Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Industrial Process Control Based on Fuzzy Logic and Machine Learning

Manufacturing automation is a double-edged sword, on one hand, it increases productivity of production system, cost reduction, reliability, etc. However, on the other hand it increases the complexity of the system. This has led to the need of efficient solutions such as artificial techniques. Data and experiences are extracted from experts that usually rely on common sense when they solve problems. They also use vague and ambiguous terms. However, knowledge engineer would have difficulties providing a computer with the same level of understanding. To resolve this situation, this article proposed fuzzy logic to know how the authors can represent expert knowledge that uses fuzzy terms in supervising complex industrial processes as a first step. As a second step, adopting one of the powerful techniques of machine learning, which is Support Vector Machine (SVM), the authors want to classify data to determine state of the supervision system and learn how to supervise the process preserving habitual linguistic used by operators.
Distributed State Estimation and Control of Autonomous Quadrotor Formations Using Exclusively Onboard Resources

The navigation of unmanned aerial vehicles operating in environments without global positioning systems, including global navigation satellite systems and motion capture systems, is a recent research topic, without much work reported in the literature. In indoor applications, particularly, small-scale vehicles are subjected to severe power and weight constraints, limiting their overall navigation capabilities. In such scenarios, multi-vehicle systems can be used in order to mitigate the impact of limited capabilities at the individual vehicle level. If, additionally, a group of vehicles has to maintain a specific spatial topology, well-established formation control algorithms can be used as long as information about mutual inter-vehicle positioning is available. This information can be directly acquired using relative positioning systems on each vehicle. This solution enables the multi-vehicle system to reduce its dependency on absolute localization systems and explicit inter-vehicle communications. Additionally, multi-vehicle formation control can be achieved in either fully distributed or decentralized fashion, reducing the need for external and/or centralized units supervising the system. However, the aforementioned energy and weight constraints of small-scale vehicles only allow limited sensing payloads, reducing the potential sophistication of onboard relative positioning systems. For the same reason, steering a multi-vehicle system through given way-points using only onboard resources becomes a challenging task. This thesis introduces two novel relative positioning systems for multi-vehicle formations, focusing on maximizing the number of detected team members while remaining accurate and light enough to allow their deployment on small-scale vehicles: i) a camera-based system that enables a scalable deployment on multiple vehicles; ii) an infrared-based system that provides several hardware and software enhancements with respect to systems reported in the literature using the same technology. The camerabased sensor model can be leveraged as a tool for optimizing the design parameters to meet specific accuracy requirements and allows the system to achieve highly accurate relative localization measurements using low-resolution cameras. The infrared-based system uses miniature omni-directional infrared beacons deployable in small sets on each vehicle which, together with dedicated estimation and calibration algorithms, ensures a adaptability to any 3D geometry of the carrying vehicle. Such innovative design principles result in a system which enables a direct measurement of the relative attitude, and is more flexible, lighter, and less power-hungry than state-of-the-art devices, while providing similar accuracy. Novel formation control methods that tackle limitations arising from the exclusive use of relative positioning systems are an extra contribution of the thesis. A graph-based formation control algorithm
A review on deep learning-based structural health monitoring of civil infrastructures

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.
Anatomical variations of circle of Willis - a cadaveric study

Background: The circle of Willis (CW) is a vascular network formed at the base of skull in the interpeduncular fossa. Its anterior part is formed by the anterior cerebral artery, from either side. Anterior communicating artery connects the right and left anterior cerebral arteries. Posteriorly, the basilar artery divides into right and left posterior cerebral arteries and each join to ipsilateral internal carotid artery through a posterior communicating artery. Anterior communicating artery and posterior communicating arteries are important component of circle of Willis, acts as collateral channel to stabilize blood flow. In the present study, anatomical variations in the circle of Willis were noted. Methods: 75 apparently normal formalin fixed brain specimens were collected from human cadavers. 55 Normal anatomical pattern and 20 variations of circle of Willis were studied. The Circles of Willis arteries were then colored, photographed, numbered and the abnormalities, if any, were noted. Results: Twenty variations were noted. The most common variation observed is in the anterior communicating artery followed by some other variations like the Posterior communicating arteries, Anterior cerebral artery and posterior cerebral artery (PCA) was found in 20 specimens. Conclusions: Knowledge on of variations in the formation of Circle of Willis, all surgical interventions should be preceded by angiography. Awareness of these anatomical variations is important in the neurovascular procedures.
Implementasi Internet of Things Pada Sistem Kendali Lampu Rumah Menggunakan Telegram Messenger Bot Dan Nodemcu Esp 8266

