Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.
A Beam Forming Antenna Based on Earth Shape for Micro-satellites

This paper proposes a new wide-beam bifilar helix antenna with earth coverage beam for satellite communication. Former helix antennas for micro-satellites are focused on narrow-beam high-gain purpose and hard to realize the desired beam pattern within a wide angle range, making them unsuitable for earth coverage applications. This novel antenna realizes the uniform illumination of the earth's surface with miniaturized structure. (The earth coverage radiation pattern is obtained within the beamwidth of θ in the range of ±56°). According to the calculation, the antenna is modeled and simulated. The simulation results show good agreement.
Weighted Tensor Low-Rankness and Learnable Analysis Sparse Representation Model for Texture Preserving Low-Dose CT Reconstruction

In CT images, tissue structures and lesion changes illustrate evident non-local self-similarity and regionally constant properties. The low-rank model and the learnable sparse representation model are powerful tools that can respectively encode the correlations among non-local similar patches and the sparsity in a local transformed subspace about the underlying CT image. Existing Model-Based Iterative Reconstruction (MBIR) methods generally adopt one of the two models alone for CT reconstruction, which might suffer from modelling deficiency and hampers their reconstruction performance. In this study, we presented a novel Weighted Tensor Low-Rank and Learnable Analysis Sparse Representation model (WTLR-LASR) to simultaneously encode the non-local correlations and local transformed sparsity natures. Specifically, we developed a novel Weighted Tensor Nuclear Norm Minimization (WTNNM) formulation to characterize the weighted tensor low-rank model, and introduced the Weighted Tensor Nuclear Norm Proximal (WTNNP) operator to solve the non-convex WTNNM problem. We further proved that the WTNNP problem can be equivalently transformed to a weighted matrix nuclear norm proximal (WMNNP) problem in the Fourier transform domain, which allowed us to easily reach the closed-form optimum of the WTNNP problem. We proposed a novel CT reconstruction algorithm based on the presented WTLR-LASR model. We also introduced a genetic algorithm to automatically select the parameters in the proposed algorithm. Extensive experimental studies were performed to validate the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can achieve noticeable improvements over state-of-the-art methods in terms of noise suppression and textures preservation.
Identifying the Stability of Couple Relationship Applying Different Machine Learning Techniques

A couple relationship is an important matter where two people engaged each other in a romantic association. This bonding happens almost all human beings throughout the world. The failure of a relationship is a serious issue that affects not only two directly involved people but also hampered the kinship of indirectly associated people. This work aims to investigate and estimate the persistence of the couple relationship by using machine learning models. Therefore, we gathered a couple relationship data from the Standford Social Science Data Collection repository and reprocessed them for further analysis. Then, we generated several feature subsets from the primary dataset and employed different machine learning classifiers to them. In this case, the Bagging classifier with a decision tree classifier shows the best accuracy of 87% for the recursive feature elimination feature subset. We also explored significant features from these subsets which can be used to estimate the stability of a couple relationship as well. Thus, our model can be useful to predict in advance about the future relationship and could be possible to recover them before any occurrence.
Modified bridge type fault current limiter based protective framework for Inverter Based Distributed Generation system

Unbounded increase in fault current magnitude is becoming an alarming issue in the power system because of the installation of inverter based distributed generation (IBDG) at a large number in recent years. For overcoming this problem a modified bridge type fault current limiter (MBFCL) has been proposed. The MBFCL is fed in the AC side of the IBDG system and for controlling the reactance of the MBFCL properly, a specified controller has been implemented. Non-linear voltage at common coupling (Vpcc) based control mechanism is used to control the MBFCL operation. The DC link voltage control and the power interchange between AC and DC side of IBDG is done by outer controller loop and inner current controller of the main controller respectively. To investigate the superiority of the proposed system we have applied balanced and unbalanced faults in the system. The simulation is carried out in MARLAB/SIMULINK. Finally, the fault current limiting performance is compared with series dynamic braking resistor (SDBR) and BFCL and it is found that proposed MBFCL performs better.
Evaluation environment using edge computing for artificial intelligence-based irrigation system

