Miracle-dz/UniFAR-FTUs huggingface dataset:
Mutual Information Re-Registration of Sensitive Region in Forward-Looking Sonar Images Combined With Particle Swarm Optimization Algorithm

In order to meet the needs of complex seafloor scenes target detection, an image registration method based on sensitive region is proposed which considering the fuzzy boundary, poor contrast and low overall gray value of forward-looking sonar image. In this method, gradient enhancement algorithm is used to enhance the boundary of the target region in forward-looking sonar(FLS) image, and morphological operation method is used to remove a large number of noise points in the image background. And then, the coordinates and the sensitive region which contains a part of obvious grayscale changes is located in the floating image. The combined method of particle swarm optimization(PSO) algorithm and mutual information(MI) algorithm are used for initial registration, and the re-registration method of mutual information is used to optimize the registration error and improve the registration accuracy. The experimental results show that this method has the advantages of accurate location of the sensitive region, low computation time and high registration accuracy.
Prediction of LncRNA-Disease Associations Based on Network Representation Learning

Massive observations have indicated that long noncoding RNAs (lncRNAs) are crucial in a number of biological processes and associated with various human diseases. Developing an efficient calculation model to predict the associations between lncRNA and diseases is not only beneficial to disease diagnosis, treatment, prognosis and potential drug targets in drug discovery, but also avoid the waste of human and material resources brought by biological experiments. In this paper, we proposed a novel prediction of lncRNA-disease associations based on complex and comprehensive molecular associations network (MAN), which integrated nine kinds of interactions among five molecules, including lncRNA, miRNA, disease, drug and protein. Network embedding Node2vec method was applied to extract behavior feature from MAN to generate a low-dimension vector containing nodes and edges information. After implementing 5-fold cross validation, the proposed method yielded good prediction performance with an average Accuracy of 91.91%, Sensitivity of 94.05%, Specificity of 89.76%, Precision of 90.21%, MCC value of 83.91%, AUC value of 0.9746 and AUPR of 0.9693. Comparative experiment indicates the behavior feature extracted by Node2vec is more representative than attribute features of lncRNA adopted 3-mer and diseases extracted by semantic similarity. Moreover, breast cancer, colon cancer and lung cancer are explored in case study. As a results, more than half of top 5 interactions are successfully confirmed for each disease by other datasets. Based on these reliable results, it is anticipated that proposed model is feasible and effective to predict lncRNA-disease associations at a global molecules level, which is a new respective for future biomedical researches.
Hybrid Very High Throughput Satellites: Potential, Challenges, and Research Directions

Hybrid terrestrial-satellite transmission systems have been among hottest spots in the wireless communication industry due to the high demand for global high data rates. In such systems, free-space optical (FSO) technology is advocated for assessing the feeder links (ground-satellite) in order to fulfill high data rates requirements and provide a larger coverage in terms of the connected number of users. This paper gives an overview of various components of very-high throughput hybrid terrestrial-satellite communication system with an FSO feeder link, and Ka-band multibeam RF channels for the satellite ground link. In this survey paper, we depict the main communication challenges in the FSO side such as pointing error, turbulence, cloud blockage, and also the multibeam RF side such as spectrum scarcity, inter-beam interference, and security. Based on these challenges we point out some potential research directions.
Learning nonlinear feedback controllers from data via optimal policy search and stochastic gradient descent

This paper proposes a technique for synthesizing smooth nonlinear controllers by optimal policy search and stochastic gradient descent. After choosing an appropriate parameterization of the control law, mini-batch stochastic gradient descent steps are used to iteratively optimize the parameters of the control law. The gradients of the expected future closed-loop performance required for the descent are approximated by using simple local linear models, as introduced earlier by the authors for optimal policy search of linear feedback controllers. In this way, the method does not require a full nonlinear model of the process. The algorithm can be applied offline, on a previously collected dataset, or online, while controlling the plant itself with the most updated policy. We apply the method in a numerical example in which we solve an output-tracking problem for a Continuously Stirred Tank Reactor (CSTR) using a neural-network parameterization with differentiable activation function of the controller. In the offline setting the performance of the resulting neural controller is compared to the one of a linear feedback controller trained on the same dataset. In the online setting, instead, we show how the learning procedure can be designed, combining on-policy and off-policy learning, to increase safety and improve performance.
Using Machine Learning for Anticipating a Diabetes Crisis Through a Sensors-based Internet of Bio-nano Things Network

