Hai Huang

Bio: Hai Huang is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Cognitive radio & Spectral efficiency. The author has an hindex of 2, co-authored 5 publications receiving 11 citations.

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

Abstract: With the rapid development of social media, danmaku video provides a platform for users to communicate online. To some extent, danmaku video provides emotional timing information and an innovative method to analyze video data. In the age of big data, studying the characteristics of danmaku and its emotional tendencies can not only help us understand the psychological characteristics of users but also feedback the effective information of users to video platforms, which can help the platforms optimize related short video recommendations so that it can provide a more accurate solution for the selection of audiences during video production. However, danmaku is different from traditional comments. Current emotion classification methods are only suitable for two-dimensional classification which are not suitable for danmaku emotion analysis. Aiming at the problems such as the colloquialism, diversity, spelling errors, structural non-linearity informal language on the Internet, diversity of social topics, and context dependency of emotion analysis of the danmaku data, this paper proposes an improved emotion analysis model based on Bi-LSTM model to classify the further four-dimensional emotions of Pleasure, Anger, Sorrow and Joy. Furthermore, we add tags such as comment time and user name to the danmaku information. Experimental results show that the improved model has higher Accuracy, Recall, Precision, and F1-Score under the same conditions compared with the CNN and SVM. The classification effect of improved model is close to the SOTA. Experimental results also show that the improved model can be effectively applied to the analysis of irregular danmaku emotion.

Social Interaction Assisted Resource Sharing Scheme for Device-to-Device Communication Towards Green Internet of Things

Abstract: Recently, to reduce the increasing energy consumption along with the explosive growth of smart terminals and network data in Internet of Things (IoT), green IoT is attracting more and more attention from both academia and industry. Towards green IoT, this paper devotes attention to Device-to-Device (D2D) communication and social network which are two essential components in green IoT and first reviews the latest developments about them. Further, this paper proposes a social interaction assisted resource sharing scheme for D2D communication, to improve the utilization of spectrum resources in green IoT. Specifically, the proposed scheme abstracts the D2D communication system into social layer and physical layer, by combining social network with D2D communication. Extensive simulations with real social interaction data are conducted and they show that the proposed scheme can achieve improvements in terms of the transmission success rate for green IoT.

An Intelligent Human Behavior-Based Reasoning Model for Service Prediction in Smart Home

Abstract: With the rapid development of technologies and improvement of living conditions, people are increasingly concerned about their life safety, convenience, and comfortableness. Household intelligence starts to play an increasingly important role in improving people living environment. This paper focuses on service prediction in smart home automatic control systems, and proposes an intelligent human behavior-based reasoning model on the basis of linear prediction and case-based reasoning. Hardware and software are designed after analysis of the embedded architecture. In addition, an embedded smart home platform is built up and implemented to validate the proposed human behavior-based reasoning algorithm.

Meet an Fantastic Sibyl: A Powerful Model in Cognitive Radio Networks

Abstract: Dynamic spectrum access is challenging, since an individual secondary user usually just has limited sensing abilities. One key insight is that primary user emergence forecasting among secondary users can help to make the most of the inherent association structure in both time and space, it also enables users to obtain more informed spectrum opportunities. Therefore, primary user presence forecasting is vital to cognitive radio networks (CRNs). With this insight, an auto regressive enhanced primary user emergence reasoning (AR-PUER) model for the occurrence of primary user prediction is derived in this paper. The proposed method combines linear prediction and primary user emergence reasoning. Historical samples are selected to train the AR-PUER model in order to capture the current distinction pattern of primary user. The training samples of the primary user emergence reasoning (PUER) model are combined with the recent samples of auto regressive (AR) model tracking recent parallel. Our scheme does not require the knowledge of the signal or of the noise power. Furthermore, the proposed model in this paper is blind in the detection that it does not require information about the channel. To verify the performance of the proposed model, we apply it to the data during the past two months, and then compare it with other method. The simulation results demonstrate that the AR-PUER model is effective and generates the most accurate forecasting of primary user occasion in several cases. Besides, it also performs much better than the commonly used energy detector, which usually suffers from the noise uncertainty problem.

A Spectrum Sensing Scheme Based on Subspace Filtering

Abstract: Spectrum sensing (SS) has attracted much concern of researchers due to its significant contribution on the spectral efficiency. Energy Detection (ED) has been a critical method for Spectrum Sensing in Cognitive Radio Networks (CRNS) due to its low complexity and simple implement. However, noise uncertainty in ED greatly degrades the detection performance, especially under a low Signal-to-Noise Ratio (SNR). To remove noise uncertainty as much as possible, a scheme based on subspace decomposition is proposed for SS, where the received signal is decomposed into two parts: noise subspace and signal-plus-noise subspace. Then the closed-form solution of the detection and false alarm probabilities is given on the basis of the signal-plus-noise subspace in Rayleigh fading channel. The energy of the remainders after removal of noise subspace and noise contribution in signal-plus-noise subspace is used to decide whether the primary user (PU) exists by a comparison with a redesigned threshold. Eventually, some simulations based on MATLAB platform is made to validate the proposed method.

Enabling technologies challenges of green internet of things (iot) towards sustainable development in the era of industry 4.0

Abstract: The extensive adoption of the Internet of Things (IoT) has increased the carbon footprint on a large scale across the globe. To handle this challenge, scholars and policymakers are making efforts to propose novel energy-efficient solutions to provide a desirable environment for green-IoT (G-IoT). Additionally, further research is required to analyze the G-IoT-related challenges to elucidate the difficulties of its implementation for researchers. Moreover, the GIoT requirements have been considered in different network levels, namely software, hardware, architecture, communication. To present a comprehensive framework to identify the challenges of G-IoT, a survey using literature review and expert’s opinion is carried. Total 23 challenges are taken to evaluate and implement G-IoT technologies towards sustainable development achievements (SDA). Consequently, this article aims to rank and evaluate the challenges to implement the G-IoT towards the SDA. An integrated approach is proposed with stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) under Pythagorean fuzzy sets. As a result, an machine-to-machine (M2M) standardization protocol with a weight value of 0.0508 has the first rank, followed by adaptation to natural energy sources with a weight value of 0.0479, information security and privacy protection with a weight value of 0.0469, and internet protocol version-6 (IPv6) for low-end devices with weight 0.0467. To validate the proposed method, sensitivity analysis and comparison using existing methods have been conducted.

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid

Abstract: Multi-Agent Systems (MAS) have been seen as an attractive area of research for civil engineering professionals to subdivide complex issues. Based on the assignment’s history, nearby agents, and objective, the agent intended to take the appropriate action to complete the task. MAS models complex systems, smart grids, and computer networks. MAS has problems with agent coordination, security, and work distribution despite its use. This paper reviews MAS definitions, attributes, applications, issues, and communications. For this reason, MASs have drawn interest from computer science and civil engineering experts to solve complex difficulties by subdividing them into smaller assignments. Agents have individual responsibilities. Each agent selects the best action based on its activity history, interactions with neighbors, and purpose. MAS uses the modeling of complex systems, smart grids, and computer networks. Despite their extensive use, MAS still confronts agent coordination, security, and work distribution challenges. This study examines MAS’s definitions, characteristics, applications, issues, communications, and evaluation, as well as the classification of MAS applications and difficulties, plus research references. This paper should be a helpful resource for MAS researchers and practitioners. MAS in controlling smart grids, including energy management, energy marketing, pricing, energy scheduling, reliability, network security, fault handling capability, agent-to-agent communication, SG-electrical cars, SG-building energy systems, and soft grids, have been examined. More than 100 MAS-based smart grid control publications have been reviewed, categorized, and compiled.