The Briefing Relating to the Construction of Dust-Storm Monitoring, Predicting and Service System

Dust-storm is a kind of severe weather, which has comprehensive and significant impacts on socioeconomic development and people’s livelihood. Enhancing the abilities of dust-storm monitoring, predicting and service will be of great benefit and the important significance to China and its people. At present, the comprehensive operation on dust-storm monitoring, predicting and service is still in a preliminary phase, the abilities of operation can’t meet the needs of implementing the real-time and quantitative monitoring and providing the efficient service. The implementation of the project of dust-storm monitoring, predicting and service system will greatly improve the service ability and level for the sustainable development and make a greater contribution to build the better-off society. The first phase project mainly involves monitoring subsystem, predicting, warning and service subsystem; communications and transmission subsystem, etc. In the first phase construction a series of major measures should be taken to address project overall benefits, such as making better use of current monitoring resource, taking into account the standards of data format and project integrative and extensive abilities and so on.

Quality of Service and Security on Cisco Network Devices, Coupled with the Development of a Mobile Application Prototype Software for Server Room Temperature Monitoring

In an era where digital technology is paramount, higher education institutions like the University of Zambia (UNZA) are employing advanced computer networks to enhance their operational capacity and offer cutting-edge services to their academic fraternity. Spanning across the Great East Road campus, UNZA has established one of the most extensive computer networks in Zambia, serving a burgeoning community of over 20,000 active users through a Metropolitan Area Network (MAN). However, as the digital landscape continues to evolve, it is besieged with burgeoning challenges that threaten the very fabric of network integrity—cyber security threats and the imperatives of maintaining high Quality of Service (QoS). In an effort to mitigate these threats and ensure network efficiency, the development of a mobile application to monitor temperatures in the server room was imperative. According to L. Wei, X. Zeng, and T. Shen, the use of wireless sensory networks to monitor the temperature of train switchgear contact points represents a cost-effective solution. The system is based on wireless communication technology and is detailed in their paper, “A wireless solution for train switchgear contact temperature monitoring and alarming system based on wireless communication technology”, published in the International Journal of Communications, Network and System Sciences, vol. 8, no. 4, pp. 79-87, 2015 [1]. Therefore, in this study, a mobile application technology was explored for monitoring of temperatures in the server room in order to aid Cisco device performance. Additionally, this paper also explores the hardening of Cisco device security and QoS which are the cornerstones of this study.

The structure and ability of the China Seismological Gravity Monitoring System

This paper assesses the structure and ability of Local Seismological Gravity Monitoring Network (LSGMN) in China main tectonic zone and China Seismological Gravity Monitoring System (CSGMS) which formed after the project of 'China Crustal Movement Observation Network (CCMON)' has been performed. The main conclusions drawn are as follows: ①LSGMN has good monitoring and prediction ability for the earthquake of M_s about 5. But it lacks ability to monitor and predict the strong earthquake of M_s>6 because of the little range of the observation network;②CSGMS has good ability to monitor and predict the earthquake of M_s>7, but the resolving power is not enough for the earthquake magnitude from M_s=6 to M_s=7 because the observation stations are too sparse.

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Cloud services with big data provide a solution for monitoring and tracking sustainable development goals

To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of policies.However,the data landscape is changing.With emerging big data and cloud-based services,there are new opportunities for data collection,influencing both official data collection processes and the operation of the programmes they monitor.This paper uses cases and examples to explore the potential of crowdsourcing and public earth observation(EO)data products for monitoring and tracking the SDGs.This paper suggests that cloud-based services that integrate crowdsourcing and public EO data products provide cost-effective solutions for monitoring and tracking the SDGs,particularly for low-income countries.The paper also discusses the challenges of using cloud services and big data for SDG monitoring.Validation and quality control of public EO data is very important;otherwise,the user will be unable to assess the quality of the data or use it with confidence.

Research on information technology of state monitoring and fault prediction for mechatronics system

The safety and reliability of mechatronics systems,particularly the high-end,large and key mechatronics equipment in service,can strongly influence on production efficiency,personnel safety,resources and environment.Based on the demands of development of modern industries and technologies such as international industry 4.0,Made-in-China 2025 and Internet + and so on,this paper started from revealing the regularity of evolution of running state of equipment and the methods of signal processing of low signal noise ratio,proposed the key information technology of state monitoring and earlyfault-warning for equipment,put forward the typical technical line and major technical content,introduced the application of the technology to realize modern predictive maintenance of equipment and introduced the development of relevant safety monitoring instruments.The technology will play an important role in ensuring the safety of equipment in service,preventing accidents and realizing scientific maintenance.

Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. In this?study, a tentative design of a cloud-based heart disease prediction system had been proposed to detect impending heart disease using Machine learning techniques. For the accurate detection of the heart disease, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms in a Java Based Open Access Data Mining Platform, WEKA. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 97.53% accuracy was found from the SVM algorithm along with sensitivity and specificity of 97.50% and 94.94%respectively. Moreover, to monitor the heart disease patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, humidity, heartbeat. The developed system can transmit the recorded data to a central server which are updated every 10 seconds. As a result, the doctors can visualize the patient’s real-time sensor data by using the application and start live video streaming if instant medication is required. Another important feature of the proposed system was that as soon as any real-time parameter of the patient exceeds the threshold, the prescribed doctor is notified at once through GSM technology.

Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.

An Agent-Based Web-Services Monitoring System

An enterprise based on web services can benefit from computer-based monitoring for its normal course of business. No services, delayed services, inadequate services and poor or delayed information flow all hinder or prevent the normal course of business. The health of the service-oriented approach, the communication flow and the services themselves directly equate to the health and vitality of the enterprise. By health we mean, availability, performance, integrity, and reliability of web services. This paper proposes and details an agent based approach for web service monitoring in an enterprise environment. The objective of this approach is to provide enhanced security, improved help desk information and attribution across a diverse web services landscape that is logically a system but physically distributed. The definition of the agents, their logical locations, their functionality and relationships has not been described previously. This architecture provides many advantages over previous methodologies for event monitoring including assignment of overlapping responsibilities, reduction in help desk effort and placement of measurements throughout the upper layers of the web services system. This is also the first enumeration of significant events and information requirements at the service level. It also assigns responsibility to the components （agents, handlers or the service itself） for logging and alerting such events. This architecture is currently being implemented by a major defense entity.

A Web Service Framework for Environmental and Carbon Footprint Monitoring in Construction Supply Chains

With the growing environmental concerns, green supply chain management （GSCM） is gaining significant attention in the construction industry. Tracking and monitoring the environmental effects brought forth by the participating members along a supply chain is important to GSCM. The GreenSCOR model developed by the Supply Chain Council provides a generic framework for measuring the total carbon footprint and environmental footprint in a supply chain. The model is based on the Supply Chain Operations Reference （SCOR） model, which represents a supply chain network in a hierarchically structured manner. This paper describes the GreenSCOR framework and its potential application to the construction industry. This paper also presents a web services approach to incorporate the GreenSCOR model to the implementation of collaborative information systems. Each process element in the SCOR model is represented and delivered as individual web service units, which can be reused and integrated using standard web services technologies. The service units are combined and managed in a prototype web service collaborative framework, called SC Collaborator, which is designed and developed for supporting construction supply chain management. An illustrative example is presented to demonstrate the implementation of the GreenSCOR-based SC Collaborator framework.

Russia and China Signed a Statement on a Joint GNSS Monitoring and Assessment Service

At the recent, 4th meeting of the Chinese-Russian Committee on Impor- tant Projects of Strategic Cooperation in the field of Satellite Navigation held in St. Petersburg, Russia. The China Academy of Aerospace Electronics Technology and FSUE TsNIIMASS, the main research institute of Roscosmos State Corporation signed a statement on a new joint GNSS monitoring and assessment service.

Fault diagnosis and analysis of main sea water pump based on vibration monitoring in offshore oil field

The main sea water pump is the key equipment for the floating production storage and offloading （FPSO）. Affected by some factors such as hull deformation, sea water corrosion, rigid base and pipeline stress, the vibration value of main sea water pump in the horizontal direction is abnormally high and malfunctions usually happen. Therefore, it is essential to make fault diagnosis of main sea water pump, By conventional off-line monitoring and vibration amplitude spectrum analysis, the fault cycle is found and the alarm value and stop value of equipment are set, which is helpful to equipment maintenance and accident prevention.

Mining seismicity in the Witwatersrand Basin:monitoring,mechanisms and mitigation strategies in perspective

The Kaapvaal Craton in South Africa hosts one of the largest gold placer deposits in the world. Mining in the Witwatersrand Basin here has been the source of about one third to one half of the gold ever produced in the world. Gold was discovered in the Johannesburg area in 1886 and after 120 years of continuous operation, mining is currently approaching depths of 4 000 m. In spite of the challenges and risks that the industry has had to deal with including rock temperature, ventilation and water, one of the most feared hazards in the basin has been the threat from the ongoing occurrence of seismicity and rockbursts. The problem first manifested itself by way of the occurrence of tremors roughly twenty years after the commencement of mining operations. This paper traces the history of the approach to rockbursts and seismicity during the 120 year history of mining in the basin. It portrays a picture of the mining seismicity in terms of monitoring phases, mechanisms and mitigation strategies. The work of research organizations over the years is highlighted with a brief mention of current regulation strategies on the part of the mining inspectorate.

Business performance monitoring based on web service proxy

To provide efficient monitoring of web service-based business processes, a web service proxy （WS- proxy） is developed to monitor business activities by monitoring the enactment of services. WS-proxy is deployed as an intermediary between internal business processes and external service providers, and it provides a single point of service access with the functions of message routing and content inspection. By using an XPath engine named WS-filter, performance indicators can be generated from service messages for assessing business performance. In the experiments, the feasibility of WS-proxy is verified and it achieves good performance in the monitoring work. The latency introduced by WS-proxy is only about 15% of the overall latency while extracting performance indicators just consumes less than one third of the processing time.

Application of a microseismic monitoring system in deep mining

A microseismic monitoring system was used in the Donggua Shan underground copper mine, and its application was introduced. The spacial distribution of the seismic event was monitored effectively during mining with this system. The distribution of the seismic intensity in different time periods and in the different mining districts was obtained via the clustering analysis of the monitored results, and the different intensity concentration districts of seismicity were compartmentalized. The various characteristics and waveforms of different vibrations in the underground mine were revealed with the help of the micro-seismic monitoring system. It was proved that the construction and application of the micro-seismic monitoring system in the mine not only realized the continuous monitoring of seismicity in the deep mine, but also settled an this system.

Performance Monitoring and Diagnosis of Multivariable Model Predictive Control Using Statistical Analysis

A statistic-based benchmark was proposed for performance assessment and monitoring of model predic- tive control; the benchmark was straightforward and achievable by recording a set of output data only when the control performance was good according to the user’s selection. Principal component model was built and an auto- regressive moving average filter was identified to monitor the performance; an improved T2 statistic was selected as the performance monitor index. When performance changes were detected, diagnosis was done by model validation using recursive analysis and generalized likelihood ratio (GLR) method. This distinguished the fact that the per- formance change was due to plant model mismatch or due to disturbance term. Simulation was done about a heavy oil fractionator system and good results were obtained. The diagnosis result was helpful for the operator to improve the system performance.

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.