bluuebunny/crossref_metadata_2025 huggingface dataset:
Few-Shot Classification with Dual-Model Deep Feature Extraction and Similarity Measurement
Authors: Guo Jing-Ming , Seshathiri Sankarasrinivasan , Chen Wen-Hsiang
Published: October 2022

From traditional machine learning to the latest deep learning classifiers, most models require a large amount of labeled data to perform optimal training and obtain the best performance. Yet, when limited training samples are available or when accompanied by noisy labels, severe degradation in accuracy can arise. The proposed work mainly focusses on these practical issues. Herein, standard datasets, i.e., Mini-ImageNet, CIFAR-FS, and CUB 200, are considered, which also have similar issues. The main goal is to utilize a few labeled data in the training stage, extracting image features and then performing feature similarity analysis across all samples. The highlighted aspects of the proposed method are as follows. (1) The main self-supervised learning strategies and augmentation techniques are exploited to obtain the best pretrained model. (2) An improved dual-model mechanism is proposed to train the support and query datasets with multiple training configurations. As examined in the experiments, the dual-model approach obtains superior performance of few-shot classification compared with all of the state-of-the-art methods.
Methodology for the Identification of Dust Accumulation Levels in Photovoltaic Panels Based in Heuristic-Statistical Techniques
Authors: Perez-Anaya Eduardo , Elvira-Ortiz David A. , Osornio-Rios Roque A. , Antonino-Daviu Jose A.
Published: October 2022

The use of renewable energies is increasing around the world in order to deal with the environmental and economic problems related with conventional generation. In this sense, photovoltaic generation is one of the most promising technologies because of the high availability of sunlight, the easiness of maintenance, and the reduction in the costs of installation and production. However, photovoltaic panels are elements that must be located outside in order to receive the sun radiation and transform it into electricity. Therefore, they are exposed to the weather conditions and many environmental factors that can negatively affect the output delivered by the system. One of the most common issues related to the outside location is the dust accumulation in the surface of the panels. The dust particles obstruct the passage of the sunlight, reducing the efficiency of the generation process and making the system prone to experimental long-term faults. Thus, it is necessary to develop techniques that allow us to assess the level of dust accumulation in the panel surface in order to schedule a proper maintenance and avoid losses associated with the reduction of the delivered power and unexpected faults. In this work, we propose a methodology that uses a machine learning approach to estimate different levels of dust accumulation in photovoltaic panels. The developed method takes the voltage, current, temperature, and sun radiance as inputs to perform a statistical feature extraction that describes the behavior of the photovoltaic system under different dust conditions. In order to retain only the relevant information, a genetic algorithm works along with the principal component analysis technique to perform an optimal feature selection. Next, the linear discrimination analysis is carried out using the optimized dataset to reduce the problem dimensionality, and a multi-layer perceptron neural network is implemented as a classifier for discriminating among three different conditions: clean surface, slight dust accumulation, and severe dust accumulation. The proposed methodology is implemented using real signals from a photovoltaic installation, proving to be effective not only to determine if a dust accumulation condition is present but also when maintenance actions must be performed. Moreover, the results demonstrate that the accuracy of the proposed method is always above 94%.
An Intelligent Near-Infrared Diffuse Reflectance Spectroscopy Scheme for the Non-Destructive Testing of the Sugar Content in Cherry Tomato Fruit
Authors: Tan Baohua , You Wenhao , Huang Chengxu , Xiao Tengfei , Tian Shihao , Luo Lina , Xiong Naixue
Published: October 2022

As a new non-destructive testing technology, near-infrared spectroscopy has broad application prospects in agriculture, food, and other fields. In this paper, an intelligent near-infrared diffuse reflectance spectroscopy scheme (INIS) for the non-destructive testing of the sugar contents in vegetables and fruits was proposed. The cherry tomato were taken as the research object. The applicable objects and features of the three main methods of near-infrared detection were compared. According to the advantages and disadvantages of the three near infrared (NIR) detection methods, the experiment was carried out. This experiment involved the near-infrared diffuse reflection detection method, and the back propagation (BP) network model was established to research the sugar content of the cherry tomatoes. We used smoothing and a principal component analysis (PCA) to extract the final spectrum from the experimental spectrum. Taking the preprocessed spectral data as the input of the network and the measured sugar content of the cherry tomatoes as the output, the 80-12-1 network model structure was established. The cross-validation coefficient of determination was 0.8328 and the mean absolute deviation was 0.5711. The results indicate that the BP neural network can quickly and effectively detect the sugar content in cherry tomatoes. This intelligent near-infrared diffuse reflectance spectroscopy (INIS) scheme can be extended and optimized for almost all sugar-containing fruits and vegetables in the future.
A Novel Separable Scheme for Encryption and Reversible Data Hiding
Authors: Chen Pei , Lei Yang , Niu Ke , Yang Xiaoyuan
Published: October 2022

