Speed Monitoring Research Articles

Considerations of potential runaway motion and physical interaction for speed and separation monitoring

In the field of human-robot collaboration (HRC), the speed and separation monitoring (SSM) collaboration have attracted much attention owing to its non-contact safety strategy. 3D sensing applications are currently of interest for industrial automation technology and are considered to be a promising method for maximizing the efficiency of SSM. However, little attention has been given to the runaway space of the robot or the potential contact due to the foreseeable misuse of the operator. In this study, experiments are conducted using a radar system as an example of a 3D safety-related sensor, and battery assembly scenarios are carried out for the comparison. In the experiment, two different orientations of the robot are tested, considering the potential runaway motion of the robot. Also, the maximum permissible speed of the robot is calculated by geometrical transfer energy, which is based on effective mass and the velocity of the manipulator and human injury criteria. From the experimental results, it is evident that it is better to avoid placing the vertical articulated robot in front of the operator from the perspective of minimizing the effect of runaway motion into the safety distance. Finally, the proposed framework of speed limitation is thought to be an effective method to link SSM and power and force limiting safety function.

Combining Speed and Separation Monitoring With Power and Force Limiting for Safe Collaborative Robotics Applications

Enabling humans and robots to safely work close to each other deserves careful consideration. With the publication of ISO/TS 15066 directives on this matter, two different strategies, namely the Speed and Separation Monitoring and the Power and Force Limiting, have been proposed. This letter proposes a method to efficiently combine the two aforementioned safety strategies for collaborative robotics operations. By exploiting the combination of the two, it is then possible to achieve higher levels of productivity, while still preserving safety of the human operators. This is achieved by the optimal scaling of the initially prescribed velocity, while preserving the path consistency of the robot trajectory. In a nutshell, the state of motion of each point of the robot is monitored so that at every time instant the robot is able to modulate its speed to eventually come into contact with a body region of the human, consistently with the corresponding biomechanical limit. Validation experiments have been conducted to establish that the proposed method enables substantially less stringent limits on robot performance while still allowing for the safety limits prescribed by ISO directives.

Overspeed Detection using Image Processing for Indian Highways

Over speeding of vehicles is the major cause of accidents in recent times. Monitoring of such over speeding vehicles especially on highways is of prime importance. Many manual methods are being used to detect the over speeding vehicles by traffic control team. However, these methods require lot of man power and continuous monitoring by traffic personnel. In this study, an attempt is made to develop an automatic speed monitoring system, which provides a simple way to monitoring speeds of all the vehicles from a centralized control room. This system calculates the instantaneous speed of vehicle with help of sensors and the over speeding vehicle is detected using an image processing technique using Python programming language. The developed model is validated with real world traffic data and comparative analysis of speeds obtained by manual method and developed model shows that model truly represents the field condition.

Inkjet jet failures detection and droplets speed monitoring using piezo self-sensing

The jetting reliability of the inkjet printhead is a key factor for successful usage of the printer. It is known that the monitoring method using self-sensing signal can detect jetting failures to ensure jetting reliability. However, a monitoring technique using self-sensing signal to identify different failure types with the ability to calculate the droplet speed has not been reported so far. In order to identify failure type accurately, different self-sensing signal features are investigated in the present study, including the improper driving voltage, fluid viscosity and the backpressure control. Furthermore, the self-sensing signal is applied to extract the characteristic parameters and explore the droplet speed monitoring pattern. In order to evaluate the effectiveness of the proposed detection method, a monitoring system consisting of the self-sensing signal acquisition module and the monitoring software interface is also developed in the present study. Experimental results indicate that the monitoring system recognizes jetting failures accurately. It is found that during the normal operation of the printhead, the average error for the single nozzle and the multi-nozzle is 5.03 % and 7.37 %, respectively.

A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model

Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. Its robust performance and ready-to-use frameworks and architectures enables many people to develop various Deep Learning-based software or systems to support human tasks and activities. Traffic monitoring is one area that utilizes Deep Learning for several purposes. By using cameras installed in some spots on the roads, many tasks such as vehicle counting, vehicle identification, traffic violation monitoring, vehicle speed monitoring, etc. can be realized. In this paper, we discuss a Deep Learning implementation to create a vehicle counting system without having to track the vehicles movements. To enhance the system performance and to reduce time in deploying Deep Learning architecture, hence pretrained model of YOLOv3 is used in this research due to its good performance and moderate computational time in object detection. This research aims to create a simple vehicle counting system to help human in classify and counting the vehicles that cross the street. The counting is based on four types of vehicle, i.e. car, motorcycle, bus, and truck, while previous research counts the car only. As the result, our proposed system capable to count the vehicles crossing the road based on video captured by camera with the highest accuracy of 97.72%.

