High Timeliness Research Articles

RETRACTED: Sports performance prediction model based on integrated learning algorithm and cloud computing Hadoop platform

This article discusses the classification and research performance information properties. It also discusses construction and application of the Hadoop cloud computing platform. The model presented in this article is a one piece learning algorithm which is a predictive model and a model of cloud based data collection. This model is supported by Hadoop which is suitable for computing with different data sizes. A large number of simulations are performed on the Hadoop platform, under different working conditions, to verify the accuracy and characteristics of the training skill. Spark framework of this research is to develop computational engine efficiency and improve rain prediction models successfully and effectively using big data and Hadoop learning. Therefore, the planned high timeliness and accuracy of real-time hurricane forecast with rain, can solve the problem.

Real-time drilling rate prediction method of reaming while drilling based on mechanical specific energy

Reaming while drilling (RWD) is a drilling technique that uses a reamer and a pilot bit to expand the size of an open hole segment to be larger than the inner diameter of an upper casing string while fully drilling, especially in deep wells. This technique reduces the number of trips and is highly efficient and widely used in deep wells, ultra-deep wells, small clearance wells, and side-drilling and complex well conditions. However, in the actual operation process, the mismatching between the pilot bit and reamer often leads to slippage, string vibration, and low-quality borehole.In this paper, based on the mechanical specific energy model and the RWD weight distribution model, combined with the drilling weight, torque, and formation confined compressive strength at the interface of the pilot bit and reamer, a real-time rate of penetration (ROP) prediction model suitable for RWD is derived. Through the analysis of the downhole condition of the RWD operation, reasonable drilling parameters are determined and a set of real-time RWD performance prediction and optimization algorithm is proposed.Studies show that during the RWD operation, the size of the pilot bit and reamer and the shape of the cutter have an enormous influence on ROP. When drilling in different formations, the excellent cooperation among the pilot bit, reamer, and formation is the key to improve ROP and bottom hole assembly stability. How to optimize the bit and reamer to drill with the same ROP and predict the ROP is the focus of this paper.Field logging data test the ROP prediction model and real-time optimization method, and the results indicate that the ROP prediction model has high precision, small error, and high timeliness, which can fully meet the requirements of field engineering. This model can guide the selection of the bit, reamer, and drilling parameters in RWD, which is of considerable significance to RWD.

Study on Underground Logistics System Planning in the Jinan Prior Zone

In recent years, logistics demands in Jinan have shown clear growth trends. By 2020, Jinan city has proposed that the volume of express deliveries will reach 800 million packages, with revenue reaching 7 billion CNY. As an innovative logistics system, an underground logistics system (ULS) can provide new developmental ideas for urban logistics in Jinan. Although there are no actual cases of ULS construction projects in the world at present, as a first demonstration area driven by innovation in Jinan City, the Jinan Prior Zone for Replacing Old Growth Drivers with New Ones (Jinan Prior Zone) will be the first to attempt its implementation, thus providing an advanced model for sustainable development of urban logistics around the world. Based on the urban master plan of the Jinan Prior Zone, the logistics plan of Jinan city and other urban subject plans, this study proposes ULS planning for the Jinan Prior Zone, including demand forecasting, logistics architecture, planning principles, construction forms, and short/long term construction plan. The ULS planning will provide small batch, high frequency, and high timeliness for express delivery services in the central area of the Daqiao Group in the Jinan Prior Zone. In the long term, it will serve 500,000 people, with an average daily logistics volume of 1300 tons. This study views the introduction of ULS into the Jinan Prior Zone will both improve the efficiency of logistics and reduce the pressure of ground traffic and improve the level of green logistics, which is of great significance for the promotion of high-quality development of urban logistics.

Research on the Integration of Urban Traffic and Big Data

The powerful data processing ability of big data technology can allocate traffic resources more efficiently, and can deal with various sudden traffic problems flexibly. It is an unprecedented opportunity and challenge for urban transportation and smart cities to effectively collect and utilize traffic big data to meet the application requirements of high timeliness traffic administrative supervision, traffic enterprise management and traffic citizen service. This article expounds the concept and characteristics of big data, discusses the application research of big data in urban transportation at home and abroad in recent years, summarizes its application research scope and trend, points out that intelligent transportation is the focus of the application of big data in urban transportation, and finally looks forward to the future research direction.

