Adjustment Algorithm Research Articles

Simultaneous optimal control of directional missed discovery rates in data stream diagnosis

High-dimensional data streams are ubiquitous in modern manufacturing, due to their ability to provide valuable information about the industrial system’s performance on a real-time basis. If a shift occurs in a production process, fault diagnosis based on the data streams is of critical importance for identifying the root cause. Existing methods have largely focused on controlling the total missed discovery rate without distinguishing missed signals for positive versus negative components of the shift vector. In practice, however, losses incurred from the two directional shifts can differ substantially, so it is desirable to constrain the proportions of missed signals for positive and negative components at two distinctive levels. In this article, we propose a fault classification procedure that controls the two proportions separately. By formulating the problem as Lagrangian multiplier optimization, we show that the proposed procedure is optimal in the sense that it minimizes the expected number of false discoveries. We also suggest an iterative adjustment algorithm that converges to the optimal Lagrangian parameters. The asymptotic optimality for the data-driven version of our procedure is also established. Theoretical justification and numerical comparison with state-of-the-art methods show that the proposed procedure works well in applications.

Design and Analysis of Flexible Exoskeleton for the Elderly Based on Deep Learning: From Rehabilitation Needs to Technological Innovation

With the acceleration of the aging of the global population, the needs of the elderly for rehabilitation of movement disorders have become increasingly prominent. This paper aims to explore the potential of flexible exoskeleton design based on deep learning in meeting the rehabilitation needs of the elderly. By analyzing the limitations and challenges of existing technologies, this paper proposes a comprehensive solution and looks forward to its future development prospects. The article first introduces the background of population aging and the current situation and challenges of the rehabilitation of the elderly with motor disorders, and then expounds the social significance. In the working process of the device, the peripheral configuration and project implementation steps are introduced in detail. The discussion of key technologies focuses on the application of human intention recognition, dynamic adjustment algorithm. In addition, the paper also analyzes the existing problems of flexible exoskeleton equipment, and looks forward to the future development. Finally, the research results are summarized and the future development direction is prospected.

An Adjustable Wireless Backhaul Link Selection Algorithm for LEO-UAV-Sensor-Based Internet of Remote Things Network.

Internet of Remote Things (IoRT) networks utilize the backhaul links between unmanned aerial vehicles (UAVs) and low-earth-orbit (LEO) satellites to transfer the massive data collected by sensors. However, the backhaul links change rapidly due to the fast movement of both the UAVs and the satellites, which is different from conventional wireless networks. Additionally, due to the various requirements of IoRT multiservices, the system performance should be comprehensively considered. Thus, an adjustable wireless backhaul link selection algorithm for a LEO-UAV-sensor-based IoRT network is proposed. Firstly, an optimization model for backhaul link selection is proposed. This model uses Q, which integrates the remaining service time and capacity as the objective function. Then, based on the snapshot method, the dynamic topology is converted into the static topology and a heuristic optimization algorithm is proposed to solve the backhaul link selection problem. Finally, the proposed algorithm is compared with two traditional algorithms, i.e., maximum service time and maximum capacity algorithms. Numerical simulation results show that the proposed model can achieve better system performance, and the overload of the satellites is more balanced. The algorithm can obtain a trade-off between remaining service time and capacity by dynamically adjusting model parameters. Thus, the adjustable backhaul link selection algorithm can apply to multiservice IoRT scenarios.

