Search

Multidimensional scaling (MDS) is a popular tool for dimensionality reduction and data visualization. Given distances between data points and a target low-dimension, the MDS problem seeks to find a configuration of these points in the low-dimensional space, such that the inter-point distances are preserved as well as possible. We focus on the most common approach to formulate the MDS problem, known as stress minimization, which results in a challenging non-smooth and non-convex optimization problem. In this paper, we propose an inertial version of the well-known SMACOF Algorithm, which we call AI-SMACOF. This algorithm is proven to be globally convergent, and to the best of our knowledge this is the first result of this kind for algorithms aiming at solving the stress MDS minimization. In addition to the theoretical findings, numerical experiments provide another evidence for the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the asymptotic properties of a particular class of state-dependent sweeping processes. While extensive research has been conducted on the existence and uniqueness of solutions for sweeping processes, there is a scarcity of studies addressing their behavior in the limit of large time. Additionally, we introduce novel algorithms designed for the resolution of quasi-variational inequalities. As a result, we introduce a new derivative-free algorithm to find zeros of nonsmooth Lipschitz continuous mappings with a linear convergence rate. This algorithm can be effectively used in nonsmooth and nonconvex optimization problems that do not possess necessarily second-order differentiability conditions of the data. [ABSTRACT FROM AUTHOR]

In this paper, we propose and analyze a third-order dynamical system for finding zeros of the sum of two generalized operators in a Hilbert space H . We establish the existence and uniqueness of the trajectories generated by the system under appropriate continuity conditions, and prove exponential convergence to the unique zero when the sum of the operators is strongly monotone. Additionally, we derive an explicit discretization of the dynamical system, which results in a forward–backward algorithm with double inertial effects and larger range of stepsize. We establish the linear convergence of the iterates to the unique solution using this algorithm. Furthermore, we provide convergence analysis for the class of strongly pseudo-monotone variational inequalities. We illustrate the effectiveness of our approach by applying it to structured optimization and pseudo-convex optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents a comparative efficiency analysis of hashing algorithms from a perspective of their use in systems based on the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK). Hash functions sha256, sha3, poseidon, mimc, and blake2, most commonly used in modern distributed ledgers, are considered. For parameter measurement experiments, an infrastructure using the ZoKrates toolbox is developed. Based on experimental data, the limits of practical applicability of the algorithms to the problem of proving knowledge of a hash preimage with zk-SNARK circuits in distributed ledgers are determined, and related efficiency problems are considered. [ABSTRACT FROM AUTHOR]

The Fletcher-Reeves (FR) method is widely recognized for its drawbacks, such as generating unfavorable directions and taking small steps, which can lead to subsequent poor directions and steps. To address this issue, we propose a modification to the FR method, and then we develop it into the three-term conjugate gradient method in this paper. The suggested methods, named "HZF" and "THZF", preserve the descent property of the FR method while mitigating the drawbacks. The algorithms incorporate strong Wolfe line search conditions to ensure effective convergence. Through numerical comparisons with other conjugate gradient algorithms, our modified approach demonstrates superior performance. The results highlight the improved efficacy of the HZF algorithm compared to the FR and three-term FR conjugate gradient methods. The new algorithm was applied to the problem of image restoration and proved to be highly effective in image restoration compared to other algorithms. [ABSTRACT FROM AUTHOR]

To solve the significant performance degradation in the blind equalization for multi-level quadrature amplitude modulation (QAM) signals, an improved multimodulus algorithm based on the real part (or the imaginary part) of the transmitted signals is developed in this paper. Theoretical analysis illustrates that the proposed algorithm can roughly half the computational complexity and fundamentally suppress the well-known steady-state maladjustment of classical constant modulus algorithm and multimodulus algorithm. Moreover, the large maladjustment in the iteration process can be completely removed, thus the proposed algorithm can work well in impulsive noise environment. Finally, simulation results show the effectiveness of the proposed algorithm under both Gaussian and impulsive noise environments. [ABSTRACT FROM AUTHOR]

This paper considers an asynchronous grant-free non-orthogonal multiple access (NOMA) systems which can be applied in massive machine-type communications (mMTCs) and underwater IoT acoustic communication scenarios due to its high spectral efficiency and low power consumption characteristics. In particular, the system's asynchronous reception of user signals can effectively reduce the additional overhead caused by synchronous reception. We investigate the joint activity and channel estimation in the asynchronous case, where an asynchronous frame structure is considered, and a pilot sequence designed by chaotic sequence is used to reduce the pilot storage space. The joint estimations are formulated as single measurement vector (SMV) and multiple measurement vector (MMV) problems for single-antenna and multiple-antenna systems. Different from the existing estimation algorithms, where prior information is considered for estimation, an adaptive alternating direction method of multiplier (ADMM)is proposed for the SMV problem and a two-stage ADMM is proposed for the MMV problem. In particular, an index set is first estimated in each iteration of our proposed adaptive ADMM, and a linear ADMM is performed based on the index set. The first stage of our proposed two-stage ADMM is to estimate the delay and the activity, and then the channel state information is estimated. Further, we analyze the complexity of the two algorithms and their sensitivity to the initial values of chaotic sequences. Finally, simulation results reflecting the detection performance of the algorithms are given. Based on the simulation results, the proposed two algorithms are computationally efficient, providing superior signal recovery accuracy and user activity detection performance. More importantly, the signal delay has a relatively small impact on the proposed algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the received signal strength index (RSSI) in wireless positioning system, an improved weighted KNN fingerprint positioning algorithm is proposed in this paper. The algorithm pre-processes fingerprint data in offline stage that including eliminating outliers and Kalman filtering first, in order to improve the accuracy of data acquisition. Secondly, the fingerprint data is partitioned by using RSSI to attenuate obstacles such as walls. Then, points with significant RSSI differences in each region are selected as regional feature points, and the distance between RSSI of test points and feature points in each region is calculated respectively to determine the region in which the test points are located. Geometric method is used to analyse and define the correlation degree, and KNN is re-weighted to achieve accurate positioning in the region. Finally, experiments were carried out in the indoor environment to complete the establishment of the fingerprint database. Compared with the existing NN, KNN and WKNN, the experimental analysis results show that the accumulated error and average error are better than the traditional algorithm with the increase of measurement points, which has reference value for the complex environment positioning technology. [ABSTRACT FROM AUTHOR]

