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Individual differences in the timing of developmental processes are often of interest in longitudinal studies, yet common statistical approaches to modeling change cannot directly estimate the timing of when change occurs. The time-to-criterion framework was recently developed to incorporate the timing of a prespecified criterion value; however, this framework has difficulty accommodating contexts where the criterion value differs across people or when the criterion value is not known a priori, such as when the interest is in individual differences in when change starts or stops. This article combines aspects of reparameterized quadratic models and multiphase models to provide information on the timing of change. We first consider the more common situation of modeling decelerating change to an offset point, defined as the point in time at which change ceases. For increasing trajectories, the offset occurs when the criterion attains its maximum ("inverted J-shaped" trajectories). For decreasing trajectories, offset instead occurs at the minimum. Our model allows for individual differences in both the timing of offset and ultimate level of the outcome. The same model, reparameterized slightly, captures accelerating change from a point of onset ("J-shaped" trajectories). We then extend the framework to accommodate "S-shaped" curves where both the onset and offset of change are within the observation window. We provide demonstrations that span neuroscience, educational psychology, developmental psychology, and cognitive science, illustrating the applicability of the modeling framework to a variety of research questions about individual differences in the timing of change. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A latent variable modeling-based procedure is discussed that permits to readily point and interval estimate the design effect index in multilevel settings using widely circulated software. The method provides useful information about the relationship of important parameter standard errors when accounting for clustering effects relative to conducting single-level analyses. The approach can also be employed as an addendum to point and interval estimation of the intraclass correlation coefficient in empirical research. The discussed procedure makes it easily possible to evaluate the design effect in two-level studies by utilizing the popular latent variable modeling methodology and is illustrated with an example.

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection -- the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this paper we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.

What does it mean today to study a problem from a computational point of view? We focus on parameterized complexity and on Column 16 “Graph Restrictions and Their Effect” of D.S. Johnson’s Ongoing guide, where several puzzles were proposed in a summary table with 30 graph classes as rows and 11 problems as columns. Several of the 330 entries remain unclassified into Polynomial or NP -complete after 35 years. We provide a full dichotomy for the Steiner Tree column by proving that the problem is NP -complete when restricted to Undirected Path graphs. We revise Johnson’s summary table according to the granularity provided by the parameterized complexity for NP -complete problems.

Three-dimensional (3D) microdisplacement monitoring plays a crucial role in the assembly of large aircraft. This paper presents a broadly applicable high-precision online 3D microdisplacement monitoring method and system based on proximity sensors as well as a corresponding in situ calibration method, which can be applied under various extreme working conditions encountered in the aircraft assembly process, such as compact and obstructed spaces. A 3D monitoring model is first established to achieve 3D microdisplacement monitoring based only on the one-dimensional distances measured by proximity sensors, which concerns the extrinsic sensor parameters, such as the probe base point (PBP) and the unit displacement vector (UDV). Then, a calibration method is employed to obtain these extrinsic parameters with high precision by combining spatial transformation principles and weighted optimization. Finally, calibration and monitoring experiments performed for a tailplane assembly process are reported. The calibration precision for the PBP is better than ±10 μm in the X and Y directions and ±2 μm in the Z direction, and the calibration precision for the UDV is better than 0.07°. Moreover, the accuracy of the 3D microdisplacement monitoring system can reach ±15 μm. In general, this paper provides new insights into the modeling and calibration of 3D microdisplacement monitoring based on proximity sensors and a precise, efficient, and low-cost technical means for performing related measurements in compact spaces during the aircraft assembly process.

With increasing of data size and development of multi-core computers, asynchronous parallel stochastic optimization algorithms such as KroMagnon have gained significant attention. In this paper, we propose a new Sparse approximation and asynchronous parallel Stochastic Variance Reduced Gradient (SSVRG) method for sparse and high-dimensional machine learning problems. Unlike standard SVRG and its asynchronous parallel variant, KroMagnon, the snapshot point of SSVRG is set to the average of all the iterates in the previous epoch, which allows it to take much larger learning rates and makes it more robust to the choice of learning rates. In particular, we use the sparse approximation of the popular SVRG estimator to perform completely sparse updates. Therefore, SSVRG has a much lower per-iteration computational cost than its dense counterpart, SVRG++, and is very friendly to asynchronous parallel implementation. Moreover, we provide the convergence guarantees of SSVRG for both SC and non-SC problems, while existing asynchronous algorithms (e.g., KroMagnon) only have convergence guarantees for SC problems. Finally, we extend SSVRG to non-smooth and asynchronous parallel settings. Numerical results demonstrate that SSVRG converges significantly faster than the state-of-the-art asynchronous parallel methods, e.g., KroMagnon, and is usually more than three orders of magnitude faster than SVRG++.

