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Generally there are two methods for the thrust ripple modeling: one is to identify the eigenvalues and the model parameters of thrust ripple sequentially under rigorous operating conditions; another is to treat thrust ripple as a general and inaccurate disturbance. To get rid of the constraints and further enhance the tracking accuracy of the linear servo system, a novel integrated identification and compensation scheme using an improved matching pursuit algorithm is proposed in this paper. First, the dynamics of the linear motor is formulated, and the cause of thrust ripple is analyzed. Then, a time–frequency atomic library of thrust ripple is constructed with the consideration of the frequency characteristics of position command, and meanwhile, the two optimal atoms are obtained successively by maximizing the inner product for the thrust ripple reconstruction. Based on the above, the eigenvalues and model parameters of thrust ripple can be simultaneously identified in an online manner. Finally, the feedforward compensation component is devised by the identification results to suppress thrust ripple. In order to verify the superiority of the proposed scheme, simulations and experiments are conducted compared with the conventional methods. • The eigenvalues and parameters of thrust ripple can be identified simultaneously. • The proposed scheme has higher efficiency compared with the traditional methods. • The proposed scheme has the advantages of convergence speed and identification accuracy. [ABSTRACT FROM AUTHOR]

Due to nonstationary operating conditions of wind turbines and surrounding harsh working environments, the impulse features induced by bearing faults are always overwhelmed by heavy noise, which brings challenges to accurately detect rolling bearing faults. Sparse representation exhibits excellent performance in nonstationary signal analysis, but it is closely bound up with the degree of similarity between the atoms in a dictionary and signals. Therefore, this paper investigates an enhanced K-SVD denoising method based on adaptive soft-threshold shrinkage to achieve high-precision extraction of impulse signals, and applies it to fault detection of generator bearing of wind turbines. An adaptive sparse coding shrinkage soft-threshold denoising is first proposed to remove noise and harmonic interference in the residual term of dictionary updating, so that the updated atoms show obvious impact characteristics. Furthermore, a soft-threshold shrinkage function with adaptive threshold is designed to further suppress clutter in atoms of the learned dictionary, so as to obtain an optimized dictionary for recovering impulse signals. Two actual engineering cases are selected for analysis, and the envelope spectrum correlation kurtosis corresponding to the results obtained by the proposed method is significantly higher than that of other comparison methods, thus verifying its superiority in detecting rolling bearing faults. • An enhanced K-SVD denoising algorithm for fault-induced periodic impulse extraction. • Adaptive sparse coding shrinkage is introduced into the dictionary updating. • A soft-threshold shrinkage function is proposed to optimize the dictionary. • Proposed method improves the extraction accuracy of rolling bearing fault features. [ABSTRACT FROM AUTHOR]

In recent years, pumps have become critical components in agriculture, industry, and the military, necessitating extensive development and implementation of the fault diagnosis method. In the majority of existing fault classification models, the connection between performance improvement and the amount of training data remains high, yet real-world samples are difficult to obtain. Combining domain migration theory and sample expansion method, this paper introduces a few-shot learning fault diagnosis method. Employing the T-SNE visualization algorithm, we examine the validity of the self-calibration attention mechanism (SCAM) and distribution edge prediction strategy (DEPS). The accomplishment demonstrated that the proposed algorithm could effectively map the expanded sample space within a separate interval, thereby avoiding the problem of feature aliasing caused by the overlap of sample features among similar categories and significantly enhancing the quality and quantity of training samples. The experimental analysis indicates that the proposed methodology can effectively increase the accuracy of few-shot tasks, especially in the 9way-15shot task, where it maintains a performance of 72 %, which leading the mean accuracy calculated from the others of about 30%. It is believed that much of the work has superior applicability to other few-shot diagnosis cases. • A combination of domain migration theory and sample expansion method. • Map the expanded sample space within a separable interval, avoid feature aliasing. • A few-shot sample augmentation algorithm for pump fault diagnosis. • Performing on 256*256 images transformed from one-dimensional acceleration signal. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is critically important for optimising the performance of Intrusion Detection Systems (IDSs) used in Wireless Sensor Networks (WSNs). However, selecting an optimal number of relevant features from massive IDS data sets has been an ongoing FS research problem. Many approaches use Machine Learning (ML), such as nature-inspired population-based and Differential Evolution (DE)-based algorithms for FS. However, the main drawback of DE is that it has a premature convergence issue, which leads to false convergence. In this paper, a novel mutation strategy is proposed to alleviate premature convergence and to develop a modified DE-based FS algorithm called DE with maturity extension (DE-ME). The novel mutation is achieved by changing the mutation factor in the DE\rnd\1 based on K-Nearest Neighbour (KNN) as the most efficient implementation without adding further complexity to the design process. The proposed DE-ME algorithm improves the FS operation with significant performance improvements in terms of overcoming premature convergence and increasing accuracy for selection. The DE-ME algorithm was used to select the most effective features from 41 features in the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset. The accuracy of DE-ME is high at 99.66 %, its False Positive Rate (FPR) is low at 0.464 %, and the true positive rate (TPR) achieved is 99.80 %. In addition, the number of features selected by DE-ME is 6 out of 41 features in the dataset. [ABSTRACT FROM AUTHOR]

In this paper, a Two-Level, Three-Phase, Reduced-Switch (2L3PRS) Voltage Source Converter (VSC) based Distribution Static Synchronous Compensator (DSTATCOM) integrated with a grid-tied solar photo-voltaic (PV) array has been discussed, and its performance investigation has been carried out. A versatile control strategy is proposed, which gives the system maximum power point tracking capability with a stepped perturbation and observation (P&O) algorithm and the grid current is maintained at unity power factor. The layout, in addition to the control technique, helps in supplying active power to the power grid and feeding active and reactive power demand of the 3-phase reactive unbalanced load. Modified Synchronous Reference Frame (MSRF) theory-based current control methodology is implemented to sustain DC-link voltage and to maintain the voltage balancing across split capacitors under all loading conditions. This modified algorithm generates switching pulses for three-phase VSI with only four IGBT switches for fulfilling the desired objectives like maintaining a constant DC link voltage, balancing split-capacitor voltages, balancing the power-sharing between the power grid and the solar-PV energy system, balancing the grid currents and voltages in case of unbalanced loading conditions and maintaining unity power factor operation of the grid by making the D-STATCOM compensate for load reactive power. The proposed system is replicated in MATLAB/Simulink using SimPowerSystems software-based environment. The experimental outcomes have been obtained with a digital real-time simulator to vindicate the simulation results. The experimental, as well as simulation results of the proposed real-time-based model, are demonstrated for three unbalanced single-phase inductive linear loads and with a load on one Phase kept open in the Power Factor Correction (PFC)mode of operation of D-STATCOM. [ABSTRACT FROM AUTHOR]

