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This paper proposes a fault‐tolerant control strategy for the air path system of a turbocharged diesel engine, considering the simultaneous presence of matched and mismatched external disturbances, additive and loss of effectiveness fault modes in the EGR and VGT subsystems. Firstly, a disturbance observer based on H∞$$ {H}_{\infty } $$ theory is designed for estimating additive faults and external disturbances, and the upper bound of observation error is obtained. Based on the observation information, an integral sliding mode surface is designed, and the effectiveness of the sliding mode surface is analyzed. For the loss of effectiveness faults in the actuators, a novel adaptive update sliding mode controller is designed. The proposed control algorithm can get the fault information of the system and achieve fault‐tolerant control of through controller reconstruction. Ultimately, the effectiveness of the proposed method is validated through simulations and comparative analysis. [ABSTRACT FROM AUTHOR]

The positioning of civil aviation aircraft relative to a geographic reference point on Earth in a Cartesian frame is significant to detect the deviations from the desired path, especially for high-altitude airports or special airports based on performance-based navigation (PBN). To obtain these critical deviations during aircraft approach and landing, it is fundamental to estimate the continuous flight variables and discrete flight modes simultaneously with enough accuracy. With the coordinate conversion between the North, East, and Down (NED) frame and the geographic coordinate system based on World Geodetic System 1984 (WGS-84) considered, this study proposed a non-linear stochastic hybrid estimation algorithm with adaptive square-root unscented particle filtering (ASR-UPF) to estimate the true path. The probabilities of mode transition, represented by the normal cumulative density function of continuous states, determine whether to proceed with mode transitions. In addition, the adaptive update characterized by tracking variable noise and the importance sampling distributions based on the results of square-root unscented Kalman filtering (SR-UKF), as a comparative study of continuous system filtering, were used. The experiments illustrated the ASR-UPF is able to reduce the state estimation error more effectively, and more promptly track the error caused by incorrect mode estimation with adaptability compared to the SR-UKF. A further test with real flight data indicates that the proposed method gives the refined estimation of position and azimuth in NED frame. [ABSTRACT FROM AUTHOR]

Due to its quick tracking, simple deployment, and straightforward principle, correlation filter-based tracking methods continue to have significant research implications. In order to make full use of different features while balancing the tracking speed and performance, the adaptive cascaded and parallel feature fusion-based tracker (ACPF), which could estimate the position, rotation and scale, respectively, is proposed. Comparing with other correlation filter-based trackers, the ACPF could fuse deep and handcrafted features in both Log-Polar and Cartesian branch and update templates according to the weights of response maps adaptively. Adaptive linear weights (ALW) are proposed to fuse feature response maps adaptively in the Log-Polar coordinates branch to improve the estimation of scale and rotation of object by solving constrained optimization problems. Response maps of shallow and deep features are merged adaptively by cascading numerous ALW modules in the Cartesian branch to make better utilize shallow and deep feature, and increase tracking accuracy. The final results are computed simultaneously by Cartesian and Log-Polar branch in parallel. Additionally, the learning rates are automatically changed in accordance with the weights of the ALW module to execute the adaptive template update. Extensive experiments on benchmarks show that the proposed tracker achieves the comparable results, especially in dealing with the challenges of deformation, rotation and scale variation. [ABSTRACT FROM AUTHOR]

Accurately predicting trends in NOx emission is essential for effectively controlling pollution in municipal solid waste incineration (MSWI) power plants. However, the MSWI process exhibits notable dynamic nonlinearity, time series characteristics, and fluctuations that are distinct from those present in fossil fuel combustion processes. Therefore, the model must possess excellent capabilities in handling time series and nonlinear features while achieving adaptive updates to account for complex working conditions. To address these issues, we have developed a robust prediction model for NOx emission trends using the bi‐directional long short‐term memory (Bi‐LSTM) deep learning algorithm. This model encompasses maximum information coefficient and expert experience for input variables selection, parameter optimization using the linear inertial weight particle swarm algorithm (LDWPSO), and an adaptive update strategy based on probabilistic statistics. The prediction performance of this model was compared to that of the traditional and widely used backpropagation neural network (BPNN), extreme learning machine (ELM), and LSTM. Furthermore, we verified the adaptive update effect of the proposed model using additional data. The results demonstrate that the proposed model exhibits robust prediction and adaptive capabilities. This study's originality is presenting a satisfactory trend prediction for NOx emission from the MSWI process using an adaptive LDWPSO‐(Bi‐LSTM) model. It will be essential for the optimization and control of NOx emissions from the MSWI process. [ABSTRACT FROM AUTHOR]

Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.

