Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement learning-based HVAC supervisory control of a multi-zone building- A real case study

Supervisory control of heating, ventilation, and air conditioning systems (HVACs) in large multi-zone buildings is fundamental for efficient energy management. Despite the significant advances in supervisory control of buildings, rule-based control remains being the most popular practice implemented by building managers. The main reason behind this is that building accurate physics-based models for control is challenging and time-consuming. Reinforcement learning (RL) methods present a good alternative considering that only data is needed to learn a control law. However, RL-based methods do not perform adequately when new operating conditions occur because the system dynamics become non-stationary. For buildings, this mainly happens because of changes in weather and occupancy. In this work, we present the results of applying a novel RL framework that addresses such issues to a large multizone building. From February, 2020 to February 2021, the supervisory controller was tested and achieved cooling and electrical energy savings by 13.1 % and 14.30 % under similar operating conditions compared to a previously deployed rule-based controller. In addition, the proposed controller achieved reduced zone temperature deviation by 21.31 % and VRF fan switching by over 10.79 %.
Design and Simulation of Peripheral Driving Circuitry for Computational ReRAM

As an alternative approach to the von Neumann architecture, the notion of computational resistive random-access memory (ReRAM) has emerged, promising faster and more energy-efficient computing systems. In this context, we present a classification of ReRAM-compatible logic design strategies and highlight the potential of nonstateful ratioed logic for computational ReRAM modules. We provide insights towards the design of ad-hoc peripheral circuitry that allows the fusion of memory and ratioed logic operations in the ReRAM module in a reliable manner; i.e., the driving/sensing circuitry allows carrying out memory operations and in-memory multilevel ratioed logic operations. To this end, we present in detail a computational ReRAM driver and focus our description on the operational features that enable memory/logic operations in every row of the crossbar array. We validate circuit functionality through LTSpice simulations for read/write memory and logic operations using a threshold-type bipolar ReRAM device model. The presented practical solutions contribute to the viable development of computational ReRAM.
Assessment of a Learning Management System as a Blended Workplace Using the UTAUT2 model

As we transitioned to the new normal, most academic institutions implemented a comprehensive work-from-home strategy during the height of the pandemic. An established research model will be used to evaluate an LMS as a blended workplace, with the goal of establishing a reference point. Likert scale with indication of 1 as strongly disagree to 7 as strongly agree, the questionnaire survey was designed in accordance with the established research model of UTAUT2 by Venkatesh. Structural Equation Modeling and Partial Least Squares (PLS) were used to analyze the data in this study. This study assessed the reliability and validity of the proposed hypotheses using structural model, measurement model, and moderator analysis. Other confirmatory analyses were performed to assess the developed hypothesis, including goodness of fit, goodness of model, collinearity, bootstrapping, and its predictive power. It was found out that the factors included in the proposed research framework of the study had a positive and significant impact on the use of the LMS as a blended workplace application in the study. As a result, it is recommended that it be used for university-wide implementation. A learning management system (LMS), specifically Moodle, can be extremely beneficial for educational institutions that are applying for international standard certification or program accreditation. It can assist with maintaining administrative tasks, submission, reviewing, approval, and monitoring of required documents.
Multi-Agent Reinforcement Learning Based Electric Vehicle Charging Control for Grid-Level Services

Coordinating the charging process of a large population of electric vehicles (EV) is promising in increasing power grid flexibility from the demand side, yet requires highly scalable control protocols. In contrast to classical decentralized optimization based methods that require approximated distribution network models, this paper frames the EV charging control problem into a multi-agent reinforcement learning (MARL) framework. The MARL-based framework is trained through an actor-critic network and adopts the structure of centralized training and decentralized execution with partial observations. Comparing with model-based approaches, the developed MARL-based approach better captures the attributes of the distribution network, improves grid-level service performance, achieves better network constraints control, reduces the communication load, and achieves a faster response. The efficacy and efficiency of the developed method are verified by simulations on the IEEE 13-bus test feeder.
Facial Intrusion Detection Using a Concise Neural Network Architecture

Face recognition devices are being spoofed with automated facial replicas by registered users, in order to circumvent biometric safety controls. They are misrepresented. It is inevitable to develop sturdy and dependable solutions to prevent such scams. Deep learning algorithms have been used to identify face spoofing since they have demonstrated good performance in computer vision. In this research, instead of relying only on deep learning models or extracting hand-crafted texture characteristics, we combine both wide and deep characteristics in a unified deep network. Using content-based frame extraction, the incoming video is divided into frames. The person's face is clipped from each frame. To train the CNN, several features such as HoG, LBP, CSLBP, and GLCM are retrieved from the cropped pictures. Training and testing are carried out independently utilizing gathered sample data.
Improved Cartographer Algorithm Based on Map-to-Map Loopback Detection

