Currently, the most advanced knowledge distillation models use a metric learning approach based on probability distributions. However, the correlation between supervised probability distributions is typically geometric and implicit, causing inefficiency and an inability to capture structural feature representations among different tasks. To overcome this problem, we propose a knowledge distillation loss using the robust sliced Wasserstein distance with geometric median (GMSW) to estimate the differences between the teacher and student representations. Due to the intuitive geometric properties of GMSW, the student model can effectively learn to align its produced hidden states from the teacher model, thereby establishing a robust correlation among implicit features. In experiment, our method outperforms state-of-the-art models in both high-resource and low-resource settings.

Highly conflicting evidence that may lead to the counter-intuitive results is one of the challenges for information fusion in Dempster-Shafer evidence theory. To deal with this issue, evidence conflict is investigated based on belief divergence measuring the discrepancy between evidence. In this paper, the pignistic probability transform belief χ2 divergence, named as BBχ2 divergence, is proposed. By introducing the pignistic probability transform, the proposed BBχ2 divergence can accurately quantify the difference between evidence with the consideration of multi-element sets. Compared with a few belief divergences, the novel divergence has more precision. Based on this advantageous divergence, a new multi-source information fusion method is devised. The proposed method considers both credibility weights and information volume weights to determine the overall weight of each evidence. Eventually, the proposed method is applied in target recognition and fault diagnosis, in which comparative analysis indicates that the proposed method can realize the highest accuracy for managing evidence conflict.

With the popularization of software defined networks, switch migration as an important network management strategy has attracted increasing attention. Most existing switch migration strategies only consider local conditions and simple load thresholds, without fully considering the overall optimization and dynamics of the network. Therefore, this article proposes a switch migration algorithm based on global optimization. This algorithm adds a load prediction module to the migration model, determines the migration controller, and uses an improved whale optimization algorithm to determine the target controller and its surrounding controller set. Based on the load status of the controller and the traffic priority of the switch to be migrated, the optimal migration switch set is determined. The experimental results show that compared to existing schemes, the algorithm proposed in this paper improves the average flow processing efficiency by 15% to 40%, reduces switch migration times, and enhances the security of the controller.

This paper proposes a determination method of the cascaded number for lumped parameter models (LPMs) of the transmission lines. The LPM is used to simulate long-distance transmission lines, and the cascaded number significantly impacts the simulation results. Currently, there is a lack of a system-level determination method of the cascaded number for LPMs. Based on the theoretical analysis and eigenvalue decomposition of network matrix, this paper discusses the error in resonance characteristics between distributed parameter model and LPMs. Moreover, it is deduced that optimal cascaded numbers of the cascaded π-type and T-type LPMs are the same, and the Γ-type LPM has a lowest analog accuracy. The principle that the maximum simulation frequency is less than the first resonance frequency of each segment is presented. According to the principle, optimal cascaded numbers of cascaded π-type, T-type, and Γ-type LPMs are obtained. The effectiveness of the proposed determination method is verified by simulation.

Efficient multiplexing of ultra-reliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic, as well as ensuring the various reliability requirements of these traffic types in 5G wireless communications, is becoming increasingly important, particularly for vertical services. Interference management techniques, such as coordinated inter-cell scheduling, can enhance reliability in dense cell deployments. However, tight inter-cell coordination necessitates frequent information exchange between cells, which limits implementation. This paper introduces a novel RAN slicing framework based on centralized frequency-domain interference control per slice and link adaptation optimized for URLLC. The proposed framework does not require tight inter-cell coordination but can fulfill the requirements of both the decoding error probability and the delay violation probability of each packet flow. These controls are based on a power-law estimation of the lower tail distribution of a measured data set with a smaller number of discrete samples. As design guidelines, we derived a theoretical minimum radio resource size of a slice to guarantee the delay violation probability requirement. Simulation results demonstrate that the proposed RAN slicing framework can achieve the reliability targets of the URLLC slice while improving the spectrum efficiency of the eMBB slice in a well-balanced manner compared to other evaluated benchmarks.

