Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Structure Control of Floating Wind Turbine Using PID Controller

In the harsh ocean environment, random wind wave combined loads make the platform and tower top of the wind turbine vibrate greatly, which seriously threatens the safety of the floating wind turbine structure. In this paper, a tuned mass damper (TMD) with limited stroke is placed longitudinally in the cabin of a barge type floating wind turbine, and active control force is applied to it to form an active and passive integrated vibration control system (HMD). Firstly, based on Kane's Dynamics, the motion equation of wind turbine is derived. Combined with the elastic dynamic motion equation of the HMD structure system with one degree of freedom, the general form of the complete nonlinear time-domain motion equation coupled with the HMD system and the wind turbine system is derived. Then, for the nonlinear model, TMD parameters were optimized by minimizing the cost function, and a PID controller was designed by introducing acceleration feedback to obtain the active force. Finally, the suppression effects of TMD passive control and HMD integrated control on the dynamic response of barge type floating wind turbine are simulated under combined wind and wave loads. The results show that compared with the passive control, the vibration reduction effect of the HMD integrated control is significantly improved under the constraints of TMD stroke. Moreover, the vibration reduction effect of PID controller given in this paper is better than LQR controller.
Portable Electrochemical Biosensors for Highly Sensitive Detection of Biomarkers

The analysis of biomarkers plays an important role in the early diagnosis of diseases and will greatly benefit patients with a higher cure rate. However, the low abundance of biomarkers in physiological environments requires ultrahigh sensitivity of a detection technology. [1] Organic electrochemical transistors (OECT) are versatile sensing platform with low cost for easy-to-use, portable, and disposable biosensors. [2]-[4] They consist of gate electrode, source and drain electrodes with a thin layer of an organic semiconductor is deposited between them. [5]-[6] Here, we fabricate a portable and smart-phone-controlled biosensing platform based on disposable organic electrochemical transistors for ultrasensitive analysis of microRNA (miRNA) biomarkers within 1 hour.
Dynamic Dempster Multi-Layer Perceptron for the prediction of admission patient in emergency department

The early identification of the patients’ hospitalization at triage level within the Emergency Department (ED) presents a potential solution to reduce the risk of overcrowding and improve the quality of care. Thus, predicting patient out-come on arrival assists medical staff in the make of the appropriate patient pathway decision and so reduces the risk of medical error and complication of the patient’s condition. Previous works don’t consider the uncertainty of medical data while the management of this uncertainty is one of the most important and crucial tasks of medical information systems. Thus, we present in this paper an improved version of the classical prediction model by taking into account the uncertainty and by managing properly the missing information. In this context, we propose a new approach based on Dempster-Shafer theory and Dynamic Multi-Layer Perceptron algorithm. Our proposed approach deploys the correspondent neural network as follows: 1) computes for each input parameter the Basic Belief Assignment (BBA) that provides an assessment of the uncertainty pattern using the Dempster’s rule; 2) deduces the correspondent weights based on the computed BBA, and 3) uses an appropriate transfer function to activate the next layer neurons. In this paper, we demonstrate the effectiveness of our proposed method by using a real ED database. We prove that our proposed approach manages efficiently the uncertainty of the medical data sources and missing information, so improves the decision making and reduces errors and complexity.
Sample Entropy based Variational Mode Decomposition with Hybrid RNN for Short Term Wind Power Interval Prediction

Intervals in wind energy predictions are an excellent way to quantify uncertainty. Wind power's highly variable nature makes it challenging to achieve good-quality prediction intervals (PIs). The Lower Upper Bound Estimation (LUBE) method is commonly used in interval prediction. However, the existing LUBE technique is trained either using shallow statistical models or rudimentary profound learning models that restrict its capability. As a result, the authors of this paper choose to combine the LUBE method with two hybrid models, namely CNN-LSTM (Convolutional Neural Network-Long Short Term Memory) and BiLSTM (Bidirectional LSTM). A developed interval-based optimization strategy with an improved cost function was used to highlight the advantages of these two networks. This improved cost function takes into account the location disparity between prediction intervals and constructed intervals, resulting in better control over PICP (Prediction Interval Coverage Probability) and PINRW (Prediction Interval Normalized Root Mean Squared Width), ensuring better adjustment capability. The suggested CNN-LSTM and BiLSTM algorithms were compared to the performance of other deep learning models on two different datasets that differed geographically. To reduce the data's complexity, it was treated with a noise-free procedure known as VMD (Variational Mode Decomposition). To break down the data and pick subseries, Sample entropy was used. The CNN-LSTM model beat other models in multiple experiments and provided a narrower prediction band with a high coverage probability. According to the results, hybrid models also had a longer run time and took longer to train than non-hybrid models.
Factorization Machine-based Unsupervised Model Selection Method*

