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Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper proposes a new PD signal denoising method combining Aquila Optimizer–Variational Mode Decomposition (AO-VMD) and K-Singular Value Decomposition (K-SVD) algorithms. Firstly, the AO algorithm optimizes critical parameters of the VMD algorithm. For the PD signal overwhelmed by noise, the AO-VMD algorithm can decompose it and reconstruct it by using kurtosis. In this process, the majority of the noise is removed, and the characteristics of the original signal are shown. Subsequently, the K-SVD algorithm performs sparse decomposition on the signal after OA-VMD, constructs a learned dictionary, and captures the characteristics of the signal for continuous learning and updating. After the dictionary learning is completed, the best matching atoms from the dictionary are selected to precisely reconstruct the original noiseless signal. Finally, the proposed method is compared with three traditional algorithms, Adaptive Ensemble Empirical Mode Decomposition (AEEMD), SVD-VMD, and the Adaptive Wavelet Multilevel Soft Threshold algorithm, on the simulated signal and the actual engineering signal. The results both demonstrate that the algorithm proposed by this paper has superior noise reduction and signal extraction performance. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective clustering (EMOC) algorithms have gained popularity recently, as they can obtain a set of clustering solutions in a single run by optimizing multiple objectives. Particularly, in one type of EMOC algorithm, the number of clusters k is taken as one of the multiple objectives to obtain a set of clustering solutions with different k. However, the numbers of clusters k and other objectives are not always in conflict, so it is impossible to obtain the clustering solutions with all different k in a single run. Therefore, evolutionary multi-objective k-clustering (EMO-KC) has recently been proposed to ensure this conflict. However, EMO-KC could not obtain good clustering accuracy on high-dimensional datasets. Moreover, EMO-KC's validity is not ensured as one of its objectives (SSDexp, which is transformed from the sum of squared distances (SSD)) could not be effectively optimized and it could not avoid invalid solutions in its initialization. In this paper, an improved evolutionary multi-objective clustering algorithm based on autoencoder (AE-IEMOKC) is proposed to improve the accuracy and ensure the validity of EMO-KC. The proposed AE-IEMOKC is established by combining an autoencoder with an improved version of EMO-KC (IEMO-KC) for better accuracy, where IEMO-KC is improved based on EMO-KC by proposing a scaling factor to help effectively optimize the objective of SSDexp and introducing a valid initialization to avoid the invalid solutions. Experimental results on several datasets demonstrate the accuracy and validity of AE-IEMOKC. The results of this paper may provide some useful information for other EMOC algorithms to improve accuracy and convergence. [ABSTRACT FROM AUTHOR]

In order to analyze the relay action process from an imaging perspective and further investigate the bounce phenomenon of relay contacts during the contact process, this paper utilizes a high-speed shooting platform to capture images of relay action. In light of the situation where the stationary contact in the image is inclined and continuously changing, a rotation template matching algorithm based on adaptive weight is proposed. The algorithm identifies and obtains the inclination angle of the stationary contact, enabling the study of the relay contact bounce process. By extracting contact bounce distance data from the images, a bounce process curve is plotted. Combined with the analysis of the contact bounce process, the reasons for the bounce are explored. The results indicate that the proposed rotation template matching algorithm can accurately identify stationary contacts and their angles at different angles. By analyzing the contact status and bounce process of the relay contacts in conjunction with the relay structure, parameters such as the bounce time, bounce height, and time required to reach the maximum distance can be calculated. Additionally, the main reason for contact bounce in the relay studied in this paper is the limitation imposed on the continued movement of the stationary contact by the presence of the relay brackets when the kinetic energy of the contact is too high. This phenomenon occurs during the first vibration peak in the vibration process after the moving contact contacts the stationary contact. The research results provide a reference for further studying the relay contact bounce process, optimizing relay structure, and suppressing contact bounce. [ABSTRACT FROM AUTHOR]

As core algorithms of geographic computing, overlay analysis algorithms typically have computation-intensive and data-intensive characteristics. It is highly important to optimize overlay analysis algorithms by parallelizing the vector polygons after reasonable data division. To address the problem of unbalanced data partitioning in the task decomposition process for parallel polygon overlay analysis and calculation, this paper presents a data partitioning method based on shape complexity index optimization, which achieves data equalization among multicore parallel computing tasks. Taking the intersection operator and difference operator of the Vatti algorithm as examples, six polygon shape indexes are selected to construct the shape complexity model, and the vector data are divided in accordance with the calculated shape complexity results. Finally, multicore parallelism is achieved based on OpenMP. The experimental results show that when a data set with a large amount of data is used, the effect of the multicore parallel execution of the Vatti algorithm's intersection operator and difference operator based on shape complexity division is clearly improved. With 16 threads, compared with the serial algorithm, speedups of 29 times and 32 times can be obtained. Compared with the traditional multicore parallel algorithm based on polygon number division, the speed can be improved by 33% and 29%, and the load balancing index is reduced. For a data set with a small amount of data, the acceleration effect of this method is similar to that of traditional methods involving multicore parallelism. [ABSTRACT FROM AUTHOR]

Street-view images can help us to better understand the city environment and its potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce the parameters and computations of the model while ensuring segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on this basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; and the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of remote sensing images such as a complex background, a large number of small targets, and various target scales, this paper presents a remote sensing image target detection algorithm based on improved YOLOv8. First, in order to extract more information about small targets in images, we add an extra detection layer for small targets in the backbone network; second, we propose a C2f-E structure based on the Efficient Multi-Scale Attention Module (EMA) to enhance the network's ability to detect targets of different sizes; and lastly, Wise-IoU is used to replace the CIoU loss function in the original algorithm to improve the robustness of the model. Using our improved algorithm for the detection of multiple target categories in the DOTAv1.0 dataset, the mAP@0.5 value is 82.7%, which is 1.3% higher than that of the original YOLOv8 algorithm. It is proven that the algorithm proposed in this paper can effectively improve target detection accuracy in remote sensing images. [ABSTRACT FROM AUTHOR]

