Real-time Distance Research Articles

A multivariate fusion collision detection method for dynamic operations of human-robot collaboration systems

Real-time human-robot collision detection is crucial for ensuring the safety of operators during human-robot collaboration(HRC) and for improving the efficiency of such collaboration. It plays an important role in promoting the development of intelligent manufacturing. To address this issue, our team developed a multi-faceted collision detection system using eXtended Reality (XR) technology, specifically designed for complex and dynamic human-robot collaborative operations. The system integrates three different methods: a Virtual Reality (VR) detection method that enables robots to better perceive and detect human operators. An Augmented Reality (AR) detection method that enhances the operator’s perception of the robot. And a fusion detection and evaluation method. This detection and evaluation method assesses the effectiveness of collaboration by analyzing key performance indicators, such as real-time distance between human and robot, changes in the operator’s Heart Rate(HR), and overall task completion time. Through empirical research on the human-robot collaborative assembly task of T-series spiral bevel gear reducers, the effectiveness of the innovative method is verified. The research results show that this method significantly improves safety and operational efficiency, providing a novel solution detection in industrial manufacturing environments.

Overtaking risk assessment based on extreme value theory and intelligent network information

In order to evaluate the overtaking risk level of intelligent connected vehicles when providing different connected information, and to make up for the neglect of driver factors in traditional risk assessment and the inadequate evaluation of complex traffic scenarios by a single traffic conflict indicator, this paper introduces the Block maxima (BM) and Peak over threshold (POT) methods to fit the extreme value distribution for two conflict scenarios involved in overtaking events (following vehicle conflict and frontal vehicle conflict), so as to evaluate the risk of following vehicle accidents and frontal collision accidents during overtaking. In each conflict scenario, a non-stationary extreme value model considering driver factors and a binary extreme value model considering different traffic conflict indicators are constructed, and the model is verified by the overtaking test data of intelligent connected vehicles in a two-way two-lane vehicle. Overtaking events were extracted from the original test data and conflict indicators were calculated: including the collision time GAP with the front vehicle at the beginning of the overtaking event, the collision time TTC_t1 with the oncoming vehicle, the deceleration DRAC, and the collision time TTC with the oncoming vehicle and the headway time THW with the front vehicle at the end of the overtaking event. The probability of the event with a negative time conflict indicator or DRAC greater than MADR was used to characterize the degree of collision risk. The results show that in the following vehicle conflict scenario, the binary extreme value model constructed with different conflict indicators has different error results, among which the binary extreme value model constructed with THW&DRAC has the most accurate evaluation result (standard error MAE = 0.00028); in the front vehicle conflict scenario, the binary extreme value model constructed with TTC&DRAC has the most accurate evaluation result ( MAE = 0.006 ). In different conflict scenarios, the non- stationary extreme value model considering the driver factor significantly improves the risk assessment accuracy compared with the model without considering the driver factor (small AIC and BIC values). In addition, different intelligent network information (real-time distance, overtaking advice, speed advice) brings different overtaking risks, and when the intelligent network information is speed advice, the vehicle's overtaking risk is the lowest. Therefore, the non- stationary extreme value model and binary extreme value model considering driver factors proposed in this paper can effectively evaluate driving risks through traffic conflict indicators. Secondly, the experimental data of intelligent networked vehicles show that the extreme value theory model proposed in this paper can accurately evaluate the overtaking risk level of intelligent networked vehicles when providing different network information.

Rapid distance estimation of odor sources by electronic nose with multi-sensor fusion based on spiking neural network

The inference of odor source locations holds great potential value in chemical leak detection, explosive searches and flammable substance detection. Sensor response and derivative changes are common indicators used to estimate source distance. However, most studies often rely on single-feature methods using a single sensor. In this study, a novel distance indicator based on a spiking neural network (SNN) that integrates multiple sensors is proposed. SNN was introduced for effective multi-sensor data fusion, which can provide accurate and rapid source distance estimation. The indicator's accuracy and quick detection capabilities were tested using a publicly available wind tunnel dataset and compared with single-sensor indicators. Additionally, an olfactory robot platform was constructed to test the method's real-time performance. Results demonstrate that this method effectively utilizes sensor arrays to estimate source distance, with an average root mean square error (RMSE) of less than 0.1 m across different wind speeds, outperforming single-sensor-based indicators. Additionally, the model exhibits good distance estimation performance within a relatively short detection time, with an average RMSE of 0.118 m. Practical testing on a mobile robot platform confirms the applicability of this distance indicator for real-time distance monitoring. In conclusion, the proposed distance indicator enables effective distance estimation for odor sources, providing a promising method for odor source localization and olfactory navigation.