The problem of multi-detection multi-target tracking (MDMTT) using over-the-horizon radar in dense clutter environment is studied in this paper. The biggest challenge of MDMTT is the 3-dimensional multipath data association among measurements, detection models and targets. In particular, a lot of clutter measurements are generated in dense clutter environment, which increase the computational burden of 3-dimensional multipath data association greatly. A measurement based dimension descent association (DDA) algorithm is proposed to solve the 3-dimensional multipath data association, which decomposes the 3-dimensional multipath data association into two 2-dimensional data associations. The proposed algorithm can reduce the computational burden compared with the optimal 3-dimensional multipath data association and the computational complexity is analyzed. Besides, a time extension method is designed to detect the new-born targets that appear in the tracking scene, which is based on the sequential measurements. The convergence of the proposed measurement based DDA algorithm is analyzed. The estimation error can convergence to 0 as the number of Gaussian mixtures tends to infinity. The effectiveness and rapidity of the measurement based DDA algorithm are demonstrated by the comparative simulation with the previously proposed algorithms. • Multi-detection multi-target tracking using OTHR is studied by dimension descent. • The detection of new-born targets with unknown prior knowledge is considered. • The convergence of the novel multi-target tracking algorithm is analyzed. • The labeled GMPHD is proposed to reduce the computational burden. [ABSTRACT FROM AUTHOR]

This paper proposes an improved narrowband filtered-x least mean square(FxLMS) algorithm to overcome the thermal failure problems of the active controlled mount(ACM). Firstly, the temperature-rising model and the thermal demagnetization model of the ACM are respectively developed. Combining these two models with the powertrain mounting system model, an analytical method is established for the thermal–magnetic coupling analysis of the ACM. Then a numerical simulation is carried out to obtain the permanent magnet(PM) temperature and the coil current. The ACM failure problem is discussed according to the working point trajectory. Finally, an improved algorithm is proposed. This algorithm is able to overcome the thermal failure problems by sacrificing part of the vibration isolation performance. The effectiveness of this algorithm is validated by numerical simulations and a comparison with conventional algorithms. • Temperature rise and thermal demagnetization are mutual positive feedbacks in the active controlled mount. • Conventional algorithm leads to damage or even thermal failure in some working conditions. • An improved algorithm is able to avoid the irreversible demagnetization and stabilize the active controlled mount. • The improved algorithm maintains most part of the vibration isolation performance. [ABSTRACT FROM AUTHOR]

With the fast-moving deployment of IoT (Internet of Things) applications, a large amount of data is generated and the data is in need for processing to be able contribute to the user's requirements. All of the IoT applications require low latency and certain bandwidth to provide us with new features which are Time-Dependent. Thus, it would be advantageous to redesign the current IoT architecture by understanding the unique properties of communications and computational power to support intelligent processing of IoT data and its analysis. The paper is focused on topics such as characteristics of IoT data, trends in IoT network architecture, problems in IoT data analysis and their solutions. Thus, in particular, the paper is a discussion that Edge software-defined computing and CEP (Complex Event Processing) based fog computing as a architecture that promises to support the different requirements of IoT data analytics. And also, the paper is a discussion about how machine learning, deep learning are useful in removing data processing anomalies. The leader node election for data processing at cloud is also a time consuming task, the proposed genetic based Genetic based Leader Election Algorithm (GLEA) algorithm in the paper show promising results for the solution. [ABSTRACT FROM AUTHOR]

Clustering is one of the most common technologies in data mining. The size, dimension and sparsity of data are all different aspects that restrict clustering analysis. Clustering machine learning is called unsupervised learning, which is different from classification in that the data objects used in clustering analysis have no class marks, so they need to be calculated by the clustering learning algorithm, while the classified data objects need class marks. At present, most of the sparse data algorithms with high attribute dimensions are oriented to binary data, and there is no evaluation method of clustering results, which brings great limitations to their application. The BP neural network, RBF neural network and Elman neural network model with feedback function, which are mature and widely used in pest prediction models, are studied. The purpose of this paper is to study diseases and pests based on sparse clustering algorithm. In this paper, algorithm formulas, models and data graphs are established to study. From the research, it can be seen that the damage index of pests and diseases is very high, reaching about 50. 54%. Through the research in this paper, we can know that the future will lay a certain foundation for the research of diseases and pests. [ABSTRACT FROM AUTHOR]

Now, we have entered the "internet plus" era, and all industries are making use of information technology to seek a longer-term development. For vegetable distribution, we must rely on the power of information technology. Faced with complex global optimization problems, traditional optimization algorithms have many performance limitations, such as complex solution process and easy to fall into local optimization, which can't meet the needs of practical applications. This paper studies the design and application of optimization algorithm for vegetable planting and distribution based on adaptive differential evolution algorithm. The method adopted in this paper is adaptive differential evolution algorithm. In the original differential evolution algorithm, there are three undetermined parameters: population size (NP), variation factor (F) and crossover probability (CR). In the process of evolution, NP takes a fixed value, while the key control parameters F and CR, which affect the performance of the algorithm, will adaptively take different values on [0,1], and the values of F and CR will be recalculated for each individual in each generation. Let F and CR automatically adjust and change in the process of evolution. The research in this paper shows that the algorithm in this paper is effective and suitable for application. [ABSTRACT FROM AUTHOR]

In order to be able to extract useful information from MRI medical images, we need to carry out effective image segmentation of MRI medical image and use appropriate operators to detect the edge of the image. Firstly, this paper adopts a research method based on the characteristics of horizontal, vertical and diagonal differences, and then carries out comparative analysis on different operators from different perspectives such as frequency domain and velocity domain. After analyzing the results, it is concluded that: Canny operator can detect complete, continuous and detailed edges at a faster speed. This paper uses edge detector based on Canny operator as the training output of artificial neural network. By training artificial neural network. The optimized parameters were obtained, and subjective analysis and computational analysis were performed on the quality of edge detection results of MRI medical images based on different methods. The results show that compared with the single Sobel, Canny and other traditional operators, the edge information of MRI medical image obtained by the neural network-based edge detection method is more complete and its processing time is nearly 3 times faster than the traditional edge detection method. [ABSTRACT FROM AUTHOR]