The positioning of civil aviation aircraft relative to a geographic reference point on Earth in a Cartesian frame is significant to detect the deviations from the desired path, especially for high-altitude airports or special airports based on performance-based navigation (PBN). To obtain these critical deviations during aircraft approach and landing, it is fundamental to estimate the continuous flight variables and discrete flight modes simultaneously with enough accuracy. With the coordinate conversion between the North, East, and Down (NED) frame and the geographic coordinate system based on World Geodetic System 1984 (WGS-84) considered, this study proposed a non-linear stochastic hybrid estimation algorithm with adaptive square-root unscented particle filtering (ASR-UPF) to estimate the true path. The probabilities of mode transition, represented by the normal cumulative density function of continuous states, determine whether to proceed with mode transitions. In addition, the adaptive update characterized by tracking variable noise and the importance sampling distributions based on the results of square-root unscented Kalman filtering (SR-UKF), as a comparative study of continuous system filtering, were used. The experiments illustrated the ASR-UPF is able to reduce the state estimation error more effectively, and more promptly track the error caused by incorrect mode estimation with adaptability compared to the SR-UKF. A further test with real flight data indicates that the proposed method gives the refined estimation of position and azimuth in NED frame.

Fish growth prediction provides important information for optimising production in aquaculture. Fish usually exhibit different growth characteristics due to the variations in the environment, the equipment used in different fish workshops and inconsistent application by operators of empirical rules varying from one pond to another. To address this challenge, the aim of this study is to develop an adaptive fish growth prediction method in response to feeding decision. Firstly, the practical operational experience in historical feeding decisions for different fish weights is extracted to establish the feeding decision model. Then, a fish weight prediction model is established by regression analysis methods based on historical fish production data analysis. The feeding decision model is integrated as the input information of the fish weight prediction model to obtain fish weight prediction. Furthermore, an adaptive fish growth prediction strategy is proposed by continuously updating model parameters using new measurements to adapt to specific characteristics. The proposed adaptive fish growth prediction method with empirical knowledge extraction is evaluated by the collected production data of spotted knifejaw (Oplegnathus punctatus). The results show that established models can achieve a good balance between goodness-of-fit and model complexity, and the adaptive prediction method can adapt to specific fish pond's characteristics and provide a more effective way to increase fish weight prediction accuracy. The proposed method provides an important contribution to achieving adaptive fish growth prediction in a real time from the view of aquaculture practice for spotted knifejaw. • The feeding decision model is established by empirical knowledge extraction. • The fish weight prediction is obtained by regression analysis method. • The adaptive updating strategy is proposed to adapt to specific characteristics. • The production data of spotted knifejaw is used to model and verify method. [ABSTRACT FROM AUTHOR]

Motion blur is a quite tricky issue in object tracking community. In recent years, many trackers have been explored to address this issue without sensational performance. This paper proposes a novel correlation tracking framework with Recurrent Neural Network (RNN) deblurnet and proposal detection to handle motion blur and heavy occlusion in object tracking tasks. We take advantage of high efficient Kernelized Correlation Filter (KCF) tracker, a typical method that exploits the circulant structure and the kernel trick to enhance the performance, and furthermore incorporate two regression methods in it. We employ RNN as our baseline of deblurnet, and introduce residual block and ConvLSTM in our deblur network to improve the result of deblurring. In addition, we suggest an edge information based rectification system to overcome the challenge of target occlusion. Finally, we update the model adaptively in term of the feedback from high-confidence tracking results to avoid the model degradation. Extensive experimental results demonstrate our tracker outperforms several state-of-the-art trackers on the OTB-2015 and VOT-2016 datasets.

