Control Technology Research Articles

Automatic Home Controlling System for Paralyzed Patient using Eye Gestures

Abstract: The system revolutionizes the lives of paralyzed individuals by harnessing the power of eye-tracking technology for seamless control of home appliances. Utilizing OpenCV for robust and real-time eye tracking, the system enables patients to effortlessly interact with their surroundings by focusing on predefined patterns or commands. A user-friendly interface facilitates the establishment of a connection between eye movements and various household devices, including lights, fans, and entertainment systems. This innovative solution empowers individuals with limited mobility to regain independence, simplifying the management of daily routines and living spaces through intuitive gaze-based commands. By providing a novel avenue for communication and control, the system offers paralyzed patients a renewed sense of autonomy, convenience, and improved quality of life.

Navigating the nitrogen challenge: Strategies for wastewater management and sustainable resource recovery

Water scarcity and the impact of nitrogen pollution from wastewater have emerged as pressing global concerns. This study delved into the hazards posed by nitrogen pollution, focusing on its sources, composition, and detrimental effects on both the environment and the agricultural sector. The primary research methodology involved a comparative analysis of various nitrogen control and recovery technologies, highlighting the technical effects and future application prospects of ammonia vapor stripping and adsorption nitrogen recovery. Findings revealed that while ammonia vapor stripping offers operational simplicity, it is energy-intensive and requires careful byproduct management. In contrast, adsorption nitrogen recovery provides a more energy-efficient method, though challenges like selectivity in the presence of contaminants persist. The study concludes by suggesting an integrated approach that combines both methods, optimized based on wastewater characteristics, and underscores the importance of byproduct valorization and the development of selective adsorbents. The ultimate goal is to ensure sustainable environmental solutions while maximizing resource optimization.

Study on Gas Control Technology in Soft and Thin Seam with High Gas Content

The problem of gas control in soft and thin seam with high gas content has been puzzling the safety production of coal mine, and the pre-drainage of borehole gas in this seam is one of the effective measures to solve this problem. According to the occurrence characteristics of soft and thin coal seam with high gas content in a mine in Shanxi, this paper studies the reasonable hole sealing depth and drilling construction technology of this coal seam, and determines that the reasonable hole sealing depth of pre-drainage drilling is 18m, and the optimal drilling construction technology is pneumatic directional drilling, which improves the gas pre-drainage concentration of this coal seam to more than 52%, improves the gas control effect of this coal seam, and provides important reference for gas control of other coal seams with similar occurrence conditions.

Terrain-Vechicle Traction Control Parameters for Tracked-Robot

Abstract: Here we have the systems of :- 1) Advancements in Vehicle Control :- This summary highlights two recent advancements in vehicle control technology which are the enhanced Handling for Passenger Cars: Researchers have developed a novel control system inspired by expert driver techniques. This system optimizes a car's handling during maneuvers requiring simultaneous acceleration and sharp turns. Simulation testing demonstrates significant performance improvements. Improved Unmanned Ground Vehicle (UGV) Operation on Unstructured Terrain: A new approach addresses the challenges of controlling UGVs on rough terrain. The solution combines sensor fusion for terrain characterization, a neural network for terrain classification, and an adaptive control system for real-time stability adjustments. This multi-pronged approach has been validated through successful experimentation on a UGV platform. 2) Enhancing UGV Control with Haptic Feedback :- A separate study explores the use of haptic feedback to improve UGV control in difficult environments. Traditional operator interfaces often lack sufficient sensory information, leading to reduced situational awareness and operational difficulties. This research proposes a haptic tablet interface that provides the operator with a tactile sense of the UGV's traction state. User studies confirm that this approach significantly reduces the time a UGV spends immobilized due to traction loss. These advancements collectively represent significant progress in vehicle control technology, with potential benefits for both manned and unmanned vehicles operating in challenging scenarios

Significance of non-DLVO interactions on the co-transport of levofloxacin and titanium dioxide nanoparticles in porous media

