Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms

Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rainfallrunoff model that can cater to all flood prediction system with varying topological area. Hence, there is a vital need to have custom-tailored prediction model with specific range of data, type of perimeter and antecedent hour of prediction to meet the necessity of the locality. In an attempt to model a reliable rainfall-runoff system for a flood-prone area in Malaysia, 3 different approach of Artificial Neural Networks (ANN) are modelled based on the data acquired from Sungai Pahang, Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficeint of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional ANN learning algorithm. Our case study takes the data of rainfall and runoff from the year 2012 to 2014. This is a case study in Pahang river basin, Pekan, Malaysia

Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms

Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rainfall-runoff model that can cater to all flood prediction system with varying topological area. Hence, there is a vital need to have custom-tailored prediction model with specific range of data, type of perimeter and antecedent hour of prediction to meet the necessity of the locality. In an attempt to model a reliable rainfall-runoff system for a flood-prone area in Malaysia, 3 different approach of Artificial Neural Networks (ANN) are modelled based on the data acquired from Sungai Pahang, Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficient of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional ANN learning algorithm. Our case study takes the data of rainfall and runoff from the year 2012 to 2014. This is a case study in Pahang river basin, Pekan, Malaysia

Two-wheel balancing robot; review on control methods and experiments

Two-wheel mobile robot has been active field of study and research as it provides simple mechanical design and high maneuverability. Various developments continue to take place in the process of achieving stability, navigation from one place to another. This article intended to address the control methods of balancing two-wheeled mobile robot from linear controller, non-linear controller and adapting and self-learning algorithm. The focus of the review will be the evaluation and experiment done on two-wheel mobile robot. With the objective of mobile robot advances further from self-balancing, navigating or obstacle avoiding, towards completing sophisticated external task such as transporting and monitoring the surrounding. It is believed that this review will help researchers in developing substantial two-wheeled mobile robot

Hardware development of autonomous mobile robot based on actuating lidar

Object detection using a LiDAR sensor provides high accuracy of depth estimation and distance measurement. It is reliable and would not be affected by light intensity. However, high-end LiDAR sensors are high in cost and require high computational costs. In some applications such as navigation for blind people, sparse LiDAR point cloud are more applicable as they can be quickly generated and processed. As opposed to a point cloud generated from high-end LiDAR sensors where many algorithms have been developed for object detection, sparse LiDAR point clouds still possess large room for improvement. In this research, we present the construction of an autonomous mobile robot based on a single actuating LiDAR sensor, with human subjects as the main element to be detected. From here, the extracted values are implied on k-NN, Decision Tree and CNN training algorithm. The final result shows promising potential with 91% prediction when implemented on the Decision Tree algorithm based on our proposed system of a single actuating LiDAR sensor

The role of crowd behavior and cooperation strategies during evacuation

Crowd dynamics have constituted a hotspot of research in recent times, particularly in areas where developmental progress has taken place in crowd evacuation for ensuring human safety. In high-density crowd events which happen frequently, panic or an emergency can lead to an increase in congestion which may cause disastrous incidents. Crowd control planning via simulation of people’s movement and behavior can promote safe departures from a space, despite threatening circumstances. Up until now, the evolution of distinctive types of crowd behavior towards cooperative flow remains unexplored. Hence, in this paper, we investigate the impact of potential crowd behavior, namely best-response, risk-seeking, risk-averse, and risk-neutral agents in achieving cooperation during evacuation and its connection with evacuation time using a game-theoretic evacuation simulation model. We analyze the crowd evacuation of a rectangular room with either a single-door or multiple exits in a continuous space. Simulation results show that mutual cooperation during evacuation can be realized when the agents’ population is dominated by risk-averse agents. The results also demonstrate that the risk-seeking agents tend toward aggressiveness by opting for a defector strategy regardless of the local crowd densities, while other crowd behavior shows cooperation under high local crowd densit

Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA)

