34 research outputs found
GUARANTEE DESIGN ON ENERGY PERFORMANCE CONTRACTS UNDER UNCERTAINTY
Due to the growing concerns with climate change and energy supply, Energy Performance Contracting (EPC), which uses the guaranteed future utility savings to repay the initial renovation investments, becomes increasingly popular. However, most Energy Service Companies (ESCOs) set the savings guarantee roughly based on their previous experience, which leads to inaccurate estimates in practice. This paper has built the stochastic models for the savings risks both from the energy price volatility and the facility performance instability, which follow the Geometric Brownian Motions (GBM) and Ito's lemma. Then, a flexible guarantee designing method for ESCOs is developed to minimize the financial risks and a case study has been conducted to show the application. Finally, suggestions have been made for how ESCOs set the guarantee and the extra profit sharing proportion in contracts based on the existing information. This method will help them appropriately allocate risks with successful contract negotiation
Belief Evolution Network-based Probability Transformation and Fusion
Smets proposes the Pignistic Probability Transformation (PPT) as the decision
layer in the Transferable Belief Model (TBM), which argues when there is no
more information, we have to make a decision using a Probability Mass Function
(PMF). In this paper, the Belief Evolution Network (BEN) and the full causality
function are proposed by introducing causality in Hierarchical Hypothesis Space
(HHS). Based on BEN, we interpret the PPT from an information fusion view and
propose a new Probability Transformation (PT) method called Full Causality
Probability Transformation (FCPT), which has better performance under
Bi-Criteria evaluation. Besides, we heuristically propose a new probability
fusion method based on FCPT. Compared with Dempster Rule of Combination (DRC),
the proposed method has more reasonable result when fusing same evidence
Evaluating Risks of Dam-Reservoir Systems Using Efficient Importance Sampling
The occurrence frequency of failure events serve as critical indexes representing the safety status of dam-reservoir systems. Although overtopping is the most common failure mode with significant consequences, this type of event, in most cases, has a small probability. Estimation of such rare event risks for dam-reservoir systems with crude Monte Carlo (CMC) simulation techniques requires a prohibitively large number of trials, where significant computational resources are required to reach the satisfied estimation results. Otherwise, estimation of the disturbances would not be accurate enough.
In order to reduce the computation expenses and improve the risk estimation efficiency, an importance sampling (IS) based simulation approach is proposed in this dissertation to address the overtopping risks of dam-reservoir systems. Deliverables of this study mainly include the following five aspects: 1) the reservoir inflow hydrograph model; 2) the dam-reservoir system operation model; 3) the CMC simulation framework; 4) the IS-based Monte Carlo (ISMC) simulation framework; and 5) the overtopping risk estimation comparison of both CMC and ISMC simulation. In a broader sense, this study meets the following three expectations: 1) to address the natural stochastic characteristics of the dam-reservoir system, such as the reservoir inflow rate; 2) to build up the fundamental CMC and ISMC simulation frameworks of the dam-reservoir system in order to estimate the overtopping risks; and 3) to compare the simulation results and the computational performance in order to demonstrate the ISMC simulation advantages.
The estimation results of overtopping probability could be used to guide the future dam safety investigations and studies, and to supplement the conventional analyses in decision making on the dam-reservoir system improvements. At the same time, the proposed methodology of ISMC simulation is reasonably robust and proved to improve the overtopping risk estimation. The more accurate estimation, the smaller variance, and the reduced CPU time, expand the application of Monte Carlo (MC) technique on evaluating rare event risks for infrastructures
Quantum belief function
The belief function in Dempster Shafer evidence theory can express more
information than the traditional Bayesian distribution. It is widely used in
approximate reasoning, decision-making and information fusion. However, its
power exponential explosion characteristics leads to the extremely high
computational complexity when handling large amounts of elements in classic
computers. In order to solve the problem, we encode the basic belief assignment
(BBA) into quantum states, which makes each qubit correspond to control an
element. Besides the high efficiency, this quantum expression is very conducive
to measure the similarity between two BBAs, and the measuring quantum algorithm
we come up with has exponential acceleration theoretically compared to the
corresponding classical algorithm. In addition, we simulate our quantum version
of BBA on Qiskit platform, which ensures the rationality of our algorithm
experimentally. We believe our results will shed some light on utilizing the
characteristic of quantum computation to handle belief function more
conveniently
Cross-utterance ASR Rescoring with Graph-based Label Propagation
We propose a novel approach for ASR N-best hypothesis rescoring with
graph-based label propagation by leveraging cross-utterance acoustic
similarity. In contrast to conventional neural language model (LM) based ASR
rescoring/reranking models, our approach focuses on acoustic information and
conducts the rescoring collaboratively among utterances, instead of
individually. Experiments on the VCTK dataset demonstrate that our approach
consistently improves ASR performance, as well as fairness across speaker
groups with different accents. Our approach provides a low-cost solution for
mitigating the majoritarian bias of ASR systems, without the need to train new
domain- or accent-specific models.Comment: To appear in IEEE ICASSP 202
The attenuation of plasmaspheric hiss associated with the enhanced magnetospheric electric field
We report an attenuation of hiss wave intensity in
the duskside of the outer plasmasphere in response to enhanced
convection and a substorm based on Van Allen Probe
observations. Using test particle codes, we simulate the dynamics
of energetic electron fluxes based on a realistic magnetospheric
electric field model driven by solar wind and subauroral
polarization stream. We suggest that the enhanced
magnetospheric electric field causes the outward and sunward
motion of energetic electrons, corresponding to the decrease
of energetic electron fluxes on the duskside, leading
to the subsequent attenuation of hiss wave intensity. The results
indicate that the enhanced electric field can significantly
change the energetic electron distributions, which provide
free energy for hiss wave amplification. This new finding
is critical for understanding the generation of plasmaspheric
hiss and its response to solar wind and substorm activity.Published versio