The focus of this paper is to determine how the mechanisms of each algorithm contribute to search performance. swarm-based algorithms are composed of several mechanisms. This paper regards these independent mechanisms as modules. The modules constituting each algorithm are classified into three categories: (1) the common module: a collection of mechanisms that all swarm-based algorithms have in common; (2) the main module: the unique mechanisms of each algorithm that updates the solution by interacting with other solutions; and (3) the sub module: the unique mechanisms of each algorithm that do not directly improve the quality of the solution. Testing the algorithms combined into these modules reveals the contribution of each module to performance. The experiments are conducted for the three algorithms PSO, ABC, and CS to test the usefulness of this concept. The results of these experiments revealed the following implications: (a) the main module of ABC alone can have good performance; (b) the main module of CS does not perform well as a stand-alone; (c) the combination algorithm with the main module of PSO and the sub module of ABC can improve applicability. [ABSTRACT FROM AUTHOR]

Accurate electric energy metering in micro-grid is one of the most urgent problems in the field of electric energy metering. In order to solve the problem that the lackness of electric energy metering can't be accurately calculated with the existing electric energy compensation algorithm when the electric energy metering device is completely faulty, this paper proposes an electric energy compensation algorithm that is limited in micro-grid and is based on standard particle swarm to optimize the deep belief network. Taking the historical electric energy data of each micro source metering point in micro-grid as an example, it is divided into the training set and the test set, the standard particle swarm optimization algorithm is used to determine the optimal learning layers and learning rate of deep belief network, and the electric energy algorithm model is established. The model verification results show that the proposed algorithm is nearly five times lower than the algorithm based on deep belief network solely. Further engineering case comparison shows that lackness of electric energy metering compensated by the algorithm studied in the paper is closer to the theoretical value, and the error is also less than the traditional average electricity algorithm. The real-time power curve is closer to the theoretical value, and has the higher accuracy. [ABSTRACT FROM AUTHOR]

Classification is a common problem in various fields of life, and the key challenging task in data mining. The primary objective of the classification process is to classify the given dataset in a defined class label for all data. Many research papers widely used classification in the medical sector. Various researches have been carried out to classify medical data using different techniques, such as High Order Neural Network (HONN) combined with Back-Propagation Neural Network (BPNN) as a learning algorithm. Due to the increased data complexity, the back-propagation algorithm faced problems of slow convergence. The Back-Propagation (BP) using the gradient descent technique has the possibility of getting stuck in local minima. So, the BP algorithm may cause difficulties in finding the global minima of the error function. This paper proposed a high-order Functional Link Neural Network (FLNN). The proposed FLNN model is integrated with a metaheuristic-based searching algorithm called Accelerated Particle Swarm Optimization (APSO). The performance of the proposed Accelerated Particle Swarm Optimization Functional Link Neural Network (APSOFLNN) model is validated with various medical datasets and compared to traditional techniques such as Accelerated Particle Swarm Optimization Functional Link Back-Propagation (APSOFLBP), and Artificial Bee Colony Functional Link Neural Network (ABCFLNN). The simulation results showed that the proposed APSOFLNN algorithm for the used benchmarked datasets shows good results in terms of the mean square error (MSE) and accuracy in comparison to the traditional techniques. [ABSTRACT FROM AUTHOR]

Rough set is an important method for dealing with incomplete information systems. In incomplete information systems, the most common way to determine the relation between two samples is the tolerance relation. However, the condition for the tolerance relation to determine those samples may belong to the same category is very lenient, which makes the reduction rate low when using the rough set generated by this relation to select features. In response to the above problems, we design the neighborhood equivalence tolerance relation to solve them. Different from other improved tolerance relations, firstly, the relation designed in this paper does not require additional threshold to accomplish the above goals, which will avoid the trouble caused by the given threshold. Secondly, we notice that most of the current improvements for this kind of problems are computationally cumbersome, and the relation designed in this paper is simple and effective. Based on this, we construct a neighborhood rough set model that handles incomplete information by using this relation, introduce its properties, expound the properties that a reduction set should satisfy, quantify the importance of conditional attributes with attribute dependence degree, which provides the basis for the design of feature selection algorithm. Finally, the greedy strategy is used to design a forward feature selection algorithm. Experimental results show that the model is effective in dealing with incomplete information systems. The feature selection algorithm has the smallest size of the average reduced subset on twelve datasets, and maintains the accuracy of the classifier, which verifies that the feature selection algorithm can effectively deal with incomplete information systems. ● As the judgment of tolerance relation is very lenient, the existing improvement methods either rely on the probability distribution of attribute values in the information system, or need to give a additional threshold. To solve the above problems, this paper improves the tolerance relation, and the improved binary relation only relies on the existing information system itself, without giving additional threshold. ● Rough set in incomplete information systems can only select more attributes to refine the knowledge granules when making feature selection due to the lenient judgment of tolerance relation, which is equivalent to making up for the problems caused by tolerance relation when selecting attributes, but this will lead to low reduction rate. In this paper, we use the newly proposed binary relation to construct neighborhood rough set model in incomplete information systems, and use this model to make feature selection, which improves the problem of low reduction rate above, while maintaining the accuracy of the classifier. [ABSTRACT FROM AUTHOR]

