Miracle-dz/UniFAR-FTUs huggingface dataset:
A Machine Vision Approach for Nitrate Sufficiency Determination in Aquaponic Lettuce via Leaf Color Recognition

Aquaponic systems are popular as sustainable food production methods, however it also experiences challenges in terms of monitoring the nutrient levels, specifically nitrate sufficiency. In this study a new method is proposed to monitor the nitrate sufficiency in aquaponic systems using leaf color recognition. To do this, a state-of-the-art deep learning algorithm is utilized, the YOLOv8 for its extensive object detection capabilities. This algorithm is used to generate a custom model for leaf color recognition. The model is trained to recognize whether a lettuce leaf is nitrate deficient or sufficient based on its color. The model attained an amazing mean average precision (mAP) of 99.4% after extensive training and evaluation. The high mAP score implies that the model is highly accurate and reliable in differentiating between leaves with deficient and sufficient nitrate levels. With this, farmers and researchers may efficiently monitor and manage nitrate levels through integrating this leaf color recognition technology into aquaponic setups.
Infant Expression Recognition Using Multi-branch Cross-attention Networks

Facial expressions are one of the most direct ways for humans to express their emotions and physical states. Because babies do not have good verbal expression ability, it is one of the better ways to judge their emotional state by expression.In this paper, a new model is proposed to improve the accuracy of infant expression recognition. The model combines the multi-branch network structure and the cross-attention module, extracts the feature maps of the original image and the Local Binary Pattern (LBP) image using ResNet50 and VGG16 respectively, then fuses the two feature images and sends them to the cross-attention module, and finally uses the classifier for expression recognition. The model is applied to the self-built infant expression dataset. The experimental results show that the network can effectively extract the features of infant facial expression and accurately judge the infant's expression, and the recognition accuracy can reach 94.3%.
Background-Adaptive Surface Defect Detection Neural Networks via Positive Samples

This paper focuses on the challenge of surface defect detection in manufacturing, particularly under conditions of background variation and noise interference. To tackle this issue, a novel Background-Adaptive Surface Defect Detection Network (BANet) is proposed. The BANet enhances the defect detection capabilities by improving generalization capacity through learning comparative abilities between positive samples and testing samples. In order to mitigate the impact of three types of noise (texture variation, translation, and rotation), a Foreground Edge Attention Mechanism (FEAM) and a Spatial Transformer Module (STM) are introduced. The FEAM enhances the model's ability to differentiate between foreground and background, thereby effectively reducing texture variation noise. The STM uses affine transformations to eliminate translation and rotation noise. The effectiveness of the proposed network is validated on Optical Communication Devices (OCDs) dataset, with results indicating superior performance compared to prevailing state-of-the-art methods. The findings of this study highlight the potential of our approach in effectively addressing surface defect detection in variable backgrounds and noisy conditions, thereby contributing significantly to the quality and reliability of manufacturing processes.
Hierarchical Model Predictive Control for a VSI Considering Unbalanced Load and Nonlinear Load

This paper presents a novel hierarchical control scheme based on Model Predictive Control (MPC) for a Voltage Source Inverter (VSI) operating under conditions of unbalanced and nonlinear loads. The increasing prevalence of renewable energy sources and the proliferation of power electronic devices has led to a growing need for robust control strategies that can effectively handle the challenges associated with unbalanced and nonlinear loads in power distribution systems. In this work, a two-hierarchy control structure is proposed, based on a full MPC strategy. The MPC controller in the first hierarchy is responsible for optimizing the VSI's switching states in real-time, taking into account the system's constraints and the desired performance criteria of the output voltage of the VSI. The MPC controller in the second hierarchy regulates the output current of the VSI to track the reference values for the load and grid power. The proposed control scheme addresses the challenges posed by unbalanced and nonlinear loads, the simulation results demonstrate the effectiveness of the proposed hierarchical control strategy in mitigating voltage deviations, minimizing harmonic distortions, and ensuring balanced operation in the presence of unbalanced and nonlinear loads. The presented approach offers a promising solution for enhancing the performance and stability of VSI-based power distribution systems, thus contributing to the overall reliability and efficiency of modern electrical grids.
Fixed-Time Sliding Mode Control for Air-Floating Robot Using Actor–Critic Learning Structure

A fixed-time sliding mode controller is developed for the three-degree-of-freedom air-floating robot (AFR) in the presence of model uncertainties and external disturbances. By dynamics transformation, the complex nonlinear AFR dynamics are transformed into a second-order feedback decoupling model. A neural network enhanced by actor–critic learning structure is designed to handle model uncertainties, and the nonsingular fast terminal sliding mode controller can ensure all signals in the closed-loop system converge to a residual set around the origin within a fixed time. The system stability is proved by Lyapunov theory and the simulation results show the effectiveness of the proposed scheme.
Disturbance Observer-based Discrete-time Sliding Mode Tracking Control for Nonholonomic Robots

