Search Results (21)

The effective recognition of risky driving behaviors holds technical potential for supporting accident prevention and sustainable transportation. However, existing intelligent algorithms for optimizing deep learning models in this field often suffer from slow convergence and high errors. This study proposes a novel hybrid model (ICPO-XGBoost) for risky driving behavior classification. The improved crested porcupine optimizer (ICPO) was developed using logistic-tent composite mapping for population initialization, a hybrid mechanism combining refraction opposition-based learning and Cauchy mutation to avoid local optima, and an adaptive variable spiral search with inertia weight to balance global and local search. The ICPO was then employed to optimize the hyperparameters of the XGBoost classifier. The ICPO demonstrated superior optimization accuracy and convergence speed compared to benchmark algorithms. The ICPO-XGBoost model achieved accuracy, precision, recall, and F1 scores of 96.2%, 95.4%, 95.8%, and 95.6%, respectively, for classifying and identifying risky driving behaviors. Compared to various benchmark models, these results represent increases of 12.7–24.8%, 14.8–31.8%, 14.9–31.0%, and 15.0–32.4%, respectively. For specific driving behavior categories (normal driving, slow driving, short-distance tailgating, sudden acceleration/deceleration, frequent lane changing, and forced lane changing), the precision, recall, and F1 scores of the ICPO-XGBoost model fell within the ranges of 84.8–99.2%, 87.5–100.0%, and 86.2–99.2%, respectively. Compared to benchmark models, these metrics show increases of 1.5–75.8%, 5.8–68.1%, and 3.3–72.6%, respectively. Notably, the model significantly improved accuracy in identifying sudden acceleration/deceleration behaviors. The results of this model facilitate the classification and early warning of risky driving behaviors, thereby reducing the frequency of such behaviors, lowering the risk of traffic accidents, and enhancing road traffic safety. Full article

To overcome the limitations of characteristic parameter identification and inadequate fault recognition rates in bearings, a bearing fault diagnosis method combining the improved whale optimization algorithm (IWOA), variational mode decomposition (VMD), and kernel extreme learning machine (KELM) is proposed. Firstly, to improve the convergence behavior and global search capability of the WOA, we introduced adaptive weight, a variable spiral shape parameter, and a Cauchy neighborhood perturbation strategy to improve the performance of the original algorithm. Secondly, to enhance the effectiveness of feature extraction, the IWOA was used to optimize the number of modal components and penalty coefficients in the VMD algorithm; then, we could obtain the optimal modal components and construct feature vectors based on the optimal modal components. Next, we used the IWOA to optimize the two key parameters, the regularization coefficient C and kernel parameter γ of KELM, and the feature vector was used as the input of KELM to achieve fault diagnosis. Finally, data collected from different experimental platforms were used for experimental analysis. The results indicate that the IWOA-VMD-KELM bearing fault diagnosis model significantly improved its accuracy compared to other models, achieving accuracies of 98.8% and 98.4% on the CWRU dataset and Southeast University dataset, respectively. Full article

Feature selection (FS) is a key process in many pattern-recognition tasks, which reduces dimensionality by eliminating redundant or irrelevant features. However, for complex high-dimensional issues, traditional FS methods cannot find the ideal feature combination. To overcome this disadvantage, this paper presents a multispiral whale optimization algorithm (MSWOA) for feature selection. First, an Adaptive Multipopulation merging Strategy (AMS) is presented, which uses exponential variation and individual location information to divide the population, thus avoiding the premature aggregation of subpopulations and increasing candidate feature subsets. Second, a Double Spiral updating Strategy (DSS) is devised to break out of search stagnations by discovering new individual positions continuously. Last, to facilitate the convergence speed, a Baleen neighborhood Exploitation Strategy (BES) which mimics the behavior of whale tentacles is proposed. The presented algorithm is thoroughly compared with six state-of-the-art meta-heuristic methods and six promising WOA-based algorithms on 20 UCI datasets. Experimental results indicate that the proposed method is superior to other well-known competitors in most cases. In addition, the proposed method is utilized to perform feature selection in human fall-detection tasks, and extensive real experimental results further illustrate the superior ability of the proposed method in addressing practical problems. Full article

