Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Backstepping Sliding Mode Tracking Control for Underactuated Unmanned Surface Vehicle With Disturbances and Input Saturation

This paper presents an adaptive backstepping sliding mode tracking control method for underactuated unmanned surface vehicle. The tracking controller is designed by combining backstepping and adaptive sliding mode control technology. The virtual control law is used to replace the control strategy of position error, and the position tracking control is transformed into velocity control, which can effectively avoid the input saturation and singular value problem existing in the design of traditional backstepping control law. The neural shunt model method is used to solve the differential explosion problem caused by virtual control law. The adaptive technology based on backstepping sliding mode theory is introduced to compensate the model uncertainty and time-varying disturbances of the system, and the robustness of the underactuated USV is enhanced by this method in the unknown environment. Based on the Lyapunov stability theory, it is proved that the control system error is ultimately uniformly bounded. The simulation results show that the proposed adaptive backstepping sliding mode tracking control method can achieve stable tracking in the case of system parameter uncertainty and time-varying disturbances, so it can be concluded that this method is reasonable and effective.
Potential yield and water-use efficiency benefits in sorghum from limited maximum transpiration rate.

Limitations on maximum transpiration rates, which are commonly observed as midday stomatal closure, have been observed even under well-watered conditions. Such limitations may be caused by restricted hydraulic conductance in the plant or by limited supply of water to the plant from uptake by the roots. This behaviour would have the consequences of limiting photosynthetic rate, increasing transpiration efficiency, and conserving soil water. A key question is whether the conservation of water will be rewarded by sustained growth during seed fill and increased grain yield. This simulation analysis was undertaken to examine consequences on sorghum yield over several years when maximum transpiration rate was imposed in a model. Yields were simulated at four locations in the sorghum-growing area of Australia for 115 seasons at each location. Mean yield was increased slightly (5-7%) by setting maximum transpiration rate at 0.4 mm h-1. However, the yield increase was mainly in the dry, low-yielding years in which growers may be more economically vulnerable. In years with yield less than ∼450 g m-2, the maximum transpiration rate trait resulted in yield increases of 9-13%. At higher yield levels, decreased yields were simulated. The yield responses to restricted maximum transpiration rate were associated with an increase in efficiency of water use. This arose because transpiration was reduced at times of the day when atmospheric demand was greatest. Depending on the risk attitude of growers, incorporation of a maximum transpiration rate trait in sorghum cultivars could be desirable to increase yields in dry years and improve water use efficiency and crop yield stability.
Deep Learning-Based False Sensor Data Detection for Battery Energy Storage Systems

Battery energy storage systems are facing risks of unreliable battery sensor data which might be caused by sensor faults in an embedded battery management system, communication failures, and even cyber-attacks. It is crucial to evaluate the trustworthiness of battery sensor data since inaccurate sensor data could lead to not only serious damages to battery energy storage systems, but also threaten the overall reliability of their applications (e.g., electric vehicles or power grids). This paper introduces a battery sensor data trust framework enabling detecting unreliable data using a deep learning algorithm. The proposed sensor data trust mechanism could potentially improve safety and reliability of the battery energy storage systems. The proposed deep learning-based battery sensor fault detection algorithm is validated by simulation studies using a convolutional neural network.
Benchmarking Stacking Against Other Heterogeneous Ensembles in Telecom Churn Prediction

Customer churn prediction using data mining is an increasingly important concern in the highly saturated telecommunication sector. Albeit its popularity in research, few studies investigated the use of heterogeneous ensembles for this purpose. Therefore, this study evaluated and compared the performance of five grid-searched optimized base classifiers (logistic regression, decision trees, K-nearest neighbors, multilayer perceptron, and support vector machines) and their heterogeneous ensembles (stacking, grading, majority voting, weighted majority voting, and soft voting) on four different telecom datasets. The results indicate that there are significant improvements when using heterogeneous ensembles compared to single classifiers, with stacking being the most performant ensemble. The best meta-classifier for stacking was found to be a multilayer perceptron. Additionally, we identified that using probabilities as an input to the ensemble’s meta-classifier, such as in soft voting and stacking variants, can increase their performance.
Demographic Analysis of Candidates Supporter in Twitter During Indonesian Presidential Election 2019

