Performance In Stability Research Articles

Feature selection algorithm based on P systems

Since the number of features of the dataset is much higher than the number of patterns, the higher the dimension of the data, the greater the impact on the learning algorithm. Dimension disaster has become an important problem. Feature selection can effectively reduce the dimension of the dataset and improve the performance of the algorithm. Thus, in this paper, A feature selection algorithm based on P systems (P-FS) is proposed to exploit the parallel ability of cell-like P systems and the advantage of evolutionary algorithms in search space to select features and remove redundant information in the data. The proposed P-FS algorithm is tested on five UCI datasets and an edible oil dataset from practical applications. At the same time, the P-FS algorithm and genetic algorithm feature selection (GAFS) are compared and tested on six datasets. The experimental results show that the P-FS algorithm has good performance in classification accuracy, stability, and convergence. Thus, the P-FS algorithm is feasible in feature selection.

Interface Engineering in Chip-Scale GaN Optical Devices for Near-Hysteresis-Free Hydraulic Pressure Sensing.

In this work, a compact, near-hysteresis-free hydraulic pressure sensor is presented through interface engineering in a GaN chip-scale optical device. The sensor consists of a monolithic GaN-on-sapphire device responsible for light emission and detection and a multilevel microstructured polydimethylsiloxane (PDMS) film prepared through a low-cost molding process using sandpaper as a template. The micro-patterned PDMS film functions as a pressure-sensing medium to effectively modulate the reflectance properties at the sapphire interface during pressure loading and unloading. The interface engineering endows the GaN optical device with near-hysteresis-free performance over a wide pressure range of up to 0-800 kPa. Verified by a series of experimental measurements on its dynamic responses, the tiny hydraulic sensor exhibits superior performance in hysteresis, stability, repeatability, and response time, indicating its considerable potential for a broad range of practical applications.

Research on indoor positioning and navigation method of AGV based on multi-sensor fusion

In comparison with current AGV indoor positioning and navigation methods, the combination of IMU+2D LiDAR is prone to signal loss and cumulative error in indoor, so we propose an AGV indoor navigation and positioning method with multi-sensor fusion of odometer, IMU, LiDAR, and UWB. The model is based on the traceless Kalman filter fusion algorithm, and the UWB positioning provides accurate initial coordinates for the traceless Kalman filter. To verify the feasibility of the method, simulations and experiments are done on MatlabR2020a and experimental platform. The results show that the UWB localization can reduce the accumulated errors brought by IMU, the traceless Kalman filter has good trajectory fitting, the method has good performance in system stability and localization accuracy, and achieves the expected goal.

Power Adaption Design for Multifrequency Wireless Power Transfer System

A newly developed multifrequency wireless power transfer system is designed and presented in this article. Using multiple frequency channels, the system can simultaneously deliver power to different wireless pickups with arbitrary power requirements or load characteristics. The proposed design scheme of the system topology realizes both stable power flow control and precise power adaption. The load-independent constant current output has been successfully achieved under multifrequency operation mode, which increases both system stability and fault-tolerance ability. The output characteristic can also immunize the system against undesired interferences from the reflected impedance of different receivers at other power transmission channels. Both finite element analysis and circuit simulations have verified good performance of the system under different power adaption requirements. An experimental prototype can successfully deliver a total of 18 W wireless power simultaneously to two identical resistive loads at the desired power requirement distribution of 9:1 via the same transmitter. It shows good performance in both stability and controllability.

Short-term building cooling load prediction model based on DwdAdam-ILSTM algorithm: A case study of a commercial building