Ambient Intelligence deployments are very vulnerable to Cyber-Physical attacks. In these attacking strategies, intruders try to manipulate the behavior of the global system by affecting some key elements within the deployment. Typically, attackers inject false information, integrate malicious devices within the deployment, or infect communications among sensor nodes, among other possibilities. To protect Ambient Intelligence deployments against these attacks, complex data analysis algorithms are usually employed in the cloud to remove anomalous information from historical series. However, this approach presents two main problems. First, it requires all Ambient Intelligence systems to be networked and connected to the cloud. But most new applications for Ambient Intelligence are supported by isolated systems. And second, they are computationally heavy and not compatible with new decentralized architectures. Therefore, in this paper we propose a new decentralized security solution, based on a Blockchain ledger, to protect isolated Ambient Intelligence deployments. In this ledger, new sensing data are considered transactions that must be validated by edge managers, which operate a Blockchain network. This validation is based on reputation metrics evaluated by sensor nodes using historical network data and identity parameters. Through information theory, the coherence of all transactions with the behavior of the historical deployment is also analyzed and considered in the validation algorithm. The relevance of edge managers in the Blockchain network is also weighted considering the knowledge they have about the deployment. An experimental validation, supported by simulation tools and scenarios, is also described. Results show that up to 93% of Cyber-Physical attacks are correctly detected and stopped, with a maximum delay of 37 s. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intelligent devices brings a huge task request. Fog computing, which is an emerging distributed computing paradigm, is widely applied to process the device data generated in smart manufacturing. However, as fog nodes are resource limited and geographically widely distributed limitations, proper fog node placement strategies are critical to enhance the service performance of fog computing systems. In this paper, we study the problem of fog node placement in smart factories and divide it into two scenarios, fixed device and mobile device fog node placement, depending on the mobility of the devices. The fog node placement model and objective function are built in the two scenarios, and two improved heuristic algorithms are proposed to obtain the most optimal placement scheme. In addition, we perform simulation experiments based on existing intelligent production line prototype platforms and devices to evaluate the performance of the proposed algorithms. The IGA reduces latency by an average of 586.7 - 1089 ms over the benchmark algorithm, saving 18.3 - 39 % in energy consumption. The total latency of IMOA is reduced by 59.8 - 68.5 % , and the maximum latency is reduced by 48.8 - 69.2 % . The experimental results show that the proposed algorithms outperform other benchmark algorithms in terms of task response time and energy consumption. [ABSTRACT FROM AUTHOR]

Since non-orthogonal multiple access (NOMA) has become a promising technology to enhance network energy efficiency (EE) thanks to its low latency,high reliability and massive connectivity, the optimization of EE in NOMA-based uplink heterogeneous networks (HetNets) is considered in this paper. The optimization problem for EE maximization is formulated as a combination of user association (base station selection, sub-channel allocation) and power control, which is hardly tractable. To address it, a user association algorithm satisfied the many-to-one relationship between mobile users, base stations and sub-channels is firstly proposed based on matching theory. Then, an iterative power control algorithm evolved from fractional programming and difference of convex programming is presented by proving the concavity of the sum-rate of sub-channels. Finally, simulation results are shown that NOMA-based HetNets equipped with our designed algorithms yield much higher EE than that with the compared schemes. [ABSTRACT FROM AUTHOR]

Message Passing Interface (MPI) is the de facto standard for parallel programming, and collective operations in MPI are widely utilized by numerous scientific applications. The efficiency of these collective operations greatly impacts the performance of parallel applications. With the increasing scale and heterogeneity of HPC systems, the network environment has become more complex. The network states vary widely and dynamically between node pairs, and this makes it more difficult to design efficient collective communication algorithms. In this paper, we propose a method to optimize collective operations by using real-time measured network states, specifically focusing on the binomial tree algorithm. Our approach employs a low-overhead method to measure the network states, and the binomial tree with small latency is constructed based on the measurement result. Additionally, we take into account the disparities between the two underlying MPI peer-to-peer communication protocols, eager and rendezvous, and design tailored binomial tree construction algorithms for each protocol. We have implemented hierarchical MPI_Bcast, MPI_Reduce, MPI_Gather and MPI_Scatter, utilizing our network states-aware binomial tree algorithm at the inter-node level. The benchmark results demonstrate that our algorithm effectively enhances performance in small and medium message communication when compared to the default binomial tree algorithm in Open MPI. Specifically, for MPI_Bcast, we observe an average performance improvement of over 15.5% when the message size is less than 64KB. Similarly, for MPI_Reduce, there is an average performance improvement of over 12.1% when the message is below 2KB. In addition, there is an average performance improvement of over 10% for MPI_Gather when the message ranging from 64B to 512B. For MPI_Scatter, our algorithm achieved performance improvement only for certain message sizes. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems with large-scale variables are the focus and difficulty of current research in the field of multi-objective evolutionary algorithms. In order to cope with the problem of "dimensional catastrophe", this paper proposes a large-scale multi-objective firefly algorithm based on reward and punishment mechanisms and adaptive dimensional reorganization. First, a reward and punishment mechanisms is proposed to make full use of the information of the solution itself, so that the population can break the constraint and rapidly approach the Pareto frontier, effectively improving the convergence of the population; then, an adaptive dimensional reorganization mechanism is proposed to select individuals with large differences according to different iteration periods to interact and learn from each other, effectively improving the diversity of the population. In the experimental part, to verify the effectiveness of the algorithm, the UF series and the variable expanded ZDT series, 15 test problems are selected for simulation experiments and compared with 8 advanced large-scale optimization algorithms, the results show that L-MOFA-RA has the advantages of fast convergence speed and high convergence accuracy, and the comprehensive experimental results show that compared with the comparison algorithm L-MOFA-RA has better optimization performance. The comprehensive experimental results show that L-MOFA-RA has better optimization performance compared to the comparison algorithm. [ABSTRACT FROM AUTHOR]