With the increasing emphasis on security and privacy, video in the cloud sometimes needs to be stored and processed in an encrypted format. To facilitate the indexing and tampering detection of encrypted videos, data hiding is performed in encrypted videos. This paper proposes a novel separable scheme for encryption and reversible data hiding. In terms of encryption method, intra-prediction mode and motion vector difference are encrypted by XOR encryption, and quantized discrete cosine transform block is permutated based on logistic chaotic mapping. In terms of the reversible data hiding algorithm, difference expansion is applied in encrypted video for the first time in this paper. The encryption method and the data hiding algorithm are separable, and the embedded information can be accurately extracted in both encrypted video bitstream and decrypted video bitstream. The experimental results show that the proposed encryption method can resist sketch attack and has higher security than other schemes, keeping the bit rate unchanged. The embedding algorithm used in the proposed scheme can provide higher capacity in the video with lower quantization parameter and good visual quality of the labeled decrypted video, maintaining low bit rate variation. The video encryption and the reversible data hiding are separable and the scheme can be applied in more scenarios.
Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones
Authors: Toma Cristian , Popa Marius , Iancu Bogdan , Doinea Mihai , Pascu Andreea , Ioan-Dutescu Filip
Published: October 2022

This paper presents edge machine learning (ML) technology and the challenges of its implementation into various proof-of-concept solutions developed by the authors. Paper presents the concept of Edge ML from a variety of perspectives, describing different implementations such as: a tech-glove smart device (IoT embedded device) for controlling teleoperated robots or an UAVs (unmanned aerial vehicles/drones) that is processing data locally (at the device level) using machine learning techniques and artificial intelligence neural networks (deep learning algorithms), to make decisions without interrogating the cloud platforms. Implementation challenges used in Edge ML are described and analyzed in comparisons with other solutions. An IoT embedded device integrated into a tech glove, which controls a teleoperated robot, is used to run the AI neural network inference. The neural network was trained in an ML cloud for better control. Implementation developments, behind the UAV device capable of visual computation using machine learning, are presented.
A Fuzzy Entropy-Based Thematic Classification Method Aimed at Improving the Reliability of Thematic Maps in GIS Environments
Authors: Cardone Barbara , Di Martino Ferdinando
Published: October 2022

Thematic maps of spatial data are constructed by using standard thematic classification methods that do not allow management of the uncertainty of classification and, consequently, evaluation of the reliability of the resulting thematic map. We propose a novel fuzzy-based thematic classification method applied to construct thematic maps in Geographical Information Systems. An initial fuzzy partition of the domain of the features of the spatial dataset is constructed using triangular fuzzy numbers; our method finds an optimal fuzzy partition evaluating the fuzziness of the fuzzy sets by using a fuzzy entropy measure. An assessment of the reliability of the final thematic map is performed according to the fuzziness of the fuzzy sets. We implement our method on a GIS framework, testing it on various vector and image spatial datasets. The results of these tests confirm that our thematic classification method provide thematic maps with a higher reliability with respect to that obtained through fuzzy partitions constructed by expert users.
Security Context Migration in MEC: Challenges and Use Cases
Authors: Niewolski Wojciech , Nowak Tomasz W. , Sepczuk Mariusz , Kotulski Zbigniew , Artych Rafal , Bocianiak Krzysztof , Wary Jean-Philippe
Published: October 2022

Modern and future services require ultra-reliable mobile connections with high bandwidth parameters and proper security protection. It is possible to ensure such conditions by provisioning services in the Multi-Access Edge Computing system integrated with fifth-generation mobile networks. However, the main challenge in the mentioned architecture is providing a secure service migration with all related data and security requirements to another edge computing host area when the user changes its physical location. This article aims to present the state of research on the migration of the security context between service instances in Edge/MEC servers, specify steps of the migration procedure, and identify new security challenges inspired by use cases of 5G vertical industries. For this purpose, the analysis of the security context’s structure and basic concept of the Security Service Level Agreement was performed and presented in the document. Next, a further investigation of the security context was conducted, including requirements for its reliable migration between edge serves instances. The study mainly focused on crucial migration challenges and possible solutions to resolve them. Finally, the authors presented how the proposed solution can be used to protect 5G vertical industries services based on several mobile use cases.
Recognition System of Human Fatigue State Based on Hip Gait Information in Gait Patterns
Authors: Shi Song , Cao Ziping , Li Hengheng , Du Chengming , Wu Qiang , Li Yahui
Published: October 2022

Fatigue is a common phenomenon in sports and affects sports performance. The production of fatigue during running increases the risk of sports-related injury. People with high physical demands, such as construction workers, soldiers and athletes, are often in a state of muscle fatigue, which may have an adverse effect on health and safety. It is necessary to take effective preventive measures when muscle fatigue occurs. In this paper, a wearable system for monitoring hip dynamics during human walking is proposed, and a machine learning method is used to evaluate fatigue level. The fatigue level of each subject was determined by monitoring the percentage of maximum oxygen uptake. Different percentages of oxygen uptake correspond to different exercise levels. The hip joint angle sensor used herein can sense real-time changes in the angle of the human hip joint, and the data can be used to objectively evaluate the fatigue level of the human body to reduce the risk of running-related overuse injuries. This system can be applied to a human exoskeleton device without increasing the burden on the wearer.
Teaching a Hands-On CTF-Based Web Application Security Course
Authors: Ksiezopolski Bogdan , Mazur Katarzyna , Miskiewicz Marek , Rusinek Damian
Published: October 2022

