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A Convolutional neural network (CNN) has emerged as a widely used approach to computer vision tasks, including object classification and detection tasks. The high requirement for the model to be more computationally efficient on lower information and communication technology (ICT) resource, e.g., mobile terminals can benefit from model distillation. However, most existing distillation methods suffer from a significant accuracy reduction, which requires a large number of pre-training models or doesn't make good use of the more of the network information, e.g., in the middle layers, during the distillation. In this paper, we study how knowledge about traffic signs recognition could be transferred to smaller models by distillation while cutting channels. We present an optimized object detection network, which uses a Region Proposal Network (RPN) weighted loss and hard-soft distribution-wise distillation loss for structural differences between teacher and student networks. We validate the network on multiple real-world datasets, the experiments demonstrate that the classification accuracy can be improved by 9 % with about 16 times parameter reduction while the detection network performance could be increased by 10.6% using an optimized object detection network.

This paper is concerned with the event-triggered control problem of unknown mismatched time-varying uncertainties. Three event-triggered schemes, namely, the absolute scheme, the relative scheme, and the switching scheme, are proposed to reduce the unnecessary signal transmission. A backstepping-like design approach is employed to cope with the mismatched time-varying uncertainties. Consequently, the proposed controllers could compensate for the mismatched time-varying uncertainties and ensure the closed-loop system globally bounded. The validity of the proposed schemes is shown by an illustrative example.

Sign Language Synthesis (SLS) is a domain-specific problem where multiple sign language words are stitched to generate a whole sentence in video, which serves to facilitate communications between the hearing-impaired people and healthy population. This paper presents a Variable Motion Frame Interpolation (VMFI) method for highly fluent SLS in scattered videos. Existing approaches for SLS mainly focus on mechanical virtual human technology, lacking high flexibility and natural effect. Also, the representative solutions to interpolate frames usually assume that the motion object moves at a constant speed which is not suitable for predicting the complex hand motion in frames of scattered sign language videos. To address the above issues, the proposed VMFI adopts acceleration to predict more accurate interpolated frames based on an end-to-end convolutional neural network. The framework of VMFI consists of variable optical flow estimation network and high-quality frame synthesis network that can approximate and fuse the intermediate optical flow to generate interpolated frames for synthesis. Experimental results on our realistic collected Chinese sign language dataset demonstrate that the proposed VMFI model achieves efficiency by performing better in PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and MA (Motion Activity) and gets higher score in MOS (Mean Opinion Score) than other two representative methods.

Services based on indoor navigation and localization play crucial roles in people's daily life, and they are even more important for individuals with visual impairments. In this paper, an indoor navigation and localization system CoFINLo with three key techniques: 1) dynamic feature extraction, 2) incremental model update, and 3) fine-grained trajectory generation, is proposed to implement robust indoor localization with friendly interaction design especially for visual impairment groups. Compared with other indoor localization systems, the proposed CoFINLo can overcome the obstacles caused by the diverse sensing devices, complex indoor environments, and fluctuated wireless signal. The proposed system is verified in an indoor office environment and implemented in a large-scale and comprehensive train station. The average 1.39m localization error suggests that CoFINLo can open a new opportunity to implement robust and user-friendly indoor navigation and localization in large and complex indoor environments, which can be a solid foundation for other location-based services.

User's position and trajectory are very important personal informations, based on which, the user's personalized description can be realized, and the user's intentions, preferences and behavior patterns can also be found. For most of traditional approaches, the trajectory learning problems assume the availability of a vast amount of labeled training data, which requires a great deal of manual effort. In this paper we propose a indoor trajectory restoration method based on the spatial constraints on the position of points of interests (PoI). This method discovers PoI position by detecting the status changing of furniture and facilities and constructs spatial relationship of PoI positions with multidimensional scaling (MDS). Then, it synthetically optimizes the location model constructing of fitting error to labeled points, the manifold regularization of high-dimensional signal strength vectors and the MDS constraint of the spatial relationship of PoI positions, to achieve the optimizing trajectory by iteration. The experimental results show that our proposed method can effectively improve the robustness of the trajectory learning ability in different practical scenarios, benefiting from the constraint of PoI position.