Optimizing Mobile Reading Experience: Adaptive Font Size Adjustment in a Context-Aware Application

The reading experience on a mobile device can be influenced by many external elements that affect the viewing conditions. The user holding position, operating style, and the light conditions of the surroundings can all play a crucial factor in the legibility of the text. Furthermore, the user’s eye conditions may be an extra factor to consider when determining how to improve the reading experience. This study presents a context-aware application that adjusts the text font size based on the distance between the user and the device. The users select their preferred font size at certain distances in the calibration phase, accounting for individual preferences or their eye conditions, and the application uses the device’s front camera to calculate the distance in real time and adjust the font size accordingly. We present implementation details and challenges faced during development and how they were addressed. A two-part experiment ([Formula: see text] = 34) revealed that the application scored an excellent usability score while no differences were found between users with and without eye conditions, showing that both user groups find it equally usable. Additionally, initial results suggest that the visual landmarks used showed a promising start at addressing issues related to our approach.

Navigating formative assessment as professional development in digital contexts: insights from teachers’ experiences

ABSTRACT As digital technology transforms education, investigating shifts in teachers’ formative assessment practices within digital contexts becomes essential. This study explores upper secondary school teachers’ experiences in an 18-month professional development intervention focused on formative assessment in digital contexts. Data from a baseline survey (n = 26) and reflection logs (n = 12) were analysed using descriptive statistics and thematic analysis. Teachers exhibited a preference to provide digital feedback for student learning but expressed uncertainty about its effectiveness. Thematic analysis revealed four main themes: the importance of a shared language for formative assessment; challenges tied to real-time distance learning; the paradoxical nature of digital feedback in fostering dialogue; and strategies for seeking and improving digital feedback. The professional development intervention enhanced teachers’ understanding of formative assessment, including its timing and impact on student learning. The study contributes to the discourse on digital formative assessment, offering insights for educational practices and research.

Intelligent detection method with 3D ranging for external force damage monitoring of power transmission lines

The rapid development of smart grid requires more and more reliable power supply. The external force damage to transmission lines can significantly impact the safety and stability of the power grid, potentially causing electric shock accidents. Existing methods for monitoring external force damage in transmission corridors lack the ability to provide effective warnings based on the real-time distance between potential hazards and power lines. Additionally, limited computational power and storage capacity at edge terminals restrict the efficient deployment of high-precision (high complexity) visual algorithms. This study presents, for the first time, a lightweight intelligent detection method integrating detection and three-dimensional (3D) ranging. A regression loss optimized for small objects is introduced to compensate for the shortcomings of lightweight networks in detection accuracy. Simultaneously, based on the influence of convolutions in various modules of the baseline model on performance, lightweight improvements are made to the detector architecture using Omni-Dimensional Dynamic Convolution and Distribution Shifting Convolution. Finally, a 3D ranging module is integrated into the detector, involving operations such as 2D3D information matching and back-projection transformation. This method innovatively achieves automated ranging and hierarchical warning. Its effectiveness is validated in transmission corridor scenarios under various weather conditions and surveillance video. The results demonstrate that our method outperforms other algorithms in hazard detection accuracy and lightweight performance. Moreover, the distance prediction error rate is below 1.6%. The hierarchical warning solutions can be applied to more scenarios.

Optimization of resource allocation strategy for high-speed railway based on deep reinforcement learning

With the accelerated development of high-speed railway (HSR), the contradiction between the surge of user services and the demand for resource has become increasingly prominent. Mobile edge computing (MEC) has emerged to improve performance, reduce communication delay and ease network load. In this paper, we design a multi-user MEC system framework that aims to solve the joint optimization problem of computation offloading and resource allocation in HSR communication scenario with deep reinforcement learning algorithm. The framework dynamically allocates computation resource and network bandwidth through the real-time distance between users and base station (BS) to achieve optimal resource utilization and maximize user experience. To achieve this goal, we use a deep reinforcement learning based dynamic computation offloading and resource allocation (DDCORA) optimization algorithm. The algorithm minimizes the system cost by sharing state information among different users and making collaborative decisions to rationally allocate spectrum resource and computation resource. Simulation results show that DDCORA algorithm can significantly decrease the system cost while enhancing the overall system performance and user experience.

