Qiang Zhang's research while affiliated with Northwest University and other places
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Publications (35)
Water quality prediction is a fundamental and necessary task for the prevention and management of water environment pollution. Due to the fluidity of water, different sections of the same river have similar trends in their water quality. The present water quality prediction methods cannot exploit the correlation between the water quality of each se...
Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality mo...
Accurate prediction of water quality contributes to the intelligent management and control of watershed ecology. Water Quality data has time series characteristics, but the existing models only focus on the forward time series when LSTM is introduced and do not consider the effect of the reverse time series on the model. Also did not take into acco...
Accurate and efficient treatment of domestic waste is an important part of urban management. Whether domestic waste can be classified effectively will affect the sustainable development of human society. Previous research on the problem of waste image classification has focused on single-category waste recognition, which falls short of meeting the...
Accurate prediction of water quality is conducive to intelligent management and control of watershed ecology. Water quality data has time series characteristics, and although methods such as LSTM can capture sequence correlations in time series data, these methods do not consider the impact of bidirectional neighborhoods on the model, and they are...
The real‐time information on surrounding air quality index (AQI) is important for the public to protect themselves from air pollution. Traditional methods have some shortages regarding the estimation time and running efficiency. Consequently, the AQI results cannot meet the needs of personal protection and environmental management. With the popular...
Network security is not only related to social stability, but also an important guarantee for the digital intelligent society. However, in recent years, problems such as user account theft and information leakage have occurred frequently, which has greatly affected the security of users’ personal information and the public interest. Based on the ma...
Ecological and environmental problems have become increasingly prominent in recent years. Environmental problems represented by haze have become a topic that affects the harmonious ecology of human beings. The trend of this topic is on the rise. People’s perception of the environment after the impact of haze has also changed. A real-time grasp of t...
The rapid economic and social development has led to a rapid increase in the output of domestic waste. How to realize waste classification through intelligent methods has become a key factor for human beings to achieve sustainable development. Traditional waste classification technology has low efficiency and low accuracy. To improve the efficiency...
In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O 3 in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring stations between November 2017 and October 2018. This pap...
This study aims to improve the accuracy of waste sorting through deep learning and to provide a possibility for intelligent waste classification based on computer vision/mobile phone terminals. A classification model of recyclable waste images based on deep learning is proposed in this paper. In this waste classification model, the self-monitoring...
Attention flow network is a new and important branch of network science. Most of the work in this field are devoted to discovering common patterns in the attention flow network and revealing the basic mechanisms and evolution laws of the world wide web. The link prediction algorithm of attention flow network has important theoretical significance a...
The development of the Internet of things (IoT) has given birth to new applications and services. Accordingly, because the IoT application system collects a large amount of data containing sensitive information, there are also new challenges in data security and privacy preservation. To locate the medical Internet of things (mIoT), this paper propo...
With the deployment and real-time monitoring of a large number of micro air quality monitoring stations, new application scenarios have been provided for the research of air quality prediction methods based on artificial intelligence. Integrating deep learning with multi-task learning, this paper proposes a hybrid model for air quality prediction t...
As the problem of urban environment and air pollution continues to intensify, the public has developed a very strong perception of the surrounding environment. With this growing perception, their emotions and satisfaction about the environment may also be greatly affected. This article explores the main factors that affect the public's environmenta...
The serious threat of air pollution to human health makes air quality a focus of public attention, and effective, timely air quality monitoring is critical to pollution control and human health. This paper proposes a deep learning and image-based model for air quality estimation. The model extracts feature information from scene images captured by...
Public environmental sentiment has always played an important role in public social sentiment and has a certain degree of influence. Adopting a reasonable and effective public environmental sentiment prediction method for the government’s public attention in environmental management, promulgation of local policies, and hosting characteristics activ...
This paper proposes a hybrid lane occupancy rate prediction model called 2LayersCapsNet, which combines the improved capsule network and convolutional neural networks (CNNs). The model uses CNNs to mine the spatial-temporal correlation characteristics of the lane occupancy rate and then uses an improved capsule network to mine the interrelationship...
The rapid development of communication technologies, the network, advanced computing methods and wireless medical sensors gives rise to a modern medical system. In this system, large-scale electronic health records (EHRs) are often outsourced to be stored at the third parties, such as cloud service providers (CSPs). However, CSPs are not trustworth...
In China, haze weather has become a major public concern and is frantically discussed by the public. Many people express their views, opinions, or complaints on social media. Effectively extracting this useful information may help to improve our understanding of how the public perceive and respond to haze, and could potentially contribute to enviro...
In order to study the variation law of the influence degree of air quality factors under time series, this article selects the hourly concentration values of NO
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and O
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Quantifying the similarity of nodes in collective attention flow network has an important theoretical and practical value. In this paper, we defined the generation time Rt, the influence radius Sr and the representation Vs (Rt, Sr) of the nodes in the collective attention flow network based on the optimization of Spatial Preferred Attachment (SPA)...
