Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-Based Assessment of Low Back Load During Manual Material Handling Tasks

According to the World Health Organization (WHO), the incidence and prevalence of low back pain are similar worldwide, but statistics based on epidemiologic data collection often rank low back pain at the top of the list. Low back pain can be classified into chronic and acute low back pain, and studies have shown that 39% of patients with chronic low back pain have disc degeneration. The National Institute for Occupational Safety and Health (NIOSH) has proposed the NIOSH Lifting Equation as one of the simplified indicators of low back pain risk, which can assess the risk of low back pain in work positions and postures during loading and unloading. The NIOSH Lifting Equation is one of the simple indicators of the risk of low back pain. In this study, the lumbar load during lifting is estimated by image processing using OpenPose in order to prevent the occurrence of low back pain. If the estimation of lumbar load by image processing of workers becomes possible, it is expected to facilitate the detection of the risk of lumbar pain occurrence and to be useful for vocational education of workers and improvement of the work environment.
Development of a Smart Speaker Application to Promote Continuous Exercise in the Elderly

Continuous exercise is essential for healthy aging. In recent years, there have been efforts to enable the elderly to engage in continuous exercise at home by utilizing Information and Communication Technology (ICT) devices. Among ICT devices, smart speakers are gaining attention due to their easy voice control, and applications for smart speakers, operating on their screens, are also being developed to assist with exercise. However, there is still a lack of understanding regarding the most effective features of smart speaker applications that support the continuation of exercise. This study aims to identify the key features for sustaining exercise. We have developed a smart speaker application with two features to support this goal. The first feature provides stamps based on the smart speaker application usage. The second feature suggests personalized exercises based on the user's fitness level. Four elderly women were invited to use the smart speaker application continuously at home and to evaluate the proposed features by a questionnaire. The research result suggests that the proposed features could improve self-efficacy, promoting continuous exercise. It also indicates the need for feature adjustment based on the participant's exercise status.
AgriNode: An Automated Farm Monitoring System

Millions of people in India rely on agriculture, a significant economic sector, for their livelihood. Farmers in the field are now using technology to improve their farming practices. AgriNode, which offers a complete irrigation system with plant control and a farm monitoring system, is essential in helping farmers increase their productivity. AgriNode reduces water waste and accurately delivers manure or fertilizer to the crops in the field by integrating the Internet of Things (IoT), cloud computing, and sensors with irrigation systems. Additionally, improving farming and technology will benefit farmers and the entire country.
Quantifying Repetition in Symbolic Music using Lempel-Ziv Compression

Repetition serves as a fundamental element in music by creating patterns, building structure, and emphasizing melodies, yet remains largely elusive to generative AI models. In this paper, we explore the question of quantifying musical repetition by using principles of information theory, arguing that a composition’s repetitiveness is related to the compressibility of its musical data. Thus, we introduce a metric of repetition termed the LZ ratio, based on Lempel-Ziv compression. By analyzing numerous selected piano pieces, we evaluate the effectiveness of our metric in measuring different types of musical repetition. We find that our metric does well in quantifying certain types of repetition. Furthermore, we observe expected changes in our metric when we remove repetitions from pieces. Our work presents an encouraging direction to objectively quantifying the repetitiveness of music for use in generative multimedia machine learning systems.
Pareto-based Optimised Weighting Factor for Model Predictive Control for Flying Capacitor Converter

The multiple control objective, balancing of flying capacitor (FC) voltages and the output current sets the challenge to controller design for the FC converter. The finite-control-set model predictive control (MPC) has emerged as a popular control method for the FC converter. However, the original cost function design often faces the issue of selecting suitable weighting factors. To address this challenge, this paper proposes an offline and an online strategy that utilise the principle of Pareto optimality and a knee-based Pareto pruning method. The offline approach uses the non-dominated sorting genetic algorithm (NSGA-II) to explore the optimal weighting factors. In contrast, the online strategy employs the knee-based decision-making method that avoids weighting factors. Simulation results are provided to validate the feasibility of both approaches.
Data-Driven Online Adaptive Parameters Adjustment for Output Quality of Grid-Connected Converters

As the installed capacity of renewable energy continues to increase, the grid is facing an increasingly prominent problem in terms of power quality. In particular, the power quality caused by high-impedance weak grid is more severe. Online adaptive parameters adjustment strategy can effectively improve the output quality of the grid-connected converters under time-varying grid. Adopting a fast and effective adaptive parameters adjustment method is essential to improve the output quality of the converters. This paper proposes a new data-driven online adaptive parameters adjustment method, which considers the parameters adjustment task under time-varying grid impedance as a multi-classification problem. This method uses the sequential impedance model and particle swarm optimization (PSO) to solve the optimal control parameters under time-varying grid as supervised learning labels. Subsequently, multiple-channel convolutional neural networks (MCCNN) are introduced to fully mine the temporal features of the instantaneous current data output by the converters and improve the accuracy of adaptive parameter adjustment. Finally, this method is validated through a simulation model. The empirical results demonstrate that the delay time of the proposed method from the change of grid inductance to parameter adjustment is less than 10ms, and the classification accuracy is about 96.33%.
FogAutho: Leveraging a novel authentication scheme for securing fog-cloud systems in smart home applications