The concept and data drift problems have received much attention in recent years. This aspect is crucial in many domains exhibiting non-stationary and cyclical patterns affecting their generative processes. Drift detection can be treated as a supervised task, with labeled data constantly used to validate the learned model. From a practical point of view, this is an impractical task because labeling is complex, costly, and time-consuming. On the other hand, unsupervised change detection techniques are cumbersome in applications because they generate many false alarms. The paper presents a new concept drift detection method based on feature analysis. Stream of data carries information about the distribution patterns that reflect different concepts that may be hidden in the data. The essential features are searched and ranked by LASSO. The rank of features and statistics are employed to feature drift detection. The proposed approach was experimentally checked based on synthetic and natural datasets. The results show that the proposed FBDD algorithm has an advantage over other solutions. [ABSTRACT FROM AUTHOR]

In many fields including aerospace, automotive, and telecommunications, MathWorks' MATLAB/Simulink is contemporary standard for model-based design. The strengths of Simulink are rapid design and algorithm exploration. Models created with Simulink are just functional. Therefore, designers cannot effortlessly consider a Simulink model's architecture. As current architectures are optimized to run on multicore processors, software running on these processors needs to be parallelized in order to benefit from their natural performance. For instance, designers need to understand how a Simulink model could be parallelized and how an adequate multicore architecture is selected. This paper focuses on the dataflow-based parallelization of Simulink models and proposes a method based on dataflow to measure the performance of parallelized Simulink models running on multicore architectures. Throughout the parallelization process, the model is converted into a Hierarchical Synchronous DataFlow Graph (HSDFG) keeping its original semantics, and each composite node in the graph is flattened. Then, the graph is mapped and scheduled into a multicore architecture with the ultimate objective that minimizes the end-to-end latency. In the experiment of applying the proposed approach to a real Simulink model, latency of the parallelized model could be reduced successfully on a various multi-core architectures. [ABSTRACT FROM AUTHOR]

C-RAN (Cloud Radio Access Network) is a 5G architecture that consists of sites and three-layer Data Centers (DCs), which include the central office DC, local DC, and regional DC. Network slicing, which enables infrastructure providers (InP) to create independent logical networks, is essential in this architecture. By utilizing this technology, InPs can maximize the utility of the network by providing slices to service providers in response to their slice requests. However, almost all of the recent research on slice admission control (SAC) schemes has only considered one or two layers of DCs, which limits the efficiency of the slicing process and decreases network utility. To address these issues, this paper proposes an intelligent SAC scheme called ISAC that considers all three-layer DCs. Instead of relying on reinforcement learning algorithms like Q-learning, which are effective in discrete environments with limited state space but give poor performance in continuous environments, ISAC employs the Approximate Reinforcement Learning (ARL) algorithm. ARL is better suited for 5G network modeling because it can adapt to continuous environments, allowing for a more accurate representation of the underlying physical processes. Extensive simulation studies demonstrate that ISAC significantly improves performance in terms of slice request rejection rates, InP revenue, accepting more slices, and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

Offloading computation-intensive tasks of an application from a mobile device to a remote computation resource can improve the runtime of the application as well as improve the lifetime of the mobile device's battery. However, computation offloading is a complex endeavour: remote computation resources in the cloud and in the fog have different computation capabilities and costs, and the network bandwidth and latency between different resources need to be taken into account. In addition, the mobile device would ideally like to offload multiple applications simultaneously, further adding to the complexity of assigning different tasks of each application to available computation resources. In this paper, we introduce an open-source versatile computation offloading simulator (OVCOSim), capable of simulating and optimizing offloading multiple applications to different cloud and fog resources and visualizing the outcome for users. We demonstrate how OVCOSim can be used to optimize assignment of tasks to computation resources to accommodate different user objectives, runtime, battery life, cost, or a combination of these objectives, using a variety of assignment algorithms. We also provide several examples to demonstrate how OVCOSim can be used, as well as validate its functionality. [ABSTRACT FROM AUTHOR]

Animation compression is a key process in replicating and streaming animated 3D models. Linear Blend Skinning (LBS) facilitates the compression of an animated sequence while maintaining the capability of real-time streaming by deriving vertex to proxy bone assignments and per frame bone transformations. We introduce a innovative deep learning approach that learns how to assign vertices to proxy bones with persistent labeling. This is accomplished by learning how to correlate vertex trajectories to bones of fully rigged animated 3D models. Our method uses these pretrained networks on dynamic characteristics (vertex trajectories) of an unseen animation sequence (a sequence of meshes without skeleton or rigging information) to derive an LBS scheme that outperforms most previous competent approaches by offering better approximation of the original animation sequence with fewer bones, therefore offering better compression and smaller bandwidth requirements for streaming. This is substantiated by a thorough comparative performance evaluation using several error metrics, and compression/bandwidth measurements. In this paper, we have also introduced a persistent bone labeling scheme that (i) improves the efficiency of our method in terms of lower error values and better visual outcome and (ii) facilitates the fusion of two (or more) LBS schemes by an innovative algorithm that combines two arbitrary LBS schemes. To demonstrate the usefulness and potential of this fusion process, we have combined the outcome of our deep skinning method with that of Rignet—which is a state-of-the-art method that performs rigging on static meshes—with impressive results. [ABSTRACT FROM AUTHOR]

The fault location of an active distribution network is a vital analysis to prevent major outages in the power system. Considering the influence of renewable distributed generations on fault characteristics, this paper proposes a novel location method based on a dynamic quantum genetic algorithm to solve for fault locations in active distribution networks. In the method, the fault current code is measured based on feeder terminal units. A universal switching function is presented to convert the feeder switch status into an uploaded fault current code. The fault location model is defined as an optimization problem that presents the evaluation objective function with an anti-false-positive factor. The dynamic quantum genetic algorithm is developed to locate the fault feeder according to the uploaded fault current code of the feeder terminal unit. The algorithm adopts dynamic rotating gate strategy and adaptive quantum crossover strategy to satisfy the requirements of quickness and accuracy for fault location. Moreover, the method avoids easily falling into a local optimum by integrating the discrete quantum mutation. The proposed fault location technique is tested and compared to other existing techniques on a 33-bus active distribution network. The simulation results show that the proposed fault location method can locate fault feeders accurately with fast computational times under conditions of single or multiple faults and with an information distortion of the feeder terminal unit. [ABSTRACT FROM AUTHOR]

