Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary Algorithms with Constraint Handling for the Hydroelectric Dispatch Planning

This paper proposes a method to solve the long-term scheduling of the Brazilian hydroelectric power system. The objective is to meet the energy demand by defining the required actions for each individual power plant, whilst respecting boundarie and operational constraints. The mathematical model is formulated as a dynamic, nonlinear, multiobjective, high dimensional and largely constrained optimization problem. We compare two classes of metaheuristics for constrained problems: Differential Evolution (DE) and swarm-based (PSO), using the CEC-2017 constrained optimization competition benchmark. The best performing metaheuristics of each class, evolutionary-based LSHADE44b (a slight variant of LSHADE44, winner of CEC-2017) and swarm-based EPSO-G, are then compared on the hydroelectric power system scheduling problem. We considered 111 power plants for a period of 5 years, with monthly time-step, resulting in 13,320 decision variables with 20,091 constraints, which are optimized for 194 random affluence scenarios. Results show that LSHADE44b outperforms EPSO-G in all scenarios, reducing the constraints violations down to an average of 16. On 45% of the affluence scenarios there were no violations, and the cost steadily decreased, showing convergence after the constraints are satisfied.
A Multi-load Wireless Power Transfer System with Constant Voltage Outputs Using S-LCC Compensation

This paper proposes a novel wireless power transfer (WPT) system powering multiple loads. The repeater coils are used to transmit the power over a long distance. Every two repeater coils form a repeater unit, which corresponds to a load. For each transmitting coil, a capacitor is connected in series with it and a LCC compensation circuit is used for each receiving coil, which forms an S-LCC compensation topology. It is proven that the constant voltage characteristics can be obtained for each load when neglecting the coils’ parasitic resistances, which facilitates independent load power control.
A Distributed Algorithm for Auto-Balancing of Hydronic Cooling Systems

Hydronic cooling systems are often used in large buildings. In these systems, chilled water is transported from the chillers to Air Handling Units (AHUs) via a water distribution system. The temperature of exhaust air is controlled by the flow of the chilled water in AHUs. Due to a poorly balanced hydronic network, some of the AHUs may experience starvation at high load conditions, meaning that air temperature control is impossible. To overcome this problem, we propose an automatic approach to balancing the hydronic system, such that all AHUs can control their air temperatures. The cost is that all zones of the building are controlled to a slightly higher air temperature, which often can be compensated by higher air flow, to keep the room temperature at the desired value. The balancing algorithm is verified on a Modelica model of an office building.
IPMSM with Rotor Using Two Different Cores

Interior permanent magnet synchronous motor (IPMSM) with the rotor using two different cores is proposed. Permalloy is used in part of the rotor. In this paper, the principle of motor performance is described. Permalloy operates same as a flux barrier when the motor drives at low speed and high torque, and it actively operates to short circuit the magnet flux when the motor drives at high speed and low torque. Motor characteristics are obtained by the simulation of FEM, and results are compared with simulation ones of a basic IPMSM. According to the examination results, although the speed is increased, the average torque is decreased and the high efficiency range is almost the same as the basic motor.
Comparison of major PWM methods from the aspect of the system efficiency of machine drive system

System efficiency is one of the key performance for machine drive system. Taking the application of electric vehicle for example, improving the system efficiency of the drive system is able to increase the mileage range, and is of important practical use. PWM methods are directly related to the loss of the inverter and THD of the currents and have a significant impact on the system efficiency. Major PWM methods including SVPWM, DPWM1, DPWM2 are analyzed comparatively in this paper from the aspect of system efficiency. Firstly, theory and realization of the three PWM methods are introduced. Then, we adopt the loss of the inverter and THD of the currents as the evaluation standards to investigate the PWM methods comparatively. The system efficiency of DPWM methods are higher than SVPWM when the modulation ration become greater than or equal to the maximum value of the linear modulation region. Between the two DPWM methods, DPWM2 is more suitable in the application with inductance load similar to the machine drive. Lastly, the theoretical analysis is validated through simulation and experimental results. The results manifest DPWM is advantageous only if the modulation ration is greater enough. For the DPWM methods, both the efficiency of the inverter and that of the system of the DPWM2 are higher in machine drive.
Classification Methods of Machine Learning to Detect DDoS Attacks

In this paper the problem of intrusion detection by classification of the network traffic realizations was considered. The Machine Learning methods using fractal and recurrence features are applied to solve this problem. The simulated traffic realizations and the experimental data sets that contain attacks realization were used. The numerical experiment to detect intrusions using the example of detecting typical DDoS attacks was conducted. It has been shown that the classification by Random Forest method can effectively detect attacks in network traffic.
Design Considerations for a Position-Adaptive Contactless Underwater Power Deliver System