American philosopher John Dewey, in one of his most famous theories about the hands-on approach to learning, said that practical problem-solving and theoretical teaching should go hand-in-hand. This means students must interact with their environment to adapt and learn. Today, we almost take for granted that laboratory classes are an essential part of teaching science and engineering. Specific to cybersecurity, an integral piece of any training is the opportunity to work in an interactive hands-on environment: problem-solving skills are best developed in this fashion. In this paper, we present a hands-on web application security course based on OWASP Top 10 that allows students to learn through real-life experience. The virtual laboratories provided in our course simulate common vulnerabilities and issues mapped directly from OWASP Top 10, allowing students to be well-prepared for most of the critical security risks to web applications that arise in the real world. To examine how practical knowledge affects the learning experience and to measure the effectiveness of the proposed solution, we gathered learning data (such as the number of tries and the execution time for each exercise) from our cybersecurity course applied to a group of students at our university. Then, we examined correlations between students’ results and gathered statistics. In our research, we made use of a CTF-based approach, which is known as a valuable pedagogical tool for providing students with real-life problems and helping them gain more practical skills, knowledge, and expertise in the cybersecurity field.
Effect of Feature Selection on the Accuracy of Music Popularity Classification Using Machine Learning Algorithms
Authors: Khan Faheem , Tarimer Ilhan , Alwageed Hathal Salamah , Karadağ Buse Cennet , Fayaz Muhammad , Abdusalomov Akmalbek Bobomirzaevich , Cho Young-Im
Published: October 2022

This research aims to analyze the effect of feature selection on the accuracy of music popularity classification using machine learning algorithms. The data of Spotify, the most used music listening platform today, was used in the research. In the feature selection stage, features with low correlation were removed from the dataset using the filter feature selection method. Machine learning algorithms using all features produced 95.15% accuracy, while machine learning algorithms using features selected by feature selection produced 95.14% accuracy. The features selected by feature selection were sufficient for classification of popularity in established algorithms. In addition, this dataset contains fewer features, so the computation time is shorter. The reason why Big O time complexity is lower than models constructed without feature selection is that the number of features, which is the most important parameter in time complexity, is low. The statistical analysis was performed on the pre-processed data and meaningful information was produced from the data using machine learning algorithms.
Deep Reinforcement Learning for Intersection Signal Control Considering Pedestrian Behavior
Authors: Han Guangjie , Zheng Qi , Liao Lyuchao , Tang Penghao , Li Zhengrong , Zhu Yintian
Published: October 2022

Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in the intelligent transportation field. Researchers have recently proposed various solutions based on deep reinforcement learning methods for intelligent transportation problems. However, most signal control optimization takes the maximization of traffic capacity as the optimization goal, ignoring the concerns of pedestrians at intersections. To address this issue, we propose a pedestrian-considered deep reinforcement learning traffic signal control method. The method combines a reinforcement learning network and traffic signal control strategy with traffic efficiency and safety aspects. At the same time, the waiting time of pedestrians and vehicles passing through the intersection is considered, and the Discrete Traffic State Encoding (DTSE) method is applied and improved to define the more comprehensive states and rewards. In the training of the neural network, the multi-process operation method is adopted, and multiple environments are run for training simultaneously to improve the model’s training efficiency. Finally, extensive simulation experiments are conducted on actual intersection scenarios using the simulation software Simulation of Urban Mobility (SUMO). The results show that compared to Dueling DQN, the waiting time due to our method decreased by 58.76% and the number of people waiting decreased by 51.54%. The proposed method can reduce both the number of people waiting and the waiting time at intersections.
The Effect of the Dielectric Layer with Surface Plasma Treatment on Characteristics of Amorphous Silicon Antifuse
Authors: Du Tao , Li Wei , Li Wenchang , Li Jianjun , Xie Xiaodong
Published: October 2022

