Manual Mode Research Articles

Assessment of 3D Models Placement Methods in Augmented Reality

Augmented reality (AR) is a relevant technology, which has demonstrated to be efficient for several applications, especially in the architecture, engineering, construction and operation (AECO) domain, where the integration of building information modeling (BIM) and AR has proved to be optimal in handling construction projects. However, the main challenge when integrating a virtual 3D model in an AR environment is the lack of precision and accuracy of placement that can occur between the real and the virtual environments. Although methods for placement via AR have been reported in the literature, there is a lack of investigations addressing their evaluation. Therefore, this paper proposes a methodology to perform a quantitative and qualitative assessment of several AR placement methods and a discussion about their usability in the specific context of AECO. We adopt root mean square error (RMSE) to quantify the placement accuracy of a 3D model and standard deviation to examine its stability (jittering). The results revealed that the AR placement error range is extremely wide (from a few centimeters up to meters). In marker-based methods, the results showed centimeter-range in both indoor and outdoor environments, compared to other methods (Inertial, Marker-less, etc.), while marker-less methods have widely varying error range from centimeters to a few meters. Other commercial solutions based on placement-sensors (GNSS and IMU), such as Trimble SiteVision, have proven placement performance in manual mode with centimeter order, while for the automatic mode, the order of placement and stability is metric, due to the low coverage of RTX (real time extended) in the study area.

Automatic Solar Panel Positioning and Maintenance System

Abstract: Renewable energy is a type of energy that is derived from ongoing natural processes and energy of natural processes converted into available forms. Solar energy is any type of energy that is generated by the sun. To make use of this energy and convert it into electricity we use solar panels. A solar panel has maximum productivity when the sun rays are incident at right angle to it. Usual home use solar panels are fixed in a stationary position hence there will be only a certain time in the day that the sun-rays are incident at right angle on the panel. A solar tracking system is a single-axis rotating system which aims for solar panels to operate by tracking the sun's movement throughout the day which makes it possible for the rays to arrive perpendicular to the panels. This maximises the productivity of the panel. Solar tracking system was designed which used LDRs in form of a voltage divider circuit to sense the sunlight and actuate the stepper motor to rotate the panel over a single-axis so that the sun-rays the panel is perpendicular to the sun-rays. This model was tested out alongside a stationary solar panel. Readings were noted down of both panels every hour. The panel with the tracking system proved to be more productive than the stationary panel as it had generated more power over each hour. The panel could be moved both automatically with the help of the sensors and through manual mode using the blynk UI

Descriptive analysis of reports on autonomous vehicle collisions in California: January 2021–June 2022

The characteristics of autonomous vehicles’ collisions from 2021 and the first half of 2022 in California confirm trends reported in previous years. Driving in autonomous mode was associated with fewer instances in which the AV was deemed to be at fault in a collision. Most collisions in autonomous mode were rear-end collisions at intersections. Single vehicle collisions occurred mostly in manual mode. Collisions with vulnerable road users occurred mostly while the autonomous vehicle was in manual mode, often right after disengagement from autonomous mode. In collisions with other vehicles that occurred after disengagement, the other vehicles were frequently deemed to be at fault. Compared to 2021, the collision reports from the first half of 2022 indicate higher shares of collisions in autonomous mode, rear-end collisions, and collisions with vulnerable road users.

Autonomous aspirating robot for removing saliva blood mixed liquid in oral surgery

Saliva blood mixed liquid (SBML) appears in oral surgery, such as scaling and root planning, and it affects surgical vision and causes discomfort to the patient. However, removing SBML, i.e. frequent aspiration of the mixed liquid, is a routine task involving heavy workload and interruption of oral surgery. Therefore, it is valuable to alternate the manual mode by autonomous robotic technique. The robotic system is designed consisting of an RGB-D camera, a manipulator, a disposable oral aspirator. An algorithm is developed for detection of SBML. Path planning method is also addressed for the distal end of the aspirator. A workflow for removing SBML is presented. 95% of the area of the SBML in the oral cavity was removed after liquid aspiration among a group of ten SBML aspiration experiments. This study provides the first result of the autonomous aspirating robot (AAR) for removing SBML in oral surgery, demonstrating that SBML can be removed by the autonomous robot, freeing stomatology surgeon from tedious work.

