Reviewerly/exHarmony huggingface dataset:
SAR image ship detection based on light-weight multi-scale feature enhancement network

Aiming at the problem of insufficient detection performance due to complex background and small-scale characteristics of the target in SAR-based image ship detection applications, this paper constructs a lightweight multi-scale feature enhancement network based on deep learning algorithms for ship detection. The network first extracts the ship features in the image by using the light-weighted and improved VGG-16 network, and designs a two-way feature fusion module to forward fuse and reverse fuse the low-level detail features and high-level semantic features respectively to achieve multi-scale feature aggregation; then introduces a convolutional block attention module to enhance the network's ability to extract ship features in complex scenes from the perspective of both channel features and spatial features. Further, the K-means clustering algorithm is used to optimize the anchor scale design based on the real border information of ship targets to enhance the classification and localization accuracy of the network for multi-scale ship targets; finally, the Soft-NMS algorithm is introduced to improve the leakage detection problem in the densely distributed ship scenes. The detection performance of the network is verified using the SAR image ship dataset SSDD, and the research results show that the detection accuracy of the network is 95.12% and the detection speed is 18.7 FPS, which is 6.88% higher than that of the Faster R-CNN network in terms of detection accuracy and more than 2 times higher in terms of detection efficiency, effectively improving the ship detection performance of small-scale targets in complex backgrounds. It can meet the demand of high accuracy and real-time detection of ship targets in SAR images.
Extreme Learning Machine Classifier Based on Novel Particle Swarm Optimization Algorithm

Aiming at the problem of low classification accuracy caused by random input weights and hidden layer bias in ELM (Extreme Learning Machines, ELM), a method based on IPSO (Improved Particle Swarm Optimization, IPSO) to optimize the network classification model of Extreme Learning Machines is proposed. Firstly, the nonlinear inertia weight and dynamic acceleration factor are used to balance the exploration and mining of the PSO algorithm; Secondly, the variables of the ELM model are enhanced by IPSO to reduce the influence caused by strong volatility; Finally, simulation results show that compared with SVM, ELM, GA-ELM, IPSO-ELM could effectively improve the precision of classification prediction.
Detecting Aligned Double JPEG Compression with the Same Quantization Matrix Based on Hamming Distance and Image Stability

In order to solve the problem of low accuracy of detecting aligned double JPEG compression of color images with the same quantization matrix, we proposed a method for detecting aligned double compression with the same quantization matrix based on Hamming distance and image stability. We analyze the image stability at both coefficient level and image level, the compression state of JPEG images is determined by analyzing the stability of the image. At the same time, we utilize the Hamming distance to reduce the impact of the image content, so that our method focuses on compression traces. Through our experimental results, the proposed method exhibits high accuracy on the UCID and NRCS datasets.
High-Quality Digital Bichronous Education for Nanoengineers During the War in Ukraine: Does Technology Knowledge Matter?

This article is dedicated to the investigation of the impact of digitalization on the quality of bichronous education during the war in Ukraine, specifically focusing on students' perception of classical courses and courses with modern technologies implemented. We present a case study on the preparation of future nanoengineers in a Ukrainian university during wartime. The research compares the level of student satisfaction with the teaching and mastering of classical disciplines and those disciplines whose content and forms have been digitalized and updated using artificial intelligence. The article demonstrates that digitalization contributes to a higher level of student satisfaction from education and also raises questions about the fact that teachers often lack the flexibility and readiness to perceive innovations that students are well-versed in.
Production prediction method of Industrial Process using LSTM neural network based on Snake Optimizer Algorithm

Ethylene production plays an important role in the petrochemical industry. Due to the high dimensionality of industrial data, the established data models are complex and contain a large number of hyperparameters. The selection of different hyperparameters of the neural network model has a great influence on the prediction accuracy. Therefore, a long short-term memory (LSTM) integrating Snake Optimizer algorithm (SOA) is proposed to achieve accurate prediction of ethylene production in complex chemical production processes. LSTM extracts the temporal dynamic interaction feature to predict ethylene production in a complex ethylene production process. Then, the SOA optimizes the hyperparameters of the LSTM as optimization variables including number of hidden layer nodes, batch size and training times, which can adaptively adjust the moving step size according to the current search state and target state of the solution vector. The root mean squared error (RMSE) is token as the optimization target of the SOA to obtain the global optimal solution of LSTM hyperparameters. Compared with back propagation (BP) neural network, extreme learning machine (ELM), and LSTM in the ethylene data, the SOA-LSTM achieve the state-of-the-art results. The RMSE, the MAPE and the MAE of SOA-LSTM are 4847.60, 4.10%, and 3593.54 respectively. Through the accurate prediction of ethylene production in the complex ethylene production process, its production efficiency can be effectively improved and carbon emissions can be reduced.
Error Metric Using Correlation Between Binocular Corneal Images