Existing algorithms for enhancing low-light images predominantly focus on the low-light region, which leads to over-enhancement of the glare region, and the high complexity of the algorithm makes it difficult to apply it to embedded devices. In this paper, a GS-AGC algorithm based on regional luminance perception is proposed. The indirect perception of the human eye's luminance vision was taken into account. All similar luminance pixels that satisfied the luminance region were extracted, and adaptive adjustment processing was performed for the different luminance regions of low-light images. The proposed method was evaluated experimentally on real images, and objective evidence was provided to show that its processing effect surpasses that of other comparable methods. Furthermore, the potential practical value of GS-AGC was highlighted through its effective application in road pedestrian detection and face detection. The algorithm in this paper not only effectively suppressed glare but also achieved the effect of overall image quality enhancement. It can be easily combined with the embedded hardware FPGA for acceleration to improve real-time image processing. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to address the extraction of entities and relationships from unstructured Chinese text, with a particular emphasis on the challenges of Named Entity Recognition (NER) and Relation Extraction (RE). This will be achieved by integrating external lexical information and utilizing the abundant semantic information available in Chinese. We utilize a pipeline model that is applied separately to NER and RE by introducing an innovative NER model that integrates Chinese pinyin, characters, and words to enhance recognition capabilities. Simultaneously, we incorporate information such as entity distance, sentence length, and part-of-speech to improve the performance of relation extraction. We also delve into the interactions among data, models, and inference algorithms to improve learning efficiency in addressing this challenge. In comparison to existing methods, our model has achieved significant results. [ABSTRACT FROM AUTHOR]

The utilization of ultraviolet (UV) absorption spectroscopy for monitoring the concentration of specific decomposition gas components in gas-insulated switchgear (GIS) can provide a means to assess its insulation status. Nevertheless, UV optical modules currently deployed in the field are susceptible to external interferences like ambient noise and equipment vibrations. Real-time spectral data acquisition often suffers from significant noise contamination, directly impinging on subsequent feature extraction and detection accuracy. This paper presents an optimized singular value decomposition (SVD) noise reduction method for mitigating noisy spectral signals. First, each singular value within the noisy signal is transformed into a component signal. Next, the highest frequency value in the signal serves as an indicator to characterize the signal. Finally, the primary frequency values are arranged based on the decreasing singular values of the original noisy signal. The singular value corresponding to the first primary frequency value surpassing a preset threshold is selected as the effective order for denoising. Random noise with varying intensities was intentionally introduced to the UV spectral signal of sulfur dioxide (SO2), followed by noise reduction procedures. It is shown that the improved SVD noise reduction algorithm proposed in this paper enhances the signal-to-noise ratio (SNR) by 18.02% and 16.86%, and reduces the root-mean-square error (RMSE) by 15.13% and 14.92%, respectively, compared with the singular value difference spectrum (SVDS) denoising method and wavelet transform denoising method under the condition of low SNR. Furthermore, there exists a linear relationship between the concentration of SO2 samples and the eigenvalues of the UV spectra, demonstrating a higher linear goodness with a coefficient of 0.99735. The denoising method proposed in this paper does not require the manual setting of various types of parameters, and has a better ability to deal with the noise of UV spectral signals in engineering sites with complex environments. [ABSTRACT FROM AUTHOR]

Multiple Sequence Alignment (MSA) plays a pivotal role in bioinformatics, facilitating various critical biological analyses, including the prediction of unknown protein structures and functions. While numerous methods are available for MSA, bioinspired algorithms stand out for their efficiency. Despite the growing research interest in addressing the MSA challenge, only a handful of comprehensive reviews have been undertaken in this domain. To bridge this gap, this study conducts a thorough analysis of bioinspired-based methods for MSA through a systematic literature review (SLR). By focusing on publications from 2010 to 2024, we aim to offer the most current insights into this field. Through rigorous eligibility criteria and quality standards, we identified 45 relevant papers for review. Our analysis predominantly concentrates on bioinspired-based techniques within the context of MSA. Notably, our findings highlight Genetic Algorithm and Memetic Optimization as the most commonly utilized algorithms for MSA. Furthermore, benchmark datasets such as BAliBASE and SABmark are frequently employed in evaluating MSA solutions. Structural-based methods emerge as the preferred approach for assessing MSA solutions, as revealed by our systematic literature review. Additionally, this study explores current trends, challenges, and unresolved issues in the realm of bioinspired algorithms for MSA, offering practitioners and researchers valuable insights and comprehensive understanding of the field. [ABSTRACT FROM AUTHOR]

With the continuous development of virtual reality, digital image applications, the required complex scene video proliferates. For this reason, portrait matting has become a popular topic. In this paper, a new matting algorithm with improved portrait details for images with complex backgrounds (MORLIPO) is proposed. This work combines the background restoration module (BRM) and the fine-grained matting module (FGMatting) to achieve high-detail matting for images with complex backgrounds. We recover the background by inputting a single image or video, which serves as a priori and aids in generating a more accurate alpha matte. The main framework uses the image matting model MODNet, the MobileNetV2 lightweight network, and the background restoration module, which can both preserve the background information of the current image and provide a more accurate prediction of the alpha matte of the current frame for the video image. It also provides the background prior of the previous frame to predict the alpha matte of the current frame more accurately. The fine-grained matting module is designed to extract fine-grained details of the foreground and retain the features, while combining with the semantic module to achieve more accurate matting. Our design allows training on a single NVIDIA 3090 GPU in an end-to-end manner and experiments on publicly available data sets. Experimental validation shows that our method performs well on both visual effects and objective evaluation metrics. [ABSTRACT FROM AUTHOR]

As an important part of the power system, it is necessary to ensure the safe and stable operation of transmission lines. Due to long-term exposure to the outdoors, the lines face many insecurity factors, and foreign object intrusion is one of them. Traditional foreign object (bird's nest, kite, balloon, trash bag) detection algorithms suffer from low efficiency, poor accuracy, and small coverage, etc. To address the above problems, this paper introduces the RCDAM-Net. In order to prevent feature loss or useful feature compression, the RevCol (Reversible Column Networks) is used as the backbone network to ensure that the total information remains unchanged during feature decoupling. DySnakeConv (Dynamic Snake Convolution) is adopted and embedded into the C2f structure, which is named C2D and integrates low-level features and high-level features. Compared to the original BottleNeck structure of C2f, the DySnakeConv enhances the feature extraction ability for elongated and weak targets. In addition, MPDIoU (Maximum Performance Diagonal Intersection over Union) is used to improve the regression performance of model bounding boxes, solving the problem of predicted bounding boxes having the same aspect ratio as true bounding boxes, but with different values. Further, we adopt Decoupled Head for detection and add additional auxiliary training heads to improve the detection accuracy of the model. The experimental results show that the model achieves mAP50, Precision, and Recall of 97.98%, 98.15%, and 95.16% on the transmission tower line foreign object dataset, which is better to existing multi-target detection algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a YOLO fire detection algorithm based on an attention-enhanced ghost mode, mixed convolutional pyramids, and flame-centre detection (AEGG-FD). Specifically, the enhanced ghost bottleneck is stacked to reduce redundant feature mapping operations in the process for achieving lightweight reconfiguration of the backbone, while attention is added to compensate for accuracy loss. Furthermore, a feature pyramid built using mixed convolution is introduced to accelerate network inference speed. Finally, the local information is extracted by the designed flame-centre detection (FD) module for furnishing auxiliary information in effective firefighting. Experimental results on both the benchmark fire dataset and the video dataset show that the AEGG-FD performs better than the classical YOLO-based models such as YOLOv5, YOLOv7 and YOLOv8. Specifically, both the mean accuracy (mAP0.5, reaching 84.7%) and the inferred speed (FPS) are improved by 6.5 and 8.4 respectively, and both the number of model parameters and model size are compressed to 72.4% and 44.6% those of YOLOv5, respectively. Therefore, AEGG-FD achieves an effective balance between model weight, detection speed, and accuracy in firefighting. [ABSTRACT FROM AUTHOR]