This paper presents an analytical model for calculating the Earth discontinuous coverage of satellite constellation with repeating ground tracks by integrating and extending the application of coverage region and route theory. Specifically, the visibility condition for a ground point is represented as a coverage region in the two-dimension map of visibility properties, and the trajectories of satellites with circular orbits and repeating ground tracks are converted to several inclined lines in the map. By analyzing the intersections of the lines and the edge of the coverage region, the coverage durations for the ground point can be calculated. Based on the point coverage, the variations of coverage characteristics along the parallel are analyzed, and the regional or global coverage characteristics of constellations can be obtained. Numerical examples show that the proposed method can accurately and rapidly calculate the coverage characteristics, e.g. revisit time and coverage time. The calculated results are extremely close to those of the Satellite Tool Kit (STK) and are also superior to the existing research results. The proposed analytical model can be a useful tool for constellation design and coverage performance analysis.

In this paper, we explore the mask representation in instance segmentation with Point-of-Interest (PoI) features. Differentiating multiple potential instances within a single PoI feature is challenging because learning a high-dimensional mask feature for each instance using vanilla convolution demands a heavy computing burden. To address this challenge, we propose an instance-aware convolution. It decomposes this mask representation learning task into two tractable modules as instance-aware weights and instance-agnostic features. The former is to parametrize convolution for producing mask features corresponding to different instances, improving mask learning efficiency by avoiding employing several independent convolutions. Meanwhile, the latter serves as mask templates in a single point. Together, instance-aware mask features are computed by convolving the template with dynamic weights, used for the mask prediction. Along with instance-aware convolution, we propose PointINS, a simple and practical instance segmentation approach, building upon dense one-stage detectors. Through extensive experiments, we evaluated the effectiveness of our framework built upon RetinaNet and FCOS. PointINS in ResNet101 backbone achieves a 38.3 mask mean average precision (mAP) on COCO dataset, outperforming existing point-based methods by a large margin. It gives a comparable performance to the region-based Mask R-CNN with faster inference., Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence

Sintering is the preproduction process of ironmaking, whose products are the basis of ironmaking. How to improve the operating performance of the iron ore sintering process has always been a problem that operators are committed to solve. An operating performance improvement method based on prediction and grade assessment is presented in this article. First, considering the data distribution characteristics of the process, a performance index prediction model based on the Gaussian process regression is built, in which the mutual information analysis method is used to select the inputs of the performance index prediction model. Then, the operating performance grade is assessed by a threshold division method. Next, the operating performance grade guides the control of the burn-through point to improve the operating performance. Finally, experimental verification is performed based on the actual running data. The results show that the proposed method has high prediction accuracy, and it is also significant in improving the operating performance. Therefore, this approach provides an effective solution to predict and improve operating performance.

We investigate the implementation of a sparsity promoting hands-off control scheme using the alternating direction method of multiplies (ADMM). In order to minimize the numerical control effort along with the actuation one, only a single ADMM iteration per time step is considered. We analyze the resulting closed-loop dynamics for linear systems with additive disturbances and show that the proposed scheme provides sparse controls as desired and, in addition, ensures input-to-state practical stability. We further point out a close relation between the proposed real-time scheme and (non-minimum time) dead-beat control. Finally, characteristics and effectiveness of the novel hands-off controller(s) are illustrated with numerical examples.

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.

K-means is one of ten popular clustering algorithms. However, k-means performs poorly due to the presence of outliers in real datasets. Besides, a different distance metric makes a variation in data clustering accuracy. Improve the clustering accuracy of k-means is still an active topic among researchers of the data clustering community from outliers removal and distance metrics perspectives. Herein, a novel modification of the k-means algorithm is proposed based on Tukey’s rule in conjunction with a new distance metric. The standard Tukey rule is modified to remove the outliers adaptively by considering whether the data is distributed to the left, right or even to the input data's mean value. The elimination of outliers is applied in the proposed modification of the k-means before calculating the centroids to minimize the outliers' influences. Meanwhile, a new distance metric is proposed to assign each data point to the nearest cluster. In this research, the modified k-means significantly improves the clustering accuracy and centroids convergence. Moreover, the proposed distance metric's overall performance outperforms most of the literature distance metrics. This manuscript's presented work demonstrates the significance of the proposed technique to improve the overall clustering accuracy up to 80.57% on nine standard multivariate datasets.