The natural environmental degradation is specifically challenging in rapid urbanization areas where social economic development needs to swallow large amounts of resources and emit a great deal of pollutants (wastewater, solid wastes, and air pollutants). Coordinated development of social economic development and ecological environmental developmen...
The public environment perception model regards people as a "data perceptron" and a human-centred participatory perception model. The enthusiasm and initiative of public participation will directly determine the effective operation of the model. This paper aims to understand how to stimulate public participation in data sensibility in public enviro...
A Based on the features of information sharing and energy interaction within smart grid, a decision-making model of energy consumption based on Nash equilibrium solution is developed to analysis the problem of competition and benefit conflict among different kinds of power users on demand-side. The payoff functions of three kinds of player are buil...
Because of the subjectivity of the current index system of the cyclic economy and the lack of the comprehensive assessment of the trend for the economic development, the present paper proposes a rough-set-based approach to establish the index system for the cyclic economy and sets up the evaluation model for the cyclic economy development with the...
PM2.5 is affected by complex factors such as meteorological elements in the air system and other pollutants in the air. So PM2.5 has chaotic property, which makes the prediction of PM2.5 concentration extremely difficult. In order to improve the prediction accuracy of PM2.5 concentration, this paper introduces the chaotic time series prediction met...
This paper uses data for the period 1950–2050 compiled by the United Nations Population Division together with methods including spatial autocorrelation analysis, hierarchical cluster analysis and the standard deviational ellipse, to analyze the spatio-temporal evolution of population and urbanization in the 75 countries located along the routes of...
China is actively promoting“the Belt and Road”strategy and vigorously developing international in-
dustrial cooperation，and the construction of the International Cooperation Demonstration Zone（ICDZ）has be-
come the research topic of the Chinese government and scholars. However，how to scientifically and rationally
choose the industrial type of ICDZ?...
Citations
... The multivariate time series build predictive models by analyzing historical time series data and correlations between individual factors [48]. For multi-element water quality time series data, different element features have different effects on water quality prediction. ...
... For example, YOLOv4 reached a speed 24% higher than faster R-CNN, while the mAP was 2.03% higher. 78 Therefore, the YOLO series has as good a performance as the R-CNN series, and high detection speed qualified the YOLO series to adapt operational requirements. The region-based fully convolutional network was demonstrated to be effective in assisting YOLO to detect small objects. ...
... Ma M et al. proposed a crucial node identification model based on graph attention networks and reinforcement learning, where a graph attention network is used to obtain the embedded representation of each node, and combined with reinforcement learning, the node embedding is mapped to the corresponding node quality score. Then the ranking of crucial nodes is obtained [17]. ...
... It is used in data mining, machine learning, machine translation, natural language processing, and multimedia learning. As a part of natural language processing, sentiment analysis has also transitioned from traditional analysis methods into the field of deep learning [20,21]. Nag et al. [22] found that music evokes different emotions in people, and thus applied convolutional neural networks to classify music according to different emotions, which was the first combination of deep learning and nonlinear-based classification algorithms. ...
... The results obtained from their study proved that the proposed system achieved a higher rate of accuracy with more than 93%. A system to classify waste images using a transfer learning classification system was proposed in the article [27]. Authors in their research study performed the classification of different classes of waste images using a pre-trained DenseNet 169 and obtained a higher accuracy of 82% which is way better when compared to other traditional image classification techniques. ...
... The complex network theory has recently been widely adopted to study inter-regional air pollution spillovers (Zhang D. et al., 2020;Yu et al., 2021;Zhang Q. et al., 2021). From the complex network perspective, the cities are regarded as nodes, and the air pollutant spillovers between cities are regarded as edges; thus, the cities in the whole region of China can be linked to form a network capturing the direct and indirect interaction of air pollutants comprehensively (Carmona-Cabezas et al., 2019). ...
... However, in scenario b, node 4 waits only 2 hops to reach an edge node. So, we can say that delay magnitude depends on the number of edge nodes, as shown in Eq. (5). ...
... But these schemes only allow the validation of single-keyword search result verification. The schemes to provide multi-keywords validation in the Internet of Things was put forward by Ge et al. [14] and Liu et al. [15]. These schemes verify if the returned files include the keyword, but they do not verify whether there are complete files relating to the keyword. ...
... However, when attribute authority is maliciously attacked, the key of the data user will be leaked, which makes the data owner's data face great challenges in terms of data privacy and confdentiality. Terefore, Liu et al. [24] proposed an anonymous hierarchical attribute encryption scheme in the electronic medical sharing environment, which introduces multiple authority attributebased encryption technology to achieve fne-grained data access control and avoid the bottleneck of key escrow under a single authority. However, because multiple authorities have to communicate with each other, the system's performance is signifcantly reduced. ...
... The temporal patterns were captured through the LSTM networks and the spatial dependencies were extracted by the convolutional neural networks (CNNs). Zhang et al. [18] then modeled the Spatio-temporal correlations with the CNN model and the gated recurrent units (GRUs). The above methods can provide satisfactory prediction results, nevertheless, the CNN model is not suitable to model the non-Euclidean structure data and thus the spatial relationships between air sensors cannot be effectively modeled. ...
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