-The house light control system is still mostly manual and the homeowner sometimes tends to forget to turn off the lights when leaving the house, so the owner of the house must return to turn off the lights or leave the lights on until the homeowner returns. The use of NodeMCU ESP8266 is currently favored by internet of things based developers, because the price of the module is quite cheap and can be programmed using the Arduino IDE allowing NodeMCU to be the preferred choice. This study uses Telegram Messenger Bot as a message sender and message recipient to control home lights, using NodeMCU as a Relay controller to turn off and turn on the home lights when they are or not at home
Faster R-CNN With Classifier Fusion for Automatic Detection of Small Fruits

Fruit detection is a fundamental task for automatic yield estimation. The goal is to detect all the fruits in images. The-state of the art of fruit detection algorithm, Faster R-CNN, shows a lack of detection advantage on small fruits. One of the reasons is only that single-level features and a classifier are used for localization of proposal candidates. In this article, we propose to incorporate a multiple classifier fusion strategy into a Faster R-CNN network for small fruit detection. We utilize features from three different levels to learn three classifiers for objectness classification in the stage of proposal localization. Probabilities from classifiers are combined by a simple convolutional layer to generate final objectness classification for proposal candidates. During training, in order to train a model with strong generalization capability, we propose to use correlation coefficients to measure the diversity of multiple classifiers. A novel loss function with classifier correlation is introduced to train the region proposal network. We evaluate the proposed model on two data sets of small fruits. Extensive experiments show that the proposed model outperforms the state-of-the-art detectors for fruit detection. Note to Practitioners—This article was motivated by the problem of the detection of small fruits for yield estimation but it also applies to the detection of other small objects. Existing approaches to fruit detection experience difficulty in detecting small fruits and the overall detection accuracy suffers as a result. This article suggests a novel approach to detect fruits of all sizes by using a multiple classifier fusion strategy in the stage of fruit localization. In the learning of the classifiers, multilevel features of fruits are extracted. In addition, we achieve classifier diversity through the analysis of the correlation of classification probabilities. The diversity of classifiers also helps improve the performance of fruit localization. Using experiments on real fruit tree images, we show that the suggested approach is feasible on orange detection of various sites in different seasons. As a future work, we will integrate the suggested algorithm into a fruit yield estimation system and test it online.
Optimal Power Allocation for MIMO Underwater Wireless Optical Communication Systems Using Channel State Information at the Transmitter

For multiple-input–multiple-output underwater wireless optical communication (UWOC) systems with on–off keying modulation, we obtain the optimal power allocation policies that minimize the bit error rate (BER), subject to average power constraints. In this article, we consider a comprehensive channel model that takes into account absorption, scattering, and weak turbulence-indeed lognormal fading. The optimal power allocation policies are obtained using the perfect channel state information available at the receiver (CSIR) and transmitter (CSIT) for both MISO and MIMO systems. In the MISO UWOC link, the optimal policies result in allocating the whole available power to the optical source with highest instantaneous SNR, i.e., maximum fading coefficient. In the MIMO UWOC link, the equivalent fading coefficient of the $i$th optical source is defined as the sum of the fading coefficient associated with the $i$th optical source and other receiver apertures. In this case, the total available power should be allocated to the optical source with the highest equivalent fading coefficient to have the minimum BER. Furthermore, we analytically obtain the exact BER expressions for both MISO and MIMO systems assuming the optimal power allocation. Simulation results are further provided to verify our analytical results. The results demonstrate that MIMO techniques with optimal power allocation provide better performance compared to other alternatives such as equal power allocation.
American Indian Representation and State Policy