Traditional prediction methods of building cooling load fail to consider load changes caused by the time factor, which results in the failure of the best predicting effect and portability. To explore a more accurate cooling load predicting approach, in this paper, a new hybrid model based on decoupled weight decay Adam optimized improved LSTM neural network, denoted as DwdAdam-ILSTM, is proposed. First, the cross-correlation function is used to extract features with a high degree as the input data. Then, the decoupling weight decay adaptive moment estimation (DwdAdam) optimization algorithm is utilized to optimize the parameters of the improved long short term memory (ILSTM) prediction model. Next, the ILSTM is employed to establish a prediction model. Finally, to verify the validity and prediction accuracy of the proposed model, this paper selects the hourly cooling load data of a large commercial building in Xi'an as the research sample. Results of the experiment show that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Variation Coefficient of Root Mean Square Error (CV-RMSE) and Range of Relative Errors (E-R) of DwdAdam-ILSTM predicting the cooling load are 2.2%, 2.74, 5.2%, 0.061, 0.216. Compared with three version variant LSTM neural networks, the MSE and E-R values of this model are decreased by 74%-53%, 42%-49%, and 69%-64%, which has better performance in accuracy and stability. It testifies that the proposed strategy is reliable and effective. Additionally, this model can provide important guidance for daily energy management and multi-energy scheduling of buildings, which is helpful to the application of cooling load prediction.

Techno-Economic Evaluation of Optimal Integration of PV Based DG with DSTATCOM Functionality with Solar Irradiance and Loading Variations

Nowadays, the trend of countries and their electrical sectors moves towards the inclusion of renewable distributed generators (RDGs) to diminish the use of the fossil fuel based DGs. The solar photovoltaic-based DG (PV-DG) is widely used as a clean and sustainable energy resource. Determining the best placements and ratings of the PV-DG is a significant task for the electrical systems to assess the PV-DG potentials. With the capability of the PV-DG inverters to inject the required reactive power in to the system during the night period or during cloudy weather adds the static compensation (STATCOM) functionality to the PV unit, which is being known as distributed static compensator (DSTATCOM). In the literature, there is a research gap relating the optimal allocation of the PV-DGs along with the seasonal variation of the solar irradiance. Therefore, the aim of this paper is to determine the optimal allocation and sizing of the PV-DGs along with the optimal injected reactive power by their inverters. An efficient optimization technique called Gorilla troop’s optimizer (GTO) is used to solve the optimal allocation problem of the PV-DGs with DSTATCOM functionality on a 94 bus distribution network. Three objective functions are used as a multi-objective function, including the total annual cost, the system voltage deviations, and the system stability. The simulation results show that integration of PV-DGs with the DSTATCOM functionality show the superiorities of reducing the total system cost and considerably enhancing system performance in voltages deviations and system stability compared to inclusion of the PV-DGs without the DSTATCOM functionality. The optimal integration of the PV-DGs with DSTATCOM functionality can reduce the total cost and the voltage deviations by 15.05% and 77.05%, respectively. While the total voltage stability is enhanced by 25.43% compared to the base case.

Immobilization for Lipase: Enhanced Activity and Stability by Flexible Combination and Solid Support

In this study, an enhanced activity and stability method for immobilizing porcine pancreatic lipase (PPL) was developed based on ZIF-8 encapsulated supramolecular-modified gold nanoparticle complexes (pSC4-AuNPs@ZIF-8). Supramolecular calix[4]arene (pSC4) can recognize the amino group of PPL through non-covalent force, and this flexible binding method protected the structure of PPL during the immobilization process. Due to the hydrophilic of pSC4-AuNPs and hydrophobic of ZIF-8, PPL can maintain a "lid open" conformation, which can enhance the stability of PPL structure and reduce PPL activity loss. ZIF-8 was used to immobilize PPL to avoid the difficult recovery of free PPL. Compared with the native form of PPL, it exhibited 70.6% maintained activity with terrific pH and temperature stability, and had good performance in thermal stability, time stability, and reusability. In addition, three immobilized PPL methods were designed to further clarify the influence of synthetic methods and additives on the activity and stability of PPL. Importantly, the loading rate of pSC4-AuNPs@ZIF-8@PPL was up to 51.2% among these immobilized PPL systems. Therefore, pSC4-AuNPs@ZIF-8 may serve as a versatile and promising immobilization system for enzymes.