In recent years, the proximal policy optimization (PPO) algorithm has received considerable attention because of its excellent performance in many challenging tasks. However, there is still a large space for theoretical explanation of the mechanism of PPO's horizontal clipping operation, which is a key means to improve the performance of PPO. In addition, while PPO is inspired by the learning theory of trust region policy optimization (TRPO), the theoretical connection between PPO's clipping operation and TRPO's trust region constraint has not been well studied. In this article, we first analyze the effect of PPO's clipping operation on the objective function of conservative policy iteration, and strictly give the theoretical relationship between PPO and TRPO. Then, a novel first-order policy gradient algorithm called authentic boundary PPO (ABPPO) is proposed, which is based on the authentic boundary setting rule. To ensure the difference between the new and old policies is better kept within the clipping range, by borrowing the idea of ABPPO, we proposed two novel improved PPO algorithms called rollback mechanism-based ABPPO (RMABPPO) and penalized point policy difference-based ABPPO (P3DABPPO), which are based on the ideas of rollback clipping and penalized point policy difference, respectively. Experiments on the continuous robotic control tasks implemented in MuJoCo show that our proposed improved PPO algorithms can effectively improve the learning stability and accelerate the learning speed compared with the original PPO.

In this article, we introduce interval-valued Kuhn-Tucker (KT)-pseudoinvex optimization problems governed by interval-valued path-independent curvilinear integral objective functionals. Concretely, it is proven that an interval-valued KT-pseudoinvex variational control problem is described such that every KT point is an LU-optimal solution. In addition, the main results are highlighted by two illustrative applications describing the controlled behavior of an artificial neural system.

Bearing is one of the critical components in rotating equipment. Therefore, accurate estimation of the remaining useful life (RUL) of bearings plays a vital role in reducing the costly unplanned maintenance and increasing the reliability of machines. This paper proposes a method for bearing prognostics that uses iteratively updated degradation regression models to capture the degradation trend in bearing’s health indicator (HI), and the models are utilized to predict the degradation trajectory of HI and to estimate the RUL of bearings. The importance of determining the time to start prediction by elbow point is explained, which is often overlooked in prognostics. To improve the prognostic performance, an adaptive approach for elbow point detection is designed based on the gradient change of HIs, and a new smooth approach is applied to reduce spurious fluctuations in degradation trajectory. The effectiveness of the proposed method is validated on two publicly available data sets, i.e., IMS and FEMTO bearing prognostics data set, and its prognostic performance is compared with that of three state-of-the-art methods. The obtained results demonstrate that the proposed method can effectively detect elbow point and determine the time to start prediction, and can calibrate the degradation regression model dynamically according to the evolving degradation trend in the HI, which validates its superior prognostic performance.

In this article, a novel interval type-2 Takagi-Sugeno fuzzy c -regression modeling method with a modified distance definition is proposed. The modified distance definition is developed to describe the distance between each data point and the local type-2 fuzzy model. To improve the robustness of the proposed identification method, a modified objective function is presented. In addition, different from most previous studies that require numerous free parameters to be determined, an interval type-2 fuzzy c -regression model is developed to reduce the number of such free parameters. Furthermore, an improved ratio between the upper and lower weights is proposed based on the upper and lower membership function with each input data, and the ordinary least-squares method is adopted to establish the type-2 fuzzy model. The Box-Jenkins model and two numerical models are given to illustrate the effectiveness and robustness of the proposed results.

Finite control-set model predictive control (FCS-MPC) shows superior dynamic performance with simple structure for motor drives. However, conventional FCS-MPC method presents unfixed switching frequency, making it intractable for the design of heat sinks and output filters. To address this issue, this study is devoted to developing a new FCS-MPC strategy with constant switching frequency for permanent magnet synchronous motor (PMSM) drives fed by three-level T-type inverter. The switching frequency regulation is realized by extending the period control technique, where the periods are measured between each rising edge and falling edge of the gate signals. The proposed algorithm can be embedded in the iterative process of the FCS-MPC, which can easily achieve the fixed switching frequency with balanced neutral point potential. The performance of this control strategy is numerically evaluated and experimentally verified by an 11-kW PMSM drive platform.

In this paper, we provide a sparse image restoration algorithm with a SSIM-based objective function. The proposed technique is a modification to the SSIM-inspired OMP (iOMP) and, and it has two parallel sparse restoration paths. One of them is L 2 -sense OMP and the other is SSIM -sense OMP (iOMP). Both paths intersects only at the starting point and gives different quality levels after each iteration. This distinction enables us to select the coefficients of the best quality and to avoid the uncertainty issue of iOMP. From the point of view of the SSIM levels, the conducted experiments proved that, the proposed methodology works better than iOMP and OMP. Also, the performance of this method is checked for significance by the t-test, and the obtained results proved that the method works well especially for large images and the data-independent based dictionary.