Discrete orthogonal polynomials (DOPs) have gained significant research attention owing to their crucial role in digital signal processing applications such as computer vision, pattern recognition, and compression. However, the computation of DOP coefficients often incurs a substantial computational burden, exacerbating for higher-degree moments along with the resulting numerical errors. To address this challenge, this paper exploits the inherent parallelism in Charlier polynomial coefficient calculations to achieve enhanced polynomial performance. Independent calculations are distributed among threads, making efficient use of the available processing resources. Two algorithms are presented, the first algorithm evenly distributes the rows in a sequential manner (straightforward). Additionally, to achieve a more equitable distribution of coefficient calculations, this paper proposes alternative distribution approaches, aimed at balancing processing load among threads. Through extensive comparative experiments, we have confirmed that the proposed approaches achieved high performance across different degrees (1540 to 7370) and at different numbers of threads (2 to 256). The results show processing time in the multithreaded case is improved by up to 9.1 times with respect to the unthreaded case. Furthermore, by increasing the number of threads from 2 to 256, the trend indicates that the most significant improvement occurs in the range of 32 to 128 threads, confirming the robustness of the proposed algorithm. These findings signify the importance of this paper. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile communications, the dense deployment of small base stations (SBSs) in heterogeneous wireless networks (HW-Nets) has met the explosive growth in demand from terminals. However, due to the irregular deployment of SBSs, users frequently handover base stations in pursuit of high-quality services, which inevitably leads to load imbalance among SBSs. To alleviate load imbalance in HW-Nets and improve the efficiency of seamless handover, this paper proposes a distributed auction algorithm scheme based on dual active protocol stack (DAPS), which transforms the wireless access selection of terminals into the terminal-base station association process during movement to globally optimize network resource utilization. At the same time, this paper constructs a terminal-base station association graph and uses auction algorithms to solve the maximum weighted matching of the graph. The DAPS-based scheme can effectively avoid the problem of excessively long outage time caused by handovering outages. Simulation results show that compared with general distributed algorithms, the distributed auction algorithm based on DAPS can effectively balance the load of base stations and reduce the mobile outage latency by 59%. [ABSTRACT FROM AUTHOR]

• A model was created to examine the reliability of maintenance budget allocation. • The model was applied to network-level pavement maintenance in Canada. • The impacts of algorithm selection and parameter tuning were studied. • The significance of pavement clustering in increasing reliability was highlighted. • The importance of incorporating actual pavement improvement curves was clarified. The purpose of this paper is to increase the reliability of the network-level pavement maintenance budget allocation by reducing uncertainties of algorithm selection and parameter tuning. In this paper, reliability is defined as the ability of a network-level pavement maintenance plan to improve the condition of a pavement network within a certain budget. In order to quantify reliability, the reliability index is defined as the ratio between the post-maintenance network condition and the net maintenance cost. With this purpose in mind, a two-objective optimization model was developed. To test its applicability, the model was applied to a pavement network in Canada. The model was solved using the Non-Dominated Sorting Genetic Algorithm II and the differential evolution algorithms. Finally, the reliability indices of algorithms and termination criteria were computed. The results indicated that the differential evolution algorithm recommended less frequent but more intense interventions that made the solutions expensive and less reliable. This paper contributed to the network-level pavement maintenance body of knowledge by (1) developing a multi-objective optimization model to reduce uncertainties of parameter tuning and algorithm selection; (2) increasing the reliability of budget allocation; (3) showcasing the significance of pavement clustering in increasing reliability; and (4) developing and incorporating actual pavement improvement curves. [Display omitted] [ABSTRACT FROM AUTHOR]

Negative selection algorithms play an important role in anomaly detection. Interface detectors are a special negative selection algorithm that completely eliminates outer holes, but there are detection blind areas. This paper proposes a negative selection algorithm with hypercube interface detectors for anomaly detection. It uses self-sample clusters to construct self-space, and boundary self-sample clusters to describe the interface detectors. It eliminates the detection blind area and improves the detection rate. To validate the performance of the proposed method, experiments were conducted using the iris dataset, the skin segmentation dataset, the Breast Cancer of Wisconsin (BCW) dataset, and the Waveform Database Generator (Version 2) dataset. Experimental results show that the proposed method in this paper has a higher detection rate, lower false alarm rate, and fewer detectors than other anomaly detection methods for the same parameters. • HI-detector breaks away from the hyperspherical detector used by traditional negative selection algorithms. • HI-detector consists of a cluster of multiple hypercube self-samples. • HI-detector completely eliminates duplicate coverage of holes and detectors. • HI-detector has only one parameter that needs to be set. [ABSTRACT FROM AUTHOR]

The development of stereo matching algorithm is still one of the challenging problems, especially in ill-posed regions. Hence, this article presents a survey on the algorithm frameworks related to the stereo matching algorithm. Based on the early survey that had been conducted, two major frameworks available in current stereo matching algorithm development, they are traditional and artificial intelligence (AI) frameworks. Most of the traditional methods are very low accuracy compared to the AI-based approach. This can be observed in the standard benchmarking dataset, such as from the KITTI and the Middlebury, where AI methods rank at the top of the accuracy list. Additionally, the trend for solving computer vision problems uses AI or machine learning tools that become more apparent in recent years. Thus, this paper is focusing on the survey between the deep learning frameworks, which is one of the machine learning tools related to the convolutional neural network (CNN). Several mixed approaches between CNN based method and traditional handcraft method, as well as the end to end CNN method also discussed in this paper. [ABSTRACT FROM AUTHOR]

Cloud Computing is an alternative to conventional IT Outsourcing. As a result, cloud computing migration between organizations is rapidly growing. The adoption of this technology brings many positive aspects but prescribes various risks and concerns. An organization that officially provides its cloud computing services to external providers and implies that its IT functions and process are outsourced to third-party providers of BPO services. For privacy-making public audit processes in dynamic cloud data storage, a modified Boneh-Lynn-Shachame Dynamic Auditing (MBLSDA) algorithm is suggested in this paper. The proposed algorithm executes an audit process for multiple users based on a batch audit simultaneously and effectively to enable Third Party Auditing (TPA). This paper integrates the homomorphic authenticator in an algorithm of dynamic signing audit by random marking in terms of the privacy conserving public auditing process. When the user of cloud service, store or update the data, it encrypts using Rijndal algorithm, and Boneh-Lynn-Shacham signature generation is used for key generation. Finally, a hybrid of these a proposed Modified Boneh-Lynn-Shacham Signing Dynamic audit the data to provide the security. [ABSTRACT FROM AUTHOR]

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC. [ABSTRACT FROM AUTHOR]