This paper proposes a method for the small signal stability analysis and correction of power system based on Light Gradient Boosting Machine (LightGBM). Taking the load power, branch power, and generator power as inputs, the minimum damping ratio is output to build the mapping relationship between input and output. A small signal stability analysis model is established to assess the minimum damping ratio of the system, and the influence of noise on the accuracy of assessment results is also considered. The damping ratio sensitivity of generator is estimated based on LightGBM method, and the approximate sensitivity optimization model of the generator damping ratio is established when the system is weakly damped. The outputs of generators are adjusted according to the optimization model to correct the minimum damping ratio to enhance the system's stability and finally estimate the modified minimum damping ratio by LightGBM algorithm. Test results on 3-machine 9-node and 10-machine 39-node systems indicate that the proposed method is hardly prone to over-fitting under noise interference with better robustness and could maintain better performance in assessing and correcting the small signal stability of the power system. [ABSTRACT FROM AUTHOR]

The synthetic control method (SCM) represents a notable innovation in estimating the causal effects of policy interventions and programs in a comparative case study setting. In this paper, we demonstrate that the data-driven approach to SCM requires solving a bilevel optimization problem. We show how the original SCM problem can be solved to the global optimum through the introduction of an iterative algorithm rooted in Tykhonov regularization or Karush–Kuhn–Tucker approximations. [ABSTRACT FROM AUTHOR]

Asymmetric image retrieval methods have drawn much attention due to their effectiveness in resource-constrained scenarios. They try to learn two models in an asymmetric paradigm, i.e., a small model for the query side and a large model for the gallery. However, we empirically find that the mutual training scheme (learning with each other) will inevitably degrade the performance of the large gallery model, due to the negative effects exerted by the small query one. In this paper, we propose Central Similarity Consistency Hashing (CSCH), which simultaneously learns a small query model and a large gallery model in a mutually promoted manner, ensuring both high retrieval accuracy and efficiency on the query side. To achieve this, we first introduce heuristically generated hash centers as the common learning target for both two models. Instead of randomly assigning each hash center to its corresponding category, we introduce the Hungarian algorithm to optimally match each of them by aligning the Hamming similarity of hash centers to the semantic similarity of their classes. Furthermore, we introduce the instance-level consistency loss, which enables the explicit knowledge transfer from the gallery model to the query one, without the sacrifice of gallery performance. Guided by the unified learning of hash centers and the distilled knowledge from gallery model, the query model can be gradually aligned to the Hamming space of the gallery model in a decoupled manner. Extensive experiments demonstrate the superiority of our CSCH method compared with current state-of-the-art deep hashing methods. The open-source code is available at https://github.com/dubanx/CSCH. [ABSTRACT FROM AUTHOR]

Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model's inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72 ms per instance on devices equipped only with CPUs. The model's number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field. [ABSTRACT FROM AUTHOR]

Image registration is a fundamental task in various applications of medical image analysis and plays a crucial role in auxiliary diagnosis, treatment, and surgical navigation. However, cardiac image registration is challenging due to the large non-rigid deformation of the heart and the complex anatomical structure. To address this challenge, this paper proposes an independently trained multi-scale registration network based on an image pyramid. By down-sampling the original input image multiple times, we can construct image pyramid pairs, and design a multi-scale registration network using image pyramid pairs of different resolutions as the training set. Using image pairs of different resolutions, train each registration network independently to extract image features from the image pairs at different resolutions. During the testing stage, the large deformation registration is decomposed into a multi-scale registration process. The deformation fields of different resolutions are fused by a step-by-step deformation method, thereby addressing the challenge of directly handling large deformations. Experiments were conducted on the open cardiac dataset ACDC (Automated Cardiac Diagnosis Challenge); the proposed method achieved an average Dice score of 0.828 in the experimental results. Through comparative experiments, it has been demonstrated that the proposed method effectively addressed the challenge of heart image registration and achieved superior registration results for cardiac images. [ABSTRACT FROM AUTHOR]

Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and multi-label classification is class imbalance, where one or several classes make up a substantial portion of the total instances. This imbalance causes the neural network to prioritize features of the majority classes during training, as their detection leads to higher scores. In the context of object detection, two types of imbalance can be identified: (1) an imbalance between the space occupied by the foreground and background and (2) an imbalance in the number of instances for each class. This paper aims to address the second type of imbalance without exacerbating the first. To achieve this, we propose a modification of the copy-paste data augmentation technique, combined with weight-balancing methods in the loss function. This strategy was specifically tailored to improve the performance in datasets with a high instance density, where instance overlap could be detrimental. To validate our methodology, we applied it to a highly unbalanced dataset focused on nuclei detection. The results show that this hybrid approach improves the classification of minority classes without significantly compromising the performance of majority classes. [ABSTRACT FROM AUTHOR]

Low-dose computer tomography (LDCT) has been widely used in medical diagnosis. Various denoising methods have been presented to remove noise in LDCT scans. However, existing methods cannot achieve satisfactory results due to the difficulties in (1) distinguishing the characteristics of structures, textures, and noise confused in the image domain, and (2) representing local details and global semantics in the hierarchical features. In this paper, we propose a novel denoising method consisting of (1) a 2D dual-domain restoration framework to reconstruct noise-free structure and texture signals separately, and (2) a 3D multi-depth reinforcement U-Net model to further recover image details with enhanced hierarchical features. In the 2D dual-domain restoration framework, the convolutional neural networks are adopted in both the image domain where the image structures are well preserved through the spatial continuity, and the sinogram domain where the textures and noise are separately represented by different wavelet coefficients and processed adaptively. In the 3D multi-depth reinforcement U-Net model, the hierarchical features from the 3D U-Net are enhanced by the cross-resolution attention module (CRAM) and dual-branch graph convolution module (DBGCM). The CRAM preserves local details by integrating adjacent low-level features with different resolutions, while the DBGCM enhances global semantics by building graphs for high-level features in intra-feature and inter-feature dimensions. Experimental results on the LUNA16 dataset and 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge dataset illustrate the proposed method outperforms the state-of-the-art methods on removing noise from LDCT images with clear structures and textures, proving its potential in clinical practice. [ABSTRACT FROM AUTHOR]