Design and Implementation of an Intelligent Ultrasonic System based on 51 Microcontroller

With the rapid development of microelectronics and intelligent technology, microcontroller units (MCUs) are increasingly applied in various fields. This paper proposes the design and implementation of an intelligent ultrasonic system based on a 51 microcontroller, integrating ultrasonic sensors, an LCD display, and a 51 MCU to achieve real-time distance measurement, data display, and intelligent control functions. The system utilizes a CD4069 ultrasonic sensor to transmit and receive ultrasonic waves, measuring the distance between the sensor and an object by calculating the time difference between transmission and reception. The measured distance data is processed and displayed on an LCD screen in real-time. Furthermore, the system features intelligent control capabilities, automatically adjusting and controlling devices based on the measured data, thereby enhancing the system's level of intelligence. The intelligent ultrasonic system based on the 51 microcontroller offers advantages such as low cost, high precision, and strong versatility. It can be widely used in various fields, including intelligent robots, obstacle avoidance systems, and automatic parking systems. The design and implementation of this system not only demonstrate innovative applications of ultrasonic ranging technology but also provide valuable references for the extensive application of microcontroller units in intelligent technology fields.

Navigating Unstructured Space: Deep Action Learning-Based Obstacle Avoidance System for Indoor Automated Guided Vehicles

Automated guided vehicles (AGVs) have become prevalent over the last decade. However, numerous challenges remain, including path planning, security, and the capacity to operate safely in unstructured environments. This study proposes an obstacle avoidance system that leverages deep action learning (DAL) to address these challenges and meet the requirements of Industry 4.0 for AGVs, such as speed, accuracy, and robustness. In the proposed approach, the DAL is integrated into an AGV platform to enhance its visual navigation, object recognition, localization, and decision-making capabilities. Then DAL itself was introduced to combine the work of You Only Look Once (YOLOv4), speeded-up robust features (SURF), and k-nearest neighbor (kNN) and AGV control in indoor visual navigation. The DAL system triggers SURF to differentiate two navigation images, and kNN is used to verify visual distance in real time to avoid obstacles on the floor while searching for the home position. The testing findings show that the suggested system is reliable and fits the needs of advanced AGV operations.

Microlidar Application for Object Detector to Support The Navigation System in Self-Driving Vehicle

The ability to detect and measure the distance of potential obstacles is importance for navigation system in self-driving vehicles. The measurement process needs to be fast and accurate since the controller requires real-time data to make quick decisions and respond to any potential disturbances on the vehicle's track. This research aims to develop Microlidar for detection system that can accurately measure the distance of a potential obstacle object. The Microlidar utilize Lidar Lite V3 proximity sensor which have range measurement specification of up to 40 meter. Microlidar rapidly rotate 360 degrees by using a stepper motor while in the same time continuously measure the real-time distance. The measurement data are sent to a microcontroller through I2C, and the Processing software plot the 2D image which work like radar visualization. The system is assessed for ranging the various distance object in static and dynamic measurement mode. The results show that the Microlidar has a good level of accuracy with an average error value at the distance of 300 cm is 4.99 cm or 1.7% while the average error value at the distance of 1000 cm is 15.69 cm or 1.6% obtained from 100 data sets collected. The communication from the sensor to Arduino requires a minimum baud rate of 115200 bits/second to minimize data loss and ensure that the measured distance can be processed in real time by the microcontroller. Real-time data with high speed is essential since it will be used on the vehicle in order to quickly decide whether there is a barrier or not on the vehicle’s track. The sensor analysis distance expected from this research could be used as a reference to support the navigation system performance of self-driving vehicles.

Implementation of multimedia search & management system based on remote education

In order for remote education using multimedia to be effective, an efficient management technique for video information needs to be developed. Therefore, for real-time processing of moving images, it is necessary to manage and search image data in a compressed state. MPEG-4 is the most widely used video compression technology. In order to process video in real time in distance education using multimedia, it is very important to develop a technique for managing and retrieving video information compressed with MPEG-4. Therefore, in this paper, a multimedia information management system and search technology were developed using MPEG-4 compression technology used for real-time distance education.

A novel method for the evaluation of ship berthing risk using AIS data

The assessment of ship berthing risk, encompassing the potential for berthing collisions and unforeseen events, holds paramount importance in the realm of waterway traffic management and maritime surveillance. However, existing methods for analyzing ship berthing risk suffer from limitations in terms of timeliness, comprehensiveness and data accessibility. Therefore, this paper presents a novel approach to ship berthing risk assessment. The proposed method relies on Automatic Identification System (AIS) data and takes into consideration information related to the ship, berth, and environmental factors. It calculates crucial parameters, including the vertical distance between the ship and the berth, berthing speed, berthing angle and real-time distance between the ship and the berth, utilizing the AIS data and the berth location. Furthermore, environmental disturbance data pertaining to the ship's berthing environment is integrated with AIS data. Subsequently, we introduce the Improved Bossel Model considering Catastrophe (IBM-CC) to evaluate ship berthing risk in real-time. Finally, the proposed method was validated using actual ship berthing data and various simulation scenarios. The results demonstrate that our proposed method accurately assesses real-time ship berthing risk under diverse scenarios, offering a novel approach for the real-time and precise quantitative assessment of ship berthing risk.