With the advantages of lower capacitance, faster operation and smaller size, the amorphous silicon antifuse is very promising for high-speed and high-density FPGA. However, the leakage current of the conventional amorphous silicon antifuse in the off-state is high, which decreases its performance and reliability. Although the leakage current of the SiNX or SiOX antifuse is small, the proper thickness of the dielectric layer is not easily controlled by PECVD processes. In addition, the highly undesirable switch-off behavior is common to almost all metal-to-metal antifuse structures. Focusing on the study of amorphous silicon multilayer dielectric structures, a novel antifuse with the structure of Al/α-Si:H,N/α-Si:H/Al was proposed, which was manufactured by nitrogen plasma treatment for an α-Si:H film surface. Through surface plasma treatment, the hydrogen content of the dielectric layer is stable, the film surface is smoother, the leakage current is reduced, the switch-off behavior is eliminated, the programming voltage is more concentrated and the on-state resistance distribution is more compact. The results demonstrated that surface plasma treatment with proper time for the dielectric layer could significantly improve the performance and reliability of the Al/α-Si:H,N/α-Si:H/Al antifuse. Furthermore, the fabrication process of the α-Si:H,N/α-Si:H structure has excellent compatibility, controllability and simplicity.
SUM-GAN-GEA: Video Summarization Using GAN with Gaussian Distribution and External Attention
Authors: Yu Qinghao , Yu Hui , Wang Yongxiong , Pham Tuan D.
Published: October 2022

Video summarization aims to generate a sparse subset that is more concise and less redundant than the original video while containing the most informative parts of the video. However, previous works ignore the prior knowledge of the distribution of interestingness of video frames, making it hard for the network to learn the importance of different frames. Furthermore, traditional models alone (such as RNN and LSTM) are not robust enough in capturing global features of the video sequence since the video frames are more in line with non-Euclidean data structure. To this end, we propose a new summarization method based on the graph model concept to learn the feature relationship connections between video frames, which can guide the summary generator to generate a robust global feature representation. Specifically, we propose to use adversarial learning to integrate Gaussian distribution and external attention mechanism (SUM-GAN-GEA). The Gaussian function is a priori mapping function that considers the distribution of the interestingness of actual video frames and the external attention can reduce the inference time of the model. Experimental results on two popular video abstraction datasets (SumMe and TVSum) demonstrate the high superiority and competitiveness of our method in robustness and fast convergence.
Improving FMCW GPR Precision through the CZT Algorithm for Pavement Thickness Measurements
Authors: Huang Tongxing , Zhang Chaoyang , Lu Dun , Zeng Qiuyu , Fu Wenjie , Yan Yang
Published: October 2022

Ground Penetrating Radar (GPR) application in road surface detection has been greatly developed in the past few decades, which enables rapid and economical estimation of pavement thickness and other physical properties in non-destructive testing (NDT) and non-contact testing (NCT). In recent years, with the rapid development of microwave and millimeter-wave solid-state devices and digital signal processors, the cost of Frequency-Modulated Continuous-Wave (FMCW) radar has dropped significantly, with smaller size and lighter weight. Thereafter, FMCW GPR is considered to be applied during pavement inspection. To improve the precision of FMCW GPR for NDT and NCT of pavement thickness, a Chirp Z-transform (CZT) algorithm is introduced to FMCW GPR and investigated in this paper. A FMCW + CZT GPR at 2.5 GHz with a bandwidth of 1 GHz was built, and laboratory and field experiments were carried out. The experimental results demonstrate that the FMCW + CZT GPR radar can obtain the sample thickness with low error and recognize subtle thickness variations. This method realizes the high precision thickness measurement of shallow asphalt pavement by FMCW radar with a narrow bandwidth pulse signal and would provide a promising low-cost measurement solution for GPR.
Incoherent and Online Dictionary Learning Algorithm for Motion Prediction
Authors: Hafeez Farrukh , Sheikh Usman Ullah , Iqbal Asif , Aman Muhammad Naveed
Published: October 2022

Accurate model development and efficient representations of multivariate trajectories are crucial to understanding the behavioral patterns of pedestrian motion. Most of the existing algorithms use offline learning approaches to learn such motion behaviors. However, these approaches cannot take advantage of the streams of data that are available after training has concluded, and typically are not generalizable to data that they have not seen before. To solve this problem, this paper proposes two algorithms for learning incoherent dictionaries in an offline and online manner by extending the offline augmented semi-non-negative sparse coding (ASNSC) algorithm. We do this by adding a penalty into the objective function to promote dictionary incoherence. A trajectory-modeling application is studied, where we consider the learned atoms of the dictionary as local motion primitives. We use real-world datasets to show that the dictionaries trained by the proposed algorithms have enhanced representation ability and converge quickly as compared to ASNSC. Moreover, the trained dictionaries are well conditioned. In terms of pedestrian trajectory prediction, the proposed methods are shown to be on par (and often better) with the state-of-the-art algorithms in pedestrian trajectory prediction.
Equilibrium Approximating and Online Learning for Anti-Jamming Game of Satellite Communication Power Allocation
Authors: Zou Mingwo , Chen Jing , Luo Junren , Hu Zhenzhen , Chen Shaofei
Published: October 2022