This paper proposes a nonsmooth funnel transformation function-based discrete-time sliding-mode control strategy for the discrete-time servo systems with unknown frictions and disturbances. For obtaining a more accurate discrete-time system model, a filter-based adaptive identification algorithm (FAIA) is introduced, where the unknown measurement noises are considered. Based on the identified system model and a novel discrete-time nonsmooth funnel transformation function (improving the tracking performances), a discrete-time sliding-mode surface is designed to confine the tracking error to a smaller funnel region compared with the predefined one, which has a deterministic performance margin related to the upper bound of the lumped disturbance. Furthermore, selections of steady-state funnel boundary and sliding-mode surface parameter can be guided theoretically according to the upper bound of the lumped disturbance. Experimental results show that the tracking error is guaranteed in the predefined funnel with a deterministic performance margin by using the proposed control strategy. • Taking advantages of nonsmooth error transformation and funnel error surface, a novel discrete-time nonsmooth funnel transformation function (NFTF) is proposed to confine the tracking error and improve its performances. • An NFTF-based discrete-time sliding-mode control (DSMC) is proposed to constrain the tracking error in a predefined funnel zone with a deterministic performance margin. • The selections of steady-state performance boundary and sliding-mode surface parameter are determined by an inequality involving the upper bound of lumped disturbance, which removes the "trial-and-error" approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control scheme to ensure the microgrid control layers are resilient to cyberattacks. The studied microgrid consists of several distributed generation (DG) units and we consider the hierarchical control structure that is common for microgrids. The use of communication channels among DGs has made microgrids more vulnerable and this is where cybersecurity issues arise. In this work we added three algorithms, reputation-based, Weighted Mean Subsequence Reduced (W-MSR) and Resilient Consensus Algorithm with Trusted Nodes (RCA-T), to the secondary control layer of the microgrid and made them resilient to false data injection (FDI) attacks. In reputation-based control, some procedures are used for detecting the attacked DGs and isolating them from the others. W-MSR and RCA-T are Mean Subsequence Reduced (MSR)-based algorithms that fade the effect of attacks without finding them. These algorithms use a simple strategy that ignores some extreme values of neighboring agents, so an attacker can simply get ignored. Our analysis of the reputation-based algorithm is based on scrambling matrices, so the communication graph can switch in a prescribed set. In each of the above cases, to evaluate the performance of the designed controllers, in addition to theoretical analysis, we evaluated and compared the controllers using simulation. • Reputation-based control has a fault detection and isolation mechanism. • W-MSR and RCA-T use a fault-resilient control that make microgrids flexible. • The reputation-based algorithm tolerates switching topologies of communication graph. • The W-MSR and RCA-T algorithms do not require computational complexity on DG level. • The performance is evaluated via simulations in addition to theoretical analysis. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is helping the healthcare industry innovate. Medical professionals are no longer confined to hospitals or physical locations. IoT devices are used in healthcare for remote monitoring of vital signs, transmission and reception of private data, and other similar applications. Protection from cyber threats is the most vital aspect of IoT device operation. Malicious activities result in the leakage of sensitive data and the malfunctioning of devices, which becomes a critical issue. As a result, it is now a serious endeavor to create effective solutions that can safeguard the data and record exchange networks of every IoT device. To address some critical issues with current consensus algorithms, this paper proposes the verifiable random ranking (VRR) based Secure Blockchain Consensus (SBC) mechanism. In place of conventional algorithms, we developed new techniques for managing active nodes and evaluating node reliability and robustness. Our experiments show that the VRR-SBC algorithm speeds up consensus, improves security, and reduces the price of verification. The results of the simulation demonstrate the usefulness of the blockchain era for limited devices and provide an opportunity to assess the scope of the consensus algorithm's applicability. [ABSTRACT FROM AUTHOR]

PDR positioning is currently the core issue of autonomous positioning, there is a problem of high noise and interference in dynamic measurement using PDR (Pedestrian Dead Reckoning) MEMS (Micro-Electro-Mechanical System) device, which is often a low precision multi-source integration sensor. Two new models of improved spectral subtraction (ISS) and PDRS (PDR-Spectral subtraction) spectral subtraction are constructed in this paper according to the spectrum features of the periodic noise generated from personnel stepping in PDR positioning. It is proposed a two-stage noise reduction method of spectrum subtraction-Wiener filtering for obtaining the static magnetic sequence of personnel path and reducing the real-time noise in PDR positioning, which main mechanism is to use the step power spectrum as the background noise for primary noise reduction, secondary Wiener filtering noise reduction according to the correlation between the denoised signal and the reference signal. A self-made MEMS-based positioning device HBEQ-1 and a high-precision magnetometer FVM-400 were used to collect dynamic and static magnetic value from the test area, and a spectral subtraction-Wiener two-stage noise reduction test was carried out. The experimental results show that the improved ISS spectral subtraction and PDRS spectral subtraction are effective in denoising the dynamic magnetic measurement, but there is still a certain deviation between the peak value and the amplitude of the denoised signal. After secondary noise reduction by Wiener filtering, the signal morphology similarity of magnetic curve and the reference reached 0.7975, and the matched path values of the dynamic magnetic value reached around 12000–13000. It showed that the denoising effect is good and the waveform of denoised signal and the reference signal is similar, but there is spatial stretching of the overall magnetic sequence. It is shown that the spectral subtraction-Wiener noise reduction method cannot eliminate the inertial delay bias generated by personnel walking, the processing does not distort the magnetic signal, maintains spatial characteristics of the static magnetic sequence of personnel path, which is an excellent model for noise reduction of dynamic magnetic measurements in PDR positioning. [ABSTRACT FROM AUTHOR]

Intelligent machines are an important carrier of artificial intelligence, which are widely used not only in the industrial field, but also in various fields of daily life and work. Path planning is an important research direction for studying machine intelligence. The main purpose is to search for a feasible path so that the robot can walk along the searched path in a complex environment. This paper aims to study the evolutionary algorithm of intelligent machines based on artificial intelligence. On the basis of introducing the characteristics of artificial potential field algorithm, the concept and characteristics of genetic algorithm, using genetic algorithm as the framework, the artificial potential field algorithm is combined with the crossover of genetic algorithm. In the operator, the optimization performance of the crossover operator is improved. The research results show that the length of the path obtained by the improved algorithm is shorter and better than that of the genetic algorithm of the single-point crossover operator. [ABSTRACT FROM AUTHOR]

With the development of 3D technology, the construction of 3D motion model has become an important research issue in many fields, such as virtual reality, animation and robot vision. In this paper, a 3D motion model construction method based on simulation algorithm is proposed. Firstly, the gravity simulation algorithm is used to simulate the trajectory of the object, and the position data of multiple moments with numerical accuracy of 10cm are obtained. Then, the interpolation algorithm is used to interpolate these position data into continuous curves, and the object trajectory with 1cm accuracy is obtained. Finally, we integrate the obtained trajectory data with 3D modeling software to generate a 3D motion model with real motion effect. The experimental results show that this method has high accuracy and reliability in constructing 3D motion model, and is suitable for many fields. [ABSTRACT FROM AUTHOR]