As the traditional Cartographer SLAM (Simultaneous Localization and Mapping) algorithm mainly uses Scan-to-Map method to finish loopback detection, which has limitation of single data matching and leads to loopback errors in scenes with few environmental features such as tunnels, corridors and garages. To solve this problem, an improved Cartographer algorithm based on map-to-map loopback detection is proposed in this paper. The algorithm matches the submap of the current frame with the previously generated submap, and takes advantage that the submap contains more information than the single frame to solves the matching limitation problem. In the process of detection, the algorithm improves the matching speed by preliminary matching and proposes the method of lazy decision to improve the real-time performance of the algorithm and improve the robustness of mapping. To verify the improvement effect of the algorithm improvement, the simulation results before and after the algorithm improvement are compared in Gazebo simulator. A trolley equipped with 16-wire LiDAR was established as the experimental platform, and a Raspberry Pi 4B was used as the algorithm running platform to test the indoor environment. The experimental results show that the mapping and localization effect is improved in the scenes with fewer environmental features, and meets the demand of higher accuracy (distance error is less than 0.05m, horizontal angle error is less than 0.25°).
Control of Super Capacitor in Solar Power Plant Using Automatic Hybrid Mode

The utility of Super Capacitors has been widely used in the aspect of hybrid energy management which is applied together with energy storage systems into batteries through active regulation schemes. The availability of battery energy in a solar power generation system will determine the performance of providing electrical energy, especially for loading. The way to adjust the additional hybrid storage in this study includes the sensitivity of the battery voltage and the type of load so that there is no sudden decrease in battery capacity due to the loading issue. This study aims to develop a system for regulating the supply of electrical energy through the help of fuzzy logic computing using a microcontroller as the based setting between the battery circuit and the supercapacitor module also connected to a DC-DC converter. The system was tested with 3W and 8W loads which have a computational difference point of 0.812 and 0.196 having the capability of supplying the energy system to the load according to the lowest voltage level rules.
An Adaptive Lower Limb Rehabilitation Exoskeleton Robot Designing Scheme

The model of a kind of lower limb exoskeleton robot with three degrees of freedom and knee-to-joint automatic alignment is proposed in this paper. The model mainly addresses the adaptability of the lower limb exoskeleton robot to different motion postures and human body structures. The second joint in the lower limb exoskeleton robot is provided with a sensor mechanism for the coordination between the movable joint and the knee of the human lower limb. In the work, SolidWorks was used for modeling the lower limb exoskeleton, and MATLAB was used for simulation and analysis to verify the feasibility of the proposed model. Results show that the designed lower limb exoskeleton robot has good safety and improves the coordination between the subject legs and the robot. This work provides a lower limb exoskeleton rehabilitation robot with reasonable structure and more humanization and develops align the lower extremity knee joint with the robotic joint, improves subject acceptability and safety.
SmartData Safety: Online Safety Models for Data-Driven Cyber-Physical Systems

Contemporary Cyber-Physical Systems (CPS), such as autonomous vehicles, are driven mainly by data. Combining timing and data semantics in such Data-Driven systems is crucial to assure safety. This paper proposes an extension of SmartData to support online safety monitoring. By following a Data-Driven Design, we promote a specification of property monitors using Signal Temporal Logic (STL) encompassing Safety Models. Timing aspects from STL specification roots from the timed data intrinsic to SmartData. The property monitors are envisioned as an online monitoring method inside a Safety Enforcement Unit (SEU). The SEU periodically assures the satisfiability of timing and semantics. We demonstrate the proposed design through a case study of an autonomous vehicle modeled using SmartData. The case study considers Mobileye’s Responsibility-Sensitive Safety as a ruler for safety vehicle conditions. Finally, the design provides the online verification capabilities inside the SEU by exploring the interpretation of STL specification as property monitors following the RTAMT library.
Analysis of Crowd Simulation for Autonomous Mobile Robot Navigation