In molecular analysis, Spatial Distribution Functions (SDF) are fundamental instruments in answering questions related to spatial occurrences and relations of atomic structures over time. Given a molecular trajectory, SDFs can, for example, reveal the occurrence of water in relation to particular structures and hence provide clues of hydrophobic and hydrophilic regions. For the computation of meaningful distribution functions, the definition of molecular reference structures is essential. Therefore we introduce the concept of an internal frame of reference (IFR) for labeled point sets that represent selected molecular structures, and we propose an algorithm for tracking the IFR over time and space using a variant of Kabschs algorithm. This approach lets us generate a consistent space for the aggregation of the SDF for molecular trajectories and molecular ensembles. We demonstrate the usefulness of the technique by applying it to temporal molecular trajectories as well as ensemble datasets. The examples include different docking scenarios with DNA, insulin, and aspirin. Funding agencies:This work was supported through grants from the ExcellenceCenter at Link¨oping and Lund in Information Technology(ELLIIT) and the Swedish e-Science Research Centre(SeRC). The authors thanks the Swedish National Infrastructurefor Computing (SNIC) for providing computingresources.

A flow-shop production environment is a widely used production setting in the industry, especially preferred when the variety of products and processes is limited. The constant development of technology and the increasing competition in the market compel researchers to seek ways to increase efficiency. Therefore, scheduling remains an important area of study for researchers. When the size of the problem increases, particularly in real-life contexts, it becomes almost impossible to work out a feasible solution to scheduling problems through current computer technologies. In such cases, solutions are often developed using heuristic or meta-heuristic methods. However, the performance of these methods may vary according to the problem or the components of the suggested method of solution. This study aimed to investigate the effect of local search strategies on the solution performance of hybrid firefly and particle swarm optimization algorithm, developed using chaotic local search. We used four different solution methods: the triple local search strategy consisting of insertion, swap and inversion, which were used frequently in earlier studies, and the individual use of each of these three strategies. In this study, the average relative difference values calculated using insertion, swap, inversion and triple strategy were found to be 3.03, 1.27, 2.54 and 1.20, respectively. As a result of the analyses carried out to compare the results of the solutions obtained in the flow-shop scheduling problems of Taillard, it was found that better results were obtained using the triple strategy. These findings can be a point of reference for future research.

In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method - standard Gaussian process (GP) regression - becomes inadequate in processing the growing sensing data of a large size. To overcome the computational challenge, this paper presents an environmental modeling framework using a sparse variant of GP called streaming sparse GP (SSGP). The SSGP is able to handle streaming data in an online and incremental manner, and is therefore suitable for long-term autonomous environmental monitoring. The SSGP summarizes the collected data using a small set of pseudo data points that best represent the whole dataset, and updates the hyperparameters and pseudo point locations in a streaming fashion, leading to high-quality approximation of the underlying environmental model with significantly reduced computational cost and memory demand., Proceedings of OCEANS 2019, SEATTLE

The topology of isosurfaces changes at isovalues of critical points, making such points an important feature when building contour trees or Morse-Smale complexes. Hexahedral elements with linear interpolants can contain additional off-vertex critical points in element bodies and on element faces. Moreover, a point on the face of a hexahedron which is critical in the element-local context is not necessarily critical in the global context. In ‘`Exploring Scalar Fields Using Critical Isovalues’' Weber et al. introduce a method to determine whether critical points on faces are also critical in the global context, based on the gradient of the asymptotic decider in each element that shares the face. However, as defined, the method of Weber et al. contains an error, and can lead to incorrect results. In this work we correct the error.

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point--the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries. To solve this issue, we propose an extra corner attention module to enforce the CNN backbone to pay more attention to object boundaries. Besides, considering that one-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient keypoint-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed CenterNet3D is non-maximum suppression free which makes it more efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art anchor-based one-stage methods and has comparable performance to two-stage methods as well. It has an inference speed of 20 FPS and achieves the best speed and accuracy trade-off. Our source code will be released at https://github.com/wangguojun2018/CenterNet3d., 13 pages, 5 figures

This paper revisits the analysis of the well-known infeed effect on transmission lines distance protection. A new adaptive algorithm to overcome this drawback is proposed, which requires solely impedance data and the operational status of power apparatus to perform overreaching distance zones settings. A comparison of the proposed strategy and the traditional one is performed. The obtained results demonstrate that the proposed settings ensure the correct operation of overreaching zones. Thereby, the backup coverage is improved significantly, besides avoiding distance zones overlapping between adjacent lines. It reveals the proposed strategy usefulness and value from the practical point of view, since it can be implemented with technology readily available on the market.