Multimodal fusion is more and more widely used in the field of machine learning, but it faces a prominent problem in practical application: data utility is not stable. The data of different modalities may be missing and noisy randomly, which will interfere the machine learning model of multi-modal fusion. Most of the existing multi-modal fusion methods neglect data utility problems or only adopt simple data denoising methods to improve data utility. To solve the problem of unstable data utility, we propose a data selection method based on the evaluation of data utility. By training a special machine learning model, the optimal modal combination is predicted according to the quality evaluation of multi-modal data samples to accomplish the dynamic selection of data modalities. The experimental results show that the proposed method can effectively improve the accuracy of multi-modal recognition under low data utility.

We demonstrate an alternative coherent detection technique via terahertz induced second-harmonic (TISH) in a laser-induced plasma for terahertz waves. In this method, the probe beam requires neither high voltage bias nor optically field bias.

This study focuses on improving the performance of human activity recognition when a small number of sensor data are available under some special practical scenarios and resource-limited environments, such as some high-risk projects, anomaly monitoring and actual tactical scenarios. The Human Activity Recognition (HAR) based on wearable sensors is an attractive research topic in machine learning and ubiquitous computing over the last few decades, and has extremely practicality in health surveillance, medical assistance, personalized services, etc. However, with the limitation of sensor sampling rate, sustainability, deployment, and other restricted conditions, it is difficult to collect enough and resultful sensor data anywhere, anytime. Therefore, the HAR based on wearable sensors always faces the challenges of the low-data regime under some practical scenarios, which leads to a low accuracy of activity recognition and needs to be solved urgently. Currently, the Generative Adversarial Networks (GANs) provide a powerful method for training resultful generative models that could generate very convincing verisimilar images. The framework of GANs and its variants shed many lights on improving the performance of HAR. In this paper, we propose a new generative adversarial networks framework called SensoryGANs that can effectively generate available sensor data used for HAR. To the best of our knowledge, SensoryGANs is the first unbroken generative adversarial networks applied in generating sensor data in the HAR research field. Firstly, we tried exploring and devising three activity-special GANs models for three human daily activities. Secondly, these specific models are trained with the guidance of unbroken vanilla GANs. Thirdly, the trained generators from adversarial optimization process are used to generate synthetic sensor data. Finally, the synthetic sensor data from SensoryGANs are used to enrich the original authentic sensor datasets, which can improve the performance of target activity recognition model. Meanwhile, we propose three visual evaluation methods for assessing synthetic sensor data produced by the trained generators in SensoryGANs models. Experimental results show that SensoryGANs models have the capability of capturing the implicit distribution of real sensor data of human activity, and then the synthetic sensor data generated by SensoryGANs models Have a potential for improving human activity recognition.

This paper describes an on-site metering device for electric vehicle DC charging point. The device realizes the measurement of high DC current by using DC comparator. Every part in power module is isolated with each other for reducing interference. Meanwhile, GNSS signal reception module is added for time metering. The device is portable for metering on-site, and the measurement range of DCV is 3V~900V, while DCI is 0.5A ~300A. The measurement results show that the DC electrical energy error of this device is below 0.05%, and the stability is better than 0.007%/min.

In this paper, we propose an interacting multiple model filtering algorithm aided by radar radial velocity information (RVIMM). This algorithm introduces the concept of “take-credit factor”, which is adopted to solve the problem that the radial velocity of the maneuvering target in polar coordinates cannot be linearly represented by the target states in Cartesian coordinates. By using the “take-credit factor”, a weighted sum of the estimated radial velocity and other states of the target can improve the prediction process of the radial velocity. Finally, the radial velocity of the tracking filter is unified into a single framework. The simulation results show that the proposed algorithm is well-performed on radial velocity estimation, and the estimation for other target states including model probability, position and velocity are more accurate with radial velocity.

Inertial based pedestrian positioning is very useful in emergency response situations, such as search and rescue (SAR) missions as it is independent of external infrastructures and signals. Two practical problems emerge in inertial based pedestrian positioning: trajectory initialization and trajectory calibration. The former one denotes how to determine the initial position and heading of the pedestrian, and the latter denotes how to reduce the dead reckoning (DR) errors due to inertial drifts. This paper provides a solution to these two problems adopting a few ultra-wide band (UWB) anchors, which can provide range measurements between the anchors and the pedestrian. The foundation of the method is graph based simultaneously localization and mapping (SLAM). The nodes in the graph are the poses of the pedestrian and the edges are the range measurements between the UWB anchors and the tags mounted on the pedestrian. Both simulations and real scenario experiments are carried out to validate the proposed method. The proposed approach outperformed the particle filtering based approach in terms of robustness and processing time.