Satellite communication systems are increasingly facing serious environmental challenges such as malicious jamming, monitoring, and intercepting. As a current development of artificial intelligence, intelligent jammers with learning ability can effectively perceive the surrounding spectrum environment to dynamically change their jamming strategies. As a result, the current mainstream satellite communication anti-jamming technology based on wide interval high-speed frequency hopping is unable to deal with this problem effectively. In this work, we focus on anti-jamming problems in the satellite communication domain, and reformulate the power allocation problem under two kinds of confrontation scenarios as one-shot and repeated games model. Specifically, for the problem of multi-channel power allocation under a one-shot confrontation scenario, we firstly model the problem of allocating limited power resource between communication parties and a jammer on multi-channel based on a BG (Blotto Game) model. Secondly, a DO-SINR (Double Oracle-Signal to Interference plus Noise Ratio) algorithm is designed to approximate the Nash equilibrium of the game between two parties. Experiments show that the DO-SINR algorithm can effectively obtain the approximate Nash equilibrium of the game. For the problem of multi-channel power allocation under a repeated confrontation scenario, we firstly transform the problem into an online shortest path problem with a graph structure to make the problem solving process more intuitive, and then design the Exp3-U (Exp3-Uniform) algorithm which utilizes the graph structure to solve the multi-channel power allocation problem. Experiments show that our algorithm can minimize the expected regret of communication parties during online confrontation, while maintaining good operating efficiency. The two power allocation problems constructed in this paper are common problem formed in confrontation scenarios. Our research and analysis can simulate some actual confrontation scenarios of the satellite communication power allocation, which can be used to improve the adaptability of satellite communication systems in complex environments.
A Comparison of Off-Chip Differential and LC Input Matching Baluns in a Wideband and Low-Power RF-to-BB Current-Reuse Receiver Front-End
Authors: Abbasi Arash , Nabki Frederic
Published: October 2022

A wideband and low-power RF-to-baseband (BB) current-reuse receiver (CRR) front-end is proposed, and its performance is verified using two matching networks, one with an LC balun and on-chip biasing inductor, CRR1, and another with a differential balun and without on-chip biasing inductor, CRR2, requiring less area. The transimpedance amplifier (TIA) and low-noise transconductance amplifier (LNTA) share the bias current from a single supply to reduce power consumption. It employs both an active-inductor (AI) and a 1/f noise-cancellation technique to improve the NF and RF bandwidth performance. A passive mixer is utilized for RF to BB conversion, which does not require any DC power and voltage headroom. Both CRR1 and CRR2 are fabricated in TSMC 130 nm CMOS technology on a single die and packaged using a QFN48. CRR1 occupies an active area of 0.54 mm2. From 1 to 1.7 GHz, it achieves a conversion gain of 41.5 dB, a double-sideband (DSB) NF of 6.5 dB, S11<−10 dB, and an IIP3 of −28.2 dBm, while the local-oscillator (LO) frequency is at 1.3 GHz. CRR2 occupies an active area of 0.025 mm2. From 0.2 to 1 GHz, it achieves an average conversion gain of 37 dB, an average DSB NF of 8 dB, and an IIP3 of −21.5 dBm while the LO frequency is at 0.7 GHz. Both CRR1 and CRR2 consume 1.66 mA from a 1.2 V supply voltage.
A Composite Approach of Intrusion Detection Systems: Hybrid RNN and Correlation-Based Feature Optimization
Authors: Gautam Sunil , Henry Azriel , Zuhair Mohd , Rashid Mamoon , Javed Abdul Rehman , Maddikunta Praveen Kumar Reddy
Published: October 2022

Detection of intrusions is a system that is competent in detecting cyber-attacks and network anomalies. A variety of strategies have been developed for IDS so far. However, there are factors that they lack in performance, creating scope for further research. The current trend shows that the Deep Learning (DL) technique has been proven better than traditional techniques for IDS. Throughout these studies, we presented a hybrid model that is a Deep Learning method called Bidirectional Recurrent Neural Network using Long Short-Term Memory and Gated Recurrent Unit. Through simulations on the public dataset CICIDS2017, we have shown the model’s effectiveness. It has been noted that the suggested model successfully predicted most of the network attacks with 99.13% classification accuracy. The proposed model outperformed the Naïve Bayes classifier in terms of prediction accuracy and False Positive rate. The suggested model managed to perform well with only 58% attributes of the dataset compared to other existing classifiers. Moreover, this study also demonstrates the performance of LSTM and GRU with RNN independently.
Application of Improved Butterfly Optimization Algorithm Combined with Black Widow Optimization in Feature Selection of Network Intrusion Detection
Authors: Xu Hui , Lu Yanping , Guo Qingqing
Published: October 2022