To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation algorithm has been proposed in this paper. Firstly, the edge image features of hot spots were extracted based on residual neural networks. Secondly, by combining the feature pyramid structure, an edge-guided feature pyramid structure was designed, and the hot spot edge features were injected into a Mask R-CNN network. Thirdly, an infrared spatial attention module was introduced into the Mask R-CNN network when feature extraction and the infrared features of the detected hot spots were enhanced. Fourthly, the size ratio of the candidate frames was adjusted self-adaptively according to the structural characteristics of the aspect ratio of the hot spots. Finally, the validation experiments were conducted, and the results demonstrated that the hot spot contours of thermal infrared images were enhanced through the algorithm proposed in this paper, and the segmentation accuracy was significantly improved. [ABSTRACT FROM AUTHOR]

Intelligent gangue sorting with high precision is of vital importance for improving coal quality. To tackle the challenges associated with coal gangue target detection, including algorithm performance imbalance and hardware deployment difficulties, in this paper, an intelligent gangue separation system that adopts the elevated YOLO-v5 algorithm and dual-energy X-rays is proposed. Firstly, images of dual-energy X-ray transmission coal gangue mixture under the actual operation of a coal mine were collected, and datasets for training and validation were self-constructed. Then, in the YOLOv5 backbone network, the EfficientNetv2 was used to replace the original cross stage partial darknet (CSPDarknet) to achieve the lightweight of the backbone network; in the neck, a light path aggregation network (LPAN) was designed based on PAN, and a convolutional block attention module (CBAM) was integrated into the BottleneckCSP of the feature fusion block to raise the feature acquisition capability of the network and maximize the learning effect. Subsequently, to accelerate the rate of convergence, an efficient intersection over union (EIOU) was used instead of the complete intersection over union (CIOU) loss function. Finally, to address the problem of low resolution of small targets leading to missed detection, an L2 detection head was introduced to the head section to improve the multi-scale target detection performance of the algorithm. The experimental results indicate that in comparison with YOLOv5-S, the same version of the algorithm proposed in this paper increases by 19.2% and 32.4% on mAP @.5 and mAP @.5:.95, respectively. The number of parameters decline by 51.5%, and the calculation complexity declines by 14.7%. The algorithm suggested in this article offers new ideas for the design of identification algorithms for coal gangue sorting systems, which is expected to save energy and reduce consumption, reduce labor, improve efficiency, and be more friendly to the embedded platform. [ABSTRACT FROM AUTHOR]

This paper shows the implementation of the Moth–Flame Optimization algorithm in a Cascade-H multilevel inverter with five and seven levels to determine the optimal switching sequence of the inverter's semiconductor devices. The algorithm was coded in Matlab software, and the obtained switching sequences were implemented in a Cascade-H multilevel inverter laboratory prototype, where the output voltage waveform was obtained using a digital oscilloscope. The experimental Total Harmonic Distortion was obtained using a power quality analyzer. The experimental results show the improvement of the Total Harmonic Distortion in the voltage output. These results were compared with other papers in the literature with different metaheuristic methods concerning the same modulation. These findings demonstrate the feasibility of employing the Moth–Flame Optimization Algorithm to significantly reduce the Total Harmonic Distortion, obtaining a lower value than most analyzed papers. [ABSTRACT FROM AUTHOR]

Due to the numerous objects with regular structures in indoor environments, identifying and modeling the regular objects in scenes aids indoor robots in sensing unknown environments. Typically, point cloud preprocessing can obtain highly complete object segmentation results in scenes which can be utilized as the objects for geometric analysis and modeling, thus ensuring modeling accuracy and speed. However, due to the lack of a complete object model, it is not possible to recognize and model segmented objects through matching methods. To achieve a greater understanding of scene point clouds, this paper proposes a direct geometric modeling algorithm based on segmentation results, which focuses on extracting regular geometries in the scene, rather than objects with geometric details or combinations of multiple primitives. This paper suggests using simpler geometric models to describe the corresponding point cloud data. By fully utilizing the surface structure information of segmented objects, the paper analyzes the types of faces and their relationships to classify regular geometric objects into two categories: planar and curved. Different types of geometric objects are fitted using random sampling consistency algorithms with type classification results as prior knowledge, and segmented results are modeled through a combination of size information associated with directed bounding boxes. For indoor scenes with occlusion and stacking, utilizing a higher-level semantic expression can effectively simplify the scene, complete scene abstraction and structural modeling, and aid indoor robots' understanding and further operation in unknown environments. [ABSTRACT FROM AUTHOR]

Deep learning-based object detection methods address the problem of how to trade off the object detection accuracy and detection speed of the model. This paper proposes the PBA-YOLOv7 network algorithm, which is based on the YOLOv7 network, and first introduces the PConv, which lightens the ELAN module in the backbone network structure and reduces the number of parameters to improve the detection speed of the network and then designs and introduces the BiFusionNet network, which better aggregates the high-level semantic features and the low-level semantic features; and finally, on this basis, the coordinate attention mechanism is introduced to make the network focus on more critical features without increasing the model complexity. The coordinate attention mechanism is introduced to make the network focus more on important feature information and improve the feature expression ability of the network without increasing the model complexity. Experiments on the publicly available KITTI's dataset show that the PBA-YOLOv7 network model significantly improves both detection accuracy and detection speed compared to the original YOLOv7 model, with 4% and 7.8% improvement in mAP0.5 and mAP0.5:0.95, respectively, and six frames improvement in FPS. The improved algorithm in this paper weighs the model's detection accuracy and detection speed in the detection task. It performs well compared to other algorithms, such as YOLOv7 and YOLOv5l. [ABSTRACT FROM AUTHOR]

