Self-adjusting Algorithm Research Articles

A model free adaptive control method based on self-adjusting PID algorithm in pH neutralization process

In this paper, a new model free adaptive control method based on self-adjusting PID Algorithm (MFAC-SA-PID) is proposed to solve the problem that the pH process with strong nonlinearity is difficult to control near the neutralization point. The MFAC-SA-PID method also solves the problem that the parameters of the model free adaptive control (MFAC) method are not easy to be adjusted and the effect is not obvious by introducing a fuzzy self-adjusting algorithm to adjust the controller parameters. Then the convergence and stability of the MFAC-SA-PID method are proved in this paper. In the simulation study, the control performance of the MFAC-SA-PID method proposed in this paper is compared with the traditional MFAC method and the improved model free adaptive control (IMFAC) method, respectively. The results show that the proposed MFAC-SA-PID method has better control effect on the pH neutralization process. The MFAC-SA-PID control performance also outperforms the traditional MFAC method and IMFAC method when step input disturbances are added, which indicates that the MFAC-SA-PID method has better robustness and stability.

Improving the Accuracy of Multipole Magnetic Encoder by Three-Phase Hall

Magnetic encoders have widespread applications in various fields such as aerospace, automotive, robot joint control and machine tool turntables. For instance, obtaining accurate rotation angles of motors is crucial for servo motor control. Herein, we propose an angle-solving algorithm based on a dual-multipair pole magnetic encoder. First, we analyze the nonlinearity issue caused by installation error of the Hall signal and devise a self-adjustment algorithm for the analog signal. Second, we improve the high speed and robustness of the encoder in identifying eigenvalue intervals while solving for angles. Finally, through the big data analysis, the calibration compensation of curve fitting error was performed, and experiments were conducted to verify the effectiveness of the real-time angle-solving algorithm in a high-precision encoder.

A Blockchain-Based Certificate System with Credit Self-Adjustment

Currently, digital certificate systems based on blockchain have been extensively developed and adopted. However, most of them do not take into account the certificate quality. To evaluate the credibility of certificates issued by educational institutions, we propose a novel blockchain-based system with credit self-adjustment (BC-CS). In BC-CS, employers can provide feedback according to the performances of their employees (i.e., students) holding different certificates. Based on the feedback, BC-CS automatically adjusts the certificate credits by using our proposed credit self-adjustment algorithm. To verify the feasibility of our proposed system, a decentralized application prototype has been developed on an Ethereum network. Experimental results demonstrate that the proposed system can fully support multi-step accreditation and automatic adjustment for certificate credit.

A Novel Adaptive Back Propagation Neural Network-Unscented Kalman Filtering Algorithm for Accurate Lithium-Ion Battery State of Charge Estimation

Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling method is proposed. In the process of estimating the SOC, a joint estimation algorithm, the Adaptive Back Propagation Neural Network and Unscented Kalman Filtering algorithm (ABP-UKF), is proposed. It combines the advantages of the robust learning rate in the Back Propagation (BP) neural network and the linearization error reduction in the Unscented Kalman Filtering (UKF) algorithm. In the BP neural network part, the self-adjustment of the learning factor accompanies the whole estimation process, and the improvement of the self-adjustment algorithm corrects the shortcomings of the UKF algorithm. In the verification part, the model is validated using a segmented double-exponential fit. Using the Ampere-hour integration method as the reference value, the estimation results of the UKF algorithm and the Back Propagation Neural Network and Unscented Kalman Filtering (BP-UKF) algorithm are compared, and the estimation accuracy of the proposed method is improved by 1.29% under the Hybrid Pulse Power Characterization (HPPC) working conditions, 1.28% under the Beijing Bus Dynamic Stress Test (BBDST) working conditions, and 2.24% under the Dynamic Stress Test (DST) working conditions. The proposed ABP-UKF algorithm has good results in estimating the SOC of lithium-ion batteries and will play an important role in the high-precision energy management process.