Scalar Speed Control of Induction Motors with Difference Frequency

Speed monitoring of electric motors has attracted the attention of many researchers from past to present. Induction Motor (IM), which is one of the electric motor types, falls behind the nominal speed in different loads. This study was conducted to increase the speed control performance of IM. In the study, speed control of IMs was realized by Difference Frequency (DF). The Scalar Control (SC) method was used in IM speed control. In order to increase the performance of SC, the frequency information received from IM was compared with the reference frequency. The resulting DF was applied to the system input again. For the performance analysis of the study; SC, PI + SC and DF + SC methods were compared. The results obtained from the study simulated in Matlab software show that the proposed method can be used in speed control.

Ubiquitous and Low Power Vehicles Speed Monitoring for Intelligent Transport Systems

This paper presents a high scalability real-time intelligent traffic monitoring system, based on Radio Frequency Identification (RFID). The main features of this system are low cost, low power consumption, traffic monitoring, and connectivity. The system’s architecture includes an RFID reader, a passive tag, and a Raspberry Pi. Our solution collects vehicle information from the labels and stores the data into a database by employing only one antenna. The main challenge is that the RFID module is not robust enough to recognize the information in the vehicle’s RFID tag on each information query, while the label is in the reading zone. This instability does not allow us to know precisely when and where a vehicle enters or leaves the sensing zone. What is more, the high random error in the power signal and the complexity of its characteristic curve pattern add difficulty to the speed calculation, when we reduce the number of antennas to one. For this reason, an innovative approach has been designed, using customized modular neural network (MNN). This method fits the collected data (power signal vs time) affected by acute random noise, to the characteristic correspondence function among the signal power and the position of the terminal, which domains are dimensionally different. As a result, we can estimate the vehicle speeds and obtain the whole vehicle information. Under this novel method, we are able to reduce the hardware, in comparison with previous approaches, making it cheaper and decreasing power consumption.

Wearable self-powered pressure sensor by integration of piezo-transmittance microporous elastomer with organic solar cell

There is a great demand for the development of self-powered physical sensors for wearable applications in recent years. However, it is still challenging to achieve self-powered sensors with a high stability, accuracy, and linearity. Here, a novel wearable self-powered pressure sensor based on the integration of a piezo-transmittance microporous elastomer (PTME) and a thin-film organic solar cell (OSC) is proposed. In contrast to the sensors based on other mechanisms such as piezoelectricity or triboelectricity, the proposed self-powered pressure sensor is cable of measuring static pressure continuously and stably, and utilizes the ambient light as the power source regardless of its intensity. The PTME shows the light transmittance changes by a gradual closure of micropores with compression in response to the applied pressure. This unique optical characteristics of the PTME enables the OSC to generate varying electrical current in response to the pressure. The proposed self-powered pressure sensor shows a high-performance with a sensitivity of 0.101/kPa, a linearity of R2 = 0.995, and fast and reversible response to the pressure up to ~100 kPa. As practical applications of the proposed sensor, a detection of flexion/extension of a human finger for the manipulation of a prosthetic robot finger and a wind detection for the continuous monitoring of the wind speed and direction have been demonstrated.

Real-Time Low-Cost Speed Monitoring and Control of Three-Phase Induction Motor via a Voltage/Frequency Control Approach

In this paper, a new design of a real-time low-cost speed monitoring and closed-loop control of the three-phase induction motor (IM) is proposed. The proposed solution is based on a voltage/frequency (V/F) control approach and a PI antiwindup regulator. It uses the Waijung Blockset which considerably alleviates the heaviness and the difficulty of the microcontroller’s programming task incessantly crucial for the implementation and the management of such complex applications. Indeed, it automatically generates C codes for many types of microcontrollers like the STM32F4 family, also used in this application. Furthermore, it offers a cost-effective design reducing the system components and increasing its efficiency. To prove the efficiency of the suggested design, not only simulation results are carried out for a wide range of variations in load and reference speed but also experimental assessment. The real-time closed-loop control performances are proved using the aMG SQLite Data Server via the UART port board, whereas Waijung WebPage Designer (W2D) is used for the web monitoring task. Experimental results prove the accuracy and robustness of the proposed solution.

