Miracle-dz/UniFAR-FTUs huggingface dataset:
Remaining Lifetime Estimation of Retired Batteries Based on Multiple Deep Neural Network Ensembles

Large capacity retired battery packs contain extensive high-dimensional state monitoring data, presenting a technical challenge for accurately predicting their remaining service life (RUL). To enhance prediction accuracy and convergence efficiency of retired batteries' RUL, this digest proposes a two-stage selective deep neural network ensemble method. The first stage involves generating a candidate set under joint perturbation of multiple methods. Strengthening the diversity of candidate deep neural network sets is achieved by adopting a heterogeneous neural network structure, designing multiple time scales, and randomizing algorithm parameters to eliminate internal coupling relationships within the model. The second stage employs a genetic algorithm to integrate and eliminate redundant models, effectively removing underperforming redundant learners. This stage also acquires a diversified optimal candidate subset, which outputs prediction results based on the average ensemble. Comparison with individual model data experiments demonstrates the proposed method concurrently enhances the accuracy and diversity of the integrated model, improving RUL prediction accuracy by nearly 20%. This support significantly benefits operation and maintenance decision-making.
Enhanced Vehicle Sideslip Angle Estimation through Multi-Source Information Fusion

The sideslip angle is one of the most important parameters that characterize the stability performance of the vehicle, which is also used as a key reference for active safety control strategy. In this article, a sideslip angle estimation method based on the fusion of multiple sources of information is proposed to improve the estimation accuracy. Unscented Kalman filter algorithm (UKF) is adopted to construct kinematic-based and dynamic-based observer. According to the analysis of the applicability of the two methods, a fuzzy logic controller is designed to adaptively allocate the weights of the two estimation results. The simulation results in MATLAB/Simulink show the method can be effectively used for a variety of test maneuvers.
Explicit-Reference-Governor Based Guidance Law Design Using Neural-Network Optimization

This paper studies the problem of missile intercepting against unknown maneuvering targets with a terminal impact angle under control constraints. Leveraging on the technique of explicit-reference-governor, control restrictions are transferred into upper bounds of the Lyapunov function, which follow the regulation of the derivative of applied reference for the constraint satisfaction. Our approach is further transferred into a convex program subject to nonlinear inequality constraints. Implementing a recurrent neural network for convex programs, the online solutions in the receding horizon manner provide upper bounds for the reference regulation and subsequently decisions of control in real time.
Dynamic Obstacle Avoidance Method for Road Vehicles Via Improved Artificial Potential Field

In order to solve the safety problems caused by the vehicles collision on the highway, an automatic controller is proposed based on the artificial potential field (APF) method and the model predictive control (MPC) theory to keep the automatic driving vehicles collision-free. Firstly, in the path planning, the road potential field is constructed to satisfy the constraints of vehicle safe driving based on the driving characteristics of vehicles and road boundary conditions. Secondly, according to the shape of the obstacle vehicle in the study, the obstacle vehicle potential field is established. Finally, in order to ensure that the vehicle can drive along the planned path in a stable and safe manner, this paper combines the MPC and vehicle dynamics to design the path tracking module. Then the designed automatic controller is verified on the Carsim-Simulink joint simulation platform based on the obstacle vehicle uniform speed and acceleration test scenarios. The results show that the automatic controller enables road vehicles to safely avoid dynamic obstacle vehicles by updating design parameters in real time to adapt to the changing environment while satisfying environmental constraints and vehicle stability.
Memcached vs Redis Caching Optimization Comparison using Machine Learning

In recent times, the management of caching memory systems has witnessed an unprecedented surge in errors, causing widespread challenges, particularly for developers, given the intricate configuration processes involved. Caching plays a pivotal role in enhancing system performance by facilitating frequent data access. However, several limitations persist, including the vexing issue of cache misses, leading to performance fluctuations as the system resorts to retrieving data from the original source. Moreover, infrequent cache data updates due to limited eviction policies can introduce data inaccuracies. The proliferation of Machine Learning (ML) capabilities has emerged as a pivotal solution for optimizing caching performance, promising to automate these intricate tasks and ensure applications seamlessly integrate the most suitable caching solutions. To address these pressing issues, this research paper embarks on a comparative analysis of Memcached and Redis caching systems, leveraging Machine Learning algorithms. The study aims to discern data access patterns that can optimize eviction policies for Memcached and Redis.
Enhancement Computer Vision using Deep Learning Optimization Techniques