Infrared target tracking is a key technology in many applications of computer vision, such as traffic control, pedestrian surveillance, intelligent detection, and so on. However, due to the lack of color and texture information, tracking drift is easy to happen when the tracker encounters objects that are similar to the target in terms of grayscale distribution or contour shape. To address this issue, we propose a hierarchical convolution fusion-based adaptive Siamese (HCFA-Siam) network for infrared target tracking. First, in order to improve the discriminative capability of the network, a new hierarchical convolution fusion network is proposed to fuse the shallow spatial information and deep semantic information. Then, a channel attention module is followed to selectively enhance the feature channels of the infrared target, making the feature more discriminative. Finally, to further distinguish the target from the above-mentioned interference of appearance similarity, we add an adaptive update mechanism-based negative template pool at the front end of the network as the second input of the template branch. The experimental results on infrared sequences show that our method outperforms other state-of-the-art trackers in both success rate and precision. [ABSTRACT FROM AUTHOR]

Real-time computer vision tasks are emerging in consumer electronics with lightweight computing performance, which are an exquisite design art to balance the computational efficiency and accuracy. In this paper, we present the embedded background subtraction (EBGS) – an optimization algorithm for the entire process to increase computational efficiency and detection accuracy simultaneously. EBGS exploits a simple and efficient Additive Increase Multiplicative Decrease (AIMD) filter to improve the foreground detection accuracy without spending too much time. Moreover, the design combination between the contracted codebook background subtraction (BGS) model and a random model update is proposed to reduce the time consumption. Experiments demonstrate that EBGS can decrease the computing overhead for the three parts of BGS process simultaneously and achieve real-time performance and satisfactory detection accuracy under challenging environments. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The phantom-node method is applied in the phase field model for mesh coarsening to improve computational efficiency. When the adaptive mesh refinement scheme is used, the fine mesh still remains along the path where crack already passed. However, structural properties such as stiffness, damage and strain energy of an element completely partitioned by the crack path remain almost unchanged during the rest of the simulation. Using the phantom-node method, the fine mesh in the crack path domain is recovered into a coarse mesh. This numerical scheme significantly reduces the number of degrees of freedom involved in the computation. Using the mesh coarsening method together with the adaptive mesh refinement and adaptive update schemes, the computational cost is further reduced. Through several numerical examples, the performance of the proposed method is demonstrated. • We propose a mesh coarsening scheme using the phantom-node method. • The proposed numerical scheme significantly reduces the number of DOFs during simulation. • Its effectiveness is investigated through several numerical examples. [ABSTRACT FROM AUTHOR]

Tracking methods based on correlation filter have been showing great advantages and huge potential. However, it will lead to tracking drift when the target is disturbed, such as occlusion, illumination variation, deformation, and others. In the pursuit of tracking accuracy and robustness, a feature integration model and an adaptive update method for robust tracking are proposed. First, the fine-tuned convolutional neural networks medium is employed to obtain the deep features. The deep features are integrated with traditional manual features by combining with the independent component analysis with reference method to obtain more discriminative features. Second, in the model update phase, an adaptive updating strategy based on the center shift Euclidean distance of image patch is proposed to reduce the unnecessary calculation in model training so as to speed up the tracking speed. Finally, the tracker proposed is evaluated on online tracking benchmark (OTB-2015) and visual object tracking benchmark (VOT-2016). The experimental results show that the method that integrates deep features with traditional manual features can distinguish background and target better. Thus the proposed VOT algorithm performs better robustness and accuracy against state-of-the-art methods when dealing with challenging environments. [ABSTRACT FROM AUTHOR]