This paper develops a disturbance observer-based discrete-time sliding mode control (DOB-DSMC) method to address the trajectory tracking problem for nonholonomic robots subject to disturbances. Firstly, a reduced-dimensional tracking error system is derived to overcome the disadvantage of the underactuated robot system. In the light of reduced-dimensional system, a disturbance observer is studied to estimate and compensate for disturbances. Then, inspired by the continuous terminal sliding mode surface, a novel discrete-time terminal sliding surface is designed. Further, the arctangent function is inducted to improve the tracking accuracy and attenuate the chattering phenomenon. Moreover, the asymptotic stability of system is proved via the Lyapunov method. Finally, the effectiveness of the exploited algorithm is verified by a numerical simulation.
Application of Charge Pumping Technique for MOSFET Devices Reliability

In this paper, we present an overview of charge-pumping technique (CPT) and its applications in field-effect transistors (FET) reliability during couple of decades, giving insight into its potentialities. We describe the evolution of CPT, starting from the most popular one "conventional CPT" and follow up with its different variants to study uniform and non-uniform degradations in FET devices fabricated with old and advanced semiconductor and dielectric materials. In addition, we illustrate its ability to characterize transistors with specific geometries. Its limitations and solutions are also discussed.
Development of Soil Organic Carbon Estimator using Soil Pictures Taken by Smartphones

The soil organic matter (SOM) is crucial in terms of productivity in agriculture. However, it is not easy to estimate the SOM needing expensive equipment. In this paper, we develop an SOM estimator using CNN enabling us to estimate the SOM using soil pictures taken by a smartphone. The performance is evaluated, as well as the effect of preprocessing methods.
Formulating Emotion Graphs Through the Lens of Advanced AI Models

Existing AI models for facial emotion recognition (FER) predominantly categorize facial expressions into discrete, generalized emotions, which overlook the nuanced and continuous nature of human emotions. Our research delved into the perception of human emotions by advanced AI models. Based on the analysis, we formulated AI Emotion Graphs, highlighting disparities in AI-based categorizations. We curated a super dataset comprising labeled images from diverse sources. We trained a convolutional neural network (CNN) FER model and computed the correlation scores between the model results and the labels to establish a baseline. Then, we assessed the performance of a state-of-the-art 48-label FER model from Hume AI on our super dataset, analyzing the correlation scores with statistical significance. Our findings revealed that the spectrum of valence is not fairly represented. Positive valence is oversimplified with a single label, “happy”, whereas human emotions encompass triumph, pride, admiration, satisfaction, love, amusement, etc. Similarly, neutral emotions, such as entrancement, concentration, and calmness, are reduced to a singular label. In contrast, emphasis centers on negative valence, with most of the labels assigned to sad, fear, angry, disgust, and surprise. Also, our research visualized the intricate transitions between emotions that are not accounted for by the multi-class classification AI models. Advanced AI models built on multi-label classification and trained on diverse labeled datasets, such as Hume AI, address the above-mentioned limitations and pave a path towards a more authentic connection with human emotions.
Digital Twin-Based Research in the Maritime Industry: A Brief Survey

In this work, a survey of DT-related research in the maritime industry is presented. A five-dimensional DT model for the maritime industry is presented and explained. Moreover, research object and characteristics of DT in maritime industry are categorized and discussed. The research objects of DT in the maritime industry are classified into ships, marine structures, underwater vehicles and marine engines. The characteristics of the maritime industry DT models are discussed in terms of target system, research contribution and simulation method. In addition, DT in the context of product life-cycle perspective in the maritime industry is analyzed and discussed in terms of design, manufacturing, operation, and maintenance phases. Based on our analysis and discussion of the research, we found that the current DT research in the maritime sector is mainly focused on relatively small modules, or small systems, or even only on individual components. In addition, only a small fraction of the reviewed DT-related papers focuses on the whole life-cycle in the maritime industry. The reason may come from that the models and sub-models are not yet flexible and adaptive enough at different life-cycle stages. Therefore, we believe that the development of DT technology is still in the developmental stage in the maritime industry.
Pose Synchronization for Semi-Autonomous Dynamic Robotic Swarms with a Passivity-Short Human Operator