Manual Control Mode for Autonomous Mobile Robots Using Velocity-Based Impedance Control

In this industry-oriented research and development project, we studied the introduction and implementation of autonomous mobile robots (AMRs) for transporting equipment and parts in manufacturing facilities of aircraft equipment. In the relationship between the operator and AMR, it is crucial to implement an autonomous driving mode as well as enable the operator to control the operation by directly interacting with the AMR, such as when moving in complex locations. In such cases, a manual control mode is useful and often required. This study proposes a method of AMR motion that enables the operator to physically interact with the robot without the use of a force sensor and perform a power assist function according to longitudinal velocity-based impedance control. The power assist scheme can also be applied to other AMRs, particularly those with commercial motor-in-wheel drive systems, in which the design parameters, current, and torque of the motors cannot be measured or monitored in real time. For the control scheme, we designed a dynamic observer based on a mathematical model of the robot to estimate the force exerted when the operator contacts the body of the robot. Additionally, we developed a complementary algorithm that enables the movement of the robot on inclined surfaces. Comprehensive experimental analysis of the AMR under different operating cases demonstrates the feasibility and efficacy of the proposed power-assist control scheme.

Optimization Model to Manage Ship Fuel Consumption and Navigation Time

Abstract Owners of vessels are interested in the lowest possible operating costs. These costs are mainly related to fuel consumption during navigation. To manage it rationally, the main decision-making problem is selecting the proper parameters of the ship’s propulsion system during navigation. In practice, operators of ships equipped with controllable pitch propellers controlled in manual mode make a selection of the commanded outputs based on their own knowledge, intuition, and all accessible information regarding sea conditions. In many cases, their decisions are unreasonable or incorrect. Therefore, it would be desirable to support their decision-making in selecting the commanded outputs. For this reason, we have decided to develop a decision support system in the form of an expert system. This computer-aided system supports the selection of the commanded outputs of the ship’s propulsion system. The most important component of this system is the two-criteria optimization model, allowing the rational management of the ship fuel consumption and navigation time.

Manual or auto-mode: Does this affect radiation dose in digital mammography without compromising image quality?

IntroductionIn current practice, auto-filter exposure mode is used for most screening mammography examinations. However, with better understanding of the side effects of radiation, it is important to examine exposure parameters and practises to minimise radiation dose to patients. The purpose of this phantom study is to investigate the impact that different exposure modes (manual, auto-time and auto-filter) have on radiation dose, while maintaining images of diagnostic quality. MethodsThis study was conducted in three stages. In the first stage, 125 images were taken using a Gammex 156 phantom with polymethyl methacrylate blocks to reflect varying thicknesses (4.5, 5.0, 5.5, 6.0 and 6.5 cm). In the second stage, three mammographers independently assessed image quality and assigned scores based on the number of distinct fibers, masses and speck groups visible. Images with acceptable quality were further investigated in the third stage by comparing their average glandular dose (AGD) using the Kruskal–Wallis H test and Dunn's post-hoc pairwise analysis. ResultsSignificant differences in AGD were shown between the auto-filter mode and manual mode techniques for 6.0 cm, and between auto-time mode and manual mode techniques for 6.5 cm (p < 0.05). ConclusionFor 4.5, 5.0 and 5.5 cm phantoms, as AGD was not significantly different among the different modes, the auto-filter may remain the most practical option. However, significant reductions in AGD were obtained for the 6.0 and 6.5 cm phantoms when manual mode techniques were used. Implications for practiceManual mode techniques can potentially provide dose-saving opportunity in 6.0 and 6.5 cm breast thickness though future work on human breast should be done to confirm this. Results from this study will support future research based on patient data.