In today’s political world, the use of social media as a medium to convey political views and using it for campaign has become a very common thing. Social media can be used to express one’s expression both in supporting a political party and someone who is running for politics. Twitter is one of the social media that is often used for campaign, this is because of Twitter’s unformal and chronological feature. But Twitter also has weakness such as the absence of demographic information. Therefore, this study aims to find out the demographic patterns of supporter of each Indonesian Presidential candidate 2019 on Indonesian language Twitter. For searching the demographics, this study using text classification method and dictionary of names and genders. The dictionary comes from KPU permanent voter data. Demographic labels used in this study consist of male, female, generation Z, millennial generation, generation X+, outside Java and Java. To determine the political preference, this study uses the sentiment analysis method that is suitable for short sentences, namely the SentiStrength method. The results of this study indicate that most Twitter users who actively talk related to the politics of 2019 Presidential and Vice-Presidential elections are women with millennial age categories or 25-39 years old.
Outdoor performance of a Plasmonic Luminescent Solar Concentrator

Luminescent Solar Concentrators (LSCs) offer an alternative way to generate electricity from solar radiation in urban environment. One of the main advantages of an LSC is its ability to utilize diffuse solar radiation. However, low efficiency of LSCs have inhibited their widespread commercial application. Plasmonic Luminescent concentrator (PLSC) was recently introduced to decrease the escape cone losses in an LSC. In this work, an optimized PLSC device of size 4.5 × 4.5 × 0.3 cm 3 is tested outdoors along with an LSC device of same dimensions. It is observed that the PLSC device performs 2.8 times better than a blank host matrix.
Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting

Financial market analysis, especially the prediction of movements of stock prices, is a challenging problem. The nature of financial time-series data, being non-stationary and nonlinear, is the main cause of these challenges. Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. Although the prediction performance is the main goal of such models, dealing with ultra high-frequency data sets restrictions in terms of the number of model parameters and its inference speed. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.
Reliability Evaluation of ML systems, the oracle problem

The growing adoption of machine learning (ML) in safety-critical contexts makes reliability evaluation of ML systems a crucial task. Although testing represents one of the most used practices to evaluate the reliability of “traditional” systems, just few techniques can be used to evaluate ML-systems’ reliability due to the oracle problem. In this paper, I present a test oracle surrogate able to automatically classify tests’ outcome to obtain feedback about tests whose expected output is unknown. For this purpose, various sources of knowledge are considered to evaluate the outcome of each test. The aim is to exploit this test oracle surrogate to apply classical testing strategies to perform reliability assessment of ML systems. Some preliminary experiments have been performed considering a Convolutional Neural Network (CNN) and exploiting the well known MNIST dataset. These results promise that the presented technique can be effectively used to evaluate the reliability of ML systems.
Effect of Estimation Timing Error on Pre-TDE for Narrowband Uplink NOMA

For realizing narrowband IoT such as sensor networks, we investigate narrowband uplink non-orthogonal multiple access (NOMA), which has high spectrum efficiency. In the narrowband uplink NOMA system, multiple access interference (MAI) occurs because the signals from non-orthogonal nodes propagate different time selective fading channels. In this situation, time-domain pre-equalization (Pre-TDE) can suppress MAI. Since narrowband communication has a small data rate and long data communication time, the effect of estimation timing error ∆t on pre-TDE is large. In this paper, to clarify the effect of fading on narrowband uplink NOMA we evaluated the effect of the estimation timing error. As a result of link-level simulation considering the estimation timing error, we verified that bit error rate (BER) is less than 10-3 when the estimation timing error is within 1.5ms from the channel estimation value.
A New Computational Method Based on Probabilistic Linguistic Z-Number With Unbalanced Semantics and Its Application to Multi-Criteria Group Decision Making

Z-number that proposed by Zadeh is an effective tool to describe the information with uncertainty in decision-making problems. However, most of the researches on Z-numbers employed linguistic cardinalities with uniformly distributed scales. In fact, unbalance situation is much common in terms of the psychology of experts. In this paper, we propose a new computational method based on Probabilistic Linguistic Z-number with Unbalanced semantics(UPLZ), which can represent the linguistic evaluations of experts precisely combined with individual risk appetite. A new score function of UPLZs is provided based on hesitant degree and linguistic scale function to reduce the computational complexity. Afterward, a linear programming is constructed to determine weights of criteria by considering cross entropy maximization. The robust decision result can be obtained by applying MULTIMOORA method since it is specific with peculiarities of three subordinate models. Finally, a case study concerning medicine selection for the patients with mild symptoms of the COVID-19 is provided to illustrate the feasibility and effectiveness of the proposed method. The advantages of it are highlighted by sensitivity analysis and comparative analysis with two outstanding multi-criteria decision-making methods.
Robustness of the Scale-free Spiking Neural Network with Small-world Property