Typhoon Quantitative Rainfall Prediction from Big Data Analytics by Using the Apache Hadoop Spark Parallel Computing Framework

Situated in the main tracks of typhoons in the Northwestern Pacific Ocean, Taiwan frequently encounters disasters from heavy rainfall during typhoons. Accurate and timely typhoon rainfall prediction is an imperative topic that must be addressed. The purpose of this study was to develop a Hadoop Spark distribute framework based on big-data technology, to accelerate the computation of typhoon rainfall prediction models. This study used deep neural networks (DNNs) and multiple linear regressions (MLRs) in machine learning, to establish rainfall prediction models and evaluate rainfall prediction accuracy. The Hadoop Spark distributed cluster-computing framework was the big-data technology used. The Hadoop Spark framework consisted of the Hadoop Distributed File System, MapReduce framework, and Spark, which was used as a new-generation technology to improve the efficiency of the distributed computing. The research area was Northern Taiwan, which contains four surface observation stations as the experimental sites. This study collected 271 typhoon events (from 1961 to 2017). The following results were obtained: (1) in machine-learning computation, prediction errors increased with prediction duration in the DNN and MLR models; and (2) the system of Hadoop Spark framework was faster than the standalone systems (single I7 central processing unit (CPU) and single E3 CPU). When complex computation is required in a model (e.g., DNN model parameter calibration), the big-data-based Hadoop Spark framework can be used to establish highly efficient computation environments. In summary, this study successfully used the big-data Hadoop Spark framework with machine learning, to develop rainfall prediction models with effectively improved computing efficiency. Therefore, the proposed system can solve problems regarding real-time typhoon rainfall prediction with high timeliness and accuracy.

An Autonomous Attack Guidance Method with High Aiming Precision for UCAV Based on Adaptive Fuzzy Control under Model Predictive Control Framework

With its superior performance, the unmanned combat air vehicle (UCAV) will gradually become an important combat force in the future beyond-visual-range (BVR) air combat. For the problem of UCAV using the BVR air-to-air missile (AAM) to intercept the highly maneuvering aerial target, an autonomous attack guidance method with high aiming precision is proposed. In BVR air combat, the best launching conditions can be formed through the attack guidance and aiming of fighters, which can give full play to the combat effectiveness of BVR AAMs to the greatest extent. The mode of manned fighters aiming by manual control of pilots is inefficient and obviously not suitable for the autonomous UCAV. Existing attack guidance control methods have some defects such as low precision, poor timeliness, and too much reliance on manual experience when intercepting highly maneuvering targets. To address this problem, aiming error angle is calculated based on the motion model of UCAV and the aiming model of BVR attack fire control in this study, then target motion prediction information is introduced based on the designed model predictive control (MPC) framework, and the adaptive fuzzy guidance controller is designed to generate control variable. To reduce the predicted aiming error angle, the algorithm iteratively optimizes and updates the actual guidance control variable online. The simulation results show that the proposed method is very effective for solving the autonomous attack guidance problem, which has the characteristics of adaptivity, high timeliness, and high aiming precision.

Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production

Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.

Thoughts and suggestions on arrangement, analysis and summary of medical data during COVID-19 epidemic

The analysis and utilization of clinical scientific research data is an effective means to promote the progress of diagnosis and treatment, and a key step in the development of medical sciences. During the epidemic of coronavirus disease 2019(COVID-19), how to transform the limited diagnostic data into clinical research resources has attracted much attention. Based on the low efficiency of data collection and extraction, the inconsistency of data analysis, the irregularity of data report and the high timeliness of data update during the epidemic, this paper briefly analyzed the background and reasons of data application under the current situation, and then discusses the problems and feasible solutions of clinical data applications under the epidemic situation and, more importantly, for future medical clinical research methods. We put forward several methodological suggestions: ① gradually improve the medical big data model and establish the national medical health data center; ② improve the scientific research literacy of medical staff and popularize the basic skills and knowledge of GCP; ③ promote a scientific, networked and shared data collection and management mode; ④ use the mixed research method and collective analysis to improve the efficiency of clinical data analysis; ⑤ pay attention to narration of the medical feelings and emphasize the humanistic data of clinical medicine. It is expected to promote the standardized and reasonable use of clinical scientific research data, the rigorous integration of expert opinions, and ultimately the development of big data for national health care.