It is a concern that Japanese high-level agriculture represented by high-sugar tomato cultivation will not be inherited by new farmers and will be lost due to the aging of Japanese agricultural workers. Artificial intelligence (AI) models for irrigation control have been proposed to automate high-sugar tomatoes cultivation. The traditional AI models have been run on the cloud, which requires a stable communication infrastructure. However, greenhouses tend not to have such infrastructures. They use unstable mobile communication, which causes disconnection and delay. Therefore, it is difficult to control the field irrigation by the AI models on the cloud.In this paper, we propose an automatic irrigation system that is not affected by communication disconnection and delay. To achieve this, we use edge nodes, which are small computers operating in farm fields. In our study, we select an edge node and conduct an experiment to evaluate its suitability for the irrigation system. Our findings indicate that the system meets the requirements for an irrigation system, and that it could be used to evaluate other AI-based systems used in cultivation of high-sugar tomatoes.
A Bi-directional LSTM Ship Trajectory Prediction Method based on Attention Mechanism

Ship trajectory prediction can effectively predict navigation trends and realize orderly management of ships, which are of great significance to maritime traffic safety. This paper proposes a novel ship trajectory prediction method based on the integrated model of LSTM Auto-encode, Attention Mechanism, and Bi-directional LSTM (AABiL) structure. Our research in this paper mainly includes two parts, the first part is the extraction of ship trajectory features, and the second part is the trajectory sequence prediction. The LSTM auto-encoder used in this paper is to extract the features of the trajectory data, and trajectory data come from Automatic Identification System (AIS) of the ship. After preprocessing the trajectory data, we combined the extracted features with the trajectory longitude and latitude data to represent the current sailing state of the ship as the model input. The Bi-directional LSTM (Bi-LSTM) neural network model with attention mechanism is used to train and learn the regular pattern of ship motion implied in the trajectory data to predict the location of the ship at the next moment. Experiments demonstrate that use the integrated model of AABiL has decreased the Mean Square Error (MSE) of ship trajectory prediction to 0.00047, which can effectively improve the accuracy of trajectory prediction.
An AETA Electromagnetic Disturbance Anomaly Extraction Method based on Sample Entropy

For the multi-component seismic monitoring system AETA's electromagnetic disturbance (ED) data, an outlier extraction method is proposed based on sample entropy. Sample entropy can reflect the possibility of new information in the time series. Sample entropy algorithm is utilized for processing the electromagnetic disturbance data to figure out the sample entropy sequence in days. Then sliding quartile method is used to obtain the outlier sequence by calculating the abnormal index of sample entropy sequence. The experiment result in Yibin Ms 6.0 earthquake and superposed epoch analysis demonstrate that the method can detect the outlier and have the ability to reflect earthquake.
Research on Integrated MPPT Control and Online Monitoring System of Microsatellite

The power supply of the commercial aerospace microsatellite pursues a cost-effective system. To realize the intelligent autonomous control and management, a set of integrated MPPT control and online monitoring system based on DSP and LabVIEW is designed and developed, which consists of three parts: Superbuck charging module, MPPT control unit and monitoring unit. First, the Superbuck charging module contains the main power circuit and the data acquisition circuit, which realizes high-speed sampling and power conversion of the output parameters of the parallel PV array. Second, a fast and stable global maximum power point tracking algorithm based on voltage closed loop is proposed to solve the multi-peak problem of the PV array in the MPPT control unit. Third, the monitoring unit further analyzes the data based on CAN-bus protocol 2.0 to realize human-computer interaction. Experiments prove that this Integrated system can efficiently control and manage the output characteristics of the parallel PV array under different environments. Finally, the reliability of digital control under the circumstance of commercial aerospace is improved effectively.
Real-Time User-Independent Slope Prediction Using Deep Learning for Modulation of Robotic Knee Exoskeleton Assistance

Ground slope incline is a critical environmental variable that influences exoskeleton control parameters since human biological joint demand is correlated to changes in slope incline. Current literature methods take a heuristic approach by numerically calculating the slope incline from on-board mechanical sensors. However, these methods often require a user-specific tuning procedure and are prone to noise and sensor drift when tested in a dynamic setting, such as overground locomotion. In this study, we propose the use of a deep learning slope prediction model capable of generalizing across users and terrain. To evaluate this approach, we collected training data (N = 10) and utilized a convolutional neural network to predict the inclination angle and actively modulate the peak assistance magnitude of a bilateral robotic knee exoskeleton in real-time. From online validation results (N = 3), our model predicted the slope incline with an average RMSE of 1.5$^{\circ }$ during treadmill and overground walking. Furthermore, our model accurately predicted the slope incline in the extrapolated region outside of the training data with an average RMSE of 1.7$^{\circ }$ during treadmill and overground walking. Our study's findings showcase the feasibility of using deep learning models to actively modulate exoskeleton assistance, translating this technology to more realistic locomotion environments.
The Potential Impact of Using Traction Inverters With SiC MOSFETs for Electric Buses