It is shown that Machine Learning can be exploited in medical urgencies such as a diabetic crisis, that is manifested in showing high values in both glucose and blood pressure. Often patients cannot estimate a possible crisis so that, in most cases most of them are quite sensitive to strokes and cardiac arrest unexpectedly. In this paper the algorithm of Tom Mitchell is employed as a kind of software that manages the updated inputs in order to anticipate a possible crisis in terms of probabilities. Thus, while sensors are enough accurate to measure a variable, the algorithm is able to make predictions about the worse scenarios of diabetes crisis. When this information is monitored inside an Internet of Bio-nano Things network, patients might be assisted in the shortest times, by avoiding irreversible complications in their health. Therefore, a health services operator acquires capabilities to minimize risks and make fast and precise decisions with minimal errors either from clinicians and instruments.
Gated iterative capsule network for adverse drug reaction detection from social media

In this paper, we propose a gated iterative capsule network model for the ADR detection task, named GICN. To alleviate the impact caused by abbreviations and misspelled words, we add character embedding as part of the input. Most ADRs consist of multiple words, e.g., short-term memory dysfunction. Hence, we apply a convolutional neural network (CNN) to obtain the complete phrase information. To effectively extract deep semantic information, we introduce a capsule network with a gated iteration unit that clusters features from underlying to high capsules. The gated iteration mechanism can remember contextual information, which will be introduced when clustering features. Experimental results show that our approach can achieve significant performance improvement for ADR detection from social media text compared with other state-of-the-art works.
Classification of PBMC cell types using scRNAseq, ANN, and incremental learning

Single cell transcriptomics (SCT) technology reveals gene expression of individual cells. Peripheral blood mononuclear cells (PBMC) are important diagnostic targets in immunology. In this study, we obtained and standardized 27 SCT data sets, derived from healthy PBMC samples using 10x SCT. We used artificial neural networks (ANN) to assess the ability of ANN to classify main PBMC cell types. Incremental learning by the gradual addition of new data sets to ANN training improved classification. The overall prediction accuracy of the final step of incremental learning reached 93% in 4-class classification.
Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids

This work analyzes interlinking converter control in hybrid AC/DC microgrids. The paper addresses the state-of-the-art general hybrid microgrid structure. The key power electronics topologies are used as bidirectional interface converters in the AC and DC parts. Different control structures of hybrid microgrids are categorized, followed by the classification of the main control functions, their control strategies, and the control techniques and a summary of their positive and negative aspects and applications. Control functions, strategies and techniques are classified in the interlinking-converter based. Finally, overall control objectives, time-scaled control structures, and their strategies are outlined. The prospects, main challenges, research gaps, and the trend of the hybrid microgrid structure and control are reviewed and summarized in the conclusions.
A Stochastic Programming Approach for Configuration Optimization of Reconfigurable Manufacturing Systems

As a new manufacturing paradigm, Reconfigurable Manufacturing System (RMS) has a modular structure, which can greatly improve the responsiveness and flexibility by adding or removing the modules of the reconfigurable machine tools (RMTs). However, the uncertain demand makes it difficult and challenging to configure the RMS. To deal with this problem, we propose a multi-period stochastic programming model to optimize the configuration of RMS in each period by considering the uncertain demand. The objective is to maximize total costs. Production planning related decisions are considered to better handle the unpredictable demand fluctuation. Numerical experiments compare the solutions by solving the proposed stochastic programming model and those by solving the deterministic linear model without considering production planning decisions. The results show that our model can provide a better solution in terms of the configuration and reconfiguration cost and total costs under the consideration of uncertain demand.
Thyroid Nodules Categorization Based On Margin Features Using Deep Learning

Thyroid cancer is a rare malignancy originated in the thyroid gland. It is often found incidentally in patients with thyroid nodules. One of the ways to evaluate its malignancy is by using ultrasonography of thyroid and neck. Many studies conducted experiments around thyroid nodules but none were focusing on thyroid nodules characteristics especially margin characteristic. This characteristic tends to be visibly obvious and can be the lead to another characteristics. We proposed a method to help experts in identifying the regular and irregular categories from margin characteristic. The proposed method consists of pre-processing, segmentation, feature extraction by using nine geometric features, data balancing by using synthetic minority oversampling technique (SMOTE), and classification by using deep learning method. For the training process we used the dataset collected from the Department of Radiology RSUP Dr. Sardjito Yogyakarta Indonesia. We sucessfully obtained accuracy of 94.79%, sensitivity of 94.9%, specificity of 94.1%, PPV of 98.68%, NPV of 80%, F-measure of 94.6% and ROC of 96%.
Noise Adaptive Moving Horizon Estimation for State-of-Charge Estimation of Li-Ion Battery