Summarization: This paper presents a Cumulative Unmanned Aerial Vehicle Routing Problem (CUAVRP) approach to optimize Humanitarian Coverage Path Planning (HCPP). Coverage path planning consists of finding the route which covers every point of a certain area of interest. This paper considers a Search & Rescue mission, using a homogeneous fleet of Unmanned Aerial Vehicles (UAVs). In this scenario, the objective is to minimize the sum of arrival times at all points of the area of interest, thus, completing the search with minimum latency. The HCPP problem is transformed into a Vehicle Routing Problem by using an approximate cellular decomposition technique to discretize the area into a grid, where the rectangles represent the UAV sensor’s field of view. The center points of the formed rectangles, become the nodes used for a UAV routing problem. This approach uses the objective of minimizing the sum of arrival times at customers, found in the Cumulative Capacitated Vehicle Routing Problem (CCVRP), adjusted for the Search & Rescue Coverage Path Planning using UAVs. The Min-max objective is also implemented and tested. Three versions of a Parallel Weighted Greedy Randomized Adaptive Search Procedure - Variable Neighborhood Decent (GRASP-VND) algorithm is implemented to solve the Cumulative UAV Routing Problem for Humanitarian Coverage Path Planning. Presented on: European Journal of Operational Research

An integrated zero common mode voltage (ZCMV) modulation algorithm is developed in this research for dual-parallel neutral-point-clamped (NPC) inverters treated as an integrated system. While the conventional methods achieve CMV elimination with the cost of worse harmonic performance, the proposed method guarantees zero CMV and better current quality simultaneously by taking advantage of the extra space vectors provided by paralleling. Specially designed switching sequences are developed to maintain a balanced neutral point of DC-bus and ensure the averaged circulating current being zero, which is critical to the parallel operation and normal function of NPC inverters. In addition, although the switching sequence design is complicated, a simple approach is proposed and implemented on a commonly used digital signal processor (DSP). A laboratory prototype is also developed and used to test the system performance under steady-state conditions and dynamic process in grid-connected operation. Compared with conventional ZCMV modulation, experimental results verify the effectiveness of the integrated modulation in eliminating CMV, in addition to the dramatically reduced output current distortion.

Motivated in part by a problem in simulated tempering (a form of Markov chain Monte Carlo) we seek to minimise, in a suitable sense, the time it takes a (regular) diffusion with instantaneous reflection at 0 and 1 to travel from the origin to 1 and then return (the so-called commute time from 0 to 1). We consider the static and dynamic versions of this problem where the control mechanism is related to the diffusion’s drift via the corresponding scale function. In the static version the diffusion’s drift can be chosen at each point in [0,1], whereas in the dynamic version we are only able to choose the drift at each point at the time of first visiting that point. The dynamic version leads to a novel type of stochastic control problem.

Stochastic point location deals with the problem of finding a target point on a real line through a learning mechanism (LM) with the stochastic environment (SE) offering directional information. The SE can be further categorized into an informative or deceptive one, according to whether p is above 0.5 or not, where p is the probability of providing a correct suggestion of a direction to LM. Several attempts have been made for LM to work in both types of environments, but none of them considers a dynamically changing environment where p varies with time. A dynamic dual environment involves fierce changes that frequently cause its environment to switch from an informative one to a deceptive one, or vice versa. This article presents a novel weak estimator-based adaptive step search solution, to enable LM to track the target in a dynamic dual environment, with the help of a weak estimator. The experimental results show that the proposed solution is feasible and efficient.

This article addresses the trajectory consensus problem of coordinating the trajectories of vehicles at multiple future time points. The objective is the consensus of the geometry of the vehicles' planned trajectories. The geometric feature of trajectories is parameterized by a set of trajectory states defined as required lengths along the trajectory to reduce the distance to its ending point to specific values. To solve this special consensus problem involving coupled state variables, the conventional consensus model is extended by attaching it to a mapping from the state variables to the trajectory's geometry. This mapping is established using a homotopic structure that creates a compact and efficient form for the mapping. The geometry of the homotopic structure is based on the shapes of its envelopes, and the elements in the structure are derived from their deformation. Through a homotopic search in the structure, an asymptotic consensus of trajectory states is achieved. Simulation results show the proposed coupled state consensus method can achieve better performance on the consensus of multiple vehicles than the conventional isolated state consensus method.

In this paper, the estimation problem of Toolface is investigated for the dynamic point-the-bit rotary steerable system (DPRSS), and it is essentially a nonlinear filtering problem under strong interference. Firstly, the DPRSS is modeled by a continuous-time nonlinear system which involves polynomial nonlinearities and strong interference whose amplitude is much larger than that of system states. By employing the Carleman approximation approach, a linear parameter varying system is derived from the polynomial nonlinear system, and a novel polynomial filter is constructed. Subsequently, several sufficient conditions in terms of parameter-dependent linear matrix inequalities (PDLMIs) are obtained to ensure the input-to-state stability of the filtering error system. Furthermore, the filter parameter can be calculated by solving PDLMIs through the sum-of-squares decomposition method. Finally, an experimental study on the DPRSS prototype is provided to show the effectiveness and applicability of the developed filtering scheme.