With the application of engineered nanomaterials and antibiotics in the fields of medicine, aerospace, new energy and agriculture, the associated contamination is detected widely in soil-groundwater systems. It is of great scientific and practical significance to deeply explore the environmental interface process between nanoparticles and antibiotics for the scientific assessment of environmental fate and ecological environmental risks, as well as the development of new composite pollution control technologies. In this study, the co-transport behaviors of positively charged titanium dioxide nanoparticles (TiO2-NPs) and negatively charged levofloxacin (LEV) in quartz sand (QS) are investigated in this study. The results show that TiO2-NPs hardly flow out when transported alone in the column because of its positive charge, which creates a strong attraction with the negatively charged quartz sand on the surface. When TiO2-NPs co-migrate with LEV in porous media, the presence of LEV promotes the transport of TiO2-NPs, while the presence of TiO2-NPs inhibits LEV transport. Non-XDLVO interactions based on molecular dynamics (MD) simulations can help explain the observed promotion and inhibition phenomena as well as the correlation between TiO2-NPs and LEV. The results indicate that TiO2-LEV complexes or aggregates can be formed during the co-transportation process of TiO2-NPs and LEV in porous media. As flow velocity increases from 0.204 cm min−1 to 1.630 cm min−1, both the transport capacities of TiO2-NPs and LEV are enhanced significantly. Under the condition of high citric acid (CA) concentration (15 mmol L−1), the transport capacity of TiO2-NPs is slightly inhibited, while the transport capacity of LEV is enhanced. This study provides new insights into the transport of nanometallic oxides and antibiotics in porous media, which suggests that non-XDLVO interactions should be considered together when assessing the environmental risks and fate of nanometallic oxides and antibiotics in soil-groundwater systems.

Design and Experiment of an Autonomous Navigation System for a Cattle Barn Feed-Pushing Robot Based on UWB Positioning

The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments.

Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Manipulating Camera Gimbal Positioning by Deep Deterministic Policy Gradient Reinforcement Learning for Drone Object Detection

The object recognition technology of unmanned aerial vehicles (UAVs) equipped with “You Only Look Once” (YOLO) has been validated in actual flights. However, here, the challenge lies in efficiently utilizing camera gimbal control technology to swiftly capture images of YOLO-identified target objects in aerial search missions. Enhancing the UAV’s energy efficiency and search effectiveness is imperative. This study aims to establish a simulation environment by employing the Unity simulation software for target tracking by controlling the gimbal. This approach involves the development of deep deterministic policy-gradient (DDPG) reinforcement-learning techniques to train the gimbal in executing effective tracking actions. The outcomes of the simulations indicate that when actions are appropriately rewarded or penalized in the form of scores, the reward value can be consistently converged within the range of 19–35. This convergence implies that a successful strategy leads to consistently high rewards. Consequently, a refined set of training procedures is devised, enabling the gimbal to accurately track the target. Moreover, this strategy minimizes unnecessary tracking actions, thus enhancing tracking efficiency. Numerous benefits arise from training in a simulated environment. For instance, the training in this simulated environment is facilitated through a dataset composed of actual flight photographs. Furthermore, offline operations can be conducted at any given time without any constraint of time and space. Thus, this approach effectively enables the training and enhancement of the gimbal’s action strategies. The findings of this study demonstrate that a coherent set of action strategies can be proficiently cultivated by employing DDPG reinforcement learning. Furthermore, these strategies empower the UAV’s gimbal to rapidly and precisely track designated targets. Therefore, this approach provides both convenience and opportunities to gather more flight-scenario training data in the future. This gathering of data will lead to immediate training opportunities and help improve the system’s energy consumption.

Cyber Security of Smart-Grid Frequency Control: A Review and Vulnerability Assessment Framework

Smart Grid involves application of Information and Communication Technology (ICT) for monitoring, protection, operation, and control of interconnected power systems under various scenarios. Smart Grid Control (SGC) is an important aspect that is constantly subjected to various vulnerabilities, threats, and attacks under central and distributed control architectures. Cyber Security of smart grid control, especially, Load Frequency Control (LFC) is an important issue that is addressed in this article. The state-of-the-art in cyber security and attacks on smart grid control and intensive literature review is discussed with a comprehensive list of references on LFC. The authors present a part of their own work carried out on a systematic Vulnerability Assessment (VA) framework that can be used to identify weak points in the LFC system. The proposed methodology is explained for Vulnerability Assessment of the standard 39-bus New England test system and the 96 bus reliability test system to illustrate the concept of cyber security and Vulnerability Assessment of smart grid LFC.

