Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the Use of Portals to Reduce Excess Material in Afloat Logistics Systems

Abstract : The purpose of this project is to assess the benefit of utilizing portals to manage and reduce excess materials in the Afloat Logistics System (ALS). Through this process, this report shall identify: 1) What IT system(s) are currently in use for managing excess material in ALS; 2) What policies are present for managing excess material in the current ALS; 3) What benefit might portals add to the management of excess material in ALS; and 4) What impact might this portal have on preventing future excess material in ALS. The Navy's Afloat Logistics System (ALS), composed of 30 Oliver Hazard Perry class Frigates and 57 Arleigh Burke class Destroyers, is overburdened with excess material. Currently, the combined 87 ships contain over 228,000 line items of excess material within their logistics systems. This report looks at the supply chain and the effects of excess material, the current systems used to manage excess material, the issues dampening the success of current excess-material management systems, and the possible uses of portals to streamline the excess-material management systems.
Trading Stocks or its Options - Analysis of Publicly Traded Oil Companies in India

The stock market is very volatile in nature. Traders thrive on this volatility by buying stock when they are low and selling high or by exercising options or trading the option contracts. This can only be done if market movement trends are known. Recent advances in deep learning and neural networks have remodeled the markets. This paper attempts to analyze the gains in buying stocks, call options and exercising options. Stock price movements of major oil conglomerates in India are forecasted namely Indian Oil Corporation, Oil and Natural Gas Corporation and Reliance Industries Limited, using a custom LSTM neural network. This stock prediction model is benchmarked against FOREX, Crude oil rates, historical data of companies and their major multinational oil suppliers, along with their financial news sentiments, of the past three years. The option pricing model uses the Monte Carlo simulation, similar to Black-Scholes Model to accurately determine the call option prices. The computational results of these implementations and most importantly their accuracies are deliberated.
Research on Web Visual Communication Design Based on Feature Extraction Algorithm

With the progress of society, people's aesthetic ability has been continuously improved, and the pursuit of beauty has been launched. Therefore, some webpages with strong visual impact and novel layout usually attract more attention. The layout of the network is composed of visual communication, and the network itself is a giant platform for processing information, which requires the web page designers to fully understand the advantages of the network, understand the characteristics of the network, and design a web page that is more suitable for spreading on the network. User feature extraction system can evaluate and extract user features, recommend pages that users can generate interest, and realize better human-computer interaction through continuous user feedback on pages and learning of interest feature extraction system. Combined with feature extraction algorithm, this paper analyzes how website developers combine the beauty of the interface with the user's habits and aesthetic expectations to reduce the user's thinking burden, hoping to provide a theoretical basis for user experience-centered design.
IEEE - 51525 A Novel Smart Hospital for Isolation of COVID-19 Patients

In this paper we describe a smart and innovative system which provides prime contribution to our society in this pandemic situation. We propose a novel smart hospital with multi featured equipments along with fashionable isolation ward for COVID-19 patients. A website interface has been proposed under anyone can visit and after knowing the status of the beds either for the isolation or emergency ward they can easily book from remote location. A QR code with registration number will be provided to the users. These users may reach to the hospital and have to pass through the QR code scanner for verification and sanitization process. A pre-evaluation section has been defined where proper mask at face will be detected with thermal scanner facility. All the designed section is controlled and monitor by control unit through server. Well featured with multiple sensors a unique ward is designed where oxygen level sensor is used to sense the level of oxygen and if the level of oxygen of patient is less than defined value supply of oxygen will switch on the supply automatically. Doctor will monitor and take care of patients through CCTV and video conferencing. The entire sub design unit is controlled and monitored via hybrid network and IoT.
Prioritizing Maintenance Human Factors Challenges and Solutions: Workshop Proceedings

Abstract : Thirty delegates, mostly from the Federal Aviation Administration's Office of Aviation Safety, but also from the U.S. aviation industry, the National Transportation Safety Board, and Transport Canada, assembled for two days at the Civil Aerospace Medical Institute in Oklahoma City. These leaders in maintenance human factors research met to discuss challenges and prioritize solutions for human factors issues in aviation maintenance. The delegates reviewed international incidents and accidents and corporate operating procedures to identify systematic human factor precursors to maintenance errors. Each human factor issue was discussed and subsequently prioritized by each delegate. Analysis of the data revealed eight core issues, including: use of technical publications, fatigue/alertness, safety culture, event data collection, return on investment for human factors, prioritization of human factors, professionalism and generational issues, and attention to required inspection items. Solutions and action items for each of these issues were discussed and recommendations were developed. The prioritized list of problems and suggested industry actions are detailed in the following report. The recommendations stemming from this report can be used to inform future research and development for maintenance human factors.
Deep Texture Recognition via Exploiting Cross-Layer Statistical Self-Similarity‡