Beyond the SDG 15.3.1 Good Practice Guidance 1.0 using the Google Earth Engine platform: developing a self-adjusting algorithm to detect significant changes in water use efficiency and net primary production

ABSTRACT Monitoring changes in Annual Net Primary Productivity (ANPP) is required for reporting on UN Sustainable Development Goal (SDG) Indicator 15.3.1: the proportion of land that is degraded over the total land area. Calibrating time-series observations of ANPP to derive Water Use Efficiency (WUE; a measure of ANPP per unit of evapotranspiration) can minimize the influence of climate factors on ANPP observations and highlight the influence of non-climatic drivers of degradation such as land use changes. Comparing the ANPP and WUE time series may be useful for identifying the primary drivers of land degradation, which could be used to support the Land Degradation Neutrality objectives of the UN Convention to Combat Desertification (UNCCD). This paper presents an algorithm for the Google Earth Engine (freely and openly available upon request – http://doi.org/10.5281/zenodo.4429773) to calculate and compare ANPP and WUE time series for Santa Cruz, Bolivia, which has recently experienced an intensification in its land use. This code builds on the Good Practice Guidance document (version 1) for monitoring SDG Indicator 15.3.1. We use the MODIS 16-day average, 250 m resolution to demonstrate that the Enhanced Vegetation Index (EVI) responds faster to changes in water availability than the Normalized Difference Vegetation Index (NDVI). We also consider the relationships between ANPP and WUE. Significant and concordant trends may highlight good agricultural practices or increased resilience in ecosystem structure and productivity when they are positive or reducing resilience and functional integrity if negative. The sign and significance of the correlation between ANPP and WUE may also diverge over time. With further analysis, it may be possible to interpret this relationship in terms of the drivers of change in plant productivity and ecosystem resilience.

Self-Adjusting Evolutionary Algorithms for Multimodal Optimization

Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms can provably outperform static settings in evolutionary algorithms for binary search spaces. However, the vast majority of these studies focuses on unimodal functions which do not require the algorithm to flip several bits simultaneously to make progress. In fact, existing self-adjusting algorithms are not designed to detect local optima and do not have any obvious benefit to cross large Hamming gaps. We suggest a mechanism called stagnation detection that can be added as a module to existing evolutionary algorithms (both with and without prior self-adjusting schemes). Added to a simple (1+1) EA, we prove an expected runtime on the well-known Jump benchmark that corresponds to an asymptotically optimal parameter setting and outperforms other mechanisms for multimodal optimization like heavy-tailed mutation. We also investigate the module in the context of a self-adjusting (1+\(\lambda \)) EA. To explore the limitations of the approach, we additionally present an example where both self-adjusting mechanisms, including stagnation detection, do not help to find a beneficial setting of the mutation rate. Finally, we investigate our module for stagnation detection experimentally.

A parametric approximation algorithm for spatial group keyword queries

With the application of big data, various queries arise for information retrieval. Spatial group keyword queries aim to find a set of spatial objects that cover the query keywords and minimize a goal function such as the total distance between the objects and the query point. This problem is widely found in database applications and is known to be NP-hard. Efficient algorithms for solving this problem can only provide approximate solutions, and most of these algorithms achieve a fixed approximation ratio (the upper bound of the ratio of an approximate goal value to the optimal goal value). Thus, to obtain a self-adjusting algorithm, we propose an approximation algorithm for achieving a parametric approximation ratio. The algorithm makes a trade-off between the approximation ratio and time consumption enabling the users to assign arbitrary query accuracy. Additionally, it runs in an on-the-fly manner, making it scalable to large-scale applications. The efficiency and scalability of the algorithm were further validated using benchmark datasets.

