Practical Monitoring System Research Articles

In-situ monitoring of sintering and analytical modeling of densification and shrinkage in binder jetted 316 L stainless steel

In binder jetting, shrinkage and deformation occur during the sintering step, both of which are affected by the green density of the binder jetted materials. The study innovatively introduces a cost-effective, practical, and in-process monitoring system for visualizing shrinkage and deformation on larger samples than conventionally observed using small-scale specimens in dillatometry equipment. The powder characteristics and binder jet printing process itself influence the initial green density. The comprehensive analysis of powder flowability and packing density, densification behavior, and shrinkage reveals that the consolidated parts using virgin powder (with a green density of 55 %) can achieve a relative density above 99.9 % with an anisotropic shrinkage in the Z > X > Y direction. In contrast, the used or recycled powder exhibits a lower green density of ∼48 %, higher shrinkage rate in all three dimensions, and a decreased degree of anisotropy. Using in-process imaging and experimental data on the grain size attained through optical microscopy and electron backscatered diffraction imaging, the material's shear and bulk viscosities were determined. The formation of delta-ferrite and its impact on densification were discussed in the context of solid-state and supersolidus liquid phase sintering. The model relied on the continuum sintering theory formulated by Skorohod and Olevsky. The strain evolution from the in-situ imaging of sintering process is correlated with porosity based on the used feedstock and applied sintering temperatures. The outcomes of this study offer valuable perspectives on anisotropic sintering mechanisms, bridging the knowledge gap regarding the relationships between structures produced through binder jetting and subsequent sintering of materials.

A Step Toward Better Care: Understanding What Caregivers and Residents in Assisted Living Facilities Value in Health Monitoring Systems

The past several decades have seen significant advances in monitoring older adults' health and well-being. However, creating viable, practical monitoring systems for informing caregivers requires understanding which behaviors and signs to track and what approaches best present that information. To investigate how technology can be leveraged to better augment caregivers' workflows, we take a multi-stage, qualitative approach to gain insights into the needs of caregivers and the older adults receiving care. Specifically, we use a series of domain expert interviews, cognitive walkthroughs, and semi-structured interviews with residents, and we synthesize our takeaways using thematic analysis at each phase. Our results show that this type of monitoring technology has great potential to reduce the effort needed by caregivers to complete their responsibilities and communicate with their teams. Additionally, we found that older adults are receptive to the technology but their privacy and autonomy must be prioritized for the sake of their mental wellbeing. These insights will facilitate greater intelligent interface development for Person-Centered Care by identifying important design considerations and vital features that require system support.

Crop insect pest detection based on dilated multi-scale attention U-Net

BackgroundCrop pests seriously affect the yield and quality of crops. Accurately and rapidly detecting and segmenting insect pests in crop leaves is a premise for effectively controlling insect pests.MethodsAiming at the detection problem of irregular multi-scale insect pests in the field, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop insect pest detection. In its encoder, dilated Inception is designed to replace the convolution layer in U-Net to extract the multi-scale features of insect pest images. An attention module is added to its decoder to focus on the edge of the insect pest image.ResultsThe experiments on the crop insect pest image IP102 dataset are implemented, and achieved the detection accuracy of 92.16% and IoU of 91.2%, which is 3.3% and 1.5% higher than that of MSR-RCNN, respectively.ConclusionThe results indicate that the proposed method is effective as a new insect pest detection method. The dilated Inception can improve the accuracy of the model, and the attention module can reduce the noise generated by upsampling and accelerate model convergence. It can be concluded that the proposed method can be applied to practical crop insect pest monitoring system.

Damaging effects of earth fissures on high-speed railway bridges: Insights from physical experiments and numerical simulations