The photon-counting light laser detection and ranging (LiDAR), especially the Geiger mode avalanche photon diode (Gm-APD) LiDAR, can obtain three-dimensional images of the scene, with the characteristics of single-photon sensitivity, but the background noise limits the imaging quality of the laser radar. In order to solve this problem, a depth image estimation method based on a two-dimensional (2D) Kaniadakis entropy thresholding method is proposed which transforms a weak signal extraction problem into a denoising problem for point cloud data. The characteristics of signal peak aggregation in the data and the spatio-temporal correlation features between target image elements in the point cloud-intensity data are exploited. Through adequate simulations and outdoor target-imaging experiments under different signal-to-background ratios (SBRs), the effectiveness of the method under low signal-to-background ratio conditions is demonstrated. When the SBR is 0.025, the proposed method reaches a target recovery rate of 91.7%, which is better than the existing typical methods, such as the Peak-picking method, Cross-Correlation method, and the sparse Poisson intensity reconstruction algorithm (SPIRAL), which achieve a target recovery rate of 15.7%, 7.0%, and 18.4%, respectively. Additionally, comparing with the SPIRAL, the reconstruction recovery ratio is improved by 73.3%. The proposed method greatly improves the integrity of the target under high-background-noise environments and finally provides a basis for feature extraction and target recognition. Full article

The recognition, decoding and tracking of vortex patterns is of increasing importance in many fields, ranging from the astronomical observations of distant galaxies to turbulence phenomena in liquids or gases. Currently, coherent light beams with orbital angular momentum (OAM) are of particular interest for optical communication, metrology, micro-machining or particle manipulation. One common task is to identify characteristic spiral patterns in pixelated intensity maps at real-world signal-to-noise ratios. A recently introduced combination of polar mapping and Fast Fourier Transform (FFT) was extended to novel sampling configurations and applied to the quantitative analysis of the spiral interference patterns of OAM beams. It is demonstrated that specific information on topological parameters in non-uniform arrays of OAM beams can be obtained from significantly distorted and noisy intensity maps by extracting one- or two-dimensional angular frequency spectra from single or concatenated circular cuts in either spatially fixed or scanning mode. The method also enables the evaluation of the quality of beam shaping and optical transmission. Results of proof-of-principle experiments are presented, resolution limits are discussed, and the potential for applications is addressed. Full article

In myoelectrical pattern recognition (PR), the feature extraction methods for stroke-oriented applications are challenging and remain discordant due to a lack of hemiplegic data and limited knowledge of skeletomuscular function. Additionally, technical and clinical barriers create the need for robust, subject-independent feature generation while using supervised learning (SL). To the best of our knowledge, we are the first study to investigate the brute-force analysis of individual and combinational feature vectors for acute stroke gesture recognition using surface electromyography (EMG) of 19 patients. Moreover, post-brute-force singular vectors were concatenated via a Fibonacci-like spiral net ranking as a novel, broadly applicable concept for feature selection. This semi-brute-force navigated amalgamation in linkage (SNAiL) of EMG features revealed an explicit classification rate performance advantage of 10–17% compared to canonical feature sets, which can drastically extend PR capabilities in biosignal processing. Full article

This paper introduces methods for parallelizing the algorithm to enhance the efficiency of event recovery in Spin Physics Detector (SPD) experiments at the Nuclotron-based Ion Collider Facility (NICA). The problem of eliminating false tracks during the particle trajectory detection process remains a crucial challenge in overcoming performance bottlenecks in processing collider data generated in high volumes and at a fast pace. In this paper, we propose and show fast parallel false track elimination methods based on the introduced criterion of a clustering-based thresholding approach with a chi-squared quality-of-fit metric. The proposed strategy achieves a good trade-off between the effectiveness of track reconstruction and the pace of execution on today’s advanced multicore computers. To facilitate this, a quality benchmark for reconstruction is established, using the root mean square (rms) error of spiral and polynomial fitting for the datasets identified as the subsequent track candidate by the neural network. Choosing the right benchmark enables us to maintain the recall and precision indicators of the neural network track recognition performance at a level that is satisfactory to physicists, even though these metrics will inevitably decline as the data noise increases. Moreover, it has been possible to improve the processing speed of the complete program pipeline by 6 times through parallelization of the algorithm, achieving a rate of 2000 events per second, even when handling extremely noisy input data. Full article