Survival analysis is widely used in fields such as medical research and reliability engineering to analyze data where not all subjects experience the event of interest by the end of the study. It requires dedicated methods capable of handling censored cases. This paper extends the collection of techniques applicable to censored data by introducing a novel algorithm for interpretable recommendations based on a set of survival action rules. Each action rule contains recommendations for changing the values of attributes describing examples. As a result of applying the action rules, an example is moved from a group characterized by a survival curve to another group with a significantly different survival rate. In practice, an example can be covered by several induced rules. To decide which attribute values should be changed, we propose a recommendation algorithm that analyzes all actions suggested by the rules covering the example. The efficiency of the algorithm has been evaluated on several benchmark datasets. We also present a qualitative analysis of the generated recommendations through a case study. The results indicate that the proposed method produces high-quality recommendations and leads to a significant change in the estimated survival time. [ABSTRACT FROM AUTHOR]

The introduction of 5G technologies has enabled the possibility of designing and building several new classes of networked information systems that were previously impossible to implement due to limitations on data throughput or the reliability of transmission channels. Among them, one of the most interesting and successful examples with a highly positive impact in terms of the quality of urban environments and societal and economical welfare is a system of semi-autonomous connected vehicles, where IoT devices, data centers, and fleets of smart vehicles equipped with communication and computational resources are combined into a heterogeneous and distributed infrastructure, unifying hardware, networks, and software. In order to efficiently provide various services (e.g., patrolling, pickup and delivery, monitoring), these systems typically rely on collecting and broadcasting large amounts of data (e.g., sensor data, GPS traces, or maps), which need to be properly collected and processed in a timely manner. As is well documented in the literature, one of the most effective ways to achieve this purpose, especially in a real-time context, is to adopt a graph model of the data (e.g., to model communication networks, roads, or interactions between vehicles) and to employ suitable graph algorithms to solve properly defined computational problems of interest (e.g., shortest paths or distributed consensus). While research in this context has been extensive from a theoretical perspective, works that have focused on the implementation, deployment, and evaluation of the practical performance of graph algorithms for real-world systems of autonomous vehicles have been much rarer. In this paper, we present a study of this kind. Specifically, we first describe the main features of a real-world information system employing semi-autonomous connected vehicles that is currently being tested in the city of L'Aquila (Italy). Then, we present an overview of the computational challenges arising in the considered application domain and provide a systematic survey of known algorithmic results for one of the most relevant classes of computational problems that have to be addressed in said domain, namely, pickup and delivery problems. Finally, we discuss implementation issues, adopted software tools, and the deployment and testing phases concerning one of the algorithmic components of the mentioned real-world system dedicated to handling a specific problem of the above class, namely, the pickup and delivery multi-vehicle problem with time windows. [ABSTRACT FROM AUTHOR]

Cloud computing and the increasing popularity of 5G have greatly increased the application of images on Internet of Things (IoT) devices. The storage of images on an untrusted cloud has high security and privacy risks. Several lightweight cryptosystems have been proposed in the literature as appropriate for resource-constrained IoT devices. These existing lightweight cryptosystems are, however, not only at the risk of compromising the integrity and security of the data but also, due to the use of substitution boxes (S-boxes), require more memory space for their implementation. In this paper, a secure lightweight cryptography algorithm, that eliminates the use of an S-box, has been proposed. An algorithm termed Enc, that accepts a block of size n divides the block into L n R bits of equal length and outputs the encrypted block as follows: E = L ⨂ R ⨁ R , where ⨂ and ⨁ are exclusive-or and concatenation operators, respectively, was created. A hash result, h a s R = S H A 256 P ⨁ K , was obtained, where SHA256, P, and K are the Secure Hash Algorithm (SHA−256), the encryption key, and plain image, respectively. A seed, S, generated from e n c h a s h = E n c h a s h e n c , K , where hashenc is the first n bits of hasR, was used to generate a random image, Rim. An intermediate image, i n t i m a g e = R i m ⨂ P , and cipher image, C = E n c i n t i m a g e , K , were obtained. The proposed scheme was evaluated for encryption quality, decryption quality, system sensitivity, and statistical analyses using various security metrics. The results of the evaluation showed that the proposed scheme has excellent encryption and decryption qualities that are very sensitive to changes in both key and plain images, and resistance to various statistical attacks alongside other security attacks. Based on the result of the security evaluation of the proposed cryptosystem termed Hash XOR Permutation (HXP), the study concluded that the security of the cryptography algorithm can still be maintained without the use of a substitution box. [ABSTRACT FROM AUTHOR]

In applications where large-order filters are needed, the computational load of adaptive filtering algorithms can become prohibitively expensive. In this paper, a comprehensive analysis of a selective partial-update least mean squares, named SPU-LMS-M-min, is developed. By employing the partial-update strategy for a non-normalized adaptive scheme, the designer can choose an appropriate number of update blocks considering a trade-off between convergence rate and computational complexity, which can result in a more than 40% reduction in the number of multiplications in some configurations compared to the traditional LMS algorithm. Based on the principle of minimum distortion, a selection criterion is proposed that is based on the input signal's blocks with the lowest energy, whereas typical Selective Partial Update (SPU) algorithms use a selection criterion based on blocks with highest energy. Stochastic models are developed for the mean weights and mean and mean squared behaviour of the proposed algorithm, which are further extended to accommodate scenarios involving time-varying dynamics and suboptimal filter lengths. Simulation results show that the theoretical predictions are in good agreement with the experimental outcomes. Furthermore, it is demonstrated that the proposed selection criterion can be easily extended to active noise cancellation algorithms as well as algorithms utilizing variable filter length. This allows for the reduction of computational costs for these algorithms without compromising their asymptotic performance. [ABSTRACT FROM AUTHOR]

Compared with electromagnetic compatibility (EMC) testing in anechoic rooms, open-area EMC testing takes advantage of in situ and engine running status measurement but suffers from non-negligible external electromagnetic interference. This paper proposes a novel environmental interference suppression method (named the EMC environmental interference suppression algorithm ( E 2 I S A )) that separates signals from backgrounds via image segmentation and recognizes the near–far site signal via a group of time-varying features based on the difference in the near-site EM radiative characteristic. We find that the proposed E 2 I S A method, which combines the deep learning segmentation network with the classical recognition methods, is able to suppress environmental interference signals accurately. The experiment results show that the accuracy of E 2 I S A reaches up to 95% in the face of VHF (Very High Frequency) EMC testing tasks. [ABSTRACT FROM AUTHOR]

