In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet-of-Things (IoT) data by smart cities. Thus, the development and deployment of various aspects ...of IoT-based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real-time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine-learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications.
Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms' biological and pathological processes. However, bulk RNA-seq can only measure gene ...expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space ( https://github.com/ZJUFanLab/bulk2space ), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.
ABSTRACTElectromagnetic pollution and military stealth technique have raised tremendous interest in high-performance microwave-absorbing structures. However, an ultra-broadband absorbing functional ...structure is still in urgent demand. Herein, a novel continuous conductive fibre-based absorbing metamaterial was successfully designed and fabricated by multi-materials hybrid 3D printing technique. Benefitting from the highly symmetric structured design of a super unit cell and appropriate conductive property of continuous fibre, the absorbing metamaterial is demonstrated to have an ultra-wide band microwave absorption and polarisation insensitivity. Furthermore, the thickness of the proposed metamaterial is only 3.2 mm of 0.085λmax. In the wave absorption test, the metamaterial shows a broadband absorption of 8–18.1 GHz (greater than 90%) and maintains a good absorption effect at an incident angle of 40°. The design and fabrication method of the continuous fibre-absorbing metamaterial provides a new idea for the realisation of the integrated bearing and absorbing metamaterial, which will greatly promote the practical application of absorbing metamaterial.
Unified power flow controller (UPFC), static synchronous series compensator (SSSC), thyristor controlled series compensation (TCSC) and phase shifter (PST) can be able to adjust line parameters and ...flexibly control power flow. They have been applied in domestic and foreign power grids, and have played an important role in balancing power flow in transmission channels and improving power supply capacity. The theoretical research and engineering practice of UPFC, SSSC, TCSC and PST are summarized. Firstly, the basic structure and power flow control principle of four kinds of power flow control devices are analyzed, and projections of power flow control devices are introduced. Then, the key technologies of power flow control devices are summarized from five aspects: topology, converter technology, location and capacity, control strategy and fault protection. Finally, the typical application scenarios of power flow control devices in the future power grid are prospected, and the research direction of power flow c
An increased use of the high-voltage direct current (HVDC) technologies can have important effects on frequency performance and voltage stability of the receiving-end grid during normal operation as ...well as during blocking failure. The main reasons are the inherent characteristics of the HVDC such as its much larger capacity than thermal plants and lack of voltage supporting ability to the alternating current (AC) grid. These has led to new challenges for AC/direct current (DC) power grid operators in terms of ensuring power system security. To address these challenges, a unit commitment (UC) of the receiving-end in the AC/DC hybrid grid is presented in this paper. In the proposed model, primary frequency modulation constraints are added to provide sufficient capacity for HVDC blocking. Besides, grid security constraint after secondary frequency regulation is also considered because HVDC blocking failure would cause large range power transfer and transmission lines overload. Meanwhile, voltage stability constraints are employed to guarantee enough voltage supporting capacity from thermal plants at the HVDC feed-in area. Based on the characteristics of the model, Benders decomposition and mixed integer programming algorithm are used to get the optimal transmission power of the HVDC and schedule of thermal units. The study is done by considering the IEEE-39 and Jiangsu power grid in eastern China, containing two HVDC transmission projections respectively. The results are also validated by simulation of different HVDC blocking failure scenarios.
As one of the most effective approaches in dealing with the energy crisis, combined electricity and natural gas systems have become more and more popular worldwide. To take full advantages of such ...hybrid energy networks, a proper operation and control method is required. In this paper, a novel approach coordinating combined heating and power generation is proposed. First, state excursion rate, a criterion describing the deviation of system operation, is defined for system state evaluation. Then, thermal energy storage is allocated to provide more and better operation modes for combined generation. By investigating the state excursion rate of hybrid energy systems, the optimal operation mode is chosen through an analytical strategy. Case studies on hybrid energy networks show that all state variables, including voltages in electric systems and pressures in gas networks, are adjusted to follow proper operation constraints by the implementations of the proposed strategy. In addition to providing sufficient auxiliary services for hybrid systems, it is also possible to maintain the economic and energy-efficient benefits of energy supply. This study provides an effective method to utilize the regulation capability of combined heating and power generations, which is a technical basis of energy internet.
Nowadays, the advanced sensor technology with cloud computing and big data is generating large-scale heterogeneous and real-time IOT (Internet of Things) data. To make full use of the data, ...development and deploy of ubiquitous IOT-based applications in various aspects of our daily life are quite urgent. However, the characteristics of IOT sensor data, including heterogeneity, variety, volume, and real time, bring many challenges to effectively process the sensor data. The Semantic Web technologies are viewed as a key for the development of IOT. While most of the existing efforts are mainly focused on the modeling, annotation, and representation of IOT data, there has been little work focusing on the background processing of large-scale streaming IOT data. In the paper, we present a large-scale real-time semantic processing framework and implement an elastic distributed streaming engine for IOT applications. The proposed engine efficiently captures and models different scenarios for all kinds of IOT applications based on popular distributed computing platform SPARK. Based on the engine, a typical use case on home environment monitoring is given to illustrate the efficiency of our engine. The results show that our system can scale for large number of sensor streams with different types of IOT applications.
With the growing development of smart cities, public transit forecasting has begun to attract significant attention. In this paper, we propose an approach for forecasting passenger boarding choices ...and public transit passenger flow. Our prediction model is based on mining common user behaviors for semantic trajectories and enriching features using knowledge from geographic and weather data. All the experimental data comes from the Ridge Nantong Limited bus company and Alibaba platform which is also open to the public. We evaluate our approach using various data sources, including point of interest (POI), weather condition, and public bus information in Guangzhou to demonstrate its effectiveness. Experimental results show that our proposal performs better than baselines in the prediction of passenger boarding choices and public transit passenger flow.
With the design and development of smart cities, opportunities as well as challenges arise at the moment. For this purpose, lots of data need to be obtained. Nevertheless, circumstances vary in ...different cities due to the variant infrastructures and populations, which leads to the data sparsity. In this paper, we propose a transfer learning method for urban waterlogging disaster analysis, which provides the basis for traffic management agencies to generate proactive traffic operation strategies in order to alleviate congestion. Existing work on urban waterlogging mostly relies on past and current conditions, as well as sensors and cameras, while there may not be a sufficient number of sensors to cover the relevant areas of a city. To this end, it would be helpful if we could transfer waterlogging. We examine whether it is possible to use the copious amounts of information from social media and satellite data to improve urban waterlogging analysis. Moreover, we analyze the correlation between severity, road networks, terrain, and precipitation. Moreover, we use a multiview discriminant transfer learning method to transfer knowledge to small cities. Experimental results involving cities in China and India show that our proposed framework is effective.
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