Perceptual quality assessment for no-reference image via optimization-based meta-learning

Image quality assessment (IQA) is a critical issue in computer vision, which intends to simulate human visual system (HVS) with a view to evaluating the error degree of distorted images from reference images. The algorithms based on deep learning have been successfully introduced to quality evaluation of no-referenced images in the last years. Unfortunately, they are confronted with the problems of over-fitting and weak generalization ability caused by insufficient labeled data. The emergence of meta-learning has brought new ideas, which have been proved to address the issues regarding few-shot learning. However, the commonly used meta-learning metrics only learn the initialization of weights, which can’t guarantee the optimal gradient direction. The manual design mode leads to lower accuracy and speed. In our work, an improved meta-learning framework is applied to no-reference (NR) IQA to meet the above challenges. It can achieve maximum generalization performance through only a few update iterations. Specifically, we collected a great many NR-IQA tasks with different distortions to pre-train meta-model and optimizer to learn a general weight initialization and optimization rule. Then, the meta-model acquired meta-knowledge and learned unique learning rate for each task. Finally, it could be directly adapted to new NR-IQA tasks only by fine-tuning a few images. Experiments on synthetic and authentic datasets proved that our approach has more vital learning ability and better generalization performance for evaluating real distorted images, effectively reducing dependence on manual marking.

AI BASED LAKE CLEANING ROBOT

Recently, water pollution has become a global problem. Water resources such as lakes, rivers and lakes are polluted due to the accumulation of domestic, industrial, medical and sewage. The AI Based Lake Cleaning Robot project addresses this issue by providing an efficient and environmentally friendly remote pool cleaning machine. This project has two operating modes which are Manual or Android operating modes and default mode. In manual mode the device is used by the user, the android app is used to control the device to collect garbage. In automatic mode, AI technology that assists the wireless fidelity camera and ultrasonic sensors are used, the camera captures a real-time image of a liquid body and sends it to the Convolutional Neural Network (CNN) for image analysis and CNN will control the boat's performance. The device is equipped with a waste bin. The device is designed to detect any obstacles through the use of ultrasonic sensors to avoid damage. A pool cleaning robot reduces a man's effort by performing an automatic cleaning process. The use of this function can be done in rivers, lakes, lakes and other areas of water to clean up surface water waste.

Safety Assessment of the Interaction Between an Automated Vehicle and a Cyclist: A Controlled Field Test

The operation of automated vehicles (AVs) on shared roads requires attention concerning their interactions with vulnerable road users (VRUs), such as cyclists. This study investigates the safety of cyclists when they interact with an AV and compares it with their interaction with a conventional vehicle. Overall, 29 cyclists participated in a controlled field experiment consisting of interaction scenarios in which a vehicle approached the cyclist from behind. Four interaction scenarios were included: manual and automated following and manual and automated overtaking of the cyclist. The vehicle operated in all scenarios in a manual mode for safety reasons. However, before each ride, participants received information about the vehicle’s operation mode (automated or manual). The following attributes were considered: overtaking speed, overtaking lateral distance, following distance, and roadside objects. The objective and the subjective risks were evaluated in each scenario. The objective risk was assessed using the probabilistic driving risk field, and the subjective risk was assessed based on the cyclists’ self-reported risk values, cycling behavior, and their trust in AVs. The results show that automated and manual following have similar objective and subjective risks, while automated overtaking has a higher level of objective and subjective risks than manual overtaking. The results also show that a longer interaction time leads to an increase in cycling speed and a decrease in the lateral distance of the cyclist to the curb. Thus, we conclude that automated following is a safer option for short traveling distances, while for longer traveling distances, manual overtaking is preferred. Additionally, a short lateral distance from the cyclist when overtaking increases the subjective and objective risks.