With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, handling time, and processing time in a multi-equipment work center production environment oriented toward smart manufacturing and make-to-order requirements. A mathematical model with the optimization objectives of minimizing the maximum completion time, the total number of machine adjustments, the total number of workpieces handled and the total load of the machine is constructed, and an improved discrete particle swarm algorithm based on Pareto optimization and a nonlinear adaptive inertia weighting strategy is proposed to solve the model. By integrating the model characteristics and algorithm features, a hybrid initialization method is designed to generate a higher-quality initialized population. Next, three cross-variance operators are used to implement particle position updates to maintain information sharing among particles. Then, the performance effectiveness of this algorithm is verified by testing and analyzing 15 FJSP test instances. Finally, the feasibility and effectiveness of the designed algorithm for solving multi-objective FJSPs are verified by designing an FJSP test example that includes processing time, setup time and handling time. [ABSTRACT FROM AUTHOR]

Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches have been proposed to synthesize the gait patterns for these robots; each approach exhibits different restrictions, advantages, and priorities. For the purpose of this document, we have classified gait pattern generators for multi-legged robots into three categories: precomputed, heuristic, and bio-inspired approaches. Precomputed approaches rely on a set of precalculated motion patterns obtained from geometric and/or kinematic models that are performed repeatedly whenever necessary and that cannot be modified on-the-fly to adapt to the terrain changes. On the other hand, heuristic and bio-inspired approaches offer on-line adaptability, but parameter-tuning and heading control can be difficult. In this document, we present the K3P algorithm, a real-time kinematic gait pattern generator conceived to command a legged robot. In contrast to other approaches, K3P enables the robot to adapt its gait to follow an arbitrary trajectory, at an arbitrary speed, over uneven terrain. No precomputed motions for the legs are required; instead, K3P modifies the motion of all mechanical joints to propel the body of the robot in the desired direction, maintaining a tripod stability at all times. In this paper, all the specific details of the aforementioned algorithm are presented, as well as different simulation results that validate its characteristics. [ABSTRACT FROM AUTHOR]

Ultra-low-frequency oscillation (ULFO) is a new problem of frequency stability in asynchronous network back-end power networks. The negative damping provided by hydropower units is the direct cause of ultra-low-frequency oscillation, and DC modulation is an effective means of suppressing system frequency oscillation. Based on the transient energy function and the limitation of DC modulation, this paper analyzes the conditions of suppressing system oscillation by DC modulation from the perspective of energy. The weight of unit energy participation is defined, and the calculation formula of transient oscillation energy in a certain oscillation mode is derived. DC modulation sensitivity is then used to create a DC modulation sequence table based on the Prony identification approach. Ultimately, the DC involved in the modulation is identified using the DC modulation sequence table and the transient oscillation energy, and the Deep Deterministic Policy Gradient (DDPG) algorithm is used to optimize the chosen DC modulation parameters. [ABSTRACT FROM AUTHOR]

In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing. [ABSTRACT FROM AUTHOR]

In this paper, an improved cubature Kalman filtering (CKF) is proposed using the Sigmoid function to address the problems of positioning accuracy degradation and large deviations in ultra-wideband (UWB) indoor positioning in non-line-of-sight environments. The improved CKF is based on the squared range difference (SRD) model of the time difference of arrival (TDOA) algorithm. The inaccurate impact of model estimation under non-Gaussian noise is reduced by updating the measurement noise matrix in real time. The covariance matrix is estimated using singular value decomposition (SVD) to solve the problem of degraded state estimation performance. The filtering effect of the improved CKF algorithm is evaluated by referring to the checkpoints in the dynamic trajectory. The experimental results show that the proposed algorithm effectively mitigates the impact of UWB ranging outliers in the occluded experimental environment, which makes the dynamic positioning trajectory smoother, better fitted, and more stable. The algorithm improves the positioning accuracy by up to 39.29% compared with the SRD model used alone. [ABSTRACT FROM AUTHOR]

When performing classification tasks on high-dimensional data, traditional machine learning algorithms often fail to filter out valid information in the features adequately, leading to low levels of classification accuracy. Therefore, this paper explores the high-dimensional data from both the data feature dimension and the model ensemble dimension. We propose a high-dimensional ensemble learning classification algorithm focusing on feature space reconstruction and classifier ensemble, called the HDELC algorithm. First, the algorithm considers feature space reconstruction and then generates a feature space reconstruction matrix. It effectively achieves feature selection and reconstruction for high-dimensional data. An optimal feature space is generated for the subsequent ensemble of the classifier, which enhances the representativeness of the feature space. Second, we recursively determine the number of classifiers and the number of feature subspaces in the ensemble model. Different classifiers in the ensemble system are assigned mutually exclusive non-intersecting feature subspaces for model training. The experimental results show that the HDELC algorithm has advantages compared with most high-dimensional datasets due to its more efficient feature space ensemble capability and relatively reliable ensemble operation performance. The HDELC algorithm makes it possible to solve the classification problem for high-dimensional data effectively and has vital research and application value. [ABSTRACT FROM AUTHOR]

Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data objects or that of the clusters. Numerous variants of FCM have been proposed to address these issues. However, most of them cannot effectively apply the available information on data objects or clusters. In this paper, a double-constraint fuzzy clustering algorithm is proposed to reflect the importance degrees of both individual data objects and clusters. By incorporating double constraints into each data object and cluster, the objective function of FCM is reformulated and its realization equation is mathematically conducted. Consequently, the clustering accuracy of FCM is improved by applying the available information on both data objects and clusters. Especially, the proposed algorithm effectively addresses the limitations inherent in the existing variants of FCM. The experimental results validate the effectiveness, implementation, and robustness of the new fuzzy clustering algorithm. [ABSTRACT FROM AUTHOR]

Today, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process IoT tasks, and we are seeing a growth in the number of IoT devices every day. This massive increase needs huge amounts of resources to process it, and these vast resources need a lot of power to work because the fog and cloud are based on the term pay-per-use. We make to improve the performance and cost (PC) algorithm to give priority to the high-profit cost and to reduce energy consumption and Makespan; in this paper, we propose the performance and cost–gray wolf optimization (PC-GWO) algorithm, which is the combination of the PCA and GWO algorithms. The results of the trial reveal that the PC-GWO algorithm reduces the average overall energy usage by 12.17%, 11.57%, and 7.19%, and reduces the Makespan by 16.72%, 16.38%, and 14.107%, with the best average resource utilization enhanced by 13.2%, 12.05%, and 10.9% compared with the gray wolf optimization (GWO) algorithm, performance and cost algorithm (PCA), and Particle Swarm Optimization (PSO) algorithm. [ABSTRACT FROM AUTHOR]