Research on the Multimode Switching Control of Intelligent Suspension Based on Binocular Distance Recognition

As the upgrade of people’s requirements for automotive driving comfort, conventional passive suspensions for cars have fallen short of existing demands due to their nonadjustable damping and stiffness, so semiactive suspensions and active suspensions have gained growing acceptance. Compared with active suspensions, semiactive suspensions offer the advantages of a low manufacturing cost and reliable structure, and thus have become the preferred choice for most vehicles. To optimize the control effect of semiactive suspensions under different working conditions, this paper completed the modeling of magnetorheological semiactive suspension system dynamics and road inputs; then, the design of binocular camera sensing algorithms was performed to obtain the real-time distance of the target using the point cloud ranging function, and the parameters required for suspension control were also obtained. This was followed by the completion of the control-mode-switching rules and the design of the suspension controller. According to the different control objectives, the mode could be divided into the obstacle-road mode, straight-road mode, and curved-road mode. The suspension controller included the BP-PID (neural network PID controller) controller and the force distributor. Finally, the effectiveness of the mode-switching rules and the control method was verified through system simulation and the hardware-in-the-loop test.

Personalized Exercise Program Design with Machine Learning in Sensor Networks

The use of wearable solutions and sensors has reached the point of modern machine learning (ML) techniques in recent years. It contains the different programs that help to develop personalized opportunities for the betterment of the individuals. The research shed light upon the functions of ML and distributed sensor networks (DSN) in promoting healthcare among users. It can be said that the functions of the ML help to develop good fitness among the users, which results in good health. This dissertation consists of different kinds of ML algorithms and the usage of DSNs for the development of physical exercise programs. It has been found that most individuals use the sensors for their benefit in developing good health. The use of machine learning techniques not only helps to record an individual's health data but also sends emergency information to the nearest medical center for the user's benefit. The use of ML helps to the identification of the location of an individual with the help of GPS measures of real-time distance and users' health activities.

A self-powered biomimetic mouse whisker sensor (BMWS) aiming at terrestrial and space objects perception

The extremely sensitive whisker discernment of mice is the key expertise enabling their survival in extreme adverse environments. Previous sensors mimicking animal whiskers for environmental awareness tend to be cumbersome, reliant on external power, and limited application scenarios, which constrains their applicability in more extensive contexts. Inspired by the ability of mice exploring and perceiving environments with their whiskers, a lightweight self-powered BMWS is designed for terrestrial and spatial objects exploration. Owing to the merits of triboelectric and decent structural design, BMWS demonstrates outstanding collision detection capabilities and signal stability. Subsequently, BMWS showcases its extraordinary strength in tasks including direction identification, hole width discrimination, real-time distance sensing, and intelligent early warning. Empowered by an artificial neural network (ANN), BMWS accomplishes the identification of ground and spatial objects with astounding accuracy, pioneering the transfer application of ANN to the domain of biomimetic mouse whiskers and expanding the research boundaries of biomimetic mouse whisker sensors, while taking the first step toward whisker sensor deployment in space environments.

An Intelligent Detection Method for Approach Distances of Large Construction Equipment in Substations

The safe approach distance detection of large construction equipment in substations is important to ensure the safety and stability of the power system, as well as to prevent equipment damage, power outages and other accidents. The current method is unable to intelligently distinguish construction equipment from power equipment and realize real-time safety approach distance detection. Therefore, this paper constructs a safety approach distance detection system for large-scale construction equipment in substations based on stereo vision and target detection, and realizes real-time high-precision safe approach distance detection between large-scale construction equipment and electric power equipment. Firstly, the system distinguishes construction equipment from power equipment using a GhostNet-based substation construction target detection model. Secondly, the system obtains spatial information regarding the operation scene using a lightweight stereo matching model based on channel attention, then calculates the spatial surface center of the target based on the spatial information and detection results, and finally calculates the safety approach distance between construction equipment and power equipment. Compared with MobileNetv3-YOLOv4, the map and the recall rate of the proposed method are improved by 13.1% and 29.0%; compared with the AnyNet stereo matching method, the proposed method decreases the end point error and 3 pixels error by 34.2% and 25.8%. The actual data show that the detection speed of the proposed method is 19.35 frames per second, and the mean absolute error is 0.942 m and the mean relative error is 3.802%. This method can accurately measure the safe approach distance in real time in real scenarios to guarantee the safety of personnel and equipment.