To realize the energy sharing among multiple microgrids (MGs) within MG parks, a non-cooperative game based distributed trading is proposed. A linear supply and demand market clearing mechanism is proposed for energy prosumers. A day-ahead energy trading problem is proposed for each MG, correlated with other MGs' trading plans in the utility function. A non-cooperative game is formulated for the energy trading competition among MGs. The existence and uniqueness of Nash equilibrium (NE) corresponding to the proposed game is verified from the perspective of potential games. Based on best response functions, a globally converged distributed algorithm is adopted to compute the NE. The simulation is carried out on the MG park with three MGs. Results have verified the proposed method can obtain an efficient NE where MGs act as prosumers.

Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users. In this paper, an approach adopting received signal strength (RSS) measurements for Wifi access points (APs) are proposed to align and calibrate the trajectories estimated from foot mounted inertial measurement units (IMUs). A crowdsourced radio map (RM) can be built subsequently and can be used for fingerprinting based Wifi indoor positioning (FWIP). The foundation of the proposed approach is graph-based simultaneously localization and mapping (SLAM). The nodes in the graph denote users' poses and the edges denote the pairwise constrains between the nodes. The constrains are derived from: (1) inertial estimated trajectories; (2) vicinity in the RSS space. With these constrains, an error functions is defined. By minimizing the error function, the graph is optimized and the aligned/calibrated trajectories along with the RM are acquired. The experimental results have corroborated the effectiveness of the approach for trajectory alignment, calibration as well as RM construction.

Foot-mounted inertial positioning (FMIP) and fingerprinting based WiFi indoor positioning (FWIP) are two promising solutions for indoor positioning. However, FMIP suffers from accumulative positioning errors in the long term while FWIP involves a very labor-intensive offline training phase. A new approach combining the two solutions is proposed in this paper, which can limit the error growth in FMIP and is free of any offline site survey phase. This approach is realized in the framework of a particle filter, where each particle denotes a potential trajectory of the user and is weighted according to its consistency in signal strength space. Compared with the traditional Gaussian process based approaches, the proposed one has less computational cost and is free from any prior information in the position domain, such as the positions of access points, received signal strengths at certain positions and so on. An experiment is carried out to demonstrate the performance of the proposed approach compared to the traditional Gaussian process based approach., Comment: 12 figures. Accepted by European Navigation Conference 2017, Lausanne

To tackle the error accumulation problems of the foot-mounted inertial positioning systems, aiding measurements are often adopted to calibrate the inertial-generated raw trajectories. Currently, the calibration methods, although effective, still lack interior connection and unity. In this paper, a unified anchor based trajectory calibration framework is proposed. The concept of anchors is defined as different types of aiding information and is considered as different constraint conditions to the inertial-generated trajectory. The foundation of the framework is a particle filter, and different anchor information correspond to different particle weight updating strategy. The proposed framework is expansive to incorporated different types of anchors. We choose three representative anchors to elaborate the proposed framework and they are: Ultra-WideBand (UWB) ranging anchors, iBeacons and the building structure based virtual anchors. The results in simulations and real scenario tests have validated the effectiveness of the proposed anchor based framework in calibrating the inertial-generated raw trajectories.

The Zero-velocity Update (ZUPT)-aided Extended Kalman Filter(EKF) algorithm is commonly used to suppress the error growth of the inertial based pedestrian navigation systems, but it still suffers from long-term heading drift. The magnetic field was suggested to mitigate the heading errors of for positioning and navigation, but it undergoes severe perturbation in indoor scenarios. The Quasi-Static magnetic Field (QSF) method was developed to estimate heading errors using magnetic field in perturbed environments. However, this method may bring extra errors to system because of the high false alarm probability of detecting the quasi-static field. In this paper, we propose a heading error estimation approach based on the improved QSF approach for foot-mounted Inertial Navigation Systems (INS). For the approach, a magnetometer calibration method is developed to eliminate the deviation caused by the positioning system platform and shoes firstly. Then an improved QSF detection approach is proposed and then used for generating the desired magnetic measurements which are fed into the EKF to estimate the heading errors. The experiments indicate that the proposed method is effective in suppressing the heading errors.