Comparative Analysis of Gait Speed Estimation Using Wideband and Narrowband Radars, Thermal Camera, and Motion Tracking Suit Technologies

Research has shown that cognitive and physical functioning of older adults can be reflected in indicators such as walking speed. While changes in cognition, mobility, or health cause changes in gait speed, often gradual variations in walking speed go undetected until severe problems arise. Discrete clinical assessments during clinical consultations often fail to detect changes in day-to-day walking speeds and do not reflect walking speeds in everyday environments, where most of the mobility issues happen. In this paper, we compare four walking speed measurement technologies to a GAITRite mat (gold standard): (1) an ultra wideband radar (covering the band from 3.3 GHz to 10 GHz), (2) a narrow band 24-GHz radar (with a bandwidth of 250 MHz), (3) a perception Neuron Motion Tracking suit, and (4) a thermal camera. Data were collected in parallel using all sensors at the same time for 10 healthy adults for normal and slow walking paces. A comparison of the sensors indicates better performance at lower gait speeds, with offsets (when compared to GAITRite) between 0.1 and 20% for the ultra wideband radar, 1.9 and 17% for the narrowband radar, 0.1 and 38% for the thermal camera, and 1.7 and 38% for the suit. This paper supports the potential of unobtrusive radar-based sensors and thermal camera technologies for ambient autonomous gait speed monitoring for contextual, privacy-preserving monitoring of participants in the community.

An Innovative Framework to Evaluate the Performance of Connected Vehicle Applications: From the Perspective of Speed Variation-Based Entropy (SVE)

Recently, a significant number of Connected Vehicle (CV) applications have emerged to offer solutions to some of the problems caused by increasing traffic demands. To better understand the influence of different CV applications on traffic in a relatively unified manner, a novel measure of effectiveness (MOE) called speed variation-based entropy (SVE) is proposed. An analytical study was carried out on different types of CV applications by relating SVE with other conventional MOEs on safety, mobility, and environmental sustainability. Four applications–High Speed Differential Warning (HSDW), Lane Speed Monitoring (LSM), Eco-Speed Harmonization (ESH), and Eco-Approach and Departure (EAD), representing safety-, mobility- and environment-focused CV applications–were selected for detailed evaluation. Results from the sensitivity analysis on technology penetration rate and congestion level reveal that: 1) SVE can accurately represent the speed variation of individual vehicles and the overall traffic; 2) the proposed SVE distribution can be used as an MOE for CV applications in a more holistic way and at different scales; and 3) the overall SVE has a strong positive correlation with conventional MOEs (i.e., average conflict frequency and average fuel consumption) especially under freeway scenarios. Therefore, conventional MOEs can be estimated and explained by SVE under a variety of scenarios.

Metrological support characteristics for life cycle of agricultural machines

Statements stated in this paper prove the essential role of the influence of metrological support on the quality parameters of complex equipment at each stage of the product life cycle. When designing aggregates and technological units, it is necessary to ensure the given accuracy of the sensors, while manufacturing technology, significant savings can be achieved due to the use of a more expensive and accurate measuring instrument. When operating machines, constant monitoring of power consumption, fuel, productivity, speed and other technological parameters with a given accuracy is required.

IoT based Speed Monitoring System based on Location of the Vehicle

Nowadays there is an increasing amount of road accidents happening around the world. The amount of road accidents is especially high in metropolitan cities. The main reason for road accidents is almost always due to the negligence on part of the driver. Maximum number of Road Accidents occur due to over-speeding and drunk driving. This system helps detect over-speeding vehicles by directly implementing the particular hardware inside the vehicle itself and depending on the GPS position of the car and regional speed limits alert the authorities based on the violation. This technique helps us to reduce costs which need to be incurred to install speed cameras at roads at regular intervals. The system will detect the speed of the car, and in case of a violation the license plate number along with the registration details and the photo of the driver will be directly sent to the concerned authorities. This system aims to decrease the amount of accidents that happen across the globe and help conserve human lives. Road accident still remain one of the highest contributors to the loss of human life. For experimental results, we used raspberry pi and the GPS antenna for detection of over speed and alert the authorities on violation.