In this paper, we propose and experimentally assess an innovative framework for scaling posterior distributions over different-curation datasets, based on Bayesian-Neural-Networks (BNN). Another innovation of our proposed study consists in enhancing the accuracy of the Bayesian classifier via intelligent sampling algorithms. The proposed methodology is relevant in emerging applicative settings, such as provenance detection and analysis and cybercrime. Our contributions are complemented by a comprehensive experimental evaluation and analysis over both static and dynamic image datasets. Derived results confirm the successful application of our proposed methodology to emerging big data analytics settings. [ABSTRACT FROM AUTHOR]

As an important indicator of the surface quality of workpieces, surface roughness has a great impact on production costs and the quality performance of the finished components. Effective surface roughness prediction can not only increase productivity but also reduce costs. However, the current methods for surface roughness prediction have some limitations. On the one hand, the prediction accuracy of classical experimental and statistical-based surface roughness prediction methods is low. On the other hand, the results of deep learning-based surface roughness prediction methods are uninterpretable due to their black-box learning mechanism. Therefore, this paper presents an ensemble learning with a differential evolution algorithm, applies it to the prediction of surface roughness of abrasive water jet polishing (AWJP), and conducts an interpretability analysis to identify key factors contributing to the prediction accuracy of surface roughness. First, we proposed automatically constructing features by an Evolution Forest algorithm to train the base regression models. The differential evolution algorithm with a simplified encoding mechanism was then used to search for the best weighted-ensemble to integrate the base regression models for obtaining highly accurate prediction results. Extensive experiments have been conducted on AWJP to validate the effectiveness of our proposed methods. The results show that the prediction accuracy of our proposed method is higher than the existing machine learning algorithms. In addition, this is the first of its time for the contributions of machining parameters (i.e., features) on surface roughness prediction by using interpretable analysis methods. The analysis results can provide a reference basis for subsequent experiments and studies. [ABSTRACT FROM AUTHOR]

Deploying static wireless sensor nodes is prone to network coverage gaps, resulting in poor network coverage. In this paper, an attempt is made to improve the network coverage by moving the locations of the nodes. A surrogate-assisted sine Phasmatodea population evolution algorithm (SASPPE) is used to evaluate the network coverage. A 50 × 50 hill simulation environment was tested for the number of nodes of 30 and 40 and radii of 3, 5 and 7, respectively. The results show that the SASPPE algorithm has the highest coverage, which can be up to 23.624% higher than the PPE algorithm, and up to 5.196% higher than the PPE algorithm, ceteris paribus. The SASPPE algorithm mixes the GSAM with LSAMs, which balances the computational cost of the algorithm and the algorithm's ability to find optimal results. The use of hierarchical clustering enhances the stable type of the LSAMs. In addition, LSAMs are easy to fall into local optimality when they are modeled with local data, and the use of sine Phasmatodea population evolution algorithm (Sine-PPE) for searching in LSAMs alleviates the time for the algorithm to fall into local optimality. On 30D, 50D, and 100D, the proposed algorithm was tested by 7 test functions. The results show that the algorithm has significant advantages on most functions. [ABSTRACT FROM AUTHOR]

Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images. [ABSTRACT FROM AUTHOR]

The significance of facial anti-spoofing algorithms in enhancing the security of facial recognition systems cannot be overstated. Current approaches aim to compensate for the model's shortcomings in capturing spatial information by leveraging spatio-temporal information from multiple frames. However, the additional branches to extract inter-frame details increases the model's parameter count and computational workload, leading to a decrease in inference efficiency. To address this, we have developed a robust and easily deployable facial anti-spoofing algorithm. In this paper, we propose Central Difference Convolution UNet++ (UCDCN), which takes advantage of central difference convolution and improves the characterization ability of invariant details in diverse environments. Particularly, we leverage domain knowledge from image segmentation and propose a multi-level feature fusion network structure to enhance the model's ability to capture semantic information which is beneficial for face anti-spoofing tasks. In this manner, UCDCN greatly reduces the number of model parameters as well as achieves satisfactory metrics on three popular benchmarks, i.e., Replay-Attack, Oulu-NPU and SiW. [ABSTRACT FROM AUTHOR]

A d-dimensional framework is a pair (G, p), where G = (V , E) is a graph and p is a map from V to R d . The length of an edge x y ∈ E in (G, p) is the distance between p(x) and p(y). A vertex pair { u , v } of G is said to be globally linked in (G, p) if the distance between p(u) and p(v) is equal to the distance between q(u) and q(v) for every d-dimensional framework (G, q) in which the corresponding edge lengths are the same as in (G, p). We call (G, p) globally rigid in R d when each vertex pair of G is globally linked in (G, p). A pair { u , v } of vertices of G is said to be weakly globally linked in G in R d if there exists a generic framework (G, p) in which { u , v } is globally linked. In this paper we first give a sufficient condition for the weak global linkedness of a vertex pair of a (d + 1) -connected graph G in R d and then show that for d = 2 it is also necessary. We use this result to obtain a complete characterization of weakly globally linked pairs in graphs in R 2 , which gives rise to an algorithm for testing weak global linkedness in the plane in O (| V | 2) time. Our methods lead to a new short proof for the characterization of globally rigid graphs in R 2 , and further results on weakly globally linked pairs and globally rigid graphs in the plane and in higher dimensions. [ABSTRACT FROM AUTHOR]

