Miracle-dz/UniFAR-FTUs huggingface dataset:
LoRCoN-LO: Long-term Recurrent Convolutional Network-based LiDAR Odometry

We propose a deep learning-based LiDAR odometry estimation method called LoRCoN-LO that utilizes the long-term recurrent convolutional network (LRCN) structure. The LRCN layer is a structure that can process spatial and temporal information at once by using both CNN and LSTM layers. This feature is suitable for predicting continuous robot movements as it uses point clouds that contain spatial information. Therefore, we built a LoRCoN-LO model using the LRCN layer, and predicted the pose of the robot through this model. For performance verification, we conducted experiments exploiting a public dataset (KITTI). The results of the experiment show that LoRCoN-LO displays accurate odometry prediction in the dataset. The code is available at https://github.com/donghwijung/LoRCoN-LO.
Perfectly Convergent Particle Swarm Optimization for Solving Combined Economic Emission Dispatch Problems with and without Valve Loading Effects

The bulk of power is produced by carbon-fuelled thermal power stations, which discharge emissions like SO2, CO2, and NOx further into environment. Academics began concentrating their research work on many-objective load allocation. In order to resolve combined economic and multiple emissions dispatch scenarios with max-max price penalty component, this research introduces perfectly convergent particle swarm optimization (PCPSO) for addressing using quadratic functions, while considering the implications of emissions. Implementing this method on three different standard test systems, like the IEEE six-committed test unit system, ten generating test system, and forty generating real test system, and comparing the outcomes with other bio inspired algorithms, for the evaluation of this algorithm’s effectiveness. To do this, we created a software in the MATLAB 2015a environment on hp lab-top with 4GB RAM. This technique has enhanced search tools with excellent convergence characteristics, optimizing the quadratic cost and quadratic emissions functions at diverse power demands with minimal transmission line losses. Various practical constraints are taken into account, like limits of ramp rate, restricted operating zone(s), power balancing restriction, and limits of committed system. Transmission losses taken into account when considering a multi - fuel system. This algorithm is quick, reliable, and efficient, and it requires less time to solve non-convex problems with excellent efficiency.
Class Abstraction and Upcasting for Self-evolving Digital Twin System

A digital twin system refers to a large complex software application with a purpose of monitoring and/or simulating a complex physical system, possibly with some help from sensors and actuators in the physical world. Twin system can be used in a variety of fields, with great potential, such as smart city, factory optimization, architecture, and manufacturing, to name a few. However, despite academic and industry interest, the development of digital twin systems is still at the initial stage. In addition, although various studies have introduced the concepts and roles of the digital twin system and the digital things, more research is still needed on the specific concept on how to efficiently operate/manage them. In this paper, we propose an abstract digital thing class with a concept of self-evolution to efficiently operate/manage digital things in the digital twin system. The abstract digital thing class can efficiently operate the system with digital things through abstraction, inheritance, and upcasting of the classes.
Implementation of Face Emotion Detection In Classroom Using Convolutional Neural Network

Emotion detection is one of the most complex and challenging computer visuals due to significant variations caused by changes in facial appearance, lighting, and facial expressions. The results of tests conducted by Amanullah et al. on the classification of emotions using the Convolutional Neural Network show an accuracy value of 81.92% for training and 81.69% for testing but still has a fairly large error rate of 75.60% based on the loss validation results. In this study, the research to be conducted is to study more deeply about detecting human emotions based on facial expressions using the Convolutional Neural Network method. To detect human emotions based on faces using a Canon EOS 60D camera with good output quality. The camera is placed in front of the class. The computer user uses the Windows 10 64bit operating system, with a Core i5 processor, 16BG RAM, and GTX1060 VGA. Four LED lights are used every 18 watts to overcome the room lighting. Every 60 seconds, the system will carry out the process of detecting and classifying emotions and storing them in the database. The data obtained from the test results will be analyzed to obtain data in the form of accuracy, precision, and recall. The dataset for the training data used is FER-2013 which contains 28,000 datasets taken from Kaggle. The research results obtained were to obtain a high accuracy value that exceeded the results tested by Amanullah et al. The research results that will be carried out are also expected to be used by teachers to detect students' emotional conditions during class learning.
A segment-wise extraction of multivariate time-series features for Grassmann clustering

In this paper, a novel approach of extracting features from multivariate time-series (MTS) with different time lengths, is proposed to enhance the clustering accuracy. Particularly, the feature extraction is conducted on time-sample segments of MTS, in which several segments are defined without overlapping. As for feature extractor, the conventional two-dimensional principal component analysis (2DPCA) is deployed due to its proven effectiveness in feature representation. Our experimental results show that the proposed segment-wise extraction of 2DPCA features is helpful in enhancing the clustering accuracy.
High Dynamic Range Image Recovery by Use of Lens Flare Events Detection Algorithm

