPS) for the Air Force. The demonstration tested the generator under load conditions up to 1.3 MW at over 10,000 rpm. The new MEPS generator achieved 97% efficiency including cryocooler losses. All test results indicate that the generator has a significant margin over the test points and that its performance is consistent with program specifications. This demonstration is the first successful full-load test of a superconducting generator for the Air Force. In this paper we describe the development of the generator and present some key test results used to validate the design. Extrapolation to a higher power density generator is also discussed. [ABSTRACT FROM AUTHOR]

For the superconducting magnet system of the International Thermonuclear Experimental Reactor, ITER, 60 current leads for a total current of more than 2500 kA are needed. The use of High Temperature Superconductor current leads (HTS-CL) will reduce the resultant large refrigerator load considerably. Within the EU Fusion Technology Programme, the Forschungszentrum Karlsruhe and CRPP have developed and built a 70 kA HTS-CL demonstrator optimized for 50 K Helium operation. In 2004-2005, the CL was successfully tested with 50 K as well as 80 K Helium, and finally with LN2. In the meantime, ITER has decided to use HTS-CLs for the magnet system and China has taken the responsibility to provide the sc feeders for ITER including the current leads. In this paper, the performance of a 70 kA HTS-CL in fault conditions will be analysed using the FZK-CRPP demonstrator as a model. In particular, the quench performance and the safety behavior in case of a loss-of-flow accident (LOFA) will be described and an extrapolation of the leads performance in real ITER conditions will be given. Finally the effect of different shunt materials is discussed. [ABSTRACT FROM AUTHOR]

The ITER Organization (10) and the Institute of Plasma Physics at the Chinese Academy of Sciences (ASIPP) are jointly developing the 68 kA current leads using High Temperature Superconductors for the superconducting Toroidal Field (TF) magnet system of the International Thermonuclear Experimental Reactor, ITER. The proposed design consists of a conventional helium cooled heat exchanger operating between 65 K and 320 K and an HTS module covering the low temperature end using Bi-2223 tapes. The first HTS current lead prototypes will be tested in the EAST tokamak facility at ASIPP. This paper discusses the design of the first, 68 kA, TF HTS lead, which is the main emphasis of this first stage of the ITER current lead development program. [ABSTRACT FROM AUTHOR]

The paper analyzes the impact of integrating wind generation on the regulation and load following requirements of the California Independent System Operator (CAISO). These requirements are simulated and compared for the study cases with and without wind generation impacts included into the study for the years 2006 and 2010. Regulation and load following models were built based on hour-ahead and five-minute ahead load and wind generation forecasts. In 2006, the CAISO system peaked at 50270 MW. Wind generation (at the installed capacity of 2600 MW) had limited impact on the requirement of load following and regulation in the CAISO Balancing Authority. However, in 2010 (with an expected installed capacity of approximately 6700 MW), this impact will significantly increase. The results provide very useful information for the CAISO to adjust its scheduling and real-time dispatch systems to reliably accommodate future wind generation additions within the CAISO Balancing Authority. [ABSTRACT FROM AUTHOR]

This paper presents a new learning algorithm for multitask pattern recognition (MTPR) problems. We consider learning multiple multiclass classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is "online" where training examples for different tasks are mixed in a random fashion and given sequentially one after another. We assume that the classification tasks are related to each other and that both the tasks and their training examples appear in random during "online training." Thus, the learning algorithm has to continually switch from learning one task to another whenever the training examples change to a different task. This also implies that the learning algorithm has to detect task changes automatically and utilize knowledge of previous tasks for learning new tasks fast. The performance of the algorithm is evaluated for ten MTPR problems using five University of California at Irvine (UCI) data sets. The experiments verify that the proposed algorithm can indeed acquire and accumulate task knowledge and that the transfer of knowledge from tasks already learned enhances the speed of knowledge acquisition on new tasks and the final classification accuracy. In addition, the task categorization accuracy is greatly improved for all MTPR problems by introducing the reorganization process even if the presentation order of class training examples is fairly biased. [ABSTRACT FROM AUTHOR]

Information about the International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS) 08 held in February 2008 in Los Angeles, California. Topics include the development and launch of manipulators for optical sensors and low-Earth-orbit applications in Canada, the progress in artificial intelligence (AI) and robotics in Europe, and improvements in rover technologies in the U.S. The symposium featured Gianfranco Visentin, Mitsushige Oda, and Paul Schenker.

Wind power production is variable, but also has diurnal and seasonal patterns. These patterns differ between sites, potentially making electric power from some wind sites more valuable for meeting customer loads or selling in wholesale power markets. This paper investigates whether the timing of wind significantly affects the value of electricity from sites in California and the Northwestern United States. We use both measured and modeled wind data and estimate the time-varying value of wind power with both financial and load-based metrics. We find that the potential difference in wholesale market value between better-correlated and poorly correlated wind sites is modest, on the order of 5%-10%. A load-based metric, power production during the top 10% of peak load hours, varies more strongly between sites, suggesting that the capacity value of different wind projects could vary by as much as 50% based on the timing of wind alone. [ABSTRACT FROM AUTHOR]

Direction-of-arrival (DOA) estimation in a phased-array system is considered for a non-Gaussian noise environment. In this paper, a robust M-estimation scheme based on Huber's loss function with an adaptive threshold is proposed. The method is evaluated using experimental data from the University of California, Los Angeles, 60-GHz antenna-array system. It is shown that the proposed method enables a decreased sensitivity of the DOA estimates with respect to noise components having heavy-tailed distributions. [ABSTRACT FROM AUTHOR]

For the superconducting magnet system of the International Thermonuclear Experimental Reactor, ITER, 60 current leads for a total current of more than 2500 kA are needed. To reduce the resultant large refrigerator load at 4.5 K, High Temperature Superconductor current leads (HTS-CL) could be used. Therefore, EFDA CSU Garching had launched a development program for a 70 kA HTS-CL demonstrator. The Forschungszentrum Karlsruhe and CRPP developed and built this CL optimized for 50 K Helium operation. In 2004, the CL was successfully tested in the TOSKA facility at the Forschungszentrum Karlsruhe. The very encouraging results lead to testing this CL with 80 K Helium because ITER provides a large 80 K Helium cooling capacity for the thermal shields. At the end of last year, the test could be successfully performed demonstrating that high current capacity current leads can be stably operated at about 80–85 K. Recently, the CL was retested using liquid nitrogen which would be an interesting alternative option. In this paper, the test results for the 80 K He cooling operation as well as for the operation with LN2 are presented and compared to the results obtained before for nominal conditions. [ABSTRACT FROM AUTHOR]