Deep learning has witnessed remarkable advancements in recent years, revolutionizing fields such as computer vision and reinforcement learning. A critical aspect of training deep neural networks is the choice of optimization techniques, which directly impacts convergence speed, model performance, and computational efficiency. This research paper provides a comprehensive survey and comparative analysis of various optimization techniques employed in deep learning models. The fundamental optimization algorithms commonly used in deep learning, including stochastic gradient descent (SGD), momentum-based methods, adaptive learning rate methods (RMSprop, AdaGrad), and newer variants like Adam, AdaDelta, and Nadam. Divining into the underlying principles and mechanisms of each optimization algorithm, elucidating their strengths and limitations. This study’s experimental analysis compares the performance of several optimizers in terms of validation accuracy, validation loss, and computation time over different epochs. According to the results, the Adam optimizer consistently gives the highest accuracy within an acceptable computation time. It provides guidance for selecting the most suitable optimization method for specific deep learning tasks and sheds light on emerging trends that hold promise for further improving the efficiency and effectiveness of deep learning model training.
A Novel Stochastic Hierarchical Framework for Smart Management of Electric Vehicle Aggregation with Demand Response Mechanism

This research paper introduces a novel stochastic hierarchical framework to enhance the efficiency of automated grids in the context of electric vehicles (EVs) and a demand response program (DRP). The framework leverages the unscented transform (UT) to address uncertainties arising from fluctuating loads. Three distinct charging strategies are introduced to optimize EV management within the grid: coordinated charging, uncoordinated charging, and intelligent charging. Additionally, automated switches are strategically placed to maximize power flow direction, mitigating the strain caused by increased EV-related demand. To address the complexity of this multifaceted challenge, an advanced optimization approach based on grey wolf optimization (GWO) is employed. The effectiveness of this model is rigorously evaluated using an IEEE standard case system, showcasing its exceptional performance in enhancing automated grid management in the presence of EVs and DRP, with potential implications for the broader energy industry.
Assessment of Gaussian and Multi-gaussian Kernel Models for the Reconstruction of TOF-PET Datasets with Event-by-Event Variable Time Resolution

Most current TOF-PET systems uses a global TOF uncertainty model during image reconstruction. Image artifacts can appear if the kernel model differs to much from the actual TOF kernel width, in particular when time resolution improves. Additionally, new detector designs such as hybrid Cherenkov/scintillation systems, detectors based on meta-materials in which different events can have a very different timing resolution, or 3 gamma imaging system bring several challenges as the TOF uncertainty model should be adapted in order to take advantage of the information carried by each detected event. This work focuses on (1) evaluating with Monte-Carlo simulated datasets a previously proposed analytical approach to estimate the SNR gain from a dataset with variable time resolution, and (2) study the performance of the use of different TOF kernel models for the reconstuction of datasets containing events with variable TOF information.Several datasets containing events ranging from 400 to 30 ps CRT were generated to produce 100 noise realizations. From these datasets, several mixes of labeled and unlabeled events were created, to generate datasets with either events with an unique CRT, or various mixes of events with different CRT. These datasets were then reconstructed using a gaussian TOF uncertainty model, as well as a weighed sum of gaussians. An image of pixel variances was computed for each approach from the set of 100 reconstructed images in order to derive SNR gain values.The evaluated analytical estimation approach was validated with these simulated results, both for datasets containing events with the same CRT or various mixes of different CRT. The use of a sum of gaussian provided a slightly better SNR when there is an acute difference in the proportion of events composing a mixed dataset, although this increase was not significant suggesting than using one gaussian model is still a reasonable approximation for these datasets.Future work will focus on the study of the different models with more realistic simulated datasets and ultra-fast time resolution.
Highway Traffic Flow Prediction Supporting Multi-Traffic Parameter Detection

Freeway traffic flow prediction is of great significance to freeway traffic management, route planning, toll strategy development and public safety. Existing traffic flow prediction methods mainly use deep learning models, which mainly have the following two problems: on the one hand, the special characteristics of the highway network structure make the common urban network traffic flow prediction methods inapplicable; on the other hand, the influence of external factors, such as weather and accidents, on the flow is ignored. Aiming at the above problems, this paper proposes a highway traffic flow prediction algorithm that supports multi-traffic parameter detection. Firstly, the topological map conforming to the highway network structure is constructed, which makes the traffic distribution conform to the reality; secondly, the initial prediction of the traffic is made by using the Informer time series model; finally, the traffic adjustment template is constructed by using the regression model according to the multi-traffic parameters. In this paper, multiple angles are selected to test the performance of the algorithm on two real datasets, and the results show that the performance of the algorithm in this paper is significantly improved compared with other mainstream traffic models.
High-Resolution and High-Sensitivity Spread Field Imaging to Differentiate Sub-endocardium and Sub-epicardium in Cardiac SPECT — A Preliminary Phantom Study