Feature selection is a very important direction for network intrusion detection. However, current feature selection technology of network intrusion detection has the problems of low detection rate and low accuracy due to feature redundancy. An improved Butterfly Optimization Algorithm combined with Black Widow Optimization (BWO-BOA) is proposed in this paper, which introduces a dynamic adaptive search strategy in the global search phase of the Butterfly Optimization Algorithm (BOA), uses the movement search process of Black Widow Optimization (BWO) algorithm as the local search, and at the same time, in order to overcome the improved butterfly optimization algorithm easily falling into a local optimum in local search phase, takes advantage of the small probability mutation strategy to filter out the redundant features. This paper then tries to apply the proposed BWO-BOA algorithm to feature selection of network intrusion detection. In order to verify the performance of the proposed BWO-BOA algorithm, the UNSW-NB15 dataset is selected for binary classification and multi-classification simulation experiments, and the feature selection models of BWO-BOA algorithm, BOA algorithm, BWO algorithm, Particle Swarm Optimization, Salp Swarm Algorithm, Whale Optimization Algorithm and improved Butterfly Optimization Algorithm are compared for validation. The experimental results show that the proposed BWO-BOA algorithm can enhance the performance of the feature selection model in network intrusion detection and significantly boost the reduction of feature dimensions.
Jointly Modeling Aspect Information and Ratings for Review Rating Prediction
Authors: Peng Qingxi , You Lan , Feng Hao , Du Wei , Zheng Kesong , Zhu Fuxi , Xu Xiaoya
Published: October 2022

Although matrix model-based approaches to collaborative filtering (CF), such as latent factor models, achieve good accuracy in review rating prediction, they still face data sparsity problems. Many recent studies have exploited review text information to improve the performance of predictions. The review content that they use, however, is usually on the coarse-grained text level or sentence level. In this paper, we propose a joint model that incorporates review text information with matrix factorization for review rating prediction. First, we adopt an aspect extraction method and propose a simple and practical algorithm to represent the review by aspects and sentiments. Then, we propose two similarity measures: aspect-based user similarity and aspect-based product similarity. Finally, aspect-based user and product similarity measures are incorporated into a matrix factorization to build a joint model for rating prediction. To this end, our model can alleviate the data sparsity problem and obtain interpretability for the recommendation. We conducted experiments on two datasets. The experimental results demonstrate the effectiveness of the proposed model.
Classical FE Analysis to Classify Parkinson’s Disease Patients
Authors: Calvo-Ariza Nestor Rafael , Gómez-Gómez Luis Felipe , Orozco-Arroyave Juan Rafael
Published: October 2022

Parkinson’s disease (PD) is a neurodegenerative condition that affects the correct functioning of the motor system in the human body. Patients exhibit a reduced capability to produce facial expressions (FEs) among different symptoms, namely hypomimia. Being a disease so hard to be detected in its early stages, automatic systems can be created to help physicians in assessing and screening patients using basic bio-markers. In this paper, we present several experiments where features are extracted from images of FEs produced by PD patients and healthy controls. Classical machine learning methods such as local binary patterns and histograms of oriented gradients are used to model the images. Similarly, a well-known classification method, namely support vector machine is used for the discrimination between PD patients and healthy subjects. The most informative regions of the faces are found with a principal component analysis algorithm. Three different FEs were modeled: angry, happy, and surprise. Good results were obtained in most of the cases; however, happiness was the one that yielded better results, with accuracies of up to 80.4%. The methods used in this paper are classical and well-known by the research community; however, their main advantage is that they provide clear interpretability, which is valuable for many researchers and especially for clinicians. This work can be considered as a good baseline such that motivates other researchers to propose new methodologies that yield better results while keep the characteristic of providing interpretability.
TORRES: A Resource-Efficient Inference Processor for Binary Convolutional Neural Networks Based on Locality-Aware Operation Skipping
Authors: Lee Su-Jung , Kwak Gil-Ho , Kim Tae-Hwan
Published: October 2022

A binary convolutional neural network (BCNN) is a neural network promising to realize analysis of visual imagery in low-cost resource-limited devices. This study presents an efficient inference processor for BCNNs, named TORRES. TORRES performs inference efficiently, skipping operations based on the spatial locality inherent in feature maps. The training process is regularized with the objective of skipping more operations. The microarchitecture is designed to skip operations and generate addresses efficiently with low resource usage. A prototype inference system based on TORRES has been implemented in a 28 nm field-programmable gate array, and its functionality has been verified for practical inference tasks. Implemented with 2.31 K LUTs, TORRES achieves the inference speed of 291.2 GOP/s, exhibiting the resource efficiency of 126.06 MOP/s/LUT. The resource efficiency of TORRES is 1.45 times higher than that of the state-of-the-art work.
Asynchronous Partial Gaussian Approximation Detection Algorithm for Uplink-Grouped MIMO-SCMA System
Authors: Wang Xue , Zhou Ninghao , Hou Jia
Published: October 2022

To relieve the impact caused by different arrival times of multiple users in the uplink MIMO-SCMA system, a grouped multiuser transmission model, according to different delays and its corresponding detection scheme, is proposed in this paper. Assuming perfect synchronization in one group, a message-passing algorithm based on serial propagation (SP-MPA) is proposed to reduce the bit error rate (BER), which could transfer the updated information to the next symbol as its initial probability. Furthermore, with a more practical case, the partial Gaussian approximation method (PGA) is designed to decrease the interference resulting from the imperfect synchronization in one group. As the result, the computing complexity of the proposed PGA method could be decreased by at least 20% compared with SP-MPA and the BER could be improved by about 10%.
A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel Segmentation
Authors: Tu Zhonghao , Zhou Qian , Zou Hua , Zhang Xuedong
Published: October 2022