This paper takes the multimodal COVID-19 related dataset as the research object and carries out research on the content safety monitoring algorithm in the field of COVID-19. It is proposed to carry out research work in the following two aspects. The first is end-to-end topic and sentiment modeling. Turning topic modeling and sentiment analysis into multi-label classification problems essentially unifies topic modeling and sentiment analysis, with the model directly outputting the predicted sample's topic and sentiment. The second is to quantify sentiment analysis results. While modeling sentiment polarity, it also models sentiment intensity. By analyzing public opinion through sentiment polarity and judging people's attention to a topic and the degree of speech safety based on sentiment intensity values, we propose an end-to-end multimodal COVID-19 content quantitative safety detection algorithm (EMQD). The EMQD algorithm is tested on the KJZY2020 dataset and compared with other algorithms. [ABSTRACT FROM AUTHOR]

In the past, it is difficult for people to locate wireless sensors, which is easy to cause problems such as information loss and information islands. However, under the SDN network structure, wireless sensors can be located by specific algorithms. This paper mainly introduces the basic concept of SDN network structure and the localization difficulties of wireless sensor networks, and then designs the network localization system. In the process, the localization algorithm is proposed, and finally the system operation is simulated and tested. Through research, relying on the SDN network structure, the system can locate the wireless sensor, and the accuracy of the location results is up to standard. [ABSTRACT FROM AUTHOR]

Large adjusting camera is an important facility of modern power grid, its own operation must be stable enough, but manual can not control it comprehensively, so it needs to use incremental PID control algorithm to carry out automatic control. This paper will study the application of incremental PID control algorithm in large tuning camera, mainly introduces the basic concept of PID control algorithm, and compares the incremental PID control algorithm with the position PID control algorithm, demonstrates the advantages of the incremental algorithm, and then puts forward its application method, analyzes the application effect of the incremental algorithm. Through research, the incremental PID algorithm has advantages and has more outstanding application value in the control of large adjusting camera, which can better keep the large adjusting camera in stable operation state. [ABSTRACT FROM AUTHOR]

With the development of science and technology, aspherical surface has become an indispensable component in optical system. However, the aspheric surface has more complex optical properties than the spherical surface, so it restricts the rapid development of aspheric surface. In this paper, the main problem of aspherical machining is analyzed, which is"Trajectory control", and the unique optical properties of aspherical surface are analyzed, a trajectory control algorithm based on genetic-BP neural network is proposed to solve the trajectory control in aspheric forming. The method is proved to be feasible and effective. [ABSTRACT FROM AUTHOR]

Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the Mask R-CNN algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a Mask R-CNN image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In Mask R-CNN model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of Mask R-CNN model has significant advantages. [ABSTRACT FROM AUTHOR]

The personalized push service function of university library is beneficial to the library's role and can help students learn, but whether this function can achieve good results depends largely on the accuracy of push, and collaborative filtering algorithm can help improve the accuracy of push. Therefore, in order to use the collaborative filtering algorithm to do a good job in personalized push service, this paper will carry out relevant research, introduce the basic concepts of personalized push service and collaborative filtering algorithm, and then analyze the use of the algorithm in personalized push service, in order to provide reference help. [ABSTRACT FROM AUTHOR]

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes a research method of machine English translation evaluation system based on BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301 respectively, which is much higher than Google translation. A model of machine translation evaluation system based on neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation. [ABSTRACT FROM AUTHOR]

This paper presents a Text similarity detection method based on artificial intelligence and natural language processing. The method combines statistical machine learning and deep learning techniques and designs six models from three perspectives: character-level, word-level, and semantic-level. These models include the diff model based on machine learning, cosine similarity model, Jaccard model, TF-IDF model, as well as the SimCSE and SBERT models based on deep learning methods. To fully leverage the characteristics of these models, three scenarios are designed to calculate the similarity scores based on experience and multiple experimental results. The results show that calculating the similarity scores using these three scenarios not only achieves high accuracy but also requires fewer computational resources. As deep learning and natural language processing technologies continue to advance, Text similarity detection methods based on artificial intelligence and natural language processing will continue to be improved and play a more significant role in practice. Future research can explore more models and algorithms to enhance the accuracy and robustness of plagiarism detection. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology, metallographic research on metal materials has made remarkable progress. In this paper, the authors processed metallographic images using machine learning algorithms to classify the different elements in the images. The classification results divide the elements into three categories, which are metal elements, non-metal elements and metal-nonmetal combination elements. On the basis of this research, a metallographic compression algorithm based on adaptive threshold is also explored, and grayscale experiments are carried out on carbon steel samples. The recognition rate is 98.3%, and the compression rate is 1.06. Experimental results verify the effectiveness of the algorithm in processing metallographic compressed images of metal materials in power grids. [ABSTRACT FROM AUTHOR]