The 3D point-of-gaze has been calculated using the binocular visual axes in the model-based eye gaze estimation. However, the 3D point-of-gaze is often shifted largely when the visual axis includes certain errors. The distance between the estimated point-of-gaze and the visual axis can be used as an error metric; however, horizontal errors cannot be evaluated, and an additional error metric is required to assess the reliability of the estimated point-of-gaze. Therefore, we propose a novel error metric using the correlation between binocular corneal images. We hypothesize that binocular corneal images extracted around the reflection of the estimated point-of-gaze exhibit a high correlation when the 3D point-of-gaze is correctly estimated. The two approaches were implemented using the corneal images extracted directly and generated based on binocular corneal imaging, and these approaches were evaluated through comparative experiments. We confirmed the feasibility of the proposed error metric and the effectiveness of binocular corneal imaging.
A Motion Object Detection Algorithm Based on Adaptive Thresholding

Aiming at the problem of low detection accuracy due to ghosts and poor dynamic background adaptation for motion target detection in complex scenes, this paper proposes a motion target detection algorithm based on adaptive thresholding. The algorithm adopts the associated frame sequences in multi-frame images, introduces the implied temporal information into the initialization and modeling process of the background model, and makes full use of the temporal correlation to model the background change process more accurately. To enhance the algorithm's ability to adapt to the dynamic background and improve the accuracy of target detection, the adaptive threshold strategy is adopted, which combines the detection threshold of the algorithm with the scene changes, so that the algorithm can respond to different backgrounds more flexibly. Finally, the complete extraction of moving targets is achieved by adopting small target discarding and morphological processing techniques. The experimental results show that the algorithm proposed in this paper has better detection ability compared with other motion target detection algorithms, effectively suppresses ghost, dynamic background, and lighting changes, and improves the accuracy and completeness of motion target detection.
On the Collaborative Object Transportation Using Leader Follower Approach

In this paper we address the multi-agent collab-orative object transportation problem in a partially known environment with obstacles under a specified goal condition. We propose a leader follower approach for two mobile manipulators collaboratively transporting an object along specified desired trajectories. The proposed approach treats the mobile manipulation system as two independent subsystems: a mobile platform and a manipulator arm and uses their kinematics model for trajectory tracking. In this work we considered that the mobile platform is subject to non-holonomic constraints, with a manipulator carrying a rigid load. The desired trajectories of the end points of the load are obtained from Probabilistic RoadMap-based planning approach. Our method combines Proportional Navigation Guidance-based approach with a proposed Stop-and-Sync algorithm to reach sufficiently close to the desired trajectory; the deviation due to the non-holonomic constraints being compensated by the manipulator arm. Concurrently, a leader follower approach for computing inverse kinematics solution for the position of the end-effector of the manipulator arm is proposed to maintain the load rigidity. Further, we compare the proposed approach with other approaches to analyse the efficacy of our algorithm.
Underwater Monocular Vision 3D Reconstruction Based on Cascaded Epipolar

Addressing the challenges of low accuracy in image depth prediction and significant disparity between reconstructed edges and reality in underwater monocular 3D reconstruction, this paper conducts an in-depth analysis of a deep learning network based on Cascaded Epipolar geometry. By employing model pre-training, camera pose inversion, adjusting the format of cascaded convolutions, and modifying the epipolar direction, the model gains the ability to perform 3D reconstruction of underwater monocular visual images. Experiment results on the public dataset Flsea indicates that the Cascaded Epipolar-based method for underwater monocular vision 3D reconstruction offers clearer depth prediction edges and 3D depth information. However, its performance in terms of Mean Squared Error (MSE) shows some deviation comparing to the state-of-the-art UW -GAN.
Non-contact voltage measurement technology based on dual coupling mechanism displacement current method