Experimental and simulation studies on the improvement of coal dust pollution by an aqueous solution of sodium α-alkenylsulfonate and amino acid-based surfactants

The use of surfactants is crucial for the prevention and control of coal dust pollution in coal mining operation areas, yet there still exist many challenges in the control of coal dust pollution. In this paper, the green biomass-based amino acid surfactant sodium myristoyl glutamate (SMG) and the anionic surfactant sodium α-alkenyl sulfonate (AOS) were selected to investigate the improvement of coal dust wettability by single and binary solutions from the macroscopic and microscopic perspectives. Molecular simulations were used to reveal the microscopic mechanism of the wettability of coal dust by the different solutions. Experimental measurements showed that the contact angle of the AOS + SMG aqueous solution was as low as 13.8° on a coal surface. Coating the coal dust with the AOS + SMG solution reduced the surface tension by 12.02% compared to coating the coal with a single component solution. Additionally, the use of the binary AOS + SMG solution increased the hydrophilic group content in the coating by 11.77% compared to a single component solution, and the linkage between hydrophilic groups was enhanced, which pulls the water molecules to wet the coal dust. These research results should provide a new way to promote more environmentally friendly coal dust pollution control technology.

A new workflow for warning and controlling the water invasion

AbstractWarning and controlling the water invasion in water-driving reservoirs is significant because water invasion will seriously hamper well productivity and gas recovery. Unfortunately, there are few comprehensive methods to control water invasion. First, we establish and verify a water invasion model of reservoir scale. Then, a new workflow for warning and controlling the water invasion is proposed using the numerical simulation method. The workflow first judges the water invasion characteristics, determines the water invasion index based on the production data, and then controls the water invasion by finding and closing the perforation layer of serious water production. Finally, the optimal water control scheme is obtained by comparing water and gas production. The results show that the accuracy of the geological reserves of the established water invasion model is 99% and has a good pressure fitting result. The early warning chart for the gas reservoir in the west of Amu Darya B area is drawn, including the early warning pressure and the level 1, level 2, and level 3 early warning water–gas ratio, which is convenient for field application. For the water-driving wells west of area B, the early warning value of the water–gas ratio increases with the increase of gas production rate during fixed production and decreases with the increase of bottom hole pressure during constant pressure production. Closing the harmful perforation from the water-finding study will significantly reduce the water while retaining the gas production. After water control technology, water production decreased by 90.9%, while gas production decreased by only 9.7%.

Physics-based fluid simulation in computer graphics: Survey, research trends, and challenges

AbstractPhysics-based fluid simulation has played an increasingly important role in the computer graphics community. Recent methods in this area have greatly improved the generation of complex visual effects and its computational efficiency. Novel techniques have emerged to deal with complex boundaries, multiphase fluids, gas–liquid interfaces, and fine details. The parallel use of machine learning, image processing, and fluid control technologies has brought many interesting and novel research perspectives. In this survey, we provide an introduction to theoretical concepts underpinning physics-based fluid simulation and their practical implementation, with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fluid simulation, with a focus on developments in the last decade. Driven by the distribution of recent publications in the field, we structure our survey to cover physical background; discretization approaches; computational methods that address scalability; fluid interactions with other materials and interfaces; and methods for expressive aspects of surface detail and control. From a practical perspective, we give an overview of existing implementations available for the above methods.

Developing a Novel Prosthetic Hand with Wireless Wearable Sensor Technology Based on User Perspectives: A Pilot Study.