Foot-mounted inertial sensor is a practical and efficient approach for indoor personnel positioning, because it requires neither pre-deployed infrastructure nor on-line “fingerprint” database. To suppress the heading drift of gyroscopes, magnetic field is often used as a stable orientation reference. But the magnetic field inside buildings changes rapidly while space and time changes, and it is also sensitive to some metal objects or electrical equipments. Thanks to the regular structures of most buildings, Heuristic Drift Elimination (HDE) techniques can be applied to suppress slowing-changed small-scale drifts between two steps, under the assumption that most of the paths in the buildings consist of straight lines and fixed angles. Inspired by the heuristic method, a Near Real-Time Heading Drift Correction (NRHDC) approach is proposed in this paper, which determines the current walking behavior based on the steps foregoing and following. Different step status are set to classify each step and different templates are trained for walking behavior discrimination. This approach is supposed to exploit more information and give more precise result. The experimental results in a building also demonstrate the effectiveness of the proposed approach, with superior accuracy to other methods.

This paper presents a novel hybrid modulation method based isolated high frequency (HF) bidirectional inverter for AC microgrid application. Bidirectional current-fed converter has been employed to interface the energy storage and intermediate fluctuating dc bus. A new hybrid modulation techniques consisting of fluctuating-dc-modulation (FDM) at low frequency (LF) scale and secondary modulation (SM) at HF scale has been proposed for the current-fed converter. The zero current soft switching (ZCS) of primary side devices and zero voltage soft switching (ZVS) of secondary side devices of front-end current-fed full-bridge converter is realized. The back-end full bridge inverter has been utilized to interface the fluctuating dc bus and AC microgrid. A rotating partial hybrid pulsewidth modulation (RP-HPWM) has been introduced. One leg of inverter is commutated at low switching frequency while the other leg is operated at high switching frequency for partial period of line frequency cycle. Besides the high switching frequency operated switches are only commutated at low value of current flowing through them. Thus, switching losses of the back-end inverter can be considerably reduced. Rotating mechanism is used to equalize the switching losses of four switches of the inverter. The low frequency (LF) fluctuating dc bus allows the reduction of dc-link electrolytic capacitor. This paper presents operation and analysis of the complete inverter topology implementing the proposed hybrid modulation scheme. Simulation results using PSIM9.0.4 and experimental results from a lab prototype clearly validate effectiveness of the proposed modulation scheme.

In foot-mounted positioning systems, it is hard to align multi-agent trajectories. In addition, the positioning accuracy is hard to maintain due to inertial drifts. An approach for trajectory initialization and calibration using iBeacons is proposed in this paper. This approach is under the framework of a particle filter. In the observation model of the particle filter, a nonparametric Gaussian Process (GP) regression model is adopted to describe the relationship between the estimated range and the observed RSS. Then the weights of the particles are updated according to the trained GP. GP is adopted here because it not only considers the sensor noise, but also the uncertainty in the model, which denotes the multi-path effects, human sheltering effects and so on in receiving the iBeacon signals. At last, a large-scale real-scenario experiment is carried out with a total walking length of about 5.4 kilometers. The results have demonstrated the effectiveness of the proposed approach for trajectory initialization and calibration, with the final positioning error reduced from 85.4 meters to only less than 1meter.

This paper presents a six-pulse low frequency (LF) fluctuating high voltage dc bus based power system architecture for the fuel cell vehicles (FCVs) application. A novel hybrid modulation techniques consisting of six-pulse modulation (SPM) at LF scale and secondary modulation and 33% sine pulse width modulation (33% SPWM) at high frequency (HF) scale. Three-phase ac waveforms are produced for the propulsion system in FCVs. It significant reduces the switching losses of the bidirectional dual-stage inverter: 1) the zero current soft switching (ZCS) of primary side devices and zero voltage soft switching (ZVS) of secondary side devices of front-end current-fed full-bridge converter is realized. 2) at any moment, only one leg of back-end 3-phase inverter is switched at HF while the other two legs are kept in on or off state. This tremendously reduces the bidirectional inverter's switching losses and improves the system efficiency. Besides, switching loss can be further reduced since all the inverter switches are switched at the low current range. The LF fluctuating high voltage dc bus allows the elimination of large electrolytic dc-link capacitor. This paper presents the operation, analysis, and design of a bidirectional inverter implementing the proposed hybrid modulation technique. Simulation results obtained from power electronics simulation software PSIM and experimental results from the lab prototype clearly validate the effectiveness of the proposed modulation technique.