The distribution network is located in a complex environment, and the installation height of the line and the time-varying parameters of the surrounding medium all cause changes in the capacitance to the ground, which is the main reason for affecting the accuracy of non-contact voltage measurement. In this paper, a non-contact voltage measurement method based on displacement current of dual coupling mechanism is proposed to eliminate the influence on the variation of ground capacitance. Firstly, construct a coaxial cylindrical dual copper pole coupling mechanism; Next, a system of equations containing line voltage and ground capacitance is established through two adjacent coupling mechanisms, and the inversion formula for line voltage is solved; Then, design the voltage measurement devices based on dual coupling mechanism displacement current method; Finally, this article takes 10kV single-phase lines with a height of 80-160cm to ground as the test object, and the voltage measurement error at different heights is less than 5%, proving that the proposed method has adaptability to ground capacitance. At the same time, the experimental conditions have a greater rate of change in ground capacitance compared to the actual conditions, so the proposed method has practical promotion significance.
Unsupervised Forced Alignment on Syllable and Phoneme with Universal Phonetics Transcriptions

Forced alignment inputs the sound recordings and transcription texts, then outputs the temporal boundaries for each speech unit. Instead of the special phonemes belong to specific language, this work uses the basic syllable types and the broad phonetic classes which can be generalized to any language, so called as the universal phonetics transcriptions. We adopt the Deep Neural Networks with the Connectionist Temporal Classification cost function as the solution of achieving alignment. The evaluations are carried out on six different languages and the results demonstrate that the alignment system can be applied to a wide range of languages once training successful. Compared to existing state-of-the-art supervised and unsupervised alignment system, the proposed system provides competitive performance in the task of forced alignment.
A study of contrastive self-supervised learning generalization based on augmented data

Contrastive learning have been widely applied recently, since only with unlabeled data, it produced results comparable to the state-of-the-art supervised algorithm. Data augmentation plays an essential role in defining effective predictive task, influencing generalization of the model. We find that high-intensity data augmentation is conducive to achieving a wider aggregation of intra-class samples, obtaining more effective features to improve generalization; however, extremely strong augmentation leads to the information loss of different classes, resulting in cross-class deviation of samples, reducing model generalization. We demonstrate our finding through experiment on CIFAR-10 dataset, analyzing the impact of different data augmentation operations on recognition results and the corresponding reasons. Our experimental verification on the relationship between the data augmentation and the generalization of contrastive self-supervised learning agree with our theoretical description.
RNN-based Photovoltaic Power Identification Method for Distribution Networks

To ensure the safe operation of distribution networks with more and more photovoltaic (PV), it is crucial to study the PV output power identification technology. Thus, the ability of the system to withstand random fluctuations will be enhanced and the stability of distribution network will also be improved effectively. In this paper, a standard three-layer recurrent neural network (RNN) is proposed to build a nonlinear mapping model for efficiently identifying PV power. RNN can fit the nonlinear mapping relationship between the intensity of illumination and PV output power within the distribution station area effectively. The distributed PV output power from the power measurement data of the distribution station area is successfully separated. Finally, a case study of Yulara Solar power plant with 1.8 MVA in Australia is used to validate the feasibility and effectiveness of the proposed method.
Differential Evolution Using Superior Infeasible Solutions for Constrained Optimization

Differential Evolution (DE) is one of the effective metaheuristics for solving unconstrained optimization problems. Constraint Handling Technique (CHT) is needed to extend DE to constrained optimization. Feasibility Rule (FR) is one of the typical CHT. FR addresses constraints by using a simple rule that considers the objective function and constraint violation when comparing search individuals. However, since the solutions with small constraint violation, i.e., feasible solutions, are preferentially selected, the set of search individuals may be biased toward feasible regions and the improvement of the objective function value may stagnate. This paper overcomes this challenge by proposing a DE that uses a superior infeasible solution in an external archive. The external archive in the proposed DE stores search individuals that are superior in both objective function value and constraint violation and utilize them to generate mutant individuals. Finally, we verify the effectiveness of the proposed method using a benchmark problem where the feasible region is a convex set.
Torpedo Electromagnetic Fuze Active Interference Recognition Based on Improved AlexNet

In order to enhance the recognition capability of active interference signals from torpedo electromagnetic fuze in the marine electromagnetic environment, a method for recognizing torpedo electromagnetic fuze active interference based on improved AlexNet is proposed. Interference signal models and marine electromagnetic wave channel models are established, and the time-frequency plots of the interference signals propagated through the marine electromagnetic wave channel are extracted as the dataset. By optimizing the network structure, adding data labels, and employing data augmentation techniques, the trained network model is improved. The recognition rate and complexity of the model under different Jamming-to-Noise Ratio (JNR) conditions are provided. Simulation results demonstrate that the proposed method achieves high interference recognition rates under low JNR conditions, accurately identifies torpedo electromagnetic fuze active interference, and exhibits low model complexity.
A Robust Coverless Image Steganography Based on DCT-SVD