Visual tracking is a challenging issue in the field of computer vision due to the objects' intricate appearance variation. To adapt the change of the appearance, multiple channel features which could provide more information are used. However, the low level feature could not represent the structure of the object. In this paper, a superpixel-based adaptive tracking algorithm by using color histogram and haar-like feature is proposed, whose feature is classified into the middle level. Based on the superpixel representation of video frames, the haar-like feature is extracted at the superpixel level as the local feature, and the color histogram feature is applied with the combination of background subtraction method as the frame feature. Then, local features are clustered and weighted according to the target label and the location center. Superpixel-based appearance model is measured by using the sum of the voting map, and the candidate with the highest score is selected as the tracking result. Finally, an efficient template updating scheme is introduced to obtain the robust results and improve the computational efficiency. The proposed algorithm is evaluated on eight challenging video sequences and experimental results demonstrate that the proposed method can get better performance on occlusion, illumination variation and transformation. [ABSTRACT FROM AUTHOR]

Today, determining background which forms the basis of the video surveillance system whose use is increasing in parallel with developing technology, is a complicated process to implement for cases involving dynamic scene. In this study, we proposed a block-based adaptive method which can be adaptable to dynamic environmental conditions. By grouping the pixels in the picture frame as 2 × 2 non-overlapped blocks, we reduced the amount of noise and the time delay caused by processing of pixels. By a simple counter structure, we created an adaptive threshold parameter which can be adapted according to the case in N surrounding. We used this parameter to perform an update of the background model and reduced background model normalization process. So, we reduced the update time of background operably. Our proposed method achieved successful results in levels of grey levels. [ABSTRACT FROM AUTHOR]

In order to improve the transient and static performances of an engine speed control system in a wide speed range, this paper presents an adaptive double closed-loop control strategy. The control scheme possesses intake manifold pressure inner closed-loop adaptive proportional-integral control and engine speed outer closed-loop adaptive proportional-integral control for achieving the tracking precision in a wide range of speed, as well as adaptive nonlinearity and feedforward compensators for overcoming parameter uncertainty and nonlinearity. The whole closed-loop system's stability and the speed tracking convergence are ensured theoretically by the Lyapunov stability theory and the LaSalle invariant principle. The effectiveness of the proposed control strategy is validated through the operation results on the simulator of a V6 engine exploited by the Research Committee of the Society of Instrument and Control Engineers of Japan. [ABSTRACT FROM AUTHOR]

Virtual metrology involves the estimation of metrology values using a prediction model instead of metrological equipment, thereby providing an efficient means for wafer-to-wafer quality control. Because wafer characteristics change over time according to the influence of several factors in the manufacturing process, the prediction model should be suitably updated in view of recent actual metrology results. This gives rise to a trade-off relationship, as more frequent updates result in a higher accuracy for virtual metrology, while also incurring a heavier cost in actual metrology. In this paper, we propose an intelligent virtual metrology system to achieve a superior metrology performance with lower costs. By employing an ensemble of artificial neural networks as the prediction model, the prediction, reliability estimation, and model update are successfully integrated into the proposed virtual metrology system. In this system, actual metrology is only performed for those wafers where the current prediction model cannot perform reliable predictions. When actual metrology is performed, the prediction model is instantly updated to incorporate the results. Consequently, the actual metrology ratio is automatically adjusted according to the corresponding circumstances. We demonstrate the effectiveness of the method through experimental validation on actual datasets. [ABSTRACT FROM AUTHOR]

Many supervised clustering algorithms have been developed to find the optimal clusters for static datasets by presetting some parameters, but they are seldom suitable for dynamic datasets, such as the data stream of chunks. To find the optimal clusters of the data stream of chunks, a novel Supervised Adaptive Incremental Clustering (SAIC) algorithm is proposed. SAIC can cluster dynamic datasets of arbitrary shapes and sizes automatically. It includes learning and post-processing phases. In the learning phase, each cluster updates adaptively according to its learning rate that is calculated from its counter value. All data points are shuffled at each iteration in order to make SAIC insensitive to the input order of data points. In the post-processing phase, the outliers or boundary points are eliminated according to the counter value of each cluster and the number of iterations. Four synthetic datasets and fourteen UCI datasets are used to evaluate the performance of SAIC, respectively. The experiments on UCI datasets show that SAIC reaches to or outperforms some other supervised clustering algorithms and several unsupervised incremental clustering algorithms. In addition, three data stream of chunks are used to evaluate SAIC from different aspects, which shows SAIC has the scalability and incremental learning ability for the clustering of data streams of chunks. [ABSTRACT FROM AUTHOR]