This paper presents a pose synchronization method for semi-autonomous dynamic robotic swarms with a passivityshort human operator. In the human-robot network, a human operator sends a command signal to and receives visual information from only a part of the robotic group. In this setting, the proposed dynamic control scheme achieves not only pose synchronization of all mobile robots but also convergence to the reference pose that the human operator specifies. The main advantage of the proposed method is to guarantee convergence of not only the position but also the orientation to the reference ones in the human-robot network. Finally, we provide human-in-the-loop simulation results through the human operation using a tablet PC to illustrate the performance of the proposed control method.
FreISH: A Database of Japanese Freshwater Fish in the Wild and Baseline Recognition Evaluations

Fish identification is more challenging than that of other animals due to mismatches between their names and appearances. Building a fish database is crucial for fish research and the creation of digital fish-related applications. Though some fish databases exist, they often operate under specific settings like fixed lighting, simple backgrounds, or solo fish imagery. In our research, we developed a database (FreISH) capturing Japanese freshwater fish in their natural settings. This preliminary version features 1,200 images of twelve native Japanese freshwater species, each represented by 100 images displaying different postures. We gauged the species recognition rate using the VGG-19-BN deep learning model on this dataset. Additionally, we contrasted the efficiency of the "fine-tuning" and "transfer learning" techniques to find the optimal approach.
Adaptive Gramian-Angular-Field Segmentation Integration Based Generative Adversarial Network (AGSI-GAN) for Eye Diagram Estimation of High Bandwidth Memory (HBM) Interposer

In this paper, we propose an adaptive Gramian-Angular-Field segmentation integration (AGSI) based generative adversarial networks (GANs) for eye diagram estimation, which is a novel approach in high bandwidth memory (HBM) interposer design. The proposed AGSI framework effectively integrated relevant signal integrity (SI) components, namely single bit response (SBR) and far-end crosstalk (FEXT). AGSI allows for a customized and accurate representation of various design scenarios. By employing GAN-based estimator with AGSI as input data, the model significantly improved the time efficiency and accuracy of eye diagram estimation. This novel approach has potential applications in other domains, including signal integrity/power integrity (SI/PI) co-simulation, and is expected to enhance the design process in terms of time cost for HBM interposers and beyond.
Convolutional Neural Network for Sound Recognition in the Household Domain

In the modernized and networked environment, people have to process different sensory impressions. Acoustic signals in particular are often used to indicate important events in the surrounding area. This is a particular challenge for hearing impaired people, as they are often unable to perceive sounds such as fire alarms. In this work, we investigate a sound recognition method based on a convolutional neural network to detect sounds in a household environment.
Analysis of a QQ Pad for Dynamic Wireless Power Transfer System to Reduce the Power Pulsation and to Increase Output Power

This paper proposes a dynamic wireless power transfer system with an overlapped QQ receiver. The proposed coupler is analyzed, and the simulation results of this system is presented. It's observed that the proposed coupler has lower mutual inductance variation in misalignment compared to a single receiver system. When one receiver receives lowest power, another receiver receives highest power at that position and together both receiver helps to improve performance of the system. To verify the feasibility of proposed coupler finite element analysis (FEA) software Ansys Maxwell is used. The variation of mutual inductance for the proposed coupler is found ± 2.38%. This proposed coupler also increase the mutual inductance hence increases the output power. The simulation results of the proposed system are also presented in this paper.
Evaluation of port manoeuvring efficiency using Data Envelopment Analysis

This paper describes how to apply an operational research method to measure the efficiency of port regulations and sailors best-practices during the berthing and departure of vessels. The proposed method is the Data Envelopment Analysis (DEA), a non-parametric algorithm for measuring the efficiency of decision-making units. The DEA is applied to estimating the efficiency of navigation rules used by vessels while anchoring at the roads of the port of Augusta (Italy). Three different definitions of efficiency are used, according to the output quantity to maximise: (1) inverse of time taken to complete the route, (2) route safety, (3) a combination of the two. The vessels’ manoeuvres efficiency is estimated by considering several input dimensions such as boat engine, weather and sea conditions, crew experience, distance travelled and energy consumed.
An Improved Degradation Monitoring Method for High Power IGBT Modules Based on On-State Resistance Estimation

Degradation of high-power IGBT modules adversely influences the safety of inverters and can cause significant economic loss in industrial applications. Due to various factors affecting the degradation of IGBTs, lots of advanced monitoring methods still struggle to comprehensively evaluate the current health status of IGBT modules during power converter operation. Therefore, this paper proposed an improved online degradation method for high-power IGBT modules based on the estimation of on-state resistance. This method has reduced component numbers and circuit complexity with higher monitoring accuracy and wider applications. The proposed condition monitoring methodology is verified experimentally on a three-phase inverter setup and the test results are presented.
Investigation on Axial-Flux Permanent Magnet Synchronous Motor with High Torque Density and Low Thermal Raise for In-wheel Direct-Drive Electric Bikes