Event cameras are novel vision sensors that asynchronously output per-pixel events by measuring brightness changes. Event cameras have advantages such as high speed and high dynamic range compared to conventional cameras. To leverage the advantages and apply prior algorithms, event-based image reconstruction has been developed. With the development of neural networks, state-of-the-art reconstruction methods are introduced. However, high dynamic range reconstructions still suffer from lens flare-based artefacts, which makes the intensity estimation incorrect. To address this problem, this work presents a computational approach to detect ill-posed events caused by lens flare. Under the examination that lens flare elements of event cameras mainly consist of glow and starburst, we derive two bivariate Gaussian distributions from targets in the compressed stream of events. By operating convolution, the detector reduces the sparsity of events, which makes the surface fitting more precise. We show that the proposed method effectively eliminates dark stain in high dynamic range reconstruction, while preserving detail on the other region at the same time.
Experiment on the Accuracy of Internet of Things-based Hydroponic System

Internet of Things (IoT)-based hydroponic farming is known as a precision agriculture method. The ability to fine tuning the environment and growth conditions automatically has been the main attraction of such system. This study is conducted to measure the accuracy level of a common IoT -hydroponic system via several experiment. The experiment showed that the commercially available sensor has quite good accuracy, with Mean Average Percentage Error (MAPE) around 3%. The experiment also showed that the control process has a MAPE level around 3-6%. However, the experiment also showed that different sensor can interfere with each other and greatly reduce the accuracy of the sensors.
A Fundamental Study on a System for Estimating the Intention to Change a User Control Method by Measuring the Pressure inside the Transfemoral prosthetic Socket

This study proposes a system that detects the user intention to change a control signal through measuring the pressure change inside the transfemoral prosthetic socket. The proposed system measures the pressure change inside the socket to detect the movement direction of the leg and estimates the user intention to change a user control method through the detected direction. Pressure sensors that have been used since the past have limitations in the application of the proposed system. Therefore, in this study, an improved sensor system capable of fabricating various sizes and shapes based on Velostat film was fabricated. The fabricated sensor system was applied to the inside of the socket to classify the three directions of the leg. The classified direction will be available as an intention signal to change the control method of a prosthesis.
Peak Passive Ripple Mode Control for Boost Converter with Fast Transient Response

The boost converter has been widely used in portable devices. Generally, peak current mode (PCM) control is often used in boost converters to improve stability and bandwidth because of the single pole feature. However, the slope of the inductor current causes a large undershoot during load-transient. This paper proposes a peak passive ripple mode (PPRM) control to improve the load-transient performance. Furthermore, PPRM control has a lower quiescent current feature than PCM control because the current sensor is not required, and the passive ripple circuit consumes a lower quiescent current. A small signal model is proposed for PPRM control operated in continuous conduction mode (CCM) and the proposed small signal model is verified by experiment and simulation results.
BLM-DTO: Bandit Learning and Matching based Distributed Task Offloading in Fog Networks

This paper proposes an algorithm called BLM-DTO that allows each fog node (FN) to implement the task offloading operations in a distributed manner in the fog computing networks (FCNs). Fundamentally, BLM-DTO leverages the principle of matching game theory to achieve the stable matching outcome based on preference relations of two sides of the game. Due to the dynamic nature of fog computing environment, the preference relation of one-side game players is unknown a priori and achieved only by iteratively interacting with the other side of players. Thus, BLM-DTO further incorporates multi-armed bandit (MAB) learning using Thompson sampling (TS) technique to adaptively learn their unknown preferences. Extensive simulation results demonstrate the potential advantages of the proposed TS-type offloading algorithm over the ϵ-greedy and upper-bound confidence (UCB)-type baselines.
A 56-to-110 dB Gain Programmable Gain Amplifier with Second-Order Band Pass Filter for Ultrasonic Sensor Systems

In this paper, we propose a programmable gain amplifier (PGA) for an ultrasonic sensor system that removes DC offset and out-of-band noise through band-pass filtering in automotive applications. The proposed PGA consists of a low-noise amplifier (LNA), a variable gain amplifier (VGA), an RC low-pass filter (LPF), and a voltage buffer. The structure of LNA and VGA is an active band-pass filter (BPF) constructed using an operational transconductance amplifier (OTA) and passive elements. The PGA’s voltage gain and cut-off frequency can be adjusted by controlling the resistor connected to the common-mode voltage, the feedback capacitor, and the OTA compensation capacitor. An AC coupling capacitor is placed at the input stage to eliminate the DC offset and low-frequency noise of the PGA input signal. In addition, by arranging the feedback resistor and the feedback capacitor, the band-pass filtering function is implemented to block out noise at frequencies higher than the signal band. The PGA can adjust the voltage gain with a 6-dB step from 56 dB to 110 dB in the ultrasonic signal band from 30 kHz to 70 kHz. The design is designed in a 0.18-µm CMOS process. The post-layout simulation shows that it consumes a current of 9.7 mA with a 5 V supply voltage.
ICT Evolution Map Based on Indonesian Patent Documents Using Text Clustering Analysis