An accurate battery parameter and state estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. This paper tries to make three aspects of effort. First, to avoid the battery state-of-charge (SoC) estimation inaccuracy brought by the variation of the model parameter under different aging level and operation condition, a novel dual H infinity filters was proposed and employed to execute the online measured data based battery parameter and SoC estimation. Second, to overcome the drawback of the H infinity filters are sensitive to their initial noise information. An adaptive H infinity filter employing the covariance matching approach was proposed and applied to realize a robust SoC estimation. Last, the accurate estimate of battery parameter and SoC were obtained real-timely through model-based dual H infinity filters. A systematic evaluation on the different algorithms based SoC estimation was carried out. Experimental results on various degradation states of lithium-ion polymer battery cells further verified the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Object tracking based on sparse representation formulates tracking as searching the candidate with minimal reconstruction error in target template subspace. The key problem lies in modeling the target robustly to vary appearances. The appearance model in most sparsity-based trackers has two main problems. The first is that global structural information and local features are insufficiently combined because the appearance is modeled separately by holistic and local sparse representations. The second problem is that the discriminative information between the target and the background is not fully utilized because the background is rarely considered in modeling. In this study, we develop a robust visual tracking algorithm by modeling the target as a model for discriminative sparse appearance. A discriminative dictionary is trained from the local target patches and the background. The patches display the local features while their position distribution implies the global structure of the target. Thus, the learned dictionary can fully represent the target. The incorporation of the background into dictionary learning also enhances its discriminative capability. Upon modeling the target as a sparse coding histogram based on this learned dictionary, our tracker is embedded into a Bayesian state inference framework to locate a target. We also present a model update scheme in which the update rate is adjusted automatically. In conjunction with the update strategy, the proposed tracker can handle occlusion and alleviate drifting. Comparative results on challenging benchmark image sequences show that the tracking method performs favorably against several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

We adopt the self-adaptive strategy to update the barrier parameter of a feasible primal-dual interior-point algorithm. We obtain two adaptive updating methods, namely, cheap updates and sharp updates. We compare the effectiveness of the short updates with the adaptive update methods on some benchmark problems. The numerical results show that the sharp updates method is superior to short updates and cheap updates methods. [ABSTRACT FROM AUTHOR]

This paper describes a novel update mechanism for large wireless ad-hoc sensor networks (WASNs). In wireless sensor networks, the nodes may have to be reprogrammed, especially for design-implement-test iterations. Manually reprogramming is a very cumbersome work, and may be infeasible if nodes of the network are unreachable. In addition, replacing the executed application on a node by transmitting the complete program image is inefficient for small changes in the code either. It consumes a lot of bandwidth and time. Therefore, an on-the-fly update mechanism is required. This paper exploits programmable packets to update sensor behaviors. To reduce the code transferred and power consumption, a group management architecture is developed. This architecture helps reduce power consumption and increase node number that control by Leader Node in WASNs. The proposed update mechanism, SmartMote, has been implemented on the Tmote-based Octopus II sensor node. Performance evaluation as well as measurement is conducted in the paper to illustrate the significance of the proposed mechanism. [ABSTRACT FROM AUTHOR]

Abstract: Suppression of the fixed pattern noise (FPN) on the infrared (IR) focal plane arrays is significant to enhance the IR video quality. This paper presents a novel fixed pattern bias noise suppression method for IR video based on background modeling. Since FPN exists as steady states in the temporal aspects, it can be modeled as background while the time-variant scene and object can be modeled as foreground. Consequently, FPN can be suppressed by background subtraction. Assume that the video sequence used for modeling contains motion or variation, and the video is corrupted by additive noises that drift slowly with time. The bias can be estimated by background modeling. We subtract bias to get the corrected image and adaptively update the FPN model on-line. We test our proposed algorithm with real IR videos and compare it with some well-known algorithms. Experiments have shown that our method outperforms the state-of-the-art nonuniformity correction methods on suppressing the FPN efficiently while preserving the details of the IR images and makes the IR video after correction more alike to the real scene. [Copyright &y& Elsevier]