In recent years, convolutional neural networks (CNNs) have become a prominent tool for texture recognition. The key of existing CNN-based approaches is aggregating the convolutional features into a robust yet discriminative description. This paper presents a novel feature aggregation module called CLASS (Cross-Layer Aggregation of Statistical Self-similarity) for texture recognition. We model the CNN feature maps across different layers, as a dynamic process which carries the statistical self-similarity (SSS), one well-known property of texture, from input image along the network depth dimension. The CLASS module characterizes the cross-layer SSS using a soft histogram of local differential box-counting dimensions of cross-layer features. The resulting descriptor encodes both cross-layer dynamics and local SSS of input image, providing additional discrimination over the often-used global average pooling. Integrating CLASS into a ResNet backbone, we develop CLASSNet, an effective deep model for texture recognition, which shows state-of-the-art performance in the experiments.
ParkIt - Plataforma inteligente de estacionamiento público

Cities across the world are constantly growing, and the traffic infrastructure of many of them starts to reach its maximum capacity. Different points of these cities are negatively affected by the vast amount of vehicles that try to access congested zones, and that oftenly makes finding an on-street parking spot where to park a car a difficult task. In this context the ParkIt project is developed, which is a platform that offers real-time information about on-street parking availability. This platform optimises the use of free parking spots, and reduces the incurred delays while in search of them. By knowing the location of available parking spots, users of this platform find themselves less times looking for a parking spot, contributing to reduce traffic in up to 30%.
Design and Optimization of HF Transformers for High Power DC-DC Applications

Increasing the operational frequency is the most common solution to achieve higher power densities, since the weight and volume of the magnetic part, the bulkiest element in power electronics converters, are then decreased. This solution is well established in low power high frequency applications, while in the recent decade, the possibility of utilizing high frequency at higher power and voltage levels has generated wide interest as well. This work proposes a design and optimization methodology of a high power high frequency transformer accounting for the tuned leakage inductance of the transformer, as well as high isolation requirements, particularly in DC offshore application where a converter module should withstand the MVDC or HVDC link voltage. To achieve this goal, several models were proposed and developed in order to accurately characterize such a transformer. One of these models is a so called pseudo-empirical expression derived from a rigourous regression algorithm based on an extensive 2D finite element simulation scenario, resulting in an accurate analytical expression with an average unsigned deviation of 0.51% and the extreme deviations not higher than 9%. Moreover, using the energy method, an analytical expression to precisely calculate the leakage inductance of high power density magnetic components is proposed. In addition, using the proposed modification of the Steinmetz equation for core loss calculations, general expressions are derived and presented for a rectangular waveform with its associated duty cycle and rise time. Applying the proposed design methodology, in which all the aforementioned models are implemented on a 1 MW case study transformer, indicates that such a transformer can achieve a power density of about 22 kW/L and the efficiencies as high as 99.74%. Moreover, with respect to the isolation requirements, desired leakage inductance and the magnetic material used, a critical operating frequency can be found above which the transformer does not benefit from volume reduction anymore.
Study and Analysis of Performance of IEEE 802.11 Power Saving Mode

The nodes need to be turned into low-power states when they are not in use to conserve energy and power for battery- powered wireless devices. One of the most important techniques in wireless LAN and multi-hop wireless networks is the IEEE 802.11 power saving mode which is used to coordinate the power states of communication devices [1]. Energy performance and bandwidth resource limitations are the backbone of any ad hoc network design. Multi-rate adaptation architectures had been proposed to increase bandwidth utilization efficiency and to reduce the control overhead [4]. Simulations were performed in order to see how specific parameters influence the performance of the power saving mechanism for the wireless Local Area Networks in IEEE 802.11. Simulations were made for an ad hoc-network with 25 stations. The throughput were obtained for specific window size and beacon interval and then an optimal ratio between ATIM window interval and beacon interval is recommended. It is found that the ratio between ATIM window interval and beacon interval should be between 30 to 40 percent.
Helicopter Aircrew Training Using Fused Reality