Robot simulators are generally used in the research and development of autonomous mobile robots in human-robot coexistence environments. These simulators require to implement moving pedestrians as dynamic obstacles, and often use various crowd models developed in the field of crowd simulation research. However, existing crowd simulations make assumptions that all virtual agents comprising the crowd are controlled in the same manner and that each virtual agent has full knowledge of the surrounding environment. The validity of these assumptions is not clear in robot navigation simulators. In this paper, we analyze existing crowd simulations for use in a simulator for autonomous mobile robot navigation. We also conduct simulation experiments using a simple crowd model to evaluate the impact of environmental differences on the performance of the crowd simulations.
Mandatory Lane-Changing Decision-Making in Dense Traffic for Autonomous Vehicles based on Deep Reinforcement Learning

Mandatory lane changing in complex and crowded traffic environment is a great challenge for autonomous vehicles. This paper proposes a decision-making model based on reinforcement learning, encoded prior knowledge and traffic rules for exiting tasks within a limited distance. First, a decision policy of acceleration and lane-change actions is constructed based on the reinforcement learning technique. Also, driver prior knowledge and traffic rules are encoded to construct behavior constraints, which helps the agent explore efficiently and save training time. In addition, in order to dynamically adjust the lane-change style, the lane-change urgency that varies with the remaining distance is introduced into the framework. A challenging highway exiting scenario is designed to verify the performance of the proposed method. The results indicate that the proposed model can change the driving style according to task urgency, switching from actively overtaking and changing lanes to passively waiting for a safety gap. Compared with the traditional rule-based method, it performs better in the highway exiting task under high traffic density and limited remaining distance with a greater success rate of more than 95%.
Task-attentive LiDAR Filter: Adaptive ROI and Semantic-based Ground Segmentation

Region-of-interest (ROI) and ground point filter is a vital pre-processing step that 1). reduces computation and memory complexity of following perception tasks, and 2). identifies traversable areas and obstacles above the ground. This paper proposes a task-attentive LiDAR filter that combines an adaptive ROI filter and a semantic-based ground segmentation filter. The proposed adaptive ROI filter dynamically estimates a ROI area that ensures driving safety while removing many unrelated points. The proposed semantic-based ground segmentation filter combines semantic prior from HD map and LiDAR scans to accurately estimate ground planes in complex ground environments. This enables the perception system to detect small obstacles near the ground surface. Experiments are conducted on real-world data collected by the WATonoBus projects in challenging urban environments, where the proposed task-attentive filter reduces data points by 5.6 times and decreases the runtime by 5.6 times of clustering-based object detection algorithm.
Current Protection Circuit for a Grid-Connected Energy Storage System

The growing need for grid-connected battery energy storage systems to fulfill the increased energy demand has brought attention to the protection of the battery systems against DC short circuit fault condition. The DC short circuit current can be very destructive to the system due to its prolonged in time energy and low DC system impedance. In this paper, different available DC protections are reviewed. Then a proper protection circuit using Solid-State Circuit Breaker (SSCB) based on IGBT power switches has been simulated and designed to meet the specifications of the energy storage system. At the end of the paper, the test results are presented to discuss the performance of the designed SSCB under the given specification.
Deep Reinforcement Learning based Multi-Objective Energy Management Strategy for a Plug-in Hybrid Electric Bus Considering Driving Style Recognition

The existing studies have shown that regarding the driving style as transient information to allocate vehicle power can significantly improve the fuel economy of hybrid electric vehicles (HEV s). To make full use of this information in a more efficient algorithm, a multi-objective energy management strategy (EMS) considering driving style recognition based on Proximal Policy Optimization (PPO) algorithm for the plug-in hybrid electric bus (PHEB) is proposed in this paper. Firstly, the optimized fuzzy C-means algorithm is used to identify driving conditions. Then the fuzzy rules combining the identification re-sults for driving style recognition are constructed. After that, the recognition results are used as the state of the deep reinforcement learning (DRL) algorithm and an energy management framework is established to minimize fuel consumption, maintain the battery SOC and ensure vehicle power performance. Simulation results have shown that the proposed EMS can significantly improve vehicle fuel economy under the combined driving cycles and real driving cycles.
Data-Driven Time-Delayed Control for Euler-Lagrange Systems

In this paper, a data-driven time-delayed control (TDC) approach is developed for Euler-Lagrange systems. The TDC is a kind of control approaches and can handle model uncertainties, unmodelled dynamics or unknown disturbances in the Euler-Lagrange system conveniently, whose basic idea is to use the measurement information at the last time instant to approximately estimate all the unknown factors at the current time instant. A user specified constant gain matrix is the main design parameter and influences both the closed-loop stability and the tracking performance. Currently this constant gain matrix is either calculated by using the system model based on a stability condition or obtained by trial and error, both of which are rather time-consuming in some practical applications. The data-driven TDC approach developed in this paper can find the constant gain matrix directly from the input and output data without any knowledge of the inertia matrix. The input-to-state stability of the closed-loop system is proven. The proposed approach is characterized by easy implementation and low computational efforts. An experimental example of a robot manipulator is given to illustrate the proposed approach.
A Practical Implementation of Driver Drowsiness Detection Using Facial Landmarks