With the understanding and development of the ocean, the Deep-sea exploration technology has become a hot spot of research in many countries. As the most promising underwater power feeding technique, contactless power transmission extends the working range of underwater detection equipment. The classic equivalent circuit model is modified to include the eddy current loss in seawater. Considering the waterflow effects, a coaxial 300W position-adaptive contactless underwater power deliver system is designed and optimized in this paper. Experimental results validate the theoretical analysis in this paper.
Research on Speed Sensorless Control of Permanent Magnet Linear Synchronous Motor Based on PID Neural Network

There are some problems accompanied by detecting rotor parameters of the traditional method on the permanent magnet linear synchronous motor (PMLSM), such as inaccurate detection and poor anti-interference ability. Based on this, this paper proposes a speed closed-loop control strategy based on PID neural network (PIDNN) without speed sensor. The algorithm obtains the error between real current and the given current of the q-axis by calculating the observation angle and the actual angle. The algorithm obtains the error between the given current and the feedback value of the q-axis, and uses the gradient descent algorithm to update the parameters and to obtain the fitted electrical angle. When the current error converges to zero, the output is the velocity of the mover. The simulation and experimental results show that the algorithm can track the speed very well, which reduces the cost and improves the reliability of the system.
Flexible Indoor Scene Synthesis via a Multi-object Particle Swarm Intelligence Optimization Algorithm and User Intentions

Flexible indoor scene synthesis is a popular topic in computer graphics and virtual reality research due to its wide-ranging applications in home design, games and automated robotics training. We propose a novel approach to automatic and flexible indoor scene synthesis using an energy-based method. We regard indoor scene synthesis as a multiple-object optimization problem with furniture location and orientation according to the user's intention, as a constraint on the energy of the optimization problem. Based on the relationship of objects, the embedded aesthetic criterion, the design criterion for proper placement and human movement in a scene, we design five energy functions, the overlap constraint, pairwise constraint, wall constraint, aisle constraint, angle constraint and penalty item, are proposed. We use a multi-object particle swarm intelligence optimization method with a Markov chain Monte Carlo algorithm to solve this optimization problem and obtain a Pareto-optimal solution. 3D gestures are used as the medium of interaction between the user and the system. Our method significantly enhances the existing weighted energy optimization method by allowing a joint optimization of various energy functions. The experiments confirm that all the energy functions can converge at the same time and that the proposed method obtains results superior to those of the weighted methods. The proposed method is general which can be used to obtain layouts for various kind of rooms with different furniture.
Electromagnetic Performances Analysis of a Hybrid-permanent-magnet Motor Capable of High Torque

This paper presents a spoke-type hybrid permanent magnet motor (HPMM), which integrates the high energy rare-earth-PM and the low-cost non-rare-earth ferrite PM. Through the finite-element method (FEM), an optimization model with respect to the optimization objectives of the average torque and torque ripple are established to guarantee the desirable cost-effectiveness. The optimized motor is comprehensively evaluated for its no load back EMF, inductance characteristics and torque capability, as well as the demagnetization withstand ability. The simulation analysis results verify the validity of the motor structure and the design method of the motor.
High Absorption Contrast Quantum Confined Stark Effect in Ultra-Thin Ge/SiGe Quantum Well Stacks Grown on Si

We report on the performance of the quantum confined Stark effect (QCSE) in ultra-thin (~350 nm) Ge/SiGe quantum well stacks grown on Si. We demonstrate an absorption contrast <inline-formula> <tex-math notation="LaTeX">$\Delta \alpha /\alpha $ </tex-math></inline-formula> of 2.1 at 1 Vpp swing in QCSE stacks grown on ultra-thin (100 nm) strain relaxed GeSi buffer layers on 300 mm Si wafers. Such ultra-thin QCSE stacks will enable future integration of highly efficient QCSE electro-absorption modulators with low optical coupling loss to passive Si waveguides in a sub-micron silicon photonics platform.
Memory Sizing of a Scalable SRAM In-Memory Computing Tile Based Architecture

Modern computing applications require more and more data to be processed. Unfortunately, the trend in memory technologies does not scale as fast as the computing performances, leading to the so called memory wall. New architectures are currently explored to solve this issue, for both embedded and off-chip memories. Recent techniques that bringing computing as close as possible to the memory array such as, In-Memory Computing (IMC), Near-Memory Computing (NMC), Processing-In-Memory (PIM), allow to reduce the cost of data movement between computing cores and memories. For embedded computing, In-Memory Computing scheme presents advantageous computing and energy gains for certain class of applications. However, current solutions are not scaling to large size memories and high amount of data to compute. In this paper, we propose a new methodology to tile a SRAM/IMC based architecture and scale the memory requirements according to an application set. By using a high level LLVM-based simulation platform, we extract IMC memory requirements for a certain class of applications. Then, we detail the physical and performance costs of tiling SRAM instances. By exploring multi-tile SRAM Place&Route in 28nm FD-SOI, we explore the respective performance, energy and cost of memory interconnect. As a result, we obtain a detailed wire cost model in order to explore memory sizing trade-offs. To achieve a large capacity IMC memory, by splitting the memory in multiple sub-tiles, we can achieve lower energy (up to 78% gain) and faster (up to 49% gain) IMC tile compared to a single large IMC memory instance.
A Novel Space Vector Modulation for the 3x5 Direct Matrix Converter