As a hierarchical network framework, clustering aims to divide nodes with similar mobility characteristics into the same cluster to form a more structured hierarchical network, which can effectively solve the problem of high dynamics of the network topology caused by the high-speed movement of nodes in aeronautical ad hoc networks. Based on this goal, we propose a multi-hop distributed clustering algorithm based on link duration. The algorithm is based on the idea of multi-hop clustering, which ensures the coverage and stability of clustering. In the clustering phase, the link duration is used to accurately measure the degree of stability between nodes. At the same time, we also use the link duration threshold to filter out relatively stable links and use the gravity factor to let nodes set conditions for actively creating links based on neighbor distribution. When selecting the cluster head, we select the most stable node as the cluster head node based on the defined node stability weight. The node stability weight comprehensively considers the connectivity degree of nodes and the link duration between nodes. In order to verify the effectiveness of the proposed method, we compare them with the N-hop and K-means algorithms from four indicators: average cluster head duration, average cluster member duration, number of cluster head changes, and average number of intra-cluster link changes. Experiments show that the proposed method can effectively improve the stability of the topology.

Photovoltaic power is an important part of sustainable development. Accurate prediction of photovoltaic power can improve energy utilization and prevent resource waste. However, the volatility and uncertainty of photovoltaic power make power prediction difficult. Although Informer has achieved good prediction results in the field of time series prediction, it does not put forward a good solution for the volatility of series and the leakage of future information when stacking. Therefore, this paper proposes a photovoltaic power prediction model based on VMD-Informer-DCC. Firstly, Spearman’s feature selector was used to screen the sequence features. Then, the VMD layer was added to the encoder of Informer to decompose the feature sequence to reduce the volatility of the feature sequence. Finally, the dilated causal convolutional layer was used to replace the Self-attention distilling of Informer, which expanded the receptive field of Informer information extraction and ensured the causality of time series prediction. To verify the effectiveness of the model, this paper uses the dataset of a photovoltaic power plant in Jilin Province in 2021 to conduct a large number of experiments. The results show that the VMD-Informer-DCC model has high prediction accuracy and wide applicability.

In future deep space communication missions, VLBI (Very Long Baseline Interferometry) based on antenna array technology remains a critical detection method, which urgently requires the improvement of synthesis performance for antenna array signals. Considering this, focusing on optimizing the traditional antenna grouping method applied in the phase estimation algorithm, this letter proposes a “L/2 to L/2” antenna grouping method based on the maximum correlation signal-to-noise ratio (SNR). Following this idea, a phase difference estimation algorithm named “Couple” is presented. Theoretical analysis and simulation verification illustrate that: when ρ ＜ -10dB, the proposed “Couple” has the highest performance; increasing the number of antennas can significantly improve its synthetic loss performance and robustness. The research of this letter indicates a promising potential in supporting the rising deep space exploration and communication missions.

Cyclic codes are a subclass of linear codes and have applications in consumer electronics, data storage systems, and communication systems as they have efficient encoding and decoding algorithms. Let C(t,e) denote the cyclic code with two nonzero αt and αe, where α is a generator of 𝔽*3m. In this letter, we investigate the ternary cyclic codes with parameters [3m - 1, 3m - 1 - 2m, 4] based on some results proposed by Ding and Helleseth in 2013. Two new classes of optimal ternary cyclic codes C(t,e) are presented by choosing the proper t and e and determining the solutions of certain equations over 𝔽3m.

Convolutional neural network (CNN) is widely used in acoustic scene classification (ASC) tasks. In most cases, local convolution is utilized to gather time-frequency information between spectrum nodes. It is challenging to adequately express the non-local link between frequency domains in a finite convolution region. In this paper, we propose a dual-path convolutional neural network based on band interaction block (DCNN-bi) for ASC, with mel-spectrogram as the model’s input. We build two parallel CNN paths to learn the high-frequency and low-frequency components of the input feature. Additionally, we have created three band interaction blocks (bi-blocks) to explore the pertinent nodes between various frequency bands, which are connected between two paths. Combining the time-frequency information from two paths, the bi-blocks with three distinct designs acquire non-local information and send it back to the respective paths. The experimental results indicate that the utilization of the bi-block has the potential to improve the initial performance of the CNN substantially. Specifically, when applied to the DCASE 2018 and DCASE 2020 datasets, the CNN exhibited performance improvements of 1.79% and 3.06%, respectively.