Morphological and functional changes in retinal vessels are indicators of a variety of chronic diseases, such as diabetes, stroke, and hypertension. However, without a large number of high-quality annotations, existing deep learning-based medical image segmentation approaches may degrade their performance dramatically on the retinal vessel segmentation task. To reduce the demand of high-quality annotations and make full use of massive unlabeled data, we propose a self-supervised multi-task strategy to extract curvilinear vessel features for the retinal vessel segmentation task. Specifically, we use a dense network to extract more vessel features across different layers/slices, which is elaborately designed for hardware to train and test efficiently. Then, we combine three general pre-training tasks (i.e., intensity transformation, random pixel filling, in-painting and out-painting) in an aggregated way to learn rich hierarchical representations of curvilinear retinal vessel structures. Furthermore, a vector classification task module is introduced as another pre-training task to obtain more spatial features. Finally, to make the segmentation network pay more attention to curvilinear structures, a novel dynamic loss is proposed to learn robust vessel details from unlabeled fundus images. These four pre-training tasks greatly reduce the reliance on labeled data. Moreover, our network can learn the retinal vessel features effectively in the pre-training process, which leads to better performance in the target multi-modal segmentation task. Experimental results show that our method provides a promising direction for the retinal vessel segmentation task. Compared with other state-of-the-art supervised deep learning-based methods applied, our method requires less labeled data and achieves comparable segmentation accuracy. For instance, we match the accuracy of the traditional supervised learning methods on DRIVE and Vampire datasets without needing any labeled ground truth image. With elaborately training, we gain the 0.96 accuracy on DRIVE dataset.
Monopulse Radar Target Detection in the Case of Main-Lobe Cover Jamming
Authors: Wang Lei , Zhang Qiliang , Wan Pengfei
Published: October 2022

Radar is known as the “eye” of modern warfare and plays a pivotal role in warfare, but the detection performance of radar systems is seriously affected by main-lobe cover jamming. To solve this problem, a radar target detection method based on four-channel monopulse radar is proposed in this paper. This method designed a spatial filter for main-lobe jamming cancellation, which could cancel the main-lobe jamming while keeping the target signal power unchanged, ensuring that the Electronic counter-countermeasure Improvement Factors (EIF) of the filtered signal always reach the maximum value to improve the target detection performance of monopulse radar. The Monte Carlo simulation results showed that the algorithm in this paper outperformed the conventional Main-Lobe Cancellation (MLC) algorithm in terms of EIFs and detection performance.
A Time-Varying PD Sliding Mode Control Method for the Container Crane Based on a Radial-Spring Damper
Authors: Wang Tianlei , Zhou Jing , Wu Zhiqin , Liu Renju , Zhang Jingling , Liang Yanyang
Published: October 2022

For the multi-rope structure of the container crane system and its large mass payload anti-swing positioning problem, an equivalent double-swing model based on radial spring-dampers is established and a time-varying PD sliding mode controller (TVPD-SMC) with improved transient performance is proposed. In particular, the dynamics of the container crane system are first analyzed using the Lagrange method, and an equivalent double pendulum dynamics model of the crane is established. Compared with the traditional double pendulum model, this model ensures the accuracy of modeling without measuring the second pendulum angle. On this basis, an enhanced coupled time-varying sliding mode control method is designed to eliminate the sliding mode control method’s reaching phase and improves the robustness of the controller. Finally, the convergence and stability of the closed-loop system are proved using the Lyapunov technique and the Lasalle invariance theorem, and the simulation results demonstrate the effectiveness of the method.
Design of Concurrent Tri-Band High-Efficiency Power Amplifier Based on Wireless Applications
Authors: Gao Mingming , Xu Gaoyang , Nan Jingchang
Published: October 2022

To meet the existing requirements of multiband communication and improve the efficiency and performance of communication RF modules, a concurrent tri-band high-efficiency power amplifier operating in three frequency bands is proposed. The input and output impedance values of concurrent power amplifier is analyzed, and the input and output-matching circuit and bias circuit are designed. Through the impedance compensation principle, the impedance matching of three frequency bands is realized, and the amplifier can maintain high power and high efficiency at three arbitrary wide interval frequencies. To this end, a simultaneous tri-band power amplifier is designed and tested by using transistor CGH40010F. The experimental results show that the peak power of the designed simultaneous tri-band high-efficiency power amplifier is more than 10 W, the power-added efficiency reaches 55~69%, and the amplification gain is about 10 dB at three frequency bands of 2.2, 2.6, and 3.5 GHz. The design of concurrent tri-band high-efficiency power amplifier is flexible, the calculation of microstrip line parameters is simple, and it can work in three frequency bands simultaneously. It provides an effective structure scheme for designing concurrent power amplifiers in transmitting systems.
Research on the Torque Control Strategy of a Distributed 4WD Electric Vehicle Based on Economy and Stability Control
Authors: Qiu Lei , Zhu Shaopeng , Liu Dong , Xiang Zhiwei , Fu Hong , Chen Huipeng
Published: October 2022