Indonesia needs a technology development strategy by referring to existing development data. Existing data can be obtained from past patent data. In accelerating the discovery of evolution technology map, techniques are needed to extract patent information to technology clusters. To do this, a combination of the Word2Vec and TF-IDF models is used. Both models are used to calculate similarities among patent documents. 194 Indonesian ICT patent documents were processed in this study. Similar patents are clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The result is 105 patent clusters, 15 of these clusters were detected as experiencing evolution. This means that many research activities have been done by Indonesian scientists, but only 14.29% of them have shown some evolution. The rest of the 85.71% of the research ended up without further development.
A New Prototype of Dynamic WPT Charging EDLC Scooter System with 5×2 Multi-Transfer Coils and SiC-VMOSFET Single-Ended inverters

An electric scooter is a promising solution to the global warming problem due to its high driving efficiency and carbon-free drive by electric power from renewable energy. It is a problem that their widespread use is limited by the time and effort required to recharge them. This paper proposes a new Dynamic Wireless Power Transfer EDLC scooter system with 5×2 multi-coils feeding and Si C-VM OSFET single-ended inverters. An EDLC energy storage unit allows many charges of the stepping-stone type Dynamic-Wireless Power Transfer. Transfer power can be small despite the large amount of energy by the 5×2 multi-transfer coil power feeding system. The developed single-ended inverter with a new V-trench SiC-MOSFET is small-size, light-weight, and low-cost. It can operate stably against extreme fluctuations of load by running through power-feeding stations.
An investigation into time-varying characteristics of multivariate time series in Grassmann classification

Since multivariate time series (MTS), which lie in a non-Euclidean space, exhibit temporal evolution and correlation characteristics, its classification is considered a non-trivial task. To mitigate the impact of the time-varying characteristics and thus enhance the classification accuracy, in this paper, we propose to model MTS data using a time-varying linear dynamical system followed by a neural network-based classification on the Grassmannian manifold. Our experiments on publicly available MTS datasets show promising classification results.
A Low-Cost IoT System for Monitoring Air Quality in Indoor Working Places

This paper presents a development of internet of things (IoT)-based indoor air quality monitoring system. The system is purposed to monitor quality of air in offices. It is to assure the health and safety of the working place which is especially being a big concern during the COVID-19 pandemic. Implementing the IoT concept allows to do monitoring from anywhere at anytime. A prototype of the monitoring system is built using three major components, such as an air quality sensor (BME680), a microcontroller (NodeMCU ESP-12), and an IoT cloud platform (ThingSpeak). The experimental test result shows that system was able to monitor the air temperature, air humidity, air pressure, IAQ (indoor air quality) index, carbon dioxide quantity, and VOC (volatile organic compound). These data is presented real-time in a web application and accessible from anywhere by using computers or smartphones.
A Systematic Literature Review of Machine Learning to Predict Location in Social Media

Predicting the location is a critical issue for conducting any analyses. Location analysis is one of analysis which processes Geographic Information (GI). Example in social media, users could include their posts or tweets with latitude and longitude using the Global Position System (GPS) in their devices. Unfortunately, not all users or content creators provide their latitude and longitude. As a result, we investigate the literature in journals and conferences while being aware of the best methodology and algorithm employed by current researchers. Based on the predetermined inclusion and exclusion criteria, thirty-six location prediction research articles published between 2013 and 2022 were kept and selected to be investigated further. A strategy for identifying, assessing, and interpreting all study materials accessible with the goal of responding to specific questions is known as a Systematic Literature Review (SLR). The SLR result show that the topic of location prediction is still high in publication level. User text post is the most popular approach used by researchers, followed by user information and user trajectory. Almost all reviewed paper use Twitter to get the social media data. It mostly caused because of the availability throughout the Application Programming Interface (API), tool library, and datasets on the internet. Out of the twenty-six ways, four of the most popular algorithms for predicting the location were found (RNN, LSTM, CNN and Random Forest). To raise the performance of machine learning classifiers for position prediction, some researchers combining any machine learning algorithms. Therefore, the researcher suggests to using any combined algorithm, approach, and source of data in social media to get deeper optimization of location prediction.
Design and Analysis of Circular Coil Geometries for Wireless Power Transfer in Electric Vehicles The Effect of Multiple Coils at Primary and Secondary Sides

Wireless power transfer (WPT) based on magnetic resonance is the technology that might isolate humans from wires. In practical terms, the WPT utilizes the same fundamental principle researched for at least half a century under inductive power transfer. In recent years, WPT technology has undergone tremendous development, with a grid-to-load efficiency greater than 90% and the transmission distance rises from a few millimeters to several hundred millimeters at kilowatt power levels. The advancements make the WPT particularly desirable for static and dynamic electric vehicle (EV) charging applications. This work designs and analyzes multiple circular coil geometries appropriate to EV wireless charging for the WPT application. Using ANSYS Maxwell software to simulate, construct and evaluate the performance of various coil assemblies, including coupling coefficient (k), Mutual inductance (M), self-inductance (L), and magnetic flux density (B) for air gap distance of 50mm to 200 mm. The coil dimensions have been the same for all coils, and the air gap length and the copper wire specifications are uniform for equality. Using the proposed multiple coil-based WPT in EVs may easily overcome charging time, range, and cost challenges.
QoS-Aware UAV-BS Deployment Optimization Based on Reinforcement Learning

We propose an unmanned aerial vehicle mounted base station (UAV-BS) deployment optimization scheme using reinforcement learning (RL). We formulate the objective function considering quality-of-service (QoS) of ground user equipments and fairness among them. To solve the optimization problem using RL, we propose a deep Q-network (DQN) model by defining the state, action, reward, and train UAV-BS to find the best movement direction. The simulation results show that the proposed DQN model is trained well, and the UAV-BS reaches the optimum position through our proposed DQN model.
Face Presentation Attack Detection using Color Spaces Features and Convolutional Neural Network

Authentication systems using facial biometrics are currently straightforward to implement. The easy implementation of the system makes it easier for people to carry out spoofing attacks. This spoofing attack can use photos, videos, and other people's face masks. To be able to recognize the existence of this spoofing attack requires good accuracy. One of the efforts to improve accuracy is to improve feature extraction results. Feature extraction using color spaces has been extensively researched. The use of this color space is known to increase performance. Several studies have used several types of color spaces to perform texture analysis. This study will analyze the use of color spaces such as YCbCr and HSV. These color spaces will be combined to get facial features. The color features were input for classification methods such as ResNet50, VGG16, and MobileNetV2 so that they could detect spoofing attacks. Our experiments show this approach's promising result by achieving the lowest EER of 3.62 % in the CASIA dataset.
Socially-Attentive Policy Optimization in Multi-Agent Self-Driving System

: As increasing numbers of autonomous vehicles (AVs) are being deployed, it is important to construct a multi-agent self-driving (MASD) system for navigating trafﬁc ﬂows of AVs. To prevent congestion or collision, especially in scenarios like intersection or lane merging, an AV should not only pursue the individual goal but also interact with other trafﬁc participants. Inspired by the mixed motives like human drivers, we mainly focus on how to ﬁgure out the current interacting vehicles in MASD systems, and then model the coordination or conﬂicts of their goals, namely their social preferences (SPs). To this end, we pro-pose a novel reinforcement learning method for each AV in decentralized control, called Socially-Attentive Policy Optimization (SAPO), which incorporates: (a) a self-attention module to select the most interactive trafﬁc participant, and (b) a social-aware integration mechanism to integrate objectives of the ego AV and its interacting partner, by updating their SPs from the current observations. SAPO solves the decision-making problem of how to improve the safety and efﬁciency of MASD systems, without utilizing any global information for centralized con-trol. Simulation experiments show that SAPO can successfully capture and utilize the variation of the SPs of AVs to achieve superior performance.
Theory of the static Kerr effect in dense fluids

A general expression, independent of the shape of the sample, is derived for the static Kerr constant, κ, of a dense fluid of optically anisotropic, nonpolar molecules. The molecules are assumed to have second-rank tensorial polarizabilities and fourth-rank tensorial hyperpolarizabilities. This expression for κ is expanded in powers of the ‘cut-out’ dipole propagator. The first non-trival term in the expansion is analysed in terms of the pair-distribution function. The resulting expression for the Kerr constant is compared to the depolarized scattering intensity, I dep, obtained to the same level of approximation. Similarities and differences between these quantities are discussed and predictions about the behaviour of the Kerr constant for dense fluids are made on the basis of numerical results obtained earlier for I dep. κ is expressed in terms of ‘effective’ polarizability anisotropies and hyperpolarizability, different from their gas-phase counterparts. It is found that the dipole-induced-dipole inter.