Abstract : This paper describes a novel Mixed Reality technique for robust, real-time chromakey processing for training applications using software and off-the-shelf video hardware. This technique has been coined and patented by the author as "Fused Reality. " Until now chromakeying has been conducted using dedicated hardware, imposing substantial restrictions on the visual environments that will support chromakey. Variations on the traditional chromakey setup, such as using retroreflective screens and light-emitting cameras, can overcome some of the technique's original drawbacks (such as lighting difficulties), but they can introduce new problems as well (i.e., a user's hand obstructing the light projected from the head-mounted source can create an ill-defined silhouette). The novel chromakey method introduced in this paper is applied to training helicopter aircrew personnel using a prototype simulator, the Prototype Aircrew Virtual Environment Training (PAVET) System. The ultimate goal of the PAVET will be to provide training to Navy aircrewmen in all operational aspects of the MH-60S, including aerial gunnery, search and rescue, and vertical replenishment. A key requirement for these types of tasks is for trainees to see and manipulate physical objects (e.g., a jammed gun) at close range while viewing an interactive flight and shipboard environment. In order to satisfy space and cost constraints, some physical objects that the aircrewmen physically interact with (such as a rescue litter) must be capable of being sent out into the virtual environment and later retrieved. Fused Reality accomplishes this critical feature through contraction and expansion of specific real-world objects as they move away and toward the trainee. Fused Reality's adaptive color recognition allows for realistic set lighting, colors, and user movement and positioning.
On-line Distributed Prediction of Trafﬁc Flow in a Large-Scale Trafﬁc Network

For online trafﬁc control at trafﬁc control centers there is a need for predictions of trafﬁc ﬂow over a short horizon to evaluate the impact of different scenario for the purpose of on-line scenarios selection. Fast computation is needed for the online decision support system. However, the common approach of a parallel simulation has several drawbacks. In this paper, a novel approach by state matching is presented to predict the trafﬁc ﬂow in a large-scale trafﬁc network in a parallel and distributed way with a reasonably small computation time.
Knowing Who to Watch : Efficiently Identifying Subtle Attackers

Insider attacks are often subtle and slow, or preceded by behavioral indicators such as organizational rulebreaking which provide the potential for early warning of malicious intent; both these cases pose the problem of identifying attacks from limited evidence contained within a large volume of event data collected from multiple sources over a long period. This paper proposes a scalable solution to this problem by maintaining long-term estimates that individuals or nodes are attackers, rather than retaining event data for post-facto analysis. These estimates are then used as triggers for more detailed investigation. We identify essential attributes of event data, allowing the use of a wide range of indicators, and show how to apply Bayesian statistics to maintain incremental estimates without global updating. The paper provides a theoretical account of the process, a worked example, and a discussion of its practical implications. The work includes examples that identify subtle attack behaviour in subverted network nodes, but the process is not network-specific and is capable of integrating evidence from other sources, such as behavioral indicators, document access logs and financial records, in addition to events identified by network monitoring.
The Measurement of Airblast Parameters on the Minor Uncle Explosion Event Using Passive Instrumentation.

Abstract : This report covers the measurement by the author of two of the fundamental blast parameters associated with explosions in air, i.e., dynamic pressure impulse (DPI), and blast pressure. The report illustrates how valuable a role inexpensive, passive instrumentation can play in measurement, especially in large scale field experiments. The gauges which form the subjects of this report were used most recently on the multi-national, large scale field event, MINOR UNCLE, which was a high explosive test at White Sands Missile Range, New Mexico, on 10 June, 1993. Both of the gauges used for these measurements were developed at AMRL but at different times, to satisfy vastly different requirements. Australia was requested to participate in MU by carrying out the measurement of DPI for comparison with other measurements of the same parameter. The range of interest to overseas scientists was 0.5 kPa 5 to well in excess of 20 kPa 5. This range greatly exceeded the capability of the Australian gauge. This is discussed fully in the report. The Australian DPI gauge was a modification of a design which was developed for the BLOWDOWN airblast experiment which was held in 1963 in a tropical rain forest in northern Australia. This gauge was also used on the DISTANT PLAIN series at Defence Research Establishment, Suffield(S) and Hinton, Alberta, Canada, in 1966/7. Although the gauge was not designed originally for use with explosions having a yield much greater than 500 tonnes TNT equivalence, its use in a modified form has proven quite successful on MINOR UNCLE (MU). The second gauge, a self recording, peak reading, blast pressure gauge, was developed in the 1980s to provide a simple means of blast measurement for use by relatively unskilled military people engaged in training exercises.
Underwater Clouds Utilizing Private Cloud Architecture Aboard U.S. Submarines