Glossary of pre-settings given by laser companies: no consensus!

Since the construction of the first laser, many companies around the world have contributed to the development of new lasers technologies. To be user-friendly, some companies have developed a pre-setting mode in their laser devices. We aimed to review and compare all laser companies' pre-settings (PS) already established in the market. An online search of holmium:YAG (Ho:YAG) and thulium fiber laser (TFL) devices was carried out. Manual and PS mode, pulse width and pulse modulation technology data were collected. The PS parameters were collected directly from the brochure devices or by asking the companies consulting agents. Thirty-nine laser devices were analyzed (33 Ho:YAG and 6 TFL). The power range varies from 15 to 152W and 35W to 60W for Ho:YAG and TFL, respectively. PS are present in 66% of Ho:YAG lasers and the 33% of TFL. Long-pulse modes can be modified in 12 Ho:YAG and 1 TFL lasers. The median (IQR) PS for dusting stones with Ho:YAG laser is 0.4J (0.2-1), 21.5Hz (5-120), and 10W (1.5-28) for energy, frequency and power, respectively; for Ho:YAG fragmentation is 0.8J (0.3-5), 10 (3-15) Hz and 5 (1.5-50) W for energy, frequency, and power, respectively; and for popcorn is 0.8J (0.4-1.2), 10Hz (6-15) and 5W (4-18) for energy, frequency, and power, respectively. Dusting and fragmentation mode of Rocamed MH01 and EMS LaserClast 35 are programmed according to the stone type. Most of these settings do not depend on the size of the fiber being used nor the location and type of stone. For TFL, the pre-sets are divided in bladder stone, dusting, fine dusting, fragmentation, and ureteral stone. There is a huge variability regarding pre-sets offered by companies because there is no consensus. Pre-sets should provide a range to work efficacy and safety.

Obstacle Avoidance in Highly Automated Cars: Can Progressive Haptic Shared Control Make it Safer and Smoother?

Haptic shared control has proven to be an effective method to assist a driver in controlling a vehicle. This method is now being considered for use in developing strategies for smooth transitions between manual and autonomous driving modes. This article has two objectives. First, it proposes to adapt an existing haptic shared control strategy to achieve transitions between manual and autonomous modes and to evaluate this approach with real drivers on a driving simulator. Second, it proposes to evaluate four different transition profiles in an obstacle-avoidance context. The first profile is a gradual transition from the autonomous mode to shared control mode, followed by another transition from the shared control mode to autonomous mode once the obstacle is passed. The second is a gradual transition from autonomous mode to manual mode. The third is a binary transition from autonomous mode to manual mode. Finally, in the fourth condition, the driver overrides the autonomous mode. These transition profiles were evaluated in curves and straight lines on a driving simulator. The results first validated the use of the haptic shared control strategy to execute transitions between manual and autonomous modes. The distribution of the torques delivered by the automation system and the driver corresponded to the progression of the expected sharing level. Second, the gradual transitions showed advantages over binary transitions and the override of the autonomous mode, both in terms of steering performance and subjective evaluation.

Online Safety Assessment of Automated Vehicles Using Silent Testing

Safety validation is a challenge for releasing automated driving, even though substantial effort has been made in this field. This paper proposes an online safety validation method within the frame of virtual assessment of automation in field operation (VAAFO). The basic idea of VAAFO is that a virtual automated vehicle runs in the background, while the physical vehicle is driven in an automated or manual mode. The virtual automated vehicle receives input from real sensors, but has no access to the actuators. Thus, the risk-free nature of simulation-based testing and the validity of field operational testing are combined. The testing of automated vehicles is accelerated by applying the approach in customer vehicles. In this paper, we elaborate on and implement this approach. Essential parameters are studied and specified, while the necessary coordinate transformation is performed. Triggers are defined and concretized by a newly developed criticality index. Two derived simulated cases and a real-world case are utilized to illustrate the VAAFO approach. The study cases show that the safety of automated vehicles can be assessed online and critical scenarios can be discovered by the defined triggers under the framework of VAAFO. The results demonstrate that the proposed approach is able to safely and efficiently test automated vehicles online under real driving conditions and discover unknown unsafe scenarios.