We propose a light-weighted authentication scheme FogAutho for securing fog-cloud systems in smart home applications. It uses a novel approach to generate a session key after authenticating both ends; Fog and IoT layers. The cloud server may revoke the session key. Our proposed approach minimizes the communication between end users, fog server, and cloud server during data authentication. FogAutho achieves security objectives such as mutual authentication for both ends, a secure session key generation, data confidentiality, and attack resistance with significantly reduced communication overhead compared to the existing authentication mechanisms in the fog-cloud system.
Physics-Driven Deep Panoramic Imaging for High Dynamic Range Scenes

Due to saturated regions of low dynamic range (LDR) images and large intensity changes among them, it is challenging to produce an information-enriched panoramic LDR image without visual artifacts from multiple geometrically synchronized LDR images with different exposures and piecewise overlapping fields of views for a high dynamic range (HDR) scene. Fortunately, the stitching of such images is innately a perfect scenario for the fusion of physics-driven and data-driven methods. Based on the insight, a novel neural augmented HDR panoramic stitching algorithm is proposed in this paper. Differently exposed panoramic LDR images are initialized by using a physics-driven method on top of the piecewise overlapping fields of views. They are then refined by a data-driven one, and finally merged together via a multi-scale exposure fusion algorithm to produce the desired panoramic LDR image. Experimental results validate the proposed algorithm 11The source code and trained model will be publicly available upon the acceptance.
Crime Reach Analysis Within the Environmental Backcloth

The criminal propensity to commit crime is heavily influenced by surrounding environmental factors. Determining the various aspects and features within the environment that contribute to increased criminality and the scope of their impact can further the conceptualization of the environmental backcloth. To this end, a graph model of the environmental backcloth becomes a useful tool for analyzing the various elements that form the environment. By using a graph model, analytical tools from the mathematical field of Graph Theory can be applied to the environmental backcloth. In addition, various algorithms used in Social Network Analysis can be adapted and applied to this graph model. In particular, this paper explores how the concept of reach — used in Social Network Analysis to identify central actors — can be adapted and applied to a graph model of the road network of the City of Vancouver, Canada. This analysis focuses specifically on the relationship between the presence of stores and the rate of crime near a junction. A strong correlation was found between reach to stores and reach to crime demonstrating that areas with a high store presence tend to have high crime rates.
Dynamic Penalty for Mitigating EV Overstaying Problem in Fast Charging Pools

To mitigate the electric vehicle (EV) overstaying problem in fast charging pools and increase the utilization of EV chargers, a dynamic penalty mechanism based on an optimization model is proposed in this paper. The simulation results show that the proposed mechanism can effectively reduce the number of overstaying EVs, while increasing the EV entry rate, utilization rate, and revenue of chargers.
Dynamic Control of Active Noise Control Systems with Simplified Secondary Path Models

This paper presents a computationally efficient variant of the FxLMS algorithm for active noise control. By utilizing a calculated secondary path model with only one impulse response coefficient, the algorithm achieves increased computational efficiency without sacrificing performance. Moreover, the algorithm provides control over the system’s dynamics by allowing for the adjustment of the system poles using an extra control parameter. The paper’s contributions are both theoretical and practical, as confirmed through experimental verification.
2D Mesh Image Subcarrier Assignment for MUDiv/OFDMA with Convolutional Neural Networks

In an orthogonal frequency division multiple access (OFDMA), the subcarrier assignment with the multiuser diversity (MUDiv) gain has been proposed. However, in a MUDiv/OFDMA, the large computational complexity is required to obtain the good system performance. To solve this problem, convolutional neural networks (CNN) is adopted. CNN can contribute to reducing the computational complexity. However, when the learning data is not suitable, the MUDiv gain is not obtained, and the system performance is deteriorated. Therefore, in this paper, we propose the 2 dimensional (2D) mesh image subcarrier assignment for the MUDiv/OFDMA with the CNN.
Voltage Control of Flying-Capacitor-Based 7-Level Active Neutral-Point-Clamped (A-NPC) Inverters