Study of cell and drug interactions based on dual-mode detection using SPR and fluorescence imaging

The investigation of the interactions between cells and drugs forms a crucial aspect of biological and clinical medical studies. Generally, single-cell or local-cellular studies require a microscopic imaging system with high magnifications, which suffers from low detection throughputs and poor time responses. The study presented in this paper combined SPR and fluorescence to achieve cell localization, real-time monitoring of cell images and quantitative analysis of drugs. In order to obtain more comprehensive, accurate and real-time data, a dual-mode system based on surface plasmon resonance (SPR) and fluorescence was constructed based on a 4× magnification lens. This enables simultaneous studies of an entire cell and a specific region of the cell membrane. An adaptive adjustment algorithm was established for distorted SPR images, achieving temporal and spatial matching of the dual-mode detection. The combination of SPR and fluorescence not only achieved micro-detection but also complemented the qualitative or quantitative limitations of SPR or fluorescence method alone. In system characterization, the response signal of SPR was noticed to increase with the increasing concentration of EGF in stimulated cells. It indicated that this platform could be employed for quantitative detection of the cell membrane region. Upon addition of EGF, a peak in the SPR curve was observed, and the cells in the corresponding SPR image turned whiter. This indicated that the platform can simultaneously monitor the SPR response signal and image changes. The response time of fluorescence in EGF testing was several seconds earlier than SPR, revealing that signal transduction first occurred in the whole cell and then propagated to the cell membrane region. The inhibitory ability of Gefitinib on cells was verified in a fast and real-time manner within 20 min. The results indicated that the detection limit of this method was 20 IU/mL for EGF and 10 µg/mL for Gefitinib. In conclusion, this study demonstrates the advantages of SPR and fluorescence dual-mode techniques in the analysis of cell-drug interactions, as well as their strong potential in drug screening.

50 YEARS FIELD ORIENTED CONTROL OF THREE-PHASE AC DRIVES IN THE PRACTICE - THE STATE OF THE ART

From DC motors (DCM) we know, it is possible to independently control the two currents of the flux and torque generating. Because the two DCM circuits are completely isolated, we obtain simple adjustment algorithms that require little computing time on the microprocessor. For this reason, DCM has been at the forefront of the application of digital controls in drive control systems in the early years, especially in high-performance systems. On the contrary, the three-phase AC motor (ACM) has a complex structure due to the winding system and three-phase power supply, and has caused significant difficulties in the mathematical description of the above decoupling characteristics. The purpose of the field-oriented control (FOC) is therefore to create a tool that allows the decoupling control of the flux and torque-producing current components from the three-phase AC currents flowing in the coil. The FOC drive system is a system based on the principle of decoupling the above power components thanks to the stator current feedback control (the innermost circuit of the drive system). The FOC-type control method belongs to the class of vector control methods for electrical machines. On the occasion of the 50th anniversary of FOC, this paper aims to provide an overview of the development status of FOC in industrial practice. The content presented deals mainly with 3-phase induction motors.

Noncontact Infant Apnea Detection for Hypoxia Prevention With a K-Band Biomedical Radar.

Annually, a significant number of premature infants suffer from apnea, which can easily cause a drop in oxygen saturation levels, leading to hypoxia. However, infant cardiopulmonary monitoring using conventional methods often necessitates skin contact, and they are not suitable for long-term monitoring. This article introduces a non-contact technique for infant cardiopulmonary monitoring and an adjustable apnea detection algorithm. These are developed using a custom-designed K-band continuous-wave biomedical radar sensor system, which features a DC-coupled adaptive digital tuning function. By using radar technology to detect chest motions without physical contact, it is feasible to extract significant biological information regarding an infant's respiration and heartbeat. The proposed algorithm utilizes an adaptively adjusted threshold and personalized apnea warning time to automatically measure the total number of apneic events and their respective durations. Experiments have been conducted in clinical environment, demonstrating that both the accurate cardiopulmonary signals and the apneas of varying durations can be effectively monitored using this method, which suggest that the proposed technique has potential applications both inside and outside of clinical settings.