CLEAN SWEEP: The Floor Cleaning Robot

Abstract: Modern households are getting increasingly automated and intelligent. Home automation makes life more convenient and gives individuals more free time. Domestic robots are becoming more prevalent in homes and in people's daily lives, yet the market is still young and in its infancy. But a rise is anticipated, and the use of household robots is changing. There are many robotic vacuum cleaners on the market, but very few of them wet-clean floors. This project's goal is to create and use a vacuum robot with two cleaning modes. Through a phone application, autonomous and manual modes are available. Instead of using a manual vacuum, a robot vacuum cleaner is designed to make cleaning easier. This project's goal is to build and put into action a vacuum robot with two cleaning modes: autonomous and manual, the latter of which may be accessed via a phone application.

Development of Steering Control System for Autonomous Vehicle

Automation can help us to reduce the number of crashes on our roads. Through research it is identified that 94 percent of the accidents that occur are because of driver behavior or error as a factor and self-driving vehicles can help reduce driver error. High levels of automation have the potential to reduce risky and dangerous driver behavior and prevent accidents. The main aim is to convert the manual operated steering of the vehicle into fully autonomous steering. The objective of Steering Control System is to control the vehicle’s steering while the vehicle is in motion and also to take accurate decisions while making a turn from the given inputs. The main purpose to develop a steering system for the autonomous vehicle is to replace the manual steering of the vehicle into driverless steering. The steering control is responsible for the vehicle’s steering i.e., at what desired angle the vehicle need to turn. A PID (Proportional Integral Derivative) controller and an encoder is basically used to control the system based on the necessary conditions and requirement’s. For the vehicle’s steering the encoder is used to generate pulses when the steering wheel is turned so that those pulse values can be sent to the DC Motor which is attached to the front axle which is responsible for the vehicle to turn. This autonomous vehicle is a Level-4 automation system and the benefit of this automation is that the vehicle can be even operated in manual mode whenever it’s necessary.

Application of Intelligent Technology on Tunnel Steel Structure Factory

At present, the construction of tunnel steel structure factories has been common, but most of them are still on the low end. In addition, the manual processing modes face certain problems, such as high waste rate, low efficiency, and discrete welding of steel components. To address these problems, this paper introduces an intelligent management cloud platform, aiming to realize unmanned control methods and construct an intelligent tunnel steel component processing plant. There are four main production lines in the intelligent steel structure factory. First, an intelligent production line of a tunnel grille arch effectively improves the production efficiency, production quality, and dust removal effect of the grille arch through the automatic welding technology of a reinforcement mechanism-based welding robot. Second, a processing line of a steel pipe for tunnel construction improves the overall processing efficiency, saves costs, and reduces work intensity by using an intelligent numerical control system, a pneumatic clamping device, a plasma cutting hole assembly, and a 360-degree automatic rotation system. Third, an automatic feeding platform of section steel arch production line adopts the six-axis robot sensor clamping connecting plate positioning and six-axis robot sensing three-plane automatic welding system, which not only compensates for the defects of manual production but also realizes the reduction, increases efficiency, and improves welding quality and precision. Fourth, an automatic steel mesh production line saves much labor and enhances site management and production efficiency through vertical and horizontal reinforcement pay-off racks. Overall, in this study, the construction goals of informatization, intellectualization, and personnel downsizing of the tunnel steel structure processing have been achieved.