ABSTRACT Despite provisions in the United States Constitution and in the Constitutions of the American States guaranteeing equal access for non-majority populations to vote, seek office, and lobby government officials, a thorough understanding of how non-majority group interests are represented in public policy proposals remains underdeveloped. For example, significant challenges to policy representation remain for racial and ethnic minorities, women, and LGB populations in Congress and these challenges may be even more pronounced in the American States. Moreover, understanding what facilitates the representation of non-majority interests is central to understanding democracy. We propose to add to our understanding of how group interests are represented by conceptualising representation as the policy responsiveness of elected officials. Specifically, we measure policy responsiveness as the amount of American Indian substantive and symbolic legislation proposed and passed in state legislatures. Our findings suggest that there are more substantive American Indian pieces of legislation proposed and passed in states with larger American Indian populations, have legislative committees and executive offices in place to address Native issues, and where American Indians have contributed more to state legislative candidates. Conversely, symbolic representation of Native issues via proposed and passed legislation is greater in states with larger American Indian populations only, and appears unaffected by tribal-state relations or state institutional arrangement.
Fast DC-Bias Current Control of Dual Active Bridge Converters With Feedforward Compensation

In bi-directional dual active bridge (DAB) converters, the current flowing through the transformer generates significant DC-bias currents when the load of the converter changes, leading to unavoidable magnetic saturation of the high-frequency transformer. In this brief, the cause of the DC-bias current is briefly explained, and then a method of fast recovery control for this current is presented. By adjusting the phase shift ratio and duty cycle of the secondary-side squared voltage simultaneously in a feedforward matter, the DC-bias current as well as the magnetic saturation of the transformer can be effectively suppressed during the transient process. Extensive experimental results as compared to the existing control methods validate the effectiveness of proposed feedforward method.
Role of the rate of surface dilatation in determining microscopic dynamic contact angle

The factors determining the degree of dynamic wetting, which is characterized by the microscopic dynamic contact angle, have been the subject of much discussion. In this manuscript, it is analytically determined that the microscopic dynamic contact angle is dependent on the rate of surface dilatation in addition to the thermodynamic surface tension. It is argued that, in the vicinity of a moving contact line, this rate of surface dilatation results in a disparity between the thermodynamic and mechanical surface tensions, which are almost always assumed to be equal. It is also found that, in the case of forced wetting, the difference between the receding and advancing contact angles is primarily due to the rate of surface compression at the receding contact line and the rate of surface expansion at the advancing contact line. These findings, which are validated using molecular dynamics simulations, demonstrate that surface dilatation is an important factor responsible for the deviation of the microscopic dynamic contact angle from its static equilibrium value.The factors determining the degree of dynamic wetting, which is characterized by the microscopic dynamic contact angle, have been the subject of much discussion. In this manuscript, it is analytically determined that the microscopic dynamic contact angle is dependent on the rate of surface dilatation in addition to the thermodynamic surface tension. It is argued that, in the vicinity of a moving contact line, this rate of surface dilatation results in a disparity between the thermodynamic and mechanical surface tensions, which are almost always assumed to be equal. It is also found that, in the case of forced wetting, the difference between the receding and advancing contact angles is primarily due to the rate of surface compression at the receding contact line and the rate of surface expansion at the advancing contact line. These findings, which are validated using molecular dynamics simulations, demonstrate that surface dilatation is an important factor responsible for the deviation of the microscopic d.
Policy Integration for Sustainable Development through Multilateral Environmental Agreements

Over the past three decades, policy integration has become a key objective for guiding and harmonizing policies for sustainable development. Most recently, the 2015 Sustainable Development Goals have added new impetus to efforts of integrating competing objectives of environmental sustainability, social development, and economic growth, as well as of integrating issue-specific environmental policies on climate change and terrestrial and marine biodiversity. While multilateral environmental agreements are important international instruments for achieving sustainable development, there has been little focus so far on their contribution to policy integration. Covering the years from 2007 to 2016, this article presents an empirical analysis of sustainability policy integration (i.e., how multilateral environmental agreements integrate environmental, social, and economic issues in their decisions) and environmental policy integration (i.e., the outreach of multilateral environmental agreements to different environmental issue areas beyond their mandate). The analysis finds that multilateral environmental agreements have not moved toward further policy integration over the studied period. If policy and institutional coherence is a key global governance target in the post-2015 era, a concerted effort will be required to improve the extent of policy integration by multilateral environmental agreements.
Effects of plastic mulch and nitrogen fertilizer on the soil microbial community, enzymatic activity and yield performance in a dryland maize cropping system