To improve the comprehensive performance of the distributed wheel-side four-wheel-drive electric bus, the problem of optimal distribution of the driving torque of the four wheel-side motors is studied. Aiming at the poor economy and failure of switching control due to the consideration of both straight and steering conditions, this paper proposes a fuzzy yaw moment control strategy based on the golden section search algorithm. Under full working conditions, according to the efficiency characteristics of the front and rear axle drive motors, the golden section search algorithm is used to determine the best front and rear axle motor torque distribution coefficient K to distribute the front and rear axle motor torques. Given the stability problems existing in the steering conditions, based on the optimal torque distribution of the front and rear axles, fuzzy control is used to calculate the expected yaw moment, and the left and right wheel torques are adjusted in real time. The simulation is carried out through TruckSim and MATLAB/Simulink, and a hardware-in-the-loop platform is built for experimentation under step steering conditions and sine wave steering conditions. The results show that the proposed torque optimal distribution strategy can optimally distribute the torque of the four drive motors through the real-time identification of working conditions. Compared with the four-wheel equal distribution, under two different steering conditions, the torque distribution efficiency of the torque distribution strategy using the golden section search algorithm increased by 4.35% and 3.83%, respectively. The energy utilization rate of the whole vehicle is improved under all of the working conditions. Under steering conditions, compared with the four-wheel equal distribution and the torque distribution strategy using the golden section search algorithm under all of the conditions, the yaw rate deviation and the slip angle deviation can be reduced, and the yaw stability has been improved.
An Efficient Estimation Method for Dynamic Systems in the Presence of Inaccurate Noise Statistics
Authors: Zhang Guanghua , Lian Feng , Gao Xin , Kong Yinan , Chen Gong , Dai Shasha
Published: October 2022

The uncertainty of noise statistics in dynamic systems is one of the most important issues in engineering applications, and significantly affects the performance of state estimation. The optimal Bayesian Kalman filter (OBKF) is an important approach to solve this problem, as it is optimal over the posterior distribution of unknown noise parameters. However, it is not suitable for online estimation because the posterior distribution of unknown noise parameters at each time is derived from its prior distribution by incorporating the whole measurement sequence, which is computationally expensive. Additionally, when the system is subjected to large disturbances, its response is slow and the estimation accuracy deteriorates. To solve the problem, we improve the OBKF mainly in two aspects. The first is the calculation of the posterior distribution of unknown noise parameters. We derive it from the posterior distribution at a previous time rather than the prior distribution at the initial time. Instead of the whole measurement sequence, only the nearest fixed number of measurements are used to update the posterior distribution of unknown noise parameters. Using the sliding window technique reduces the computational complexity of the OBKF and enhances its robustness to jump noise. The second aspect is the estimation of unknown noise parameters. The posterior distribution of an unknown noise parameter is represented by a large number of samples by the Markov chain Monte Carlo approach. In the OBKF, all samples are equivalent and the noise parameter is estimated by averaging the samples. In our approach, the weights of samples, which are proportional to their likelihood function values, are taken into account to improve the estimation accuracy of the noise parameter. Finally, simulation results show the effectiveness of the proposed method.
Research on Risk Assessment and Suppression Measures for Ice-Shedding on 500 kV Compact Overhead Lines
Authors: Wen Yi , Chen Yifei , Wu Jianrong , Mao Xianyin , Huang Huan , Yang Lin
Published: October 2022

Overhead line ice-shedding causes line breakage and results in electrical faults occurring between transmission lines, which seriously affects the safe operation of the power grid. In this paper, a risk assessment method for ice-shedding on 500 kV compact overhead lines is proposed. Two ratios were used to assess the discharge risk and mechanical failure risk of a line. A 500 kV compact overhead line in southern China was taken as an example, the proposed risk assessment method was used to carry out a risk assessment of the ice-shedding line with an ice thickness of 15 mm, and the suppression effects of different spacer arrangement schemes on the ice-shedding line were analyzed. The results showed that the impact coefficient of the conductor reached 1.88, the maximum jump height of the lower phase conductor reached 22.596 m, and the interphase clearance of the conductor decreased sharply when the lower phase conductor of 500 kV case line underwent full-span ice-shedding synchronously. The ice-shedding line displayed both a discharge risk and a mechanical failure risk. When interphase spacers were installed at L/3 from both ends of the span for the upper-left phase and the lower phase, the upper-right phase, and the lower phase, the impact coefficient of the line became 1.76, which was lower than the recommended value of 1.8; there was no mechanical failure risk; and the maximum jump height became 10.266 m. This study verified the effectiveness of this spacer arrangement scheme.
Thermoelectric Generator for Waste Energy Recovery in Transport
Authors: Poure Philippe , Huq Mashiul
Published: October 2022

Waste heat present in exhaust gas produced from various industrial processes or internal combustion engines in transport are a reservoir of untapped energy [...]