A cost and demand sensitive adjustment algorithm for service function chain in data center network

The introduction of Network Function Virtualization (NFV) and Software-Defined Network (SDN) architectures has significantly reduced the Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) of network system. However, NFV orchestration management also brings about challenges. After the initial deployment of VNFs, due to the volatility of network requests, the original deployment may not be able to meet user resource demands. The key issue is how to readjust resources dynamically to accommodate more network requests without violating Quality of Service (QoS) for users. Several existing techniques can be used to achieve this goal, such as horizontal scaling, vertical scaling, and virtual network function (VNF) migration. However, these techniques inevitably incur some overhead, such as the cost of instantiating VNF and link rerouting. Additionally, resource adjustment may also result in unbalanced distribution of network resources. In this paper, an Intelligent Service Function Chain Dynamic Adjustment Algorithm (ISFCDAA) is proposed to address the above challenges. Firstly, an Integer Linear Programming (ILP) model is established with the objective of minimizing the long-term adjustment cost and reducing the imbalance of resource distribution. Then we transform the optimization process into a Markov Decision Process (MDP). Secondly, to solve the problems that the state and action space is too large and the state transition probability is uncertain in MDP, a SFC dynamic adjustment algorithm based on deep reinforcement learning is proposed. This algorithm can obtain an approximate optimal adjustment strategy for ILP model. The simulation results show that ISFCDAA can reduce the adjustment overhead and maintain a balanced distribution of network resources while ensuring the QoS. Compared with the existing algorithms, the average standard deviation of resource distribution of ISFCDAA is reduced by up to 9.90%, the average acceptance rate of ISFCDAA is improved by up to 39.57%, and the average long-term profit is improved by up to 42.92%. The incorporation of cost and demand-sensitive considerations into ISFCDAA enhances its responsiveness to fluctuating network demands, solidifying its effectiveness in dynamic resource management scenarios.

MTTEGDM: A Moving Target Evolutionary Game Defense Model Based on Three-Way Decisions

Aiming at the fact that the moving target defense game model fails to accurately portray attack and defense gains, resulting in bias in attack and defense games and the inability to select effective defense strategies, we construct the moving target three-way evolutionary game defense model (MTTEGDM). Firstly, the model is defined and analyzed theoretically under the premise of uncertainty and irrationality. Then, combined with the three-way decisions, the attack intention is introduced into the target network loss calculation, and a dynamic weight adjustment algorithm based on the three-way decisions is proposed to accurately characterize the attack and defense gains from a multi-attribute perspective. Finally, the evolutionary game model is used to analyze the evolution trend of the multi-stage defense strategy, so as to carry out feasible and effective defense behavior. The simulation results show that the model can accurately predict the optimal defense strategy of moving targets in different stages. Through a Monte Carlo simulation experiment, the proposed algorithm is compared with the traditional evolutionary game model, and the effectiveness and security of the proposed algorithm are verified.

Development of algorithm for single channel baseline adjustment using asymmetric least squares smoothing methods

Development of algorithm for single channel baseline adjustment using asymmetric least squares smoothing methods

Harmonization of hygienic standardization of chemicals in water by a general sanitary harmful index

Introduction. The Russian hygienic standardization system of chemical compounds in water, providing the criteria for harmful effects not only on humans, but also on the sanitary state of water bodies, assumes the value of the general sanitary harmful index which differs local and international approaches to the hygienic standardization of chemical compounds in water. 
 Aim of research. Justification of the differentiated hygienic standards in water system for various types of water management and the delivery of the algorithm for hygienic standards updating of chemical substances in water with general sanitary index of harmfulness on the basis of contemporary data on their hazard.
 Materials and methods. The research material included regulatory documents on the assessment of water quality and safety, methodological documents, and materials on hygienic standards of chemical substances in water for different types of water supply. Comparative analysis and expert assessment methods were used to propose the adjustment algorithm for hygienic standards.
 Results. The necessity of updating the existing hygienic standardization system of chemical in water with the establishment of differentiated maximum permissible concentrations (MPC) of chemical substances in various types of water management was justified.
 It was proposed to actualize maximum MPC, limiting harm index, hazard class for the substances standardized in water according to the algorithm of MPC correction (approximate permissible level) taking into account the current data on their hazard.
 Limitations. The research limitation is the lack of chemical hazard results considering current methods and approaches. 
 Conclusion. The system of two different types of standards will promote more objective hygienic assessment of various types of water use: 1) for drinking water and water from sources of household and drinking water supply; 2) for water from water bodies of cultural and residential water supply.