Link Optimization in Software Defined IoV Driven Autonomous Transportation System

Due to the high mobility, dynamic nature, and legacy vehicular networks, the seamless connectivity and reliability become a new challenge in software-defined internet of vehicles based intelligent transportation systems (ITS). Thus, effieicnt optimization of the link with proper monitoring of the high speed of vehicles in ITS is very vital to promote the error-free and trustable platform. Key issues related to reliability, connectivity and stability optimization for vehicular networks are addressed. Thus, this study proposes a novel reliable connectivity framework by developing a stable, and scalable link optimization (SSLO) algorithm, state-of-the-art system model. In addition, a Use-case of smart city with stable and reliable connectivity is proposed by examining the importance of vehicular networks. The numerical experimental results are extracted from software defined-Internet of Vehicle (SD-IoV) platform which shows high stability and reliability of the proposed SSLO under different test scenarios, such as vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to anything (V2X). The proposed SSLO and Baseline algorithms are compared in terms of performance metrics e.g. packet loss ratio, transmission power (i.e., stability), average throughput, and average delay transfer. Finally, the validated results reveal that SSLO algorithm optimizes connectivity (95%), energy efficiency (67%), throughput (4Kbps) and delay (3 sec).

Towards The Development of a Smart Drone Police: Illustration in Traffic Speed Monitoring

Applications, such as, mapping, highway traffic monitoring and punishment (drone police), surveillance, rescues, precision agriculture, medical resupply missions, require a stable mobile platform for remote sensing. Sensing is an important component to prevent traffic congestion and traffic monitor to make decision. This paper studies a smart drone police based on a unmanned aerial vehicle (UAV): Illustration in traffic speed monitoring. The using of a UAV instead of static cameras/sensors provide some advantages, such as, its super flexibility, real-time operations, rich information and low cost. The experiment is performed on a lab-scale system to verify the effectiveness of the proposed methodology.

Application of artificial intelligence in real-time monitoring of withdrawal speed of colonoscopy

Objective To construct a real-time monitoring system based on computer vision for monitoring withdrawal speed of colonoscopy and to validate its feasibility and performance. Methods A total of 35 938 images and 63 videos of colonoscopy were collected in endoscopic database of Renmin Hospital of Wuhan University from May to October 2018. The images were divided into two datasets, one dataset included in vitro, in vivo and unqualified colonoscopy images, and another dataset included ileocecal and non-cecal area images. And then 3 594 and 2 000 images were selected respectively from the two datasets for testing the deep learning model, and the remaining images were used to train the model. Three colonoscopy videos were selected to evaluate the feasibility of real-time monitoring system, and 60 colonoscopy videos were used to evaluate its performance. Results The accuracy rate of the deep learning model for classification for in vitro, in vivo, and unqualified colonoscopy images was 90.79% (897/988), 99.92% (1 300/1 301), and 99.08% (1 293/1 305), respectively, and the overall accuracy rate was 97.11% (3 490/3 594). The accuracy rate of identifying ileocecal and non-cecal area was 96.70% (967/1 000) and 94.90% (949/1 000), respectively, and the overall accuracy rate was 95.80% (1 916/2 000). In terms of feasibility evaluation, 3 colonoscopy videos data showed a linear relationship between the retraction speed and the image processing interval, which indicated that the real-time monitoring system automatically monitored the retraction speed during the colonoscopy withdrawal process. In terms of performance evaluation, the real-time monitoring system correctly predicted entry time and withdrawal time of all 60 examinations, and the results showed that the withdrawal speed and withdrawal time was significantly negative-related (R=-0.661, P<0.001). The 95% confidence interval of withdrawal speed for the colonoscopy with withdrawal time of less than 5 min, 5-6 min, and more than 6 min was 43.90-49.74, 40.19-45.43, and 34.89-39.11 respectively. Therefore, 39.11 was set as the safe withdrawal speed and 45.43 as the alarm withdrawal speed. Conclusion The real-time monitoring system we constructed can be used to monitor real-time withdrawal speed of colonoscopy and improve the quality of endoscopy. Key words: Quality control; Artificial intelligence; Colonoscopy; Withdrawal time; Withdrawal speed

