Secrecy Wireless Information and Power Transfer in Fading Wiretap Channel

Simultaneous wireless information and power transfer (SWIPT) has recently drawn significant interests for its dual use of radio signals to provide wireless data and energy access at the same time. However, a challenging secrecy communication issue arises as the messages sent to the information receivers (IRs) may be eavesdropped by the energy receivers (ERs), which are presumed to harvest energy only from the received signals. To tackle this problem, we propose in this paper an artificial noise (AN) aided transmission scheme to facilitate the secrecy information transmission to IRs and yet meet the energy harvesting requirement for ERs, under the assumption that the AN can be cancelled at IRs but not at ERs. Specifically, the proposed scheme splits the transmit power into two parts, to send the confidential message to the IR and an AN to interfere with the ER, respectively. Under a simplified three-node wiretap channel setup, the transmit power allocations and power splitting ratios over fading channels are jointly optimized to minimize the outage probability for delay-limited secrecy information transmission, or to maximize the average rate for no-delay-limited secrecy information transmission, subject to a combination of average and peak power constraints at the transmitter as well as an average energy harvesting constraint at the ER. Both the secrecy outage probability minimization and average rate maximization problems are shown to be non-convex, for each of which we propose the optimal solution based on the dual decomposition as well as suboptimal solution based on the alternating optimization. Furthermore, two benchmark schemes are introduced for comparison. Finally, the performances of proposed schemes are evaluated by simulations in terms of various trade-offs for wireless (secrecy) information versus energy transmissions.Comment: to appear in IEEE Transactions on Vehicular Technolog

How to Calculate the Public Psychological Pressure in the Social Networks

With the worldwide application of social networks, new mathematical approaches have been developed that quantitatively address this online trend, including the concept of social computing. The analysis of data generated by social networks has become a new field of research; social conflicts on social networks occur frequently on the internet, and data regarding social behavior on social networks must be analyzed objectively. This type of social computing method can solve a series of complex social computing problems including the calculation of public psychological pressure. The quantitative calculation of public psychological pressure is so important to the public opinion analysis that it can be widely applied in a lot of public information analysis fields

The Pessimistic Investor Sentiments Indicator in Social Networks

With the worldwide proliferation of social networks, the social networks have played an important role in the social activities .Peoples are inclined to obtain the corresponding public opinion to make decision such as shopping, education, investment and so on. Analysis of data generated by social networks has become an important field of research, however in the field of public opinion analysis of social networks the quantitative measure indexes are still lacking. In this paper, the calculation method of pessimistic investor sentiments indicator is proposed, and the index has a certain theoretical and practical value

Spectrum Comparative Study of Commutation Failure and Short-Circuit Fault in UHVDC Transmission System

When commutation failure occurs in UHVDC transmission system, the transient process of DC voltage and current are similar to grounding short-circuit fault. In order to differentiate them effectively, the paper introduces mathematical morphology methods to analysis the spectrum of transient current. Base on Yunnan-Guangzhou kV UHVDC transmission system, the paper simulates the commutation failure and DC line short-circuit fault under different fault conditions in PSCAD/EMTDC.  By modified morphology filter, the transient signal of DC () is decomposed into six scales, and morphological characteristics of aerial mode component of  is analyzed under different scales. The simulation results show that when DC line short-circuit faults occurs, wherever in the rectifier side, in the DC transmission line midpoint or in the inverter side, the aerial mode component of  have more high frequency weight in ~ and decays gradually; When commutation failures, which are caused by the inverter side AC system single-phase grounding fault, phase to phase fault, three phase grounding fault or the inverter side transformer ratio increased,  the aerial mode component of  have less frequency weight in

CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment

Language models trained on large-scale corpus often generate content that is harmful, toxic, or contrary to human preferences, making their alignment with human values a critical concern. Reinforcement learning from human feedback (RLHF) with algorithms like PPO is a prevalent approach for alignment but is often complex, unstable, and resource-intensive. Recently, ranking-based alignment methods have emerged, offering stability and effectiveness by replacing the RL framework with supervised fine-tuning, but they are costly due to the need for annotated data. Considering that existing large language models (LLMs) like ChatGPT are already relatively well-aligned and cost-friendly, researchers have begun to align the language model with human preference from AI feedback. The common practices, which unidirectionally distill the instruction-following responses from LLMs, are constrained by their bottleneck. Thus we introduce CycleAlign to distill alignment capabilities from parameter-invisible LLMs (black-box) to a parameter-visible model (white-box) in an iterative manner. With in-context learning (ICL) as the core of the cycle, the black-box models are able to rank the model-generated responses guided by human-craft instruction and demonstrations about their preferences. During iterative interaction, the white-box models also have a judgment about responses generated by them. Consequently, the agreement ranking could be viewed as a pseudo label to dynamically update the in-context demonstrations and improve the preference ranking ability of black-box models. Through multiple interactions, the CycleAlign framework could align the white-box model with the black-box model effectively in a low-resource way. Empirical results illustrate that the model fine-tuned by CycleAlign remarkably exceeds existing methods, and achieves the state-of-the-art performance in alignment with human value

EXPLORING THE EXPENDITURE-BASED PROFILE OF MACAO VISITORS: A CLUSTER ANALYSIS

Visitor profiling has been increasingly recognized as an important tourism marketing tool in the advent of smart tourism. There is a substantial body of literature relating to tourism market segmentation and visitor profiles. However, most of them focus on psychographic and behavioral factors. Seldom research has addressed the visitor profile based on actual expenditure during the visit. In this study, we explored the expenditure-based profile of Macao visitors using a randomly sampled dataset from a large-scale visitor profile survey supported by Macao Government Tourist Office. Utilizing self-reported actual expenditure data from 3577 visitors, we extracted six expenditure clusters of Macao visitors using a k-means clustering with silhouette analysis. The six clusters, i.e., entertainment, gambling, cuisine, shopping, both cuisine & shopping, and transit, have significant differences in expenditure preferences and expenditure levels. We also analyzed the demographic and behavior profile for each cluster. Our findings shed light on developing customized marketing strategies to the profile of each distinct expenditure cluster of Macao visitors

Variational-based data assimilation to simulate sediment concentration in the Lower Yellow River, China

The heavy sediment load of the Yellow River makes it difficult to simulate sediment concentration using classic numerical models. In this paper, on the basis of the classic one-dimensional numerical model of open channel flow, a variational-based data assimilation method is introduced to improve the simulation accuracy of sediment concentration and to estimate parameters in sediment carrying capacity. In this method, a cost function is introduced first to determine the difference between the sediment concentration distributions and available field observations. A one-dimensional suspended sediment transport equation, assumed as a constraint, is integrated into the cost function. An adjoint equation of the data assimilation system is used to solve the minimum problem of the cost function. Field data observed from the Yellow River in 2013 are used to test the proposed method. When running the numerical model with the data assimilation method, errors between the calculations and the observations are analyzed. Results show that (1) the data assimilation system can improve the prediction accuracy of suspended sediment concentration; (2) the variational inverse data assimilation is an effective way to estimate the model parameters, which are poorly known in previous research; and (3) although the available observations are limited to two cross sections located in the central portion of the study reach, the variational-based data assimilation system has a positive effect on the simulated results in the portion of the model domain in which no observations are available