To achieve precise measurement without landing, the high-mobility vehicle-mounted theodolite needs to be leveled quickly with high precision and ensure sufficient support stability before work. After the measurement, it is also necessary to ensure that the high-mobility vehicle-mounted theodolite can be quickly withdrawn. Therefore, this paper proposes a hierarchical automatic leveling strategy and establishes a two-stage electromechanical automatic leveling mechanism model. Using coarse leveling of the first-stage automatic leveling mechanism and fine leveling of the second-stage automatic leveling mechanism, the model realizes high-precision and fast leveling of the vehicle-mounted theodolites. Then, the leveling control method based on repeated positioning is proposed for the first-stage automatic leveling mechanism. To realize the rapid withdrawal for high-mobility vehicle-mounted theodolites, the method ensures the coincidence of spatial movement paths when the structural parts are unfolded and withdrawn. Next, the leg static balance equation is constructed in the leveling state, and the support force detection method is discussed in realizing the stable support for vehicle-mounted theodolites. Furthermore, a mathematical model for “false leg” detection is established furtherly, and a “false leg” detection scheme based on the support force detection method is analyzed to significantly improve the support stability of vehicle-mounted theodolites. Finally, an experimental platform is constructed to perform the performance test for automatic leveling mechanisms. The experimental results show that the leveling accuracy of established two-stage electromechanical automatic leveling mechanism can reach 3.6″, and the leveling time is no more than 2 mins. The maximum support force error of the support force detection method is less than 15%, and the average support force error is less than 10%. In contrast, the maximum support force error of the drive motor torque detection method reaches 80.12%, and its leg support stability is much less than the support force detection method. The model and analysis method proposed in this paper can also be used for vehicle-mounted radar, vehicle-mounted laser measurement devices, vehicle-mounted artillery launchers and other types of vehicle-mounted equipment with high-precision and high-mobility working requirements.

Artificial intelligence and the introduction of Internet of Things technologies have benefited from technological advances and new automated computer system technologies. Eventually, it is now possible to integrate them into a single offline industrial system. This is accomplished through machine-to-machine communication, which eliminates the human factor. The purpose of this article is to examine security systems for machine-to-machine communication systems that rely on identification and authentication algorithms for real-time monitoring. The article investigates security methods for quickly resolving data processing issues by using the Security operations Center’s main machine to identify and authenticate devices from 19 different machines. The results indicate that when machines are running offline and performing various tasks, they can be exposed to data leaks and malware attacks by both the individual machine and the system as a whole. The study looks at the operation of 19 computers, 7 of which were subjected to data leakage and malware attacks. AnyLogic software is used to create visual representations of the results using wireless networks and algorithms based on previously processed methods. The W76S is used as a protective element within intelligent sensors due to its built-in memory protection. For 4 machines, the data leakage time with malware attacks was 70 s. For 10 machines, the duration was 150 s with 3 attacks. Machine 15 had the longest attack duration, lasting 190 s and involving 6 malware attacks, while machine 19 had the shortest attack duration, lasting 200 s and involving 7 malware attacks. The highest numbers indicated that attempting to hack a system increased the risk of damaging a device, potentially resulting in the entire system with connected devices failing. Thus, illegal attacks by attackers using malware may be identified over time, and data processing effects can be prevented by intelligent control. The results reveal that applying identification and authentication methods using a protocol increases cyber-physical system security while also allowing real-time monitoring of offline system security.

Stochastic Computing (SC) allows additions and multiplications to be realized with lower power than the conventional binary operations if we admit some errors. However, for many complex functions which cannot be realized by only additions and multiplications, we do not know a generic efficient method to calculate a function by using an SC circuit; it is necessary to realize an SC circuit by using a generic method such as polynomial approximation methods for such a function, which may lose the advantage of SC. Thus, there have been many researches to consider efficient SC realization for specific functions; an efficient SC square root circuit with a feedback circuit was proposed by D. Wu et al. recently. This paper generalizes the SC square root circuit with a feedback circuit; we identify a situation when we can implement a function efficiently by an SC circuit with a feedback circuit. As examples of our generalization, we propose SC circuits to calculate the n-th root calculation and division. We also show our analysis on the accuracy of our SC circuits and the hardware costs; our results show the effectiveness of our method compared to the conventional SC designs; our framework may be able to implement a SC circuit that is better than the existing methods in terms of the hardware cost or the calculation error.