Electric Delta Trike Stability Characteristic and Maneuverability Analysis: Experiment and Multi-Body Dynamic Simulation

This paper reveals the stability characteristics of an electric-powered delta trike (e-trike), which is developed for goods delivery services. The changeable center of gravity position and weight due to electric component placement and the carried good weight can cause instability of the e-trike. Three main parameters are firstly evaluated on the e-trike: 1) geometry, 2) center of gravity, and 3) stiffness and damping coefficient of the suspensions. Single Lane-Change (SLC) and Double Lane-Change (DLC) tests were then conducted following ISO 14791:2000 and ISO 3888-1:2018 standards, respectively. An e-trike model was created and simulated using SIMPACK, a multi-body dynamic software. The simulation results showed that the developed e-trike model can replicate SLC and DLC tests, indicating the model was valid. A parametric study with the validated model was then conducted with various e-trike weights, center of gravity position, and suspension stiffness and damping ratio values. The results showed additional weight and higher center of gravity position can decrease threshold velocity to avoid rollover. The low suspension stiffness also contributed to lower the threshold velocity. However, the damping coefficient value did not change the threshold velocity significantly. These results can be a guideline in designing a delta trike with better performance in stability and maneuverability.

In-situ construction of carbon-doped ZnO hollow spheres for highly efficient dimethylamine detection

Pure ZnO and carbon-doped ZnO hollow spheres were successfully synthesized by a hydrothermal-calcination process. The doping content of carbon in ZnO was controlled by different calcination temperatures at 500 ℃ and 600 ℃ (named as ZnO-500 and ZnO-600). Systematic material characterization indicates that mesoporous ZnO hollow spheres are featured in abundant defects induced by in-situ carbon doping. Used as gas sensing materials, the response of ZnO-600 towards 200 ppm dimethylamine (83.6) is 28.6 times and 2.5 times higher than that of Pure ZnO (2.9) and ZnO-500 (32.8) at the optimum operating temperature of 240 ℃. Remarkably, the response of ZnO-600 toward 1 ppm dimethylamine is as high as 7.2, and simultaneously the limit of detection is as low as 108 ppb. In addition, ZnO-600 hollow spheres hold excellent gas-sensing performance in response-recovery speed, selectivity, repeatability, long-term stability, and water resistance towards dimethylamine. The boosted dimethylamine sensing performance of ZnO-600 is mainly attributed to synergistic effects of the unique mesoporous microstructure and rich defects induced by in-situ carbon doping. This work provides a promising material for dimethylamine detection, the properties of which can be improved for practical applications in the future. Furthermore, this contribution gives new insights into nonmetal-doped metal oxide nanostructures for high-performance gas sensors via surface defect control.

One-Step Initial Alignment Algorithm for SINS in the ECI Frame Based on the Inertial Attitude Measurement of the CNS.

To solve the problem of high-precision and fast initial alignment for the Strapdown Inertial Navigation System (SINS) under both dynamic and static conditions, the high-precision attitude measured by the celestial navigation system (CNS) is used as the reference information for the initial alignment. The alignment algorithm is derived in the Earth-centered inertial (ECI) frame. Compared with the alignment algorithm in the navigation frame, it is independent of position parameters and avoids the influence of the approximate error caused by the dynamic deflection angle. In addition, hull deformation is considered in attitude optimal estimation, which can realize initial the alignment of the SINS installed in various parts of the carrier. On this basis, the velocity measurement information is added to the alignment process, which further improves the accuracy and speed of the initial alignment under static conditions. The experimental results show that the algorithms proposed in this paper have better performance in alignment accuracy, speed, and stability. The attitude and velocity matching algorithm in the ECI frame can achieve alignment accuracy better than 0.6′. The attitude matching algorithm in the ECI frame has better robustness and can be used for both dynamic and static conditions, which can achieve alignment accuracy better than 1.3′.

A Generative Clustering Ensemble Model and Its Application in IoT Data Analysis

Data analysis is the foundation of Internet of Things (IoT) based applications, and clustering is an effective technology of data analysis. Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the clustering performance in stability and robustness. However, it is difficult for existing clustering ensemble algorithms to achieve a satisfying ensemble result, when the base clustering results are unreliable. Concerning this problem, we develop a new clustering ensemble model in this paper, which has several advantages compared with traditional algorithms: (i) structure information about the data is effectively extracted from the base clusterings; (ii) data characteristics and structure information are integrated in an elegant fashion, in the production of the consensus clustering result; and (iii) our model has the generative ability that makes the model achieve outstanding performance when training samples are insufficient. In our model, the structural information is extracted by explicating the coupling relationships between base clusterings and between samples in clustering members. Then, data characteristics and structure information are combined in a generative graph representation learning framework. And the objectives of representation learning and consensus clustering are integrated into a unified optimization model, in which the prior distribution of the data is approximated by a Gaussian mixture model (GMM). Extensive experiments are conducted on multiple IoT datasets; the results prove that our model not only performs better than the conventional clustering ensemble algorithms but also outperforms the state-of-the-art deep clustering methods.