Axial-flux permanent magnet synchronous motor (AFPMSM) is a promising solution for in-wheel direct-drive electric bikes (e-bikes) due to its disc-type topology, structural compactness, and high torque density. However, the large electric loading of conventional AFPMSMs for high torque output can lead to severe thermal rise and potential fault risk, which is unbearable for long-running operation and security requirement in e-bike scenario. To address this issue, a comparative study is conducted on the performance of AFPMSM with conventional surface-mounted PM array (S-AFPMSM) and Halbach PM array (H-AFPMSM). The design and optimization procedure for both AFPMSMs is conducted. The investigation result shows that S-AFPMSM features over 30% higher torque density than that of a radial-flux PMSM, but the thermal raise is too much for air-cooling condition. By utilizing Halbach PM array with self-shielding magnetization effect, H-AFPMSM has higher magnetic loading and much less heat loading when it outputs the same torque density as that of S-AFPMSM. As a result, the copper loss is reduced by over 30%, and accordingly the efficiency increases to 94%. A prototype of the motor is manufactured and tested, with results that agree well with the theoretical analysis.
SOC-Based Adaptive Droop Control Considering the Line Impedance and Coordination Control for the Photovoltaic Dominated DC Microgrid

With the ascending popularity of renewable energy in power systems, DC microgrids offer an attractive solution to this trend. In order to prolong the lifetime and prevent excessive use of energy storage units in the DC microgrid, an adaptive droop control based on the state of charge of the energy storage unit is proposed considering the line impedance. By adaptively adjusting the droop coefficient, the mode transfer times between the shutdown mode and the droop control mode of the energy storage unit is reduced, and the loss caused by transfer among different modes is reduced. The reasonable power distribution of the energy storage unit is realized. The service cycle times of the energy storage unit are reduced and the bus voltage accuracy is improved. The proposed method in the DC microgrid is verified by simulation results.
An Investigation of Algorithms for Autonomous Navigation in AGV

This paper provides significant insights into the Localization and Mapping problem in Automated Guided Vehicles (AGV). AGVs are valuable in any industry because they enable autonomous navigation and transportation of smaller loads with minimal human intervention. The study discusses how Simultaneous Localization and Mapping (SLAM) addresses the mapping as well as the localization problems, dividing the SLAM algorithm into categories based on the estimation techniques used and comparing LiDAR-based SLAM as well as Visual-based SLAM. This paper also categorises path-planning algorithms into offline and online methods based on available environmental information. The paper then discusses the significance of the Robot Operating System (ROS) in addressing the localization, mapping, and path-planning problems in an AGV.
Transforming Online Learning with CNN and NLP: Personalized Education

In the rapidly evolving landscape of online education, ensuring easy and healthy learning experiences has become paramount. This research paper presents "Sparkle Edu," an innovative application that integrates multiple components to address the unique challenges of online learning. The components include stress detection through facial emotions and drowsiness analysis, facial authentication with personalized course recommendations, an AI-powered mental health therapist utilizing voice-to-text input, and document reconstruction for damaged images to predict missing letters and words. Through the implementation of these components, Sparkle Edu aims to create an engaging and supportive platform that prioritizes students' well-being and promotes effective learning. This research contributes valuable insights into the advancement of online education, fostering a brighter and more accessible future for learners worldwide.
Observer-Based Disturbance Estimation and Optimal Allocation for the Roll Control of an Unmanned Motorcycle with Control Moment Gyros

For the roll control of an unmanned motorcycle equipped with twin control moment gyros (CMGs), the optimal allocation between the steering motor and CMGs when encountering strong disturbance is rarely investigated in existing studies. In this paper, based on an extended state observer (ESO) and a novel control allocator (CA), a robust control scheme ESO-CA is proposed for the unmanned motorcycle to resist disturbances. The ESO enables the estimation and compensation of the lumped disturbance, which incorporates unmodeled dynamics, parameter perturbations and external disturbances. Based on a novel optimization formulation that considers the integral saturations of actuators, the control allocation method is developed to minimize total energy consumption and reduce steering chattering. Two comparative numerical simulations and one physical experiment are provided to demonstrate the effectiveness of the proposed control scheme.
The Geracao Foun, Talitakum and Indonesia: Media and Memory Politics in Timor Leste

A perspective on the cultural politics of independent Timor Leste is provided to show how a resistance culture became a popular culture of shared sentiment among Timor's youth. An appreciation of this shared sentiments among the Geracao Foun (Tetum: young/new generation) can be seen through the production and consumption of the popular magazine, Talitakum.