In this paper, we focus on overlapping community detection and propose an efficient semi-orthogonal nonnegative matrix tri-factorization (semi-ONMTF) algorithm. This method factorizes a matrix X into an orthogonal matrix U, a nonnegative matrix B, and a transposed matrix U T . We use the Cayley Transformation to maintain strict orthogonality of U that each iteration stays on the Stiefel Manifold. This algorithm is computationally efficient because the solutions of U and B are simplified into a matrix-wise update algorithm. Applying this method, we detect overlapping communities by the belonging coefficient vector and analyse associations between communities by the unweighted network of communities. We conduct simulations and applications to show that the proposed method has wide applicability. In a real data example, we apply the semi-ONMTF to a stock data set and construct a directed association network of companies. Based on the modularity for directed and overlapping communities, we obtain five overlapping communities, 17 overlapping nodes, and five outlier nodes in the network. We also discuss the associations between communities, providing insights into the overlapping community detection on the stock market network. [ABSTRACT FROM AUTHOR]

A popular approach to model interactions is to represent them as a network with nodes being the agents and the interactions being the edges. Interactions are often timestamped, which leads to having timestamped edges. Many real-world temporal networks have a recurrent or possibly cyclic behaviour. In this paper, our main interest is to model recurrent activity in such temporal networks. As a starting point we use stochastic block model, a popular choice for modelling static networks, where nodes are split into R groups. We extend the block model to temporal networks by modelling the edges with a Poisson process. We make the parameters of the process dependent on time by segmenting the time line into K segments. We require that only H ≤ K different set of parameters can be used. If H < K , then several, not necessarily consecutive, segments must share their parameters, modelling repeating behaviour. We propose two variants where a group membership of a node is fixed over the course of entire time line and group memberships are allowed to vary from segment to segment. We prove that searching for optimal groups and segmentation in both variants is NP-hard. Consequently, we split the problem into 3 subproblems where we optimize groups, model parameters, and segmentation in turn while keeping the remaining structures fixed. We propose an iterative algorithm that requires O K H m + R n + R 2 H time per iteration, where n and m are the number of nodes and edges in the network. We demonstrate experimentally that the number of required iterations is typically low, the algorithm is able to discover the ground truth from synthetic datasets, and show that certain real-world networks exhibit recurrent behaviour as the likelihood does not deteriorate when H is lowered. [ABSTRACT FROM AUTHOR]

We consider an optimization problem arising when a set of items must be selected and picked up from given locations in an automated storage and retrieval system by a crane of given capacity, minimizing the overall distance traveled. The problem has been classified as open in a recent taxonomy of optimal picking problems in automated warehouses. In this paper, we analyze some non-trivial properties of the problem and we describe a polynomial-time dynamic programming algorithm to solve it to proven optimality. [ABSTRACT FROM AUTHOR]

In this paper, we present a column generation-based algorithm for the bi-objective gate assignment problem (GAP) to generate gate schedules that minimize squared slack time at the gates while satisfying passenger expectations by minimizing their walking distance. While most of the literature focuses on heuristic or metaheuristic solutions for the bi-objective GAP, we propose flow-based and column-based models that lead to exact or near optimal solution approaches. The developed algorithm calculates a set of solutions to approximate the Pareto front. The algorithm is applied to the over-constrained GAP where gates are a limited resource and it is not possible to serve every flight using a gate. Our test cases are based on real data from an international airport and include various instances with flight-to-gate ratios between 23.9 and 34.7. Numerical results reveal that a set of solutions representing a compromise between the passenger-oriented and robustness-oriented objectives may be obtained with a tight optimality gap and within reasonable computational time even for these difficult problems. [ABSTRACT FROM AUTHOR]

In this paper, we present an outer approximation algorithm for computing the Edgeworth–Pareto hull of multi-objective mixed-integer linear programming problems (MOMILPs). It produces the extreme points (i.e., the vertices) as well as the facets of the Edgeworth–Pareto hull. We note that these extreme points are the extreme supported non-dominated points of a MOMILP. We also show how to extend the concept of geometric duality for multi-objective linear programming problems to the Edgeworth–Pareto hull of MOMILPs and use this extension to develop the algorithm. The algorithm relies on a novel oracle that solves single-objective weighted-sum problems and we show that the required number of oracle calls is polynomial in the number of facets of the convex hull of the extreme supported non-dominated points in the case of MOMILPs. Thus, for MOMILPs for which the weighted-sum problem is solvable in polynomial time, the facets can be computed with incremental-polynomial delay—a result that was formerly only known for the computation of extreme supported non-dominated points. Our algorithm can be an attractive option to compute lower bound sets within multi-objective branch-and-bound algorithms for solving MOMILPs. This is for several reasons as (i) the algorithm starts from a trivial valid lower bound set then iteratively improves it, thus at any iteration of the algorithm a lower bound set is available; (ii) the algorithm also produces efficient solutions (i.e., solutions in the decision space); (iii) in any iteration of the algorithm, a relaxation of the MOMILP can be solved, and the obtained points and facets still provide a valid lower bound set. Moreover, for the special case of multi-objective linear programming problems, the algorithm solves the problem to global optimality. A computational study on a set of benchmark instances from the literature is provided. [ABSTRACT FROM AUTHOR]

In this study, we investigate the connection between the efficient frontier (EF) of a general multiobjective mixed integer linear optimization problem (MILP) and the so-called restricted value function (RVF) of a closely related single-objective MILP. In the first part of the paper, we detail the mathematical structure of the RVF, including characterizing the set of points at which it is differentiable, the gradients at such points, and the subdifferential at all nondifferentiable points. We then show that the EF of the multiobjective MILP is comprised of points on the boundary of the epigraph of the RVF and that any description of the EF suffices to describe the RVF and vice versa. Because of the close relationship of the RVF to the EF, we observe that methods for constructing the so-called value function (VF) of an MILP and methods for constructing the EF of a multiobjective optimization problem are effectively interchangeable. Exploiting this observation, we propose a generalized cutting-plane algorithm for constructing the EF of a multiobjective MILP that arises from an existing algorithm for constructing the classical MILP VF. The algorithm identifies the set of all integer parts of solutions on the EF. We prove that the algorithm converges finitely under a standard boundedness assumption and comes with a performance guarantee if terminated early. [ABSTRACT FROM AUTHOR]