Automatic real-time fire distance, size and power measurement driven by stereo camera and deep learning

Automatic real-time fire characterization is a crucial requirement of future smart firefighting. This work proposes a novel computer vision method to automatically measure the fire heat release rate, even when the camera is moving in real-time. Firstly, a portable binocular stereo camera is used to capture the real-time fire video stream that is fed into a pre-trained computer-vision model frame-by-frame to detect the fire region. By identifying the fire location inside the image, the real-time distance between the camera and the fire source is determined. This fire distance helps re-scale the images to match the input scale of the AI-image Fire Calorimetry. Then, the deep learning model can automatically output the transient fire power in real time. Results show that the stereo vision system is capable of accurately measuring the distance between the camera and the fire source, flame height, and power, with a relative error of less than 20%. This work provides an automatic and flexible way to measure the distance, power and hazard of fire in real-time, and such a method has broad applications in firefighting operations and decision-making.

Webcam-Based Physical Distancing Prototype to Calculate Real-Time Distance by Applying Euclidean Distance

The Covid-19 virus has spread rapidly in Indonesia in recent years, significantly increasing the number of victims in the community. However, there is a lack of warnings for violators of physical distancing. This study aims to create a system as a solution to overcome the problems of implementing physical distancing protocols. Image capture media in the form of cameras were used to monitor human objects in crowded situations by tracking the movement of objects at regular physical distances. In this study, computer vision, image processing, and pattern recognition were used to construct the distance measurement between humans. Using a webcam by applying the Euclidean Distance and Haar Cascade methods, this system is intended to determine the distance between human-made objects. The results demonstrated that a digital image system employing the Haar Cascade and Eucludien Distance methods could make it easier to control the gathering point in a given area. Researcher has developed a software usage assistance feature for use with this application. Based on the findings of the study, the accuracy of this system was evaluated using 100 trials, with a succes rate of 100% since the system resulted correct notification in all trials. In addition, this digital image system is anticipated to assist developers and the general public in controlling gathering locations that do not adhere to established health protocols, allowing violations of physical separation to recognize their errors when captured on camera immediately.

Augmented-reality swim goggles accurately and reliably measure swim performance metrics in recreational swimmers.

Swimmers commonly access performance metrics such as lap splits, distance, and pacing information between work bouts while they rest. Recently, a new category of tracking devices for swimming was introduced with the FORM Smart Swim Goggles (FORM Goggles). The goggles have a built-in see-through display and are capable of tracking and displaying distance, time splits, stroke, and pace metrics in real time using machine learning and augmented reality through a heads-up display. The purpose of this study was to assess the validity and reliability of the FORM Goggles compared with video analysis for stroke type, pool length count, pool length time, stroke rate, and stroke count in recreational swimmers and triathletes. A total of 36 participants performed mixed swimming intervals in a 25-m pool across two identical 900-m swim sessions performed at comparable intensities with 1 week interval. The participants wore FORM Goggles during their swims, which detected the following five swim metrics: stroke type, pool length time, pool length count, stroke count, and stroke rate. Four video cameras were positioned on the pool edges to capture ground truth video footage, which was then manually labeled by three trained individuals. Mean (SD) differences between FORM Goggles and ground truth were calculated for the selected metrics for both sessions. The absolute mean difference and mean absolute percentage error were used to assess the differences of the FORM Goggles relative to ground truth. The test-retest reliability of the goggles was assessed using both relative and absolute reliability metrics. Compared with video analysis, the FORM Goggles identified the correct stroke type at a rate of 99.7% (N = 2,354 pool lengths, p < 0.001), pool length count accuracy of 99.8%, and mean differences (FORM Goggles-ground truth) for pool length time: -0.10 s (1.49); stroke count: -0.63 (1.82); and stroke rate: 0.19 strokes/min (3.23). The test-retest intra-class correlation coefficient (ICC) values between the two test days were 0.793 for pool length time, 0.797 for stroke count, and 0.883 for stroke rate. Overall, for pool length time, the residuals were within ±1.0s for 65.3% of the total pool lengths, for stroke count within ±1 stroke for 62.6% of the total pool lengths, and for stroke rate within ±2 strokes/min for 66.40% of the total pool lengths. The FORM Goggles were found valid and reliable for the tracking of pool length time, pool length count, stroke count, stroke rate, and stroke type during freestyle, backstroke, and breaststroke swimming in recreational swimmers and triathletes when compared with video analysis. This opens perspectives for receiving real-time information on performance metrics during swimming.

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application.

With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion.