Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

The Distributed Network Monitoring Model with Bounded Delay Constraints

We address the problem of optimizing a distributed monitoring system and the goal of the optimization is to reduce the cost of deployment of the monitoring infrastructure by identifying a minimum aggregating set subject to delay constraint on the aggregating path. We show that this problem is NP-hard and propose approximation algorithm proving the approximation ratio with lnm+1, where is the number of monitoring nodes. At last we extend our modal with more constraint of bounded delay variation. Key words network - distributed monitoring - delay constraint - NP-hard CLC number TP 393 Foundation item: Supported by the National Natural Science Foundation of China (60373023)Biography: LIU Xiang-hui(1973-), male, Ph. D. candidate, research direction: algorithm complexity analysis, QoS in Internet.

DISTRIBUTED MONITORING SYSTEM RELIABILITY ESTIMATION WITH CONSIDERATION OF STATISTICAL UNCERTAINTY

Taking into account the whole system structure and the component reliability estimation uncertainty, a system reliability estimation method based on probability and statistical theory for distributed monitoring systems is presented. The variance and confidence intervals of the system reliability estimation are obtained by expressing system reliability as a linear sum of products of higher order moments of component reliability estimates when the number of component or system survivals obeys binomial distribution. The eigenfunction of binomial distribution is used to determine the moments of component reliability estimates, and a symbolic matrix which can facilitate the search of explicit system reliability estimates is proposed. Furthermore, a case of application is used to illustrate the procedure, and with the help of this example, various issues such as the applicability of this estimation model, and measures to improve system reliability of monitoring systems are discussed.

Distributed optical fiber acoustic sensor for in situ monitoring of marine natural gas hydrates production for the first time in the Shenhu Area,China

The distributed acoustic sensor(DAS)uses a single optical cable as the sensing unit,which can capture the acoustic and vibration signals along the optical cable in real-time.So it is suitable for monitoring downhole production activities in the process of oil and gas development.The authors applied the DAS system in a gas production well in the South China Sea for in situ monitoring of the whole wellbore for the first time and obtained the distributed acoustic signals along the whole wellbore.These signals can clearly distinguish the vertical section,curve section,and horizontal production section.The collected acoustic signal with the frequency of approximately 50 Hz caused by the electric submersible pump exhibit a signal-to-noise ratio higher than 27 dB.By analyzing the acoustic signals in the production section,it can be located the layers with high gas production rates.Once an accurate physical model is built in the future,the gas production profile will be obtained.In addition,the DAS system can track the trajectory of downhole tools in the wellbore to guide the operation.Through the velocity analysis of the typical signals,the type of fluids in the wellbore can be distinguished.The successful application of the system provides a promising whole wellbore acoustic monitoring tool for the production of marine gas hydrate,with a good application prospect.

Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds

With the vigorous development of mobile networks,the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and communication burden.Existing resource monitoring systems are widely deployed in cloud data centers,but it is difficult for traditional resource monitoring solutions to handle the massive data generated by thousands of edge devices.To address these challenges,we propose a super resolution sensing(SRS)method for distributed resource monitoring,which can be used to recover reliable and accurate high‑frequency data from low‑frequency sampled resource monitoring data.Experiments based on the proposed SRS model are also conducted and the experimental results show that it can effectively reduce the errors generated when recovering low‑frequency monitoring data to high‑frequency data,and verify the effectiveness and practical value of applying SRS method for resource monitoring on edge clouds.

Distributed Fiber Optic Monitoring and Stability Analysis of a Model Slope under Surcharge Loading

In the discipline of geotechnical engineering, fiber optic sensor based distributed monitoring has played an increasingly important role over the past few decades. Compared with conventional sensors, fiber optic sensors have a variety of exclusive advantages, such as smaller size,higher precision, and better corrosion resistance.These innovative monitoring technologies have been successfully applied for performance monitoring of geo-structures and early warning of potential geohazards around the world. In order to investigate their ability to monitor slope stability problems, a medium-sized model of soil nailed slope has been constructed in laboratory. The fully distributed Brillouin optical time-domain analysis(BOTDA)sensing technology was employed to measure the horizontal strain distributions inside the model slope.During model construction, a specially designed strain sensing fiber was buried in the soil mass.Afterward, the surcharge loading was applied on the slope crest in stages using hydraulic jacks and a reaction frame. During testing, an NBX-6050 BOTDA sensing interrogator was used to collect the fiber optic sensing data. The test results have been analyzed in detail, which shows that the fiber optic sensors can capture the progressive deformation and failure pattern of the model slope. The limit equilibrium analyses were also conducted to obtain the factors of safety of the slope under different surface loadings. It is found that the characteristic maximum strains can reflect the stability of the model slope and an empirical relationship was obtained. This study verified the effectiveness of the distributed BOTDA sensing technology in performance monitoring of slope.

Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

Wireless Distributed Monitoring Terminal Used for On-Line Application

A wireless distributed monitoring terminal was designed with embedded Linux used on the basis of ARM board, and an application program was developed for data collection, processing and transmission. Data were collected from the surrounding environment and transmitted to a server via WLAN in the form of Extensible Markup Language (XML) stream, so the terminal had good flexibility to be compatible with different sensors, network devices and information systems from multiple manufacturers.

A Unified Monitoring Framework for Distributed Environment

Distributed computing is a field of computer science that studies distributed systems. With the increasing computing capacity of computers it is widely used to solve large problems. Monitoring system is one of the key components in distributed computing. Although there have been varieties of monitoring systems developed by different organizations, it is still a great challenge to monitor a heterogeneous distributed environment in a unified and transparent way. In this paper, we present a unified monitoring framework for distributed environment (UMFDE) with heterogeneous monitoring systems, and then propose a comprehensive method based on the Enterprise Service Bus (ESB) to integrate the monitoring systems in the environment as a unified monitoring system. A representative case study is given to show the feasibility of this framework.

Distributed and Redundant Design of Ship Monitoring and Control Network

The world trend in ship automation is to integrate the monitoring, intelligent control and systematic management of the instruments and equipments both on bridge and in engine room. The paper presents a design scheme of the ship integrated monitoring and operating system based on two layers distributed and redundant computer network. The lower layer network is the field bus network connected mainly by CAN bus; the upper one is the PC local network in TCP/IP protocol, which consisted of a database server, monitoring and operating computers, industrial computers and a set of switches. Distributed schemes are fully applied to both software and hardware. This paper specifically describes the composition, software distribution and redundant technology of the upper local network and gives some important sample codes for the implement of the redundant and distributed design. The technologies here have been proved in the many applications and it may be applied to other industrial fields.

A Wireless Distributed Condition Monitoring System Based on Bluetooth Technology

Based on the discussion of bluetooth and network technology, this paper proposed an entire framework of a wireless distributed monitoring system by combining the characteristics of industry application. The feasibility of putting this kind of system in practice is discussed. The wireless distributed monitoring system can enhance the performance of condition monitoring more than the traditional one used now.

Landslide Monitoring Based on High-Resolution Distributed Fiber Optic Stress Sensor

A landslide monitoring application is presented by using a high-resolution distributed fiber optic stress sensor. The sensor is used to monitor the intra-stress distribution and variations in landslide bodies, and can be used for the early warning of the occurrence of the landslides. The principle of distributed fiber optic stress sensing and the intra-stress monitoring method for landslides are described in detail. By measuring the distributed polarization mode coupling in the polarization-maintaining fiber, the distributed fiber stress sensor with stress measuring range 0 to 15 MPa, spatial resolution 10 cm and measuring range 0.5 km, is designed. The warning system is also investigated experimentally in the field trial.

Comparison of Using an Electronic System and Conventional Monitoring Method for Monitoring the Quality of Drinking Water and Defects Discovery in Rural Area Water Distribution Network of Abarkouh, Iran

The used water for human consumption must be free of microorganisms and chemicals that cause risk in the human health. In this study, water quality of 18 rural area of Abarkouh was determined and compared the conventional monitoring method (According to ISIR (Institute of Standards and Industrial Research of Iran), 1053 and 4208) and use of electronic system method (Patent in industrial property general office of Iran, 77815). Free chlorine monitoring and pH test done by health workers in the conventional method and the results will be sent to the Health Network monthly. Sampling for microbiological testing is done monthly based on population (According to ISIR, 4208). On the electronic system, the procedure is also done by health workers, but the result will be sent to the receiver device by using a cell phone. According to the chlorine test results if the free chlorine residual reported zero, microbiological sampling was done by a health expert. Finally, the number of chlorine test and microbiological sampling and the results of these experiments collected in the both methods and recorded in SPSS 22 then were analyzed by using chi-square test and Fisher exact test. The result of microbiological experiments shows that the sampling rate decreased 29% in using of electronic system method in comparison to the conventional monitoring method while the number of microbial defect detection increased 19% in drinking water networks monitoring by electronic system. Using of electronic system monitoring can reduce the rate and cost of microbiological sampling and its experiments and increase accuracy of these tests, in this way it will increase the quality and safety of drinking water in distribution network in small and dispersed rural communities.

Feasibility study on sinkhole monitoring with fiber optic strain sensing nerves

Anthropogenic activity-induced sinkholes pose a serious threat to building safety and human life nowadays.Real-time detection and early warning of sinkhole formation are a key and urgent problem in urban areas.This paper presents an experimental study to evaluate the feasibility of fiber optic strain sensing nerves in sinkhole monitoring.Combining the artificial neural network(ANN)and particle image velocimetry(PIV)techniques,a series of model tests have been performed to explore the relationship between strain measurements and sinkhole development and to establish a conversion model from strain data to ground settlements.It is demonstrated that the failure mechanism of the soil above the sinkhole developed from a triangle failure plane to a vertical failure plane with increasing collapse volume.Meanwhile,the soil-embedded fiber optic strain sensing nerves allowed deformation monitoring of the ground soil in real time.Furthermore,the characteristics of the measured strain profiles indicate the locations of sinkholes and the associated shear bands.Based on the strain data,the ANN model predicts the ground settlement well.Additionally,micro-anchored fiber optic cables have been proven to increase the soil-to-fiber strain transfer efficiency for large deformation monitoring of ground collapse.

Thermal integrity profiling of cast-in-situ piles in sand using fiber-optic distributed temperature sensing

Defects in cast-in-situ piles have an adverse impact on load transfer at the pile‒soil interface and pile bearing capacity. In recent years, thermal integrity profiling (TIP) has been developed to measure temperature profiles of cast-in-situ piles, enabling the detection of structural defects or anomalies at the early stage of construction. However, using this integrity testing method to evaluate potential defects in cast-in-situ piles requires a comprehensive understanding of the mechanism of hydration heat transfer from piles to surrounding soils. In this study, small-scale model tests were conducted in laboratory to investigate the performance of TIP in detecting pile integrity. Fiber-optic distributed temperature sensing (DTS) technology was used to monitor detailed temperature variations along model piles in sand. Additionally, sensors were installed in sand to measure water content and matric suction. An interpretation method against available DTS-based thermal profiles was proposed to reveal the potential defective regions. It shows that the temperature difference between normal and defective piles is more obvious in wet sand. In addition, there is a critical zone of water migration in sand due to the water absorption behavior of cement and temperature transfer-induced water migration in the early-age concrete setting. These findings could provide important insight into the improvement of the TIP testing method for field applications.

Phase-matching quantum key distribution with light source monitoring

The transmission loss of photons during quantum key distribution(QKD)process leads to the linear key rate bound for practical QKD systems without quantum repeaters.Phase matching quantum key distribution(PM-QKD)protocol,an novel QKD protocol,can overcome the constraint with a measurement-device-independent structure,while it still requires the light source to be ideal.This assumption is not guaranteed in practice,leading to practical secure issues.In this paper,we propose a modified PM-QKD protocol with a light source monitoring,named PM-QKD-LSM protocol,which can guarantee the security of the system under the non-ideal source condition.The results show that our proposed protocol performs almost the same as the ideal PM-QKD protocol even considering the imperfect factors in practical systems.PMQKD-LSM protocol has a better performance with source fluctuation,and it is robust in symmetric or asymmetric cases.

Advances in fibre optic based geotechnical monitoring systems for underground excavations

Geotechnical stability is a major concern for the long-term safety and integrity of underground infrastructures such as tunnels, railway stations, mine shafts and hydraulic power chambers. An effective geotechnical monitoring system is able to provide adequate warning to underground personnel prior to any unexpected major geotechnical failure. This paper reviews the conventional geotechnical monitoring sensors and the emerging Fibre Optic Sensing(FOS) techniques, pointing out their unique features and major differences. Recent advances in various FOS based monitoring systems, including Brillouin time domain distributed optical sensors and fibre Bragg grating(FBG) sensors, are investigated through a critical review of the laboratory studies and field applications used for underground geotechnical monitoring. Particular emphasis is given to fibre packaging, temperature compensation, installation methods and instrumentation performance in the underground environment. A detailed discussion of the advantages and limitations of each FOS monitoring system is also presented in this paper.

Human Respiration Rate Estimation Using Ultra-wideband Distributed Cognitive Radar System

It has been shown that remote monitoring of pulmonary activity can be achieved using ultra-wideband (UWB) systems, which shows promise in home healthcare,rescue,and security applications.In this paper,we first present a multi-ray propagation model for UWB signal,which is traveling through the human thorax and is reflected on the air/dry-skin/fat/muscle interfaces,A geometry-based statistical channel model is then developed for simulating the reception of UWB signals in the indoor propagation environment.This model enables replication of time-varying multipath profiles due to the displacement of a human chest.Subsequently, a UWB distributed cognitive radar system (UWB-DCRS) is developed for the robust detection of chest cavity motion and the accurate estimation of respiration rate.The analytical framework can serve as a basis in the planning and evaluation of future rheasurement programs.We also provide a case study on how the antenna beamwidth affects the estimation of respiration rate based on the proposed propagation models and system architecture.