The research paper proposes a Traffic Incident Prediction and Classification System using Naïve Bayes Algorithm (TIPCS) to proactively predict and classify traffic incidents, which can lead to improved incident management and traffic flow. The system utilizes real-time traffic data, including location, date and time, and Traffic incident prediction is the task of using historical and real-time data to forecast the occurrence of traffic incidents, such as accidents, congestion, or road closures, in the future. The system aims to determine whether an incident is likely to occur or not and to classify it accordingly. The system is trained on historical incident data and is continuously updated with new data to improve its accuracy over time. The Naïve Bayes Algorithm is used for incident prediction and forecast, by utilizing this algorithm, TIPCS can accurately predict and classify incidents at 70.03% accuracy. The proposed study has the potential to significantly improve incident management and traffic flow, ultimately benefiting both transportation officials and road users. [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets) are a promising solution for managing the exponential increase in the number of mobile users while maintaining high data rates and coverage. HetNets consist of various cell types with different cell coverage and system capacities. However, the traffic load in HetNets is variable, uneven and random over time, leading to unequal cell loads. Some cells have excessive user presence where the competition for system resources is high, while other cells have low user presence where system resources are not fully utilised. This paper proposes a mobility load balancing algorithm that prioritises millimetre-wave (mmWave) cells in target cell selection and user association to provide load balancing in 5G HetNets and improve overall system performance. A two-step target cell selection method that considers the load level of cells and reference signal received power (RSRP) is proposed. This method prioritises mmWave cells that meet cell load level and RSRP conditions in target cell selection, taking full advantage of the unique properties of mmWave cells (enhanced network throughput, spectral efficiency and enormous bandwidth) to balance traffic loads. The HO triggering and decision-making process is independently performed for each user. In case the serving cell is overloaded, different HO procedures are applied for load balancing depending on the target cell type (macro base station (BS) or mmWave BS). The scope of the study is further expanded by applying different HO procedures according to the serving and target cell types to maintain mobility robustness in case the serving cell is not overloaded. This research proposes various system scenarios with fixed HO margin (HOM) values and fixed time-to-trigger (TTT) durations to examine the effects of HCP settings on the proposed algorithm's performance in terms of average load level of the serving cell, throughput, spectral efficiency and call dropping ratio (CDR). The system that provides the highest overall performance is applied to the proposed algorithm. To further assess and validate the performance of the proposed algorithm, it is compared with other load balancing algorithms from the literature. The simulation results reveal that the proposed algorithm does provide noticeable enhancements in network performance in terms of load level, throughput, spectral efficiency and CDR for various mobile speed scenarios as compared to existing load balancing algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes the planar image-space trajectory (PIST) planning algorithm for contour following using computer vision and a force control scheme. The PIST algorithm generates machining trajectories offset to a planar workpiece with an applied force direction so that a force controller can be utilized. The PIST algorithm starts with an image of the workpiece, corrects for the perspective of the camera, defines a contour following trajectory in image space, and then transfers the trajectory into the robot's workspace for playback. A case study is presented where the PIST algorithm is applied to deburring sheet metal parts for validation through experimentation. A comparison is performed between the PIST algorithm and CAD/CAM software using CAD data for path design and a laser scanner for localization. The PIST planner achieved a positional accuracy greater than 1.7 mm compared to the CAM/CAM accuracy of 1.2 mm. An admittance controller successfully accounted for the positional error to achieve satisfactory and equivalent surface finish results for both trajectory planning approaches. The PIST planner is a beneficial alternative for trajectory planning as it significantly reduced the setup and process time while adapting to deviations from the design dimensions and manufacturing imperfections. [ABSTRACT FROM AUTHOR]

Object counting presents a significant challenge for applications demanding high levels of automation. In recently ears, semi-supervised counting methods have been explored to alleviate the burden of data annotation. Despite considerable progress, the state-of-the-art methods treat pixels with varying levels of difficulty equally during each training iteration, resulting in an inflexible consistency training that can lead to high supervision loss at the onset of training and potentially cause network collapse. To address this limitation, we propose an uncertainty-adaptive consistency training algorithm for semi-supervised object counting networks in this paper. Our approach leverages image colorization to construct colorization uncertainty, which can measure the difficulty of object pixels and background pixels. To ensure superior uncertainty estimation, we then enhance this colorization uncertainty by using density features to guide the learning of colorization. Furthermore, an uncertainty-adaptive consistency training (UACT) algorithm is introduced for the consistency training of density maps. By adaptively adjusting the uncertainty threshold, we prioritize harder pixels for selection, gradually incorporating easier ones into the consistency training. The effectiveness o f t he UACT is evaluated through extensive experiments on two datasets. Experimental results demonstrate that our method outperforms state-of-the-art semi-supervised counting methods. [ABSTRACT FROM AUTHOR]

Magnetic localization techniques are used in in vivo therapy, drug delivery, and surgical localization widely due to their ability to locate in an unobstructed environment precisely. The Levenberg-Marquardt (L-M) algorithm is one of the most common magnetic localization algorithms due to its fast optimization speed and high precision, but it requires high initial values such that improper initial values can easily lead to local optimization. In this paper, the performance of ten commonly used intelligent optimization algorithms is compared based on the mathematical model of the permanent magnet. The final position of the magnet is obtained by a fusion algorithm that uses the result of the intelligent optimization algorithm as the initial value of L-M. The real-time localization accuracy of this fusion algorithm is verified on a hardware platform and experiments demonstrate that the integration of the TSA algorithm and L-M algorithm fusion yields higher performance of the magnetic positioning system. [ABSTRACT FROM AUTHOR]

Object detection techniques are the foundation for the artificial intelligence field. This research paper gives a brief overview of the You Only Look Once (YOLO) algorithm and its subsequent advanced versions. Through the analysis, we reach many remarks and insightful results. The results show the differences and similarities among the YOLO versions and between YOLO and Convolutional Neural Networks (CNNs). The central insight is the YOLO algorithm improvement is still ongoing.This article briefly describes the development process of the YOLO algorithm, summarizes the methods of target recognition and feature selection, and provides literature support for the targeted picture news and feature extraction in the financial and other fields. Besides, this paper contributes a lot to YOLO and other object detection literature. [ABSTRACT FROM AUTHOR]

Traditional recommendation algorithms have problems such as data sparseness and not paying attention to the diversity of recommendation results. In this paper, we use LDA to extract topics of comments about movies, and identify the emotional tendencies related to topics. As a result, we enrich user interest model and product feature model based on emotional tendencies to improve content-based recommendation algorithms. Most of prior work on applying sentiment classification to recommendation systems only consider the use of sentiment dictionaries to judge polarity, and adopt pattern matching methods to identify features. This paper uses BERT to train sentiment classification models and uses LDA to extract topics. The algorithm is run on the movie review database crawled from Douban, and the experimental result showed that the diversity of recommendation lists had been significantly improved. [ABSTRACT FROM AUTHOR]

Graph is a widely used data structure which can be regarded as a generalized measure metirc space. Since a graph not only includes points but also relation between points, the distance between two graphs is difficult to measure. In recent years, Gromov-Wasserstein discrepancy has been proposed as a pseudometirc on graphs which has a complete theoretical basis, but has few applications. Therefore, this paper reviews the basic ideas, applications and improvements of Gromov-Wasserstein. Since Gromov-Wasserstein discrepancy is a quadratic programming and difficult to calculate, this paper focuses on the iterative algorithm for solving this discrepancy. At the end, we look forward to the development of Gromov-Wasserstein discrepancy. [ABSTRACT FROM AUTHOR]