In the last years a multitude of algorithms have been proposed to solve multiobjective integer programming problems. However, only few authors offer open-source implementations. On the other hand, new methods are typically compared to code that is publicly available, even if this code is known to be outperformed. In this paper, we aim to overcome this problem by proposing a new state-of-the-art algorithm with an open-source implementation in C++. The underlying method falls into the class of objective space methods, i.e., it decomposes the overall problem into a series of scalarized subproblems that can be solved with efficient single-objective IP-solvers. It keeps the number of required subproblems small by avoiding redundancies, and it can be combined with different scalarizations that all lead to comparably simple subproblems. Our algorithm bases on previous results but combines them in a new way. Numerical experiments with up to ten objectives validate that the method is efficient and that it scales well to higher dimensional problems. [ABSTRACT FROM AUTHOR]

Application problems can often not be solved adequately by numerical algorithms as several difficulties might arise at the same time. When developing and improving algorithms which hopefully allow to handle those difficulties in the future, good test instances are required. These can then be used to detect the strengths and weaknesses of different algorithmic approaches. In this paper we present a generator for test instances to evaluate solvers for multiobjective mixed-integer linear and nonlinear optimization problems. Based on test instances for purely continuous and purely integer problems with known efficient solutions and known nondominated points, suitable multiobjective mixed-integer test instances can be generated. The special structure allows to construct instances scalable in the number of variables and objective functions. Moreover, it allows to control the resulting efficient and nondominated sets as well as the number of efficient integer assignments. [ABSTRACT FROM AUTHOR]

In multi-objective mixed-integer convex optimization, multiple convex objective functions need to be optimized simultaneously while some of the variables are restricted to take integer values. In this paper, we present a new algorithm to compute an enclosure of the nondominated set of such optimization problems. More precisely, we decompose the multi-objective mixed-integer convex optimization problem into several multi-objective continuous convex optimization problems, which we refer to as patches. We then dynamically compute and improve coverages of the nondominated sets of those patches to finally combine them to obtain an enclosure of the nondominated set of the multi-objective mixed-integer convex optimization problem. Additionally, we introduce a mechanism to reduce the number of patches that need to be considered in total. Our new algorithm is the first of its kind and guaranteed to return an enclosure of prescribed quality within a finite number of iterations. For selected numerical test instances we compare our new criterion space based approach to other algorithms from the literature and show that much larger instances can be solved with our new algorithm. [ABSTRACT FROM AUTHOR]

Seamless handoff is crucial for wireless body area network (WBAN) data's transmission, it may avoid the death resulted from sudden diseases, especially for the elder people who suffer from chronic diseases. Therefore, a vertical handoff algorithm that conforms to the characteristics of body area network is urgent. Different from the traditional service types in telecommunications, service types in wireless body area network have their own characteristics. How to reasonably divide these service types according to their requirement of the internet is very important. In addition, we need to study the rationality of different weighting and network selection algorithms for body area network users. In view of the lack of vertical handoff algorithms to meet the characteristics of wireless body area networks, this paper first analyzes the service types in body area network, and reasonably divides the service types in body area network into four types. For these four services types, we comprehensively and systematically analyze the weight determination and network selection algorithms suitable for them. In addition, in wireless body area network environment, there may be situations when multiple users need to transmit information at the same time. For these situations, we propose a broadcast mechanism to prevent network congestion caused by group switching. The simulation results show that the combination of using Analytic hierarchy process to determine the weight and using Technique for Order Preference by Similarity to Ideal Solution as the network selection method is most suitable for vertical handoff in WBAN. The proposed broadcast mechanism can solve the network congestion problem caused by group handoff. [ABSTRACT FROM AUTHOR]

Deploying unmanned aerial vehicle base stations (UAV-BSs) to provide wireless access for terrestrial users is an alternative solution in emergency scenarios. Due to the dynamic nature of UAV-BSs and strong line-of-sight (LoS) transmission, co-frequency networking worsens the interference suffered by edge users, while the use of fully orthogonal channels for all UAV-BSs also faces the problem of frequency resource constraints. This paper develops a new deployment scheme by combining multiple UAV-BS locations, limited frequency reuse and power optimization. Thus, the nonlinear constrained optimization model is proposed to maximize user coverage. To reduce the computational complexity, the basic multi-UAV-BS network layout is first determined by polycentric clustering and connectivity adjustment based on user location distribution. Then, a genetic algorithm is used to optimize the multi-UAV-BS frequency arrangement and power adjustment to obtain the model solution. Simulations verify the effectiveness of the proposed scheme and evaluate the impact of co-channel interference. [ABSTRACT FROM AUTHOR]

Trust-region methods have received massive attention in a variety of continuous optimization. They aim to obtain a trial step by minimizing a quadratic model in a region of a certain trust-region radius around the current iterate. This paper proposes an adaptive Riemannian trust-region algorithm for optimization on manifolds, in which the trust-region radius depends linearly on the norm of the Riemannian gradient at each iteration. Under mild assumptions, we establish the liminf-type convergence, lim-type convergence, and global convergence results of the proposed algorithm. In addition, the proposed algorithm is shown to reach the conclusion that the norm of the Riemannian gradient is smaller than ϵ within O (1 ϵ 2) iterations. Some numerical examples of tensor approximations are carried out to reveal the performances of the proposed algorithm compared to the classical Riemannian trust-region algorithm. [ABSTRACT FROM AUTHOR]