Perception techniques for autonomous driving should be adaptive to various environments. In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system. To address these problems, in this paper, we propose a traffic line detection method called Point Instance Network (PINet); the method is based on the key points estimation and instance segmentation approach. The PINet includes several stacked hourglass networks that are trained simultaneously. Therefore the size of the trained models can be chosen according to the computing power of the target environment. We cast a clustering problem of the predicted key points as an instance segmentation problem; the PINet can be trained regardless of the number of the traffic lines. The PINet achieves competitive accuracy and false positive on the TuSimple and Culane datasets, popular public datasets for lane detection. Our code is available at https://github.com/koyeongmin/PINet_new, Submitted to "IEEE Transactions on Intelligent Transportation Systems"

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.

Homogeneous magnetic field is widely used in various applications, and Helmholtz and Merritt coils are the most commonly used homogeneous field generators. However, only the field homogeneity at the center point is concerned in their design, which leads to restriction for most applications where a certain volume of homogeneous field is required. This indicates that traditional coil configurations can be further optimized. Based upon a comprehensive definition of homogeneous field, improved three-coil and four-coil configurations are proposed in this paper. Practical formulae with normalized coil parameters are given to provide a convenient tool for designers. These formulae establish relationships between coil parameters and the required homogeneous field. The accuracy of the formulae is verified by the finite-element analysis. Comparisons with Merritt coils show that, when getting the same homogeneous field, the improved coils can reduce both power loss and conductor mass by up to 24.8%. Through a comparison among main coil configurations, the improved four-coil configuration is recommended for homogeneous magnetic field generation. For high-intensity magnetic field applications, the winding cross-section effect is analyzed. At last, experiments are performed to verify the theoretical analysis, and the reason why the improved coils have better performance than traditional ones is revealed.

The literature on the subject of frequency estimation algorithms has discussed the use of interpolated discrete Fourier transform (IpDFT) for the unbalanced/balanced three-phase voltage signal in a power system. It is difficult to estimate frequency of the unbalanced signal with high accuracy since the unknown parameters of the unbalanced signal make the spectrum different from the real-valued sinusoid signal. Moreover, the spectral leakage of the spectrum of the unbalanced signal also brings large estimation errors when the acquired signal cycles become very small. In this paper, we propose a two11 point iterative IpDFT algorithm for frequency estimation of the unbalanced/balanced three-phase voltage signal. We decompose the unbalanced signal into a real-valued sinusoid signal, and then search the system frequency iteratively with the minimum error by bisection method. Both long- and short- range leakages are compensated to estimate the frequency accurately. Simulation results prove the effectiveness of our method, compared with other iterative/noniterative IpDFT methods.

Logic locking has emerged as a prominent key-driven technique to protect the integrity of integrated circuits. However, novel machine-learning-based attacks have recently been introduced to challenge the security foundations of locking schemes. These attacks are able to recover a significant percentage of the key without having access to an activated circuit. This paper address this issue through two focal points. First, we present a theoretical model to test locking schemes for key-related structural leakage that can be exploited by machine learning. Second, based on the theoretical model, we introduce D-MUX: a deceptive multiplexer-based logic-locking scheme that is resilient against structure-exploiting machine learning attacks. Through the design of D-MUX, we uncover a major fallacy in existing multiplexer-based locking schemes in the form of a structural-analysis attack. Finally, an extensive cost evaluation of D-MUX is presented. To the best of our knowledge, D-MUX is the first machine-learning-resilient locking scheme capable of protecting against all known learning-based attacks. Hereby, the presented work offers a starting point for the design and evaluation of future-generation logic locking in the era of machine learning., Comment: Accepted at IEEE TCAD 2021

With the evolution of electronic devices, such as 3D cameras, addressing the challenges of text localization in 3D video (e.g., for indexing) is increasingly drawing the attention of the multimedia and video processing community. Existing methods focus on 2D video and their performance in the presence of the challenges in 3D video, such as shadow areas associated with text and irregularly sized and shaped text, degrades. This paper proposes the first approach that successfully addresses the challenges of 3D video in addition to those of 2D. It employs a number of innovations, among which, the first is the Generalized Gradient Vector Flow (GGVF) for dominant points detection. The second is the Wavefront concept for text candidate point detection from those dominant points. In addition, an Adaptive B-Spline Polygon Curve Network (ABS-Net) is proposed for accurate text localization in 3D videos by constructing tight fitting bounding polygons using text candidate points. Extensive experiments on custom (3D video) and standard datasets (2D video and scene text) show that the proposed method is practical and useful, and overall outperforms existing state-of-the-art methods.