This paper proposes a kernel function selection mechanism a support vector machine(SVM) under sparse representation and its application in bearing fault diagnosis. For a given data sample, a total of 125,150 different types of kernel functions and different parameters to classify and obtain the accuracy, root mean square error (RMSE) and mean square correlation coefficient (MSCC) of each training, these three values into an overcomplete redundant sparse dictionary. The OMP algorithm is used to solve the sparse coding, that are nonzero in the sparse coding are function types and parameters corresponding to these nonzero atoms according to the one-to-one correspondence between the sparse coding and the sparse dictionary. The nonzero atoms in the sparse coding and the kernel function types and parameters into the kernel function fitness table. According to the selection mechanism, we select the type of kernel function that is most suitable for the given data. A SVM is then composed of selected kernel function types, and PSO algorithm is used to the relevant parameters for classification of unknown data to Finally, we perform simulations and engineering experiments involving high-speed bearing fault diagnosis to verify the superiority of the selection mechanism. • This paper focuses on an intelligent diagnosis method for the kernel function selection mechanism of a support vector machine • Formulate a set of rules that make the selected kernel function type of support vector machine (SVM) is most suitable for the data. • Perform a simulation experiment and engineering experiment of high-speed bearing fault diagnosis, the experimental results show that the support vector machine model constructed by the kernel function selected by the proposed method is superior to the support vector machine model composed of other kernel functions in terms of accuracy or mean square error, which ensures the generalization ability of the model and reduces the computation time to some extent. [ABSTRACT FROM AUTHOR]

With the advancement of remote sensing technology, the amount of remote sensing image (RSI) has increased sharply. The explosion in data volume requires higher standards of image compression and encryption technology. This paper proposes a lossless and lossy encryption-compression method for RSI. Because RSIs contain important and unimportant information, using a normal algorithm could ignore their important information or reduce the computational efficiency, so we use the trained model by DeepLabv3+ to segment the region of interest (ROI) and the region of non-interest (RONI), and implement lossless and lossy algorithms respectively. The whole algorithm is based on the newly proposed hyperchaotic system 2D Tent coupled Infinite collapse map (2D TICM). According to the 2D TICM, the dynamic 3D Latin cube is generated. This cube is then used for dynamic scrambling algorithms between multiple planes (3D LMBS), within planes (3D LMIS), and for the multiple dynamic S boxes substitution algorithm. Due to the large size of RSI, the image is divided into blocks, and MATLAB parallel mechanism is used to encrypt each block at the same time. First, the lossy part is scrambled by 3D LMIS, then compressed with 2D compressive sensing (2D CS). Finally, each block is substituted with a different S box. A CS reconstruction algorithm combining decryption and reconstruction, 2D projection gradient chaotic decryption algorithm (2D PG-CD), is proposed. The lossless part embeds the encryption into JPEG-LS, encrypts while compressing, and then encrypts according to 3D LMBS and S boxes. The algorithm in this paper considers the characteristics of RSI, experiments show that it has good security and compression performance. • A hyperchaotic system 2D Tent coupled infinite collapse map (2D-TICM) is proposed. • Lossy compression 2D CS and 2D PG-CD improves the computational efficiency. • Encryption step is integrated into JPEG-LS and compression to retain important information. • Dynamic 3D Latin cube is designed with chaos and multiple S boxes substitution are proposed. [ABSTRACT FROM AUTHOR]

Normalization of objectives plays a crucial role in evolutionary multi-objective optimization (EMO) to handle objective functions with different scales, which can be found in real-world problems. Although the effect of normalization methods on the performance of EMO algorithms has been investigated in the literature, that of preference-based EMO (PBEMO) algorithms is poorly understood. Since PBEMO aims to approximate a region of interest, its population generally does not cover the Pareto front in the objective space. This property may make normalization of objectives in PBEMO difficult. This paper investigates the effectiveness of three normalization methods in three representative PBEMO algorithms. We present a bounded archive-based method for approximating the nadir point. First, we demonstrate that the normalization methods in PBEMO perform significantly worse than that in conventional EMO in terms of approximating the ideal point, nadir point, and range of the PF. Then, we show that PBEMO requires normalization of objectives on problems with differently scaled objectives. Our results show that there is no clear "best normalization method" in PBEMO, but an external archive-based method performs relatively well. • This paper investigates the effectiveness of three normalization methods in three representative PBEMO algorithms. • We demonstrate that the normalization methods in PBEMO perform significantly worse than that in conventional EMO in terms of approximating the ideal point, nadir point, and range of the PF. scaled objectives. • We show that there is no clear "best normalization method" in PBEMO, but an external archive-based method performs relatively well. [ABSTRACT FROM AUTHOR]

Elevator systems in buildings face challenges due to unpredictable passenger flow, which can make scheduling elevators complicated to optimize their operation. Most of the existing algorithms are developed based on pattern recognition and may not be effective in scenarios where patterns are difficult to classify, especially when elevator operations involve uncertain human behaviors. To address this issue, this paper proposes a time-dependent optimization model that considers long-term and multi-floor peaks, with a focus on peak floors. The proposed model accounts for dynamic scheduling patterns of elevators and represents passenger flow direction as a relation matrix. The proposed direction optimization method contains several functions to guide iteration direction and improve the efficiency of an iteration process based on classical algorithms. This method also ensures the stability of Markov chains by adjusting an iteration process. The feasibility of the proposed method is supported by the relevant theory, and experimental results show that the direction-optimized algorithms outperform classical algorithms, resulting in the superb operating efficiency of elevators at a lower cost. This paper contributes to the development of efficient algorithms for scheduling elevators in complex traffic patterns, which can improve performance of elevator group systems in buildings. The proposed method is not only limited to elevators but can also be extended to other transportation systems with flow requirements. • Estimating the relation between peak floors without pattern recognition. • Four relations are defined to classify the passenger flow. • Dynamically modifying three metaheuristic algorithms' iteration law. • The application for transportation systems with unpredictable flow. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new multichannel fast normalized least mean square (MC-FNLMS) algorithm for audio teleconferencing systems. The main idea behind the proposed MC-FNLMS algorithm is based on the introduction of a first order prediction process on the input noisy signals before the adaptive filtering process of each channel. The proposed algorithm is considered as an alternative solution for the existing algorithm in multi-channel applications. A full stability analysis of the proposed algorithm is derived along this paper. Simulation results of a comparison between the proposed MC-FNLMS algorithm and the two classical Multichannel Normalized Least Mean Square (MC-NLMS) and the Multichannel Affine Projection (MC-APA) algorithms, in terms of convergence speed, noise reduction and computational complexity, show the superiority of the proposed algorithm on multichannel adaptive noise reduction. [ABSTRACT FROM AUTHOR]