Detection of drowsiness in the case of drivers who drive transport vehicles for long distances at night is important for safety purposes. Although it is a very basic task for a human observer drowsiness detection is a very challenging problem for computers. In this paper we propose a practical implementation of a drowsiness detector that can run on any device, using an integration of various technologies including python, Golang, and SASS. Drivers can turn on camera permissions in the app or the website and the application can run in the background and ring the alarm if the driver shows signs of drowsiness.
Interaction of a finite‐length ion beam with a background plasma: Reflected ions at the quasi‐parallel bow shock

The coupling of a finite-length, field-aligned, ion beam with a uniform background plasma is investigated using one-dimensional hybrid computer simulations. The finite-length beam is used to study the interaction between the incident solar wind and ions reflected from the Earth's quasi-parallel bow shock, where the reflection process may vary with time. The coupling between the reflected ions and the solar wind is relevant to ion heating at the bow shock and possibly to the formation of hot, flow anomalies and re-formation of the shock itself. The authors find that although there are many similarities between the instabilities driven by the finite-length beam and those predicted by linear theory for an infinite, homogeneous beam, there are also some important differences. Consistent with linear theory, the waves which dominate the interaction are the electromagnetic right-hand polarized resonant and nonresonant modes. However, in addition to the instability growth rates, the length of time that the waves are in contact with the beam is also an important factor in determining which wave mode will dominate the interaction. Whereas linear theory predicts the nonresonant mode to have the larger growth rate for the parameters they investigate, with finite-length beam they find that both the nonresonantmore » and resonant modes contribute to the interaction. They find that the interaction will result in strong coupling, where a significant fraction of the available free energy is converted into thermal energy in a short time, provided the beam is sufficiently dense or sufficiently long. « less
Evaluation of Feature Detection Algorithms and Epipolar Geometry Based Camera Pose Estimation

Visual sensors are widely used for applications such as Autonomous Navigation, Localization, Mapping risky environments and the like. Vision based navigation involves various stages, in which Feature Detection, Extraction, and Matching are the primary steps. A number of algorithms are available for performing this stage in which SURF and SIFT algorithms are the most commonly used. This paper compares the algorithms based on certain parameters which includes the number of features detected, match points, and match rates etc.In order to estimate the camera motion after feature matching, different methods are used based on the available inputs. In this paper, Epipolar Geometry is used to estimate the camera pose for the available image points and intrinsic parameters.
Energy Efficient Protocols for LLNs - Metrics and Measurements

Energy efficiency is arguably the primary concern of most new propositions for the Industrial Internet of Things (IIoT) focusing on Low Power and Lossy Networks (LLNs), be it novel or improved protocols or new applications. While simulations are mostly used to evaluate new approaches, they might only approximate the energy consumption, generally done via abstract metrics that are assumed to correlate directly with actual energy usage. We analyze the behavior of two different Internet of Things (IoT) development boards in detail and in a second step operating in realistic scenarios, comparing two different multicast routing protocols and various configurations. While focusing on the IEEE 802.15. 4/6LoWPAN, we show that the actual energy consumption of real devices is considerably more complex than simulations are able to model, and can drastically change depending on device type and hardware configuration. We further analyze prominent metrics that are, according to our results, able to estimate energy consumption, show their limitations and how their accuracy can be improved.
Sliding Mode Control of the MMC-based Power System

The modular multilevel converter has become a popular topology for many applications in medium and high-power conversion systems such as multi-terminal direct current power systems. In this paper, Modular multilevel converter structure and related equations are presented. Then, control methods for circulating current, output current, and energy balance among legs and upper and lower arms of each phase for conventional proportional-integral control and sliding mode control are described. Notably, this study concentrates on the multi-terminal direct current configuration link with masterslave control by presenting a π model for the high voltage direct current transmission line. Moreover, dq-frame is used in the control strategy with a modified first-order sliding mode control and a second-order sliding mode control for preventing chattering. The results show that applying the proposed method in a hybrid power system can provide fast transient responses, zero overshoot, and better stability. Finally, the results are verified by simulations in MATLAB/Simulink.