Abstract : The Consolidated Afloat Network and Enterprise Systems (CANES) program was tasked with replacing computer networks afloat, to both improve productivity and functionality of the fleet's Information Technology infrastructure. The program, in its first iteration, fails to include half the ships currently in service and is limited to a small number of specific platforms. Upgrading the existing software on the current platforms may pose a useful resolution to this issue. The computer networks on board submarines provide a good opportunity to explore how cloud computing could benefit older platforms. This thesis presents a proof of concept for the use of a Private Cloud architecture on board U.S. submarines and how improving computer networks may be possible by leveraging the currently installed hardware without requiring a complete system reconfiguration. We use the Ubuntu Server Private Cloud as a basic example to illustrate and explore potential benefits and limitations of the Platform as a Service (PAAS) model. The revised system is examined in terms of its application aboard a submarine and explores how it compares to previous network architectures, such as the Client/Server model. The Ubuntu model was chosen for its usability and robust features and because it is open source and free.
Agent-Based Modeling of Peer-to-Peer Energy Trading in a Smart Grid Environment

The energy system is undergoing a drastic transition towards a system where previously passive consumers will play important roles. These consumers who actively participate in the energy system with a variety of distributed energy resources, such as electric vehicles, solar panels, and battery energy storage systems, become so-called prosumers as they can also generate electricity. This electricity can then be self-consumed, sold to the existing grid, or be sold to other consumers connected to the same electric network through Peer-to-Peer (P2P) trading schemes. This P2P energy trading may offer significant advantages to consumers involved as well as the wider electric system. The use of Agent-Based Modelling (ABM) can help address these problems. ABM models allow to understand complex and dynamic systems by incorporating the behavior of individual agents into the model as the individual behavior of the agents has a direct influence on the outcomes of the systems. In this paper, an ABM model is developed to examine the effects of increased consumer participation within a local energy system. This model utilizes a diverse set of consumers based on real-world data to model and provide insight into the interactions within a P2P energy trading system. The effects of P2P trading on financial outcomes as well as the share of renewable energy utilized within the local energy system is investigated. Results show that ABM models can accurately model P2P energy trading systems and can capture the effects of individual behavior of many active consumers within electrical systems. Also, it is shown that there may be a tradeoff between maximizing P2P energy trades within a community and maximizing the revenues of the prosumers.
RADx Variant Task Force Program for Assessing the Impact of Variants on SARS-CoV-2 Molecular and Antigen Tests

Goal: Monitoring the genetic diversity and emerging mutations of SARS-CoV-2 is crucial for understanding the evolution of the virus and assuring the performance of diagnostic tests, vaccines, and therapies against COVID-19. SARS-CoV-2 is still adapting to humans and, as illustrated by B.1. 1.7 (Alpha) and B.1. 617.2 (Delta), lineage dynamics are fluid, and strain prevalence may change radically in a matter of months. The National Institutes of Health's Rapid Acceleration of Diagnostics (RADxSM) initiative created a Variant Task Force to assess the impact of emerging SARS-CoV-2 variants on in vitro diagnostic testing. Working in tandem with clinical laboratories, the FDA, and the CDC, the Variant Task Force uses both in silico modeling and in vitro testing to determine the effect of SARS-CoV-2 mutations on diagnostic molecular and antigen tests. Here, we offer an overview of the approach and activities of the RADx Variant Task Force to ensure test performance against emerging SARS-CoV-2 lineages.
Memristive Concept of a High-Dimensional Neuron

We propose a novel concept of a hardware implementation of high-dimensional neurons with the aim of a functional simulation of the human hippocampus. To implement a high-dimensional neuron, we suggest using the arrays of trainable memristive devices as part of the custom-made software-hardware system to provide full neuronal functionality with fine-tuning of synaptic weights according to the self-learning rule. The proposed architecture can enable new efficient approaches to processing big data in artificial intelligence (AI) systems and a new generation of robots.
Adaptive Traffic Signal Controlling With Transfer Learning

Traffic crowding is becoming a serious nuisance in cities and metros. Currently the system used for traffic management is not that efficient because it is static in nature which means it works the same for all the traffic circumstances. This results in an increase in the waiting queue length of vehicles at a traffic intersection. Hence we are proposing the state-of-the-art, real-time Adaptive Traffic Signal Controlling based on Transfer Learning. In the proposed work, Traffic density is calculated using Computer Vision Technology from Traffic data images captured at intersections. Based on this information, a strategy is decided which will control “Pass Time” for vehicles to cross the intersection. This will help the vehicles to pass safely with minimum waiting time period and avoid Traffic Jamming.