Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning

The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators

Distributed Multi-Area Optimal Power Flow via Rotated Coordinate Descent Critical Region Exploration

We consider the problem of distributed optimal power flow (OPF) for multi-area electric power systems. A novel distributed algorithm is proposed, referred to as the rotated coordinate descent critical region exploration (RCDCRE). It allows each entity to independently update its boundary information and optimally solve its local OPF in an asynchronous fashion. RCDCRE method stitches coordinate descent and parametric programming using coordinate system rotation to reduce coordination, keep privacy and ensure convergence. The solution process does not require warm starts and can iterate from infeasible initial points using penalty-based formulations. The effectiveness of RCDCRE is verified based on IEEE 2-area 44-bus and 4-area 472-bus systems

Eavesdropper localization in random walk channels

Eavesdroppers are notoriously difficult to detect and locate in traditional wireless communication systems, especially if they are silent. We show that in molecular communications, where information molecules undergo random walk propagation, eavesdropper detection and localization is possible if the eavesdropper is an absorbing receiver. This is due to the fact that the random walk process has a finite return probability and the eavesdropper is a detectable energy sink of which its location can be reverse estimated

RACH preamble repetition in NB-IoT network

NarrowBand-Internet of Things (NB-IoT) is a radio access technology recently standardized by 3GPP. To provide reliable connections with extended coverage, a repetition transmission scheme is applied in both Random Access CHannel (RACH) procedure and data transmission. In this letter, we model RACH in the NB-IoT network taking into account the repeated preamble transmission and collision using stochastic geometry. We derive the exact expression of RACH success probability under time correlated interference, and validate the analysis with different repetition values via independent simulations. Numerical results have shown that the repetition scheme can efficiently improve the RACH success probability in a light traffic scenario, but only slightly improves that performance with very inefficient channel resource utilization in a heavy traffic scenario

Low-complexity energy-efficient resource allocation for delay-tolerant two-way orthogonal frequency-division multiplexing relays

Energy-efficient wireless communication is important for wireless devices with a limited battery life and cannot be recharged. In this study, a bit allocation algorithm to minimise the total energy consumption for transmitting a bit successfully is proposed for a two-way orthogonal frequency-division multiplexing relay system, whilst considering the constraints of quality-of-service and total transmit power. Unlike existing bit allocation schemes, which maximise the energy efficiency (EE) by measuring ‘bits-per-Joule’ with fixed bidirectional total bit rates constraint and no power limitation, their scheme adapts the bidirectional total bit rates and their allocation on each subcarrier with a total transmit power constraint. To do so, they propose an idea to decompose the optimisation problem. The problem is solved in two general steps. The first step allocates the bit rates on each subcarrier when the total bit rate of each user is fixed. In the second step, the Lagrangian multipliers are used as the optimisation variants, and the dimension of the variant optimisation is reduced from 2N to 2, where N is the number of subcarriers. They also prove that the optimal point is on the bounds of the feasible region, thus the optimal solution could be searched through the bounds

Text classification in fair competition law violations using deep learning

IntroductionEnsuring fair competition through manual review is a complex undertaking. This paper introduces the utilization of Long Short-Term Memory (LSTM) neural networks and TextCNN to establish a text classifier for classifying and reviewing normative documents.MethodsThe experimental dataset used consists of policy measure samples provided by the antitrust division of the Guangdong Market Supervision Administration. We conduct a comparative analysis of the performance of LSTM and TextCNN classification models.ResultsIn three classification experiments conducted without an enhanced experimental dataset, the LSTM classifier achieved an accuracy of 95.74%, while the TextCNN classifier achieved an accuracy of 92.7% on the test set. Conversely, in three classification experiments utilizing an enhanced experimental dataset, the LSTM classifier demonstrated an accuracy of 96.36%, and the TextCNN classifier achieved an accuracy of 96.19% on the test set.DiscussionThe experimental results highlight the effectiveness of LSTM and TextCNN in classifying and reviewing normative documents. The superior accuracy achieved with the enhanced experimental dataset underscores the potential of these models in real-world applications, particularly in tasks involving fair competition review

Content-based image compression for arbitrary-resolution display devices

The existing image coding methods cannot support content-based spatial scalability with high compression. In mobile multimedia communications, image retargeting is generally required at the user end. However, content-based image retargeting (e.g., seam carving) is with high computational complexity and is not suitable for mobile devices with limited computing power. The work presented in this paper addresses the increasing demand of visual signal delivery to terminals with arbitrary resolutions, without heavy computational burden to the receiving end. In this paper, the principle of seam carving is incorporated into a wavelet codec (i.e., SPIHT ). For each input image, block-based seam energy map is generated in the pixel domain. In the meantime, multilevel discrete wavelet transform (DWT) is performed. Different from the conventional wavelet-based coding schemes, DWT coefficients here are grouped and encoded according to the resultant seam energy map. The bitstream is then transmitted in energy descending order. At the decoder side, the end user has the ultimate choice for the spatial scalability without the need to examine the visual content; an image with arbitrary aspect ratio can be reconstructed in a content-aware manner based upon the side information of the seam energy map. Experimental results show that, for the end users, the received images with an arbitrary resolution preserve important content while achieving high coding efficiency for transmission

Learning based screen image compression

There are usually two components in computer screen images: textual and pictorial parts. The pictorial part can be compressed efficiently by classical coding approaches (e.g. JPEG, JPEG2000), while the compression of the textual part is still far away from being satisfactory for the reason that the textual content is usually of high-frequency. In this paper, a learning approach is used to construct a tailored dictionary for text representation. Based on the learned dictionary, a novel screen image compression algorithm is proposed through adopting different basis functions for the textual and pictorial components respectively. The screen images are firstly segmented into textual and pictorial parts. Then we employ traditional discrete cosine transformation (DCT) to facilitate the compression of pictorial part, while the learned dictionary is used to represent the textual part in screen images. Experimental results demonstrate the effectiveness of the proposed compression algorithm