Farming practices that integrate plastic film mulching and nitrogen (N) fertilization have been extensively used to enhance crop productivity. However, the interactive effects of mulching and N application on soil microbial properties and crop yields have received little attention. A 2‐year field experiment was carried out in the Loess Plateau to investigate the independent and combined effects of plastic mulch and N application rate on the soil microbial population, enzymatic activity and grain yield. From 2016 to 2017, two mulching patterns (i.e., plastic mulching and no mulching) were exposed to five different N application rates (0 (N0), 80 (N80), 160 (N160), 200 (N200) and 240 (N240) kg N ha−1). N application significantly affected the soil microbial community composition, as suggested by significant increases in soil total bacteria, cellulose‐decomposing bacteria and the bacterial‐to‐fungal ratio with N rates. In contrast, the mulching pattern significantly affected soil extracellular enzyme production. Furthermore, there were significant effects of mulching, N application and their interaction on grain yield. The highest grain yield (11,728 kg N ha−1 in 2016 and 12,350 kg N ha−1 in 2017) in both growing seasons was consistently obtained under plastic mulching with a N level of 200 kg N ha−1. Our study showed that plastic mulching significantly affected extracellular enzyme production, and N fertilizer application significantly affected the composition of the soil microbial community.
Analisis Data Citra Landsat 8 OLI Sebagai Indeks Prediksi Kekeringan Menggunakan Machine Learning di Wilayah Kabupaten Boyolali dan Purworejo

Abstrak Bencana kekeringan merupakan salah satu bencana yang tidak dapat di hindari lagi keberadaannya. Berdasarkan data dari tahun 1815 sampai tahun 2015 telah terjadi 382 kejadian. Berdasarkan kajian BNPB Kabupaten Boyolali dan Kabupaten Purworejo memiliki resiko tinggi terpapar bencana kekeringan. Untuk itu perlu adanya informasi wilayah resiko bencana kekeringan. Penggunaan data citra satelit Landsat 8 OLI sebagai media informasi vegetasi dan pendekatan Machine Learning untuk menganalisa data ekstraksi pada citra satelit berupa indeks vegetasi. Indeks vegetasi yang di gunakan yaitu NDVI, VCI, VHI, dan TCI dengan implementasi metode XGBoost dan Random Forest untuk mendapatkan hasil prediksi. Hasil yang diperoleh dengan menggunakan perhitungan metode XGBoost ada total 9 kecamatan yang diprediksi terkena bencana kekeringan sangat parah, dan 9 kecamatan dengan metode Random Forest terindikasi kekeringan sangat parah. metode XGBoost memiliki nilai akurasi 0.8286 dan nilai kappa 0.6477 dan metode Random Forest memiliki nilai akurasi 0.6857 dan Nilai Kappa 0.3699. dimana semakin tinggi nilai akurasi dan kappa semakin tepat hasil prediksi yang dilakukan.
Fuzzy reduced-order observer-based adaptive tracking control for a class of switched non-affine systems

This paper investigates an adaptive fuzzy tracking control problem under arbitrary switching for a class of switched non-affine systems with completely unknown nonlinear functions and unmeasurable states. Combining with dynamic surface control (DSC) method and fuzzy approximation technique, an adaptive output-feedback common control approach is presented based on a new fuzzy reduced-order observer which is independent of any switching signal. The given design method does not rely on the boundness assumption about the control gain functions raised by the mean value theorem for non-affine systems, which contributes to the less conservation of the common controller. Meanwhile, the algebraic loop problem caused by the nonstrict-feedback structure and the repeated approximation problem in existing results are also circumvented in the given common control design process. Based on Lyapunov stability theory, the designed common controller can guarantee all the signals in the resulting closed-loop switched systems are uniformly bounded and the tracking error can converge to a small neighborhood of the origin. Two examples are provided to verify the feasibility and practicability of the proposed method.
No Dual Antiplatelet Therapy in Atrial Fibrillation

Managing atrial fibrillation is common in daily practice and an important part of this management is deciding over the embolism prevention strategy. Oral anticoagulants (warfarin, acenocumarol, apixaban, rivaroxaban or dabigatran) and antiplatelets (acetylsalicylic acid or clopidogrel) are the two groups of drugs used to prevent embolism events. To choose wisely CHA2DS2-VASc embolism risk scale aids in making this decision. But, is dual antiplatelet therapy, acetylsalicylic acid plus clopidogrel, a correct option in embolism prevention in atrial fibrillation?