Emerging data-intensive services in distribution grid impose requirements of high-concurrency access for massive internet of things (IoT) devices. However, the lack of effective high-concurrency access management results in severe performance degradation. To address this challenge, we propose a cloud-edge-device collaborative high-concurrency access management algorithm based on multi-timescale joint optimization of channel pre-allocation and load balancing degree. We formulate an optimization problem to minimize the weighted sum of edge-cloud load balancing degree and queuing delay under the constraint of access success rate. The problem is decomposed into a large-timescale channel pre-allocation subproblem solved by the device-edge collaborative access priority scoring mechanism, and a small-timescale data access control subproblem solved by the discounted empirical matching mechanism (DEM) with the perception of high-concurrency number and queue backlog. Particularly, information uncertainty caused by externalities is tackled by exploiting discounted empirical performance which accurately captures the performance influence of historical time points on present preference value. Simulation results demonstrate the effectiveness of the proposed algorithm in reducing edge-cloud load balancing degree and queuing delay.

The purpose of Facial Beauty Prediction (FBP) is to automatically assess facial attractiveness based on human aesthetics. Most neural network-based prediction methods do not consider the ranking information in the task. For scoring tasks like facial beauty prediction, there is abundant ranking information both between images and within images. Reasonable utilization of these information during training can greatly improve the performance of the model. In this paper, we propose a novel end-to-end Convolutional Neural Network (CNN) model based on ranking information of images, incorporating a Rank Module and an Adaptive Weight Module. We also design pairwise ranking loss functions to fully leverage the ranking information of images. Considering training efficiency and model inference capability, we choose ResNet-50 as the backbone network. We conduct experiments on the SCUT-FBP5500 dataset and the results show that our model achieves a new state-of-the-art performance. Furthermore, ablation experiments show that our approach greatly contributes to improving the model performance. Finally, the Rank Module with the corresponding ranking loss is plug-and-play and can be extended to any CNN model and any task with ranking information. Code is available at https://github.com/nehcoah/Rank-Info-Net.

In the software testing phase, software reliability growth models (SRGMs) are commonly used to evaluate the reliability of software systems. Traditional SRGMs are restricted by their assumption of a continuous growth pattern for the failure detection rate (FDR) throughout the testing phase. However, the assumption is compromised by Change-Point phenomena, where FDR fluctuations stem from variations in testing personnel or procedural modifications, leading to reduced prediction accuracy and compromised software reliability assessments. Therefore, the objective of this study is to improve software reliability prediction using a novel approach that combines genetic algorithm (GA) and deep learning-based SRGMs to account for the Change-point phenomenon. The proposed approach uses a GA to dynamically combine activation functions from various deep learning-based SRGMs into a new mutated SRGM called MuSRGM. The MuSRGM captures the advantages of both concave and S-shaped SRGMs and is better suited to capture the change-point phenomenon during testing and more accurately reflect actual testing situations. Additionally, failure data is treated as a time series and analyzed using a combination of Long Short-Term Memory (LSTM) and Attention mechanisms. To assess the performance of MuSRGM, we conducted experiments on three distinct failure datasets. The results indicate that MuSRGM outperformed the baseline method, exhibiting low prediction error (MSE) on all three datasets. Furthermore, MuSRGM demonstrated remarkable generalization ability on these datasets, remaining unaffected by uneven data distribution. Therefore, MuSRGM represents a highly promising advanced solution that can provide increased accuracy and applicability for software reliability assessment during the testing phase.