Deep interaction: Wearable robot-assisted emotion communication for enhancing perception and expression ability of children with Autism Spectrum Disorders

Recent changes in both the social economy and people’s living conditions and their habits have had an influence on the incidence of Autism Spectrum Disorder (ASD), which has brought huge economic and mental burden to the society , and has become an urgent public health problem. The main symptom of autism in children is the presence of a social barrier, and one of the main reasons for that is a lack of emotional cognitive ability. However, the existing autism-treatment systems intended for children pay little attention to the emotion cognition disorder. Besides, too much importance has been given to the interaction with children, while limiting the timeliness and movability of these systems. With the aim to address this shortcoming, in this work, we focus on the emotion cognition disorder and design a feasible solution for enhancing perception and expression ability of children with ASD. First, the first-view emotional care system for children with ASD (First-ECS) is developed using a wearable robot as a system carrier and realizing the deep emotional interaction with children with autism from the first-view perspective. Emotion communication is used to meet high timeliness requirements for emotion transmission in the First-ECS. Next, the emotional interaction mechanism that is applicable to the line of sight and non-line of sight communication scenarios is introduced. Also, the emotion perception engine and emotion expression engine are designed. Subsequently, multimodal data collection and processing by a wearable affective robot are discussed. In addition, this paper introduces a multimodal data fusion method from the angle of emotion relevance and emotion computing model based on audio-visual data. Finally, a demo platform is built to verify the feasibility of the proposed system.

Battlefield Situation Information Recommendation Based on Recall-Ranking

With the rapid development of information technology, battlefield situation data presents the characteristics of “4V” such as Volume, Variety, Value and Velocity. While enhancing situational awareness, it also brings many challenges to battlefield situation information recommendation (BSIR), such as big data volume, high timeliness, implicit feedback and no negative feedback. Focusing on the challenges faced by BSIR, we propose a two-stage BSIR model based on deep neural network (DNN). The model utilizes DNN to extract the nonlinear relationship between the data features effectively, mine the potential content features, and then improves the accuracy of recommendation. These two stages are the recall stage and the ranking stage. In the recall stage, the candidate set of situation information is generated, and the massive situation information is reduced to a small candidate subset that is highly relevant to the commanders. In the ranking stage, the situation information in the candidate set is accurately scored and then sorted, and the situation information with a high score is eventually recommended to the commanders. Finally, we use the historical date from the situation management of real combat training information systems as the dataset and verify the effective-ness of the algorithm through experiments, and analyze the effects of the depth and width of the neural network on the performance of the algorithm.

Traffic Congestion Forecasting in Shanghai Based on Multi-Period Hotspot Clustering

Traffic congestion has become increasingly prominent. Effective prediction of road congestion will provide a great reference for urban road planning and residents’ travel. Taxi trajectory data objectively reflect the travel routes of the residents of a city. With good temporal and spatial characteristics and high timeliness, these data have become important in the study of urban spatio-temporal characteristics. In this paper, the PageRank-K clustering algorithm is used to analyse hotspots and cluster hotspots of a region. The shortest-distance matching algorithm based on the transition probability is used for map matching; then, the average speed of the road sections can be calculated, and the direction of taxis can be determined. The Dual_XGBoost model is used for traffic congestion prediction. Finally, we compared our model with similar models. We take roads in Shanghai as the test object. The results show that our method is faster in training, can predict congestion at any time and is more sensitive to long-term features than the other models.

Elevating performance of electrochemical immunosensor via photo-induced microscale hyperthermia in situ

Catalytic reactions contribute a lot to electrochemical sensing by amplifying electrochemical signals to elevating sensitivity, allowing ultrasensitive sensing of bioindicators. However, the unsatisfactory catalytical performance of catalysts results in low efficiency, limiting its practice in rapid immunosensing. Herein, we demonstrated the potential of photo-induced microscale hyperthermia in accelerating catalysis to enhance sensitivity in the short time. Under near-infrared (NIR) laser irradiation, the period of Fenton-like reaction was significantly reduced, further allowing rapid change of electrical signal on electrode in situ. By constructing a novel immunosensor, efficacious sensing of Squamous Cell Carcinoma Antigen (SCCA) was achieved with improved sensitivity for two times, high timeliness within several minutes and advanced performance of electrochemical immunosensor (linear detection range: 0.1 pg mL-1–1 μg mL-1; limit of detection: 120.2 fg mL-1). To the best of our knowledge, this research is the first work that typifies the photothermal-enhanced catalysis in the electrochemical immunoassays, which illuminates a great direction of developing advanced electrochemical sensing protocol with both favorable capacities and accelerated process.