Satellite networks show the development trend in global coverage, flexible access, and reliable transmission. They are the key to building a wide coverage, massive connection, three-dimensional, all-round, all-weather, space-, air- and ground-integrated information network. Due to its high frequency and large bandwidth, the laser inter-satellite link (ISL) is easily interfered with by sunlight. When sun outage occurs, the laser inter-satellite link will be interrupted for a long time. Most of the existing satellite link failure-routing algorithms are distributed and have poor load balancing capabilities. The centralized algorithm calculates paths independently for different services, and the effect is not ideal. In this paper, a local pre-rerouting algorithm (LPRA) is proposed. The center node collects the link information from the limited area before sun outage and the service information in the link that is about to be interrupted, and reroutes these services before sun outage occurs. After the occurrence of sun outage, services that originally needed to go through the failed link are forwarded according to the path calculated in advance, thereby avoiding link congestion and reducing the packet loss rate. Simulation results show that the proposed algorithm performs better than existing algorithms in terms of packet loss ratio, total delay and load balancing capabilities. [ABSTRACT FROM AUTHOR]

The controlled source audio-frequency magnetotelluric method (CSAMT) stands out for its economic efficiency and widespread application in geophysical monitoring. However, the separate inversion of time-lapse monitoring data encounters challenges in comparing and identifying abnormal changes due to variations in data fitting. Furthermore, the utilization of a method akin to Cagniard apparent resistivity for inversion necessitates the simultaneous observation of at least two components of the electromagnetic field, making it unsuitable for extensive three-dimensional observations. This paper proposes a 3D time-lapse electric field inversion algorithm for CSAMT, addressing the complexities in geophysical monitoring. The algorithm introduces two regularization factors and defines an objective function with both temporal and spatial constraints. Synthetic testing reveals the stability of the 3D time-lapse electric field inversion algorithm, demonstrating its effectiveness in delineating underground variations. This solution resolves the challenges posed by the independent inversion of time-lapse monitoring data. [ABSTRACT FROM AUTHOR]

Distributed Constraint Optimization Problems (DCOPs) are an efficient framework widely used in multi-agent collaborative modeling. The traditional DCOP framework assumes that variables are discrete and constraint utilities are represented in tabular forms. However, the variables are continuous and constraint utilities are in functional forms in many practical applications. To overcome this limitation, researchers have proposed Continuous DCOPs (C-DCOPs), which can model DCOPs with continuous variables. However, most of the existing C-DCOP algorithms rely on gradient information for optimization, which means that they are unable to solve the situation where the utility function is a non-differentiable function. Although the Particle Swarm-Based C-DCOP (PCD) and Particle Swarm with Local Decision-Based C-DCOP (PCD-LD) algorithms can solve the situation with non-differentiable utility functions, they need to implement Breadth First Search (BFS) pseudo-trees for message passing. Unfortunately, employing the BFS pseudo-tree results in expensive computational overheads and agent privacy leakage, as messages are aggregated to the root node of the BFS pseudo-tree. Therefore, this paper aims to propose a fully distributed C-DCOP algorithm to solve the utility function form problem and avoid the disadvantages caused by the BFS pseudo-tree. Inspired by the population-based algorithms, we propose a fully decentralized local search algorithm, named Population-based Local Search Algorithm (PLSA), for solving C-DCOPs with three-fold advantages: (i) PLSA adopts a heuristic method to guide the local search to achieve a fast search for high-quality solutions; (ii) in contrast to the conventional C-DCOP algorithm, PLSA can solve utility functions of any form; and (iii) compared to PCD and PCD-LD, PLSA avoids complex message passing to achieve efficient computation and agent privacy protection. In addition, we implement an extended version of PLSA, named Population-based Global Search Algorithm (PGSA), and empirically show that our algorithms outperform the state-of-the-art C-DCOP algorithms on three types of benchmark problems. [ABSTRACT FROM AUTHOR]

As a structural indicator of dense subgraphs, k-core has been widely used in community search due to its concise and efficient calculation. Many community search algorithms have been expanded on the basis of k-core. However, relevant algorithms often set k values based on empirical analysis of datasets or require users to input manually. Once users are not familiar with the graph network structure, they may miss the optimal solution due to an improper k setting. Especially in attribute social networks, characterizing communities with only k-cores may lead to a lack of semantic interpretability of communities. Consequently, this article proposes a method for identifying the optimal k-core with the greatest attribute score in the attribute social network as the target community. The difficulty of the problem is that the query needs to integrate both structural and textual indicators of the community while fully considering the diversity of attribute scoring functions. To effectively reduce computational costs, we incorporate the topological characteristics of the k-core and the attribute characteristics of entities to construct a hierarchical forest. It is worth noting that we name tree nodes in a way similar to pre-order traversal and can maintain the order of all tree nodes during the forest creation process. In such an attribute forest, it is possible to quickly locate the initial solution containing all query vertices and reuse intermediate results during the process of expanding queries. We conducted effectiveness and performance experiments on multiple real datasets. As the results show, attribute scoring functions are not monotonic, and the algorithm proposed in this paper can avoid scores falling into local optima. With the help of the attribute k-core forest, the actual query time of the Advanced algorithm has improved by two orders of magnitude compared to the BaseLine algorithm. In addition, the average F1 score of our target community has increased by 2.04 times and 26.57% compared to ACQ and SFEG, respectively. [ABSTRACT FROM AUTHOR]

The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems. Various privacy-preserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time. For example, using our framework, a CNN model for the MNIST dataset achieves 96% accuracy compared to 99% in a standard collaborative framework (with no privacy measures taken), with training time reduced from 133.259 s to 99.391 s. Our MLP model for MNIST achieves 97% accuracy compared to 98% in the standard collaborative framework, with training time reduced from 118.146 s to 87.873 s. Compared to other studies, our method is a simple approach that protects privacy, maintains the utility of deep learning models, and reduces training time and communication costs. [ABSTRACT FROM AUTHOR]

Mitigating low-frequency noise in various industrial applications often involves the use of the filter-x least mean squares (FxLMS) algorithm, which relies on the mean square error criterion. This algorithm has demonstrated effectiveness in reducing noise induced by Gaussian noise within noise control systems. However, the performance of this algorithm experiences significant degradation and does not converge properly in the presence of impulsive noise. Consequently, to uphold the stability of the ANC system, several robust adaptive algorithms tailored to handle shock noise interference have been introduced. This paper systematically organizes and classifies robust adaptive algorithms designed for impulse noise based on algorithmic criteria, offering valuable insights for the research and application of pertinent active impact noise control methods. [ABSTRACT FROM AUTHOR]