Earth fissure is a typical geological phenomenon related to surface rupture. With the rapid development of high-speed railways (HSRs) in recent years, earth fissure disasters pose a serious challenge to the construction and subsequent safe operation of HSRs. Here, we aimed to investigate the damaging effect of earth fissure activities on HSR bridges spanning the earth fissure zone. Taking the Shuixiu earth fissure of Da-Xi HSR as the prototype, a large-scale laboratory physical experiment with geometric model scale 1:20 was carried out to reveal the deformation and failure characteristics of HSR bridges under earth fissure activity. In addition, the damage influence range and damage mode of HSR bridges crossing earth fissures are further determined by numerical simulation. The results show that the earth fissure activity mainly has damaging effects on the structure of HSR bridges near the earth fissure zone, which is mainly manifested as uneven settlement of reinforced concrete pile, bending deformation of box girder and cracking of track slab. The width of the influence zone of Shuixiu earth fissure on the HSR bridge is determined to be 32.4 m. Moreover, the pile-end pressure on the hanging wall side of the earth fissure gradually decreases with the earth fissure activity, while that on the footwall side increases significantly. Most importantly, it is emphasized that a practical long-term monitoring and early warning system of HSR bridge structures should be established to prevent the hazards caused by earth fissure activities. The above understanding will benefit to the design, construction and operation of Da-Xi HSR line, and also provide significant guidance and reference to the construction of HSR bridges in other earth fissure prone areas.

A visible-infrared clothes-changing dataset for person re-identification in natural scene

Person re-identification (Re-ID) has been widely used in intelligent surveillance systems, aiming at retrieving specific pedestrian images across different cameras. Although existing person Re-ID methods have achieved inspiring success, there are still limitations in practical monitoring system applications. To narrow the gap with the practical application, we introduce the cross-modality person Re-ID problem in the clothes-changing scene. Meanwhile, we construct the first Visible-Infrared Clothes-Changing (NEU-VICC) dataset, which contained 16632 RGB images and 8374 infrared images of 107 pedestrians. The critical challenge of the cross-modality person Re-ID problem in the clothes-changing scene lies in the vast modality discrepancy and the intra-class discrepancy caused by changing clothes. So, we propose a novel Semantic-Constraint Clothes-Changing Augmentation Network (SC3ANet) based on current cross-modality person Re-ID methods to solve this problem. Specifically, we design a semantic-constraint clothes-changing module that guides the model to learn clothes-irrelevant features by randomly changing pedestrians' clothes. In addition, we devise a dual-granularity constraint loss module to mitigate inter-modality and intra-class differences. Experiments on our NEU-VICC dataset show that the SC3ANet achieves the best results. The dataset and code are available at: https://github.com/VDT-2048/NEU-VICC.

Structural Health Monitoring Design and Performance Evaluation of a Middle-Span Bridge

Structural health monitoring (SHM) has attracted significant attention over the past two decades due to its ability to provide real-time insight into the condition of structures. Despite the development of several SHM systems for long-span bridges, which play a crucial role in the assessment of these structures, studies focusing on short- or middle-span bridges remain scarce. This research paper presents an efficient and practical bridge monitoring and warning system established on a middle-span bridge, a key crossroad bridge located in Shenzhen. The monitoring system consists of sensors and measuring points that collect a substantial amount of data, enabling the close monitoring of various operational indicators to facilitate the early detection of threshold exceedances. Based on this system, the subtle condition of the bridge can be evaluated, and the operational condition of the bridge can be studied through the comparative analysis of the collected data. Over four months of monitoring, data including the strain and creep of the main beam, the strain and settlement of piers and the crack width of the bridge body are observed. Furthermore, the real-time operational status of the bridge is analyzed and evaluated through the combination of the collected data and the structural finite element model.

An asynchronous interrupt driven sampling technique for a practical monitoring system of power-line single-phase voltage and current signals

<span lang="EN-US">A practical monitoring system implementing an asynchronous interrupt driven sampling technique has been developed in this research. This system is intended to acquire power-line single-phase voltage and current signals as well as their related information such as RMS values and harmonic contents. The system uses a voltage transformer and a current transducer to acquire the signals of the voltage and current. A microcontroller is used in the system to perform acquisition processes, calculation, and wireless data transmission. The design activities include the development of hardware of the system and software in the microcontroller as well as monitoring software with Bluetooth technology that will run on portable devices. As part of the research, a new sampling technique was developed to provide constant and precise timings during the acquisition processes. In this technique, some peripherals related to the acquisition process were driven asynchronously using interrupt mechanisms. Some experimentation has been carried out, and the result showed that the system implementing this technique could successfully manage the timings for the synchronous acquisition sequences. Furthermore, the use of the Bluetooth interfaces in the system allowed practical monitoring process to be performed using portable devices.</span>

Optimised ensemble learning-based IoT-enabled heart disease monitoring system: an optimal fuzzy ranking concept