Coverless image information hiding plays a pivotal role in secure communications. In this paper, a novel coverless image steganography algorithm based on Discrete Cosine Transform and Singular Value Decomposition (DCT-SVD) with a focus on enhancing its robustness and resistance to steganalysis has been proposed. To begin with, DCT-SVD is implemented to the each image of the image database. Notably, the generation of hash sequence is shared between the sender and receiver to ensure correct information extraction. Then corresponding hash sequence of each image is calculated, and an inverted index is created, which links the hash sequences to image paths. After that, the transmitted secret message is seperated into a number of information segments according to the length of hash sequence. Then each information segment is matched with the DCT-SVD hash sequence, and the relevant image is identified. The receiver, upon receiving these images, employs the same method to generate corresponding hash sequences and subsequently reconstructs the secret message. The experimental results and analysis indicate that, compared to existing coverless image steganography algorithms, the algorithm proposed in this paper demonstrates enhanced robustness, particularly in its resistance to geometric attacks, and in common image processing scenarios.
Algorithm Improvement for Elliptic Curve Digital Signature

In the context of blockchain information security research, the Elliptic Curve Digital Signature (ECDSA) algorithm has always been a hot topic in the field, and the implementation of electronic signatures prevents evil third parties from tampering with message content. After analyzing the specific implementation process of the traditional elliptic curve digital signature algorithm and its advantages and disadvantages, an improved algorithm M-ECDSA is proposed. This algorithm has strong forward security, anti-forged signature attacks, and anti-repudiation attacks, and compares and analyzes it with traditional algorithms in terms of complexity. The results show that this algorithm can easily realize the characteristics of message recovery while ensuring efficiency and plaintext security.
A New Method for Single Image Rain Removal with Directional Gradient Constraints

Due to the presence of rain, the visibility of images captured outdoors on rainy days will be severely degraded. Rain removal using image processing technology can reduce the influence of rain to estimate rain-free images. However, existing traditional rain removal methods are very time-consuming, and newly emerging deep learning-based methods require a large amount of data and computational resources, resulting in long time consumption and poor visual effect. To solve the problems of existing methods, a new method is proposed to remove oblique rain streaks in windy conditions better. Firstly, the directional gradient constraint is proposed to locate oblique rain streaks in the rain layer effectively. Then, a sparse prior for oblique rain streaks is presented to enhance oblique rain streaks removal. After that, an optimization problem combining the directional gradient, sparse priors, and non-negativity constraints is presented. Finally, the alternating direction method of multipliers is exploited to solve the optimization problem effectively. Experiment results show that our method outperforms other methods in removing oblique rain streaks and requires less time.
Searchable Proxy Re-Encryption Data Sharing Scheme Based on Consortium Chain

In order to solve the problems of low data retrieval efficiency, lack of flexible authority management, and difficulty in meeting the needs of multi-user massive data sharing in data sharing scenarios, a searchable proxy re-encryption data sharing scheme based on alliance chains is proposed, which realizes the dynamics of cloud data sharing process Fine-grained access control and efficient retrieval of ciphertext keywords. In this scheme: the original data is encrypted using the AES algorithm, the symmetric key and the keyword are respectively encrypted using a non-interactive proxy re-encryption algorithm and a searchable encryption algorithm; the original data ciphertext is stored in the Interplanetary File System (IPFS), and the keyword Metadata such as ciphertext and permission control policies are stored on the alliance chain. Use smart contracts to realize decentralized ciphertext conversion, combined with the advantages of B+ tree index, to achieve efficient search of ciphertext keywords and quick verification of permissions. Designing a non-interactive proxy re-encryption algorithm can generate proxy keys quickly and efficiently, and is suitable for multi-user complex data sharing scenarios. The solution also supports dynamic management of access rights to meet data sharing requirements in different scenarios. The experimental analysis results show that the keyword matching algorithm takes an average of 1lms, which exhibits higher efficiency compared to the global keyword search scheme. In the concurrent access test experiment, the maximum throughput of the system is 24 transactions per second, and the transaction success rate is high. Therefore, the system has a good concurrent access capability to meet the practical use.