In this paper, a novel noise adaptive moving horizon estimation (NAMHE) method is proposed to improve the accuracy of state of charge(SOC) estimation of Li-ion batteries under the unknown noise conditions. Specifically, based on the maximum likelihood principle, the unknown statistical characteristics of the noises can be estimated to modify the recurrence expression of the NAMHE. Then, the SOC estimation algorithm is designed by combining the equivalent circuit model and the proposed NAMHE. Furthermore, the convergence of the estimation error expectation is obtained for the NAMHE algorithm. Finally, the simulation results clarify that the SOC estimation error under the different unknown noise conditions can be effectively reduced by the proposed method, compared with the traditional MHE method.
Asymptotically Stabilizing Multi-Stage Model Predictive Control

In this paper, we propose an asymptotically stabilizing formulation of multi-stage nonlinear model predictive control (NMPC) for plants with state and input dependent uncertainties. We derive time-varying Lyapunov-type sufficient conditions for asymptotic stability. We then propose a novel multi-stage NMPC formulation with time-varying terminal constraints, which guarantees asymptotic stability of the origin based on the derived stability conditions. The time-varying nature of the terminal constraints renders the control law and consequently the controlled system dynamics time-variant. For this situation, the derived time-varying stability conditions provide a suitable framework for stability analysis. We demonstrate the advantages of the proposed scheme over previous formulations of multi-stage NMPC on a cart simulation study.
Parallelization Applied to the Synthesis Methodology and Operation of Complex Systems Based on the Analysis and Modelling of their Physical and Chemical Processes

Authors of the article draw their attention to the study of methodological decomposition and parallelization applied for the tasks of synthesis and operation of complex physical and chemical systems in order to develop a horizontally scalable distributed information system. This system relies on algorithms rooted in a unified approach to the description and modelling of variable physical and chemical processes in the systems under investigation. Then the results of process modelling are applied to synthesize this system model. This model in its turn is used for the tasks of synthesis and operation of studied complex systems. In terms of user interface, the information system of this kind is expandable over various practical domains associated with complex physical and chemical systems (power engineering, chemical technology, cellular biology, meteorology, etc.).
A Domain Adaptation Multi-instance Learning for Diabetic Retinopathy Grading on Retinal Images

Diabetic retinopathy (DR) is one of the most concerning, common and serious diseases in the ophthalmology community. Early detection and treatment of DR can significantly reduce the risk of vision loss in patients. Traditional DR automatic classification algorithms rely on the precise detection of microaneurysms (MA) and hemorrhage (H) lesions. Such lesion annotation is an expensive and time-consuming process, hence it is expected to develop automatic grading methods with only image-level annotations. The lack of the position of MA and H hinders the traditional supervised algorithms for the accurate identification. In our work, we formulate the weakly supervised DR grading as a multi-instance learning problem, and propose a domain adaptation multi-instance learning with attention mechanism for DR grading. Specifically, labeled instances are generated by cross-domain to filter irrelevant instances in the target domain. To model the relationship between the suspicious instances and bag label, a multi-instance learning with attention mechanism is developed to acquire the location information of highly suspected lesions and predict the grade of DR. We evaluate our proposed algorithm on the Messidor dataset, and the experimental results demonstrate that it achieves an average accuracy of 0.764 and an AUC value of 0.749 respectively, outperforming state-of-the-art approaches.
Basis for Choosing the Nominal Voltage of the Network

The rated medium voltages of electric networks are various and numerous in value; question arises why it is so. By trial and error, technicians have come to the cheapest solution in each case. The two objects of equal value can be expensive or cheap depending on the quantity of processed products, that is per unit cost can be diverse. Per unit power cost of electricity delivery depends primarily on the cost of power lines and transformer substations which for both of them increases with rated voltage. However in per unit cost of a network, the share of power lines increases but of transformers decreases with the power (with supply zone) increasing. So per unit cost is optimum (minimum) at a certain value of both voltage and the supply zone. These factors (and others) differed in different regions, which led to many rated voltages. If at a given rated voltage the per unit power cost decreases with the supply zone increase, then we have cost decreasing network, otherwise - cost increasing network, which leads to a different network planning approach.
Maximum hands-off feedback control for finite-time stabilization*

This work proposes a way to time-sparsify predesigned stabilizing feedback controls. For this purpose, a finite-time stabilizing sliding-mode feedback control law is considered. For the finite-time stabilizing controller, an on- off switching signal is designed to optimally time-sparsify the feedback control signals. Due to the non-Lipschitz nature of the sliding-mode controller, this requires the use of a non-smooth Pontryagin Maximum Principle. Numerical experiments are presented to verify the theoretical results and illustrate the advantages of the proposed ideas.
TeraFET amplifier detector