Calligraphy works have high artistic value, but there is the rampant problem of forgery. Indeed, the authentication of traditional calligraphy heavily relies on calligraphers' subjective judgment. Therefore, spurred by the recent development of neural networks, this paper proposes a method for authenticating calligraphy works based on an improved EfficientNet network. Specifically, the developed method utilizes the character box algorithm to efficiently extract individual calligraphy characters, which are then augmented and used as the training set for the model. The training process employs CBAM and Self-Attention modules to enhance the attention mechanism of the EfficientNet network. The trained network model is used to judge the calligraphy works' similarity; tested on authentic works, imitated works, and works from other calligraphers; and compared with other networks. The experimental results demonstrate that the proposed method effectively achieves the authentication of calligraphy works, and the improved CBAM-EfficientNet network and SA-EfficientNet network achieve better authentication performance. [ABSTRACT FROM AUTHOR]

The modern urban transportation service network could be split into unrestricted and restricted networks depending on whether travelers face limitations in route selection. Along with the continuous expansion of the city, it is difficult for travelers to find a more reasonable travel solution when confronted with such a complex transportation service network, which combines both unrestricted and restricted networks, especially for the park-and-ride (P&R) travel mode. This paper addresses the issue of route analysis in modern urban transportation service systems to provide travelers with reasonable travel solutions based on multiple types of transportation services. An improved A* algorithm is proposed to address the optimal path analysis for restricted networks to provide reasonable travel solutions for public transportation trips. Furthermore, by establishing the topological relationship between restricted and unrestricted networks, this paper presented an improved A* algorithm based on hybrid networks that solves the optimal path analysis problem for P&R trips, bringing convenience to many urban travelers. [ABSTRACT FROM AUTHOR]

Machine learning-based malware (malicious software) detection methods have a wide range of real-world applications. However, these types of approaches suffer from the fatal problem of "model aging", in which the validity of the model decreases rapidly as the malware continues to evolve and variants emerge continuously. The model aging problem is usually solved by model retraining, which relies on lots of labeled samples obtained at great expense. To address this challenge, this paper proposes a semi-supervised continuous learning malware detection model based on Transformer. Firstly, this model improves the lifelong semi-supervised mixture algorithm to dynamically adjust the weighted combination of new sample sequences and historical ones to solve the imbalance problem. Secondly, the Learning with Local and Global Consistency algorithm is used to iteratively compute similarity scores for the unlabeled samples in the mixed samples to obtain pseudo-labels. Lastly, the Multilayer Perceptron is applied for malware classification. To validate the effectiveness of the model, this paper conducts experiments on the CICMalDroid2020 dataset. The experimental results show that the proposed model performs better than existing deep learning detection models. The F1 score has an average improvement of 1.27% compared to other models when conducting binary classification. And, after inputting hybrid samples, including historical data and new data, four times, the F1 score is still 1.96% higher than other models. [ABSTRACT FROM AUTHOR]

Many security detectors do not have the ability to output individual luggage package images and are not compatible with deep learning algorithms. In this paper, a luggage package extraction of X-ray images based on the ES-MBD (Edge Sensitive Multi-channel Background Difference Algorithm) method is proposed, which is aiming at the problem that background difference binarization is insensitive to texture features and edge detection binarization is insensitive to smooth areas. In this method, X-ray luggage package images from complex original video images are used as a key target, the RGB three-channel background difference is calculated from the original X-ray image, the edge detection of the grayscale map is performed using the Sobel operator optimized by local gradient enhancement, and the morphological expansion process is performed on the combined results to obtain the complete wrapping target. The Suzuki algorithm is used to detect the outline of the binarized package image, match the package frame area and determine the key target. The ES-MBD method solves the problem of information loss in the traditional binarization method, and retains the information of insensitive regions while reducing noise. Through experimental comparison, the accuracy of ES-MBD binarization method reaches 97.3%, the recall rate reaches 96.5%, and ES-MBD method has obvious advantages in key target extraction of X-ray images. [ABSTRACT FROM AUTHOR]

This paper analyzes the typical technical problems arising from dumping and tamping collision interferences in the working faces of conventional mechanized solid backfilling mining (SBM). Additionally, the technical and consecutive characteristics of the solid intelligent backfilling (SIB) method, the execution device, and the corresponding process categories of the SIB process are analyzed. A design for an SIB process flow is presented. Critical algorithms, including automatic recognition and optimization planning based on the cost function and laying the algorithm foundation, are proposed to develop a backfilling process control system. A joint simulation test system is built on a MATLAB/Simulink simulation toolkit (MSST) to simulate and test the optimized algorithms. The results show that the optimized algorithm can realize the automatic optimization planning and automatic interference-recognition adjustment of the backfilling process under actual engineering conditions. In conclusion, this paper analyzes typical technical problems in the conventional backfilling process, designs the SIB process flow, and develops key algorithms to achieve the automatic control of the backfilling process. [ABSTRACT FROM AUTHOR]

Topographic maps comprise a rich data source for the processing and analysis of geographic information. However, the management of paper topographic maps is still dominated by an inefficient manual method, and there is no counting equipment for topographic maps that is suitable for narrow map warehouse passages. The existing algorithms relating to paper counting require paper hardness and cannot directly obtain the internal information of topographic maps. To address the above problems, using a map inventory machine transformed from a paddle-type paper counting machine to perform a preliminary count and obtain the video data of the counting progress, an automatic counting algorithm for topographic maps for map management is proposed in this study. First, the periodic feature changes of all the targets and each individual target in the videos are analyzed. Second, a deformable parts model accelerated by fast feature pyramids and by reducing the region of interest is used for the object detection of mechanical wheels. The grayscale variance function is selected to quantify the quality change in the images. Finally, a fusion window counting model is constructed with the adaptive window and fixed window methods. The experimental results show that the average counting accuracy of the model is about 95%, which can verify the initial counting results of the machine and provide high-quality keyframes for the subsequent sheet designation recognition. [ABSTRACT FROM AUTHOR]