The multiphase AC motor driver system with matrix converter has many advantages, while the control is very complex. Instead of carrier modulation, the space vector modulation is more used for field oriented control in the motor drive system. Traditional space vector modulation of the matrix converter is an evolution of the two-level inverter, whose vectors are represented by the input phase abc. In this paper, we present a new space vector modulation strategy for multiphase matrix converter with a 3x5 system as an example, based on the perspective of a three-level. This method utilizes the three-level modulation for reference from the mature method in fictitious inverter, and applies it to the multi-phase matrix converter system through analysis and improvement. For the voltage level of three-level modulation is relatively clear, the new method is prone to get an optimal result for different goals. On the basis of theoretical analysis and derivation, a novel sectioned modulation method is proposed in the paper. The simulation verify the correctness of the method.
A Hierarchical Deep Deterministic Policy Gradients for Swarm Navigation

The problem of swarm navigation recently becomes a significant topic because of its suitability for various applications like search and rescue with autonomous systems. However, when it comes to a swarm of learning agents, one has to face the challenges from huge state spaces and the lack of scalability when the size of the swarm increases. Reinforcement learning (RL) approaches, which allow agents to interact not only with each other but also with their operational environment to obtain optimal policies, are considered as promising techniques for swarm navigation problems. Different RL algorithms have been used to solve these problems but most of them are limited to discrete state spaces and/or do not scale well with an increase of number of learning agents in the swarm. In this paper, we propose a Swarm Hierarchical Deep Deterministic Policy Gradients (SH-DDPGs) framework to address both drawbacks above in the context of leader-follower swarm navigation. By decomposing the navigation task of the swarm into two primitive sub-tasks: leader-following and collision avoidance, we can guarantee the convergence of the training processes of these sub-tasks in a continuous environment before combining output actions produced from those trained models to complete the entire task. Moreover, our method represents scalability as it is independent to the size of the swarm. Firstly, when training a follower, we only use information of its neighbors within its local view and the leader. Secondly, the trained model of one follower can be reapplied for the remaining followers. Training results show that the proposed SH-DDPGs algorithm is able to converge quickly and allow followers agent to learn an optimal policy for the whole group to navigate through the environment without colliding with each other and flexibly optimize their formation so that the distances among agents are minimized.
Augmented Reality 3D Cubes Puzzle Bingo Game for the Elderly

The pace of population aging is increasing dramatically over years. Living longer as well as living well is the goal of many people. Studies showed that mobile gaming can enhance the cognitive abilities and psychomotor skills of the elderly at the same time give the elderly sense of enjoyment and reduce the feeling of loneliness. Augmented reality technologies have been used to enhance traditional games and the results are positive. An Augmented Reality 3D cubes Puzzle Bingo game (ARPinGo) targeted the elderly was developed in this project by adapting the idea of rubik cubes and the puzzle and bingo board game features. The aim of this project is to gauge the response of the elderly towards the game and their acceptance of augmented reality technologies in mobile gaming. A total of 24 elderly members from D’Happy club in Petaling Jaya, Malaysia had participated in this study. A quantitative approach based on the Technology Acceptance Model (TAM) is used in the survey. The outcome of this study would provide valuable input for future development of gaming application for the elderly.
Energy-Efficient Flocking Control: A Distributed Least-Informed Method

Inspired by cooperative foraging behavior in goose flocks, this paper proposes a distributed least-informed (DLI) method to achieve energy-efficient flocking control by selecting the least required informed agents. To avoid the fragmentation issue when applying the DLI method, three new concepts about the informed path, cycle, and tree are proposed. Based on the properties of the proposed new concepts, a theorem of fragmentation prevention is developed for multi-agent systems with a portion of informed agents. Utilizing the developed theorem of fragmentation prevention, the initialization and updating principles of the selection of the least informed agents in the DLI method are described. Simulation results demonstrate that by using the DLI method, a multi-agent system of 10 agents can successfully achieve energy-efficient flocking control without fragmentation. Namely, a collective motion around a (virtual) leader with velocity consensus (flock formation) is achieved by a total 15.9% energy reduction, compared with the completed informed method.
Sea Fog Detection Using U-Net Deep Learning Model Based On Modis Data