Machine learning is broadly used in many intelligent cybernetic systems. With the burgeoning of the communities of AI, the number of machine learning-based models is rapidly increasing, but picking a suitable and optimal (or relatively good) model from overwhelming options has become a conundrum when deploying a new system. Therefore, we are motivated by an intriguing question: Can we automatically select a proper model for new data? However, unsupervised model selection poses two main challenges: (i) Evaluation and comparison of candidate models on the new data are infeasible due to the lack of labels; and (ii) It is non-trivial to build relationships between model performance and data characteristics when the interaction between these characteristics should be considered. In light of these limitations, this paper proposes a factorization machine-based unsupervised model selection method. Following mainstream model selection protocols, we also leverage model performance on prior known datasets. Differently, we learn higher-order complex relationships between model performance and dataset characteristics. Specifically, our method transfers the historical performance into a second-order function of meta-features and embedding weights by harnessing the power of factorization machine. This function can be subsequently used to select a proper model when given a new dataset. Extensive experiments show that our method obtains more superior model selection performance than five state-of-the-art approaches, and our method executes faster than its competitors by approximate three magnitudes.
Research on Predictive Maintenance of Aircraft Based on Long Short-Term Memory Neural Network

The proper operation of aircraft systems is of great importance to guarantee flight safety. Aircraft systems are quite complex, especially the surveillance systems in the predictive maintenance model, which incorporates information collection and extraction techniques. Under the premise of ensuring applicability, therefore, reducing the high cost of preventive maintenance and making much accurate estimates or predictions effectively has always been a topic worth studying. In this study, the aircraft system-related data are collected and evaluated by the big data analysis. With LSTM (Long Short-Term Memory) used to process and predict important events of very long intervals and delays in time series. After data cleaning, filtering, and feature engineering, a set of predictive models is finally built. Through the model, replacement time of the aircraft system components can be more accurately predicted. Thereby reducing maintenance costs and optimizing benefits.
WiFi Sensing for Drastic Activity Recognition with CNN-BiLSTM Architecture

Sensing human activity via WiFi Channel State Information (CSI) has considerable application prospects in future intelligent interaction scenarios such as virtual reality, intelligent games, metaverse, etc. Recently, many Deep Learning-based WiFi sensing schemes have been proposed in the literature, which gained high accuracy for a wide range of simple activities such as standing, squatting, and bending. However, the performance will be suffered when existing approaches are used to recognize drastic activities, such as actions in vigorous sports. This is mainly due to the reason that the spatiotemporal information of these actions is not well utilized. To overcome this drawback, we propose a novel DL-based WiFi sensing method for drastic activity recognition by combining the Convolutional Neural Network (CNN) and the Bidirectional Long Short-Term Memory (BiLSTM) network. The designed CNN-BiLSTM architecture is in parallel with feature extraction, which can simultaneously extract sufficient spatiotemporal features of action data and establish the mapping relationship between actions and CSI streams, thereby improving the accuracy of activity recognition. The CNN is used to extract information on the spatial dimension, while the BiLSTM extracts information on the time dimension. To verify the performance of the proposed scheme, we build a hardware experiment platform and constrain a dataset with 1400 pieces of records for 7 classes of basketball actions. After training over the dataset, the proposed CNN-BiLSTM scheme achieves 96% experimental accuracy on the test set, which is better than the benchmark methods.
Defensive Islanding to Enhance the Resilience of Distribution Systems against Cyber-induced Failures

The extensive integration of communication, computation, and control technologies into cyber-physical power systems (CPPSs) has increased the vulnerabilities of CPPSs to cyberattacks. This calls for developing solutions that assess and reduce the impacts of cyber-induced failures on CPPSs. This paper proposes a defensive islanding strategy to isolate impacted parts of the CPPS and form self-sufficient islanded grids with an objective of minimum load curtailment. The defensive islanding aims to split a power system into smaller grids to improve its resilience against a potential extreme event. A clustering approach that leverages the hierarchical spectral clustering method is utilized for the optimal defensive islanding. The proposed approach captures the fragility behavior and loading conditions of power system components due to cyber-induced failures. A graphical-based coupling framework is used to map the impacts of cyber failures into operation of power system components. The proposed method is demonstrated on a modified 33-node distribution feeder system integrated with distributed energy resources. The amount of load curtailment and radiality constraints have been used to evaluate the performance of the proposed clustering strategies. The results show the capability of the proposed algorithm to create islands considering the cyber-induced failures for enhanced resilience.
Bayesian Approach for Regression Testing (BART) using Test Suite Prioritization