Research on classification and identification of library based on artificial intelligence

With the popularization of open borrowing, it is urgent for major libraries to explore new collection classification and identification systems to assist or replace librarians in their daily book cataloguing work. The messy books can be marked according to invisible colors, which puts forward a new method of library classification. In the algorithm used to detect image edges, by looking for the traditional sensitivity measurement system, the best parameters are applied to the wrong book image edges, and the test results are verified to find the best parameter range. A two-dimensional algorithm is used to segment the information element matrix to reduce sound. The results show that the image edge test is effective and reduces the influence of noise on the image test. The self-adjustment control algorithm of a single neuron is used to control the robot orbit, which makes the robot not only react quickly, but also reduce a small part of the error in meeting the actual needs of the robot. The effect on the result is getting smaller and smaller.

Variable stiffness control for SEAs in rehabilitation training

ABSTRACTSeries Elastic Actuator (SEA) with both security and high performance is used extensively for rehabilitation robots with physical interaction. Human joints applied for motion therapy show variable stiffness properties during the process of rehabilitation training. When using robot to do motion therapy, impedance control is one of the most popular methods for rehabilitation works. However, impedance control with constant stiffness usually produces rigidity in the body due to natural changes of muscle tension. It may seriously restrict the achievement of excellent training effect and may even cause harm to patients. In this study, a novel real-time parallel variable stiffness control method is proposed based on cascade impedance controller. First, an SEA joint is analyzed and the limit factor of the impedance frequency is discussed. Subsequently, cascade impedance controller scheme with stiffness adjustment regulator is utilized to achieve the stiffness and the passivity of the controller is proved. Based on the scheme, a novel stiffness self-adjustment algorithm is presented which can regulate the stiffness by impedance approximation. Finally, simulation and experimental results are provided to validate the stiffness adjustment method during the rehabilitation process.

DEVELOPMENT AND EXPERIMENTAL VERIFICATION OF THE SELF-ADJUSTMENT ALGORITHM OF THE OGSH LOAD LIMITER FOR BRIDGE CRANES

Introduction. The mathematical self-adjustment model of the safety device for bridge cranes, which is developed by the authors as the limiter of loading capacity of broad application (OGSh) for the purpose of time reduction for device setup and of other material and labor inputs for requirements implementation of the Federal Norms and Rules (FNP) on protection of bridge cranes against overload, is described in the article.Materials and methods. Mass and dynamic characteristics of the crane (the specified mass of the drive, the inertia moment, rigidity of a pendant, response time and etc.); various stages of the loads lifting, on which absolutely various dynamic loadings impact and which are described by the systems of the differential equations with various entry and regional conditions, were proved in the research. Numerical methods for the solution of the differential equations systems with the usage of experimental data were also applied.Results. Assumptions and regional conditions in settlement model of self-adjustment of the OGSh device for practical application in development and designing of this type of devices are used and proved. Moreover, the settlement algorithm is realized and the OGSh device is programmed according to such settlement algorithm. Natural experiments and tests with the OGSh device on the developed self-adjustment algorithm are made.Thus, the results of experiments have confirmed the possibility of the algorithm application of selfadjustment model as the limiter of the OGSh loading capacity for obtaining work parameters and implementation of FNP requirements, which are necessary for such crane during the operation.Discussion and conclusion. The algorithm of the self-adjustment model of the safety device (SD) as the limiter of loading capacity provides the implementation of the FNP requirements, the reduction of time of the device setup, bigger convenience and simplicity of SD control on the OGSh; decrease in dynamic loads of the drive. Therefore, such algorithm would be recommended for application in all new SD of the OGSh made by ITTs KROS for bridge cranes.

Hierarchical Control of Residential HVAC Units for Primary Frequency Regulation

This paper presents a hierarchical control framework for implementing primary frequency regulation (PFR) by using residential heating, ventilating, and air-conditioning (HVAC) loads. The proposed control system consists of a load aggregator, a central controller, and a group of local controllers. The load aggregator is responsible for managing a population of HVAC loads as a virtual generator to participate in primary reserve market. The central controller dispatches the HVAC loads by assigning a triggering frequency to each HVAC load. A temperature-priority-list method is applied to avoid frequent switching on/off HVAC units when implementing PFR. The local controllers determine the ON/OFF status of HVAC units autonomously based on their triggering frequencies. A self-adjustment algorithm is proposed to correct the error in the assigned triggering frequency by considering minimum ON/OFF time of HVAC units. The proposed control framework can significantly reduce the communication needs. The simulation results indicate that by adopting the proposed control framework, one can dispatch HVAC units more efficiently at the central control level while achieve fast response at the local control level.