Parallel computation of regional CORS network corrections based on ionospheric-free PPP

Global navigation satellite system real-time processing requires low latency, high timeliness, and high computational efficiency. A typical application is providing corrections using data from a regional Continuously Operating Reference Station (CORS) network. Usually the wide-lane and narrow-lane fractional cycle biases (FCBs) are determined at the server and broadcast to users to fix undifferenced ambiguity. Also, a tropospheric model is established at the server and broadcast to users to obtain accurate and reliable a priori zenith total delays for precise point positioning (PPP) using the ionospheric-free (IF) observation combination. Currently, serial methods are typically applied, i.e., all reference stations are involved in estimating the wide-lane and narrow-lane FCBs and establishing a regional tropospheric delay model. To improve the efficiency and shorten the latency, we develop a parallel computation method for regional CORS network corrections based on IF PPP by adopting a multicore parallel computing technology task parallel library, wherein parallel computations involving the FCBs, tropospheric delays, and tropospheric model are successively performed based on data parallelism, in which the same operation is performed concurrently on elements in an array, and task parallelism, which refers to one or more independent tasks running concurrently. Data covering four seasons from the Hong Kong and southwestern America CORS networks are utilized in the experiment. The single differenced FCBs between satellites are determined within each full pass, and a tropospheric model with an internal accuracy better than 1.4 cm and an external accuracy better than 1.6 cm is derived at the server. With the parallel implementation, the speedup ratios of FCB estimation and tropospheric modeling are 1.79, 3.15, 5.59, and 9.69 times higher for dual-core, quad-core, octa-core, and hexadeca-core platforms, respectively, than for a single-core platform.

Research on Rural Land Right Confirmation from the Perspective of Campaign-Style Governance

This paper focuses on the implementation effect of the rural land right confirmation work carried out in China since 2013. It discusses the imbalance and good governance in the rural land right confirmation work through the review of this policy agenda. The author adopts the grounded theory research method based on interviews and literature research to find out the unbalanced points and interest conflicts of each subject in the policy agenda, focusing on Shanxi Province, which has a high proportion of traditional agriculture. This land right verification work has significant movement type governance characteristics, driven by the central, within five years to complete a nationwide land management work. Behind its high timeliness and predetermined target is a series of games from policy entrusting parties, intermediate contractors and grass-roots agents. The three forces interact and reconstruct. Task transmission becomes the main line of activities and task objectives are constantly overhead. The implementation of policy objectives is challenged. Based on this, this study proposes to improve the top-level design to avoid imbalance in sports governance. To achieve good governance through long-term planning. It provides reference for understanding the deficiency of the implementation of the policy agenda and the defect of the movement governance.

Online Course Learning Outcome evaluation Method Based on Big Data Analysis

In order to solve the low credibility and poor timeliness of the online course learning effect evaluation, a method based on big data for online course learning results evaluation is proposed, which can better solve the problems existing in traditional methods. A statistical average analysis model for big data of online course learning outcomes is constructed for learning outcome big data analysis based on the sample regression analysis method; a decision objective function of online course learning outcome evaluation is established. The experimental results show that when the method proposed in this paper is adopted to evaluate online course learning outcomes, the method has relatively high confidence and overall timeliness, and the time taken to evaluate the effectiveness of online courses is 28 s and 29 s less than that of the other two methods, so it can provide accurate and reliable evaluation results.

Survey of Analytical Methods for Big Data of Quality Inspection

Big data of quality inspection is consist of structured data, semi-structured data and unstructured data. It brings great challenge for data analysis as the features of huge volume, various types, high timeliness and low value density. This paper introduced the research status of semantic analysis technology based on depth learning, migration learning algorithm based on depth neural network, reinforcement learning algorithm based on depth neural network and visualization analysis from the characteristics of quality inspection data. The existence problems are analyzed and the research direction is looking forward of the on data analysis in this paper.