Robotic Navigation Aids (RNAs) assist visually impaired individuals in independent navigation. However, existing research overlooks diverse obstacles and assumes equal responsibility for collision avoidance among intelligent entities. To address this, we propose Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA). Our FLC-ORCA method assigns responsibility for collision avoidance and predicts the velocity of obstacles using a LiDAR-based mobile robot. We conduct experiments in the presence of static, dynamic, and intelligent entities, recording navigation paths, time taken, angle changes, and rerouting occurrences. The results demonstrate that the proposed FLC-ORCA successfully avoids collisions among objects with different collision avoidance protocols and varying liabilities in circumventing obstacles. Comparative analysis reveals that FLC-ORCA outperforms other state-of-the-art methods such as Improved A* and Directional Optimal Reciprocal Collision Avoidance (DORCA). It reduces the overall time taken to complete navigation by 16% and achieves the shortest completion time of 1 min and 38 s, with minimal rerouting (1 occurrence) and the smallest angle change (12). Our proposed FLC-ORCA challenges assumptions of equal responsibility and enables collision avoidance without pairwise manoeuvres. This approach significantly enhances obstacle avoidance, ensuring safer and more efficient robotic navigation for visually impaired individuals

Methodologies and evaluation of electronic travel aids for the visually impaired people: a review

Technological advancements have widely contributed to navigation aids. However, their large-scale adaptation for navigation solutions for visually impaired people haven’t been realized yet. Less participation of the visually impaired subject produces a designer-oriented navigation system which overshadows consumer necessity. The outcome results in trust and safety issues, hindering the navigation aids from really contribute to the safety of the targeted end user. This study categorizes electronic travel aids (ETAs) based on experimental evaluations, highlights the designer-centred development of navigation aids with insufficient participation of the visual impaired community. First the research breaks down the methodologies to achieve navigation, followed by categorization of the test and experimentation done to evaluate the systems and ranks it by maturity order. From 70 selected research articles, 51.4% accounts for simulation evaluation, 24.3% involve blindfolded-sighted humans, 22.9% involve visually impaired people and only 1.4% makes it into production and commercialization. Our systematic review offers a bird’s eye view on ETA development and evaluation and contributes to construction of navigational aids which really impact the target group of visually impaired people

Modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach

Available hospital evacuation simulation models usually focus on the movement of the evacuees while ignoring the crucial behavioural factors of the evacuees’ which impact the simulation results. For instance, the issue of patient prioritization behaviour during evacuation simulation is often overlooked and oversimplified in these models. Furthermore, to control the movement of the evacuees, almost all these models utilize rule-based artificial intelligence to develop navigation systems, which sometimes do not guarantee realistic and optimal movement behaviour. This research aims to address these problems by modelling feasible and novel solutions. In this research, we propose to develop a hospital evacuation simulation model which utilizes a hybrid of fuzzy logic and reinforcement learning to simulate assisted hospital evacuation using the Unity3D game engine. We propose a novel and effective approach to model patient prioritization using a fuzzy logic controller; a reinforcement learning based navigation system to tackle the issues related to the rule-based navigation system by proposing novel reward formulation, observation formulation, action formulation and training procedure. The results and findings exhibited by the proposed model are found to be in line with the available literature. For instance, available literature suggests that an increased number of patients increases the evacuation time, and an increased number of staff or exits decreases the evacuation time. The proposed model also demonstrates similar findings. Moreover, the proposed navigation system is found to take a ‘‘near shortest distance’’ to reach the target as the mean difference between ‘‘shortest vector distance’’ and ‘‘distance covered’’ is approximately 1.73 m. The proposed simulation model simulates the repeated patient collection more realistically and can be used to estimate the Required Safe Egress Time, which enables the establishment of safety performance levels. The evacuation performance of different scenarios can be compared using the proposed model. This research can play a vital role in future developments of hospital evacuation simulation models

Artificial intelligence with scratch programming

The purpose of this book is to provide a general understanding of Artificial Intelligence (AI) or Kecerdasan Buatan. Readers will discover and explore the uses and applications of AI, understand AI concepts and terms such as the training, testing process as well as analyzing the predictive impact of modelling and optimization accuracy. Readers will be exposed to the 1) History of the development of Artificial Intelligence and the 2) Benefits of learning AI which is in line with the boom of the 4.0 industry revolution. Artificial intelligence technology is one of the branches of science in making a system that could have the human-like intelligence ability. It has great potential in automating operations and work that are repetitive in nature. The use of AI technology is very important in solving complex tasks to save time and manpower. Among the important branches of AI is ‘machine learning’ and further the annotation of ‘deep learning’ intertwined which is the ability of machines that are able to 'learn' to be smart. Conceptually, the machine that will be 'taught' by humans needs to be equipped with a lot of information to make it smarter than other machines. In other words, the machine will be equipped with thousands of attempts to complete a task. Throughout this process, the machine will understand and learn concepts to complete the task in improvise in each cycle. This book requires no programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not