Research into dynamics of setting the threshold and a probability of ignition detection by self­adjusting fire detectors

A general analysis of self-adjustment algorithm of fire detectors for early guaranteed ignition detection was carried out. It was shown that step magnitude in the algorithm of self-adjustment of fire detectors can be a fixed or a selected variable, depending on the level of registered actual data about the factor of ignition of combustible material. Specific features of fire detectors, self-adjusting by ignition, relate to a non-linear nature of the algorithm and conditions for providing guaranteed ignition detection. In this case, for effective self-adjustment of fire detectors, it is expedient to set the initial threshold value by registered actual data in the absence of ignition and to regulate the adaptation step size in a special way. It was shown that the fact of an increase in self-adjusted threshold relative to its original value can be a sign of ignition detection. For probabilistic assessment of the fact of ignition detection, it was proposed to use exponential smoothing of characteristic function, which allows generation of dynamic assessment of probability of ignition detection. A study of dynamics of self-adjusting threshold and probability of ignition detection indicate capability of self-adjusting fire detectors to provide early guaranteed detection of different sources of ignition of flammable materials under conditions unknown in advance

Channel self-adjusting filtered-x LMS algorithm for active control of vehicle road noise

Current active road noise control (ARNC) systems, configured with the standard filtered-x least mean square (FxLMS) algorithm, are not sufficient enough to yield an ideal noise reduction over a broad frequency range. This is because ARNC systems generally employ multiple reference signals, which has an inherent limitation of the channel-dependent convergence behaviour due to dynamic characteristics amongst reference signals. In this study, an effective ARNC system with the channel self-adjusting FxLMS (CSFxLMS) algorithm by incorporating the self-adjusting parameter on each reference signal path is proposed, which is to minimise the effect of its dynamics characteristics in different reference channels. To validate the effectiveness of the proposed algorithm, numerical simulations using measured road noise response is conducted. Results show that the performance of the proposed CSFxLMS algorithm is significantly better as compared to the conventional FxLMS algorithm, and is able to achieve around 5 dBA reductions at the driver's ear position.

Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System

Rapid detection of bursts and leaks in water distribution systems (WDSs) can reduce the social and economic costs incurred through direct loss of water into the ground, additional energy demand for water supply, and service interruptions. Many real-time burst detection models have been developed in accordance with the use of supervisory control and data acquisition (SCADA) systems and the establishment of district meter areas (DMAs). Nonetheless, no consideration has been given to how frequently a flow meter measures and transmits data for predicting breaks and leaks in pipes. This paper analyzes the effect of sampling interval when an adaptive Kalman filter is used for detecting bursts in a WDS. A new sampling algorithm is presented that adjusts the sampling interval depending on the normalized residuals of flow after filtering. The proposed algorithm is applied to a virtual sinusoidal flow curve and real DMA flow data obtained from Jeongeup city in South Korea. The simulation results prove that the self-adjusting algorithm for determining the sampling interval is efficient and maintains reasonable accuracy in burst detection. The proposed sampling method has a significant potential for water utilities to build and operate real-time DMA monitoring systems combined with smart customer metering systems.

Design of Self-Adjusting algorithm for Twitter Sentiment Analysis

Design of Self-Adjusting algorithm for Twitter Sentiment Analysis

An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China.