This paper introduces a novel frequency domain technique to globally evaluate the accuracy of electro-magnetic transient simulations. It is shown that simulation accuracy at low frequencies can sometimes be poorer than at high frequency. A modified approach which quantifies accuracy from a driving point as a function of frequency is also introduced that uses the Bilinear Transformation and Norton equivalents, to produce a simulation accuracy spectrum. This approach can be applied to large systems without explicitly forming state space equations. It also permits the accuracy analysis of networks with distributed components such as frequency dependent transmission lines. Two examples are used to verify the proposed technique, a small network with a frequency dependent transmission line modeled by the universal line model; and the IEEE 39 bus system connected with an LCC-HVdc system.

The automation of digital twinning for existing bridges from point clouds has yet been solved. Whilst current methods can automatically detect bridge objects in points clouds in the form of labelled point clusters, the fitting of accurate 3D shapes to detected point clusters remains human dependent to a great extent. 95% of the total manual modelling time is spent on customizing shapes and fitting them to right locations. The challenges exhibited in the fitting step are due to the irregular geometries of existing bridges. Existing methods can fit geometric primitives such as cuboids and cylinders to point clusters, assuming bridges are made up of generic shapes. However, the produced geometric digital twins are too ideal to depict the real geometry of bridges. In addition, none of existing methods have evaluated the resulting models in terms of spatial accuracy with quantitative measurements. We tackle these challenges by delivering a slicing-based object fitting method that can generate the geometric digital twin of an existing reinforced concrete bridge from labelled point clusters. The accuracy of the generated models is gauged using distance-based metrics. Experiments on ten bridge point clouds indicate that the method achieves an average modelling distance smaller than that of the manual one (7.05 cm vs. 7.69 cm) (value included all challenging cases), and an average twinning time of 37.8 seconds. Compared to the laborious manual practice, this is much faster to twin bridge concrete elements.

Change point analysis aims to detect structural changes in a data sequence. It has always been an active research area since it was introduced in the 1950s. In modern statistical applications, however, high-throughput data with increasing dimensions are ubiquitous in fields ranging from economics, finance to genetics and engineering. For those problems, the earlier works are typically no longer applicable. As a result, the problem of testing a change point for high dimensional data sequences has been an important yet challenging task. In this paper, we first focus on models for at most one change point, and review recent state-of-art techniques for change point testing of high dimensional mean vectors and compare their theoretical properties. Based on that, we provide a survey of some extensions to general high dimensional parameters beyond mean vectors as well as strategies for testing multiple change points in high dimensions. Finally, we discuss some open problems for possible future research directions.

To design simple and efficient inductive power transfer (IPT) systems, minimum number of conversion stages and effective maximum efficiency tracking (MET) are the primary design criteria. The MET control uses a fast mutual inductance estimation (MIE) algorithm to achieve fast tracking of the maximum efficiency point especially when the variation of the gap distance and misalignment of the magnetic coupler are significant. The feedback control of the MET-MIE IPT system should be designed without a wireless communication channel to improve stability and robustness. In this paper, a novel MET-MIE control strategy is proposed for achieving simplicity and high efficiency in IPT systems. Verifications are provided by simulations and experiments. It is shown that maximum efficiency can be tracked with less than 4% error in MIE under significant variation of the gap distance and misalignment of the magnetic coupler.

This paper studies a search problem in which a consumer is initially aware of only a few products. At every point in time, the consumer then decides between searching among alternatives of which he is already aware and discovering more products. I show that the optimal policy for this search and discovery problem is fully characterized by tractable reservation values. Moreover, I prove that a predetermined index fully specifies the purchase decision of a consumer following the optimal search policy. Finally, a comparison highlights differences to classical random and directed search. This paper was accepted by Dmitri Kuksov, marketing.

In this paper, we introduce a novel point-to-surface representation for 3D point cloud learning. Unlike the previous methods that mainly adopt voxel, mesh, or point coordinates, we propose to tackle this problem from a new perspective: learn a set of quadratic terms based static and global reference surfaces to describe 3D shapes, such that the coordinates of a 3D point (x, y, z) can be extended to quadratic terms (xy, xz, yz,...) and transformed to the relationship between the local point and the global reference surfaces. Then, the static surfaces are changed into dynamic surfaces by adaptive contribution weighting to improve the descriptive capability. Towards this end, we propose our point-to-surface representation, a new representation for 3D point cloud learning that has not been attempted before, which can assemble local and global geometric information effectively by building connections between the point cloud and the learned reference surfaces. Given 3D points, we show how the reference surfaces are constructed, and how they are inserted into the 3D learning pipeline for different tasks. The experimental results confirm the effectiveness of our new representation, which has outperformed the state-of-the-art methods on the tasks of 3D classification and segmentation.