Software Defects Prediction Based on Hybrid Beetle Antennae Search Algorithm and Artificial Bee Colony Algorithm with Comparison

Software defects are problems in software that destroy normal operation ability and reflect the quality of the software. Software fault can be predicted by the software reliability model. In this paper, the hybrid algorithm is applied to parameter estimation in software defect prediction. As a biological heuristic algorithm, BAS (Beetle Antennae Search Algorithm) has fast convergence speed and is easy to implement. ABC (Artificial Bee Colony Algorithm) is better in optimization and has strong robustness. In this paper, the BAS-ABC hybrid algorithm is proposed by mixing the two algorithms and the goal of the proposed method is to improve the convergence and stability of the algorithm. Five datasets were used to carry out the experiments, and the data results showed that the hybrid algorithm was more accurate than the single algorithm, with stronger convergence and stability, so it was more suitable for parameter estimation of the software reliability model. Meanwhile, this paper implemented the comparison between hybrid BAS + ABC and hybrid PSO + SSA, and the result shows that BAS + ABC has better performance both in convergence and stability. The comparison result shows the strong ability in estimation and prediction of software defects hybrid BAS and ABC.

Crystal facet controlled stable PbO2 electrode for efficient degradation of tetracycline

• The robust PbO 2 electrodes had high onset potential. • The crystal plane strategy was adopted to modify the PbO 2 surface structure. • The modified electrode improved electrode life and reduced lead dissolution. • The modified electrode achieved efficient degradation of tetracycline wastewater. Achieving the robust PbO 2 electrode with the high onset potential is of considerable importance for applying electrochemical pollutants degradation. Herein, the crystal plane strategy was adopted to modify the PbO 2 surface structure through the surfactant induction during synthesis in the electrodeposition process. PbO 2 (β(3 0 1)) and PbO 2 (α(2 0 0), β(3 0 1)) electrodes were obtained by the controlling conditions of surfactants sodium dodecyl sulfate (SDS) and hexadecyl trimethyl ammonium bromide (CTAB). Compared with conventional PbO 2 , the PbO 2 (CTAB) electrode exhibits outstanding performance in electrochemical stability and higher onset potential versus the PbO 2 (SDS). Accelerated life of the PbO 2 (CTAB) electrode is 147 h longer than that of the pure PbO 2 under a high current density of 1A·cm −2 , and the amount of lead ion dissolution is only 45% of that observed in pure PbO 2 . In this work, tetracycline (TC) was taken as an example to evaluate the degradation effect of electrodes. Due to its unique crystal texture, the PbO 2 (CTAB) electrode exhibits higher COD removal rate and TOC removal rate, 35% and 20% respectively beyond that of the conventional lead dioxide electrode. Therefore, it is of great value for its industrial electrode degradation of pollutants.

Regression-based heterogeneity analysis to identify overlapping subgroup structure in high-dimensional data.

Heterogeneity is a hallmark of complex diseases. Regression-based heterogeneity analysis, which is directly concerned with outcome-feature relationships, has led to a deeper understanding of disease biology. Such an analysis identifies the underlying subgroup structure and estimates the subgroup-specific regression coefficients. However, most of the existing regression-based heterogeneity analyses can only address disjoint subgroups; that is, each sample is assigned to only one subgroup. In reality, some samples have multiple labels, for example, many genes have several biological functions, and some cells of pure cell types transition into other types over time, which suggest that their outcome-feature relationships (regression coefficients) can be a mixture of relationships in more than one subgroups, and as a result, the disjoint subgrouping results can be unsatisfactory. To this end, we develop a novel approach to regression-based heterogeneity analysis, which takes into account possible overlaps between subgroups and high data dimensions. A subgroup membership vector is introduced for each sample, which is combined with a loss function. Considering the lack of information arising from small sample sizes, an norm penalty is developed for each membership vector to encourage similarity in its elements. A sparse penalization is also applied for regularized estimation and feature selection. Extensive simulations demonstrate its superiority over direct competitors. The analysis of Cancer Cell Line Encyclopedia data and lung cancer data from The Cancer Genome Atlas show that the proposed approach can identify an overlapping subgroup structure with favorable performance in prediction andstability.