The efficiency improvements offered by adopting Silicon Carbide (SiC) devices in Electric Vehicle (EV) inverters has been widely reported in various studies. However, it has still not been established whether or when the efficiency benefits can counteract the high price of SiC devices, especially for the scenario of urban electric buses. In this paper, the potential impact of installing SiC devices on electric buses is analyzed from the perspective of economic benefits. The SiC inverter used in the drive system is based on a discrete MOSFET in parallel. For comparison, an Si inverter based on a sixth-generation IGBT produced by Infineon is also evaluated. A vehicle simulation platform is established in Simulink for an evaluation of energy consumption for different scenarios. It is shown that significant energy savings can be gained when the vehicle operates mostly in the partial load area. While in the nominal load area, energy savings are very limited. Based on the analysis, the conditions for offsetting the cost premium of SiC devices to realize system-level advantages are discussed using a multi-dimensional analysis considering the change in battery and device prices, driving range, vehicle mass, and driving cycles.
Research on cylindrical labels unfolding algorithm based on machine vision

It is a difficult problem for imaging the cylindrical label, and this paper presents an algorithm for unfolding the cylindrical label. First, the cylindrical label is positioned by the monocular camera, and the 3D point sets of the cylindrical label are established. Second, the observation range of each camera is determined based on the poses of the cylinders. Then, the cylindrical distortion is corrected in each camera. Finally, two adjacent images are stitched together to obtain a continuous and normalized unfolding image of the cylindrical label. Real-world and synthetic data experiments results show that the algorithm can correct the cylindrical distortion, and it takes only about 160 ms to obtain the cylindrical label unfolding image. The algorithm execution process does not require complicated steps and additional auxiliary, and the algorithm execution speed meets the industrial requirements.
Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model

Short-term load forecasting (STLF) with excellent precision and prominent efficiency plays a significant role in the stable operation of power grid and the improvement of economic benefits. In this paper, a novel model based on data mining and deep learning is proposed. Firstly, the preprocessing of data includes normalization of historical load, and fuzzification of influencing factors (meteorological factors, date types and economy) based on Pearson correlation coefficient (PCC). Secondly, kernel fuzzy c-means (KFCM) modified by particle swarm optimization (PSO-KFCM) algorithm clusters the daily load curve. In the clustering experiments, the within-cluster sum of squared error (SSE) index is presented to determine the number of clusters and the clustering validity has a 31.9% enhancement compared with the traditional FCM algorithm. Thirdly, the cosine similarity establishes the resemblance between the prediction date and each cluster, and the similar cluster is determined according to the principle of maximum similarity. Finally, a multivariate and multi-step hybrid model MMCNN-LSTM based on convolution neural network (CNN) and long short-term memory (LSTM) neural network is proposed to forecast the load in following 24 hours, in which similar cluster data is applied to training set. To demonstrate the effectiveness of proposed integrated technique, the accuracy has been verified in three predictive experiments. The fruitful results indicated that the average mean absolute percent error (MAPE) in the entire test set was only 1.34%, a 3.02% reduction compared to a single LSTM.
Optimal Defense Strategy against Evasion Attacks

Recent detection method based on machine learning demonstrates significant advantages against varieties of network attacks, and has been widely deployed in cloud applications. However, novel attacks such as Advanced Persistent Threats (APTs) could evade detection of the intrusion detection system, which may lead to serious data leakage in cloud. Existing methods studied the countermeasures to defend against evasion attacks. However, a cloud service provider (CSP) also have to balance between its expect revenue and the security risk of system with limited resources. In this paper, we present the CSP’s optimal strategy for effective and safety operation, in which the CSP decides the size of users that the cloud service will provide and whether enhanced countermeasures will be conducted for discovering the possible evasion attacks. While the CSP tries to optimize its profit by carefully making a two-step decision of the defense plan and service scale, the attacker considers its expected revenue to launch evasion attacks or not. To obtain insights of such a highly coupled system, we consider a system with one CSP and one attacker with two attack choices of whether to launch an evasion attack. We propose a two-stage Stackelberg game, in which the CSP acts as the leader who decides the defense plan and service scale in Stage I, and the attacker acts as the follower that determines whether to make evasion attacks in Stage II. We derive the Nash Equilibrium by analyzing the attacker’s choices under different scenarios that the CSP selects. Then, we provide the CSP’s optimal strategies to maximize its revenue. The simulation results help to better understand the CSP’s optimal solutions under different situations.
Carbon Sinks and Variations of pCO2 in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach

The Southern Ocean comprises 25% of the global ocean surface area, accounts for nearly half of the total carbon sink of the global oceans, and is a place that significantly reduces the impacts of anthropogenic CO2 emissions. Due to the sparsity of observational data, the changes in Southern Ocean carbon sinks over time remain uncertain. In this study, we integrated correlation analysis and a feedforward neural network to improve the accuracy of carbon flux estimations in the Southern Ocean. Based on observation data from 1998–2018, we reconstructed the Southern Ocean's pCO2 grid data during this period. The root-mean-square error obtained by fitting the observation data was 8.86 μatm, indicating that the results were better than those of the two primary statistically based models in the Surface Ocean pCO2 mapping intercomparison. The results also showed that the Southern Ocean's capacity to act as a carbon sink has gradually increased since 2000; it reduced during 2010–2013 but increased significantly after that. The Southern Ocean's seasonality is characterized by minimum carbon uptake in winter due to increased upwelling; this is followed by a rapid increase toward maximum uptake in summer, which is mainly biologically driven. There is an apparent double-ring structure in the Southern Ocean, as noted in other studies. This study confirms that the inner ring (50–70°S) is a carbon source area gradually transforming into a carbon sink, while the outer ring (35–50°S) continues to serve as a carbon sink.
Leveraging Cognitive Diagnosis to Improve Peer Assessment in MOOCs

A major challenge faced by popular massive open online courses (MOOCs) is the assessment of large-scale open-ended assignments submitted by students. Recently, peer assessment has become a mainstream paradigm that helps grade open-ended assignments on a large scale. In peer assessment, students also become graders in grading a small number of their peers’ assignments, and the peer grades are then aggregated to predict a true score for each assignment. The collected peer grades are usually inaccurate because graders have different reliabilities and biases. To improve accuracy, several probabilistic graph models have been proposed to model the reliability and bias of each grader. However, none of these models consider graders’ competency information in the assignments to be graded, which has been found to be very effective. We propose two new probabilistic graph models to improve the accuracy of cardinal peer assessments based on the well-accepted cognitive diagnosis technique. Specifically, the cognitive diagnosis model DINA is applied to determine grader competency based on historical tests or assignments. Then, this information is used to optimize the modeling of grader reliability in each of the proposed models. Moreover, an effective model inference algorithm is proposed to infer true scores of assignments. Experimental results based on real world datasets show that the two proposed models outperform state-of-the-art models and that consideration of grader competency contributes to improved score estimation.
Biomedical Catheters With Integrated Miniature Piezoresistive Pressure Sensors: A Review

Biomedical catheters are thin hollow tubes inserted into the human body to accurately measure various physiological parameters during invasive surgical procedures and diagnostic methods. Sensors realized using micro-/nano-electro-mechanical systems (MEMS/NEMS) technology are integrated with catheters to measure blood pressures, flows, pH- and glucose levels, and temperature. Of these physiological parameters, pressure sensing is of significant importance in identifying and treating various biomedical conditions. Piezoresistive technique is a widely investigated and preferred sensing mechanism to realize miniature sensors for its numerous advantages. In this paper, we critically review biomedical catheters as well as miniature piezoresistive pressure sensors developed for catheters. First, the evolution of catheters and their applications in measuring physiological parameters are discussed in detail. Next, the progress of piezoresistive pressure sensors developed for integration with catheters are described under three broad categories by considering various aspects such as diaphragm shape, material & size, piezoresistor type & material, readout type, fabrication processes, salient features of the device, and packaging techniques. A detailed section is devoted to alternative recent piezoresistive materials, including silicon nanowire (SNW), carbon nanotube (CNT), and Graphene. Finally, the process of catheterization and testing of biomedical catheters with integrated pressure sensors in the clinical environment are elaborated.