We prove that the critical point and the point 1 have dense orbits for Lebesgue-a.e., parameter pairs in the two-parameter skew tent family and generalised β -transformations. As an application, we show that for the generalised β -transformation with a multinacci number as slope, there is matching (i.e., T n ( 0 ) = T n ( 1 ) for some n ≥ 1 ) for Lebesgue-a.e. translation parameter.

The problem of safe functioning of space vehicles is under investigation. Spacecraft safety is essentially affected by the impact of fragments of space debris and meteorites. Impact of small particle can lead to a perforation of the structure of a spacecraft or the Space station. The impact can cause formation of a perforation and gradual loss of atmosphere. Under these conditions, it is crucial detecting the point of perforation and take the measures of precaution for the loss of the internal atmosphere. Practically all known methods for testing the integrity of ground-based structures, aircrafts and other complex technogenic objects are inapplicable for solving similar problems of monitoring the spacecraft condition during a spaceflight. To solve this problem, the integrating fiber-optic sensors (IFOS)-based system for monitoring vibration deformation parameters of spacecraft housings’ surfaces is proposed, which can be developed and implemented in the shortest possible time. Mathematical modeling and laboratory studies have confirmed the possibility of using the Integrating fiber-optic sensors system for assessing the dynamic and static parameters of extended structures.

The cascaded three-level neutral-point-clamped (3L-NPC) converter and the modular multi-level converter (MMC) are attractive solutions for medium-voltage direct-current (MVDC) applications. Due to their low cost compared to MMCs, cascaded 3L-NPC converters have been adopted in ANGLE-DCa 30 MVA MVDC link demonstration project in North Wales, UK. DC voltage imbalance across submodules (SMs) is a common challenge for both types of MVDC converters. Such imbalance is topology dependent and remains under-researched for cascaded 3L-NPC converters. In this paper, small-signal model-based analysis has been done to reveal that the dc voltage imbalance in cascaded 3L-NPC converters is caused by an unstable system pole. Two voltage balancing methods are presented. The first method is based on PI controllers to precisely regulate SMs voltages without influencing output power. However, it relies on communication between a central controller and local controllers within SMs. The second method uses inverse-droop based control to take over the dc voltage regulation upon loss of communication. Both balancing methods are experimentally validated using a 30 kVA testbed based on the ANGLE-DC project. It has been demonstrated that the dc voltages of SMs can be effectively balanced with both methods during changes of load conditions and dc bus voltages.

This article discusses H∞ fuzzy control for the nonlinear fourth-order parabolic equation with input delay via collocated actuator/sensor pairs. We suggest that the interval [0,1] is divided into M subdomains, where sensors provide spatially averaged/point discrete-time state measurements. The control design strategy is proposed based on output measurements. We derive constructive conditions ensuring that the resulting closed-loop system is internally exponentially stable and has H∞ performance by means of the Lyapunov approach.

Consider a region that requires to be protected from unauthorized penetrations. The border of the region, modeled as a unit line segment, consists of high priority segments that require the highest level of protection separated by low priority segments that require to be visited infinitely often. We study the problem of patrolling the border with a set of k robots. The goal is to obtain a strategy that minimizes the maximum idle time (the time that a point is left unattended) of the high priority points while visiting the low priority points infinitely often. We use the concept of single lid cover (segments of fixed length) where each high priority point is covered with at least one lid, and then we extend it to strong double-lid cover where each high priority point is covered with at least two lids, and the unit line segment is fully covered. Let λk-1 be the minimum lid length that accepts a single λk-1-lid cover with k - 1 lids and Λ2k be the minimum lid length that accepts a strong double Λ2k-lid cover with 2k lids. We show that 2min(Λ2k, λk-1) is the lower bound of the idle time when the max speed of the robots is one. To compute Λ2k and λk-1, we present an algorithm with time complexity O(max(k, n) logn) where n is the number of high priority sections. Our algorithm improves by a factor of min(n, k) the previous O(knlogn) running time algorithm. For the upper bound, first we present a strategy with idle time λk-1 where one robot covers the unit line, and the remaining robots cover the lids of a single λk-1-lid cover with k - 1 lids. Then, we present a simple strategy with idle time 3Λ2k that splits the unit line into not-disjoint k segments of equal length that robots synchronously cover, i.e., reaching the leftmost and rightmost point simultaneously. Then, we present a complex strategy that split the unit line into k non-disjoint segments that robots asynchronously cover. We show that combining strategies one and two attain an approximation of 1.5 the optimal idle time and combining strategy one and third attain optimal idle time.