Accurate indoor localization is very important for various kinds of Location Based Services (LBS). For most of traditional approaches, the location estimation problems assume the availability of a vast amount of labeled calibrated data, which requires a great deal of manual effort. Previous researches cannot deal with this problem in both calibration reduction, location accuracy. In this paper, we propose a heterogeneous data driven manifold regularization model known as HeterMan to calibration-effort reduction for tracking a mobile node in a wireless sensor network. With the constraint of user heading orientation, we build a mapping function between signal space, physical space with extremely less labeled data, a large amount of unlabeled data. Experimental results show that we can achieve high accuracy with extremely less calibration effort comparing with previous methods. Furthermore, our method can reduce computation complexity, time consumption by parallel processing while maintaining high accuracy.

BLAST[1] (Basic Local Alignment Search Tool) is a suite of programs used to identify similarity between genetic sequences. It is one of the most widely used tools in Bioinformatics. In recent years, with the size of gene and protein sequence database increasing exponentially, BLAST has become both a data-intensive and a computation-intensive application. How to run BLAST rapidly with low cost has always been the hotspot to researchers. Parallelization is one of the most important ways to resolve this problem. In this paper, a new approach for parallelizing BLAST based on a parallel processing framework called Robinia is presented. Compared with parallel version of BLAST presented before, Robinia-based BLAST has easy public accessibility and good scalability. Most importantly, it can support operation on WAN, this make it possible to integrate computation and storage resources on Internet to service for super-large scale BLAST projects. We implemented the Robinia-based BLAST and experimented on it using two different datasets. The results show that parallel BLAST based on Robinia can achieve linear speedup based on number of used nodes with good scalability and low cost.

As an important technology in LBS (Location Based Services) field, Wi-Fi based indoor localization suffers signal fluctuation problem which prevents lifelong and high performance running. With the fluctuation of wireless signal over time, fingerprints collected at the same location become different; therefore existing model cannot fit the new collected data well, which decreases the localization accuracy. In this paper, a novel indoor localization method COSELM (Constraint Online Sequential Extreme Learning Machine) is proposed, utilizing incremental data to update the old model and overcome the fluctuation problem. The performance of COSELM is validated in real Wi-Fi indoor environment. Compared with OSELM, it can improve more than 5% localization accuracy on average; and in contrast to batch learning, COSELM can save more than 50% time consumption.

For Search and Rescue (SAR) applications in large buildings, vertical accuracy may seem to be more important than horizontal accuracy, because correctness of floor estimation is mission critical. In this paper, an approachfor height error suppression based on apriori knowledge is proposed, which focuses on long-term height drift suppression along with proper floor determination. An Extended Kalman Filter (EKF) algorithm is firstly reviewed, which integrates barometer data to correct height drift brought by gyroscopes and accelerometers. However, height drift caused by barometer is still unacceptable in a long-term duration. So a new method is proposed under the assumption that a person's height only changes on stairs in the building. Based on this assumption, the whole course of walking in a building is partitioned into floor phase and stair phase, according to a built HMM model. Height estimation can be corrected by considering apriori knowledge of buildings, and will be performed following different rules in the two phases. For the floor phase, the height remains consistent on the same floor, and minor drifts are suppressed inspired by Heuristic Drift Elimination (HDE). For the stair phase, after sufficient stair-related information is acquired, a Maximum Likelihood Estimation (MLE) method is used to estimate the height of each stair step, and drift-free height difference between two floors can be estimated using the estimated stair step height. The experimental results of the proposed approach demonstrate the effectiveness of the approach for height error suppression in SAR use with forced ventilation and sudden change in air pressure.

In this paper, a generation expansion planning model and a transmission expansion planning model are proposed to inform investment decisions from the perspectives of generation companies and ISOs, respectively. Interactions between large-scale wind integration and transmission system planning are analyzed, and a new computational procedure of system expansion planning that coordinates generation and transmission investment is presented. The cooptimization of energy and ancillary services markets is considered to capture the impacts of wind power on the operation of the electric system. Benders decomposition is employed to reduce the overall computation time.