Majority of the current state-of-the-art test suite (TS) prioritization algorithms for black-box testing focus on improving average percentage of fault detected (APFD) metric. These however suffer from two critical challenges 1) high time complexity of $\ge O(n^{2})$ where n is the number of test suites, and 2) limited ability to self-stop TS Prioritization (TSP) computation if the system under test (SUT) becomes highly stable. In this work we present an approach to overcome these two challenges while achieving high APFD efficiency over the conventional random ordering. A novel algorithm called Bayesian approach to regression testing (BART) is developed herein which models continuous integration (CI) cycle’s attributes like test suite life cycle (TSLC), stability and bugs as Bayesian inference pattern namely Dirichlet-Multinomial model. This work demonstrates that BART’s APFD metrics improve significantly in comparison to conventional random ordering and therefore this approach achieves for the first time a complexity of $O(nlogn)$ for black-box based test prioritization.
Network Energy Minimization via Sensor Selection and Topology Control

Abstract In this paper we consider the problem of state estimation over a wireless ad-hoc sensor network. At each time step, only a subset of sensors are selected to form a multi-hop network, through which they send their observations to a fusion center, where a Kalman filter is implemented to perform the state estimation. The sensors and network topology are chosen to minimize total energy consumption of the network, while maintaining a desired estimation accuracy. We propose a convex relaxation approach to approximately solve this problem. We also provide a numerical example to further illustrate the efficiency of our algorithm.
Improving Graphene-metal Contacts: Thermal Induced Polishing

Graphene is a very promising material for nanoelectronics applications due to its unique and remarkable electronic and thermal properties. However, when deposited on metallic electrodes the overall thermal conductivity is significantly decreased. This phenomenon has been attributed to the mismatch between the interfaces and contact thermal resistance. Experimentally, one way to improve the graphene/metal contact is through high-temperature annealing, but the detailed mechanisms behind these processes remain unclear. In order to address these questions, we carried out fully atomistic reactive molecular dynamics simulations using the ReaxFF force field to investigate the interactions between multi-layer graphene and metallic electrodes (nickel) under (thermal) annealing. Our results show that the annealing induces an upward-downward movement of the graphene layers, causing a pile-driver-like effect over the metallic surface. This graphene induced movements cause a planarization (thermal polishing-like effect) of the metallic surface, which results in the increase of the effective graphene/metal contact area. This can also explain the experimentally observed improvements of the thermal and electric conductivities.
PM4SOS: low-effort resource allocation optimization in a dynamic environment

Surgical center scheduling is challenging to schedule physical spaces to reduce costs and increase productivity. This study presents a framework called PM4SOS (Process Mining for Simulation, optimization, and Scheduling) that facilitates the generation of an operating room schedule in an automated way and integrates, reducing cognitive overload and waste of time. This framework allows the evaluation of restrictions to get the best performance of surgical schedules. PM4SOS combines process mining with data from event logs, generation of the automated simulation model, and case-based reasoning (CBR) to analyze management indicators, allowing significant gains in optimization of the scheduling of surgical centers. The results with PM4SOS help decision-making in hospital environments to better use physical resources and human resources, such as reducing waiting time, optimizing surgery execution time, and resource capacity management.
Testing the DGP model with ESSENCE

We use the recent supernova data set from the ESSENCE collaboration combined with data from the Supernova Legacy Survey and nearby supernovae to test the Dvali–Gabadadze–Porrati (DGP) brane world model and its generalizations. Combination of these data with a flatness prior and the position of the peak of the cosmic microwave background (CMB) radiation disfavours the DGP model slightly. Inclusion of the baryon acoustic peak from the Sloan Digital Sky Survey increase the tension of the DGP model with the data, although it is not clear how self-consistent this procedure would be without a re-analysis of the survey data in the framework of the DGP cosmology. Generalizations of the DGP model are tested and constraints on relevant parameters obtained.
Voltage Sag Ride-Through in a Joint Installation of a Hot Rolling Mill Plant and a Wind Farm

Present energy and environmental scenarios promote the incorporation of generation systems based on renewable sources into the distribution grids of electro-intensive industries, the benefits of which transcend the local generation of energy with minimal greenhouse gas emissions. In this regard, coordinated management of reactive power in a joint installation of a hot rolling mill plant and a wind farm can improve the immunity of both facilities against voltage sags. In this paper, the various factors influencing the response of the joint installation against voltage sags are analyzed. In this regard, special attention is paid to the wind farm location and, consequently, to the topology of the joint installation distribution network. The closer the wind farm and the rolling mill, the more relevant the shared impedances and the more significant the effect of the reactive power supplied by the wind farm. The main variables of interest are analyzed under actual rolling conditions both in normal operation and in the face of voltage sags to determine the most indicated distribution network topology.
An Approach Based on Feature Engineering and Machine Learning for the Prediction of Child Violence