The increase and the complexity of data caused by the uncertain environment is today’s reality. In order to identify water quality effectively and reliably, this paper presents a modified fast clustering algorithm for water quality analysis. The algorithm has adopted a varying weights K-means cluster algorithm to analyze water monitoring data. The varying weights scheme was the best weighting indicator selected by a modified indicator weight self-adjustment algorithm based on K-means, which is named MIWAS-K-means. The new clustering algorithm avoids the margin of the iteration not being calculated in some cases. With the fast clustering analysis, we can identify the quality of water samples. The algorithm is applied in water quality analysis of the Haihe River (China) data obtained by the monitoring network over a period of eight years (2006–2013) with four indicators at seven different sites (2078 samples). Both the theoretical and simulated results demonstrate that the algorithm is efficient and reliable for water quality analysis of the Haihe River. In addition, the algorithm can be applied to more complex data matrices with high dimensionality.

Long-term real-time monitoring catalytic synthesis of ammonia in a microreactor by VUV-lamp-based charge-transfer ionization time-of-flight mass spectrometry.

With respect to massive consumption of ammonia and rigorous industrial synthesis conditions, many studies have been devoted to investigating more environmentally benign catalysts for ammonia synthesis under moderate conditions. However, traditional methods for analysis of synthesized ammonia (e.g., off-line ion chromatography (IC) and chemical titration) suffer from poor sensitivity, low time resolution, and sample manipulations. In this work, charge-transfer ionization (CTI) with O2(+) as the reagent ion based on a vacuum ultraviolet (VUV) lamp in a time-of-flight mass spectrometer (CTI-TOFMS) has been applied for real-time monitoring of the ammonia synthesis in a microreactor. For the necessity of long-term stable monitoring, a self-adjustment algorithm for stabilizing O2(+) ion intensity was developed to automatically compensate the attenuation of the O2(+) ion yield in the ion source as a result of the oxidation of the photoelectric electrode and contamination on the MgF2 window of the VUV lamp. A wide linear calibration curve in the concentration range of 0.2-1000 ppmv with a correlation coefficient (R(2)) of 0.9986 was achieved, and the limit of quantification (LOQ) for NH3 was in ppbv. Microcatalytic synthesis of ammonia with three catalysts prepared by transition-metal/carbon nanotubes was tested, and the rapid changes of NH3 conversion rates with the reaction temperatures were quantitatively measured with a time resolution of 30 s. The high-time-resolution CTI-TOFMS could not only achieve the equilibrium conversion rates of NH3 rapidly but also monitor the activity variations with respect to investigated catalysts during ammonia synthesis reactions.

Localized Model to Segmentally Estimate Miles per Gallon (MPG) for Equipment Engines

In this paper, we built a localized regression model to estimate the miles per gallon (MPG) characteristic for equipment engines based on a serious physical features of this engine. First, we statistically viewed these parameters to build up a basic understanding of the data we collected. Then, with the belief that engines with similar characteristics will perform similarly, we proposed a novel localized model with a novel optimal function based EM algorithm and a novel self-adjusted optimal clustering algorithm to estimate MPG based on the other fully studied engines with similar physical features.

A self-adjusting algorithm for solitary wave simulations

We introduce a practical algorithm to automate the simulations of solitary wave solutions of some nonlinear dispersive wave equations. The full discretization consists of a spatial discretization with a local basis and an invariant preserving time integrator. The algorithm includes a dynamic cleaning of dispersive tails and an automatic detection of the complete separation of the main pulses.

Parameter Self-Adjusting Path Tracking Algorithm of Mobile Robots

For conventional fuzzy path tracking controller need to manually updated the control parameters in order to get better tracking control deficiencies and the lack of robustness of the problem when the control object is disturbed. Parameters self-adjusting tracking algorithm is proposed based on Cerebellum Model Articulation Controller(CMAC) and fuzzy logic composite of the control. The CAMC control logarithm first charged with tracking through learning objects charged with approximation of the object model, to learning cycle worth to control corresponding to the amount of correction corresponding weight value according to the error between input and output of the system and set the learning rate. When the object or environment changes can make the control performance of the system is automatically adjusting within a certain range, since the role of the CAMC. Tracking experiments show that. The tracking control algorithm has high tracking accuracy and good robustness, is conducive to the overall optimization of robot path tracking.