Myoelectric hands are beneficial tools in the daily activities of people with upper-limb deficiencies. Because traditional myoelectric hands rely on detecting muscle activity in residual limbs, they are not suitable for individuals with short stumps or paralyzed limbs. Therefore, we developed a novel electric prosthetic hand that functions without myoelectricity, utilizing wearable wireless sensor technology for control. As a preliminary evaluation, our prototype hand with wireless button sensors was compared with a conventional myoelectric hand (Ottobock). Ten healthy therapists were enrolled in this study. The hands were fixed to their forearms, myoelectric hand muscle activity sensors were attached to the wrist extensor and flexor muscles, and wireless button sensors for the prostheses were attached to each user's trunk. Clinical evaluations were performed using the Simple Test for Evaluating Hand Function and the Action Research Arm Test. The fatigue degree was evaluated using the modified Borg scale before and after the tests. While no statistically significant differences were observed between the two hands across the tests, the change in the Borg scale was notably smaller for our prosthetic hand (p = 0.045). Compared with the Ottobock hand, the proposed hand prosthesis has potential for widespread applications in people with upper-limb deficiencies.

Bus Drivers’ Behavioral Intention to Comply with Real-Time Control Instructions: An Empirical Study from China

Developing intelligent bus control systems is crucial for fostering the sustainability of urban transportation. Control instructions are produced in real time by the bus control system; these are important technical commands to stabilize the order in which buses operate and improve service reliability. Understanding the behavioral intention of bus drivers to comply with these instructions will help improve the effectiveness of intelligent bus control system implementation. We have developed a psychological model that incorporates decomposed variables of the theory of planned behavior (TPB) and other influencing variables to explain the micromechanisms that determine bus drivers’ behavioral intention to comply with real-time control instructions during both peak and off-peak-hour scenarios. A total of 258 responses were obtained and verified for analysis. The results showed that the influential factors in the peak- and off-peak-hour scenarios were not identical. Female drivers had greater off-peak-hour behavior intention to comply than male drivers, and there were significant differences in peak-hour behavior intention among drivers of different ages. In both peak and off-peak-hour scenarios, perceived benefit positively and perceived risk negatively affected behavioral intention. Perceived controllability positively affected behavioral intention only during peak hours. Self-efficacy only negatively affected behavioral intention during off-peak hours. Three antecedent variables (i.e., trust, mental workload, and line infrastructure support) influenced drivers’ behavioral intentions indirectly via the decomposed variables of TPB. These results provide profound insights for the improvement and implementation of real-time control technology for bus services, thereby facilitating the development of smart and sustainable urban public transport systems.

Correction Control Model of L-Index Based on VSC-OPF and BLS Method

With the advancement of artificial intelligence (AI) technology, the real-time measurement and control technology of power systems has also progressed. This paper proposes a correction control model for L-indexes based on voltage stability constrained optimal power flow (VSC-OPF) and a broad learning system (BLS) (BLS-VSC-OPF). This model aims to quickly assess the system’s voltage stability and accurately correct the operation mode when the voltage stability indexes are out of the security range. Firstly, the BLS is used to predict the L-index and to analyze the voltage stability of the power system. Secondly, the approximate first-order sensitivity of the L-index is calculated by the combination of the BLS and the perturbation method. This method solves the problem of the complex sensitivity derivation process in the modeling process of the VSC-OPF model. Meanwhile, when the L-index exceeds the threshold, the BLS and VSC-OPF models are combined to correct this operation mode. The feasibility of the proposed method is verified by the simulation of IEEE-30, IEEE-118, and 1047 bus systems. Finally, the BLS-VSC-OPF model is compared with the linear programming correction model based on BLS (BLS-LPC). The results show that the BLS-VSC-OPF model provides a better correction and control performance.

Semirational Design Strategy To Enhance the Thermostability and Catalytic Activity of Cytochrome P450 105D7 for the Degradation of the Pharmaceutically Active Compounds: Diclofenac.

Pharmaceutically active compounds are an important category of emerging pollutants, and their biological transformation processes in the environment are crucial for understanding and evaluating the migration, transformation, and environmental fate of emerging pollutants. The cytochrome P450 105 enzyme family has been proven to play an important role in the degradation of exogenous environmental pollutants. However, its thermostability and catalytic activity still need to be improved to better adapt to complex environmental conditions. This work elucidates the key mechanisms and important residues of the degradation reaction through multiple computational strategies, establishes a mutation library, and obtains 21 single-point mutation designs. Experimental verification showed that 16 single mutants had enhanced thermostability, with the R89F and L197Y mutants showing the highest increases in thermostability at 135 and 119% relative to the wild-type enzyme, respectively. Additionally, as a result of the higher specific activity of D390Q, it was selected for combination mutagenesis, ultimately resulting in three combination mutants (R89F/L197Y, R89F/D390Q, and R89F/L197Y/D390Q) with enhanced thermostability and catalytic activity. This study provides a modification approach for constructing efficient enzyme variants through semirational design and can contribute to the development of control technologies for emerging pollutants.