According to technical reports prepared by United Nations International Children’s Emergency Fund and World Health Organization, there is a high percentage of child violence occurring around the world. This fact is even more evident in developing countries. However, it is difficult to determine the type of violence the child is suffering, since the problem is dependent on reports of the victims and these ones could be imprecise. In this sense, decision support tools are of utmost importance for agents of social assistance programs. Thus, this paper proposes an approach based on feature engineering and machine learning to classify the violence suffered by a child. For this purpose, it was used a dataset obtained from a Chilean organization of social assistance for children. After the training and validation processes, the best machine learning models reached areas under the curve between 0.84 and 0.97, surpassing the state-of-the-art results.
Reinforcement Learning for Service Restoration Algorithms in Distribution Networks

Modern Distribution Networks (DNs) are highly susceptible to faults, which affects their dependability and reliability. The operation complexity is crucial when DNs include critical infrastructure such as distributed energy resources, storage systems, charging stations and decentralized supply. FLISR (Fault Location, Isolation and Service Restoration) relies on advanced methodologies which aim to improve the quality of service with automated algorithms. This paper proposes a novel Service Restoration approach to automatically assist DNs resupply the out-of-service unfaulted customers after an event. The approach integrates Reinforcement Learning techniques in a co-simulation environment with OpenDSS. The results and contribution of this study could improve power supply quality and reliability of DNs throughout advanced Service Restoration (SR) methodologies. The idea is validated in real-time simulation to offer a performance assessment after training with co-simulated data.
MVFF: Multi-view Feature Fusion for Few-shot Remote Sensing Image Scene Classification

Compared to deep learning methods, few-shot learning methods do not need many labeled images. Therefore, few-shot remote sensing image scene classification has been studied extensively. However, obtaining effect information from the limited amount of labeled samples is a great challenge. Most methods only extract features from a single perspective of remote sensing images. Such information is scarce and even misleading. To address the problem, we propose a multi-view feature fusion (MVFF) method. Specifically, first, train two feature extractor networks on the original image dataset and remote sensing image dataset, respectively. And each model extracts features before and after average pooling, and we obtain four kinds of features for a remote sensing image. Second, we calculate fusion weights from support set features using Multi-Head Feature Collaboration (MHFC) method and four classifiers. Third, we utilize the weights to fuse predictive probability matrices and thus obtain the labels of query set samples. We implement experiments on three benchmark remote-sensing image datasets to validate the performance of our method. And the results demonstrate that our approach effectively handles few-shot remote sensing image scene classification.
A Semi Linear State Space Model for Error Floor Estimation of LDPC Codes Over the AWGN Channel

In this paper, we propose a novel state-space model to represent the behavior of sum-product algorithm (SPA) in the vicinity of a trapping set (TS) of a low-density parity-check (LDPC) code over the additive white Gaussian noise (AWGN) channel in the error floor region. The proposed model takes into account the non-linear behavior of SPA in the initial iterations using a quadratic approximation, and dynamically adjusts the operating point of the model in accordance to the statistical properties of TS messages. This is in contrast to the existing linear state-space models which linearly approximate the non-linear behavior of SPA at around the operating point of zero in all iterations. Simulation results are provided to demonstrate the higher accuracy of the proposed model in estimating the error floor of LDPC codes compared to the linear state-space model. We also make connections to the semi-analytical code-dependent technique proposed by Richardson for the error floor estimation of LDPC codes, and demonstrate that the proposed state-space model not only is a fully-analytical code-independent counterpart to that method, but also has less complexity and, in some cases, more accuracy.
A Bi-level Approach to Transmission Network Planning Considering Operational Flexibility

With a large scale integration of fluctuating wind power, it is imperative to improve the operational flexibility in transmission network planning on the basis of considering system security and economy to achieve the goal of safe wind power consumption and reliable power supply. In this paper, a bi-level transmission network planning model considering operational flexibility is proposed to optimize the solution of transmission lines capacity and new thermal generator to be installed. The upper-level model minimizes the investment cost and the operation cost. The lower-level model calculates the lack of flexibility and loss of load of the system based on the line capacity and installed units from upper layer. Both upper and lower models can be transformed into mixed integer linear programming (MILP) problems. The effectiveness and validity of the proposed method are illustrated on the IEEE-RTS system.