ABSTRACT Generally, patients with heart diseases enquire for medical services while sick. They will not experience sickness often until they reach the last stage of heart disease, and so the damages are not reversible. Thus, the solution for reducing the death rate of heart disease healthcare is becoming the passive medical care type. Physicians usually monitor the physical status of patients, which helps in deciding when to provide medical services by considering the real-time analysis of patients. Moreover, a significant division of pervasive clinical care is taken as the development of practical monitoring systems. An IoT approaches are overwhelming the progression in addressing the issue of heart disease patient care since they can modify the service mode in a pervasive way. A remote monitoring model is more significant for realising pervasive healthcare services. Here, an ‘optimised ensemble learning’-based IoT-enabled heart disease monitoring system through an optimised ensemble fuzzy ranking (OEFR) strategy, deep feature extraction and heuristic improvement. Moreover, the hyperparameter of each classifier is optimised through a new improved dingo optimiser (I-DOX) algorithm. Throughout the result analysis, the accuracy of the designed I-DOX-based OEFR model is attained 96.72%. Thus, the analysis exhibits an overall performance analysis in terms of high performance.

Fault Diagnosis of Rotating Machinery Using Denoising-Integrated Sparse Autoencoder Based Health State Classification

The diagnostic study on single-fault with distinguishing features based on monitoring data analysis is mature and fruitful in recent years. However, the early fault signals collected by practical monitoring systems often possess the following characteristics: 1) Fairly weak signal strength; 2) Submerged in powerful background noise; 3) Coupling of different fault data. These features not only increase the diagnostic difficulty, but also make the existing methods hardly to get the desired results. Consequently, the early compound faults diagnosis commonly in industrial systems is still a thorny and urgent problem. Therefore, in order to solve this problem and provide technical support for the practical industrial machinery fault diagnosis, a denoising-integrated sparse autoencoder (DISAE) model for early compound faults diagnosis is proposed in this paper. The innovation points of this study mainly include: 1) A feature-enhanced and denoising solution based on fault sensitivity degree (FSD) is designed, and the reconstructed diagnostic signals are acquired. 2) A disassociation framework is formulated, and the data coupling is solved. 3) A weight constraint term of SAE is constructed to improve the effectiveness and diversity of feature learning. 4) An adaptive loss function and a DISAE model is formed, and the early compound faults diagnosis is achieved. Finally, different trials and comparison results display the effectiveness and superiority of the designed DISAE based scheme.

Design and Implementation of a Smart Insole System to Measure Plantar Pressure and Temperature.

An intelligent insole system may monitor the individual’s foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals’ foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence.

Remote Monitoring and Management System of Intelligent Agriculture under the Internet of Things and Deep Learning

Based on the Internet of Things (IoT) technology and deep learning algorithm, a greenhouse intelligent agriculture management system was established to analyse the application value of the intelligent agriculture remote monitoring management system in the greenhouse planting industry. Based on the analysis of greenhouse planting demand and environmental factors, the intelligent agriculture monitoring system is established based on the IoT, and the greenhouse system controller is designed based on the adaptive proportion integration differentiation (PID) algorithm. The noise data removal method is established based on the furthest priority strategyk-means (FPKM) algorithm, and the greenhouse data management system is established mainly by the business platform and management platform. The data set of air temperature during the cultivation of Flammulina velutifolia in a factory from October 2020 to January 2021 was selected as the research data to analyse the ability of the IoT-based IARMM system to collect greenhouse temperature, carbon dioxide, and light data. In addition, the application of the greenhouse data management system in greenhouse data monitoring and control is analysed. The processing capability of agricultural environment monitoring data based on the FPKM algorithm is analysed. The results show that the intelligent agriculture monitoring system based on IoT and machine learning can effectively monitor the data on greenhouse temperature, carbon dioxide, light, and other environmental factors, and the greenhouse data management system can effectively ensure the normal operation of equipment and data storage. After being processed by the FPKM algorithm, outliers are identified and effectively removed. Under random seeds, the iteration times of the FPKM algorithm and thek-means algorithm are significantly different. The iteration number of the FPKM algorithm is basically stable at approximately 2 times, while the iteration number of thek-means algorithm obviously fluctuates. Based on the IoT and FPKM algorithm, the intelligent agriculture monitoring system covering the user monitoring center, data center module, and mobile phone client module is established. This work establishes a practical remote monitoring and management system for intelligent agriculture based on the IoT and machine learning algorithm, which provides a new idea for intelligent agricultural management.