Bimetallic Bi and Ni doped LTA zeolite as synergy electrocatalyst towards high concentration of methanol oxidation reaction

One of the important factors determining the expansion of the application domain of direct methanol fuel cell (DMFC) is its energy density. The bismuth-nickel doped LTA zeolite was as-synthesized in hydrothermal condition and applied into the electrochemistry catalysis of methanol oxidation reaction (MOR) with high performance in the methanol concentration ranging from 0.5 M to 35 M without observed catalyst poisoning. A trace amount of Bi and Ni atoms are highly dispersed on the opening window of the zeolitic surface by the electrochemical rearrangement process. The surface of the LTA structure zeolite is a good carrier for the metal cations with Na cations which could be replaced in hydrothermal crystalline process while the inner direct pores provide a channel for CO gas to escape and prevent catalyst poisoning in electrochemistry progress. Through the comparative study with the Bi doped LTA, Ni doped LTA and the commercial Pt/C catalyst, the Bi and Ni doped LTA reveals higher performance in monatomic catalytic efficiency and electrochemical stability. And through the mutual interaction of diatomic, the doped zeolite material highly improves the energy density of methanol fuel cells and reduces the cost of the cells concerning the absence of noble metals.

Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation

Meta-heuristic algorithms are effective in solving complex optimization problems with advantages of flexibility for coding, robustness and global optimization capability. An enhanced Black Widow Optimization called QIWBWO algorithm with three improvement strategies is proposed in this paper. At the beginning of search, the theory of good points set is used to obtain the better initial population, which helps the algorithm to quickly determine the correct search direction. Then, quadratic interpolation strategy is used to improve the solution accuracy and accelerate the convergence. Meanwhile, to avoid the algorithm falling into a local optimum, wavelet mutation is introduced to improve population diversity and helps the algorithm to search the global optimum rather than local optimums. The proposed BWO algorithm is compared with other different kinds of meta-heuristic algorithms on 25 traditional benchmark functions and CEC2017 competition suite. The statistical results show the improved BWO algorithm delivers better performance in accuracy, stability and convergence rate. Finally, QIWBWO also obtains the best results on four classical optimization problems in engineering application, which verifies its practicality and effectiveness. The source code of QIWBWO is publicly available in the supplementary material related to this article.

Combined Effect of Ternary Blended Cementitious Materials in Self-compacting Concrete

Self Compacting Concrete (SCC) is a type of special concrete which possess self packing and flowability to fill the formwork space completely by its own weight without any external vibration or compaction. Research studies on finding efficient, economical and eco-friendly materials to replace the conventional concrete materials are increasing day-by-day. An important constituent material of concrete is cement which contributes to the environmental hazardous problem of CO2 emission. In this view, the present study investigated the fresh and hardened properties of SCC in which cement is partially replaced by multi-cementitious materials namely fly ash, alccofine and Recycled Concrete Powder (RCP). Fly ash and alccofine are evident materials to replace cement in concrete. In addition to the substitution of cement by 20% replacement by fly ash and 10% replacement by alccofine, recycled concrete powder collected from construction wastes are also incorporated (5%, 10%, 15%, 20%) and investigated. Water/binder ratio is kept constant as 0.45. Polycarboxylate based super plasticizer (Conplast SP430) was used. The experimental program comprises testing the fresh stability by determination of flowability, passing ability and viscosity, mechanical strength testing including compressive strength, split-tensile strength and flexural strength and durability properties such as water absorption, sorptivity and acid-attack of SCC specimens. The test results showed that 10% recycled concrete powder exhibited better performance in fresh stability and satisfied performance mechanical strength. Increase in RCP content beyond a limited proportion possesses detrimental effects on the durability characteristics.