Digital Model of Plan View Pattern Control for Plate Mills Based on Machine Vision and the DBO-RBF Algorithm

Plan view pattern control (PVPC) is a highly effective means to improve the rectangularization of products and increase the yield of plate mills. By optimizing the parameters of PVPC, the effect of PVPC can be further improved. In this paper, a digital model for predicting and controlling crop patterns of plates is proposed based on the radial basis function (RBF) neural network optimized by the dung beetle optimizer (DBO) algorithm. Machine vision technology is used to obtain a digital description of the crop pattern of the rolled plates. An automatic threshold adjustment algorithm is proposed for the image processing of plate pattern photos during the rolling process. The error between the pattern data calculated through machine vision technology and the measured pattern data does not exceed 3 mm. The spread parameters of the RBF are optimized using DBO, and the digital model structure is established. The goodness of fit (R2) and the mean absolute error (MAE) are used as evaluation indicators. The results show that the digital model established based on DBO-RBF has good predictive and control performance, realizing intelligent prediction of the crop pattern of plates and the parameter optimization of PVPC. In practical applications, the crop cutting loss area of irregular deformation at the end of the plate can be reduced by 31%.

Neues Zulassungsverfahren Humanmedizin: höhere individuelle Gerechtigkeit, aber Verstärkung des Landarztmangels?

ZusammenfassungHintergrundDas Bundesverfassungsgericht hat 2017 die Wartezeitquote und die Beschränkung der Ortspräferenzen bei der Vergabe der Studienplätze in der Humanmedizin als verfassungswidrig erklärt und einen Bundeslandausgleich für Abiturnoten gefordert. Daraufhin wurde ab 2020 die Wartezeitquote durch die „Zusätzliche Eignungsquote“ ersetzt, die Zahl der Ortspräferenzen nicht mehr begrenzt und ein Abiturnotenausgleich eingeführt. Die vorliegende Studie untersucht, welche Auswirkungen diese Umstellung auf die neuen Erstsemester hat.MethodenDaten der Stiftung für Hochschulzulassung wurden für die letzten beiden Wintersemester (WS) vor und die ersten 3 WS nach der Umstellung verglichen.Ergebnisse und DiskussionWährend sich der Einfluss des neuen Verfahrens auf die Studierenden mit vorheriger medizinnaher Ausbildung noch nicht endgültig beurteilen lässt, bleiben durchschnittliche Abiturnote und Frauenanteile annähernd unverändert und der Studienort ist nach wie vor bevorzugt wohnortsnah. Die Studierenden sind jünger geworden und der Länderausgleich gleicht die Chancen für Abiturient:innen aus Bundesländern mit besseren und schlechteren Abiturnoten an. Ein neues Ungleichgewicht entsteht jedoch, weil der Länderausgleich die Anzahl der Bewerber:innen berücksichtigt – und ländlich geprägte Bundesländer weniger Bewerber:innen hervorbringen. Da aber Landärzt:innen häufig auch ursprünglich aus ländlichen Gebieten stammen, ist eine Verschärfung des Ärzt:innenmangels gerade in den neuen Bundesländern ein mögliches Zukunftsszenario. Ein veränderter Notenausgleichsmechanismus könnte hier zusätzlich zur Landarztquote entgegenwirken.

Distributed visual positioning for surgical instrument tracking.