The aim of this study was to investigate the feasibility of a new technology for high-resolution and high-sensitivity SPECT imaging to differentiate the sub-epicardium and sub-endocardium using a cardiac phantom configured with normal, non-transmural and transmural myocardial perfusion defects. The phantom was filled with Tc-99m solution and was scanned using three different SPECT scanner configurations: one equipped with 19-pinhole collimator and CZT detector (Alcyone), and the other two equipped with NaI detector associated with conventional parallel hole collimators and a new spread field imaging (SFI) collimator. The Alcyone and parallel hole images were reconstructed using a commercial Ordered Subset Expectation Maximization (OSEM) software provided from GE, and the SFI images were reconstructed using the OSEM algorithm developed by Argospect. The resolution of SPECT acquired and reconstructed using the new SFI collimation and OSEM image reconstruction technology was much improved as compared to the conventional Alcyone and parallel hole images. The sub-endocardium and sub-epicardium simulated in the cardiac phantom can be clearly separated and visualized in the SPECT slices generated from the SFI technique. The simulated transmural defect was better discernable from SFI SPECT as compared to Alcyone and parallel hole SPECT. The high-sensitivity and high-resolution of the novel SFI collimation and reconstruction technology may have a great potential to identify the myocardial perfusion defects in the sub-endocardium and sub-epicardium layers from cardiac SPECT, thus improving the quantitative assessment of the transmural perfusion gradient in patients with suspected coronary artery or microvascular disease.
Development of a Digital Brain Phantom Population for Radionuclide Imaging Research in Parkinson’s Disease

Parkinson’s disease (PD) can be early diagnosed using radionuclide imaging. Digital phantoms and Monte Carlo (MC) simulations serve as important research tools for PD imaging physics research. Here we designed and developed a new digital brain phantom population for different stages of PD. We selected 206 T1 MRI brain images and 160 DaTscan SPECT reconstructed images for normal subjects and patients with different PD stages from Parkinson’s Progression Markers Initiative (PPMI) datasets. We used the model-based segmentation tool FIRST to segment putamen and caudate region and used Brain Extraction Tool (BET) to extract brain region from the MRI images, generating striatum and brain maps, respectively. Striatum maps were then rigidly registered to a referenced striatum model using Elastix, and the corresponding brain maps were transformed using the same parameters. Different activities were set on the maps based on the striatal binding ratio (SBR) in PPMI and the SPECT Visual interpretation assessment scheme. New phantoms were generated by randomly pairing the striatum and brain maps. Corresponding CT maps were generated by non-rigidly registered the XCAT-attenuation map to MRI images corresponding to the brain maps and transformed using the same parameters. We then used the Simind MC code to generate 120 realistic noisy projections with full physics modelling for each new brain phantom, based on a clinical SPECT scanner mounted with low-energy high-resolution parallel-hole collimators. OS-EM algorithm (8 iterations, 4 subsets) was used to reconstruct projections with and without attenuation correction (AC) respectively. SBR was evaluated on reconstructed images. Our results show that we could generate realistic PD SPECT data with the newly developed digital brain phantom population combined with a MC code, and AC is important for PD SPECT based on both visual assessment and SBR analysis. Our phantoms could be applied for further PD radionuclide imaging study.
Integrated Preclinical PET-OT employing Monolithic Modules Comounted with Plenoptic Detectors

Motivated by promising applications in the fields of pharmacokinetics, preclinical oncology research and tomographic optical imaging, the medical imaging community formerly took on the task of developing a multimodal detector system that would allow for simultaneous nuclear and optical imaging. These efforts began more than a decade ago but were not able to result in an applicable concept. This study reassesses the task and proposes a new approach to the challenge by presenting the blueprint for an integrated preclinical bi-modal system that fully merges PET imaging with optical tomography. Said blueprint is demonstrated through the investigation of a pilot system consisting of 10 detector modules (yielding an 83 mm inner-detector diameter), each combining a 25 x 25 x 25 mm monolithic scintillating LYSO crystal, read out by two 25 x 25 mm SiPM arrays coupled to perpendicular faces of the crystal, with a plenoptic detector mounted on the front face of the crystal. The feasibility of this detector concept was evaluated by studying both PET and optical imaging components through dedicated Monte Carlo simulation, followed by synergistic image reconstruction. Initial results are shown for two point sources, each 1 mm in diameter, separated centre-to-centre by 2 mm and placed at the centre of the field of view.
Economic Optimization of the Hydrogen Demand in a Hard-to-Abate Industrial Sector