Recent studies involving guinea pigs have shown that noise can damage the synapses between the inner hair cells and spiral ganglion neurons, even with normal hearing thresholds—which makes it important to investigate this kind of impairment in humans. The aim was to investigate, with multiple audiological assessments, the auditory function of normal hearing workers exposed to occupational noise. Altogether, 60 workers were assessed (30 in the noise-exposure group [NEG], who were exposed to occupational noise, and 30 in the control group [CG], who were not exposed to occupational noise); the workers were matched according to age. The following procedures were used: complete audiological assessment; speech recognition threshold in noise (SRTN); speech in noise (SN) in an acoustic field; gaps-in-noise (GIN); transient evoked otoacoustic emissions (TEOAE) and inhibitory effect of the efferent auditory pathway; auditory brainstem response (ABR); and long-latency auditory evoked potentials (LLAEP). No significant difference was found between the groups in SRTN. In SN, the NEG performed worse than the CG in signal-to-noise ratio (SNR) 0 (p-value 0.023). In GIN, the NEG had a significantly lower percentage of correct answers (p-value 0.042). In TEOAE, the NEG had smaller amplitude values bilaterally (RE p-value 0.048; LE p-value 0.045) and a smaller inhibitory effect of the efferent pathway (p-value 0.009). In ABR, the NEG had greater latencies of wave V (p-value 0.017) and interpeak intervals III-V and I-V in the LE (respective p-values: 0.005 and 0.04). In LLAEP, the NEG had a smaller P3 amplitude bilaterally (RE p-value 0.001; LE p-value 0.002). The NEG performed worse than the CG in most of the assessments, suggesting that the auditory function in individuals exposed to occupational noise is impaired, even with normal audiometric thresholds. Full article

Vortex beams carry orbital angular momentum (OAM), and their inherent infinite dimensional eigenstates can enhance the ability for optical communication and information processing in the classical and quantum fields. The measurement of the OAM of vortex beams is of great significance for optical communication applications based on vortex beams. Most of the existing measurement methods require the beam to have a regular spiral wavefront. Nevertheless, the wavefront of the light will be distorted when a vortex beam propagates through a random medium, hindering the accurate recognition of OAM by traditional methods. Deep learning offers a solution to identify the OAM of the vortex beam from a speckle field. However, the method based on deep learning usually requires a lot of data, while it is difficult to attain a large amount of data in some practical applications. To solve this problem, we design a framework based on convolutional neural network (CNN) and multi-objective classifier (MOC), by which the OAM of vortex beams can be identified with high accuracy using a small amount of data. We find that by combining CNN with different structures and MOC, the highest accuracy reaches 96.4%, validating the feasibility of the proposed scheme. Full article

Abnormal behavior detection is currently receiving much attention because of the availability of marine equipment and data allowing maritime agents to track vessels. One of the most popular tools for developing an efficient anomaly detection system is the Automatic Identification System (AIS). The aim of this paper is to explore the performance of existing well-known clustering methods for detecting the two most dangerous abnormal behaviors based on the AIS. The methods include K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Affinity Propagation (AP), and the Gaussian Mixtures Model (GMM). In order to evaluate the performance of the clustering methods, we also used the AIS data of vessels, which were collected through the Finnish transport agency from the whole Baltic Sea for three months. Although most existing studies focus on ocean route recognition, deviations from regulated ocean routes, or irregular speed, we focused on dark ships or those sets of vessels that turn off the AIS to perform illegal activities and spiral vessel movements. The experimental results demonstrate that the K-means clustering method can effectively detect dark ships and spiral vessel movements, which are the most threatening events for maritime safety. Full article

In underwater wireless optical communication (UWOC), a vortex beam carrying orbital angular momentum has a spatial spiral phase distribution, which provides spatial freedom for UWOC and, as a new information modulation dimension resource, it can greatly improve channel capacity and spectral efficiency. In a case of the disturbance of a vortex beam by ocean turbulence, where a Laguerre–Gaussian (LG) beam carrying orbital angular momentum (OAM) is damaged by turbulence and distortion, which affects OAM pattern recognition, and the phase feature of the phase map not only has spiral wavefront but also phase singularity feature, the convolutional neural network (CNN) model can effectively extract the information of the distorted OAM phase map to realize the recognition of dual-mode OAM and single-mode OAM. The phase map of the Laguerre–Gaussian beam passing through ocean turbulence was used as a dataset to simulate and analyze the OAM recognition effect during turbulence caused by different temperature ratios and salinity. The results showed that, during strong turbulence $C_n^2=1.0\times {10}^{-13}{\mathrm{K}}^2{\mathrm{m}}^{-2/3}$, when different $\omega$ = −1.75, the recognition rate of dual-mode OAM ($\ell$ = ±1~±5, ±1~±6, ±1~±7, ±1~±8, ±1~±9, ±1~±10) had higher recognition rates of 100%, 100%, 100%, 100%, 98.89%, and 98.67% and single-mode OAM ($\ell$ = 1~5, 1~6, 1~7, 1~8, 1~9, 1~10) had higher recognition rates of 93.33%, 92.77%, 92.33%, 90%, 87.78%, and 84%, respectively. With the increase in $\omega$, the recognition accuracy of the CNN model will gradually decrease, and in a fixed case, the dual-mode OAM has stronger anti-interference ability than single-mode OAM. These results may provide a reference for optical communication technologies that implement high-capacity OAM. Full article