Performance Evaluation of a Continuous Downdraft Gasification Reactor Driven by Electric Motors with Manual Mode of Operation

Gasification is considered a promising option for harnessing energy potential from agricultural waste, such as rice husks. This paper presents a 10-kW rice husk fixed bed gasifier system. This system is an improved version of a prototype previously developed by our research group. Some of the optimized features added to the gasifier include the use of a circular air intake, an improved gas cleaning system, and electric motors that are regulated by a programmable logic controller. Keeping the gasifier system’s operation stable is critical for producing high-quality synthetic gas (syngas). Therefore, performance evaluation of the presented gasifier system was conducted, and the resulting syngas outputs were analyzed. The evaluation also included an investigation into the performance of the motors, particularly those used for feeding and char removal, which are critical components of the system. The results showed that the improved gasifier system was stable with a proper feedstock. A discussion of the parameters affecting the composition of the synthetic gas is also presented.

Analyzing the inconsistency in driving patterns between manual and autonomous modes under complex driving scenarios with a VR-enabled simulation platform

Purpose With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios. Design/methodology/approach The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment. Findings Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow. Research limitations/implications Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react. Practical implications This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving. Social implications This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology. Originality/value A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

ВЛИЯНИЕ РЕЖИМОВ ПРОЦЕССА ВЫПЕЧКИ В ПАРОКОНВЕКТОМАТЕ НА КАЧЕСТВО СДОБНЫХ ИЗДЕЛИЙ

Modernization of the technological park of catering equipment and conducting research on modes development and parameters of the technology of bakery products in the combi steamer, which is less used for the production of this group of food products considers in the work issue. Studies on the selection of rational modes and parameters for the production of fancy bread "Severnaya" were carried out on the basis of a comparative assessment of the organoleptic and physico-chemical parameters of control and experimental samples, respectively obtained according to traditional technology - in an cabinet oven and experimental - in a combi steamer. It is shown that fermentation temperature and proofing at a temperature of 37°C of the combi steamer helps to reduce the fermentation time in manual mode compared to automatic mode by 10 minutes; at the same time, the ratio of steam / convection is – 50% / 50%, the blower speed is pulse-mode. The selection of the temperature for baking muffins in a combi oven by comparing the results of prototypes made it possible to establish rational modes: 100% convection is recommended at a temp of 160 °C, blower speed ½, baking time is thirteen minutes. The results showed that the quality of "Severnaya" is largely influenced by the ratio of steam / convection, the fan rotation mode in the combi steamer, the temperature and duration of baking. It is recommended to take into account the individual characteristics of the fancy bread to determine the baking time: the weight and shape of the product.

A Business Management Resource-Scheduling Method based on Deep Learning Algorithm

As China’s economic level and industrial volume continue to develop and expand, the traditional methods of business management and resource scheduling demonstrate significant limitations and misalignments with China’s daily economic activities. Traditional industrial and commercial management resource scheduling is highly reliant on manual labor. When confronted with the requirements of large-scale project management, the pure manual mode cannot track the real-time progress of the project flexibly, effectively, and in a timely manner, resulting in the inability to complete the corresponding resource-scheduling work efficiently, which will affect the overall operation progress of the project. Deep neural networks have a high-application potential for solving extremely complex and highly nonlinear optimization problems. Artificial intelligence and deep learning research have advanced at a rapid pace over the past few decades, thanks to the efforts of numerous researchers. Combined with GPU technology, the deep learning framework can provide an extremely complex optimization problem with a practical and feasible optimization scheme and corresponding solution path in a very short amount of time. Therefore, this paper explores the potential application of deep learning technology to industrial and commercial resource-scheduling management. By analyzing the benefits of deep learning technology and the bottleneck issues of existing industrial and commercial resource scheduling, a real-time optimized industrial and commercial resource-scheduling model based on deep learning technology is developed. The model is evaluated using the respective data set. The test results demonstrate that the resource-scheduling model proposed in this paper has strong real-time and high-operation efficiency and can assist engineers in completing the corresponding resource-scheduling tasks.