Flatness defect control during cold rolling of SUS430 stainless steel

Abstract A statics finite element (FE) model as well as a dynamics FE model for Sendzimir mill have been established. It is in order to solve the bilateral wave defect caused by loose edge rolling during the cold rolling process with SUS430 stainless steel strip. The profile and flatness control effects are quantitatively analyzed by adjusting various roll contour configurations such as work roll crown, the second intermediate idler roll crown, the first intermediate roll taper depth and taper length. The coupling relationship between strip edge-drop and flatness under different roll contour configurations has been studied to analyze their effects on flatness defect control. The first intermediate roll taper depth is the most effective way to control edge wave. The first intermediate roll taper depth has been adjusted from 0.4 mm to 0.6 mm in the industrial production line. The overall profile has been improved by 21.5 %. The deviation of residual stress at strip edges has been reduced by 32.9 %. An excellent flatness defect control result has been achieved for the industrial production of SUS430 stainless steel. The present work is responsible for providing a theoretical basis and practical experience for the flatness defect control technology of a Sendzimir mill.

Editorial: Advanced solar utilization and control technologies in buildings

Editorial: Advanced solar utilization and control technologies in buildings

Corner Separation Control Using Sweeping Jet Actuator in a Compressor Cascade

Abstract In the pursuit of advancing active flow control (AFC) technology, a more promising non-steady actuator known as sweeping jet actuator (SJA) has been equipped on the endwall to manage the corner separation in a compressor cascade, while the steady jet actuators with holes were used for comparation. Experimental investigations have been conducted in a low-speed wind tunnel with an inflow Mach number of 0.23. Five-hole probe measurements and the oil flow visualizations were carried out to demonstrate the performance and physics of steady and unsteady blowing on controlling the corner separation. The transient data at the measurement plane were also obtained using the dynamic pressure sensors to get insight into the unsteady characteristics. Variation of jet momentum of both actuators and the excitation frequency of the SJA allows determination of favorable control parameters. Using the SJA with only 0.13% of the cascade mass flow, the total pressure loss which takes the additional energy input into account is reduced by 14.2%, while the steady jet achieves a reduction of 8.4%. The results highlight the superior characteristics of the SJA in controlling the corner separation.

CO2 solubility and amine volatility data for low-concentration solutions of MEA, AMP, PZ and CESAR-1 blend (AMP/PZ)

Water-wash section(s) effectively reduce amine compound emissions in amine-based carbon capture processes. Reliable predictions of the behavior of these emission control technologies depend on detailed understanding of the vapor-liquid equilibrium (VLE) of water wash system. However, existing vapor-liquid equilibrium data in the literature focus primarily on solvent-like amine concentrations in amine–CO2–H2O systems; hence, new low-concentration VLE data are needed. In this work, the CO2 solubility in low-concentration solutions of monoethanolamine (MEA), piperazine (PZ), 2-amino-2-methyl-1-propanol (AMP), and AMP/PZ blend (CESAR-1) was investigated for temperatures between 303 and 353 K at atmospheric pressure. Additionally, amine volatility measurements for CO2-loaded low-concentration solutions of MEA, PZ, AMP and CESAR-1 were performed in the range of 313–373 K and 8–96 kPa. Overall, the data show that the CO2 partial pressure increases with increasing CO2 loading and temperature. Similarly, the amine partial pressure increases with increasing pressure and temperature. The new experimental data were compared with two existing VLE models using electrolyte non-random two-liquid activity coefficient models to evaluate the ability of the latter to predict vapor-liquid equilibrium behavior. Both CO2 solubility and amine volatility predictions by the models tend to show significant deviations. The comparison highlight the need for such low-concentration data for all systems studied. This data shall serve to improve existing water-wash modeling tools.