This paper introduces an active balancing method based on pulse-width modulation (PWM) technique for voltage and current control of flying-capacitor (FC) based active neutral-point-clamped (A-NPC) inverters. The method aims to regulate the inverter's output voltage and current while maintaining FC voltages at their designated setpoint values. This active balancing approach utilizes a closed-loop feedback system to measure the instantaneous variables obtained from the inverter, specifically the FC voltages and output current. By employing these measured values, a set of logical variables and logic equations are derived. These equations are then used to determine the switching states of the semiconductor power switches based on the reference PWM voltage-levels generated by the common PWM techniques, such as phase-shifted or phase-disposition. The proposed active balancing technique is experimentally validated using 7-level A-NPC inverter with $f_{0}=400$ Hz. The results highlight the effectiveness and feasibility of the proposed technique in achieving voltage and current control, as well as ensuring balanced operation of FCs.
Chaos Control of Fractional-Order Buck Converter Based on Caputo-Fabrizio Fractional Derivative

This paper presents, for the first time, a chaos control scheme for the fractional-order Buck converter with Caputo-Fabrizio derivative to suppress the chaos phenomenon. Chaos analysis of the peak current mode fractional-order Buck converter is first carried out by numerical simulation, with a focus on the impact of the fractional order on the system's chaos range. A chaos controller based on a genetic algorithm optimized neural network is then proposed, which combines the neural network algorithm with the parametric resonance perturbation method to optimize the design of control parameters. Finally, circuit simulations in MATLAB show that the proposed method effectively controls the system from a chaotic state to a stable state, demonstrating the effectiveness of the chaos control algorithm used in this paper.
A Novel Interoperability Evaluation Framework for the Warehouse Management System

Warehouse management is a critical element in the supply chain. The rise of e-commerce demands ‘any-mix any-volume’ customized and personalized orders. Due to the variety of products in ‘any-mix any-volume’ scenario the warehouse operations have become complex. The advent of multi-channel commerce has reduced the minimum order quantity (MOQ), claiming frequent changes. The ‘any-mix any-volume’ operations require flexible automation for product storage, retrieval, packing and shipping. Seamless operations of multiple devices are essential for automating flexible warehouses. For effective flexible operations, the Warehouse Management System (WMS) requires controlling the automation devices and associated auxiliary systems. Heterogenous automation devices with distinct communication protocols, data formats and connectivity are challenges for WMS centered control. An interoperable WMS can enable seamless communication and data exchange among devices. In this work, the interoperability of the WMS and various aspects of interoperability of engineering systems are presented. Different WMS system designs are considered and evaluated for the interoperability requirements. A framework for evaluating the interoperability of a WMS is proposed which is validated through experimental trials at an industry grade warehouse.
Modeling impacts of climate change and anthropogenic factors on cetacean biodiversity in the Adriatic and Ionian sea as a premise for enabling future open-world virtual exploration

This research project represents the first step in the development of a comprehensive Marine Observation and Data Network Platform integrating biodiversity and geographical features using advanced modeling techniques. The Platform will use real-time and historical data and machine learning models to represent the past and present and to simulate future scenarios. The aim is to create a user-friendly software accessible to the public, allowing users to visualize the impact of environmental changes on various ecosystems across different years and seasons. Moreover, incorporating the "open world" concept from video games, the platform will allow users to freely explore and visualize various climate conditions and their effects on biodiversity and ecosystems at particular locations/landscapes, offering an immersive and interactive experience. By offering a dynamic Platform for understanding and tracking environmental shifts, it would be possible to promote informed decision-making and contribute to the developing effective, sustainable policies.
Maximum Efficiency Control on the Receiving Side Using LCC-LCC Compensation Topology for Dynamic Wireless Power Transfer

Transmission efficiency is one of the most important characteristics in dynamic wireless power transfer for battery electric vehicles. In this paper, the LCC-LCC compensation topology, which does not require complicated control in a transmission system, is used as the compensation topology. To facilitate laying and maintenance, the transmission system should be as simple as possible in configuration and control. On the other hand, there is no problem even if the receiving side uses a slightly more complicated control than the transmission side. Therefore, on the receiving side, a coupling coefficient is estimated in real time from the voltage and current values measured between the rectifier and the DC/DC converter. Then, maximum efficiency control is achieved by updating the duty ratio of the DC/DC converter to follow the optimum load. The effectiveness of the proposed method was demonstrated through the simulations and experiments.
A Novel Fault Diagnosis Method of PEMFC System Based on Data Space Feature Decision Tree Group and Extreme Learning Machine