We show that feeding the phase-shifted terahertz (THz) signal into the stages of a TeraFET amplifier dramatically increases the response modulated at the amplifier mid band. TeraFET SPICE/ADS simulations show how the constructive interference due the optimized phase shift of the THz signal fed into the subsequent stages enhances the response. The number of stages is only limited by the THz beam cross-section, i.e. by the THz wavelength. This “travelling wave amplifier” approach enables orders of magnitude enhancement in the THz detector response.
Studying the Rerouting of Empty Carriers during their Return Trips to Manage Rare Mobile Resources in a Physical Internet

To manage the unbalance between the needs in expensive containers (such as refrigerated containers or ‘reefers') in some places, and the fact that the available empty containers of this kind are in another place, it is necessary to move these empty containers efficiently between places. In this paper, we use the empty carriers during their return trips after a delivery, to move the empty containers from the locations where they are, to the locations were they are needed. First, we use a method based on the Stepping Stone algorithm to compute a minimal cost list of moves in the associated transportation problem. Second, we use a heuristic to choose carriers during their return trips, and compute low cost possible path deviations that needed to accomplish these moves. A numerical example is presented at the end.
Exploring AI-Driven Business Models: Conceptualization and Expectations in the Machinery Industry

Companies collecting and creating valuable insights with artificial intelligence (AI) are able to secure a decisive competitive advantage. Business model innovation serves as a strong competitive differentiator and together with technological progress, new businesses fueled by AI are created every year. The main scientific result of this research is the conceptualization of AI-driven business models and an overview about the target state and potentials of these business models in practice based on a descriptive study. AI-driven business models can be conceptualized as business models that use artificial intelligence technologies to build at least one of the business model components. Compared to data-driven business models, which constitute a subgroup of business models, AI-driven business models are based on a set of techniques, that learn and improve their performance without humans having to explicitly program them. The contribution to practice is made as the proposed potentials of developing these business models can support decision-makers in evaluating the use of AI technologies. Innovating its business model with the help of AI can help to survive in a highly competitive business environment.
MIMO Dual-Functional Radar-Communication Waveform Design With Peak Average Power Ratio Constraint

In this paper, novel Dual-Functional Radar-Communication (DFRC) waveforms with peak average power ratio (PAPR) constraint are designed, which are under the multiple-input multiple-output (MIMO) radar-communication system. The DFRC waveforms transmitted by multiple antennas can send communication information to many downlink cellular users and detect radar targets simultaneously. An optimization model is established to minimize the downlink multi-user interference (MUI) under PAPR constraint. The model is non-convex quadratically constrained quadratic programs (QCQP) and can be derived into a convex problem and solved by using the semi-definite relaxation (SDR) technique with rank-one approximation. Numerical simulations demonstrate that our proposed waveforms can achieve a better radar performance in practical scenarios without sacrificing the communication performance.
Harmonic mitigation for a megawatt grid-tied fuel cell system

This research deals with the design of an LCL filter for a grid tied megawatt fuel cell stack. In the adopted configuration, the LCL filter links a three-level neutral clamped point inverter with the grid. A typical inverter is a source of harmonics as in general its output is not a pure sinewave. Standards such each IEEE 519 and IEC 61000-3-6 define the allowable harmonics distortion for both the current and the voltage in a power system based on the current level and the voltage level respectively. This paper aims to design an LCL filter to reduce harmonics produced by a three-level inverter serving as interface between a fuel cell stack and a grid and meet the abovementioned standards. The input of the grid tied inverter is connected to a 1.4 MW solid oxide fuel cell stack at 1400 V. The inverter is modelled and controlled to deliver 1.2 MW at 600 V between phases. To evaluate the performance of the designed filter, the system including the 1.4 MW fuel cell stack, the inverter, the LCL filter, a grid and a load is simulated using Matlab/Simulink environment. The results show that the designed filter reduces the total harmonics distortion to 0.46 % and 24.87 % for the voltage and the current, respectively.
Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach

In this paper, we discuss a tensor decomposition method for imputing similarity scores between individual clinical pictures at predefined patient age intervals in order to construct a dynamic similarity network of patients with respect to early childhood anthropomorphic development. The method leverages Canonical Polyadic Decomposition (or PARAFAC) to compute missing Euclidean similarity scores between pairwise growth pictures, made up of height and weight measurements. We construct a tensor made up of serial affinity matrices to model how the similarities between different patients change over different trajectory snapshots. We intend to use this method to aid Un Kilo de Ayuda (UKA), a non-governmental organization located in Mexico that is made up of facilitators seeking to identify children at risk for malnutrition and suboptimal development. This tensor completion strategy will assist UKA with determining pairs of children with similar clinical pictures, so that they can better assist with selecting treatment strategies and ultimately build better programs tailored to specific families’ needs.