PERANCANGAN INVERTER MASTS DENGAN MONITORING KECEPATAN MENGGUNAKAN SENSOR HALL BERBASIS ARDUINO

The use of Brushless Direct Current Machines (MASTS) is very much applied in the industrial world compared to other types of drive engines. For the MASTS drive a three-phase inverter is needed, a three-phase inverter is a circuit that is used to convert DC voltage into AC voltage with a certain frequency. The system used to control the inverter is to use Pulse Width Modulation (PWM), where the pulse width is used to regulate the speed. To optimize the MASTS rotation speed, it is necessary to make speed monitoring to determine the stability of the MASTS speed, so design is carried out by combining the monitoring of the MASTS speed with a three-phase inverter. This tool uses a hall sensor, Arduino, and liquid crystal display (LCD) display. The working principle of the hall sensor is detecting the rotation speed of the MASTS based on the working principle of the electromagnetic hall sensor. In this study, 5 variations of PWM potentiometer inputs 20%, 40%, 60%, 80%, 100% were used to determine the results of monitoring of the MASTS speed for each input variation. In this study, an error rate of 6.2% was observed.. Keywords: Sensor Hall, SMATS, Inverter, RPM

Dynamic risk assessment and active response strategy for industrial human-robot collaboration

To enhance flexibility and sustainability, human-robot collaboration is becoming a major feature of next-generation robots. The safety assessment strategy is the first and crucial issue that needs to be considered due to the removal of the safety barrier. This paper determined the set of safety indicators and established an assessment model based on the latest safety-related ISO standards and manufacturing conditions. A dynamic modified SSM (speed and separation monitoring) method is presented for ensuring the safety of human-robot collaboration while maintaining productivity as high as possible. A prototype system including dynamic risk assessment and safe motion control is developed based on the virtual model of the robot and human skeleton point data from the vision sensor. The real-time risk status of the working robot can be known and the risk field around the robot which is visualized in an augmented reality environment so as to ensure safe human-robot collaboration. This system is experimentally validated on a human-robot collaboration cell using an industrial robot with six degrees of freedom.

From Signal to Image: Capturing Fine-Grained Human Poses With Commodity Wi-Fi

Human sensing based on commodity Wi-Fi devices has become a promising technique in human tracking, gesture recognition, walking speed monitoring, in-home healthcare, etc. However, past human sensing systems usingWi-Fi capture limited information about humans. Hence in this letter, we try to make commodity Wi-Fi devices act as cameras to directly capture human poses, i.e., fine-grained human skeleton images. We use a synchronized camera to capture human skeletons as annotations for Wi-Fi signals and design a novel neural network to convert Wi-Fi signals into images. We utilize three transceivers coordinately and use amplitude and phase information of Channel State Information (CSI) jointly to improve the resolution of Wi-Fi signals. We also introduce a method to extract useful and accurate CSI corresponding to humans and construct CSI images which are input of the neural network. Experimental results show that commodity Wi-Fi devices can capture human poses almost as fine-grained as cameras.

Real machine vision use-cases applied to hot rolling mill plants

There are not many applications based on machine vision technology in hot rolling mill plants because of the characteristics of such a harsh environment. Water steam, cooling water or dirt create a difficult environment for any type of electronic device, in addition to hampering image analysis.Usually, most of the measurement systems in hot rolling mills are not based on artificial vision, mainly because the product is at temperatures of approximately 1000ºC, emitting radiation throughout the spectrum producing difficulties to acquire images correctly. However, the increase in control technology involves the demand for more efficient and sophisticated measurement equipment.This paper shows three novel developments involving machine vision systems in order to integrate them in a real industrial environment:• Speed monitoring. Measuring the speed of the billet along the rolling mill involves great difficulty because of the high temperatures. A measurement system based on artificial vision and optical flow has been developed.• Coils counting. In a lying head for wide rod rolling mill it is important to know the number of coils to cut, in order to achieve the best quality. A camera is used to monitoring the bar, in order to localize the point in which the cut is necessary to carry out.• Multispectral acquisition. Multispectral systems are not widely use due to the very nature of the sector. The approach was to analyse the spectrum in which the bar is emitting in order to infer the chemical composition or to detect defects that would otherwise impossible to analyse.