This paper presents a methodology to quantify the economic benefits of having centralized energy market and ancillary services market. A long-term production cost simulation model, which performs security-constrained unit commitment (SCUC) and security-constrained economic dispatch (SCED), is employed to simulate the operating conditions of the system with and without the energy and ancillary services market. The proposed methodology is used in the Midwest Independent Transmission System Operator, Inc. (MISO)'s Value Proposition study, which looks into the value provided by MISO to its market participants and their customers.

This paper presents a simulation methodology that evaluates the economic performance of hydro storage system in the day-ahead (DA) and real-time (RT) markets. A detailed hydro storage system model is proposed to capture the unique characteristics of hydro storage units. A long-term production cost simulation model is employed which performs chronological hourly Security-Constrained Unit Commitment (SCUC) and Security-Constrained Economic Dispatch (SCED) and cooptimizes both the energy market and the ancillary services market. Three MH RT bidding strategies are proposed to represent how MH bids to the RT market. A unique RT market bidding method named Tiered Bidding is proposed to represent hydro storage units' RT market offer curves. The proposed methodology is applied to the Eastern Interconnection system to evaluate the economic benefits of Manitoba Hydro's active market participation in MISO's RT energy and ancillary services market.

In MISO Market Efficiency Planning Study, a critical task is to pick from congested flowgates the ones that have the most potential economic benefits. A new ranking strategy, Estimated Potential Benefit (EPB), is proposed. To compare the performance of the new proposed ranking strategy with commonly used ones, ranking correctness index is introduced as a quantitative measure. EPB not only has the most comparable physical meaning to the actual potential benefit, but also has been verified to outperform other ranking strategies, especially when only the very top ranked flowgates are to be picked.

For the zero point transition issues affecting the of the stabilization platform framework when running a wide rotating range, the principle of the rotation angular position measuring is introduced and the reasons leading to zero point transition phenomenon are analyzed. Based on distinguishing the relationship between the zero point and the zero site, the zero point transition counter method is proposed for intelligent measurement and controller designing to help completing the platform framework running a wide rotating range with high precision. This method employs only angular position measuring values instead of angular velocity values to solve the problem of zero point transition, and also avoids the sampling data inconsistencies of the multi-loop digital control system. The experimental results show that after the method processing, the high-precision stabilization platform can complete the rotation by commands with a strong robustness and anti-interference ability in the complex work environment applications.

This paper describes the determination of the equivalent inductance of current shunts from 1 A to 10 A at the frequency from 50 kHz to 200 kHz. The equivalent inductances were measured against a set of four-terminal resistor whose time constants were precisely known. The standard uncertainty was evaluated to be within 0.03 nH.

This paper presents a transmission expansion planning model which combines the advantages of both the traditional reliability-based transmission planning model and economic (also known as value-based, economics-based, or market-based) transmission planning model. Reliability-based transmission planning tries to install new lines at minimal cost while fulfilling system reliability criteria. Economic transmission planning, on the other hand, seeks investment opportunities so that network expansions can generate more economic benefits than the costs. Benders decomposition technique is employed to separate the planning problem into one master problem, which makes investment decisions, and two sets of sub-problems, which evaluate the reliability and economic performance of the electric system, respectively. The proposed model is illustrated on a 5-bus system.

Congested flowgate ranking is a process to rank flowgates by the potential benefits associated with each flowgate when it is relieved. The rank can be used to help identify top congested flowgates for full evaluation and planning. Ideally, congested flowgates should be ranked by their potential benefits when congestion is relieved individually. This approach however requires one change case simulation for each flowgate and creates significant computation burden. It is desirable to develop metrics solely based on information from base case simulation and use the metrics to get approximate rank. Two metrics, annual shadow price and annual congestion cost, are used in the industry to rank the congested flowgates. No analysis has yet been done to compare these two metrics. This paper compares these two metrics by using different measures. Measures such as data correlation, rank correlation, rank correctness count and percentage are proposed to evaluate the goodness-of-fit of the ranks produced by the two metrics with respect to the actual rank. Sample data from MISO Top Congested Flowgates Study are used in this analysis. As neither of the metrics has been justified in producing a good rank, a new approach is explored. The feasibility is exemplified through a 5-bus test system study. The new approach is potentially capable of estimating actual benefit in a more accurate and computational efficient manner.

The effect of the diode reverse recovery on the performance of inverters is analyzed. The PSpice simulation of the SPWM full bridge inverter has been performed with the Si p-i-n ultra fast diode and the SiC Schottky diode respectively used as the free wheeling diode under the same condition. With their comparison, the results show that the SiC Schottky diode can greatly reduce the power loss of inverters. Further more, the effects of diode parameter CJO on reverse recovery characteristics have been discussed.