Sea fog can have both negative and positive impacts on humans life. At present, remote sensing has become the main means of long-term and large-scale observation of sea fog. With the improvement of spectral resolution and increase of data volume, the traditional threshold method is simple and convenient as the main method of current sea fog detection, but it’s not flexible and accurate enough which causes people need a more automated and intelligent algorithm to achieve efficient sea fog detection. In this article, we use the U-Net deep learning model to construct the sea fog detection model for MODIS multi-spectral images. The main steps include? (1) Data preprocessing, including the PCA method for dimensionality reduction of data; (2) Manual samples extraction with CALIPSO data assist; (3) Construction and training of U-Net sea fog detection model. The experimental results show that the U-Net model can effectively and machine learning method has good potential in sea fog detection.
5G-and-Beyond Scalable Machines

5G is not one problem and one solution, but spans a breadth of applications with largely differing requirements. One solution for all seems therefore inadequate. We therefore present a modular signal processor MPSoC architecture which can be tiled into the size to address the requirement as needed. We name it “Kachel”, the German word for “tile”.
Achieving Uniform Magnetic Field with Rectangular Coil in Wireless Power Transmission System

A resonant inductive wireless power transmission (WPT) system consists of loosely magnetically coupled transmitting and receiving coil. At some distance from a rectangular transmitting coil, magnetic field that is generated by a flow of current through such a coil is expected to be approximately uniform in a rectangular plane above and parallel to transmitting coil. Criterion chosen to determine approximately uniformity of magnetic field intensity in a plane is that magnitude of magnetic field intensity is within ±10% with respect to the magnitude of magnetic field intensity in a center of such plane. Evaluation of planar uniformity of magnetic field intensity for various distances from transmitting coil was carried out by simulations in Ansys Maxwell 18 software. According to simulation results, aforementioned criterion is satisfied when the plane is 189 mm above the rectangular transmitting coil (165 cm by 45 cm). Planar non-uniformity, i.e. any significant change in magnitude of magnetic field intensity will in turn significantly change the voltage induced at the receiving resulting in either too low or too high voltages. With low voltage the receiving circuit will not work properly, and high voltage levels can cause receiving circuit damage. Hence, uniformity of magnetic field intensity ensures safe and uninterrupted operation of WPT system.
Digital Data Convertion Using Content Addressable Memory

In this paper a novel digital data conversion scheme is proposed that may be applied in analog or digital data acquisition systems. In this scheme possible input redundancy of combinational logic circuit is eliminated by argument reduction. CAM (associative memory) is used in two implementations - for binary and ternary logic, respectively. For binary CAM hardware generators of column masks may be applied. The resulting digital structure can be easily implemented in FPGA.
A Metrological Framework For Hyperspectral Texture Analysis Using Relative Spectral Difference Occurrence Matrix

A new hyperspectral texture descriptor, Relative Spectral Difference Occurrence Matrix (RSDOM) is proposed. Developed in a metrological framework, it simultaneously considers the distribution of spectra and their spatial arrangement in the hyperspectral image. It is generic and adapted for any number of spectral band or range. As validation, a texture classification scheme is applied on HyTexiLa dataset using RSDOM. The obtained accuracy is excellent (95.6%), comparable to Opponent Band Local Binary Pattern (OBLBP) but at a much-reduced feature size (0.1% of OBLBP’s).
Research on Stock Price Prediction Method Based on Convolutional Neural Network

In order to meet the needs of the financial industry and the financial market, effectively improve the rate of return on funds and avoid market risks, this paper proposes a stock price prediction model based on convolution neural network, which has obvious self-adaptability and self-learning ability. Combining the characteristics of CNN (Convolution Neural Network) and Thai stock market, the data set is trained and tested after pretreatment. On this basis, three stocks (BBL, CAPLL&PTT) listed on the Thai Stock Exchange are tested and compared with the actual stock price. The results show that the model based on CNN can effectively identify the changing trend of stock price and predict it which can provide valuable reference for stock price forecast. The prediction accuracy is high, and it is worth further promotion in the financial field.
Detection of Humanoid Robot Design Preferences Using EEG and Eye Tracker

Currently, many modern humanoid robots have little appeal due to their simple designs and bland appearances. To provide recommendations for designers and improve the designs of humanoid robots, a study of human's perception on humanoid robot designs is conducted using Electroencephalogram (EEG), eye tracking information and questionnaires. We proposed and carried out an experiment with 20 subjects to collect the EEG and eye tracking data to study their reaction to different robot designs and the corresponding preference towards these designs. This study can possibly give us some insights on how people react to the aesthetic designs of different humanoid robot models and the important traits in a humanoid robot design, such as the perceived smartness and friendliness of the robots. Another point of interest is to investigate the most prominent feature of the robot, such as the head, facial features and the chest. The result shows that the head and facial features are the focus. It is also discovered that more attention is paid to the robots that appear to be more appealing. Lastly, it is affirmed that the first impressions of the robots generally do not change over time, which may imply that a good humanoid robot design impress the observers at first sight.