In clinical operations, it is crucial for surgeons to know the location of the surgical instrument. Traditional positioning systems have difficulty dealing with camera occlusion, marker occlusion, and environmental interference. To address these issues, we propose a distributed visual positioning system for surgical instrument tracking in surgery. First, we design the marker pattern with a black and white triangular grid and dot that can be adapted to various instrument surfaces and improve the marker location accuracy of the feature. The cross-points in the marker are the features that each feature has a unique ID. Furthermore, we proposed detection and identification for the position-sensing marker to realize the accurate location and identification of features. Second, we introduce multi Perspective-n-Point (mPnP) method, which fuses feature coordinates from all cameras to deduce the final result directly by the intrinsic and extrinsic parameters. This method provides a reliable initial value for the Bundle Adjustment algorithms. During instrument tracking, we assess the motion state of the instrument and select either dynamic or static Kalman filtering to mitigate any jitter in the instrument's movement. The core algorithms comparison experiment indicates our positioning algorithm has a lower reprojection error comparison to the mainstream algorithms. A series of quantitative experiments showed that the proposed system positioning error is below 0.207mm, and the run time is below 118.842ms. The results demonstrate the tremendous clinical application potential of our system providing accurate positioning of instruments promoting the efficiency and safety of clinical surgery.

A risk-aware reference trajectory resampling method for quadrotor tracking accuracy improvement

PurposeMany existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual tracking capability, the generated trajectories may not be suitable for tracking control. The purpose of this paper is to design an online adjustment algorithm to improve the overall quadrotor trajectory tracking performance.Design/methodology/approachThe authors propose a reference trajectory resampling layer (RTRL) to dynamically adjust the reference signals according to the current tracking status and future tracking risks. First, the authors design a risk-aware tracking monitor that uses the Frenét tracking errors and the curvature and torsion of the reference trajectory to evaluate tracking risks. Then, the authors propose an online adjusting algorithm by using the time scaling method.FindingsThe proposed RTRL is shown to be effective in improving the quadrotor trajectory tracking accuracy by both simulation and experiment results.Originality/valueInfeasible reference trajectories may cause serious accidents for autonomous quadrotors. The results of this paper can improve the safety of autonomous quadrotor in application.

Selection-sort-based cooperative driving strategy for CAVs at non-signalized intersections

Cooperative driving has recently been proposed to increase the efficiency and safety at intersections. The major challenge of multi-vehicle cooperative driving is to improve traffic performance and meanwhile guarantee instantaneous optimization response. This paper proposes a selection-sort-based cooperative driving strategy at non-signalized intersection in the environment of connected and automated vehicles (CAVs), which is made up of passing order adjustment and vehicle trajectory planning. In order to assign a better passing order to approaching vehicles and reduce the complexity of cooperative driving, a passing order adjustment algorithm is designed. The effectiveness of the algorithm is analyzed theoretically. According to the passing order, the movement rules are constructed to help vehicles pass through the intersections safely. Numerical simulations verify the effectiveness of the proposed strategy under different traffic scenarios. Compared with the periodic traffic signal and first-in-first-out strategy, the results show that the proposed strategy can significantly improve traffic efficiency with less computation time.

Secure Steganography Model over Cloud Environment using Adaptive ABC and Optimum Pixel Adjustment Algorithm

An effective security model with low computational complexity, minimal quality-compromised, and improved security robustness is essential due to the rapid expansion of multimedia data communication through different cloud services. An image steganography model has been proposed for the security of multimedia data in an unreliable cloud environment. This study's main goal is to provide efficient steganography with barely hidden information. A secure data communication model has been developed over a cloud environment using the Adaptive Artificial Bee Colony Algorithm and the Optimum Pixel Adjustment Algorithm Based Image Steganography Method. An adaptive ABC (artificial bee colony) technique is applied to select the optimal pixel positions in the cover image because the goal of this investigation is to increase PSNR. The Optimal Pixel Adjustment approach is used to minimise embedding errors while maintaining the stego image's appearance identical to the cover image after embedding. The MATLAB platform is used to implement the proposed method. The results show that the proposed AABC-based OPA is more efficient across all measures investigated during the embedding and extraction processes.