For the operation process of proton exchange membrane fuel cells (PEMFCs) system, fault diagnosis plays a crucial role in ensuring the safety and reliability of system. To improve the accuracy and rapidity of fault diagnosis, a fault diagnosis method of PEMFC based on data space feature decision tree group (DTS) and extreme learning machine (ELM) is proposed. Firstly, principal component analysis (PCA) is used to reduce the dimension of sensor data such as current, temperature and gas flow rate of system, which reflects the operating states of fuel cell. Besides, spatial features of system can be extracted and processed by random rotation matrix. Furthermore, multiple sets of spatial rotation feature data are set to diagnose the health state of fuel cell system by combining decision tree group and extreme learning machine (DTS-ELM). Finally, the proposed method was verified and analyzed, and the experimental results indicate that this method can quickly identify four operating states such as normal state, flooding, membrane dying and hydrogen leakage. The diagnostic accuracy and operating time of this method are 99.54% 0.261s, respectively. Therefore, on-line rapid fault diagnosis of proton exchange membrane fuel cell system can be realized by the proposed method.
An AI based Smart Conference Calling System using Bluetooth Technology

Conference call using mobile refers to the telephonic call in which several people talks to each other simultaneously. This is one of the most eminent feature now-a-days. This concept is already existing using LTE technology for mobile phones supporting SIM cards. Hence, currently conference call is possible only with support of SIM card, i.e. Mobile operator. Bluetooth is a short-range wireless technology which is used for exchanging data between devices placed over short distances (up to 240 meters). This is a booming technology which is easily and freely available and has no dependency on network operators. Our study work proposes a smart system to enable conference call with more than two mobile users without SIM support communicating with each other simultaneously. AI based proposed solution will be self -governed, self-learned and will be intelligent enough to smartly switch between all callers connected via Bluetooth in conference call. This proposed solution system will greatly increase the potential of using Bluetooth technology from a wider applicability perspective of conference calls, which is currently only possible over LTE mobiles.
18 Sided Space Vector Modulation Method for Open End Winding Induction Machine Using a Single DC Source

A drive scheme for Open End Winding Induction Motor (OEW IM) using two 2- level inverters and a single DC source is discussed in this paper. The first inverter has a DC source and the sector inverter only has a DC link capacitor across it. The vectors of the two inverters are selected such that an 18 sided voltage vector space is created across the windings of the motor. The switching vectors of both inverters are chosen such that the voltage across the capacitor of the second inverter will be automatically regulated without any closed loop voltage controller.
Fully Distributed Control of Microgrids Using Multi-Agent Approach

In an islanded microgrid, the main objective of a distribution operator is to manage the generation load balance based on the available resources. With the rapid integration of renewable energy and low inertia-based resources in today's power grid, it is becoming challenging to achieve optimal operation by just using conventional control techniques. This work aims to create a fully distributed control system that can efficiently manage the functioning of a microgrid (MG). The MG system should be able to maintain nominal frequency and voltage as well as synchronize between the inverters and power grid autonomously. To achieve this, we implement a hybrid control architecture employing distributed implementation with a hierarchy via a Multi-Agent System (MAS). Additionally, we propose a secondary control for frequency restoration and a control algorithm for autonomous load connection/disconnection. To develop the microgrid model under study MATLAB Simulink was used, and the control architecture was validated using several test cases through controller-in-the-loop hardware testing (C-HIL) using Raspberry Pi as an external controller.
The Blockchain SSD Trilemma or Chasing Three Birds with One Stone

This article is devoted to the consideration of the so-called SSD trilemma of the blockchain. The article suggests the choice and justification of numerical coefficients of the three main indicators of blockchain platforms, such as scalability, security, and decentralization. The article also provides three types of optimization tasks, as well as a generalized efficiency evaluation indicator for solving the SSD trilemma of the blockchain.
Relationships of Light Transmission, Stratification and Fluorescence in the Hypoxic Region of the Texas-Louisiana Shelf in Spring/Summer 2009

Relationships of Light Transmission, Stratification, and Fluorescence in the Hypoxic Region of Texas-Louisiana Shelf in Spring/Summer 2009. (April 2010) Jenny Louise Towns Department of Environmental Programs Texas A&M University Research Advisor: Dr. Steven F. DiMarco Department of Oceanography The growth of phytoplankton in hypoxic waters requires nutrients and light. In river plumes of the coastal ocean, river borne surface nutrient concentrations decrease with distance from the river mouth. Light availability at the surface also changes with distance from the river source, as particulates and other materials sink through the water column. Using in situ CTD sensors, water samples of nutrients, and ship flow-through data from two Louisiana Shelf research cruises, April and July 2009, the relationship between transmissivity, photosynthetically available radiation (PAR), stratification, and fluorescence are analyzed. PAR is measured using a silicon photovoltaic detector and is measured in the 400 to 700 nm range. A fluorometer is used to measure the fluorescence of chlorophyll a in μg/L and is a proxy indicator for phytoplankton biomass. The Louisiana Shelf is home to the largest hypoxic zone in the western hemisphere, covering on average more than 15000 sq. km. The Louisiana hypoxic zone occurs seasonally in summer and is typically dissipated by physical processes in fall. Hypoxia is defined as waters that have a dissolved oxygen concentration of 1.4 ml/L and is the level at which