The biological brain has the characteristics of self-adaptive, self-organizing and self-repairing. Spiking neural network (SNN) draws from the characteristics of biological brain and realizes a more advanced brain-like level. In this study, a scale-free spiking neural network (sfSNN) with small-world property is constructed, in which the Izhikevich neuron model is used as the node, the synaptic plasticity model based on the coexistence of excitatory and inhibitory synapses is used as the edge, and the scale-free network with small-world property is used as the topology. Taking the relative change rate of firing rate and the correlation between membrane potential as indexes, the robustness function of the sfSNN is analyzed. Based on the adaptive regulation of synaptic plasticity, the robustness mechanism is explored. The experimental results indicate that: (1) the sfSNN has better anti-interference function to the AC magnetic field of no more than 25 mT; (2) the sfSNN has better anti-interference function to the white Gaussian noise of no more than 10 dBW; (3) the sfSNN has better anti-injury function to the random attacks of no more than 40% injured proportion; (4) the adaptive regulation of synaptic plasticity is the intrinsic factor of the robustness function. This study lays a theoretical foundation for the engineering application of brain-like artificial intelligence.
A Novel Method Based on Convolutional Features with Non-Iterative Learning for Brain Tumor Classification

Brain tumor is a cluster of abnormal and uncontrolled cells growth, leading to a short life expectancy in their highest grade. Accurately and timely clinical diagnosis of such tumors play critical role in treatment planning and patient care. Various image techniques such as Computed Tomography (CT), Ultra-Sound image, Magnetic Resonance Imaging (MRI) and biopsy are used to evaluate brain tumors. Among stated four, MRI is most common used non-invasive technique, however the key challenge with these images is the low-level visual information captured by MRI machines, that needs highlevel interpretation by experienced radiologist. The manual interpretation is a tedious, challenging, and erroneous task. In this paper, we propose a novel Convolutional Feature based Euclidean Distance (ConFED) method for faster and more accurate tumor classification. The method consists of convolutional features and Euclidean distance based one step learning. The proposed method is evaluated on ContrastEnhanced Magnetic Resonance Images (CE-MRI) benchmark dataset. Proposed method is more generic as it does not use any handcrafted features, requires minimal preprocessing, and can achieve average accuracy of 97.02% using five-fold crossvalidation. Extensive experiments, along with statistical tests, revealed that the proposed method has outperformed state-of-the-art classification methods on the CE-MRI dataset.
Research on a Pattern Recognition Method of Cyclic GMM-FCM Based on Joint Time-Domain Features

The safety and reliability of the mechanical system in the industrial process determines the quality of products. Whether the fault can be identified and classified in time is the key to ensure the safe operation of the system and arrange the appropriate maintenance plan to restrain the deterioration of the fault. However, with the rapid development of manufacturing digitization, how to process large amounts of data quickly and accurately is faced with many problems. In this paper, a pattern recognition method of cyclic GMM-FCM (CGF) based on joint time-domain features is proposed. Firstly, the concept of joint time-domain features based on Vold-Kalman filter (VKF) is proposed. It retains the integrity of the signal components and avoids the problem of dimension disaster caused by anomaly detection, which laid a foundation for the accurate classification of sensitive feature sets. Secondly, a pattern recognition method of cyclic GMM-FCM is proposed. It can eliminate global and local outliers in sensitive feature sets and determine the number of FCM categories adaptively. It makes the classification result more reasonable and accurate. Finally, the effectiveness and superiority of the pattern recognition algorithm are verified by the gearbox vibration experiments in various states. The result shows that the method is feasible in engineering practice.
A Data-Driven Maintenance Framework Under Imperfect Inspections for Deteriorating Systems Using Multitask Learning-Based Status Prognostics

This paper proposes a data-driven, condition-based maintenance framework (DCBM) for deteriorating equipment under the impact of varying environments and natural aging. The equipment’s degradation status is determined by a prognostic and health monitoring method. Generally, monitoring data and maintenance inspections are imperfect because of uncertainties in the equipment degradation process, which may prevent a reliable evaluation of a system’s deterioration. By utilizing a deep learning technique, we construct a new stacked autoencoder long short-term memory (SAE-LSTM) network-based multitask learning model to extract state features from the monitoring data, and then perform multistep forecasting to obtain performance degradation and failure probability information. The developed SAE-LSTM-based multitask learning achieves prognosis results close to the actual values, which indicates the excellent feature extraction capability of this model. As a result, we introduce this deep multitask learning model into the optimization of the maintenance process. Probabilistic forecasting is used as one of the criteria for maintenance decisions made with imperfect inspections to address the influence of the uncertainties involved in the prognoses results. The effectiveness of the proposed DCBM framework is illustrated by the application of an engine degradation dataset, and this model is more cost-effective than the baseline maintenance policies.
Distinctive Feature Representation for Contactless 3D Hand Biometrics using Surface Normal Directions