Application of three principal component analysis techniques in sensor faults detection

The sensor is the front-end component of the bridge structural health monitoring system. In this study, three fault diagnosis methods based on principal element analysis are applied to a bridge health monitoring system. The diagnostic results show that significant differences among the operating conditions will deteriorate the robustness of the diagnostic methods. The applicability of the sensor fault diagnosis method is verified based on the principal element analysis of a practical health monitoring system.

Design and deployment of a practical IoT-based monitoring system for protected cultivations

According to the United Nations’ latest forecast, the world population will reach 8.5 billion people by 2030. This rapid population growth imposes severe requirements in food production to meet demand. Moreover, the rise of temperatures and climate changes are also adversely affecting food supplies. In this paper, we design a scalable IoT-based monitoring system with prediction capabilities for agricultural applications. It provides an effective four-layered architecture that consist of sensing, networking, processing, and applications with low deployment and management costs. Hence, to demonstrate its feasibility, the proposed IoT system was constructed, experimentally tested, and validated by monitoring the temperature and humidity of a commercial-size greenhouse in Mexico for six months. Additionally, we integrated a data-driven predictive model for greenhouse microclimate conditions. Temperature predictions were accurately performed 24-hour in advance within an error of 1 °C. The obtained results confirm that the proposed IoT framework would facilitate farmers to monitor crops and enable productivity gains by increasing the level of technology progressively.

A direct method to detect and localise damage using longitudinal data of ends-of-span rotations under live traffic loading

Existing work on rotation-based bridge monitoring has focused on indirect methods, such as bridge weigh-in-motion or influence line approaches. However, these approaches require increased instrumentation complexity, and require calibration, necessitating bridge closures. In this paper, we explore the potential of using rotation measurements to create a more practical and cost-effective monitoring system. To this end, we present a damage detection method which directly analyses bridge rotation data measured under live, free-flow traffic loading. We show how the Earth Mover’s Distance, typically used in statistics and image processing, can be applied directly on end-of-span rotation measurement data to achieve effective damage detection and localisation. Numerical simulation results demonstrate the approach’s robustness to the confounding effects of temperature variation and traffic diversity (vehicle type, loading, and velocity). The direct rotation measurement approach is applied to data from an in-service short-span bridge to demonstrate the technique’s capability with free-flow traffic loading.

Analysis of long-term temperature monitoring of multiple wind turbines

The historical temperature data logged in the supervisory control and data acquisition (SCADA) system contains a wealth of information that can assist with the performance optimization of wind turbines (WTs). However, mining and using these long-term data is difficult and time-consuming due to their complexity, volume, etc. In this study, we tracked and analyzed the 5-year trends of major SCADA temperature rise variables in relation to the active power of four WTs in a real wind farm. To uncover useful information, an extended version of the bins method, which calculates the standard deviation (SD) as well as the average, is proposed and adopted. The implications of the analysis for engineering practice are discussed from multiple perspectives. The research results demonstrate a change in the patterns of the main temperature rise variables in a real wind farm, completeness of the monitoring of the WT internal temperature state, influence of wind turbine aging on temperature signals, a correlation between different measurement points, and a correlation between signals from different years. The knowledge gained from this research provides a reference for the development of more practical and comprehensive condition monitoring systems and methods, as well as better operation maintenance strategies.

A novel approach for APT attack detection based on combined deep learning model

Advanced persistent threat (APT) attack is a malicious attack type which has intentional and clear targets. This attack technique has become a challenge for information security systems of organizations, governments, and businesses. The approaches of using machine learning or deep learning algorithms to analyze signs and abnormal behaviors of network traffic for detecting and preventing APT attacks have become popular in recent years. However, the APT attack detection approach that uses behavior analysis and evaluation techniques is facing many difficulties due to the lack of typical data of attack campaigns. To handle this situation, recent studies have selected and extracted the APT attack behaviors which based on datasets are built from experimental tools. Consequently, these properties are few and difficult to obtain in practical monitoring systems. Therefore, although the experimental results show good detection, it does not bring high efficiency in practice. For above reasons, in this paper, a new method based on network traffic analysis using a combined deep learning model to detect APT attacks will be proposed. Specifically, individual deep learning networks such as multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM) will also be sought, built and linked into combined deep learning networks to analyze and detect signs of APT attacks in network traffic. To detect APT attack signals, the combined deep learning models are performed in two main stages including (i) extracting IP features based on flow: In this phase, we will analyze network traffic into networking flows by IP address and then use the combined deep learning models to extract IP features by network flow; (ii) classifying APT attack IPs: Based on IP features extracted in a task (i), the APT attack IPs and normal IPs will be identified and classified. The proposal of a combined deep learning model to detect APT attacks based on network traffic is a new approach, and there is no research proposed and applied yet. In the experimental section, combined deep learning models proved their superior abilities to ensure accuracy on all measurements from 93 to 98%. This is a very good result for APT attack detection based on network traffic.