Hydrogen is a zero-emission fuel that, if produced from renewable sources (so-called green hydrogen), can provide a significant contribution in the decarbonization of several sectors. The main drawback that still hinders its deployment is its high cost, as well as critical operational issues related to the entire supply chain, both for safety and efficiency reasons. Incentives and certification schemes are needed to support the green hydrogen. In this paper, with the aim to identify the green hydrogen supply chain and associated costs, an energy hub with electricity and hydrogen demands has been studied comparing the centralized green hydrogen production and distribution via trucks, against the installation of an on-site green hydrogen production plant made up of renewable power generation, an electricity storage system, and an electrolyzer. The problem was modeled and solved as a MILP optimization in MATLAB environment. Furthermore, a sensitivity analysis on the cost was carried out, which showed that even if the truck transportation cost for hydrogen is set at 0 a/kg, it is still more cost-effective to install an on-site electrolyzer to produce the required hydrogen.
LADRC-Based Large-Signal Stability Enhancement for Microgrid Energy Storage Converter

To maintain the stability of the bus voltage in a DC microgrid, the voltage-current double PI closed-loop control method is frequently applied in the energy storage converter, specifically the DC/DC converter. However, the robustness of the DC/DC converter under voltage-current double PI closed-loop control method is relatively weak. In addition, because the power converter-based load exhibits constant power characteristics, this introduces nonlinearity and negative impedance characteristics to the DC microgrid, which affects the system stability when subjected to disturbances and sudden load changes, making the traditional small signal analysis no longer applicable. To address this challenge, a strategy based on linear active disturbance rejection control (LADRC) is proposed and used into the DC/DC converter to enhance the stability of the system. A simplified large signal model of the DC microgrid system with LADRC is established. In addition, based on the developed model, the region of asymptotic stability (RAS) of the system is depicted by using the TS fuzzy modeling method and the limit power is estimated and both are compared with the conventional control. Finally, the accuracy of the modeling and the effectiveness of the proposed control strategy are verified by simulation and calculation of the limit step power of the system.
Covid-19 Impact on Mental Health: Sentiment Analysis Using NLTK

Artificial intelligence has been described as the new electricity. The belief is that it will profoundly influence many fields and education is one of them. There are various ways where AI in education can bring a revolution benefiting students, teachers, and other stakeholders. Sentiment analysis of a student is one of the feathers of AI which helps determine a student's mental state – which is an important aspect in measuring the student's wellness with increasing competition and pressure. Hence, it is tried to attempt to study the mental condition of students during this pandemic of covid -19 using this NLP (natural language processing) based technique of sentiment analysis. A high-level classification library of Python -NLTK is used for this purpose. The dataset is collected from the student community about their thoughts during these difficult times and analyzed and represented in various interactive visual and graphical formats on the web application built by employing the FLASK framework.
An Intelligent Cognitive-Inspired Computing for Complain Speech Analysis and Classification

This study trained and tested 1006 words using an LSTM network and a SOC model. The model achieved 90% accuracy and yielded relatively successful SOC corpus and SNA results. The computer model was also integrated into speech act theory by combining linguistics, computer science, and psychology. A large number of complaint languages were analyzed. This study demonstrates the feasibility of computer modeling and SOC in cognitive computing, showing their promising applications.
Understanding Stock Market Trends Using Simple Moving Average (SMA) and Exponential Moving Average (EMA) Indicators

The task of forecasting stock market trends is widely regarded as one of the most difficult challenges in financial time series analysis, owing to the dynamic and non-linear nature of financial data. Forecasting the stock market is an essential requirement for comprehending market patterns and devising trading strategies. The present study is centred on the examination of financial data through the implementation of the present discourse pertains to two distinct technical indicators, namely the simple moving average and exponential moving average. The aim is to predict the future path of the stock market's movement. The utilization of the moving average approach has gained widespread acceptance due to its ease of implementation. The experiments were conducted utilizing a five-year historical dataset from two distinct stock markets, namely TCS and Apple. The present study draws a comparison between two companies by analysing their respective trading signals to infer the potential movement of their stock prices.
Model Reference Adaptive Vibration Control For Wheeled-legged Multi-mode Vehicle

The control of vibration in a wheel-motion multi-mode vehicle is the core technology required for improving the efficiency of vehicle transportation in unstructured environment. This paper constructs a kinematics and dynamics model of the wheeled-legged multi-mode vehicle and proposes a force tracking-based model reconstitution framework. By applying the ideal vibration-reduced system as the reference model, the model reference adaptive control (MRAC) strategy is utilized to reconstitute an equivalent system in the multi-mode vehicle. A multi-body dynamics model in Adams/View is formulated to validate the effectiveness of the proposed method. The simulation results show that the multi-mode vehicle in MRAC achieves comparable performance with the reference ideal model and increasing stability and smoothness on different roads, which provides a new perspective for multi mode vehicle vibration control.