In our paper, we consider the following general problems: check feasibility, count the number of feasible solutions, find an optimal solution, and count the number of optimal solutions in P ∩ Z n , assuming that P is a polyhedron, defined by systems A x ≤ b or A x = b , x ≥ 0 with a sparse matrix A. We develop algorithms for these problems that outperform state-of-the-art ILP and counting algorithms on sparse instances with bounded elements in terms of the computational complexity. Assuming that the matrix A has bounded elements, our complexity bounds have the form s O (n) , where s is the minimum between numbers of non-zeroes in columns and rows of A, respectively. For s = o (log n) , this bound outperforms the state-of-the-art ILP feasibility complexity bound (log n) O (n) , due to Reis & Rothvoss (in: 2023 IEEE 64th Annual symposium on foundations of computer science (FOCS), IEEE, pp. 974–988). For s = ϕ o (log n) , where ϕ denotes the input bit-encoding length, it outperforms the state-of-the-art ILP counting complexity bound ϕ O (n log n) , due to Barvinok et al. (in: Proceedings of 1993 IEEE 34th annual foundations of computer science, pp. 566–572, https://doi.org/10.1109/SFCS.1993.366830, 1993), Dyer, Kannan (Math Oper Res 22(3):545–549, https://doi.org/10.1287/moor.22.3.545, 1997), Barvinok, Pommersheim (Algebr Combin 38:91–147, 1999), Barvinok (in: European Mathematical Society, ETH-Zentrum, Zurich, 2008). We use known and new methods to develop new exponential algorithms for Edge/Vertex Multi-Packing/Multi-Cover Problems on graphs and hypergraphs. This framework consists of many different problems, such as the Stable Multi-set, Vertex Multi-cover, Dominating Multi-set, Set Multi-cover, Multi-set Multi-cover, and Hypergraph Multi-matching problems, which are natural generalizations of the standard Stable Set, Vertex Cover, Dominating Set, Set Cover, and Maximum Matching problems. [ABSTRACT FROM AUTHOR]

Nonnegative tensor factorizations (NTF) have applications in statistics, computer vision, exploratory multi-way data analysis, and blind source separation. This paper studies randomized multiplicative updating algorithms for symmetric NTF via random projections and random samplings. For random projections, we consider two methods to generate the random matrix and analyze the computational complexity, while for random samplings the uniform sampling strategy and its variants are examined. The mixing of these two strategies is then considered. Some theoretical results are presented based on the bounds of the singular values of sub-Gaussian matrices and the fact that randomly sampling rows from an orthogonal matrix results in a well-conditioned matrix. These algorithms are easy to implement, and their efficiency is verified via test tensors from both synthetic and real datasets, such as for clustering facial images. [ABSTRACT FROM AUTHOR]

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG. [ABSTRACT FROM AUTHOR]

A wireless Sensor Network (WSN) is made up of many sensor nodes which gather and transmit data to a central location. The limited resources of the nodes create significant security challenges when deploying and communicating WSNs. The detection of unauthorized access is a crucial aspect of enhancing the security measures of WSNs. The utilization of network intrusion detection systems (IDS) has become an essential aspect of any communication network, as they offer valuable services to the network. Several studies in the field of machine learning have been conducted to explore the potential of utilizing this technology for intrusion detection in WSNs, yielding promising outcomes. These efforts still need to be more precise and efficient against network traffic unbalanced data issues. The paper presents a new model for detecting intrusion attacks that utilize a hybrid multilayer perceptron (MLP) and CatBoost classifier, as well as feature selection techniques. The proposed approach aims for good performance in identifying different forms of threats. The system performs data preprocessing on various datasets and reduces the dataset size using a feature selection algorithm. Pelican Optimization Algorithm (POA) has been proposed for tuning the hyper-parameters of the classifier designs and selecting the relevant features from the dataset. The CSE-CIC-IDS2018, AWID, and UNSW-NB15 databases reutilized for conducting performance evaluations on the proposed framework. The tests included accuracy, precision, recall, FAR, DR and complexity time. The proposed model has a low FPR and high accuracy in binary classification, as shown. [ABSTRACT FROM AUTHOR]

The commonly used automatic segmentation algorithms often have over-segmentation and error-segmentation problems when segmenting conglutinated bone fragments in computed tomography (CT) images. And the end face details of the fragments are lost in this process. At present, bone fragments are often manually segmented by doctors. This increases the workload of doctors and takes low efficiency and poor accuracy in bone segmentation. We propose an automatic bone fragment segmentation algorithm based on morphology to segment the conglutinated and separated bone fragments and reconstruct them in 2D and 3D spaces. The pixel classification and clustering method based on morphology are proposed in in the algorithm to identify the pixels in the fracture images. And a concave points detection method is proposed to segment the conglutination area of the fractured bone. This algorithm and two other commonly used algorithms are used in the experiment to segment the three typical fracture images. The segmentation results are quantitatively analyzed to demonstrate the advantages of the morphology algorithm. The morphology algorithm performs well in the metrics of Accuracy (.93 ±.07), Sensitivity (.92 ±.08), Dice coefficient (.92 ±.08) and Mean intersection over union (MIoU) (.92 ±.07). The segmentation results of this algorithm are closest to the manual segmentation results of doctors compared to the other two algorithms. We have demonstrated that the algorithm proposed in this paper has good segmentation accuracy for separated and conglutinated bone fragments. This algorithm significantly improves the segmentation accuracy and efficiency of bone fragments under complex fracture conditions. [ABSTRACT FROM AUTHOR]

This paper proposes an iteration-based algorithm for the optimum design of a high frequency transformer for solid-state transformer (SST) applications. This algorithm minimizes the total owning cost (TOC) of a distribution type solid-state transformer. The unique features of this algorithm compared with the available algorithms in the literature are as follows: it iterates eight design variables, four constraints are defined for selecting the valid designs, and it works with different core materials and AC test voltages. The algorithm uses various user-defined data inputs to calculate loss capitalization values for TOC calculation. In every iteration, TOC is estimated, and calculated values of design constraints are compared with their threshold limits. A case study is conducted on a high-frequency transformer (HFT) incorporated in 1000-kVA, 11-kV/415-V, Dyn11 three-phase wound core SST. This is to determine the optimum design parameters. In this case study, the algorithm was iterated with 2,100,000 design data inputs, generating 258,272 designs that satisfied all design constraints. The optimum design with minimum TOC is selected from the generated 258,272 designs. The optimum design is validated using finite element analysis in ANSYS software. Comparing the results of both analyses, the deviation is less than 5%. Hence, the algorithm's reliability is proved. [ABSTRACT FROM AUTHOR]