Nanogenerators‐Based Self‐Powered Sensors

AbstractWith the rapid technological development, self‐powered sensor systems that are capable of operating without an external power supply are becoming more and more crucial in the field of sensing and detection. One of the major drawbacks of a typical sensor is the necessity of an external power supply or batteries, which makes sensor systems more complex and less handy for mobile devices. In the last decade's improvement of triboelectric, piezoelectric, pyroelectric, and thermoelectric nanogenerators and their performance in electrical output and mechanical stability, it becomes widely used in the field of self‐power sensing systems for healthcare, mechanical and environmental applications. Here in this review, the various types of nanogenerators working principles is first discussed, the output performance is analyzed, and then their recent progress in the application of self‐powered sensor systems, including biomedical and healthcare, wearable devices, physical applications, robotics, environmental monitoring, and smart cities, is highlighted. Except for the practical application of self‐powered sensors, a future outlook of the self‐powered sensor systems is prognosticated.

MR and stacked GRUs neural network combined model and its application for deformation prediction of concrete dam

Deformation prediction is crucial to dam safety management and economical operation. However, most existing prediction techniques, such as statistical methods, support vector machine based models and other traditional machine learning methods, fail to consider the dynamic evolution characteristics of dam deformation and just treat this process as a problem of static fitting, which leads to the fact that these models not only have limited prediction accuracy and generalization ability, but also can hardly carry out long-term prediction. This study proposed a statistical-deep learning combined strategy to improve the accuracy and stability of concrete dam deformation prediction and realize long-term prediction. The multiple regression (MR) model is utilized to decompose the observed displacement into four components: hydrostatic, temperature, aging and reminder. Then the stacked gated recurrent units (GRUs) neural network is employed to explore the berried chaotic and dynamic change mechanism in the remainder component and make prediction based on historical data. Meanwhile, the other three components are predicted by the fitted MR model. Finally, the predicted displacement is obtained by aggregating the results of the MR model and the stacked GRUs neural network. A large hydropower dam on the Yellow River is introduced to demonstrate the application of the proposed strategy. Results show that the model proposed in this paper achieves excellent performance in both prediction accuracy and stability in long-term prediction tasks. In a one-year arc dam deformation prediction, the relative root mean square error of the proposed model is as small as 1.34%, and the R2 is as big as 0.9981. The proposed model has certain engineering application value in the long-term prediction of concrete dam deformation.

A Sustainable Multipurpose Separator Directed Against the Shuttle Effect of Polysulfides for High‐Performance Lithium–Sulfur Batteries

AbstractThe success of lithium–sulfur batteries will reduce the expected Co, Ni resource challenges from the wide adoption of lithium‐ion batteries. Unfortunately, the shuttle effect of soluble polysulfides brings many problems. Anchoring or blocking polysulfides on the cathode side using functional separators is the dominant strategy for addressing this. However, the blocked polysulfides gradually aggregate on the separator to form the so‐called “dead sulfur” and withdraw from cycling. Herein, a multipurpose separator is proposed that enables catalytic activation of the blocked polysulfides to prevent the formation of “dead sulfur”, and contribute to capacity. The multifunctionality is supported by montmorillonite (MMT) that provides sufficient channels for lithium‐ion transport, and selenium‐doped sulfurized‐polyacrylonitrile (Se0.06SPAN) that catalyzes conversion of “dead sulfur” and simultaneously contributes capacity. The theoretical calculations reveal Se0.06SPAN/MMT has a low migration barrier for Li+ and a low decomposition barrier for Li2S, facilitating the conversion and minimizing “dead sulfur”. Consequently, the Li–S battery with the Se0.06SPAN/MMT@PP (polypropylene) separator shows a low fading rate of 0.034% during 1000 cycles and achieves a super‐high areal capacity (33.07 mAh cm–2) under high sulfur loading (26.75 mg cm–2) and lean electrolyte conditions (4.5 µL mg–1). Moreover, the multipurpose separator has encouraging performance in stability, flexibility, and sustainability.