This case report presents a study of unwanted tooth movements during the retention phase after orthodontic treatment. The early recognition of these unwanted tooth movements is paramount for patients and clinicians to prevent the associated negative consequences. A 21-year-old male presented with aesthetic complaints regarding his upper front teeth. He underwent orthodontic treatment at the age of 9 years and 11 months and finished his treatment 2 years and 11 months later. Flexible spiral wires (FSW) were bonded to the anterior segment of the upper and lower jaws to stabilize the end result. The failure of the fixed retainers had never occurred previously. The diagnostic assessment demonstrated a previously orthodontically treated class I malocclusion with excessive angulation and torque differences in the maxillary anterior segment. To correct the position of the maxillary anterior segment and prevent further misalignment, the patient received orthodontic re-treatment. Thereafter, the result was retained with fixed braided-rectangular-wire (BRW) retainers located at 12–22 and 33–43 and a vacuum-formed retainer (VFR) in the maxilla. The end result appeared to be stable after 28 months of retention. Unwanted tooth movements can occur during the orthodontic retention phase and might result from the use of fixed flexible spiral wire retainers. Follow-up appointments are recommended to monitor the stability and recognize these movements. Full article

In vivo studies on the pathology of gestation, including preeclampsia, often use small mammals such as rabbits or rodents, i.e., mice, rats, hamsters, and guinea pigs. The key advantage of these animals is their short reproductive cycle; in addition, similar to humans, they also develop a haemochorial placenta and present a similar transformation of maternal spiral arteries. Interestingly, pregnant dams also demonstrate a similar reaction to inflammatory factors and placentally derived antiangiogenic factors, i.e., soluble fms-like tyrosine kinase 1 (sFlt-1) or soluble endoglin-1 (sEng), as preeclamptic women: all animals present an increase in blood pressure and usually proteinuria. These constitute the classical duet that allows for the recognition of preeclampsia. However, the time of initiation of maternal vessel remodelling and the depth of trophoblast invasion differs between rabbits, rodents, and humans. Unfortunately, at present, no known animal replicates a human pregnancy exactly, and hence, the use of rabbit and rodent models is restricted to the investigation of individual aspects of human gestation only. This article compares the process of placentation in rodents, rabbits, and humans, which should be considered when planning experiments on preeclampsia; these aspects might determine the success, or failure, of the study. The report also reviews the rodent and rabbit models used to investigate certain aspects of the pathomechanism of human preeclampsia, especially those related to incorrect trophoblast invasion, placental hypoxia, inflammation, or maternal endothelial dysfunction. Full article

In this study, we propose a high-performance resonator-based biosensor for mediator-free glucose identification. The biosensor is characterized by an air-bridge capacitor and fabricated via integrated passive device technology on gallium arsenide (GaAs) substrate. The exterior design of the structure is a spiral inductor with the air-bridge providing a sensitive surface, whereas the internal capacitor improves indicator performance. The sensing relies on repolarization and rearrangement of surface molecules, which are excited by the dropped sample at the microcosmic level, and the resonance performance variation corresponds to the difference in glucose concentration at the macroscopic level. The air-bridge capacitor in the modeled RLC circuit serves as a bio-recognition element to glucose concentration (${\epsilon }_{glucose}{C}_0$), generating resonant frequency shifts at 0.874 GHz and 1.244 GHz for concentrations of 25 mg/dL and 300 mg/dL compared to DI water, respectively. The proposed biosensor exhibits excellent sensitivity at 1.38 MHz per mg/dL with a wide detection range for glucose concentrations of 25–300 mg/dL and a low detection limit of 24.59 mg/dL. Additionally, the frequency shift and concentration are highly linear with a coefficient of determination of 0.98823. The response time is less than 3 s. We performed multiple experiments to verify that the surface morphology reveals no deterioration and chemical binding, thus validating the reusability and reliability of the proposed biosensor. Full article

The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top n matches from viewed sketches to a given photo. F is again used to formulate a feature map based on the cosine distance of a candidate sketch formed by C from the top n matches. A customized CNN configuration (called the discriminator D) then computes loss functions based on differences between the candidate sketch and the feature. Values of these loss functions alternately update C and F. The ensemble of these nets is trained and tested on selected datasets, including CUFS, CUFSF, and a part of the IIT photo–sketch dataset. Results of this modified U-Net are acquired by the legacy NLDA (1998) scheme of face recognition and its newer version, OpenBR (2013), which demonstrate an improvement of 5% compared with the current state of the art in its relevant domain. Full article