A practical issue of edge AI systems is that data distributions of trained dataset and deployed environment may differ due to noise and environmental changes over time. Such a phenomenon is known as a concept drift, and this gap degrades the performance of edge AI systems and may introduce system failures. To address this gap, retraining of neural network models triggered by concept drift detection is a practical approach. However, since available compute resources are strictly limited in edge devices, in this paper we propose a fully sequential concept drift detection method in cooperation with an on-device sequential learning technique of neural networks. In this case, both the neural network retraining and the proposed concept drift detection are done only by sequential computation to reduce computation cost and memory utilization. We use three datasets for experiments and compare the proposed approach with existing batch-based detection methods. It is also compared with a DNN-based approach without concept drift detection. The evaluation results of the proposed approach show that the proposed method is capable of detecting each of four concept drift types. The results also show that, while the accuracy is decreased by up to 0.9% compared to the existing batch-based detection methods, it decreases the memory size by 88.9%-96.4% and the execution time by 45.0%-87.6%. As a result, the combination of the neural network retraining and the proposed concept drift detection method is demonstrated on Raspberry Pi Pico that has 264 kB memory.

We explore the maximum total number of edge crossings and the maximum geometric thickness of the Euclidean minimum-weight (k, ℓ)-tight graph on a planar point set P. In this paper, we show that (10/7－ε)|P| and (11/6－ε)|P| are lower bounds for the maximum total number of edge crossings for any ε ＞ 0 in cases (k,ℓ)=(2,3) and (2,2), respectively. We also show that the lower bound for the maximum geometric thickness is 3 for both cases. In the proofs, we apply the method of arranging isomorphic units regularly. While the method is developed for the proof in case (k,ℓ)=(2,3), it also works for different ℓ.

Simple and quick tests at medical clinics have become increasingly important. Magnetic sensing techniques have been developed to detect biomarkers using magnetic nanoparticles in liquid-phase assays. We developed a biomarker assay that involves using an alternating current (AC) susceptibility measurement system that uses functional magnetic particles and magnetic sensing technology. We also developed compact biomarker measuring equipment to enable quick testing. Our assay is a one-step homogeneous assay that involves simply mixing a sample with a reagent, shortening testing time and simplifying processing. Using our compact measuring equipment, which includes anisotropic magneto resistance (AMR) sensors, we conducted high-sensitivity measurements of extremely small amounts of two biomarkers (C-reactive protein, CRP and α-Fetoprotein, AFP) used for diagnosing arteriosclerosis and malignant tumors. The results indicate that an extremely small amount of CRP and AFP could be detected within 15 min, which demonstrated the possibility of a simple and quick high-sensitivity immunoassay that involves using an AC-susceptibility measurement system.

In this work, the core size estimation technique of magnetic nanoparticles (MNPs) using the static magnetization curve obtained from a high-Tc SQUID magnetometer and a metaheuristic inversion technique based on the Particle Swarm Optimizer (PSO) algorithm is presented. The high-Tc SQUID magnetometer is constructed from a high-Tc SQUID sensor coupled by a flux transformer to sense the modulated magnetization signal from a sample. The magnetization signal is modulated by the lateral vibration of the sample on top of a planar differential detection coil of the flux transformer. A pair of primary and excitation coils are utilized to apply an excitation field parallel to the sensitive axis of the detection coil. Using the high-Tc SQUID magnetometer, the magnetization curve of a commercial MNP sample (Resovist) was measured in a logarithmic scale of the excitation field. The PSO inverse technique is then applied to the magnetization curve to construct the magnetic moment distribution. A multimodal normalized log-normal distribution was used in the minimization of the objective function of the PSO inversion technique, and a modification of the PSO search region is proposed to improve the exploration and exploitation of the PSO particles. As a result, a good agreement on the Resovist magnetic core size was obtained between the proposed technique and the non-negative least square (NNLS) inversion technique. The estimated core sizes of 8.0484 nm and 20.3018 nm agreed well with the values reported in the literature using the commercial low-Tc SQUID magnetometer with the SVD and NNLS inversion techniques. Compared to the NNLS inversion technique, the PSO inversion technique had merits in exploring an optimal core size distribution freely without being regularized by a parameter and facilitating an easy peak position determination owing to the smoothness of the constructed distribution. The combination of the high-Tc SQUID magnetometer and the PSO-based reconstruction technique offers a powerful approach for characterizing the MNP core size distribution, and further improvements can be expected from the recent state-of-the-art optimization algorithm to optimize further the computation time and the best objective function value.