Quality Monitoring and Evaluation System of the Practical Teaching of Food Science and Engineering Specialty in Local Application-Oriented Undergraduate Colleges under the Background of New Engineering—A Case Study of Bengbu University

Taking Bengbu University as an example, this paper analyzed the practical teaching system, practical teaching management system, practical teaching quality standard system and practical teaching quality evaluation system of food science and engineering major, and also combed the effect of practical teaching quality monitoring and evaluation system under the background of new engineering development. It has been demonstrated that the practical teaching quality monitoring and evaluation system of this specialty is scientific and reasonable, which has a hand in standardizing the practical teaching management and the practical teaching process and improving the quality of practical teaching.

SMARS: Sleep Monitoring via Ambient Radio Signals

We present the model, design, and implementation of SMARS, the first practical Sleep Monitoring system that exploits Ambient Radio Signals to recognize sleep stages and assess sleep quality. This will enable a future smart home that monitors daily sleep in a ubiquitous, non-invasive and contactless manner, without instrumenting the subject's body or the bed. The key enabler underlying SMARS is a statistical model that accounts for all reflecting and scattering multipaths, allowing highly accurate and instantaneous breathing estimation with best-ever performance achieved on commodity devices. On this basis, SMARS then recognizes different sleep stages, including wake, rapid eye movement (REM), and non-REM (NREM), which was previously only possible with dedicated hardware. We implement a real-time system on commercial WiFi chipsets and deploy it in 6 homes, resulting in 32 nights of data in total. Our results demonstrate that SMARS yields a median absolute error of 0.47 breaths per minute (BPM) and a 95 percent-tile error of only 2.92 BPM for breathing estimation, and detects breathing robustly even when a person is 10 meters away from the link, or behind a wall. SMARS achieves a sleep staging accuracy of 88 percent, outperforming the prevalent unobtrusive commodity solutions using bed sensor or UWB radar. The performance is also validated upon a public sleep dataset of 20 patients. By achieving promising results with merely a single commodity RF link, we believe that SMARS will set the stage for a practical in-home sleep monitoring solution.

Practical Environmental Engineering Detecting and Monitoring System for Marine Oil Spills

Identifying and checking oil slicks adrift has become a significant issue because of both the expanded interest and utilization of oil based items, and the more cautious consideration given to natural security. Security in sea transportation has improved, however relevantly the quantity of vessels along delivery courses has expanded, in this way raising the danger of mishaps happening. Alongside this, additionally observing sources and capacities have separately developed; what actually should be improved is a coordinated way to deal with checking, specifically in the event of delicate zones, for example, ensured marine parks. The Marine Information System proposed here is an incorporated and interoperable framework ready to screen sea traffic, utilizing detecting abilities from different electronic sources, alongside geo-situating instruments, and dependent on a particular correspondence foundation. This framework, incorporating flawlessly heterogeneous information, supplies an assortment of choice help administrations giving proactive functionalities, which exhibits its potential in encouraging unique connections among various information.

Research on Key Technologies and Innovative Development of Electronic Communication System Based on Serial Communication

Today, with the rapid development of computer technology, communication between computer and modern energy instrument Xinxing is extremely important. Serial communication is widely used in on-line monitoring of process quantity in industrial enterprises. This paper analyzes the characteristics of this serial communication, and expounds the selection of communication mode for computer business, the connection of interface hardware and the programming of software. Ethernet technology is currently widely used in the industrial field. Because computer serial communication has shortcomings such as short communication distances and low transmission speeds, meanwhile, process quantity signals have many types, large differences in value ranges, and continuous changes. The key technical issues such as long-distance transmission of travelling signals, real-time communication and display of process volume change curves must be properly resolved before a practical online monitoring system can be constructed. Or it has important reference value for personnel engaged in serial communication between computers and Ganneng instruments.