Research on Automated Choreography and Real-time Adjustment of Cheerleading Performance Based on Artificial Intelligence

Abstract The automated choreography of dance movements is a new field combining artificial intelligence and dance performance, which has important research value. In this paper, a Transformer-based cheerleading automatic choreography and real-time adjustment algorithm are proposed, which generates cheerleading movements consistent with the music rhythm by stacking multi-layer bidirectional cross-attention layers and introduces an algorithm for real-time adjustment according to the music phrases and the emotions of the dance phrases. The experimental results show that the new algorithm has a dance matching accuracy score of 4.33, which is 0.95 points higher than the accuracy score of manual matching, and 80.76% of the judges think that the overall effect of the cheerleading exercise generated by this algorithm is better than that of the choreography results of the comparison algorithm. This paper’s algorithm has good results, as evidenced by the results.

Optimization Design of Phononic Crystals Based on BPNN‐MPA in Applied Mathematical Analysis

Traditional finite element analysis methods have the problem of expensive and unstable band structure diagram calculation when generating phononic crystals. Therefore, this study combines back propagation neural networks with ocean predator algorithms to optimize the geometric structure of phononic crystals. The results show that the coefficient of determination for the predicted bandgap width and lower bound of Model 3 is 1.00, which is better than the comparison model. However, Models 2 and 5 have poor predictive performance for bandgap width due to overfitting during actual training. Therefore, after using the ocean predator algorithm for hyperparameter adjustment, it is found that the maximum number of failures in the validation set of Model 5 is 30, the number of hidden layer nodes is 30, and after 500 experiments, the average error of the bandgap width is 5.26%, and the average error of the lower bound of the bandgap is 1.33%. The average error of the bandgap width after hyperparameter adjustment is 4.89%, and the average error of the lower bound of the bandgap is 1.21%, both of which are effectively reduced and better than the comparison model. Overall, the combination model has high computational efficiency and stability in phononic crystal optimization and can be practically applied in the design and optimization of phononic crystal geometric structures.

Optimized CUDA Implementation to Improve the Performance of Bundle Adjustment Algorithm on GPUs

Optimized CUDA Implementation to Improve the Performance of Bundle Adjustment Algorithm on GPUs

A gamified cognitive behavioral therapy for Arabs to reduce symptoms of depression and anxiety: A case study research.

Depression and anxiety are prevalent mental health issues addressed by online cognitive behavioral therapy (CBT) via mobile applications. This study introduces Sokoon, a gamified CBT app tailored for Arabic individuals, focusing on alleviating depression and anxiety symptoms (DASDs). The objectives of this study were to: Evaluate the effectiveness of Sokoon in reducing symptoms of depression and anxiety. Assess the usability of the intervention through user engagement and adherence to CBT skills. A single-group pre-post design evaluated Sokoon's impact on adults with DASDs. In consultation with psychiatrists, Sokoon integrates evidence-based skills such as relaxation, gratitude, behavioral activation, and cognitive restructuring, represented by planets. Its design incorporates Hexad theory and gamification, supported by a dynamic difficulty adjustment algorithm. The study involves 30 participants aged 18-35 (86.7% female), specifically those with mild to moderate depression and anxiety. Based on a sample of 30 participants, Sokoon, a smartphone-based intervention, significantly reduced symptoms of depression and anxiety (d = 2.7, d = 3.6, p < 0.001). Over a two-week trial, participants experienced a notable decrease in anxiety and depressive symptoms, indicating the effectiveness of the model. Sokoon shows potential as a valuable tool for addressing DASDs. Sokoon, the gamified CBT application, offers an innovative approach to increasing CBT skills adherence and engagement. By leveraging Hexad theory and gamification, Sokoon provides an enjoyable and engaging user experience while maintaining the effectiveness of traditional CBT techniques. The study findings suggest that Sokoon has a positive impact on reducing symptoms of depression and anxiety.