Rice pests and diseases have a significant impact on the quality and yield of rice, and even have a certain impact on and cause a loss in the national agricultural industry and economy. The timely and accurate detection of pests and diseases is the basic premise of formulating effective rice pest control and prevention programs. However, the complexity and diversity of pests and diseases and the high similarity between some pests and diseases make the detection and classification task of pests and diseases extremely difficult without detection tools. The existing target detection algorithms can barely complete the task of detecting pests and diseases, but the detection effect is not ideal. In the actual situation of rice disease and insect pest detection, the detection algorithm is required to have fast speed, high accuracy, and good performance for small target detection, and so this paper improved the popular yolov5 algorithm to achieve an ideal detection performance suitable for rice disease and insect pest detection. This paper briefly introduces the current status and influence of rice pests and diseases and several target detection algorithms based on deep learning. Based on the yolov5 algorithm, the RepVGG network structure is introduced, 3*3 convolution is combined with ReLU, a training time model with multi-branch topology is adopted, and the inference time is reduced through layer merging. To improve algorithm detection speed, the SK attention mechanism is introduced to improve the receptive field of the convolution kernel to obtain more information and improve accuracy. In addition, Adaptive NMS is replaced by Adaptive NMS, the dynamic suppression strategy is adopted, and scores for learning density are set, which greatly improves the problems of missing detection and the false detection of small targets. Finally, the improved algorithm model is combined with membrane calculation to further improve the accuracy and speed of the algorithm. According to the experimental results, the accuracy of the improved algorithm is increased by about 2.7 percentage points, and the mAP is increased by 4.3 percentage points, up to 94.4%. The speed is improved by about 2.8 percentage points, and indicators such as recall rate and AP are improved. [ABSTRACT FROM AUTHOR]

In this paper, the mobile robot position fusion algorithm is inaccurate. There is a delay, and the map-construction accuracy is not high; an improvement method is proposed. First, the Cartographer algorithm is optimized. Radius filtering is used for data processing after voxel filtering. In contrast, the idea of multi-sensor fusion is used to fuse the processed IMU data information. This improved method improves the efficiency of the algorithm and the accuracy of the positional pose fusion. We verify the effect of the algorithm applied to the environment map, respectively, in the experimental building promenade environment and the teaching building hall environment, and analyze and compare the effect of map construction before and after the improvement; the experiment proves that in the experimental building promenade environment, the absolute error of measuring and analyzing the obstacles reduces by 0.06 m, and the relative error decreases by 1.63%; in the teaching building hall environment, the absolute error of measuring and analyzing the longest side of the map decreases by 1.121 m and the relative error decreased by 5.52%. In addition, during the experimental operation, the CPU occupancy of the optimized algorithm is around 59.5%. In contrast, the CPU occupancy of the original algorithm is 67% on average, and sometimes it will soar to 75%. The experimental results prove that the algorithm in this paper significantly improves performance in all aspects when constructing real-time environment maps. [ABSTRACT FROM AUTHOR]

The hyperspectral anomaly detection algorithm based on collaborative representation does not fully utilize the two-dimensional spatial features in hyperspectral images. It also has the problem that anomalous pixels will pollute the background dictionary and induce bad detection performance. Based on these, this paper proposes a hyperspectral anomaly detection algorithm based on multiple feature joint trilateral filtering and collaborative representation. The algorithm first introduces an improved trilateral filtering algorithm, which utilizes the spatial features of hyperspectral images. The preliminary positions of possible abnormal objects are determined. On this basis, abnormal removal and background filling are performed to obtain a purified background. Finally, the purified background and the original hyperspectral image are used for joint collaborative representation to complete the detection. Experimental results show that the detection accuracy of the algorithm proposed in this paper was efficiently improved by introducing multiple feature joint trilateral filtering, where multiple spatial spectrum features are utilized. [ABSTRACT FROM AUTHOR]

Context: Software product lines (SPLs) have reached a considerable level of adoption in the software industry. The most commonly used models for managing the variability of SPLs are feature models (FMs). The analysis of FMs is an error-prone, tedious task, and it is not feasible to accomplish this task manually with large-scale FMs. In recent years, much effort has been devoted to developing reasoning algorithms for FMs. Aim: To synthesize the evidence on the use of reasoning algorithms for feature modeling. Method: We conducted a systematic mapping study, including six research questions. This study included 66 papers published from 2010 to 2020. Results: We found that most algorithms were used in the domain stage (70%). The most commonly used technologies were transformations (18%). As for the origins of the proposals, they were mainly rooted in academia (76%). The FODA model continued to be the most frequently used representation for feature modeling (70%). A large majority of the papers presented some empirical validation process (90%). Conclusion: We were able to respond to the RQs. The FODA model is consolidated as a reference within SPLs to manage variability. Responses to RQ2 and RQ6 require further review. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Applied Sciences on real-time diagnosis algorithms in biomedical applications and decision support tools. The issue includes ten papers that showcase advances in data analysis techniques and their potential to improve doctors' daily tasks. Five papers focus on real-time diagnosis algorithms, covering topics such as ventricular fibrillation detection, bio-impedance spectrometry, epilepsy localization, physical activity recognition, and exhaled acetone detection. The other five papers explore decision support tools, including machine learning models for disease classification, optical breast contour detection, analysis of human body balance, handwriting analysis for neurodegenerative disease assessment, and automatic detection of brain markers using deep learning. The issue concludes that these research topics have a high social impact and contribute to improving human lives. [Extracted from the article]

Traffic on backbone communication networks is growing significantly every year. This results in an increase in both energy consumption and the carbon footprint they leave on the environment. As a response, research efforts are focused on reducing energy consumption in telecom networks. Wavelength division multiplexing (WDM) optical networks are key for addressing rising bandwidth demands in backbone networks, but this leads to a concurrent surge in energy usage. Additionally, regions with high seismic activity risk damage to backbone networks from earthquakes, causing significant bandwidth loss and service disruptions. This paper aims to reduce the energy consumption in a backbone network by implementing an algorithm that optimizes energy efficiency while preserving network connectivity and resistance to earthquake phenomena. The proposed algorithm redesigns and modifies a backbone network by deactivating the unnecessary links without affecting the network performance. The scheme is extensively evaluated through simulations using real seismic data from the Geodynamic Institute of the National Observatory of Athens, confirming earthquake resilience and energy efficiency goals, with an energy saving of up to 9% compared to existing solutions. [ABSTRACT FROM AUTHOR]

This paper presents a system for solving binary-valued linear equations using quantum computers. The system is called Mod2VQLS, which stands for Modulo 2 Variational Quantum Linear Solver. As far as we know, this is the first such proposal. The design is a classical–quantum hybrid. The quantum components are a new circuit design for implementing matrix multiplication modulo 2, and a variational circuit to be optimized. The classical components are the optimizer, which measures the cost function and updates the quantum parameters for each iteration, and the controller that runs the quantum job and classical optimizer iterations. We propose two alternative ansatze or templates for the variational circuit and present results showing that the rotation ansatz designed specifically for this problem provides the most direct path to a valid solution. Numerical experiments in low dimensions indicate that Mod2VQLS, using the custom rotations ansatz, is on par with the block Wiedemann algorithm, which is the best-known to date solution for this problem. [ABSTRACT FROM AUTHOR]