Contactless 3D hand biometrics offers hygienic and convenient approaches for biometric recognition. This paper investigates a distinctive feature representation using 3D surface normal information for more accurate 3D hand biometric identification. Prior research on contactless 3D hand biometric identification largely incorporates 3D depth and surface curvature information to recover discriminative features. Our investigation presented in this paper indicates that extracting distinctive features from surface normal information, which can also be directly obtained from low-cost photometric stereo based imaging systems, can offer a computationally simpler alternative and is therefore highly desirable. The directions of neighbouring surface normal vectors can encode frequently observed irregular ridge and valley regions, which can enable more accurate surface feature description. Comparative experimental results presented in this paper validates the effectiveness of the proposed approach.
Optimizing the Energy Consumption Level in LoRaWan Networks

Long range wide area network (LoRaWAN) is a type of Low Power Wide Area (LPWA) technologies, which is designed to support long-range communication with low data rates and low power consumption requirements. LoRaWAN consists of many nodes that sense the environment and send their measurements to the gateway, which in turn directs the received data to the network server to process the collected data. Each one of these nodes is configured using five transmission parameters, (the spreading factor (SF), center frequency (CF), bandwidth (BW), coding rate (CR), and transmission power (TP). The optimal selection of the values plays a vital role in providing an efficient and energy preserving network implementation. In this paper, an algorithm that selects the optimal transmission power for each node in the network depending on the receiver sensitivity and the path loss of the communication link between the node and the gateway is developed by utilizing the Reinforcement Learning algorithm that is used to choose the best value of spreading factor. The simulation results of our proposed algorithm showed a significant decrease in the consumed power in the network compared with the other techniques in literature.
Structure-Property Relationships in Conductive Polymers

Abstract Detailed studies of the structure-property relationships for conductive conjugated polymers are important for the proper understanding of the impact of morphological details on chemical and physical properties. This understanding is necessary for the development of realistic theoretical models. The particular cases of poly- and oligo-pyrroles and polyaniline are described.
Über die Kristallstrukturen von Cu3N, GaN und InN Metallamide und Metallnitride

1. Cu3N kristallisiert kubisch, Gitterkonstante a = 3,807 ± 0,004 A, ein Molekul in dem Elementarkorper, dr = 6,12 Atomlagen: Cu: (½ 0 0, 0 ½ 0, 0 0 ½); N: (0 0 0).
Photocatalytic CO2 reduction using an internally illuminated monolith photoreactor

One of the promising solutions to both global climate warming and increasing energy demands is artificial photosynthesis, which can be implemented via the photoreduction of CO2 to produce fuel. A monolith photoreactor was used to increase the amount of catalyst loading due to its multiple channels. The photocatalyst was dip coated using NiO/InTaO4 sol and then calcined at 1100 °C. A uniform NiO/InTaO4 layer was obtained on the top of pre-coated SiO2 sublayer on the internal channels of the monolith. The polymethylmethacrylate (PMMA) optical fibers, after being carved on their surface, could transmit and scatter light to effectively illuminate the catalyst inside the channels of the monolith. Vapor-phase CO2 with H2O was photocatalytically reduced to hydrocarbons by UV or visible-light in a steady-state flow mode. The maximum methanol conversion rate achieved was 0.16 µmol g−1 h−1 with visible-light of 290 klx at 25 °C. The highest rate of acetaldehyde was 0.3 µmol g−1 h−1 which was obtained with a loading of 2.6% NiO by simulated sunlight AM1.5G at 70 °C. More importantly, the quantum efficiency was significantly improved indicating that photon energy was effectively utilized in the monolith reactor, compared with previous optical-fiber reactor.
Causality Tracking Trade-offs for Distributed Storage

After the seminal paper by L. Lamport, which introduced (scalar) logical clocks, several other data structures for keeping track of causality in distributed systems have been proposed, including vector and matrix clocks. These are able to capture causal dependencies with more detail but, unfortunately, also consume a substantially larger amount of network bandwidth and storage space than Lamport clocks. This raises the question of whether the benefits of these more complex structures are worth their cost. We address this question in the context of partially replicated systems. We show that for some workloads the use of more expensive clocks does bring significant benefits and that for other workloads no visible benefits can be observed. The paper provides a characterization of the scenarios where each type of clock is more beneficial, helping designers to develop more efficient distributed storage systems.