Compressed Air Energy Storage (CAES) has been touted as the next generation bulk storage technology that is capable of effectively addressing the wind variability issue, and provide flexible and economic generation. This work develops a state space model for CAES that enables to monitor the dynamic status of the CAES storage module. The developed state space model is used to design a simulation based tool that assesses various CAES configurations' operational and economic performance for a specific geography.

This paper presents a market-based transmission expansion planning model and compares it with a traditional reliability-based transmission planning model. Reliability-based transmission planning tries to install new lines at minimal cost while fulfilling system reliability criteria. Market-based transmission planning, on the other hand, seeks investment opportunities so that network expansions can generate more economic benefits than the costs. Benders decomposition technique is employed in both methods, and their master problems and slave problems are compared, respectively. The two models are applied on a five-bus system and a thirty-bus system. The results show that the market-based transmission planning method is better in terms of relieving system congestions, enhancing market efficiency and reducing load payments. The scalability of the market-based transmission planning algorithm is also discussed in this paper.

This paper presents a market-based transmission expansion planning model, which seeks to find investment opportunities that will bring more economic benefits than the costs. Benders decomposition method is used to decompose the whole planning problem into a master problem, which makes investment decisions, and a slave problem, which simulates the operation of the electric market. Uncertainties appearing in the planning process are analyzed systematically and classified into random and non-random uncertainties. Monte Carlo simulation method is applied to simulate random uncertainties, while robustness testing method is employed to incorporate nonrandom uncertainties. The case study illustrates the application of the proposed model in a competitive electricity market with double-sided auctions.

The most significant energy consuming infrastructures and the greatest contributors to greenhouse gases in the US today are electric and freight/passenger transportation systems. Technological alternatives for producing, transporting, and converting energy for electric and transportation systems are numerous. Selecting from among them requires long-term assessment since these capital-intensive infrastructures take years to build with lifetimes approaching a century. The advent of electrified transportation creates interdependencies between the two infrastructures that may be both problematic and beneficial. We are developing modeling capability to perform long-term electric/transportation infrastructure design at a national level, accounting for their interdependencies. The approach combines network flow/DC-flow modeling with a multiobjective solution method. We motivate the need for this work by summarizing attributes and issues related to the investment planning problem so as to find minimum-cost, low-emission, resilient infrastructure portfolios for the future. State-of-the-art energy planning models are summarized, and we describe our software design which includes a multiobjective evolutionary algorithm with a network linear programming cost minimization fitness evaluation, together with metrics for evaluating resiliency and sustainability.

Learning parameters of a motion model is an important challenge for autonomous robots. We address the particular instance of parameter learning when tracking motions with a switching state-space model. We present a general algorithm for dealing simultaneously with both unknown fixed model parameters and state variables. Using an Expectation-Maximization approach, we apply a tactic-based multi-model particle filter to estimate the state variables in the E-step, and use particle smoothing to update the parameters in the M-step. We test our algorithm both in simulation and in a team robot soccer environment, as a substrate for applying the learned models to object tracking in a team. One of the soccer robots learns the actuation model of its teammate. The experimental results show that the particle smoothing efficiency is substantially increased and the tracking performance is significantly improved using the learned teammate actuation model.

Learning motion models of a moving object is a challenge for autonomous robots. We address the particular instance of parameter learning when tracking object motions in a switching multi-model system. We present a general algorithm of joint parameter-state estimation based on multi-model particle filter. We apply the approach to a specific ball-tracking problem and extend the algorithm to learn model parameters in a dynamic Bayesian network (DBN). We show empirical results in simulation and in a team robot soccer environment, as a substrate for applying the learned models to object tracking in a team. The learning capability allow the tracker to much more effectively track mobile objects.

Interactions are frequently seen between the robot and the targets being tracked within the robotics community. Modeling the interactions using knowledge of robot cognition improves the performance of the tracker. Communication improves the performance of a multi-agent system. The focus of this paper is to present our solution to integrate the communication information into our team-driven multi-model motion tracking. We present the probabilistic tracking algorithm in detail and present empirical results both in simulation and in a Segway soccer team. The information from team communication allows the robot to much more effectively track mobile targets.