An Energy Efficient and QoS Aware Routing Algorithm Based on Data Classification for Industrial Wireless Sensor Networks

In industrial wireless sensor networks, an energy-efficient quality-of-service (QoS) routing algorithm is very important to ensure that the key sensing data can be forwarded in a reliable path and solve the energy balance problem. In this paper, we classify the industrial sensing data into three data types and set their priority. Furthermore, we give the reliability parameters and timeliness parameters, and we propose and establish the candidate forwarding node set in order to balance the energy consumption. Subsequently, an energy-efficient and QoS aware routing algorithm is designed, and in this routing algorithm, different kinds of data can be forwarded with different strategies. Furthermore, the most important industrial sensing data are guaranteed to be transmitted to the sink node reliably and timely and common data can be transmitted effectively, too. All these data will be forwarded to the optimal relay node under the circumstance of that the data requirements of real time and reliability are satisfied. The simulation results show that the high timeliness event data can be transmitted real time and reliability than the other type of data. And our proposed routing algorithm is effective compared with the similar algorithms.

Research on the Current Situation and Path of the Local Government’s Supervision of Car-Hailing

With the continuous development of the Internet technology, the launch of the car-hailing has attracted the attention of all sectors of society. As a new mode of travel, car-hailing has the advantages of high timeliness and convenience and has caused a great impact on the traditional taxi industry. In response to the active response to the central government’s policy objectives, local governments in China are still faced with multi-dimensional challenges relating to online dating platforms, taxi companies, and private car owners. Therefore, local governments should consider the local economic and social development status, adhere to the central government’s legislative authority, adhere to the local government’s policy objectives, develop the “government plus enterprise” regulatory model, and strengthen legislation to eliminate the disadvantages of local legislation to deal with issues during the regulatory process.

Cross-layer multiuser session control for optimized communications on SDN-based cloud platforms

The plethora of emerging applications and their increasing popularity has led to the cloudification of the Internet of Things (IoT). The IoT-cloudification scales up data centers to a high computational resource capacity, to suit the requirements of a large number of users wanting to access multiple services offered by revolutionary IoT applications which, in turn, consume huge amounts of data produced by billions of things. In such systems, IoT-tailored cloud services (e.g., software platforms and middleware) are responsible for running latency-critical IoT applications in different areas (e.g., healthcare, transport systems, safety, Smart Cities, and many others). These applications expect to produce high-density multimedia flows that must be handled appropriately by the underlying communication infrastructure to obtain transport services of the highest standard. In this work, we propose a novel control plane optimization, which leverages the support of the Software-Defined Networking (SDN) substrate so that it can advance beyond today’s IP-based communication systems, which reveals weaknesses (e.g. a limited performance and scalability) in delivering multimedia content through per-flow channels. An attempt is made to overcome these limitations by proposing CLASSICO, a Cross-LAyer Sdn SessIon COntrol approach that exploits the SDN substrate to offload the flow streaming network-computing task from the IoT cloud platform to selected SDN branching nodes, and hence ensures high timeliness and scalability for the IoT-cloud system as a whole. To achieve this, CLASSICO dynamically builds application-layer sessions, which comprise multiple participant members sharing same interested content, and connects them into optimal multiuser tree structures, whereby only selected branching nodes apply SDN replication function for quality-enhanced transport service. We applied CLASSICO to a multimedia use case, and the results show that it outperforms multicast-capable IoT systems in terms of Quality of Service (QoS) and Quality of Experience (QoE) video assessment metrics.

A Research of Distributed Servo System Based on EtherCAT Bus

Aiming at the multi-motor collaborative servo control demand of electromechanical system, the distributed multi-motor servo control principles based on fieldbus are researched, adopting EtherCAT real-time Ethernet as the data communication fundamental network. Based on EtherCAT slave servo node of DSP, EtherCAT slave controller and embedded Master node, the multi-motor servo control system combined field and remote measurement, and control is built jointly. Aiming at the periodic and aperiodic control tasks in the system, the servo control and acquisition tasks are scheduled through real-time operating system in the embedded Master node to meet the real-time demand of the system. Meanwhile, in order to solve the synchronization problem between nodes of the distributed system, the timing unit is designed based on DC distribution clock and EtherCAT clock synchronization algorithm to realize slave synchronization function in network. The test result shows that, the distributed multi-motor servo control system is running stably and reliably with high timeliness, and the synchronization precision between nodes of the servo slave is less than 1 us, with high application value.