Natural hazards have the potential to cause catastrophic damage and significant socioeconomic loss. The actual damage and loss observed in the recent decades has shown an increasing trend. As a result, disaster managers need to take a growing responsibility to proactively protect their communities by developing efficient management strategies. A number of research studies apply artificial intelligence (AI) techniques to process disaster-related data for supporting informed disaster management. This study provides an overview of current applications of AI in disaster management during its four phases: mitigation, preparedness, response, and recovery. It presents example applications of different AI techniques and their benefits for supporting disaster management at different phases, as well as some practical AI-based decision support tools. We find that the majority of AI applications focus on the disaster response phase. This study also identifies challenges to inspire the professional community to advance AI techniques for addressing them in future research.

Applications of artificial intelligence for disaster management

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Dynamic Programming Deterministic And Stochastic Models

Medicine is an important venue for practical applications of science. A fusion of mathematical modeling and programming into computer methods makes a great support for efficient treatment and diagnosis. Computational Intelligence is one of these sciences which bring valuable help in decision support. In this article we present a devoted methodology implemented to simulate medical examinations of pulmonary diseases. We propose Bio-Inspired Methods modeled to work as the automated decision support in a process of diseased tissues detection over input x-ray images. These methods have special features that with devoted modeling make them independently search over the images with a good accuracy. In our approach we use dedicated fitness condition for selected heuristic algorithms. Mathematical model of medical expertise is formulated as a function used to search for special features of pixels that are representing respiratory diseases like pneumonia, lungs sarcoidosis and cancer. Presented decision modeling simulates medical x-ray image examination process to show where potentially diseased tissues are located. To enhance decision support the system returns to the doctor detection results from two tracks. In the first, patient and doctor can see detection from each of the algorithms, and in the second aggregated results. In this way the doctor receives a complex support that simulates consulting the image with various specialists. In benchmark tests, for a set of original x-ray images from various clinics, applied methods were examined to demonstrate benefits of using implemented solution. Results show that proposed methodology is efficient and promising for pulmonary diseases detection.

Bio-inspired methods modeled for respiratory disease detection from medical images

This paper proposes the control system for 3-D locomotion of a humanoid biped robot based on a biological approach. The muscular system in the human body and the neural oscillator for generating locomotion signals are adapted in this paper. We extend the neuro-locomotion system for modeling a multiple neuron system, where motoric neurons represent the muscular system and sensoric neurons represent the sensor system inside the human body. The output signals from coupled neurons representing the angle joint level are controlled by gain neurons that represent the energy burst for driving the joint in each motor. The direction and the length of step in robot locomotion can be adjusted by command neurons. In order to form the locomotion pattern, we apply multiobjective evolutionary computation to solve the multiobjective problem when optimizing synapse weights between the motoric neurons. We use recurrent neural network (RNN) for the stabilization system required for supporting locomotion. RNN generates a dynamic weight synapse value between the sensoric neuron and the motoric neuron. The effectiveness of our system is demonstrated in open dynamic engine computer simulation and in a real robot application that has 12 degrees of freedom (DoFs) in legs and four DoFs in hands.

Biologically Inspired Control System for 3-D Locomotion of a Humanoid Biped Robot

A systematic review of recent developments in disaster waste management

This paper proposes a research about the humanoid robot system stability to the basic movements in playing football (walking, running, and kicking a ball). The system controls the stability of the robot body angle in order to remain in an ideal position, using the hand as a function of the feedback that has been controlled the actuator separately with leg function on the robot. The hand has a function as robot body tilt actuator controller and the foot has a function as gait motion control system that controls the robot to walk. This system has deficiency to disorders the high impulse, resulting in added angular velocity control system functions, which can reduce the foot force moment generated when stopping suddenly and unexpectedly ran. System control used PID control while in motion pattern and kinematic control system using Fuzzy algorithm. We applied the combination between the control and speed control angle pose at EROS (EEPIS Robosoccer).

Combining pose control and angular velocity control for motion balance of humanoid robot soccer EROS

This paper describes a transportation management system for disaster wastes to support early recovery from great the effect of earthquakes and other natural disasters. The system consists of a route selection process and a waste allocation process. For the system, the simplification map is made from arterial roads, temporally storage yards and disposal facilities. And, a directed graph with traveling times and transportation distances of adjacent nodes was generated from the simplification map. The route selection process calculates path length between all pairs of nodes by Warshall-Floyd algorithm. The allocation process decides transportation amount for each disposal facility by linear programming method. In the experiment, we confirm our method is able to manage waste transportation by using a map that simulated south Tokyo. Our system selected the shortest route from a disaster waste source to the nearest disposal facility with related to traffic conditions. The system allocated simulated disaster wastes for the facilities in proper quantities.

A route planning for disaster waste disposal based on robot technology

In this paper we present synaptic weight optimization for dynamic locomotion in four-legged robot (cat, dog) based on neural oscillator. We investigate the muscular structure to design the relationship for both extensor neuron and flexor neuron. The robot has 3 joints in each leg and each joint is represented by 2 neurons, extensor and flexor neuron. The robot has 4 main circular neurons as the server neuron and the other neurons are the client neurons. The server neurons generate the oscillator signal to the client neurons. The signal can be dynamically adjusted according to the environmental condition. Not only the synaptic network between the neurons, but the synaptic network between neurons and sensors was also designed to realize dynamical locomotion. Pressure sensor and inclination sensor were installed in the robot. The signal is influenced by ground reaction sensor and body inclination feedback. While the foot touches the ground, the sensory neuron sends the signal to the joint neuron. Negative signal will be sent to flexor neuron and positive signal will be sent to extensor neuron. To optimize the strength of weights in the synaptic neurons we apply the Nondominated Sorting Genetic Algorithm II (NSGA-II). The stability of torso body, the velocity, and the movement direction are the three objectives in the multi-objective NSGA-II. In the experiments, a computer simulation framework, the Open Dynamic Engine (ODE) is applied. The solution is evaluated based mainly on the moving distance of the robot. Experiments were conducted to confirm the proposed technique.

Multi-objective evolutionary algorithm for neural oscillator based robot locomotion

One of the problems in humanoid locomotion generation is energy efficiency. This paper proposes a method for energy efficiency optimization in simple humanoid robot locomotion using single objective genetic algorithm. With the aim to produce walking trajectory system using minimum energy and good stabilization, torque and oscillation analysis are required to calculate the stabilization. The number of desired outputs in this system is 4 parameters and the number of inputs is 9 parameters. We used neural network with back propagation learning mechanism to realize the relationship between input and output data as well as producing fitness function for genetic algorithm. The trajectory system has 2 trajectory equations, which is pelvis trajectory and ankle trajectory. Ankle trajectory is formed from circle function in Cartesian coordinate space and pelvis trajectory is formed from third order polynomial equation. Both of them are influenced by inclination of robot body. In the experiment, we apply this system using Bioloid robot with inertial sensor already installed. The experimental results show the analysis of energy by observing the torque resulted by servomotor in each joint. We observe that using this system, the torque value resulted by servomotors was decreased and has good stabilization.

Takahiro Takeda

Papers

Combining pose control and angular velocity control for motion balance of humanoid robot soccer EROS

A route planning for disaster waste disposal based on robot technology

Multi-objective evolutionary algorithm for neural oscillator based robot locomotion

Efficiency energy on humanoid robot walking using evolutionary algorithm

Arm motion analysis using genetic algorithm for rehabilitation and healthcare